diff --git a/Paper - Project 4 - Determining Startup Success and Factors - VZ.pdf b/Paper - Project 4 - Determining Startup Success and Factors - VZ.pdf new file mode 100644 index 0000000..cd67007 Binary files /dev/null and b/Paper - Project 4 - Determining Startup Success and Factors - VZ.pdf differ diff --git a/Presentation - Project 5.pptx b/Presentation - Project 5.pptx new file mode 100644 index 0000000..1bb1571 Binary files /dev/null and b/Presentation - Project 5.pptx differ diff --git a/your-project/README.md b/your-project/README.md index c7951f8..9e7f0a4 100644 --- a/your-project/README.md +++ b/your-project/README.md @@ -14,25 +14,40 @@ - [Links](#links) ## Project Description -Write a short introduction to your project: 3-5 sentences about the context of your topic and why you chose it. +The Startup Ecosystem has been flourishing over the past decades. As Startups are essential leaders of today's innovation as well as potential employers for Bootcamp Alumi's, I decided to dedicate my second project to investigating past developments in the Startup Ecosystem, Correlations of different Parameters and prospect future developments in order to determine future success factors. ## Questions & Hypotheses -What are the questions you would like to answer with your analysis? What did you feel were the answers to those questions before answering them with data? +Questions I would like to answer in my project : +- How did the number of companies founded by year develop over time? +- How many companies are there by sector and country? +- How was the growth of number of companies founded by sector in the past years? +- How is the expected growth for the coming years by sector? +- What is the probability of being acquired by sector? +- What is the probability of being funded and acquired by sector? +- Which sector pays the most for acquisition? +- How many Unicorns are there by sector and Country? +- Hypothesis 1: There is a positive correlation between the amount of companies founded and the amount of funding by sector. +- Hypothesis 2: There is a positive correlation between the amount of companies founded and closed in a sector. +- Hypothesis 3: There is a correlation between the amount of funding and the probability of being acquired. ## Dataset -What dataset (or datasets) did you use? What are the different sources you used (e.g. APIs, web scrapping, etc.)? Provide links to the data if available and describe the data briefly. +I used csv files provided by Crunchbase and Webscrapping from CB Insight's Unicorn Tracker ## Workflow -Outline the workflow you used in your project. What are the steps you went through? +- Brainstorming and Research +- Data Search +- Data Cleaning +- Data Restructuring +- Calculations of Porbability and Correlation +- Data Visualisation +- Preparation of Presentation and Paper ## Organization -How did you organize your work? Did you use any tools like a kanban board? - -What does your repository look like? Explain your folder and file structure. +Yes, I used a Trello Board and To-Do Lists to Organize my work. ## Links Include links to your repository, slides and kanban board. Feel free to include any other links associated with your project. -[Repository](https://github.com/) -[Slides](https://slides.com/) -[Trello](https://trello.com/en) +[Repository](https://github.com/VickyZauner/Project-Week-5-Your-Own-Project) +[Slides](https://github.com/VickyZauner/Project-Week-5-Your-Own-Project) +[Trello](https://trello.com/b/kpihzTiJ/project-4-sucessful-factors-in-the-startup-economy) diff --git a/your-project/code/Project 5 - Analysing Prob of Acquiring or IPO ing.ipynb b/your-project/code/Project 5 - Analysing Prob of Acquiring or IPO ing.ipynb new file mode 100644 index 0000000..a4b286b --- /dev/null +++ b/your-project/code/Project 5 - Analysing Prob of Acquiring or IPO ing.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Project 5 - Analysing Success by Funding.ipynb b/your-project/code/Project 5 - Analysing Success by Funding.ipynb new file mode 100644 index 0000000..a4b286b --- /dev/null +++ b/your-project/code/Project 5 - Analysing Success by Funding.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Project 5 - Building the DataFrame for Analysing Success.ipynb b/your-project/code/Project 5 - Building the DataFrame for Analysing Success.ipynb new file mode 100644 index 0000000..7209db6 --- /dev/null +++ b/your-project/code/Project 5 - Building the DataFrame for Analysing Success.ipynb @@ -0,0 +1,4971 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Success 1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3051: DtypeWarning: Columns (3,7,9,10,17,18,21,22,23,25,26,29,30,37) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Unnamed: 0 id name category_code status \\\n", + "0 0 c:1 Wetpaint web operating \n", + "1 1 c:10 Flektor games_video acquired \n", + "2 2 c:100 There games_video acquired \n", + "3 3 c:10000 MYWEBBO network_hosting operating \n", + "4 4 c:10001 THE Movie Streamer games_video operating \n", + "\n", + " founded_at closed_at acquired_at country_code state_code ... year_founded \\\n", + "0 2005-10-17 NaN NaN USA WA ... 2005.0 \n", + "1 NaN NaN 2007-05-30 USA CA ... NaN \n", + "2 NaN NaN 2005-05-29 USA CA ... NaN \n", + "3 2008-07-26 NaN NaN NaN NaN ... 2008.0 \n", + "4 2008-07-26 NaN NaN NaN NaN ... 2008.0 \n", + "\n", + " year_closed month_closed duration year_acquired month_acquired \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN 2007.0 5.0 \n", + "2 NaN NaN NaN 2005.0 5.0 \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " t_unt_acq term_code price_amount price_currency_code \n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN 20000000.0 USD \n", + "2 NaN cash 0.0 USD \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_acq_joined_time = pd.read_csv(r'data/comps_acq_joined_time.csv')\n", + "comps_acq_joined_time.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Unnamed: 0 int64\n", + "id object\n", + "name object\n", + "category_code object\n", + "status object\n", + "founded_at object\n", + "closed_at object\n", + "acquired_at object\n", + "country_code object\n", + "state_code object\n", + "city object\n", + "region object\n", + "funding_total_usd float64\n", + "year_founded float64\n", + "year_closed float64\n", + "month_closed float64\n", + "duration object\n", + "year_acquired float64\n", + "month_acquired float64\n", + "t_unt_acq object\n", + "term_code object\n", + "price_amount float64\n", + "price_currency_code object\n", + "dtype: object" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_acq_joined_time.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Success by not being closed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# making dataframe for counting growth rate\n", + "comps_closed_ann = comps_acq_joined_time.groupby(['year_closed']).count()\n", + "comps_closed_ann = comps_closed_ann.reset_index()\n", + "comps_closed_ann = comps_closed_ann[['year_closed', 'year_founded', 'name']]\n", + "comps_closed_ann.rename(columns = {'name' : 'number'}, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1998.0" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = comps_closed_ann[comps_closed_ann['year_closed'] < 1998].index\n", + "comps_closed_ann.drop(idx , inplace=True)\n", + "comps_closed_ann.year_closed.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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year_closedyear_foundednumberyoy_closed
152010.02262920.315315
162011.03494540.554795
172012.06337930.746696
182013.0517648-0.182850
192014.011-0.998457
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" + ], + "text/plain": [ + " year_closed year_founded number yoy_closed\n", + "15 2010.0 226 292 0.315315\n", + "16 2011.0 349 454 0.554795\n", + "17 2012.0 633 793 0.746696\n", + "18 2013.0 517 648 -0.182850\n", + "19 2014.0 1 1 -0.998457" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_closed_ann['yoy_closed'] = (comps_closed_ann['number'] -comps_closed_ann['number'].shift(1)) / comps_closed_ann['number'].shift(1)\n", + "comps_closed_ann.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2012.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = comps_closed_ann[comps_closed_ann['year_closed'] >= 2013].index\n", + "comps_closed_ann.drop(idx , inplace=True)\n", + "comps_closed_ann.year_closed.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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year_closedyear_foundednumberyoy_closed
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category_codeyear_foundedcountry_codeidfounded_atclosed_atduration
0advertising1902.0USA2200
1advertising1911.0USA1100
2advertising1915.0USA1100
3advertising1917.0USA1100
4advertising1919.0USA1100
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" + ], + "text/plain": [ + " category_code year_founded country_code id founded_at closed_at \\\n", + "0 advertising 1902.0 USA 2 2 0 \n", + "1 advertising 1911.0 USA 1 1 0 \n", + "2 advertising 1915.0 USA 1 1 0 \n", + "3 advertising 1917.0 USA 1 1 0 \n", + "4 advertising 1919.0 USA 1 1 0 \n", + "\n", + " duration \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies_industry_year = pd.read_csv(r'data/companies_industry_year.csv')\n", + "companies_industry_year.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1999.0" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_founded_closed = companies_industry_year.copy()\n", + "idx = comps_founded_closed[comps_founded_closed['year_founded'] < 1999].index\n", + "comps_founded_closed.drop(idx , inplace=True)\n", + "comps_founded_closed.year_founded.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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110advertising1999.0DEU1100
111advertising1999.0ESP1100
112advertising1999.0FRA1100
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" + ], + "text/plain": [ + " category_code year_founded country_code id founded_at closed_at \\\n", + "108 advertising 1999.0 AUS 1 1 0 \n", + "109 advertising 1999.0 CAN 9 9 0 \n", + "110 advertising 1999.0 DEU 1 1 0 \n", + "111 advertising 1999.0 ESP 1 1 0 \n", + "112 advertising 1999.0 FRA 1 1 0 \n", + "\n", + " duration \n", + "108 0 \n", + "109 0 \n", + "110 0 \n", + "111 0 \n", + "112 0 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_founded_closed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "comps_founded_closed = comps_founded_closed.drop(columns = 'country_code')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeyear_foundedidfounded_atclosed_atduration
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1advertising2000.0818100
2advertising2001.0959500
3advertising2002.0696944
4advertising2003.011711733
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" + ], + "text/plain": [ + " category_code year_founded id founded_at closed_at duration\n", + "0 advertising 1999.0 88 88 0 0\n", + "1 advertising 2000.0 81 81 0 0\n", + "2 advertising 2001.0 95 95 0 0\n", + "3 advertising 2002.0 69 69 4 4\n", + "4 advertising 2003.0 117 117 3 3" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_founded_closed = comps_founded_closed.groupby(['category_code', 'year_founded']).sum()\n", + "comps_founded_closed = comps_founded_closed.reset_index()\n", + "comps_founded_closed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeyear_foundedidfounded_atclosed_atdurationratio_fc
0advertising1999.08888000.000000
1advertising2000.08181000.000000
2advertising2001.09595000.000000
3advertising2002.06969440.057971
4advertising2003.0117117330.025641
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" + ], + "text/plain": [ + " category_code year_founded id founded_at closed_at duration ratio_fc\n", + "0 advertising 1999.0 88 88 0 0 0.000000\n", + "1 advertising 2000.0 81 81 0 0 0.000000\n", + "2 advertising 2001.0 95 95 0 0 0.000000\n", + "3 advertising 2002.0 69 69 4 4 0.057971\n", + "4 advertising 2003.0 117 117 3 3 0.025641" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_founded_closed['ratio_fc'] = comps_founded_closed['closed_at'] / comps_founded_closed['founded_at']\n", + "comps_founded_closed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "comps_founded_closed_2 = comps_founded_closed[['category_code', 'ratio_fc', 'id']]" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_coderatio_fcid
0advertising0.0176383498
1analytics0.011702730
2automotive0.01848796
3biotech0.0230392058
4cleantech0.035122863
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" + ], + "text/plain": [ + " category_code ratio_fc id\n", + "0 advertising 0.017638 3498\n", + "1 analytics 0.011702 730\n", + "2 automotive 0.018487 96\n", + "3 biotech 0.023039 2058\n", + "4 cleantech 0.035122 863" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_founded_closed_2 = comps_founded_closed_2.groupby(['category_code']).agg({'ratio_fc':'mean','id':'sum'})\n", + "comps_founded_closed_2 = comps_founded_closed_2.reset_index()\n", + "comps_founded_closed_2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# comps_founded_closed_2.to_csv(r'data/comps_ratio_fc.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# companies_yig = pd.read_csv(r'data/companies_year_industry_geography.csv')\n", + "# companies_yig.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slope: 32808.10484195256\n", + "Intercept: 682.2024870134363\n", + "rvalue: 0.2349734639742888\n", + "pvalue: 0.13415724135698337\n", + "stderr: 21458.501552927584\n" + ] + }, + { + "data": { + "image/png": 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idfunding_round_idobject_idfunded_atfunding_round_typefunding_round_coderaised_amount_usdraised_amountraised_currency_codepre_money_valuation_usd...post_money_valuationpost_money_currency_codeparticipantsis_first_roundis_last_roundsource_urlsource_descriptioncreated_bycreated_atupdated_at
011c:42006-12-01series-bb8500000.08500000.0USD0.0...0.0NaN200http://www.marketingvox.com/archives/2006/12/2...NaNinitial-importer2007-07-04 04:52:572008-02-27 23:14:29
122c:52004-09-01angelangel500000.0500000.0USD0.0...0.0USD201NaNNaNinitial-importer2007-05-27 06:08:182013-06-28 20:07:23
233c:52005-05-01series-aa12700000.012700000.0USD115000000.0...0.0USD300http://www.techcrunch.com/2007/11/02/jim-breye...Jim Breyer: Extra $500 Million Round For Faceb...initial-importer2007-05-27 06:09:102013-06-28 20:07:23
344c:52006-04-01series-bb27500000.027500000.0USD525000000.0...0.0USD400http://www.facebook.com/press/info.php?factsheetFacebook Fundinginitial-importer2007-05-27 06:09:362013-06-28 20:07:24
455c:72992006-05-01series-bb10500000.010500000.0USD0.0...0.0NaN200http://www.techcrunch.com/2006/05/14/photobuck...PhotoBucket Closes $10.5M From Trinity Venturesinitial-importer2007-05-29 11:05:592008-04-16 17:09:12
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5 rows × 23 columns

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" + ], + "text/plain": [ + " id funding_round_id object_id funded_at funding_round_type \\\n", + "0 1 1 c:4 2006-12-01 series-b \n", + "1 2 2 c:5 2004-09-01 angel \n", + "2 3 3 c:5 2005-05-01 series-a \n", + "3 4 4 c:5 2006-04-01 series-b \n", + "4 5 5 c:7299 2006-05-01 series-b \n", + "\n", + " funding_round_code raised_amount_usd raised_amount raised_currency_code \\\n", + "0 b 8500000.0 8500000.0 USD \n", + "1 angel 500000.0 500000.0 USD \n", + "2 a 12700000.0 12700000.0 USD \n", + "3 b 27500000.0 27500000.0 USD \n", + "4 b 10500000.0 10500000.0 USD \n", + "\n", + " pre_money_valuation_usd ... post_money_valuation \\\n", + "0 0.0 ... 0.0 \n", + "1 0.0 ... 0.0 \n", + "2 115000000.0 ... 0.0 \n", + "3 525000000.0 ... 0.0 \n", + "4 0.0 ... 0.0 \n", + "\n", + " post_money_currency_code participants is_first_round is_last_round \\\n", + "0 NaN 2 0 0 \n", + "1 USD 2 0 1 \n", + "2 USD 3 0 0 \n", + "3 USD 4 0 0 \n", + "4 NaN 2 0 0 \n", + "\n", + " source_url \\\n", + "0 http://www.marketingvox.com/archives/2006/12/2... \n", + "1 NaN \n", + "2 http://www.techcrunch.com/2007/11/02/jim-breye... \n", + "3 http://www.facebook.com/press/info.php?factsheet \n", + "4 http://www.techcrunch.com/2006/05/14/photobuck... \n", + "\n", + " source_description created_by \\\n", + "0 NaN initial-importer \n", + "1 NaN initial-importer \n", + "2 Jim Breyer: Extra $500 Million Round For Faceb... initial-importer \n", + "3 Facebook Funding initial-importer \n", + "4 PhotoBucket Closes $10.5M From Trinity Ventures initial-importer \n", + "\n", + " created_at updated_at \n", + "0 2007-07-04 04:52:57 2008-02-27 23:14:29 \n", + "1 2007-05-27 06:08:18 2013-06-28 20:07:23 \n", + "2 2007-05-27 06:09:10 2013-06-28 20:07:23 \n", + "3 2007-05-27 06:09:36 2013-06-28 20:07:24 \n", + "4 2007-05-29 11:05:59 2008-04-16 17:09:12 \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_rounds = pd.read_csv(r'data/initial/funding_rounds.csv')\n", + "funding_rounds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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000c:1Wetpaintweboperating2005-10-17NaNUSAWASeattleSeattle39750000.02005.010.0NaNNaNNaN
111c:10Flektorgames_videoacquiredNaNNaNUSACACulver CityLos Angeles0.0NaNNaNNaNNaNNaN
222c:100Theregames_videoacquiredNaNNaNUSACASan MateoSF Bay0.0NaNNaNNaNNaNNaN
333c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNunknown0.02008.07.0NaNNaNNaN
444c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNunknown0.02008.07.0NaNNaNNaN
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" + ], + "text/plain": [ + " Unnamed: 0 Unnamed: 0.1 id name category_code \\\n", + "0 0 0 c:1 Wetpaint web \n", + "1 1 1 c:10 Flektor games_video \n", + "2 2 2 c:100 There games_video \n", + "3 3 3 c:10000 MYWEBBO network_hosting \n", + "4 4 4 c:10001 THE Movie Streamer games_video \n", + "\n", + " status founded_at closed_at country_code state_code city \\\n", + "0 operating 2005-10-17 NaN USA WA Seattle \n", + "1 acquired NaN NaN USA CA Culver City \n", + "2 acquired NaN NaN USA CA San Mateo \n", + "3 operating 2008-07-26 NaN NaN NaN NaN \n", + "4 operating 2008-07-26 NaN NaN NaN NaN \n", + "\n", + " region funding_total_usd year_founded month_founded year_closed \\\n", + "0 Seattle 39750000.0 2005.0 10.0 NaN \n", + "1 Los Angeles 0.0 NaN NaN NaN \n", + "2 SF Bay 0.0 NaN NaN NaN \n", + "3 unknown 0.0 2008.0 7.0 NaN \n", + "4 unknown 0.0 2008.0 7.0 NaN \n", + "\n", + " month_closed duration \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN " + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies1990.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Method Nr. 1" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usdyear_foundedmonth_foundedyear_closedmonth_closedduration
0c:1Wetpaintweboperating2005-10-17NaNUSAWASeattleSeattle39750000.02005.010.0NaNNaNNaN
1c:10Flektorgames_videoacquiredNaNNaNUSACACulver CityLos Angeles0.0NaNNaNNaNNaNNaN
2c:100Theregames_videoacquiredNaNNaNUSACASan MateoSF Bay0.0NaNNaNNaNNaNNaN
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNunknown0.02008.07.0NaNNaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNunknown0.02008.07.0NaNNaNNaN
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" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaN \n", + "2 c:100 There games_video acquired NaN \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaN USA WA Seattle Seattle \n", + "1 NaN USA CA Culver City Los Angeles \n", + "2 NaN USA CA San Mateo SF Bay \n", + "3 NaN NaN NaN NaN unknown \n", + "4 NaN NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \\\n", + "0 39750000.0 2005.0 10.0 NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 7.0 NaN NaN \n", + "4 0.0 2008.0 7.0 NaN NaN \n", + "\n", + " duration \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies_funding = companies1990.drop(['Unnamed: 0', 'Unnamed: 0.1'], axis = 1)\n", + "companies_funding.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "192075" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(companies_funding)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "192075" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(companies_funding.funding_total_usd)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "companies_funding['funding_total_usd'] = companies_funding['funding_total_usd'].replace(0, np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "164696" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies_funding.funding_total_usd.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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00c:1Wetpaintweboperating2005-10-17NaNNaNUSAWA...2005.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
11c:10Flektorgames_videoacquiredNaNNaN2007-05-30USACA...NaNNaNNaNNaN2007.05.0NaNNaN20000000.0USD
22c:100Theregames_videoacquiredNaNNaN2005-05-29USACA...NaNNaNNaNNaN2005.05.0NaNcash0.0USD
33c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaN...2008.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
44c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaN...2008.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows × 23 columns

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" + ], + "text/plain": [ + " Unnamed: 0 id name category_code status \\\n", + "0 0 c:1 Wetpaint web operating \n", + "1 1 c:10 Flektor games_video acquired \n", + "2 2 c:100 There games_video acquired \n", + "3 3 c:10000 MYWEBBO network_hosting operating \n", + "4 4 c:10001 THE Movie Streamer games_video operating \n", + "\n", + " founded_at closed_at acquired_at country_code state_code ... year_founded \\\n", + "0 2005-10-17 NaN NaN USA WA ... 2005.0 \n", + "1 NaN NaN 2007-05-30 USA CA ... NaN \n", + "2 NaN NaN 2005-05-29 USA CA ... NaN \n", + "3 2008-07-26 NaN NaN NaN NaN ... 2008.0 \n", + "4 2008-07-26 NaN NaN NaN NaN ... 2008.0 \n", + "\n", + " year_closed month_closed duration year_acquired month_acquired \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN 2007.0 5.0 \n", + "2 NaN NaN NaN 2005.0 5.0 \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " t_unt_acq term_code price_amount price_currency_code \n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN 20000000.0 USD \n", + "2 NaN cash 0.0 USD \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_acq_joined_time = pd.read_csv(r'data/comps_acq_joined_time.csv')\n", + "comps_acq_joined_time.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "192715" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(comps_acq_joined_time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# e.g. sort by industries, see how many get funding and how much" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# grouped by industry and then devided by count" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "average_by_industry = comps_acq_joined_time.copy()\n", + "average_by_industry['number'] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeUnnamed: 0funding_total_usdyear_foundedyear_closedmonth_closedyear_acquiredmonth_acquiredprice_amountnumber
0advertising5756595861.231554e+109753590.0195100.0538.0623160.02021.02.147999e+105979
1analytics1022290706.698312e+091706091.030181.077.080494.0287.05.430000e+081019
2automotive301369033.389967e+09291169.08047.029.010061.031.07.643000e+09273
3biotech4762572526.516285e+104886393.0235359.0646.0828212.02740.01.391689e+114230
4cleantech1996451713.808582e+102170640.0144825.0437.0213131.0786.01.461245e+101862
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" + ], + "text/plain": [ + " category_code Unnamed: 0 funding_total_usd year_founded year_closed \\\n", + "0 advertising 575659586 1.231554e+10 9753590.0 195100.0 \n", + "1 analytics 102229070 6.698312e+09 1706091.0 30181.0 \n", + "2 automotive 30136903 3.389967e+09 291169.0 8047.0 \n", + "3 biotech 476257252 6.516285e+10 4886393.0 235359.0 \n", + "4 cleantech 199645171 3.808582e+10 2170640.0 144825.0 \n", + "\n", + " month_closed year_acquired month_acquired price_amount number \n", + "0 538.0 623160.0 2021.0 2.147999e+10 5979 \n", + "1 77.0 80494.0 287.0 5.430000e+08 1019 \n", + "2 29.0 10061.0 31.0 7.643000e+09 273 \n", + "3 646.0 828212.0 2740.0 1.391689e+11 4230 \n", + "4 437.0 213131.0 786.0 1.461245e+10 1862 " + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "average_by_industry = average_by_industry.groupby('category_code').sum().reset_index()\n", + "average_by_industry.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "average_by_industry = average_by_industry[['category_code', 'funding_total_usd', 'number']]" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codefunding_total_usdnumberaverage_funding
0advertising1.231554e+1059792.059799e+06
1analytics6.698312e+0910196.573417e+06
2automotive3.389967e+092731.241746e+07
3biotech6.516285e+1042301.540493e+07
4cleantech3.808582e+1018622.045426e+07
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" + ], + "text/plain": [ + " category_code funding_total_usd number average_funding\n", + "0 advertising 1.231554e+10 5979 2.059799e+06\n", + "1 analytics 6.698312e+09 1019 6.573417e+06\n", + "2 automotive 3.389967e+09 273 1.241746e+07\n", + "3 biotech 6.516285e+10 4230 1.540493e+07\n", + "4 cleantech 3.808582e+10 1862 2.045426e+07" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "average_by_industry['average_funding'] = average_by_industry.funding_total_usd / average_by_industry.number\n", + "average_by_industry.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexcategory_codefunding_total_usdnumberaverage_funding
024nanotech2.160290e+09703.086129e+07
14cleantech3.808582e+1018622.045426e+07
235semiconductor9.999885e+096361.572309e+07
33biotech6.516285e+1042301.540493e+07
42automotive3.389967e+092731.241746e+07
520medical1.301831e+1011291.153083e+07
619manufacturing5.530671e+096049.156740e+06
721messaging2.027797e+092946.897268e+06
81analytics6.698312e+0910196.573417e+06
934security6.979331e+0911166.253881e+06
1013government2.037835e+08355.822386e+06
1111finance7.345703e+0913215.560714e+06
1225network_hosting1.208540e+1022975.261385e+06
1336social6.473194e+0913074.952712e+06
149enterprise2.117987e+1042904.937033e+06
1510fashion2.606760e+095384.845280e+06
1615health7.551738e+0916254.647223e+06
1714hardware1.226610e+1027294.494723e+06
1822mobile2.976642e+1068134.369062e+06
1926news3.147230e+097364.276128e+06
2032real_estate1.652770e+094343.808225e+06
2123music2.044819e+095773.543880e+06
2239transportation1.580356e+094673.384059e+06
2316hospitality2.475313e+097373.358634e+06
2440travel2.454250e+099052.711878e+06
2537software4.005558e+10173502.308679e+06
260advertising1.231554e+1059792.059799e+06
2729pets1.140078e+08572.000136e+06
2831public_relations5.352326e+0927191.968491e+06
297ecommerce1.711374e+1088701.929395e+06
3012games_video1.366549e+1074261.840223e+06
3127nonprofit2.950901e+081731.705723e+06
3233search3.113459e+0921551.444760e+06
3330photo_video6.857056e+085371.276919e+06
348education3.591608e+0928141.276336e+06
3541web1.799777e+10150331.197218e+06
366design1.843784e+082706.828828e+05
375consulting2.232984e+0947844.667608e+05
3818local3.118630e+087164.355629e+05
3928other5.148330e+09127254.045839e+05
4038sports2.600040e+086444.037329e+05
4117legal3.665113e+089213.979493e+05
\n", + "
" + ], + "text/plain": [ + " index category_code funding_total_usd number average_funding\n", + "0 24 nanotech 2.160290e+09 70 3.086129e+07\n", + "1 4 cleantech 3.808582e+10 1862 2.045426e+07\n", + "2 35 semiconductor 9.999885e+09 636 1.572309e+07\n", + "3 3 biotech 6.516285e+10 4230 1.540493e+07\n", + "4 2 automotive 3.389967e+09 273 1.241746e+07\n", + "5 20 medical 1.301831e+10 1129 1.153083e+07\n", + "6 19 manufacturing 5.530671e+09 604 9.156740e+06\n", + "7 21 messaging 2.027797e+09 294 6.897268e+06\n", + "8 1 analytics 6.698312e+09 1019 6.573417e+06\n", + "9 34 security 6.979331e+09 1116 6.253881e+06\n", + "10 13 government 2.037835e+08 35 5.822386e+06\n", + "11 11 finance 7.345703e+09 1321 5.560714e+06\n", + "12 25 network_hosting 1.208540e+10 2297 5.261385e+06\n", + "13 36 social 6.473194e+09 1307 4.952712e+06\n", + "14 9 enterprise 2.117987e+10 4290 4.937033e+06\n", + "15 10 fashion 2.606760e+09 538 4.845280e+06\n", + "16 15 health 7.551738e+09 1625 4.647223e+06\n", + "17 14 hardware 1.226610e+10 2729 4.494723e+06\n", + "18 22 mobile 2.976642e+10 6813 4.369062e+06\n", + "19 26 news 3.147230e+09 736 4.276128e+06\n", + "20 32 real_estate 1.652770e+09 434 3.808225e+06\n", + "21 23 music 2.044819e+09 577 3.543880e+06\n", + "22 39 transportation 1.580356e+09 467 3.384059e+06\n", + "23 16 hospitality 2.475313e+09 737 3.358634e+06\n", + "24 40 travel 2.454250e+09 905 2.711878e+06\n", + "25 37 software 4.005558e+10 17350 2.308679e+06\n", + "26 0 advertising 1.231554e+10 5979 2.059799e+06\n", + "27 29 pets 1.140078e+08 57 2.000136e+06\n", + "28 31 public_relations 5.352326e+09 2719 1.968491e+06\n", + "29 7 ecommerce 1.711374e+10 8870 1.929395e+06\n", + "30 12 games_video 1.366549e+10 7426 1.840223e+06\n", + "31 27 nonprofit 2.950901e+08 173 1.705723e+06\n", + "32 33 search 3.113459e+09 2155 1.444760e+06\n", + "33 30 photo_video 6.857056e+08 537 1.276919e+06\n", + "34 8 education 3.591608e+09 2814 1.276336e+06\n", + "35 41 web 1.799777e+10 15033 1.197218e+06\n", + "36 6 design 1.843784e+08 270 6.828828e+05\n", + "37 5 consulting 2.232984e+09 4784 4.667608e+05\n", + "38 18 local 3.118630e+08 716 4.355629e+05\n", + "39 28 other 5.148330e+09 12725 4.045839e+05\n", + "40 38 sports 2.600040e+08 644 4.037329e+05\n", + "41 17 legal 3.665113e+08 921 3.979493e+05" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "average_by_industry.sort_values(by='average_funding', ascending=False).reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "# average_by_industry.to_csv(r'data/average_funding_by_industry.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "count_f_by_industry = average_by_industry.groupby('category_code').count().reset_index()\n", + "count_f_by_industry.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Success Master DF" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idipo_idobject_idvaluation_amountvaluation_currency_coderaised_amountraised_currency_codepublic_atstock_symbolsource_urlsource_descriptioncreated_atupdated_at
011c:16540.000000e+00USD0.0USD1980-12-19NASDAQ:AAPLNaNNaN2008-02-09 05:17:452012-04-12 04:02:59
122c:12420.000000e+00USD0.0NaN1986-03-13NASDAQ:MSFTNaNNaN2008-02-09 05:25:182010-12-11 12:39:46
233c:3420.000000e+00USD0.0NaN1969-06-09NYSE:DISNaNNaN2008-02-09 05:40:322010-12-23 08:58:16
344c:590.000000e+00USD0.0NaN2004-08-25NASDAQ:GOOGNaNNaN2008-02-10 22:51:242011-08-01 20:47:08
455c:3171.000000e+11USD0.0NaN1997-05-01NASDAQ:AMZNNaNNaN2008-02-10 23:28:092011-08-01 21:11:22
566c:14383.500000e+08USD87000000.0USD2006-09-29SFLYhttp://www.nasdaq.com/markets/ipos/company/shu...NaN2008-02-11 06:03:562013-05-20 03:48:47
677c:26441.600000e+08USD0.0NaN2006-05-01DIVXNaNNaN2008-02-25 23:52:112008-06-15 02:58:53
788c:25840.000000e+00USD0.0USD2004-03-01OPESFNaNNaN2008-02-27 20:22:312012-05-08 00:09:32
899c:27200.000000e+00USD0.0NaN1999-12-02XOXONaNNaN2008-02-29 00:31:342011-08-22 02:11:44
91011c:33166.000000e+09USD0.0USD1988-08-12NASDAQ:BMCNaNNaN2008-03-18 14:07:102013-10-21 18:08:28
\n", + "
" + ], + "text/plain": [ + " id ipo_id object_id valuation_amount valuation_currency_code \\\n", + "0 1 1 c:1654 0.000000e+00 USD \n", + "1 2 2 c:1242 0.000000e+00 USD \n", + "2 3 3 c:342 0.000000e+00 USD \n", + "3 4 4 c:59 0.000000e+00 USD \n", + "4 5 5 c:317 1.000000e+11 USD \n", + "5 6 6 c:1438 3.500000e+08 USD \n", + "6 7 7 c:2644 1.600000e+08 USD \n", + "7 8 8 c:2584 0.000000e+00 USD \n", + "8 9 9 c:2720 0.000000e+00 USD \n", + "9 10 11 c:3316 6.000000e+09 USD \n", + "\n", + " raised_amount raised_currency_code public_at stock_symbol \\\n", + "0 0.0 USD 1980-12-19 NASDAQ:AAPL \n", + "1 0.0 NaN 1986-03-13 NASDAQ:MSFT \n", + "2 0.0 NaN 1969-06-09 NYSE:DIS \n", + "3 0.0 NaN 2004-08-25 NASDAQ:GOOG \n", + "4 0.0 NaN 1997-05-01 NASDAQ:AMZN \n", + "5 87000000.0 USD 2006-09-29 SFLY \n", + "6 0.0 NaN 2006-05-01 DIVX \n", + "7 0.0 USD 2004-03-01 OPESF \n", + "8 0.0 NaN 1999-12-02 XOXO \n", + "9 0.0 USD 1988-08-12 NASDAQ:BMC \n", + "\n", + " source_url source_description \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 http://www.nasdaq.com/markets/ipos/company/shu... NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "\n", + " created_at updated_at \n", + "0 2008-02-09 05:17:45 2012-04-12 04:02:59 \n", + "1 2008-02-09 05:25:18 2010-12-11 12:39:46 \n", + "2 2008-02-09 05:40:32 2010-12-23 08:58:16 \n", + "3 2008-02-10 22:51:24 2011-08-01 20:47:08 \n", + "4 2008-02-10 23:28:09 2011-08-01 21:11:22 \n", + "5 2008-02-11 06:03:56 2013-05-20 03:48:47 \n", + "6 2008-02-25 23:52:11 2008-06-15 02:58:53 \n", + "7 2008-02-27 20:22:31 2012-05-08 00:09:32 \n", + "8 2008-02-29 00:31:34 2011-08-22 02:11:44 \n", + "9 2008-03-18 14:07:10 2013-10-21 18:08:28 " + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ipos = pd.read_csv(r'data/initial/ipos.csv')\n", + "ipos.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "comps_acq_joined_time = pd.read_csv(r'data/comps_acq_joined_time.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "comps_acq_joined_time.rename(columns = {'id' : 'object_id'}, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "192715" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(comps_acq_joined_time)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0c:1Wetpaintweboperating2005-10-17NaNNaNUSAWASeattleSeattle39750000.02005.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1c:10Flektorgames_videoacquiredNaNNaN2007-05-30USACACulver CityLos Angeles0.0NaNNaNNaNNaN2007.05.0NaNNaN20000000.0USDNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2c:100Theregames_videoacquiredNaNNaN2005-05-29USACASan MateoSF Bay0.0NaNNaNNaNNaN2005.05.0NaNcash0.0USDNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " object_id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaN \n", + "2 c:100 There games_video acquired NaN \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at acquired_at country_code state_code city region \\\n", + "0 NaN NaN USA WA Seattle Seattle \n", + "1 NaN 2007-05-30 USA CA Culver City Los Angeles \n", + "2 NaN 2005-05-29 USA CA San Mateo SF Bay \n", + "3 NaN NaN NaN NaN NaN unknown \n", + "4 NaN NaN NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded year_closed month_closed duration \\\n", + "0 39750000.0 2005.0 NaN NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 NaN NaN NaN \n", + "4 0.0 2008.0 NaN NaN NaN \n", + "\n", + " year_acquired month_acquired t_unt_acq term_code price_amount \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 2007.0 5.0 NaN NaN 20000000.0 \n", + "2 2005.0 5.0 NaN cash 0.0 \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " price_currency_code id ipo_id valuation_amount valuation_currency_code \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 USD NaN NaN NaN NaN \n", + "2 USD NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " raised_amount raised_currency_code public_at stock_symbol source_url \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " source_description created_at updated_at \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN " + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "master_merged = pd.merge(comps_acq_joined_time, ipos, on = 'object_id', how='left').drop(columns = 'Unnamed: 0')\n", + "master_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "192719" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(master_merged)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "# master_merged.to_csv(r'data/success_master_merged.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [], + "source": [ + "master_merged_slimmed = master_merged.drop(columns = ['updated_at', 'created_at', 'source_description', 'source_url', 'stock_symbol'])" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0c:1Wetpaintweboperating2005-10-17NaNNaNUSAWASeattleSeattle39750000.02005.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1c:10Flektorgames_videoacquiredNaNNaN2007-05-30USACACulver CityLos Angeles0.0NaNNaNNaNNaN2007.05.0NaNNaN20000000.0USDNaNNaNNaNNaNNaNNaNNaN
2c:100Theregames_videoacquiredNaNNaN2005-05-29USACASan MateoSF Bay0.0NaNNaNNaNNaN2005.05.0NaNcash0.0USDNaNNaNNaNNaNNaNNaNNaN
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " object_id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaN \n", + "2 c:100 There games_video acquired NaN \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at acquired_at country_code state_code city region \\\n", + "0 NaN NaN USA WA Seattle Seattle \n", + "1 NaN 2007-05-30 USA CA Culver City Los Angeles \n", + "2 NaN 2005-05-29 USA CA San Mateo SF Bay \n", + "3 NaN NaN NaN NaN NaN unknown \n", + "4 NaN NaN NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded year_closed month_closed duration \\\n", + "0 39750000.0 2005.0 NaN NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 NaN NaN NaN \n", + "4 0.0 2008.0 NaN NaN NaN \n", + "\n", + " year_acquired month_acquired t_unt_acq term_code price_amount \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 2007.0 5.0 NaN NaN 20000000.0 \n", + "2 2005.0 5.0 NaN cash 0.0 \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " price_currency_code id ipo_id valuation_amount valuation_currency_code \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 USD NaN NaN NaN NaN \n", + "2 USD NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " raised_amount raised_currency_code public_at \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN " + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.options.display.max_columns = None\n", + "master_merged_slimmed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "# master_merged_slimmed.to_csv(r'data/success_master_merged_slimmed.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Success by being Acquired" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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00c:1Wetpaintweboperating2005-10-17NaNNaNUSAWA...2005.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
11c:10Flektorgames_videoacquiredNaNNaN2007-05-30USACA...NaNNaNNaNNaN2007.05.0NaNNaN20000000.0USD
22c:100Theregames_videoacquiredNaNNaN2005-05-29USACA...NaNNaNNaNNaN2005.05.0NaNcash0.0USD
33c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaN...2008.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
44c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaN...2008.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows × 23 columns

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" + ], + "text/plain": [ + " Unnamed: 0 id name category_code status \\\n", + "0 0 c:1 Wetpaint web operating \n", + "1 1 c:10 Flektor games_video acquired \n", + "2 2 c:100 There games_video acquired \n", + "3 3 c:10000 MYWEBBO network_hosting operating \n", + "4 4 c:10001 THE Movie Streamer games_video operating \n", + "\n", + " founded_at closed_at acquired_at country_code state_code ... year_founded \\\n", + "0 2005-10-17 NaN NaN USA WA ... 2005.0 \n", + "1 NaN NaN 2007-05-30 USA CA ... NaN \n", + "2 NaN NaN 2005-05-29 USA CA ... NaN \n", + "3 2008-07-26 NaN NaN NaN NaN ... 2008.0 \n", + "4 2008-07-26 NaN NaN NaN NaN ... 2008.0 \n", + "\n", + " year_closed month_closed duration year_acquired month_acquired \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN 2007.0 5.0 \n", + "2 NaN NaN NaN 2005.0 5.0 \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " t_unt_acq term_code price_amount price_currency_code \n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN 20000000.0 USD \n", + "2 NaN cash 0.0 USD \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_acq_joined_time = pd.read_csv(r'data/comps_acq_joined_time.csv')\n", + "comps_acq_joined_time.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# of company being" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Correlation of total funding amount and being acquired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Success by IPOing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ipos = pd.read_csv(r'data/initial/ipos.csv')\n", + "ipos.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Project 5 - Creating the Company DataFrame.ipynb b/your-project/code/Project 5 - Creating the Company DataFrame.ipynb new file mode 100644 index 0000000..c734729 --- /dev/null +++ b/your-project/code/Project 5 - Creating the Company DataFrame.ipynb @@ -0,0 +1,4318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating the Company Dataframe - merging with Python" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3051: DtypeWarning: Columns (3,7,9,10,17,18,21,22,23,25,26,29,30,37) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + }, + { + "data": { + "text/html": [ + "
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idobject_idoffice_iddescriptionregionaddress1address2cityzip_codestate_codecountry_codelatitudelongitudecreated_atupdated_at
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45c:77NaNSF BaySuite 200654 High StreetPalo Alto94301CAISR0.0000000.000000NaNNaN
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11c:15SF BaySan FranciscoCAUSA
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13c:18SF BayPalo AltoCAUSA
14c:19Los AngelesLos AngelesCAUSA
15c:20SF BaySan JoseCAUSA
16c:21United States - OtherNaNNaNUSA
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18c:23New YorkNew YorkNYUSA
19c:24California - OtherNaNCAUSA
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112702f:15096HyderabadHyderabadNaNIND
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" + ], + "text/plain": [ + " id region city state_code country_code\n", + "0 c:1 Seattle Seattle WA USA\n", + "1 c:3 SF Bay Pleasanton CA USA\n", + "2 c:4 SF Bay San Francisco CA USA\n", + "3 c:5 SF Bay Menlo Park CA USA\n", + "4 c:7 SF Bay Palo Alto CA ISR" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "offices_companies.rename(columns = {'object_id' : 'id'}, inplace = True)\n", + "offices_companies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "102135" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(offices_companies)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "196553" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(companies1)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identity_typeentity_idparent_idnamenormalized_namepermalinkcategory_codestatusfounded_at...last_milestone_atmilestonesrelationshipscreated_bycreated_atupdated_atregion_ycity_ystate_code_ycountry_code_y
0c:1Company1NaNWetpaintwetpaint/company/wetpaintweboperating2005-10-17...2013-09-18517initial-importer2007-05-25 06:51:272013-04-13 03:29:00SeattleSeattleWAUSA
1c:1Company1NaNWetpaintwetpaint/company/wetpaintweboperating2005-10-17...2013-09-18517initial-importer2007-05-25 06:51:272013-04-13 03:29:00New YorkNew YorkNYUSA
2c:10Company10NaNFlektorflektor/company/flektorgames_videoacquiredNaN...NaN06initial-importer2007-05-31 21:11:512008-05-23 23:23:14Los AngelesCulver CityCAUSA
3c:100Company100NaNTherethere/company/theregames_videoacquiredNaN...2011-09-23412initial-importer2007-08-06 23:52:452013-11-04 02:09:48SF BaySan MateoCAUSA
4c:10002Company10002NaNSynergie Mediasynergie media/company/synergie-mediaadvertisingoperating2007-06-27...NaN02NaN2008-08-24 17:39:102008-09-06 14:19:19AgadirAgadirNaNMAR
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5 rows × 44 columns

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" + ], + "text/plain": [ + " id entity_type entity_id parent_id name normalized_name \\\n", + "0 c:1 Company 1 NaN Wetpaint wetpaint \n", + "1 c:1 Company 1 NaN Wetpaint wetpaint \n", + "2 c:10 Company 10 NaN Flektor flektor \n", + "3 c:100 Company 100 NaN There there \n", + "4 c:10002 Company 10002 NaN Synergie Media synergie media \n", + "\n", + " permalink category_code status founded_at ... \\\n", + "0 /company/wetpaint web operating 2005-10-17 ... \n", + "1 /company/wetpaint web operating 2005-10-17 ... \n", + "2 /company/flektor games_video acquired NaN ... \n", + "3 /company/there games_video acquired NaN ... \n", + "4 /company/synergie-media advertising operating 2007-06-27 ... \n", + "\n", + " last_milestone_at milestones relationships created_by \\\n", + "0 2013-09-18 5 17 initial-importer \n", + "1 2013-09-18 5 17 initial-importer \n", + "2 NaN 0 6 initial-importer \n", + "3 2011-09-23 4 12 initial-importer \n", + "4 NaN 0 2 NaN \n", + "\n", + " created_at updated_at region_y city_y \\\n", + "0 2007-05-25 06:51:27 2013-04-13 03:29:00 Seattle Seattle \n", + "1 2007-05-25 06:51:27 2013-04-13 03:29:00 New York New York \n", + "2 2007-05-31 21:11:51 2008-05-23 23:23:14 Los Angeles Culver City \n", + "3 2007-08-06 23:52:45 2013-11-04 02:09:48 SF Bay San Mateo \n", + "4 2008-08-24 17:39:10 2008-09-06 14:19:19 Agadir Agadir \n", + "\n", + " state_code_y country_code_y \n", + "0 WA USA \n", + "1 NY USA \n", + "2 CA USA \n", + "3 CA USA \n", + "4 NaN MAR \n", + "\n", + "[5 rows x 44 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Join with Company Name\n", + "offices_companies_names = pd.merge(companies1, offices_companies, on = 'id' )\n", + "offices_companies_names.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "102135" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(offices_companies_names)\n", + "# Here there are some companies that do not appear in the office column and some companies with more than one office" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identity_typenamecategory_codestatusfounded_atclosed_atshort_descriptiondescriptionoverviewtag_listcountry_code_xstate_code_xcity_xregion_xfirst_investment_atlast_investment_atinvestment_roundsinvested_companiesfirst_funding_atlast_funding_atfunding_roundsfunding_total_usdfirst_milestone_atlast_milestone_atmilestonesregion_ycity_ystate_code_ycountry_code_y
0c:1CompanyWetpaintweboperating2005-10-17NaNNaNTechnology Platform CompanyWetpaint is a technology platform company that...wiki, seattle, elowitz, media-industry, media-...USAWASeattleSeattleNaNNaN002005-10-012008-05-19339750000.02010-09-052013-09-185SeattleSeattleWAUSA
1c:1CompanyWetpaintweboperating2005-10-17NaNNaNTechnology Platform CompanyWetpaint is a technology platform company that...wiki, seattle, elowitz, media-industry, media-...USAWASeattleSeattleNaNNaN002005-10-012008-05-19339750000.02010-09-052013-09-185New YorkNew YorkNYUSA
2c:10CompanyFlektorgames_videoacquiredNaNNaNNaNNaNFlektor is a rich-media mash-up platform that ...flektor, photo, videoUSACACulver CityLos AngelesNaNNaN00NaNNaN00.0NaNNaN0Los AngelesCulver CityCAUSA
3c:100CompanyTheregames_videoacquiredNaNNaNNaNNaNThere.com is an online virtual world where any...virtualworld, there, teensUSACASan MateoSF BayNaNNaN00NaNNaN00.02003-02-012011-09-234SF BaySan MateoCAUSA
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category_codeid
0advertising6098
1analytics1022
2automotive291
3biotech4430
4cleantech1940
5consulting5006
6design281
7ecommerce9065
8education2901
9enterprise4441
10fashion563
11finance1386
12games_video7520
13government43
14hardware2951
15health1698
16hospitality768
17legal1012
18local785
19manufacturing680
20medical1153
21messaging296
22mobile6862
23music581
24nanotech70
25network_hosting2350
26news768
27nonprofit184
28other13617
29pets61
30photo_video544
31public_relations2846
32real_estate474
33search2182
34security1171
35semiconductor696
36social1310
37software17922
38sports675
39transportation489
40travel936
41web15118
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" + ], + "text/plain": [ + " category_code id\n", + "0 advertising 6098\n", + "1 analytics 1022\n", + "2 automotive 291\n", + "3 biotech 4430\n", + "4 cleantech 1940\n", + "5 consulting 5006\n", + "6 design 281\n", + "7 ecommerce 9065\n", + "8 education 2901\n", + "9 enterprise 4441\n", + "10 fashion 563\n", + "11 finance 1386\n", + "12 games_video 7520\n", + "13 government 43\n", + "14 hardware 2951\n", + "15 health 1698\n", + "16 hospitality 768\n", + "17 legal 1012\n", + "18 local 785\n", + "19 manufacturing 680\n", + "20 medical 1153\n", + "21 messaging 296\n", + "22 mobile 6862\n", + "23 music 581\n", + "24 nanotech 70\n", + "25 network_hosting 2350\n", + "26 news 768\n", + "27 nonprofit 184\n", + "28 other 13617\n", + "29 pets 61\n", + "30 photo_video 544\n", + "31 public_relations 2846\n", + "32 real_estate 474\n", + "33 search 2182\n", + "34 security 1171\n", + "35 semiconductor 696\n", + "36 social 1310\n", + "37 software 17922\n", + "38 sports 675\n", + "39 transportation 489\n", + "40 travel 936\n", + "41 web 15118" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies_by_sector = pd.DataFrame([companies_by_sector.category_code, companies_by_sector.id]).transpose()\n", + "companies_by_sector" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "companies_by_sector.to_csv(r'data/companies_by_sector.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# next step visualize the data!" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0c:1Company1NaNWetpaintwetpaint/company/wetpaintweboperating2005-10-17...2008-05-19339750000.02010-09-052013-09-18517initial-importer2007-05-25 06:51:272013-04-13 03:29:00
1c:10Company10NaNFlektorflektor/company/flektorgames_videoacquiredNaN...NaN00.0NaNNaN06initial-importer2007-05-31 21:11:512008-05-23 23:23:14
2c:100Company100NaNTherethere/company/theregames_videoacquiredNaN...NaN00.02003-02-012011-09-23412initial-importer2007-08-06 23:52:452013-11-04 02:09:48
3c:10000Company10000NaNMYWEBBOmywebbo/company/mywebbonetwork_hostingoperating2008-07-26...NaN00.0NaNNaN00NaN2008-08-24 16:51:572008-09-06 14:19:18
4c:10001Company10001NaNTHE Movie Streamerthe movie streamer/company/the-movie-streamergames_videooperating2008-07-26...NaN00.0NaNNaN00NaN2008-08-24 17:10:342008-09-06 14:19:18
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5 rows × 40 columns

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" + ], + "text/plain": [ + " id entity_type entity_id parent_id name \\\n", + "0 c:1 Company 1 NaN Wetpaint \n", + "1 c:10 Company 10 NaN Flektor \n", + "2 c:100 Company 100 NaN There \n", + "3 c:10000 Company 10000 NaN MYWEBBO \n", + "4 c:10001 Company 10001 NaN THE Movie Streamer \n", + "\n", + " normalized_name permalink category_code \\\n", + "0 wetpaint /company/wetpaint web \n", + "1 flektor /company/flektor games_video \n", + "2 there /company/there games_video \n", + "3 mywebbo /company/mywebbo network_hosting \n", + "4 the movie streamer /company/the-movie-streamer games_video \n", + "\n", + " status founded_at ... last_funding_at funding_rounds \\\n", + "0 operating 2005-10-17 ... 2008-05-19 3 \n", + "1 acquired NaN ... NaN 0 \n", + "2 acquired NaN ... NaN 0 \n", + "3 operating 2008-07-26 ... NaN 0 \n", + "4 operating 2008-07-26 ... NaN 0 \n", + "\n", + " funding_total_usd first_milestone_at last_milestone_at milestones \\\n", + "0 39750000.0 2010-09-05 2013-09-18 5 \n", + "1 0.0 NaN NaN 0 \n", + "2 0.0 2003-02-01 2011-09-23 4 \n", + "3 0.0 NaN NaN 0 \n", + "4 0.0 NaN NaN 0 \n", + "\n", + " relationships created_by created_at updated_at \n", + "0 17 initial-importer 2007-05-25 06:51:27 2013-04-13 03:29:00 \n", + "1 6 initial-importer 2007-05-31 21:11:51 2008-05-23 23:23:14 \n", + "2 12 initial-importer 2007-08-06 23:52:45 2013-11-04 02:09:48 \n", + "3 0 NaN 2008-08-24 16:51:57 2008-09-06 14:19:18 \n", + "4 0 NaN 2008-08-24 17:10:34 2008-09-06 14:19:18 \n", + "\n", + "[5 rows x 40 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BIG DATAFRAME" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Okay... cleaining up Objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3051: DtypeWarning: Columns (3,7,9,10,17,18,21,22,23,25,26,29,30,37) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + }, + { + "data": { + "text/html": [ + "
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0c:1Company1NaNWetpaintwetpaint/company/wetpaintweboperating2005-10-17NaNwetpaint-inc.comhttp://wetpaint-inc.comBachelrWetpainthttp://s3.amazonaws.com/crunchbase_prod_assets...40154NaNTechnology Platform CompanyWetpaint is a technology platform company that...wiki, seattle, elowitz, media-industry, media-...USAWASeattleSeattleNaNNaN002005-10-012008-05-19339750000.02010-09-052013-09-18517initial-importer2007-05-25 06:51:272013-04-13 03:29:00
1c:10Company10NaNFlektorflektor/company/flektorgames_videoacquiredNaNNaNflektor.comhttp://www.flektor.comNaNhttp://s3.amazonaws.com/crunchbase_prod_assets...18685NaNNaNFlektor is a rich-media mash-up platform that ...flektor, photo, videoUSACACulver CityLos AngelesNaNNaN00NaNNaN00.0NaNNaN06initial-importer2007-05-31 21:11:512008-05-23 23:23:14
2c:100Company100NaNTherethere/company/theregames_videoacquiredNaNNaNthere.comhttp://www.there.comNaNhttp://s3.amazonaws.com/crunchbase_prod_assets...10734NaNNaNThere.com is an online virtual world where any...virtualworld, there, teensUSACASan MateoSF BayNaNNaN00NaNNaN00.02003-02-012011-09-23412initial-importer2007-08-06 23:52:452013-11-04 02:09:48
3c:10000Company10000NaNMYWEBBOmywebbo/company/mywebbonetwork_hostingoperating2008-07-26NaNmywebbo.comhttp://www.mywebbo.comNaNNaN00NaNNaNBRAND NEW ONLINE SOCIAL NETWORKING WEBSITE,FOR...social-network, new, website, web, friends, ch...NaNNaNNaNunknownNaNNaN00NaNNaN00.0NaNNaN00NaN2008-08-24 16:51:572008-09-06 14:19:18
4c:10001Company10001NaNTHE Movie Streamerthe movie streamer/company/the-movie-streamergames_videooperating2008-07-26NaNthemoviestreamer.comhttp://themoviestreamer.comNaNhttp://s3.amazonaws.com/crunchbase_prod_assets...20074NaNNaNThis company shows free movies online on their...watch, full-length, moives, online, for, free,...NaNNaNNaNunknownNaNNaN00NaNNaN00.0NaNNaN00NaN2008-08-24 17:10:342008-09-06 14:19:18
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" + ], + "text/plain": [ + " id entity_type entity_id parent_id name \\\n", + "0 c:1 Company 1 NaN Wetpaint \n", + "1 c:10 Company 10 NaN Flektor \n", + "2 c:100 Company 100 NaN There \n", + "3 c:10000 Company 10000 NaN MYWEBBO \n", + "4 c:10001 Company 10001 NaN THE Movie Streamer \n", + "\n", + " normalized_name permalink category_code \\\n", + "0 wetpaint /company/wetpaint web \n", + "1 flektor /company/flektor games_video \n", + "2 there /company/there games_video \n", + "3 mywebbo /company/mywebbo network_hosting \n", + "4 the movie streamer /company/the-movie-streamer games_video \n", + "\n", + " status founded_at closed_at domain \\\n", + "0 operating 2005-10-17 NaN wetpaint-inc.com \n", + "1 acquired NaN NaN flektor.com \n", + "2 acquired NaN NaN there.com \n", + "3 operating 2008-07-26 NaN mywebbo.com \n", + "4 operating 2008-07-26 NaN themoviestreamer.com \n", + "\n", + " homepage_url twitter_username \\\n", + "0 http://wetpaint-inc.com BachelrWetpaint \n", + "1 http://www.flektor.com NaN \n", + "2 http://www.there.com NaN \n", + "3 http://www.mywebbo.com NaN \n", + "4 http://themoviestreamer.com NaN \n", + "\n", + " logo_url logo_width logo_height \\\n", + "0 http://s3.amazonaws.com/crunchbase_prod_assets... 401 54 \n", + "1 http://s3.amazonaws.com/crunchbase_prod_assets... 186 85 \n", + "2 http://s3.amazonaws.com/crunchbase_prod_assets... 107 34 \n", + "3 NaN 0 0 \n", + "4 http://s3.amazonaws.com/crunchbase_prod_assets... 200 74 \n", + "\n", + " short_description description \\\n", + "0 NaN Technology Platform Company \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " overview \\\n", + "0 Wetpaint is a technology platform company that... \n", + "1 Flektor is a rich-media mash-up platform that ... \n", + "2 There.com is an online virtual world where any... \n", + "3 BRAND NEW ONLINE SOCIAL NETWORKING WEBSITE,FOR... \n", + "4 This company shows free movies online on their... \n", + "\n", + " tag_list country_code state_code \\\n", + "0 wiki, seattle, elowitz, media-industry, media-... USA WA \n", + "1 flektor, photo, video USA CA \n", + "2 virtualworld, there, teens USA CA \n", + "3 social-network, new, website, web, friends, ch... NaN NaN \n", + "4 watch, full-length, moives, online, for, free,... NaN NaN \n", + "\n", + " city region first_investment_at last_investment_at \\\n", + "0 Seattle Seattle NaN NaN \n", + "1 Culver City Los Angeles NaN NaN \n", + "2 San Mateo SF Bay NaN NaN \n", + "3 NaN unknown NaN NaN \n", + "4 NaN unknown NaN NaN \n", + "\n", + " investment_rounds invested_companies first_funding_at last_funding_at \\\n", + "0 0 0 2005-10-01 2008-05-19 \n", + "1 0 0 NaN NaN \n", + "2 0 0 NaN NaN \n", + "3 0 0 NaN NaN \n", + "4 0 0 NaN NaN \n", + "\n", + " funding_rounds funding_total_usd first_milestone_at last_milestone_at \\\n", + "0 3 39750000.0 2010-09-05 2013-09-18 \n", + "1 0 0.0 NaN NaN \n", + "2 0 0.0 2003-02-01 2011-09-23 \n", + "3 0 0.0 NaN NaN \n", + "4 0 0.0 NaN NaN \n", + "\n", + " milestones relationships created_by created_at \\\n", + "0 5 17 initial-importer 2007-05-25 06:51:27 \n", + "1 0 6 initial-importer 2007-05-31 21:11:51 \n", + "2 4 12 initial-importer 2007-08-06 23:52:45 \n", + "3 0 0 NaN 2008-08-24 16:51:57 \n", + "4 0 0 NaN 2008-08-24 17:10:34 \n", + "\n", + " updated_at \n", + "0 2013-04-13 03:29:00 \n", + "1 2008-05-23 23:23:14 \n", + "2 2013-11-04 02:09:48 \n", + "3 2008-09-06 14:19:18 \n", + "4 2008-09-06 14:19:18 " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objects = pd.read_csv(r'data/initial/objects.csv')\n", + "pd.options.display.max_columns = None\n", + "objects.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identity_typenamecategory_codestatusfounded_atclosed_atoverviewtag_listcountry_codestate_codecityregionfirst_investment_atlast_investment_atinvestment_roundsinvested_companiesfirst_funding_atlast_funding_atfunding_roundsfunding_total_usdfirst_milestone_atlast_milestone_atmilestones
0c:1CompanyWetpaintweboperating2005-10-17NaNWetpaint is a technology platform company that...wiki, seattle, elowitz, media-industry, media-...USAWASeattleSeattleNaNNaN002005-10-012008-05-19339750000.02010-09-052013-09-185
1c:10CompanyFlektorgames_videoacquiredNaNNaNFlektor is a rich-media mash-up platform that ...flektor, photo, videoUSACACulver CityLos AngelesNaNNaN00NaNNaN00.0NaNNaN0
2c:100CompanyTheregames_videoacquiredNaNNaNThere.com is an online virtual world where any...virtualworld, there, teensUSACASan MateoSF BayNaNNaN00NaNNaN00.02003-02-012011-09-234
3c:10000CompanyMYWEBBOnetwork_hostingoperating2008-07-26NaNBRAND NEW ONLINE SOCIAL NETWORKING WEBSITE,FOR...social-network, new, website, web, friends, ch...NaNNaNNaNunknownNaNNaN00NaNNaN00.0NaNNaN0
4c:10001CompanyTHE Movie Streamergames_videooperating2008-07-26NaNThis company shows free movies online on their...watch, full-length, moives, online, for, free,...NaNNaNNaNunknownNaNNaN00NaNNaN00.0NaNNaN0
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" + ], + "text/plain": [ + " id entity_type name category_code status \\\n", + "0 c:1 Company Wetpaint web operating \n", + "1 c:10 Company Flektor games_video acquired \n", + "2 c:100 Company There games_video acquired \n", + "3 c:10000 Company MYWEBBO network_hosting operating \n", + "4 c:10001 Company THE Movie Streamer games_video operating \n", + "\n", + " founded_at closed_at overview \\\n", + "0 2005-10-17 NaN Wetpaint is a technology platform company that... \n", + "1 NaN NaN Flektor is a rich-media mash-up platform that ... \n", + "2 NaN NaN There.com is an online virtual world where any... \n", + "3 2008-07-26 NaN BRAND NEW ONLINE SOCIAL NETWORKING WEBSITE,FOR... \n", + "4 2008-07-26 NaN This company shows free movies online on their... \n", + "\n", + " tag_list country_code state_code \\\n", + "0 wiki, seattle, elowitz, media-industry, media-... USA WA \n", + "1 flektor, photo, video USA CA \n", + "2 virtualworld, there, teens USA CA \n", + "3 social-network, new, website, web, friends, ch... NaN NaN \n", + "4 watch, full-length, moives, online, for, free,... NaN NaN \n", + "\n", + " city region first_investment_at last_investment_at \\\n", + "0 Seattle Seattle NaN NaN \n", + "1 Culver City Los Angeles NaN NaN \n", + "2 San Mateo SF Bay NaN NaN \n", + "3 NaN unknown NaN NaN \n", + "4 NaN unknown NaN NaN \n", + "\n", + " investment_rounds invested_companies first_funding_at last_funding_at \\\n", + "0 0 0 2005-10-01 2008-05-19 \n", + "1 0 0 NaN NaN \n", + "2 0 0 NaN NaN \n", + "3 0 0 NaN NaN \n", + "4 0 0 NaN NaN \n", + "\n", + " funding_rounds funding_total_usd first_milestone_at last_milestone_at \\\n", + "0 3 39750000.0 2010-09-05 2013-09-18 \n", + "1 0 0.0 NaN NaN \n", + "2 0 0.0 2003-02-01 2011-09-23 \n", + "3 0 0.0 NaN NaN \n", + "4 0 0.0 NaN NaN \n", + "\n", + " milestones \n", + "0 5 \n", + "1 0 \n", + "2 4 \n", + "3 0 \n", + "4 0 " + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "relevant = objects.drop(columns=['entity_id', 'parent_id', 'normalized_name', 'permalink', 'domain', 'homepage_url', 'twitter_username', 'logo_url', 'logo_width', 'logo_height', 'description', 'short_description', 'relationships', 'created_by', 'created_at', 'updated_at' ])\n", + "pd.options.display.max_columns = None\n", + "relevant.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identity_typenamecategory_codestatusfounded_atclosed_atoverviewtag_listcountry_codestate_codecityregionfirst_investment_atlast_investment_atinvestment_roundsinvested_companiesfirst_funding_atlast_funding_atfunding_roundsfunding_total_usdfirst_milestone_atlast_milestone_atmilestones
0c:1CompanyWetpaintweboperating2005-10-17NaNWetpaint is a technology platform company that...wiki, seattle, elowitz, media-industry, media-...USAWASeattleSeattleNaNNaN002005-10-012008-05-19339750000.02010-09-052013-09-185
1c:10CompanyFlektorgames_videoacquiredNaNNaNFlektor is a rich-media mash-up platform that ...flektor, photo, videoUSACACulver CityLos AngelesNaNNaN00NaNNaN00.0NaNNaN0
2c:100CompanyTheregames_videoacquiredNaNNaNThere.com is an online virtual world where any...virtualworld, there, teensUSACASan MateoSF BayNaNNaN00NaNNaN00.02003-02-012011-09-234
3c:10000CompanyMYWEBBOnetwork_hostingoperating2008-07-26NaNBRAND NEW ONLINE SOCIAL NETWORKING WEBSITE,FOR...social-network, new, website, web, friends, ch...NaNNaNNaNunknownNaNNaN00NaNNaN00.0NaNNaN0
4c:10001CompanyTHE Movie Streamergames_videooperating2008-07-26NaNThis company shows free movies online on their...watch, full-length, moives, online, for, free,...NaNNaNNaNunknownNaNNaN00NaNNaN00.0NaNNaN0
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" + ], + "text/plain": [ + " id entity_type name category_code status \\\n", + "0 c:1 Company Wetpaint web operating \n", + "1 c:10 Company Flektor games_video acquired \n", + "2 c:100 Company There games_video acquired \n", + "3 c:10000 Company MYWEBBO network_hosting operating \n", + "4 c:10001 Company THE Movie Streamer games_video operating \n", + "\n", + " founded_at closed_at overview \\\n", + "0 2005-10-17 NaN Wetpaint is a technology platform company that... \n", + "1 NaN NaN Flektor is a rich-media mash-up platform that ... \n", + "2 NaN NaN There.com is an online virtual world where any... \n", + "3 2008-07-26 NaN BRAND NEW ONLINE SOCIAL NETWORKING WEBSITE,FOR... \n", + "4 2008-07-26 NaN This company shows free movies online on their... \n", + "\n", + " tag_list country_code state_code \\\n", + "0 wiki, seattle, elowitz, media-industry, media-... USA WA \n", + "1 flektor, photo, video USA CA \n", + "2 virtualworld, there, teens USA CA \n", + "3 social-network, new, website, web, friends, ch... NaN NaN \n", + "4 watch, full-length, moives, online, for, free,... NaN NaN \n", + "\n", + " city region first_investment_at last_investment_at \\\n", + "0 Seattle Seattle NaN NaN \n", + "1 Culver City Los Angeles NaN NaN \n", + "2 San Mateo SF Bay NaN NaN \n", + "3 NaN unknown NaN NaN \n", + "4 NaN unknown NaN NaN \n", + "\n", + " investment_rounds invested_companies first_funding_at last_funding_at \\\n", + "0 0 0 2005-10-01 2008-05-19 \n", + "1 0 0 NaN NaN \n", + "2 0 0 NaN NaN \n", + "3 0 0 NaN NaN \n", + "4 0 0 NaN NaN \n", + "\n", + " funding_rounds funding_total_usd first_milestone_at last_milestone_at \\\n", + "0 3 39750000.0 2010-09-05 2013-09-18 \n", + "1 0 0.0 NaN NaN \n", + "2 0 0.0 2003-02-01 2011-09-23 \n", + "3 0 0.0 NaN NaN \n", + "4 0 0.0 NaN NaN \n", + "\n", + " milestones \n", + "0 5 \n", + "1 0 \n", + "2 4 \n", + "3 0 \n", + "4 0 " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies = relevant[relevant.id.str.startswith('c')]\n", + "companies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# relevant data for companies founded by year, industry and region\n", + "#companies_for_analysis = companies[companies.id, companies.name, companies.category_code, companies.status, companies.founded_at, companies.closed_at, companies.country_code, companies.state_code, companies.city, companies.region]" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "companies_for_analysis = companies.drop(columns=['entity_type', 'overview', 'tag_list', 'first_investment_at', 'last_investment_at', 'investment_rounds', 'invested_companies', 'first_funding_at', 'last_funding_at', 'funding_rounds', 'first_milestone_at', 'last_milestone_at', 'milestones'])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usd
0c:1Wetpaintweboperating2005-10-17NaNUSAWASeattleSeattle39750000.0
1c:10Flektorgames_videoacquiredNaNNaNUSACACulver CityLos Angeles0.0
2c:100Theregames_videoacquiredNaNNaNUSACASan MateoSF Bay0.0
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNunknown0.0
4c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNunknown0.0
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" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaN \n", + "2 c:100 There games_video acquired NaN \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaN USA WA Seattle Seattle \n", + "1 NaN USA CA Culver City Los Angeles \n", + "2 NaN USA CA San Mateo SF Bay \n", + "3 NaN NaN NaN NaN unknown \n", + "4 NaN NaN NaN NaN unknown \n", + "\n", + " funding_total_usd \n", + "0 39750000.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 " + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies_for_analysis.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "companies_for_analysis.to_csv('data/companies_for_analysis.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Project 5 - Main file.ipynb b/your-project/code/Project 5 - Main file.ipynb new file mode 100644 index 0000000..9fdfe1c --- /dev/null +++ b/your-project/code/Project 5 - Main file.ipynb @@ -0,0 +1,9131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 1 - having a general look at the data set" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Dataset from Kaggle: https://www.kaggle.com/arindam235/startup-investments-crunchbase, 2015" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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permalinknamehomepage_urlcategory_listmarketfunding_total_usdstatuscountry_codestate_coderegion...secondary_marketproduct_crowdfundinground_Around_Bround_Cround_Dround_Eround_Fround_Ground_H
0/organization/waywire#waywirehttp://www.waywire.com|Entertainment|Politics|Social Media|News|News17,50,000acquiredUSANYNew York City...0.00.00.00.00.00.00.00.00.00.0
1/organization/tv-communications&TV Communicationshttp://enjoyandtv.com|Games|Games40,00,000operatingUSACALos Angeles...0.00.00.00.00.00.00.00.00.00.0
2/organization/rock-your-paper'Rock' Your Paperhttp://www.rockyourpaper.org|Publishing|Education|Publishing40,000operatingESTNaNTallinn...0.00.00.00.00.00.00.00.00.00.0
3/organization/in-touch-network(In)Touch Networkhttp://www.InTouchNetwork.com|Electronics|Guides|Coffee|Restaurants|Music|i...Electronics15,00,000operatingGBRNaNLondon...0.00.00.00.00.00.00.00.00.00.0
4/organization/r-ranch-and-mine-R- Ranch and MineNaN|Tourism|Entertainment|Games|Tourism60,000operatingUSATXDallas...0.00.00.00.00.00.00.00.00.00.0
5/organization/club-domains.Club Domainshttp://nic.club/|Software|Software70,00,000NaNUSAFLFt. Lauderdale...0.00.00.07000000.00.00.00.00.00.00.0
6/organization/fox-networks.Fox Networkshttp://www.dotfox.com|Advertising|Advertising49,12,393closedARGNaNBuenos Aires...0.00.00.00.00.00.00.00.00.00.0
7/organization/0-6-com0-6.comhttp://www.0-6.com|Curated Web|Curated Web20,00,000operatingNaNNaNNaN...0.00.02000000.00.00.00.00.00.00.00.0
8/organization/004-technologies004 Technologieshttp://004gmbh.de/en/004-interact|Software|Software-operatingUSAILSpringfield, Illinois...0.00.00.00.00.00.00.00.00.00.0
9/organization/01games-technology01Games Technologyhttp://www.01games.hk/|Games|Games41,250operatingHKGNaNHong Kong...0.00.00.00.00.00.00.00.00.00.0
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" + ], + "text/plain": [ + " permalink name \\\n", + "0 /organization/waywire #waywire \n", + "1 /organization/tv-communications &TV Communications \n", + "2 /organization/rock-your-paper 'Rock' Your Paper \n", + "3 /organization/in-touch-network (In)Touch Network \n", + "4 /organization/r-ranch-and-mine -R- Ranch and Mine \n", + "5 /organization/club-domains .Club Domains \n", + "6 /organization/fox-networks .Fox Networks \n", + "7 /organization/0-6-com 0-6.com \n", + "8 /organization/004-technologies 004 Technologies \n", + "9 /organization/01games-technology 01Games Technology \n", + "\n", + " homepage_url \\\n", + "0 http://www.waywire.com \n", + "1 http://enjoyandtv.com \n", + "2 http://www.rockyourpaper.org \n", + "3 http://www.InTouchNetwork.com \n", + "4 NaN \n", + "5 http://nic.club/ \n", + "6 http://www.dotfox.com \n", + "7 http://www.0-6.com \n", + "8 http://004gmbh.de/en/004-interact \n", + "9 http://www.01games.hk/ \n", + "\n", + " category_list market \\\n", + "0 |Entertainment|Politics|Social Media|News| News \n", + "1 |Games| Games \n", + "2 |Publishing|Education| Publishing \n", + "3 |Electronics|Guides|Coffee|Restaurants|Music|i... Electronics \n", + "4 |Tourism|Entertainment|Games| Tourism \n", + "5 |Software| Software \n", + "6 |Advertising| Advertising \n", + "7 |Curated Web| Curated Web \n", + "8 |Software| Software \n", + "9 |Games| Games \n", + "\n", + " funding_total_usd status country_code state_code \\\n", + "0 17,50,000 acquired USA NY \n", + "1 40,00,000 operating USA CA \n", + "2 40,000 operating EST NaN \n", + "3 15,00,000 operating GBR NaN \n", + "4 60,000 operating USA TX \n", + "5 70,00,000 NaN USA FL \n", + "6 49,12,393 closed ARG NaN \n", + "7 20,00,000 operating NaN NaN \n", + "8 - operating USA IL \n", + "9 41,250 operating HKG NaN \n", + "\n", + " region ... secondary_market product_crowdfunding \\\n", + "0 New York City ... 0.0 0.0 \n", + "1 Los Angeles ... 0.0 0.0 \n", + "2 Tallinn ... 0.0 0.0 \n", + "3 London ... 0.0 0.0 \n", + "4 Dallas ... 0.0 0.0 \n", + "5 Ft. Lauderdale ... 0.0 0.0 \n", + "6 Buenos Aires ... 0.0 0.0 \n", + "7 NaN ... 0.0 0.0 \n", + "8 Springfield, Illinois ... 0.0 0.0 \n", + "9 Hong Kong ... 0.0 0.0 \n", + "\n", + " round_A round_B round_C round_D round_E round_F round_G round_H \n", + "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "5 0.0 7000000.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "7 2000000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + "[10 rows x 39 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Investments_VC = pd.read_csv(r'data/initial/investments_VC.csv', encoding = \"ISO-8859-1\")\n", + "Investments_VC.head(10)\n", + "# Intersting: Can use as control Data if Thesis is valid" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "54294" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(Investments_VC)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Datasets from Kaggle: https://www.kaggle.com/justinas/startup-investments, Crunchbase Snapshot 2013" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idfunding_round_idfunded_object_idinvestor_object_idcreated_atupdated_at
011c:4f:12007-07-04 04:52:572008-02-27 23:14:29
121c:4f:22007-07-04 04:52:572008-02-27 23:14:29
233c:5f:42007-05-27 06:09:102013-06-28 20:07:23
344c:5f:12007-05-27 06:09:362013-06-28 20:07:24
454c:5f:52007-05-27 06:09:362013-06-28 20:07:24
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" + ], + "text/plain": [ + " id funding_round_id funded_object_id investor_object_id \\\n", + "0 1 1 c:4 f:1 \n", + "1 2 1 c:4 f:2 \n", + "2 3 3 c:5 f:4 \n", + "3 4 4 c:5 f:1 \n", + "4 5 4 c:5 f:5 \n", + "\n", + " created_at updated_at \n", + "0 2007-07-04 04:52:57 2008-02-27 23:14:29 \n", + "1 2007-07-04 04:52:57 2008-02-27 23:14:29 \n", + "2 2007-05-27 06:09:10 2013-06-28 20:07:23 \n", + "3 2007-05-27 06:09:36 2013-06-28 20:07:24 \n", + "4 2007-05-27 06:09:36 2013-06-28 20:07:24 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "investments = pd.read_csv(r'data/initial/investments.csv')\n", + "investments.head()\n", + "# Interesting: Funding round dat, number of rounds, date and time in between investments and investor relevant" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80902\n" + ] + } + ], + "source": [ + "print(len(investments))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idacquisition_idacquiring_object_idacquired_object_idterm_codeprice_amountprice_currency_codeacquired_atsource_urlsource_descriptioncreated_atupdated_at
011c:11c:10NaN20000000.0USD2007-05-30http://venturebeat.com/2007/05/30/fox-interact...Fox Interactive confirms purchase of Photobuck...2007-05-31 22:19:542008-05-21 19:23:44
127c:59c:72cash60000000.0USD2007-07-01http://www.techcrunch.com/2007/07/02/deal-is-c...Deal is Confirmed: Google Acquired GrandCentral2007-07-03 08:14:502011-05-06 21:51:05
238c:24c:132cash280000000.0USD2007-05-01http://www.techcrunch.com/2007/05/30/cbs-acqui...CBS Acquires Europe’s Last.fm for $280 million2007-07-12 04:19:242008-05-19 04:48:50
349c:59c:155cash100000000.0USD2007-06-01http://techcrunch.com/2007/05/23/100-million-p...$100 Million Payday For Feedburner – This Deal...2007-07-13 09:52:592012-06-05 03:22:17
4510c:212c:215cash25000000.0USD2007-07-01http://blog.seattlepi.nwsource.com/venture/arc...seatlepi.com2007-07-20 05:29:072008-02-25 00:23:47
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" + ], + "text/plain": [ + " id acquisition_id acquiring_object_id acquired_object_id term_code \\\n", + "0 1 1 c:11 c:10 NaN \n", + "1 2 7 c:59 c:72 cash \n", + "2 3 8 c:24 c:132 cash \n", + "3 4 9 c:59 c:155 cash \n", + "4 5 10 c:212 c:215 cash \n", + "\n", + " price_amount price_currency_code acquired_at \\\n", + "0 20000000.0 USD 2007-05-30 \n", + "1 60000000.0 USD 2007-07-01 \n", + "2 280000000.0 USD 2007-05-01 \n", + "3 100000000.0 USD 2007-06-01 \n", + "4 25000000.0 USD 2007-07-01 \n", + "\n", + " source_url \\\n", + "0 http://venturebeat.com/2007/05/30/fox-interact... \n", + "1 http://www.techcrunch.com/2007/07/02/deal-is-c... \n", + "2 http://www.techcrunch.com/2007/05/30/cbs-acqui... \n", + "3 http://techcrunch.com/2007/05/23/100-million-p... \n", + "4 http://blog.seattlepi.nwsource.com/venture/arc... \n", + "\n", + " source_description created_at \\\n", + "0 Fox Interactive confirms purchase of Photobuck... 2007-05-31 22:19:54 \n", + "1 Deal is Confirmed: Google Acquired GrandCentral 2007-07-03 08:14:50 \n", + "2 CBS Acquires Europe’s Last.fm for $280 million 2007-07-12 04:19:24 \n", + "3 $100 Million Payday For Feedburner – This Deal... 2007-07-13 09:52:59 \n", + "4 seatlepi.com 2007-07-20 05:29:07 \n", + "\n", + " updated_at \n", + "0 2008-05-21 19:23:44 \n", + "1 2011-05-06 21:51:05 \n", + "2 2008-05-19 04:48:50 \n", + "3 2012-06-05 03:22:17 \n", + "4 2008-02-25 00:23:47 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acquisitions = pd.read_csv(r'data/initial/acquisitions.csv')\n", + "acquisitions.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idobject_iddegree_typesubjectinstitutiongraduated_atcreated_atupdated_at
01p:6117MBANaNNaNNaN2008-02-19 03:17:362008-02-19 03:17:36
12p:6136BAEnglish, FrenchWashington University, St. Louis1990-01-012008-02-19 17:58:312008-02-25 00:23:55
23p:6136MSMass CommunicationBoston University1992-01-012008-02-19 17:58:312008-02-25 00:23:55
34p:6005MSInternet TechnologyUniversity of Greenwich2006-01-012008-02-19 23:40:402008-02-25 00:23:55
45p:5832BCSComputer Science, PsychologyRice UniversityNaN2008-02-20 05:28:092008-02-20 05:28:09
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" + ], + "text/plain": [ + " id object_id degree_type subject \\\n", + "0 1 p:6117 MBA NaN \n", + "1 2 p:6136 BA English, French \n", + "2 3 p:6136 MS Mass Communication \n", + "3 4 p:6005 MS Internet Technology \n", + "4 5 p:5832 BCS Computer Science, Psychology \n", + "\n", + " institution graduated_at created_at \\\n", + "0 NaN NaN 2008-02-19 03:17:36 \n", + "1 Washington University, St. Louis 1990-01-01 2008-02-19 17:58:31 \n", + "2 Boston University 1992-01-01 2008-02-19 17:58:31 \n", + "3 University of Greenwich 2006-01-01 2008-02-19 23:40:40 \n", + "4 Rice University NaN 2008-02-20 05:28:09 \n", + "\n", + " updated_at \n", + "0 2008-02-19 03:17:36 \n", + "1 2008-02-25 00:23:55 \n", + "2 2008-02-25 00:23:55 \n", + "3 2008-02-25 00:23:55 \n", + "4 2008-02-20 05:28:09 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "degrees = pd.read_csv(r'data/initial/degrees.csv')\n", + "degrees.head()\n", + "# Very interesting Data, can find Position and Company via object ID and analyse Degree relationship towards success" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7149" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "array = degrees.degree_type.unique()\n", + "len(array)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idfunding_round_idobject_idfunded_atfunding_round_typefunding_round_coderaised_amount_usdraised_amountraised_currency_codepre_money_valuation_usd...post_money_valuationpost_money_currency_codeparticipantsis_first_roundis_last_roundsource_urlsource_descriptioncreated_bycreated_atupdated_at
011c:42006-12-01series-bb8500000.08500000.0USD0.0...0.0NaN200http://www.marketingvox.com/archives/2006/12/2...NaNinitial-importer2007-07-04 04:52:572008-02-27 23:14:29
122c:52004-09-01angelangel500000.0500000.0USD0.0...0.0USD201NaNNaNinitial-importer2007-05-27 06:08:182013-06-28 20:07:23
233c:52005-05-01series-aa12700000.012700000.0USD115000000.0...0.0USD300http://www.techcrunch.com/2007/11/02/jim-breye...Jim Breyer: Extra $500 Million Round For Faceb...initial-importer2007-05-27 06:09:102013-06-28 20:07:23
344c:52006-04-01series-bb27500000.027500000.0USD525000000.0...0.0USD400http://www.facebook.com/press/info.php?factsheetFacebook Fundinginitial-importer2007-05-27 06:09:362013-06-28 20:07:24
455c:72992006-05-01series-bb10500000.010500000.0USD0.0...0.0NaN200http://www.techcrunch.com/2006/05/14/photobuck...PhotoBucket Closes $10.5M From Trinity Venturesinitial-importer2007-05-29 11:05:592008-04-16 17:09:12
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5 rows × 23 columns

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" + ], + "text/plain": [ + " id funding_round_id object_id funded_at funding_round_type \\\n", + "0 1 1 c:4 2006-12-01 series-b \n", + "1 2 2 c:5 2004-09-01 angel \n", + "2 3 3 c:5 2005-05-01 series-a \n", + "3 4 4 c:5 2006-04-01 series-b \n", + "4 5 5 c:7299 2006-05-01 series-b \n", + "\n", + " funding_round_code raised_amount_usd raised_amount raised_currency_code \\\n", + "0 b 8500000.0 8500000.0 USD \n", + "1 angel 500000.0 500000.0 USD \n", + "2 a 12700000.0 12700000.0 USD \n", + "3 b 27500000.0 27500000.0 USD \n", + "4 b 10500000.0 10500000.0 USD \n", + "\n", + " pre_money_valuation_usd ... post_money_valuation \\\n", + "0 0.0 ... 0.0 \n", + "1 0.0 ... 0.0 \n", + "2 115000000.0 ... 0.0 \n", + "3 525000000.0 ... 0.0 \n", + "4 0.0 ... 0.0 \n", + "\n", + " post_money_currency_code participants is_first_round is_last_round \\\n", + "0 NaN 2 0 0 \n", + "1 USD 2 0 1 \n", + "2 USD 3 0 0 \n", + "3 USD 4 0 0 \n", + "4 NaN 2 0 0 \n", + "\n", + " source_url \\\n", + "0 http://www.marketingvox.com/archives/2006/12/2... \n", + "1 NaN \n", + "2 http://www.techcrunch.com/2007/11/02/jim-breye... \n", + "3 http://www.facebook.com/press/info.php?factsheet \n", + "4 http://www.techcrunch.com/2006/05/14/photobuck... \n", + "\n", + " source_description created_by \\\n", + "0 NaN initial-importer \n", + "1 NaN initial-importer \n", + "2 Jim Breyer: Extra $500 Million Round For Faceb... initial-importer \n", + "3 Facebook Funding initial-importer \n", + "4 PhotoBucket Closes $10.5M From Trinity Ventures initial-importer \n", + "\n", + " created_at updated_at \n", + "0 2007-07-04 04:52:57 2008-02-27 23:14:29 \n", + "1 2007-05-27 06:08:18 2013-06-28 20:07:23 \n", + "2 2007-05-27 06:09:10 2013-06-28 20:07:23 \n", + "3 2007-05-27 06:09:36 2013-06-28 20:07:24 \n", + "4 2007-05-29 11:05:59 2008-04-16 17:09:12 \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_rounds = pd.read_csv(r'data/initial/funding_rounds.csv')\n", + "funding_rounds.head()\n", + "# Very interesting data, amount, round name, need to look up investor by funding_round_id investor ID as well as company" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idfund_idobject_idnamefunded_atraised_amountraised_currency_codesource_urlsource_descriptioncreated_atupdated_at
011f:371Second Fund2008-12-16300000000.0USDhttp://www.pehub.com/26194/dfj-dragon-raising-...peHub2008-12-17 03:07:162008-12-17 03:07:16
144f:17Sequoia Israel Fourth Fund2008-12-17200750000.0USDhttp://www.pehub.com/26725/sequoia-israel-rais...Sequoia Israel Raises Fourth Fund2008-12-18 22:04:422008-12-18 22:04:42
255f:951Tenth fund2008-08-11650000000.0USDhttp://venturebeat.com/2008/08/11/interwest-cl...Venture Beat2008-12-31 09:47:512008-12-31 09:47:51
366f:192New funds acquireNaN625000000.0USDhttp://venturebeat.com/2008/07/28/us-venture-p...U.S. Venture Partners raises $625M fund for ne...2009-01-01 18:13:442009-01-01 18:16:27
477f:519Third fund2008-05-20200000000.0USDhttp://venturebeat.com/2008/05/20/disneys-stea...Venture Beat2009-01-03 09:51:582013-09-03 16:34:54
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" + ], + "text/plain": [ + " id fund_id object_id name funded_at \\\n", + "0 1 1 f:371 Second Fund 2008-12-16 \n", + "1 4 4 f:17 Sequoia Israel Fourth Fund 2008-12-17 \n", + "2 5 5 f:951 Tenth fund 2008-08-11 \n", + "3 6 6 f:192 New funds acquire NaN \n", + "4 7 7 f:519 Third fund 2008-05-20 \n", + "\n", + " raised_amount raised_currency_code \\\n", + "0 300000000.0 USD \n", + "1 200750000.0 USD \n", + "2 650000000.0 USD \n", + "3 625000000.0 USD \n", + "4 200000000.0 USD \n", + "\n", + " source_url \\\n", + "0 http://www.pehub.com/26194/dfj-dragon-raising-... \n", + "1 http://www.pehub.com/26725/sequoia-israel-rais... \n", + "2 http://venturebeat.com/2008/08/11/interwest-cl... \n", + "3 http://venturebeat.com/2008/07/28/us-venture-p... \n", + "4 http://venturebeat.com/2008/05/20/disneys-stea... \n", + "\n", + " source_description created_at \\\n", + "0 peHub 2008-12-17 03:07:16 \n", + "1 Sequoia Israel Raises Fourth Fund 2008-12-18 22:04:42 \n", + "2 Venture Beat 2008-12-31 09:47:51 \n", + "3 U.S. Venture Partners raises $625M fund for ne... 2009-01-01 18:13:44 \n", + "4 Venture Beat 2009-01-03 09:51:58 \n", + "\n", + " updated_at \n", + "0 2008-12-17 03:07:16 \n", + "1 2008-12-18 22:04:42 \n", + "2 2008-12-31 09:47:51 \n", + "3 2009-01-01 18:16:27 \n", + "4 2013-09-03 16:34:54 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funds = pd.read_csv(r'data/funds.csv')\n", + "funds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idipo_idobject_idvaluation_amountvaluation_currency_coderaised_amountraised_currency_codepublic_atstock_symbolsource_urlsource_descriptioncreated_atupdated_at
011c:16540.000000e+00USD0.0USD1980-12-19NASDAQ:AAPLNaNNaN2008-02-09 05:17:452012-04-12 04:02:59
122c:12420.000000e+00USD0.0NaN1986-03-13NASDAQ:MSFTNaNNaN2008-02-09 05:25:182010-12-11 12:39:46
233c:3420.000000e+00USD0.0NaN1969-06-09NYSE:DISNaNNaN2008-02-09 05:40:322010-12-23 08:58:16
344c:590.000000e+00USD0.0NaN2004-08-25NASDAQ:GOOGNaNNaN2008-02-10 22:51:242011-08-01 20:47:08
455c:3171.000000e+11USD0.0NaN1997-05-01NASDAQ:AMZNNaNNaN2008-02-10 23:28:092011-08-01 21:11:22
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" + ], + "text/plain": [ + " id ipo_id object_id valuation_amount valuation_currency_code \\\n", + "0 1 1 c:1654 0.000000e+00 USD \n", + "1 2 2 c:1242 0.000000e+00 USD \n", + "2 3 3 c:342 0.000000e+00 USD \n", + "3 4 4 c:59 0.000000e+00 USD \n", + "4 5 5 c:317 1.000000e+11 USD \n", + "\n", + " raised_amount raised_currency_code public_at stock_symbol source_url \\\n", + "0 0.0 USD 1980-12-19 NASDAQ:AAPL NaN \n", + "1 0.0 NaN 1986-03-13 NASDAQ:MSFT NaN \n", + "2 0.0 NaN 1969-06-09 NYSE:DIS NaN \n", + "3 0.0 NaN 2004-08-25 NASDAQ:GOOG NaN \n", + "4 0.0 NaN 1997-05-01 NASDAQ:AMZN NaN \n", + "\n", + " source_description created_at updated_at \n", + "0 NaN 2008-02-09 05:17:45 2012-04-12 04:02:59 \n", + "1 NaN 2008-02-09 05:25:18 2010-12-11 12:39:46 \n", + "2 NaN 2008-02-09 05:40:32 2010-12-23 08:58:16 \n", + "3 NaN 2008-02-10 22:51:24 2011-08-01 20:47:08 \n", + "4 NaN 2008-02-10 23:28:09 2011-08-01 21:11:22 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ipos = pd.read_csv(r'data/initial/ipos.csv')\n", + "ipos.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idobject_idmilestone_atmilestone_codedescriptionsource_urlsource_descriptioncreated_atupdated_at
01c:122008-06-09otherSurvives iPhone 3G Stevenotehttp://www.techcrunch.com/2008/06/10/twitter-f...Twitter Fails To Fail, Community Rejoices2008-06-18 08:14:062008-06-18 08:14:06
12c:31382008-06-17otherTwhirl announces support for Seesmic video pla...http://www.inquisitr.com/1103/seesmic-now-avai...Seesmic Now Available In Twhirl2008-06-18 08:46:282008-06-18 08:46:28
23c:592008-06-18otherMore than 4 Billion videos viewed at Google Si...http://www.comscore.com/press/release.asp?pres...11 Billion Videos Viewed Online in the U.S. in...2008-06-18 08:50:242008-06-18 08:50:24
34c:3142008-06-18otherReddit goes Open Sourcehttp://blog.reddit.com/2008/06/reddit-goes-ope...reddit goes open source2008-06-19 04:14:002008-06-19 04:14:00
45c:3142008-01-22otherAdds the ability to create your own Redditshttp://www.techcrunch.com/2008/01/22/reddit-ad...Reddit Adds Ability to Create Your Own “Redd...2008-06-19 04:15:532008-06-19 04:15:53
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" + ], + "text/plain": [ + " id object_id milestone_at milestone_code \\\n", + "0 1 c:12 2008-06-09 other \n", + "1 2 c:3138 2008-06-17 other \n", + "2 3 c:59 2008-06-18 other \n", + "3 4 c:314 2008-06-18 other \n", + "4 5 c:314 2008-01-22 other \n", + "\n", + " description \\\n", + "0 Survives iPhone 3G Stevenote \n", + "1 Twhirl announces support for Seesmic video pla... \n", + "2 More than 4 Billion videos viewed at Google Si... \n", + "3 Reddit goes Open Source \n", + "4 Adds the ability to create your own Reddits \n", + "\n", + " source_url \\\n", + "0 http://www.techcrunch.com/2008/06/10/twitter-f... \n", + "1 http://www.inquisitr.com/1103/seesmic-now-avai... \n", + "2 http://www.comscore.com/press/release.asp?pres... \n", + "3 http://blog.reddit.com/2008/06/reddit-goes-ope... \n", + "4 http://www.techcrunch.com/2008/01/22/reddit-ad... \n", + "\n", + " source_description created_at \\\n", + "0 Twitter Fails To Fail, Community Rejoices 2008-06-18 08:14:06 \n", + "1 Seesmic Now Available In Twhirl 2008-06-18 08:46:28 \n", + "2 11 Billion Videos Viewed Online in the U.S. in... 2008-06-18 08:50:24 \n", + "3 reddit goes open source 2008-06-19 04:14:00 \n", + "4 Reddit Adds Ability to Create Your Own “Redd... 2008-06-19 04:15:53 \n", + "\n", + " updated_at \n", + "0 2008-06-18 08:14:06 \n", + "1 2008-06-18 08:46:28 \n", + "2 2008-06-18 08:50:24 \n", + "3 2008-06-19 04:14:00 \n", + "4 2008-06-19 04:15:53 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "milestones = pd.read_csv(r'data/milestones.csv')\n", + "milestones.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3051: DtypeWarning: Columns (3,7,9,10,17,18,21,22,23,25,26,29,30,37) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + }, + { + "data": { + "text/html": [ + "
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0c:1Company1NaNWetpaintwetpaint/company/wetpaintweboperating2005-10-17...2008-05-19339750000.02010-09-052013-09-18517initial-importer2007-05-25 06:51:272013-04-13 03:29:00
1c:10Company10NaNFlektorflektor/company/flektorgames_videoacquiredNaN...NaN00.0NaNNaN06initial-importer2007-05-31 21:11:512008-05-23 23:23:14
2c:100Company100NaNTherethere/company/theregames_videoacquiredNaN...NaN00.02003-02-012011-09-23412initial-importer2007-08-06 23:52:452013-11-04 02:09:48
3c:10000Company10000NaNMYWEBBOmywebbo/company/mywebbonetwork_hostingoperating2008-07-26...NaN00.0NaNNaN00NaN2008-08-24 16:51:572008-09-06 14:19:18
4c:10001Company10001NaNTHE Movie Streamerthe movie streamer/company/the-movie-streamergames_videooperating2008-07-26...NaN00.0NaNNaN00NaN2008-08-24 17:10:342008-09-06 14:19:18
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5 rows × 40 columns

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" + ], + "text/plain": [ + " id entity_type entity_id parent_id name \\\n", + "0 c:1 Company 1 NaN Wetpaint \n", + "1 c:10 Company 10 NaN Flektor \n", + "2 c:100 Company 100 NaN There \n", + "3 c:10000 Company 10000 NaN MYWEBBO \n", + "4 c:10001 Company 10001 NaN THE Movie Streamer \n", + "\n", + " normalized_name permalink category_code \\\n", + "0 wetpaint /company/wetpaint web \n", + "1 flektor /company/flektor games_video \n", + "2 there /company/there games_video \n", + "3 mywebbo /company/mywebbo network_hosting \n", + "4 the movie streamer /company/the-movie-streamer games_video \n", + "\n", + " status founded_at ... last_funding_at funding_rounds \\\n", + "0 operating 2005-10-17 ... 2008-05-19 3 \n", + "1 acquired NaN ... NaN 0 \n", + "2 acquired NaN ... NaN 0 \n", + "3 operating 2008-07-26 ... NaN 0 \n", + "4 operating 2008-07-26 ... NaN 0 \n", + "\n", + " funding_total_usd first_milestone_at last_milestone_at milestones \\\n", + "0 39750000.0 2010-09-05 2013-09-18 5 \n", + "1 0.0 NaN NaN 0 \n", + "2 0.0 2003-02-01 2011-09-23 4 \n", + "3 0.0 NaN NaN 0 \n", + "4 0.0 NaN NaN 0 \n", + "\n", + " relationships created_by created_at updated_at \n", + "0 17 initial-importer 2007-05-25 06:51:27 2013-04-13 03:29:00 \n", + "1 6 initial-importer 2007-05-31 21:11:51 2008-05-23 23:23:14 \n", + "2 12 initial-importer 2007-08-06 23:52:45 2013-11-04 02:09:48 \n", + "3 0 NaN 2008-08-24 16:51:57 2008-09-06 14:19:18 \n", + "4 0 NaN 2008-08-24 17:10:34 2008-09-06 14:19:18 \n", + "\n", + "[5 rows x 40 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objects = pd.read_csv(r'data/initial/objects.csv')\n", + "objects.head()\n", + "# Very interesting: Data on Investors, People and Companies & Category Code as well as status\n", + "# Next step: trennen" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Company', 'FinancialOrg', 'Person', 'Product'], dtype=object)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objects.entity_type.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['web', 'games_video', 'network_hosting', 'advertising',\n", + " 'cleantech', nan, 'enterprise', 'other', 'consulting', 'mobile',\n", + " 'health', 'software', 'analytics', 'finance', 'education',\n", + " 'medical', 'manufacturing', 'biotech', 'ecommerce',\n", + " 'public_relations', 'hardware', 'search', 'news', 'government',\n", + " 'security', 'photo_video', 'travel', 'semiconductor', 'social',\n", + " 'legal', 'transportation', 'hospitality', 'sports', 'nonprofit',\n", + " 'fashion', 'messaging', 'music', 'automotive', 'design',\n", + " 'real_estate', 'local', 'nanotech', 'pets'], dtype=object)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objects.category_code.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "462651\n" + ] + } + ], + "source": [ + "print(len(objects))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idobject_idoffice_iddescriptionregionaddress1address2cityzip_codestate_codecountry_codelatitudelongitudecreated_atupdated_at
01c:11NaNSeattle710 - 2nd AvenueSuite 1100Seattle98104WAUSA47.603122-122.333253NaNNaN
12c:33HeadquartersSF Bay4900 Hopyard RdSuite 310Pleasanton94588CAUSA37.692934-121.904945NaNNaN
23c:44NaNSF Bay135 Mississippi StNaNSan Francisco94107CAUSA37.764726-122.394523NaNNaN
34c:55HeadquartersSF Bay1601 Willow RoadNaNMenlo Park94025CAUSA37.416050-122.151801NaNNaN
45c:77NaNSF BaySuite 200654 High StreetPalo Alto94301CAISR0.0000000.000000NaNNaN
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" + ], + "text/plain": [ + " id object_id office_id description region address1 \\\n", + "0 1 c:1 1 NaN Seattle 710 - 2nd Avenue \n", + "1 2 c:3 3 Headquarters SF Bay 4900 Hopyard Rd \n", + "2 3 c:4 4 NaN SF Bay 135 Mississippi St \n", + "3 4 c:5 5 Headquarters SF Bay 1601 Willow Road \n", + "4 5 c:7 7 NaN SF Bay Suite 200 \n", + "\n", + " address2 city zip_code state_code country_code latitude \\\n", + "0 Suite 1100 Seattle 98104 WA USA 47.603122 \n", + "1 Suite 310 Pleasanton 94588 CA USA 37.692934 \n", + "2 NaN San Francisco 94107 CA USA 37.764726 \n", + "3 NaN Menlo Park 94025 CA USA 37.416050 \n", + "4 654 High Street Palo Alto 94301 CA ISR 0.000000 \n", + "\n", + " longitude created_at updated_at \n", + "0 -122.333253 NaN NaN \n", + "1 -121.904945 NaN NaN \n", + "2 -122.394523 NaN NaN \n", + "3 -122.151801 NaN NaN \n", + "4 0.000000 NaN NaN " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "offices = pd.read_csv(r'data/initial/offices.csv')\n", + "offices.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idobject_idfirst_namelast_namebirthplaceaffiliation_name
01p:2BenElowitzNaNBlue Nile
12p:3KevinFlahertyNaNWetpaint
23p:4RajuVegesnaNaNZoho
34p:5IanWenigNaNZoho
45p:6KevinRoseRedding, CAi/o Ventures
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" + ], + "text/plain": [ + " id object_id first_name last_name birthplace affiliation_name\n", + "0 1 p:2 Ben Elowitz NaN Blue Nile\n", + "1 2 p:3 Kevin Flaherty NaN Wetpaint\n", + "2 3 p:4 Raju Vegesna NaN Zoho\n", + "3 4 p:5 Ian Wenig NaN Zoho\n", + "4 5 p:6 Kevin Rose Redding, CA i/o Ventures" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "people = pd.read_csv(r'data/initial/people.csv')\n", + "people.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idrelationship_idperson_object_idrelationship_object_idstart_atend_atis_pastsequencetitlecreated_atupdated_at
011p:2c:1NaNNaN08Co-Founder/CEO/Board of Directors2007-05-25 07:03:542013-06-03 09:58:46
122p:3c:1NaNNaN1279242VP Marketing2007-05-25 07:04:162010-05-21 16:31:34
233p:4c:3NaNNaN04Evangelist2007-05-25 19:33:032013-06-29 13:36:58
344p:5c:32006-03-012009-12-0114Senior Director Strategic Alliances2007-05-25 19:34:532013-06-29 10:25:34
466p:7c:42005-07-012010-04-0511Chief Executive Officer2007-05-25 20:05:332010-04-05 18:41:41
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" + ], + "text/plain": [ + " id relationship_id person_object_id relationship_object_id start_at \\\n", + "0 1 1 p:2 c:1 NaN \n", + "1 2 2 p:3 c:1 NaN \n", + "2 3 3 p:4 c:3 NaN \n", + "3 4 4 p:5 c:3 2006-03-01 \n", + "4 6 6 p:7 c:4 2005-07-01 \n", + "\n", + " end_at is_past sequence title \\\n", + "0 NaN 0 8 Co-Founder/CEO/Board of Directors \n", + "1 NaN 1 279242 VP Marketing \n", + "2 NaN 0 4 Evangelist \n", + "3 2009-12-01 1 4 Senior Director Strategic Alliances \n", + "4 2010-04-05 1 1 Chief Executive Officer \n", + "\n", + " created_at updated_at \n", + "0 2007-05-25 07:03:54 2013-06-03 09:58:46 \n", + "1 2007-05-25 07:04:16 2010-05-21 16:31:34 \n", + "2 2007-05-25 19:33:03 2013-06-29 13:36:58 \n", + "3 2007-05-25 19:34:53 2013-06-29 10:25:34 \n", + "4 2007-05-25 20:05:33 2010-04-05 18:41:41 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "relationships = pd.read_csv(r'data/relationships.csv')\n", + "relationships.head()\n", + "# Very interesting to piece together founder qualities with success" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CompanyValuation ($B)Date JoinedCountryIndustrySelect Investors
0Toutiao (Bytedance)$754/7/2017ChinaArtificial intelligenceSequoia Capital China, SIG Asia Investments, S...
1Didi Chuxing$5612/31/2014ChinaAuto & transportationMatrix Partners, Tiger Global Management, Sof...
2Stripe$361/23/2014United StatesFintechKhosla Ventures, LowercaseCapital, capitalG
3SpaceX$33.312/1/2012United StatesOtherFounders Fund, Draper Fisher Jurvetson, Rothe...
4Airbnb$187/26/2011United StatesTravelGeneral Catalyst Partners, Andreessen Horowit...
\n", + "
" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined Country \\\n", + "0 Toutiao (Bytedance) $75 4/7/2017 China \n", + "1 Didi Chuxing $56 12/31/2014 China \n", + "2 Stripe $36 1/23/2014 United States \n", + "3 SpaceX $33.3 12/1/2012 United States \n", + "4 Airbnb $18 7/26/2011 United States \n", + "\n", + " Industry Select Investors \n", + "0 Artificial intelligence Sequoia Capital China, SIG Asia Investments, S... \n", + "1 Auto & transportation Matrix Partners, Tiger Global Management, Sof... \n", + "2 Fintech Khosla Ventures, LowercaseCapital, capitalG \n", + "3 Other Founders Fund, Draper Fisher Jurvetson, Rothe... \n", + "4 Travel General Catalyst Partners, Andreessen Horowit... " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unicorntracker = pd.read_csv(r'data/cbinsights_unicorntracker.csv').drop(columns='Unnamed: 0')\n", + "unicorntracker.head()\n", + "# interesting because Unicorns as of April 2020" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CompanyValuation ($B)Date JoinedCountryIndustrySelect InvestorsSelect Investors_new
0Toutiao(Bytedance)75.04/7/2017ChinaArtificialintelligenceSequoiaCapitalChina,SIGAsiaInvestments,SinaWei...NaN
1DidiChuxing56.012/31/2014ChinaAuto&transportationNaNMatrixPartners,TigerGlobalManagement,SoftbankC...
2Stripe36.01/23/2014UnitedStatesFintechKhoslaVentures,LowercaseCapital,capitalGNaN
3SpaceX33.312/1/2012UnitedStatesOtherNaNFoundersFund,DraperFisherJurvetson,RothenbergV...
4Airbnb18.07/26/2011UnitedStatesTravelNaNGeneralCatalystPartners,AndreessenHorowitz,ENI...
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" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined Country \\\n", + "0 Toutiao(Bytedance) 75.0 4/7/2017 China \n", + "1 DidiChuxing 56.0 12/31/2014 China \n", + "2 Stripe 36.0 1/23/2014 UnitedStates \n", + "3 SpaceX 33.3 12/1/2012 UnitedStates \n", + "4 Airbnb 18.0 7/26/2011 UnitedStates \n", + "\n", + " Industry Select Investors \\\n", + "0 Artificialintelligence SequoiaCapitalChina,SIGAsiaInvestments,SinaWei... \n", + "1 Auto&transportation NaN \n", + "2 Fintech KhoslaVentures,LowercaseCapital,capitalG \n", + "3 Other NaN \n", + "4 Travel NaN \n", + "\n", + " Select Investors_new \n", + "0 NaN \n", + "1 MatrixPartners,TigerGlobalManagement,SoftbankC... \n", + "2 NaN \n", + "3 FoundersFund,DraperFisherJurvetson,RothenbergV... \n", + "4 GeneralCatalystPartners,AndreessenHorowitz,ENI... " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unicornsentire = pd.read_csv(r'data/cbinsights_entire_unicorn_tracker_sorted.csv').drop(columns='Unnamed: 0')\n", + "unicornsentire.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 2 - Exploring the Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check Nans and Dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 3 - answering general questions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### I. Companies Founded by Year, Industry and Geography" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Analyse" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usd
0c:1Wetpaintweboperating2005-10-17NaNUSAWASeattleSeattle39750000.0
1c:10Flektorgames_videoacquiredNaNNaNUSACACulver CityLos Angeles0.0
2c:100Theregames_videoacquiredNaNNaNUSACASan MateoSF Bay0.0
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNunknown0.0
4c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNunknown0.0
\n", + "
" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaN \n", + "2 c:100 There games_video acquired NaN \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaN USA WA Seattle Seattle \n", + "1 NaN USA CA Culver City Los Angeles \n", + "2 NaN USA CA San Mateo SF Bay \n", + "3 NaN NaN NaN NaN unknown \n", + "4 NaN NaN NaN NaN unknown \n", + "\n", + " funding_total_usd \n", + "0 39750000.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 " + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Companies Founded by Year\n", + "companies = pd.read_csv(r'data/companies_for_analysis.csv').drop(columns = ['Unnamed: 0'])\n", + "companies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "companies.founded_at = companies.founded_at.astype('datetime64')\n", + "companies.closed_at = companies.closed_at.astype('datetime64')" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "companies['year_founded'], companies['month_founded'] = companies['founded_at'].dt.year, companies['founded_at'].dt.month" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1901.0" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies.year_founded.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "comps_consolidated_ann = companies.groupby(['year_founded']).count()\n", + "comps_consolidated_ann = comps_consolidated_ann.reset_index()\n", + "comps_consolidated_ann = comps_consolidated_ann[['year_founded', 'name']]\n", + "comps_consolidated_ann.rename(columns = {'name' : 'number'}, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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year_foundednumber
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1102011.011216
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1122013.05828
1132014.016
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" + ], + "text/plain": [ + " year_founded number\n", + "109 2010.0 10183\n", + "110 2011.0 11216\n", + "111 2012.0 10584\n", + "112 2013.0 5828\n", + "113 2014.0 16" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_consolidated_ann.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Histogram\n", + "comps_consolidated_ann.plot(x='year_founded', y= 'number', kind = 'hist')" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ], + "text/plain": [ + " year_founded number yoy_growth\n", + "109 2010.0 10183 0.138020\n", + "110 2011.0 11216 0.101444\n", + "111 2012.0 10584 -0.056348\n", + "112 2013.0 5828 -0.449358\n", + "113 2014.0 16 -0.997255" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# average annual growth in company's founded\n", + "comps_consolidated_ann['yoy_growth'] = (comps_consolidated_ann['number'] -comps_consolidated_ann['number'].shift(1)) / comps_consolidated_ann['number'].shift(1)\n", + "comps_consolidated_ann.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# comps_consolidated_ann.to_csv(r'data/comps_cons_ann_growth.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Same thing for companies closed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# --> use growth rate for probability of growing" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Hypothesis\n", + "# Can you say with a 95% confidence that company foundings will grow by x% yoy" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Statistsics for Reliability\n", + "#--> how many companies were founded, what was the everage growth and where " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Companies Founded by Industry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Companies Founded by Industry & Year" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeid
0advertising6098
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2automotive291
3biotech4430
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" + ], + "text/plain": [ + " category_code id\n", + "0 advertising 6098\n", + "1 analytics 1022\n", + "2 automotive 291\n", + "3 biotech 4430\n", + "4 cleantech 1940" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Industry\n", + "comp_by_ind = pd.read_csv(r'data/companies_by_sector.csv').drop(columns = ['Unnamed: 0'])\n", + "comp_by_ind.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeid
0software17922
1web15118
2other13617
3ecommerce9065
4games_video7520
5mobile6862
6advertising6098
7consulting5006
8enterprise4441
9biotech4430
10hardware2951
11education2901
12public_relations2846
13network_hosting2350
14search2182
15cleantech1940
16health1698
17finance1386
18social1310
19security1171
20medical1153
21analytics1022
22legal1012
23travel936
24local785
25hospitality768
26news768
27semiconductor696
28manufacturing680
29sports675
30music581
31fashion563
32photo_video544
33transportation489
34real_estate474
35messaging296
36automotive291
37design281
38nonprofit184
39nanotech70
40pets61
41government43
\n", + "
" + ], + "text/plain": [ + " category_code id\n", + "0 software 17922\n", + "1 web 15118\n", + "2 other 13617\n", + "3 ecommerce 9065\n", + "4 games_video 7520\n", + "5 mobile 6862\n", + "6 advertising 6098\n", + "7 consulting 5006\n", + "8 enterprise 4441\n", + "9 biotech 4430\n", + "10 hardware 2951\n", + "11 education 2901\n", + "12 public_relations 2846\n", + "13 network_hosting 2350\n", + "14 search 2182\n", + "15 cleantech 1940\n", + "16 health 1698\n", + "17 finance 1386\n", + "18 social 1310\n", + "19 security 1171\n", + "20 medical 1153\n", + "21 analytics 1022\n", + "22 legal 1012\n", + "23 travel 936\n", + "24 local 785\n", + "25 hospitality 768\n", + "26 news 768\n", + "27 semiconductor 696\n", + "28 manufacturing 680\n", + "29 sports 675\n", + "30 music 581\n", + "31 fashion 563\n", + "32 photo_video 544\n", + "33 transportation 489\n", + "34 real_estate 474\n", + "35 messaging 296\n", + "36 automotive 291\n", + "37 design 281\n", + "38 nonprofit 184\n", + "39 nanotech 70\n", + "40 pets 61\n", + "41 government 43" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comp_by_ind = comp_by_ind.sort_values(by = \"id\", ascending = False).reset_index(drop=True)\n", + "comp_by_ind" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "123186" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(comp_by_ind['id'])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeidpercent
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" + ], + "text/plain": [ + " category_code id percent\n", + "0 software 17922 0.145487\n", + "1 web 15118 0.122725\n", + "2 other 13617 0.110540\n", + "3 ecommerce 9065 0.073588\n", + "4 games_video 7520 0.061046" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comp_by_ind['percent'] = comp_by_ind['id'] / sum(comp_by_ind['id'])\n", + "comp_by_ind.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/ipykernel_launcher.py:8: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. \n", + " \n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "labels = comp_by_ind['category_code']\n", + "sizes = comp_by_ind['percent']\n", + "patches, texts = plt.pie(sizes, shadow=True, startangle=90)\n", + "plt.legend(patches, labels, loc=\"best\")\n", + "plt.axis('equal')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Growth yoy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Companies founded by Industry and Year" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usdyear_foundedmonth_foundedyear_closedmonth_closed
0c:1Wetpaintweboperating2005-10-17NaTUSAWASeattleSeattle39750000.02005.010.0NaNNaN
1c:10Flektorgames_videoacquiredNaTNaTUSACACulver CityLos Angeles0.0NaNNaNNaNNaN
2c:100Theregames_videoacquiredNaTNaTUSACASan MateoSF Bay0.0NaNNaNNaNNaN
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaN
\n", + "
" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaT \n", + "2 c:100 There games_video acquired NaT \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaT USA WA Seattle Seattle \n", + "1 NaT USA CA Culver City Los Angeles \n", + "2 NaT USA CA San Mateo SF Bay \n", + "3 NaT NaN NaN NaN unknown \n", + "4 NaT NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \n", + "0 39750000.0 2005.0 10.0 NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 7.0 NaN NaN \n", + "4 0.0 2008.0 7.0 NaN NaN " + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Companies Founded by Geography" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atstate_codecityregionfunding_total_usd
country_code
AFG8888804888
AGO2222200122
AIA1101000111
ALB10101010800101010
AND1111100111
.................................
VNM676766675810666767
YEM2222200222
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ZMB2222100222
ZWE4444400444
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175 rows × 10 columns

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" + ], + "text/plain": [ + " id name category_code status founded_at closed_at \\\n", + "country_code \n", + "AFG 8 8 8 8 8 0 \n", + "AGO 2 2 2 2 2 0 \n", + "AIA 1 1 0 1 0 0 \n", + "ALB 10 10 10 10 8 0 \n", + "AND 1 1 1 1 1 0 \n", + "... ... ... ... ... ... ... \n", + "VNM 67 67 66 67 58 1 \n", + "YEM 2 2 2 2 2 0 \n", + "ZAF 277 277 249 277 225 5 \n", + "ZMB 2 2 2 2 1 0 \n", + "ZWE 4 4 4 4 4 0 \n", + "\n", + " state_code city region funding_total_usd \n", + "country_code \n", + "AFG 4 8 8 8 \n", + "AGO 0 1 2 2 \n", + "AIA 0 1 1 1 \n", + "ALB 0 10 10 10 \n", + "AND 0 1 1 1 \n", + "... ... ... ... ... \n", + "VNM 0 66 67 67 \n", + "YEM 0 2 2 2 \n", + "ZAF 2 252 277 277 \n", + "ZMB 0 2 2 2 \n", + "ZWE 0 4 4 4 \n", + "\n", + "[175 rows x 10 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_founded_geo = companies.groupby(['country_code']).count()\n", + "comps_founded_geo" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# comps_founded_geo = comps_founded_geo.reset_index()\n", + "# comps_founded_geo = comps_founded_geo[['country_code', 'name']]\n", + "# comps_founded_geo.rename(columns = {'name' : 'number'}, inplace = True)\n", + "# comps_founded_geo.sort_values(by = 'number', ascending = False).reset_index(drop=True)\n", + "# comps_founded_geo.drop(columns= ['level_0','index'])\n", + "comps_founded_geo = comps_founded_geo.sort_values(by = 'number', ascending = False).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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country_codenumber
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" + ], + "text/plain": [ + " country_code number\n", + "0 USA 51637\n", + "1 GBR 7372\n", + "2 IND 3924\n", + "3 CAN 3728\n", + "4 DEU 1921" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_founded_geo = comps_founded_geo.drop(columns = ['index', 'level_0'])\n", + "comps_founded_geo.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# comps_founded_geo.to_csv(r'data/comps_founded_geo.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# After 1990" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usdyear_foundedmonth_foundedyear_closedmonth_closed
0c:1Wetpaintweboperating2005-10-17NaTUSAWASeattleSeattle39750000.02005.010.0NaNNaN
1c:10Flektorgames_videoacquiredNaTNaTUSACACulver CityLos Angeles0.0NaNNaNNaNNaN
2c:100Theregames_videoacquiredNaTNaTUSACASan MateoSF Bay0.0NaNNaNNaNNaN
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaN
\n", + "
" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaT \n", + "2 c:100 There games_video acquired NaT \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaT USA WA Seattle Seattle \n", + "1 NaT USA CA Culver City Los Angeles \n", + "2 NaT USA CA San Mateo SF Bay \n", + "3 NaT NaN NaN NaN unknown \n", + "4 NaT NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \n", + "0 39750000.0 2005.0 10.0 NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 7.0 NaN NaN \n", + "4 0.0 2008.0 7.0 NaN NaN " + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usdyear_foundedmonth_foundedyear_closedmonth_closed
0c:1Wetpaintweboperating2005-10-17NaTUSAWASeattleSeattle39750000.02005.010.0NaNNaN
1c:10Flektorgames_videoacquiredNaTNaTUSACACulver CityLos Angeles0.0NaNNaNNaNNaN
2c:100Theregames_videoacquiredNaTNaTUSACASan MateoSF Bay0.0NaNNaNNaNNaN
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaN
\n", + "
" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaT \n", + "2 c:100 There games_video acquired NaT \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaT USA WA Seattle Seattle \n", + "1 NaT USA CA Culver City Los Angeles \n", + "2 NaT USA CA San Mateo SF Bay \n", + "3 NaT NaN NaN NaN unknown \n", + "4 NaT NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \n", + "0 39750000.0 2005.0 10.0 NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 7.0 NaN NaN \n", + "4 0.0 2008.0 7.0 NaN NaN " + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies1990 = companies.copy()\n", + "\n", + "idx = companies1990[companies1990['year_founded'] < 1990].index\n", + "companies1990.drop(idx , inplace=True)\n", + "companies1990.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "# companies1990.to_csv(r'data/companies1990.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usdyear_foundedmonth_foundedyear_closedmonth_closed
00c:1Wetpaintweboperating2005-10-17NaNUSAWASeattleSeattle39750000.02005.010.0NaNNaN
11c:10Flektorgames_videoacquiredNaNNaNUSACACulver CityLos Angeles0.0NaNNaNNaNNaN
22c:100Theregames_videoacquiredNaNNaNUSACASan MateoSF Bay0.0NaNNaNNaNNaN
33c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNunknown0.02008.07.0NaNNaN
44c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNunknown0.02008.07.0NaNNaN
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 id name category_code status \\\n", + "0 0 c:1 Wetpaint web operating \n", + "1 1 c:10 Flektor games_video acquired \n", + "2 2 c:100 There games_video acquired \n", + "3 3 c:10000 MYWEBBO network_hosting operating \n", + "4 4 c:10001 THE Movie Streamer games_video operating \n", + "\n", + " founded_at closed_at country_code state_code city region \\\n", + "0 2005-10-17 NaN USA WA Seattle Seattle \n", + "1 NaN NaN USA CA Culver City Los Angeles \n", + "2 NaN NaN USA CA San Mateo SF Bay \n", + "3 2008-07-26 NaN NaN NaN NaN unknown \n", + "4 2008-07-26 NaN NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \n", + "0 39750000.0 2005.0 10.0 NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 7.0 NaN NaN \n", + "4 0.0 2008.0 7.0 NaN NaN " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies1990 = pd.read_csv(r'data/companies1990.csv')\n", + "companies1990.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "companies1990['duration'] = companies1990['year_closed'] - companies1990['year_founded']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "companies_y_i_g = companies1990.copy()\n", + "companies_y_i_g = companies_y_i_g.groupby(['category_code', 'year_founded', 'country_code']).size()\n", + "companies_y_i_g = pd.DataFrame(companies_y_i_g)\n", + "# companies_y_i_g.to_csv(r'data/companies_year_industry_geography.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0
category_codeyear_foundedcountry_code
advertising1990.0ESP1
ITA1
USA3
1991.0CAN1
POL1
USA3
1992.0BRA1
USA9
1993.0CAN1
GBR3
USA10
1994.0CAN1
GBR1
HUN1
IND1
JAM1
USA10
1995.0ARE1
CAN3
DEU2
ESP1
GBR1
IND1
MYS1
NLD2
NZL1
USA20
1996.0ARE1
AUS1
CAN2
DEU1
FRA1
GBR1
GRC1
USA20
1997.0AUS1
ESP1
FRA1
GBR3
ISR1
\n", + "
" + ], + "text/plain": [ + " 0\n", + "category_code year_founded country_code \n", + "advertising 1990.0 ESP 1\n", + " ITA 1\n", + " USA 3\n", + " 1991.0 CAN 1\n", + " POL 1\n", + " USA 3\n", + " 1992.0 BRA 1\n", + " USA 9\n", + " 1993.0 CAN 1\n", + " GBR 3\n", + " USA 10\n", + " 1994.0 CAN 1\n", + " GBR 1\n", + " HUN 1\n", + " IND 1\n", + " JAM 1\n", + " USA 10\n", + " 1995.0 ARE 1\n", + " CAN 3\n", + " DEU 2\n", + " ESP 1\n", + " GBR 1\n", + " IND 1\n", + " MYS 1\n", + " NLD 2\n", + " NZL 1\n", + " USA 20\n", + " 1996.0 ARE 1\n", + " AUS 1\n", + " CAN 2\n", + " DEU 1\n", + " FRA 1\n", + " GBR 1\n", + " GRC 1\n", + " USA 20\n", + " 1997.0 AUS 1\n", + " ESP 1\n", + " FRA 1\n", + " GBR 3\n", + " ISR 1" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies_y_i_g.head(40)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Companies Closed" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usdyear_foundedmonth_foundedyear_closedmonth_closed
0c:1Wetpaintweboperating2005-10-17NaTUSAWASeattleSeattle39750000.02005.010.0NaNNaN
1c:10Flektorgames_videoacquiredNaTNaTUSACACulver CityLos Angeles0.0NaNNaNNaNNaN
2c:100Theregames_videoacquiredNaTNaTUSACASan MateoSF Bay0.0NaNNaNNaNNaN
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaN
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" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaT \n", + "2 c:100 There games_video acquired NaT \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaT USA WA Seattle Seattle \n", + "1 NaT USA CA Culver City Los Angeles \n", + "2 NaT USA CA San Mateo SF Bay \n", + "3 NaT NaN NaN NaN unknown \n", + "4 NaT NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \n", + "0 39750000.0 2005.0 10.0 NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 7.0 NaN NaN \n", + "4 0.0 2008.0 7.0 NaN NaN " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "companies['duration'] = companies['year_closed'] - companies['year_founded']" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usdyear_foundedmonth_foundedyear_closedmonth_closedduration
0c:1Wetpaintweboperating2005-10-17NaTUSAWASeattleSeattle39750000.02005.010.0NaNNaNNaN
1c:10Flektorgames_videoacquiredNaTNaTUSACACulver CityLos Angeles0.0NaNNaNNaNNaNNaN
2c:100Theregames_videoacquiredNaTNaTUSACASan MateoSF Bay0.0NaNNaNNaNNaNNaN
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaNNaN
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" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaT \n", + "2 c:100 There games_video acquired NaT \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaT USA WA Seattle Seattle \n", + "1 NaT USA CA Culver City Los Angeles \n", + "2 NaT USA CA San Mateo SF Bay \n", + "3 NaT NaN NaN NaN unknown \n", + "4 NaT NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \\\n", + "0 39750000.0 2005.0 10.0 NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 7.0 NaN NaN \n", + "4 0.0 2008.0 7.0 NaN NaN \n", + "\n", + " duration \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# Invesigate duration by Industry" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 NaN\n", + "1 NaN\n", + "2 NaN\n", + "3 NaN\n", + "4 NaN\n", + " ..\n", + "196548 NaN\n", + "196549 NaN\n", + "196550 NaN\n", + "196551 NaN\n", + "196552 NaN\n", + "Name: duration, Length: 196553, dtype: float64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comp_duration = companies.copy()\n", + "comp_duration.duration.astype('float64') " + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/pandas/core/indexing.py:671: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " self._setitem_with_indexer(indexer, value)\n" + ] + }, + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usdyear_foundedmonth_foundedyear_closedmonth_closedduration
0c:1WetpaintwebNaN2005-10-17NaTUSAWASeattleSeattle39750000.02005.010.0NaNNaNNaN
1c:10Flektorgames_videoacquiredNaTNaTUSACACulver CityLos Angeles0.0NaNNaNNaNNaNNaN
2c:100Theregames_videoacquiredNaTNaTUSACASan MateoSF Bay0.0NaNNaNNaNNaNNaN
3c:10000MYWEBBOnetwork_hostingNaN2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaNNaN
4c:10001THE Movie Streamergames_videoNaN2008-07-26NaTNaNNaNNaNunknown0.02008.07.0NaNNaNNaN
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" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web NaN 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaT \n", + "2 c:100 There games_video acquired NaT \n", + "3 c:10000 MYWEBBO network_hosting NaN 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video NaN 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaT USA WA Seattle Seattle \n", + "1 NaT USA CA Culver City Los Angeles \n", + "2 NaT USA CA San Mateo SF Bay \n", + "3 NaT NaN NaN NaN unknown \n", + "4 NaT NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \\\n", + "0 39750000.0 2005.0 10.0 NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 7.0 NaN NaN \n", + "4 0.0 2008.0 7.0 NaN NaN \n", + "\n", + " duration \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "comp_duration['status'].loc[(comp_duration['status'] != 'acquired')] = np.NaN\n", + "comp_duration.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"Column 'duration' does not exist!\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcomp_duration\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomp_duration\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'category_code'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'year_founded'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'country_code'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'duration'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dur_sum'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'dur_count'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'count'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'nr'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'count'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'acq'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'count'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, func, axis, *args, **kwargs)\u001b[0m\n\u001b[1;32m 6704\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6705\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6706\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6707\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6708\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_aggregate\u001b[0;34m(self, arg, axis, *args, **kwargs)\u001b[0m\n\u001b[1;32m 6718\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6719\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6720\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6721\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6722\u001b[0m \u001b[0magg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maggregate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_aggregate\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mSpecificationError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nested renamer is not supported\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 345\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Column '{k}' does not exist!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 348\u001b[0m \u001b[0marg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: \"Column 'duration' does not exist!\"" + ] + } + ], + "source": [ + "comp_duration = comp_duration[['category_code', 'year_founded', 'country_code']].agg({'duration': [('dur_sum', 'sum'), ('dur_count', 'count')], 'nr' : ('id', 'count'), 'acq': ('status', 'count')})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Companies Founded & Closed by Industry & Year & Geography" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamestatusfounded_atclosed_atstate_codecityregionfunding_total_usdmonth_foundedyear_closedmonth_closedduration
category_codeyear_foundedcountry_code
advertising1902.0USA2222022222000
1911.0USA1111011111000
1915.0USA1111001111000
1917.0USA1111011111000
1919.0USA1111011111000
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" + ], + "text/plain": [ + " id name status founded_at \\\n", + "category_code year_founded country_code \n", + "advertising 1902.0 USA 2 2 2 2 \n", + " 1911.0 USA 1 1 1 1 \n", + " 1915.0 USA 1 1 1 1 \n", + " 1917.0 USA 1 1 1 1 \n", + " 1919.0 USA 1 1 1 1 \n", + "\n", + " closed_at state_code city region \\\n", + "category_code year_founded country_code \n", + "advertising 1902.0 USA 0 2 2 2 \n", + " 1911.0 USA 0 1 1 1 \n", + " 1915.0 USA 0 0 1 1 \n", + " 1917.0 USA 0 1 1 1 \n", + " 1919.0 USA 0 1 1 1 \n", + "\n", + " funding_total_usd month_founded \\\n", + "category_code year_founded country_code \n", + "advertising 1902.0 USA 2 2 \n", + " 1911.0 USA 1 1 \n", + " 1915.0 USA 1 1 \n", + " 1917.0 USA 1 1 \n", + " 1919.0 USA 1 1 \n", + "\n", + " year_closed month_closed duration \n", + "category_code year_founded country_code \n", + "advertising 1902.0 USA 0 0 0 \n", + " 1911.0 USA 0 0 0 \n", + " 1915.0 USA 0 0 0 \n", + " 1917.0 USA 0 0 0 \n", + " 1919.0 USA 0 0 0 " + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies_sub = companies.copy()\n", + "companies_sub = companies_sub.groupby(['category_code', 'year_founded', 'country_code']).count()\n", + "companies_sub.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idfounded_atclosed_atduration
category_codeyear_foundedcountry_code
advertising1902.0USA2200
1911.0USA1100
1915.0USA1100
1917.0USA1100
1919.0USA1100
1922.0USA1100
1927.0USA1100
1929.0USA1100
1932.0AUT1100
1935.0USA1100
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" + ], + "text/plain": [ + " id founded_at closed_at duration\n", + "category_code year_founded country_code \n", + "advertising 1902.0 USA 2 2 0 0\n", + " 1911.0 USA 1 1 0 0\n", + " 1915.0 USA 1 1 0 0\n", + " 1917.0 USA 1 1 0 0\n", + " 1919.0 USA 1 1 0 0\n", + " 1922.0 USA 1 1 0 0\n", + " 1927.0 USA 1 1 0 0\n", + " 1929.0 USA 1 1 0 0\n", + " 1932.0 AUT 1 1 0 0\n", + " 1935.0 USA 1 1 0 0" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies_sub = companies_sub[[ 'id', 'founded_at', 'closed_at', 'duration']]\n", + "companies_sub.head(10) # how do I get this out of the index?" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "companies_sub.to_csv(r'data/companies_industry_year.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# companies['year_closed'], companies['month_closed'] = companies['closed_at'].dt.year, companies['closed_at'].dt.month" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "# companies.to_csv(r'data/companiesThur.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ib. Acquired " + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usdyear_foundedmonth_foundedyear_closedmonth_closed
11c:10Flektorgames_videoacquiredNaNNaNUSACACulver CityLos Angeles0.0NaNNaNNaNNaN
22c:100Theregames_videoacquiredNaNNaNUSACASan MateoSF Bay0.0NaNNaNNaNNaN
1313c:1001FriendFeedwebacquired2007-10-01NaNUSACAMountain ViewSF Bay5000000.02007.010.0NaNNaN
1818c:10014Mobclixmobileacquired2008-03-01NaNUSACAPalo AltoSF Bay0.02008.03.0NaNNaN
4242c:100265Coastal Supply CompanyNaNacquiredNaNNaNNaNNaNNaNunknown0.0NaNNaNNaNNaN
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" + ], + "text/plain": [ + " Unnamed: 0 id name category_code status \\\n", + "1 1 c:10 Flektor games_video acquired \n", + "2 2 c:100 There games_video acquired \n", + "13 13 c:1001 FriendFeed web acquired \n", + "18 18 c:10014 Mobclix mobile acquired \n", + "42 42 c:100265 Coastal Supply Company NaN acquired \n", + "\n", + " founded_at closed_at country_code state_code city region \\\n", + "1 NaN NaN USA CA Culver City Los Angeles \n", + "2 NaN NaN USA CA San Mateo SF Bay \n", + "13 2007-10-01 NaN USA CA Mountain View SF Bay \n", + "18 2008-03-01 NaN USA CA Palo Alto SF Bay \n", + "42 NaN NaN NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "13 5000000.0 2007.0 10.0 NaN NaN \n", + "18 0.0 2008.0 3.0 NaN NaN \n", + "42 0.0 NaN NaN NaN NaN " + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acquired = companies1990.copy()\n", + "\n", + "idx = acquired[acquired['status'] != 'acquired'].index\n", + "acquired.drop(idx , inplace=True)\n", + "acquired.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8922" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(acquired)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamecategory_codestatusfounded_atclosed_atcountry_codestate_codecityregionfunding_total_usdyear_foundedmonth_foundedyear_closedmonth_closedduration
0c:1Wetpaintweboperating2005-10-17NaNUSAWASeattleSeattle39750000.02005.010.0NaNNaNNaN
1c:10Flektorgames_videoacquiredNaNNaNUSACACulver CityLos Angeles0.0NaNNaNNaNNaNNaN
2c:100Theregames_videoacquiredNaNNaNUSACASan MateoSF Bay0.0NaNNaNNaNNaNNaN
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNunknown0.02008.07.0NaNNaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNunknown0.02008.07.0NaNNaNNaN
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" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaN \n", + "2 c:100 There games_video acquired NaN \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaN USA WA Seattle Seattle \n", + "1 NaN USA CA Culver City Los Angeles \n", + "2 NaN USA CA San Mateo SF Bay \n", + "3 NaN NaN NaN NaN unknown \n", + "4 NaN NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \\\n", + "0 39750000.0 2005.0 10.0 NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 7.0 NaN NaN \n", + "4 0.0 2008.0 7.0 NaN NaN \n", + "\n", + " duration \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companies1990.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "192075" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(companies1990)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "acquired.drop(columns= 'Unnamed: 0', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idacquisition_idacquiring_object_idacquired_object_idterm_codeprice_amountprice_currency_codeacquired_atsource_urlsource_descriptioncreated_atupdated_at
011c:11c:10NaN20000000.0USD2007-05-30http://venturebeat.com/2007/05/30/fox-interact...Fox Interactive confirms purchase of Photobuck...2007-05-31 22:19:542008-05-21 19:23:44
127c:59c:72cash60000000.0USD2007-07-01http://www.techcrunch.com/2007/07/02/deal-is-c...Deal is Confirmed: Google Acquired GrandCentral2007-07-03 08:14:502011-05-06 21:51:05
238c:24c:132cash280000000.0USD2007-05-01http://www.techcrunch.com/2007/05/30/cbs-acqui...CBS Acquires Europe’s Last.fm for $280 million2007-07-12 04:19:242008-05-19 04:48:50
349c:59c:155cash100000000.0USD2007-06-01http://techcrunch.com/2007/05/23/100-million-p...$100 Million Payday For Feedburner – This Deal...2007-07-13 09:52:592012-06-05 03:22:17
4510c:212c:215cash25000000.0USD2007-07-01http://blog.seattlepi.nwsource.com/venture/arc...seatlepi.com2007-07-20 05:29:072008-02-25 00:23:47
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" + ], + "text/plain": [ + " id acquisition_id acquiring_object_id acquired_object_id term_code \\\n", + "0 1 1 c:11 c:10 NaN \n", + "1 2 7 c:59 c:72 cash \n", + "2 3 8 c:24 c:132 cash \n", + "3 4 9 c:59 c:155 cash \n", + "4 5 10 c:212 c:215 cash \n", + "\n", + " price_amount price_currency_code acquired_at \\\n", + "0 20000000.0 USD 2007-05-30 \n", + "1 60000000.0 USD 2007-07-01 \n", + "2 280000000.0 USD 2007-05-01 \n", + "3 100000000.0 USD 2007-06-01 \n", + "4 25000000.0 USD 2007-07-01 \n", + "\n", + " source_url \\\n", + "0 http://venturebeat.com/2007/05/30/fox-interact... \n", + "1 http://www.techcrunch.com/2007/07/02/deal-is-c... \n", + "2 http://www.techcrunch.com/2007/05/30/cbs-acqui... \n", + "3 http://techcrunch.com/2007/05/23/100-million-p... \n", + "4 http://blog.seattlepi.nwsource.com/venture/arc... \n", + "\n", + " source_description created_at \\\n", + "0 Fox Interactive confirms purchase of Photobuck... 2007-05-31 22:19:54 \n", + "1 Deal is Confirmed: Google Acquired GrandCentral 2007-07-03 08:14:50 \n", + "2 CBS Acquires Europe’s Last.fm for $280 million 2007-07-12 04:19:24 \n", + "3 $100 Million Payday For Feedburner – This Deal... 2007-07-13 09:52:59 \n", + "4 seatlepi.com 2007-07-20 05:29:07 \n", + "\n", + " updated_at \n", + "0 2008-05-21 19:23:44 \n", + "1 2011-05-06 21:51:05 \n", + "2 2008-05-19 04:48:50 \n", + "3 2012-06-05 03:22:17 \n", + "4 2008-02-25 00:23:47 " + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acquisitions = pd.read_csv(r'data/initial/acquisitions.csv')\n", + "acquisitions.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# join on object id" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "acquisitions_tbj = acquisitions.copy()\n", + "acquisitions_tbj = acquisitions_tbj.drop(columns = 'id')" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "acquisitions_tbj = acquisitions_tbj.rename(columns={\"acquired_object_id\": \"id\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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acquisition_idacquiring_object_ididterm_codeprice_amountprice_currency_codeacquired_atsource_urlsource_descriptioncreated_atupdated_at
01c:11c:10NaN20000000.0USD2007-05-30http://venturebeat.com/2007/05/30/fox-interact...Fox Interactive confirms purchase of Photobuck...2007-05-31 22:19:542008-05-21 19:23:44
17c:59c:72cash60000000.0USD2007-07-01http://www.techcrunch.com/2007/07/02/deal-is-c...Deal is Confirmed: Google Acquired GrandCentral2007-07-03 08:14:502011-05-06 21:51:05
28c:24c:132cash280000000.0USD2007-05-01http://www.techcrunch.com/2007/05/30/cbs-acqui...CBS Acquires Europe’s Last.fm for $280 million2007-07-12 04:19:242008-05-19 04:48:50
39c:59c:155cash100000000.0USD2007-06-01http://techcrunch.com/2007/05/23/100-million-p...$100 Million Payday For Feedburner – This Deal...2007-07-13 09:52:592012-06-05 03:22:17
410c:212c:215cash25000000.0USD2007-07-01http://blog.seattlepi.nwsource.com/venture/arc...seatlepi.com2007-07-20 05:29:072008-02-25 00:23:47
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" + ], + "text/plain": [ + " acquisition_id acquiring_object_id id term_code price_amount \\\n", + "0 1 c:11 c:10 NaN 20000000.0 \n", + "1 7 c:59 c:72 cash 60000000.0 \n", + "2 8 c:24 c:132 cash 280000000.0 \n", + "3 9 c:59 c:155 cash 100000000.0 \n", + "4 10 c:212 c:215 cash 25000000.0 \n", + "\n", + " price_currency_code acquired_at \\\n", + "0 USD 2007-05-30 \n", + "1 USD 2007-07-01 \n", + "2 USD 2007-05-01 \n", + "3 USD 2007-06-01 \n", + "4 USD 2007-07-01 \n", + "\n", + " source_url \\\n", + "0 http://venturebeat.com/2007/05/30/fox-interact... \n", + "1 http://www.techcrunch.com/2007/07/02/deal-is-c... \n", + "2 http://www.techcrunch.com/2007/05/30/cbs-acqui... \n", + "3 http://techcrunch.com/2007/05/23/100-million-p... \n", + "4 http://blog.seattlepi.nwsource.com/venture/arc... \n", + "\n", + " source_description created_at \\\n", + "0 Fox Interactive confirms purchase of Photobuck... 2007-05-31 22:19:54 \n", + "1 Deal is Confirmed: Google Acquired GrandCentral 2007-07-03 08:14:50 \n", + "2 CBS Acquires Europe’s Last.fm for $280 million 2007-07-12 04:19:24 \n", + "3 $100 Million Payday For Feedburner – This Deal... 2007-07-13 09:52:59 \n", + "4 seatlepi.com 2007-07-20 05:29:07 \n", + "\n", + " updated_at \n", + "0 2008-05-21 19:23:44 \n", + "1 2011-05-06 21:51:05 \n", + "2 2008-05-19 04:48:50 \n", + "3 2012-06-05 03:22:17 \n", + "4 2008-02-25 00:23:47 " + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acquisitions_tbj.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0c:1Wetpaintweboperating2005-10-17NaNUSAWASeattleSeattle...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1c:10Flektorgames_videoacquiredNaNNaNUSACACulver CityLos Angeles...1.0c:11NaN20000000.0USD2007-05-30http://venturebeat.com/2007/05/30/fox-interact...Fox Interactive confirms purchase of Photobuck...2007-05-31 22:19:542008-05-21 19:23:44
2c:100Theregames_videoacquiredNaNNaNUSACASan MateoSF Bay...20.0c:377cash0.0USD2005-05-29http://www.there.com/pr_acquisition.htmlMakena Technologies Acquires There from Forter...2007-08-07 05:01:462011-08-22 00:03:07
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNunknown...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaN \n", + "2 c:100 There games_video acquired NaN \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region ... \\\n", + "0 NaN USA WA Seattle Seattle ... \n", + "1 NaN USA CA Culver City Los Angeles ... \n", + "2 NaN USA CA San Mateo SF Bay ... \n", + "3 NaN NaN NaN NaN unknown ... \n", + "4 NaN NaN NaN NaN unknown ... \n", + "\n", + " acquisition_id acquiring_object_id term_code price_amount \\\n", + "0 NaN NaN NaN NaN \n", + "1 1.0 c:11 NaN 20000000.0 \n", + "2 20.0 c:377 cash 0.0 \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " price_currency_code acquired_at \\\n", + "0 NaN NaN \n", + "1 USD 2007-05-30 \n", + "2 USD 2005-05-29 \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " source_url \\\n", + "0 NaN \n", + "1 http://venturebeat.com/2007/05/30/fox-interact... \n", + "2 http://www.there.com/pr_acquisition.html \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " source_description created_at \\\n", + "0 NaN NaN \n", + "1 Fox Interactive confirms purchase of Photobuck... 2007-05-31 22:19:54 \n", + "2 Makena Technologies Acquires There from Forter... 2007-08-07 05:01:46 \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " updated_at \n", + "0 NaN \n", + "1 2008-05-21 19:23:44 \n", + "2 2011-08-22 00:03:07 \n", + "3 NaN \n", + "4 NaN \n", + "\n", + "[5 rows x 26 columns]" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_acq_joined = pd.merge(companies1990, acquisitions_tbj, on = 'id', how='outer')\n", + "comps_acq_joined.head() # what if I want it to fill the rest with nans?" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "192715" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(comps_acq_joined)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [], + "source": [ + "# convert date to datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "comps_acq_joined.acquired_at = comps_acq_joined.acquired_at.astype('datetime64')" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "comps_acq_joined['year_acquired'], comps_acq_joined['month_acquired'] = comps_acq_joined['acquired_at'].dt.year, comps_acq_joined['acquired_at'].dt.month" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "# duration" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "comps_acq_joined['t_unt_acq'] = comps_acq_joined['year_acquired'] - comps_acq_joined['year_founded']" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "pd.options.display.max_columns = None" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2c:100Theregames_videoacquiredNaNNaNUSACASan MateoSF Bay0.0NaNNaNNaNNaNNaN20.0c:377cash0.0USD2005-05-29http://www.there.com/pr_acquisition.htmlMakena Technologies Acquires There from Forter...2007-08-07 05:01:462011-08-22 00:03:072005.05.0NaN
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" + ], + "text/plain": [ + " id name category_code status founded_at \\\n", + "0 c:1 Wetpaint web operating 2005-10-17 \n", + "1 c:10 Flektor games_video acquired NaN \n", + "2 c:100 There games_video acquired NaN \n", + "3 c:10000 MYWEBBO network_hosting operating 2008-07-26 \n", + "4 c:10001 THE Movie Streamer games_video operating 2008-07-26 \n", + "\n", + " closed_at country_code state_code city region \\\n", + "0 NaN USA WA Seattle Seattle \n", + "1 NaN USA CA Culver City Los Angeles \n", + "2 NaN USA CA San Mateo SF Bay \n", + "3 NaN NaN NaN NaN unknown \n", + "4 NaN NaN NaN NaN unknown \n", + "\n", + " funding_total_usd year_founded month_founded year_closed month_closed \\\n", + "0 39750000.0 2005.0 10.0 NaN NaN \n", + "1 0.0 NaN NaN NaN NaN \n", + "2 0.0 NaN NaN NaN NaN \n", + "3 0.0 2008.0 7.0 NaN NaN \n", + "4 0.0 2008.0 7.0 NaN NaN \n", + "\n", + " duration acquisition_id acquiring_object_id term_code price_amount \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN 1.0 c:11 NaN 20000000.0 \n", + "2 NaN 20.0 c:377 cash 0.0 \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " price_currency_code acquired_at \\\n", + "0 NaN NaT \n", + "1 USD 2007-05-30 \n", + "2 USD 2005-05-29 \n", + "3 NaN NaT \n", + "4 NaN NaT \n", + "\n", + " source_url \\\n", + "0 NaN \n", + "1 http://venturebeat.com/2007/05/30/fox-interact... \n", + "2 http://www.there.com/pr_acquisition.html \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " source_description created_at \\\n", + "0 NaN NaN \n", + "1 Fox Interactive confirms purchase of Photobuck... 2007-05-31 22:19:54 \n", + "2 Makena Technologies Acquires There from Forter... 2007-08-07 05:01:46 \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " updated_at year_acquired month_acquired t_unt_acq \n", + "0 NaN NaN NaN NaN \n", + "1 2008-05-21 19:23:44 2007.0 5.0 NaN \n", + "2 2011-08-22 00:03:07 2005.0 5.0 NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN " + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_acq_joined.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# filter relevant data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "comps_acq_joined_filtered = comps_acq_joined[['id', 'name', 'category_code', 'status', 'founded_at', 'closed_at', 'acquired_at', 'country_code', 'state_code', 'city', 'region','funding_total_usd', 'year_founded', 'year_closed', 'month_closed', 'duration', 'year_acquired', 'month_acquired', 't_unt_acq', 'term_code', 'price_amount', 'price_currency_code']]" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2c:100Theregames_videoacquiredNaNNaN2005-05-29USACASan MateoSF Bay0.0NaNNaNNaNNaN2005.05.0NaNcash0.0USD
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaTNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaTNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
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1c:10Flektorgames_videoacquiredNaTNaT2007-05-30USACACulver CityLos Angeles0.0NaNNaNNaNNaT2007.05.0NaTNaN20000000.0USD
2c:100Theregames_videoacquiredNaTNaT2005-05-29USACASan MateoSF Bay0.0NaNNaNNaNNaT2005.05.0NaTcash0.0USD
3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaTNaTNaNNaNNaNunknown0.02008.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaTNaTNaNNaNNaNunknown0.02008.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
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3c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaTNaTNaNNaNNaNunknown0.02008.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
4c:10001THE Movie Streamergames_videooperating2008-07-26NaTNaTNaNNaNNaNunknown0.02008.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
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7c:100042HostNineNaNoperatingNaTNaTNaTNaNNaNNaNunknown0.0NaNNaNNaNNaTNaNNaNNaTNaNNaNNaN
8c:10005Thomas PublishingadvertisingoperatingNaTNaTNaTUSANYNew YorkNew York0.0NaNNaNNaNNaTNaNNaNNaTNaNNaNNaN
9c:100062Vetter Idea Management Systementerpriseoperating2011-08-01NaTNaTNaNNaNNaNunknown0.02011.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
10c:100068ChatRandomgames_videooperating2011-02-01NaTNaTNaNNaNNaNunknown0.02011.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
11c:10008ScapadasAmorosasotheroperating2007-03-01NaTNaTNaNNaNNaNunknown0.02007.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
12c:10009dimension5 labsadvertisingoperating2008-08-01NaTNaTUSANMSanta FeSanta Fe0.02008.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
13c:1001FriendFeedwebacquired2007-10-01NaT2009-08-10USACAMountain ViewSF Bay5000000.02007.0NaNNaNNaT2009.08.0679 dayscash_and_stock47500000.0USD
14c:10010Whooligangames_videooperating2007-12-01NaTNaTNaNNaNNaNunknown0.02007.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
15c:10011PoetryVisualized.comgames_videooperating2008-01-01NaTNaTUSACAJulianSan Diego0.02008.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
16c:10012moviestring.comgames_videoclosed2008-08-222010-01-01NaTNaNNaNNaNunknown0.02008.02010.01.0497 daysNaNNaNNaTNaNNaNNaN
17c:10013The Adoryconsultingoperating2008-01-01NaTNaTNaNNaNNaNunknown0.02008.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
18c:10014Mobclixmobileacquired2008-03-01NaT2010-09-30USACAPalo AltoSF Bay0.02008.0NaNNaNNaT2010.09.0943 daysNaN0.0USD
19c:10015Fitbithealthoperating2007-10-01NaTNaTUSACASan FranciscoSF Bay68069200.02007.0NaNNaNNaTNaNNaNNaTNaNNaNNaN
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" + ], + "text/plain": [ + " id name category_code status \\\n", + "0 c:1 Wetpaint web operating \n", + "1 c:10 Flektor games_video acquired \n", + "2 c:100 There games_video acquired \n", + "3 c:10000 MYWEBBO network_hosting operating \n", + "4 c:10001 THE Movie Streamer games_video operating \n", + "5 c:10002 Synergie Media advertising operating \n", + "6 c:10003 Green Basti Ecosystems cleantech operating \n", + "7 c:100042 HostNine NaN operating \n", + "8 c:10005 Thomas Publishing advertising operating \n", + "9 c:100062 Vetter Idea Management System enterprise operating \n", + "10 c:100068 ChatRandom games_video operating \n", + "11 c:10008 ScapadasAmorosas other operating \n", + "12 c:10009 dimension5 labs advertising operating \n", + "13 c:1001 FriendFeed web acquired \n", + "14 c:10010 Whooligan games_video operating \n", + "15 c:10011 PoetryVisualized.com games_video operating \n", + "16 c:10012 moviestring.com games_video closed \n", + "17 c:10013 The Adory consulting operating \n", + "18 c:10014 Mobclix mobile acquired \n", + "19 c:10015 Fitbit health operating \n", + "\n", + " founded_at closed_at acquired_at country_code state_code city \\\n", + "0 2005-10-17 NaT NaT USA WA Seattle \n", + "1 NaT NaT 2007-05-30 USA CA Culver City \n", + "2 NaT NaT 2005-05-29 USA CA San Mateo \n", + "3 2008-07-26 NaT NaT NaN NaN NaN \n", + "4 2008-07-26 NaT NaT NaN NaN NaN \n", + "5 2007-06-27 NaT NaT MAR NaN Agadir \n", + "6 2008-08-20 NaT NaT IND NaN Vadodara \n", + "7 NaT NaT NaT NaN NaN NaN \n", + "8 NaT NaT NaT USA NY New York \n", + "9 2011-08-01 NaT NaT NaN NaN NaN \n", + "10 2011-02-01 NaT NaT NaN NaN NaN \n", + "11 2007-03-01 NaT NaT NaN NaN NaN \n", + "12 2008-08-01 NaT NaT USA NM Santa Fe \n", + "13 2007-10-01 NaT 2009-08-10 USA CA Mountain View \n", + "14 2007-12-01 NaT NaT NaN NaN NaN \n", + "15 2008-01-01 NaT NaT USA CA Julian \n", + "16 2008-08-22 2010-01-01 NaT NaN NaN NaN \n", + "17 2008-01-01 NaT NaT NaN NaN NaN \n", + "18 2008-03-01 NaT 2010-09-30 USA CA Palo Alto \n", + "19 2007-10-01 NaT NaT USA CA San Francisco \n", + "\n", + " region funding_total_usd year_founded year_closed month_closed \\\n", + "0 Seattle 39750000.0 2005.0 NaN NaN \n", + "1 Los Angeles 0.0 NaN NaN NaN \n", + "2 SF Bay 0.0 NaN NaN NaN \n", + "3 unknown 0.0 2008.0 NaN NaN \n", + "4 unknown 0.0 2008.0 NaN NaN \n", + "5 Agadir 0.0 2007.0 NaN NaN \n", + "6 Vadodara 0.0 2008.0 NaN NaN \n", + "7 unknown 0.0 NaN NaN NaN \n", + "8 New York 0.0 NaN NaN NaN \n", + "9 unknown 0.0 2011.0 NaN NaN \n", + "10 unknown 0.0 2011.0 NaN NaN \n", + "11 unknown 0.0 2007.0 NaN NaN \n", + "12 Santa Fe 0.0 2008.0 NaN NaN \n", + "13 SF Bay 5000000.0 2007.0 NaN NaN \n", + "14 unknown 0.0 2007.0 NaN NaN \n", + "15 San Diego 0.0 2008.0 NaN NaN \n", + "16 unknown 0.0 2008.0 2010.0 1.0 \n", + "17 unknown 0.0 2008.0 NaN NaN \n", + "18 SF Bay 0.0 2008.0 NaN NaN \n", + "19 SF Bay 68069200.0 2007.0 NaN NaN \n", + "\n", + " duration year_acquired month_acquired t_unt_acq term_code \\\n", + "0 NaT NaN NaN NaT NaN \n", + "1 NaT 2007.0 5.0 NaT NaN \n", + "2 NaT 2005.0 5.0 NaT cash \n", + "3 NaT NaN NaN NaT NaN \n", + "4 NaT NaN NaN NaT NaN \n", + "5 NaT NaN NaN NaT NaN \n", + "6 NaT NaN NaN NaT NaN \n", + "7 NaT NaN NaN NaT NaN \n", + "8 NaT NaN NaN NaT NaN \n", + "9 NaT NaN NaN NaT NaN \n", + "10 NaT NaN NaN NaT NaN \n", + "11 NaT NaN NaN NaT NaN \n", + "12 NaT NaN NaN NaT NaN \n", + "13 NaT 2009.0 8.0 679 days cash_and_stock \n", + "14 NaT NaN NaN NaT NaN \n", + "15 NaT NaN NaN NaT NaN \n", + "16 497 days NaN NaN NaT NaN \n", + "17 NaT NaN NaN NaT NaN \n", + "18 NaT 2010.0 9.0 943 days NaN \n", + "19 NaT NaN NaN NaT NaN \n", + "\n", + " price_amount price_currency_code \n", + "0 NaN NaN \n", + "1 20000000.0 USD \n", + "2 0.0 USD \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 47500000.0 USD \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "17 NaN NaN \n", + "18 0.0 USD \n", + "19 NaN NaN " + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_acq_joined_time.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "comps_acq_joined_time.to_csv(r'data/comps_acq_joined_time.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### II. Trending according to Funding Amounts by Year, Industry (incl. Growth) and Geography" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Analyse" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Companies recieving Funding by Year / Industry / Geography" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Hypothesis" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Statistics for Reliability" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### III. Money: Geography, Industry, Development over time by Industry and Funding Round" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Analyse" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Hypothesis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Correlation of Funding Amount and Geography" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Correlation of Funding Amount and Industry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Correlation of Funding Amount and Founders' Education" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Correlation of Funding Amount and time founded" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Statistics for Reliability" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Conclusions " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 4 - Analysis on Success" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### I. Define Success" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Most successful Startups (Google, etc. Metrics as a Benchmark)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Still exist" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Down Rounds" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Growth in Valuation" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### II. Likelyhood of Success" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Correlation of s" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Project 5 - Projecting Growth.ipynb b/your-project/code/Project 5 - Projecting Growth.ipynb new file mode 100644 index 0000000..83a9774 --- /dev/null +++ b/your-project/code/Project 5 - Projecting Growth.ipynb @@ -0,0 +1,1457 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Projecting Growth" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Overall" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assuming a world without Corona and Recession" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " year_founded number yoy_growth\n", + "89 1990.0 349 0.026471\n", + "90 1991.0 351 0.005731\n", + "91 1992.0 392 0.116809\n", + "92 1993.0 485 0.237245\n", + "93 1994.0 528 0.088660" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = ecosystem[ecosystem['year_founded'] < 1990].index\n", + "ecosystem.drop(idx , inplace=True)\n", + "ecosystem.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2012.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# getting rid of 2013 and 2014\n", + "\n", + "idx = ecosystem[ecosystem['year_founded'] >= 2013].index\n", + "ecosystem.drop(idx , inplace=True)\n", + "ecosystem.year_founded.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 23.000000\n", + "mean 0.170481\n", + "std 0.150929\n", + "min -0.106007\n", + "25% 0.095052\n", + "50% 0.138020\n", + "75% 0.240194\n", + "max 0.515957\n", + "Name: yoy_growth, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecosystem.yoy_growth.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import sem, t\n", + "from scipy import mean" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/ipykernel_launcher.py:5: DeprecationWarning: scipy.mean is deprecated and will be removed in SciPy 2.0.0, use numpy.mean instead\n", + " \"\"\"\n" + ] + } + ], + "source": [ + "confidence = 0.95\n", + "data = ecosystem.yoy_growth\n", + "\n", + "n = len(data)\n", + "m = mean(data)\n", + "std_err = sem(data)\n", + "h = std_err * t.ppf((1 + confidence) / 2, n - 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.10521491813916703\n" + ] + } + ], + "source": [ + "start = m - h\n", + "print(start)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2357480735364004\n" + ] + } + ], + "source": [ + "end = m + h\n", + "print(end)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# After 2000" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " year_founded number yoy_growth\n", + "99 2000.0 2264 0.134837\n", + "100 2001.0 2024 -0.106007\n", + "101 2002.0 2001 -0.011364\n", + "102 2003.0 2268 0.133433\n", + "103 2004.0 2610 0.150794" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = ecosystem[ecosystem['year_founded'] < 2000].index\n", + "ecosystem.drop(idx , inplace=True)\n", + "ecosystem.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 13.000000\n", + "mean 0.145392\n", + "std 0.142630\n", + "min -0.106007\n", + "25% 0.101444\n", + "50% 0.138020\n", + "75% 0.239464\n", + "max 0.394960\n", + "Name: yoy_growth, dtype: float64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecosystem.yoy_growth.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/ipykernel_launcher.py:5: DeprecationWarning: scipy.mean is deprecated and will be removed in SciPy 2.0.0, use numpy.mean instead\n", + " \"\"\"\n" + ] + } + ], + "source": [ + "confidence = 0.95\n", + "data = ecosystem.yoy_growth\n", + "\n", + "n = len(data)\n", + "m = mean(data)\n", + "std_err = sem(data)\n", + "h = std_err * t.ppf((1 + confidence) / 2, n - 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.10521491813916703\n" + ] + } + ], + "source": [ + "start2000 = m - h\n", + "print(start)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2357480735364004\n" + ] + } + ], + "source": [ + "end2000 = m + h\n", + "print(end)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# After 2005" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " year_founded number yoy_growth\n", + "104 2005.0 3235 0.239464\n", + "105 2006.0 4246 0.312519\n", + "106 2007.0 5923 0.394960\n", + "107 2008.0 7350 0.240925\n", + "108 2009.0 8948 0.217415" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = ecosystem[ecosystem['year_founded'] < 2005].index\n", + "ecosystem.drop(idx , inplace=True)\n", + "ecosystem.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 8.000000\n", + "mean 0.198550\n", + "std 0.138078\n", + "min -0.056348\n", + "25% 0.128876\n", + "50% 0.228439\n", + "75% 0.258824\n", + "max 0.394960\n", + "Name: yoy_growth, dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecosystem.yoy_growth.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/ipykernel_launcher.py:5: DeprecationWarning: scipy.mean is deprecated and will be removed in SciPy 2.0.0, use numpy.mean instead\n", + " \"\"\"\n" + ] + } + ], + "source": [ + "confidence = 0.95\n", + "data = ecosystem.yoy_growth\n", + "\n", + "n = len(data)\n", + "m = mean(data)\n", + "std_err = sem(data)\n", + "h = std_err * t.ppf((1 + confidence) / 2, n - 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.10521491813916703\n" + ] + } + ], + "source": [ + "start2005 = m - h\n", + "print(start)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2357480735364004\n" + ] + } + ], + "source": [ + "end2005 = m + h\n", + "print(end)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### By Industry" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeyear_foundedcountry_code0
0advertising1990.0ESP1
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2advertising1990.0USA3
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" + ], + "text/plain": [ + " category_code year_founded country_code 0\n", + "0 advertising 1990.0 ESP 1\n", + "1 advertising 1990.0 ITA 1\n", + "2 advertising 1990.0 USA 3\n", + "3 advertising 1991.0 CAN 1\n", + "4 advertising 1991.0 POL 1" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_year_ind = pd.read_csv(r'data/companies_year_industry_geography.csv')\n", + "comps_year_ind.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# after 2000" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeyear_foundedcountry_code0
51advertising1999.0AUS1
52advertising1999.0CAN9
53advertising1999.0DEU1
54advertising1999.0ESP1
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" + ], + "text/plain": [ + " category_code year_founded country_code 0\n", + "51 advertising 1999.0 AUS 1\n", + "52 advertising 1999.0 CAN 9\n", + "53 advertising 1999.0 DEU 1\n", + "54 advertising 1999.0 ESP 1\n", + "55 advertising 1999.0 FRA 1" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = comps_year_ind[comps_year_ind['year_founded'] < 1999].index\n", + "comps_year_ind.drop(idx , inplace=True)\n", + "comps_year_ind.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " category_code year_founded 0\n", + "0 advertising 1999.0 88\n", + "1 advertising 2000.0 81\n", + "2 advertising 2001.0 95\n", + "3 advertising 2002.0 69\n", + "4 advertising 2003.0 117" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_year_ind_grouped = comps_year_ind.groupby(['category_code', 'year_founded']).sum().reset_index()\n", + "comps_year_ind_grouped.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Growth" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeyear_founded0yoy_growth
605web2010.09760.073707
606web2011.011200.147541
607web2012.0935-0.165179
608web2013.0319-0.658824
609web2014.02-0.993730
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" + ], + "text/plain": [ + " category_code year_founded 0 yoy_growth\n", + "605 web 2010.0 976 0.073707\n", + "606 web 2011.0 1120 0.147541\n", + "607 web 2012.0 935 -0.165179\n", + "608 web 2013.0 319 -0.658824\n", + "609 web 2014.0 2 -0.993730" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comps_year_ind_grouped['yoy_growth'] = (comps_year_ind_grouped['0'] -comps_year_ind_grouped['0'].shift(1)) / comps_year_ind_grouped['0'].shift(1)\n", + "comps_year_ind_grouped.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "idx = comps_year_ind_grouped[comps_year_ind_grouped['year_founded'] < 2000].index\n", + "comps_year_ind_grouped.drop(idx , inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeyear_founded0yoy_growth
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2advertising2001.0950.172840
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" + ], + "text/plain": [ + " category_code year_founded 0 yoy_growth\n", + "1 advertising 2000.0 81 -0.079545\n", + "2 advertising 2001.0 95 0.172840\n", + "3 advertising 2002.0 69 -0.273684\n", + "4 advertising 2003.0 117 0.695652\n", + "5 advertising 2004.0 112 -0.042735" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = comps_year_ind_grouped[comps_year_ind_grouped['year_founded'] > 2012].index\n", + "comps_year_ind_grouped.drop(idx , inplace=True)\n", + "comps_year_ind_grouped.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats\n", + "import math\n", + "\n", + "list_of_industries = list(comps_year_ind_grouped.category_code.unique())\n", + "lower_con = []\n", + "upper_con = []\n", + "\n", + "\n", + "for industry in list_of_industries:\n", + " temp = comps_year_ind_grouped.loc[comps_year_ind_grouped.category_code == industry]\n", + " n = len(temp.yoy_growth)\n", + " m = temp.yoy_growth.mean()\n", + " std_err = temp.yoy_growth.sem()\n", + " std = temp.yoy_growth.std()\n", + " h = std_err * t.ppf((1 + confidence) / 2, n - 1)\n", + " lower, upper = stats.norm.interval(0.95, m, scale = (std / math.sqrt(n)))\n", + " lower_con.append(lower)\n", + " upper_con.append(upper)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IndustryLowerUpper
0advertising-0.009478370.306652
1analytics0.04242640.767002
2automotive-0.06289210.661293
3biotech-0.05505990.144694
4cleantech-0.07210290.311477
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" + ], + "text/plain": [ + " Industry Lower Upper\n", + "0 advertising -0.00947837 0.306652\n", + "1 analytics 0.0424264 0.767002\n", + "2 automotive -0.0628921 0.661293\n", + "3 biotech -0.0550599 0.144694\n", + "4 cleantech -0.0721029 0.311477" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(data=[list_of_industries, lower_con, upper_con]).transpose()\n", + "df.columns=['Industry', 'Lower', 'Upper']\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(r'growth_industry_confidence.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### By geographical Orientation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Companies being closed" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Ratio in general" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Overall" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Project 5 - Scraping CB Insights.ipynb b/your-project/code/Project 5 - Scraping CB Insights.ipynb new file mode 100644 index 0000000..fc50b16 --- /dev/null +++ b/your-project/code/Project 5 - Scraping CB Insights.ipynb @@ -0,0 +1,6692 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Try 1" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from bs4 import BeautifulSoup\n", + "import pandas as pd\n", + "import requests" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://www.cbinsights.com/research-unicorn-companies'" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "page = requests.get(url)\n", + "content = page.content" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "soup = BeautifulSoup('content')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "The Complete List of Unicorn Companies\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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The Global Unicorn Club

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(including whisper valuations)

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Current Private Companies Valued At $1B+



Total Number of Unicorn Companies: 469

Total Cumulative Valuation: ~ $1,378B

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DOWNLOAD THE FULL LIST OF BILLION DOLLAR COMPANIES TO SEE THEIR FUNDING DATA, INVESTORS, AND MORE

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CompanyValuation ($B)Date JoinedCountryIndustrySelect Investors
Toutiao (Bytedance)$754/7/2017ChinaArtificial intelligenceSequoia Capital China, SIG Asia Investments, Sina Weibo, Softbank Group
Didi Chuxing\n", + "\t $5612/31/2014ChinaAuto & transportationMatrix Partners, Tiger\n", + "\t Global Management, Softbank Corp.,
Stripe$361/23/2014United StatesFintechKhosla Ventures, LowercaseCapital, capitalG
SpaceX$33.312/1/2012United StatesOtherFounders Fund, Draper\n", + "\t Fisher Jurvetson, Rothenberg Ventures
Airbnb$187/26/2011United StatesTravelGeneral Catalyst Partners,\n", + "\t Andreessen Horowitz, ENIAC Ventures
Kuaishou$181/1/2015ChinaMobile & telecommunicationsMorningside Venture Capital, Sequoia Capital, Baidu
One97 Communications$165/12/2015IndiaFintechIntel Capital, Sapphire\n", + "\t Ventures, Alibaba Group
Epic Games$1510/26/2018United StatesOtherTencent Holdings, KKR, Smash Ventures
DJI Innovations$155/6/2015ChinaHardwareAccel Partners, Sequoia\n", + "\t Capital
Grab$14.312/4/2014SingaporeAuto & transportationGGV Capital, Vertex Venture\n", + "\t Holdings, Softbank Group
Beike Zhaofang$147/18/2019ChinaInternet software & servicesTencent Holdings, Hillhouse Capital Management, Source Code Capital
DoorDash$12.63/1/2018United StatesSupply chain, logistics, & deliverySoftbank Group, Sequoia Capital, Khosla Ventures
Snowflake Computing$12.41/25/2018United StatesData management & analyticsRedpoint Ventures,Iconiq Capital, Madrona Venture Group
Palantir Technologies$12.185/5/2011United StatesData management & analyticsRRE Ventures, Founders\n", + "\t Fund, In-Q-Tel
JUUL Labs$1212/20/2017United StatesConsumer & retailTiger Global Management
Bitmain Technologies$127/6/2018ChinaHardwareCoatue Management, Sequoia Capital China, IDG Capital
Samumed$128/6/2018United StatesHealthVickers Venture Partners, IKEA GreenTech
Wish$11.25/18/2015United StatesE-commerce & direct-to-consumerFounders Fund, GGV Capital, Digital Sky Technologies
Global Switch$11.0812/22/2016United KingdomHardwareAviation Industry Corporation of China, Essence Financial, Jiangsu Sha Steel Group
Go-Jek$108/4/2016IndonesiaSupply chain, logistics, & deliveryFormation Group, Sequoia Capital India, Warburg Pincus
Nubank$103/1/2018BrazilFintechSequoia Capital, Redpoint e.ventures, Kaszek Ventures
Oyo Rooms$109/25/2018IndiaTravelSoftBank Group, Sequoia Capital India,Lightspeed India Partners
Ripple$1012/20/2019United StatesFintechIDG Capital, Venture51, Lightspeed Venture Partners
Coupang$95/28/2014South KoreaE-commerce & direct-to-consumerSequoia Capital, Founder\n", + "\t Collective, Wellington Management
Guazi (Chehaoduo)$93/12/2016ChinaE-commerce & direct-to-consumerSequoia Capital China, GX Capital
Coinbase$88/10/2017United StatesFintechY Combinator, Union Square Ventures, DFJ Growth
BYJU'S$87/25/2017IndiaEdtechTencent Holdings, Lightspeed India Partners, Sequoia Capital India
Robinhood$84/26/2017United StatesFintechGoogle Ventures, Andreessen Horowitz, DST Global
Yuanfudao$7.85/31/2017ChinaEdtechTencent Holdings, Warbug Pincus, IDG Capital
Instacart$7.612/30/2014United StatesSupply chain, logistics, & deliveryKhosla Ventures, Kleiner\n", + "\t Perkins Caufield & Byers, Collaborative Fund
SenseTime$7.57/11/2017ChinaArtificial intelligenceStar VC, IDG Capital, Infore Capital, Alibaba Group
Snapdeal$75/21/2014IndiaE-commerce & direct-to-consumerSoftBankGroup, Blackrock, Alibaba Group
Roivant Sciences$711/13/2018United StatesHealthSoftBankGroup, Founders Fund
Tokopedia$712/12/2018IndonesiaE-commerce & direct-to-consumerSoftBankGroup, Alibaba Group, Sequoia Capital India
Argo AI$707/12/2019United StatesArtificial intelligenceVolkswagen Group, Ford Autonomous Vehicles
Automation Anywhere$6.87/2/2018United StatesArtificial intelligenceGeneral Atlantic, Goldman Sachs, New Enterprise Associates
Tanium$6.73/31/2015United StatesCybersecurityAndreessen Horowitz,\n", + "\t Nor-Cal Invest, TPG Growth
Ziroom$6.61/17/2018ChinaE-commerce & direct-to-consumerSequoia Capital China, Warburg Pincus, General Catalyst
UiPath$6.43/2/2018United StatesArtificial intelligenceAccel, capitalG, Earlybrid Venture Capital, Seedcamp
Compass$6.48/31/2016United StatesE-commerce & direct-to-consumerFounders Fund, Thrive Capital, Wellington Management
Magic Leap$6.310/21/2014United StatesHardwareObvious Ventures, Qualcomm Ventures, Andreessen Horowitz
Samsara Networks$6.33/22/2018United StatesHardwareAndreessen Horowitz, General Catalyst
Ola Cabs$6.3210/27/2014IndiaAuto & transportationAccel Partners, SoftBank Group, Sequoia Capital
Databricks$6.22/5/2019United StatesData management & analyticsAndreessen Horowitz, New Enterprise Associates, Battery Ventures
Manbang Group$64/24/2018ChinaSupply chain, logistics, & deliverySoftbank Group, CapitalG
Unity Technologies$67/13/2016United StatesOtherSequoia Capital, iGlobe Partners, DFJ Growth
Revolut$5.54/26/2018United KingdomFintechindex Ventures, DST Global, Ribbit Capital
Lianjia (Homelink)$5.84/8/2016ChinaE-commerce & direct-to-consumerTencent, Baidu, Huasheng Capital
Chime$5.83/5/2019United StatesFintechForerunner Ventures, Crosslink Capital, Homebrew
EasyHome$5.72/12/2018ChinaConsumer & retailAlibaba Group, Boyu Capital, Borui Capital
Vice Media$5.78/17/2013United StatesInternet software & servicesTechnology Crossover Ventures, A&E Television Networks
Intarcia\n", + "\t Therapeutics$5.54/1/2014United StatesHealthNew Enterprise Associates,\n", + "\t New Leaf Venture Partners, Charter Venture Capital
Klarna$5.512/12/2011SwedenFintechInstitutional Venture\n", + "\t Partners, Sequoia Capital, General Atlantic
GuaHao (We Doctor)$5.59/22/2015ChinaHealthTencent, Morningside Group
HashiCorp$5.111/1/2018United StatesInternet software & servicesRedpoint Ventures, True Ventures, Mayfield Fund
United Imaging Healthcare$59/14/2017ChinaHealthChina Life Insurance, China Development Bank Capital, CITIC Securities International
UBTECH Robotics$57/26/2016ChinaHardwareCDH Investments, Goldstone Investments, Qiming Venture Partners
Krafton Game Union$58/9/2018South KoreaOtherTencent Holdings, Stonebridge Capital, IMM Investment
Machine Zone$57/16/2014United StatesMobile & telecommunicationsJ.P. Morgan Chase & Co., Menlo Ventures
WM Motor$53/8/2019ChinaAuto & transportationBaidu Capital, Linear Venture, Tencent
Royole Corporation$58/18/2015ChinaHardwareWarmsun Holding, IDG Capital Partners
Hello TransTech$56/01/2018ChinaAuto & transportationAnt Financial Services Group, GGV Capital
Tempus$53/21/2018United StatesHealthNew Enterprise Associates, T. Rowe Associates, Lightbank
Toast$4.97/10/2018United StatesFintechBessemer Venture Partners, Tiger Global Management, Google Ventures
Meizu Technology$4.587/23/2014ChinaHardwareTelling Telecommunication Holding Co., Alibaba Group
Fanatics$4.56/6/2012United StatesE-commerce & direct-to-consumerSoftBank Group, Andreessen Horowitz, Temasek Holdings
SoFi$4.52/3/2015United StatesFintechBaseline Ventures, DCM Ventures, Institutional Venture Partners
Vipkid$4.58/23/2017ChinaEdtechSequoia Capital China, Tencent Holdings, Sinovation Ventures
Confluent$4.51/23/2019United StatesData management & analyticsBenchmark, Sequoia Capital, Index Ventures
Ginkgo BioWorks$4.212/14/2017United StatesHealthY Combinator, Data Collective, MassVentures
Yello\n", + "\t Mobile$411/11/2014South KoreaMobile & telecommunicationsFormation 8
Houzz$49/30/2014United StatesE-commerce & direct-to-consumerNew Enterprise Associates,\n", + "\t Sequoia Capital, Comcast Ventures
Face++ (Megvii)$410/31/2017ChinaArtificial intelligenceAnt Financial Services Group, Russia-China Investment Fund, Foxconn Technology Company
Roblox$49/4/2018United StatesInternet software & servicesAtlos Ventures, Index Ventures, First Round Capital
Impossible Foods$45/13/2019United StatesConsumer & retailKhosla Ventures, Horizons Ventures, Temasek Holdings
TripActions$411/8/2018United StatesTravelAndreessen Horowitz, Lightspeed Venture Partners, Zeev Ventures
XPeng Motors$48/2/2018ChinaAuto & transportationMorningside Venture Capital, Foxconn Technology Company, Alibaba Group
OpenDoor Labs$3.811/30/2016United StatesE-commerce & direct-to-consumerNorwest Venture Partners, New Enterprise Associates, Khosla Ventures
\n", + "\n", + "Gusto\n", + "$3.8\n", + "12/18/2015\n", + "United States\n", + "Fintech\n", + "General Catalyst Partners, Google Ventures, Kleiner Perkins Caufield & Byers\n", + "\n", + "\n", + "Auto1 Group\n", + "$3.54\n", + "8/3/2015\n", + "Germany\n", + "E-commerce & direct-to-consumer\n", + "Digital Sky Technologies, Piton Capital, DN Capital, SoftBank Group\n", + "\n", + "\n", + "Otto Bock HealthCare\n", + "$3.5\n", + "6/24/2017\n", + "Germany\n", + "Health\n", + "EQT Partners\n", + "\n", + "\n", + "Arrival\n", + "$3.91\n", + "1/15/2020\n", + "United Kingdom\n", + "Auto & transportation\n", + "Kia Motors Company, Hyundai Motor Company\n", + "\n", + "\n", + "Indigo Agriculture\n", + "$3.5\n", + "9/26/2017\n", + "United States\n", + "Artificial intelligence\n", + "Activant Capital Group, Alaska Permanent Fund, Baillie Gifford & Co.\n", + "\n", + "\n", + "Greensill\n", + "$3.5\n", + "7/16/2018\n", + "United Kingdom\n", + "Fintech\n", + "SoftBank Group, General Atlantic\n", + "\n", + "\n", + "TransferWise\n", + "$3.5\n", + "1/26/2015\n", + "United Kingdom\n", + "Fintech\n", + "IA Ventures, Index, Ventures, SV Angel\n", + "\n", + "\n", + "N26\n", + "$3.5\n", + "1/10/2019\n", + "Germany\n", + "Fintech\n", + "Redalpine Venture Partners, Earlybird Venture Capital, Valar Ventures\n", + "\n", + "\n", + "Root Insurance\n", + "$3.5\n", + "8/22/2018\n", + "United States\n", + "Fintech\n", + "Tiger Global Management, Ribbit Capital, Redpoint Ventures\n", + "\n", + "\n", + "Rivian\n", + "$3.5\n", + "9/10/2019\n", + "United States\n", + "Auto & transportation\n", + "Amazon, Ford Motor Company, Cox Automotive\n", + "\n", + "\n", + "Freshworks\n", + "$3.5\n", + "7/31/2018\n", + "United States\n", + "Internet software & services\n", + "Accel, Tiger Global Management, capitalG\n", + "\n", + "\n", + "Youxia Motors\n", + "$3.35\n", + "4/2/2018\n", + "China\n", + "Auto & transportation\n", + "China Environmental Protection Industry, China Fortune Ocean\n", + "\n", + "\n", + "Cloudwalk\n", + "$3.32\n", + "10/8/2018\n", + "China\n", + "Artificial intelligence\n", + "Oriza Holdings, Guangdong Technology Financial Group\n", + "\n", + "\n", + "Rubrik\n", + "$3.3\n", + "4/28/2017\n", + "United States\n", + "Data management & analytics\n", + "Greylock Partners, Lightspeed Venture Partners, Khosla Ventures\n", + "\n", + "\n", + "Swiggy\n", + "$3.6\n", + "6/21/2018\n", + "India\n", + "Supply chain, logistics, & delivery\n", + "Accel India, SAIF Partners, Norwest Venture Partners\n", + "\n", + "\n", + "The Hut Group\n", + "$3.25\n", + "08/13/2017\n", + "United Kingdom\n", + "E-commerce & direct-to-consumer\n", + "KKR, Old Mutual Global Investors, Artemis Investment Management\n", + "\n", + "\n", + "GRAIL\n", + "$3.2\n", + "3/1/2017\n", + "United States\n", + "Health\n", + "Kleiner Perkins Caufield & Byers, Amazon, Merck & Co.\n", + "\n", + "\n", + "Oscar\n", + "\t Health\n", + "$3.2\n", + "4/20/2015\n", + "United States\n", + "Health\n", + "BoxGroup, Formation8,\n", + "\t Khosla Ventures\n", + "\n", + "\n", + "Zoox\n", + "$3.2\n", + "5/27/2016\n", + "United States\n", + "Artificial intelligence\n", + "AID Partners, Draper Fisher Jurvetson\n", + "\n", + "\n", + "Flexport\n", + "$3.2\n", + "4/30/2018\n", + "United States\n", + "Supply chain, logistics, & delivery\n", + "Bloomberg Beta, Founders Fund, First Round Capital\n", + "\n", + "\n", + "Canva\n", + "$3.2\n", + "01/08/2018\n", + "Australia\n", + "Internet software & services\n", + "Sequoia Capital China, Blackbird Ventures, Matrix Partners\n", + "\n", + "\n", + "Automattic\n", + "$3\n", + "5/27/2013\n", + "United States\n", + "Internet software & services\n", + "Insight Venture Partners,\n", + "\t Lowercase Capital, Polaris Partners\n", + "\n", + "VANCL\n", + "$3\n", + "12/14/2010\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Ceyuan Ventures, QiMing\n", + "\t Venture Partners, Temasek Holdings\n", + "\n", + "\n", + "BGL Group\n", + "$3\n", + "11/24/2017\n", + "United Kingdom\n", + "Fintech\n", + "CPP Investment Board\n", + "\n", + "\n", + "Circle Internet Financial\n", + "$3\n", + "5/15/2018\n", + "United States\n", + "Fintech\n", + "General Catalyst, Digital Currency Group, Accel\n", + "\n", + "\n", + "Xiaohongshu\n", + "$3\n", + "3/31/2016\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "GGV Capital, ZhenFund, Tencent\n", + "\n", + "\n", + "SouChe Holdings\n", + "$3\n", + "11/1/2017\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Morningside Ventures, Warburg Pincus, CreditEase Fintech Investment Fund\n", + "\n", + "\n", + "Niantic\n", + "$4\n", + "11/24/2017\n", + "United States\n", + "Mobile & telecommunications\n", + "\n", + "Nintendo, Google, Pokemon Company International, Spark Capital\n", + "\n", + "\n", + "Horizon Robotics\n", + "$3\n", + "2/27/2019\n", + "China\n", + "Artificial intelligence\n", + "Hillhouse Capital Management, Linear Venture, Morningside Venture Capital\n", + "\n", + "\n", + "UCommune\n", + "$3\n", + "1/18/2017\n", + "China\n", + "Other\n", + "Ant Financial Services Group, Dahong Group, Sequoia Capital China\n", + "\n", + "\n", + "Netskope\n", + "$3\n", + "11/13/2018\n", + "United States\n", + "Cybersecurity\n", + "Lightspeed Venture Partners, Social Capital, Accel\n", + "\n", + "\n", + "Pony.ai\n", + "$3\n", + "7/11/2018\n", + "United States\n", + "Artificial intelligence\n", + "Sequoia Capital China, IDG Capital, DCM Ventures\n", + "\n", + "\n", + "Lixiang Automotive\n", + "$2.93\n", + "6/28/2019\n", + "China\n", + "Auto & transportation\n", + "Future Capital Discovery Fund, Shougang Fund, BlueRun Ventures\n", + "\n", + "\n", + "Affirm\n", + "$2.9\n", + "12/11/2017\n", + "United States\n", + "Fintech\n", + "Andreessen Horowitz, Khosla Ventures, Singapore Wealth Fund\n", + "\n", + "\n", + "OVO\n", + "$2.9\n", + "3/14/2019\n", + "Indonesia\n", + "Fintech\n", + "Grab, Tokopedia, Tokyo Century Corporation\n", + "\n", + "\n", + "Yixia\n", + "$2.9\n", + "11/24/2015\n", + "China\n", + "Mobile & telecommunications\n", + "Sequoia Capital China, Sina Weibo, Kleiner Perkins Caufield & Byers, Redpoint Ventures\n", + "\n", + "\n", + "Kuayue Express\n", + "$2.88\n", + "10/23/2018\n", + "China\n", + "Supply chain, logistics, & delivery\n", + "Sequoia Capital China, Eastern Bell Capital\n", + "\n", + "\n", + "Meicai\n", + "$2.8\n", + "1/11/2018\n", + "China\n", + "Mobile & telecommunications\n", + "Tiger Global Management, Blue Lake Capital, ZhenFund\n", + "\n", + "\n", + "GoodRx\n", + "$2.8\n", + "8/6/2018\n", + "United States\n", + "Health\n", + "Silver Lake Partners, SV Angel, Upfront Ventures\n", + "\n", + "\n", + "GitLab\n", + "$2.77\n", + "9/19/2018\n", + "United States\n", + "Internet software & services\n", + "Google Ventures, ICONIQ Capital, Khosla Ventures\n", + "\n", + "\n", + "Convoy\n", + "$2.75\n", + "9/21/2018\n", + "United States\n", + "Supply chain, logistics, & delivery\n", + "Greylock Partners, capitalG, Y Combinator\n", + "\n", + "\n", + "Nuro\n", + "$2.7\n", + "2/11/2019\n", + "United States\n", + "Auto & transportation\n", + "SoftBank Group, Greylock Partners, Gaorong Capital\n", + "\n", + "\n", + "OneTrust\n", + "$2.7\n", + "7/11/2019\n", + "United States\n", + "Internet software & services\n", + "Insight Partners\n", + "\n", + "\n", + "Wemakeprice\n", + "$2.33\n", + "9/9/2015\n", + "South Korea\n", + "E-commerce & direct-to-consumer\n", + "IMM Investment, NXC\n", + "\n", + "\n", + "Brex\n", + "$2.6\n", + "10/5/2018\n", + "United States\n", + "Fintech\n", + "DST Global, Ribbit Capital, Greenoaks Capital Management\n", + "\n", + "\n", + "Monzo\n", + "$2.55\n", + "10/31/2018\n", + "United Kingdom\n", + "Fintech\n", + "Passion Capital, Thrive Capital, Orange Digital Ventures\n", + "\n", + "\n", + "23andMe\n", + "$2.5\n", + "7/3/2015\n", + "United States\n", + "Health\n", + "Google Ventures, New Enterprise Associates, MPM Capital\n", + "\n", + "\n", + "Vista Global\n", + "$2.5\n", + "8/23/2017\n", + "Malta\n", + "Other\n", + "Rhone Capital\n", + "\n", + "\n", + "Zhihu\n", + "$2.5\n", + "1/12/2017\n", + "China\n", + "Internet software & services\n", + "Tencent Holdings, Sinovation Ventures, Qiming Venture Partners \n", + "\n", + "\n", + "Aihuishou\n", + "$2.5\n", + "7/12/2018\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Morningside Ventures, JD.com, Tiantu Capital\n", + "\n", + "\n", + "Aurora\n", + "$2.5\n", + "2/7/2019\n", + "United States\n", + "Auto & transportation\n", + "Index Ventures, Greylock Partners, Lightspeed Venture Partners\n", + "\n", + "\n", + "Bird Rides\n", + "$2.78\n", + "05/29/2018\n", + "United States\n", + "Auto & transportation\n", + "Tusk Ventures, Craft Ventures, Sequoia Capital\n", + "\n", + "\n", + "BYTON\n", + "$2.5\n", + "4/20/2018\n", + "China\n", + "Auto & transportation\n", + "FAW Group, Tencent Holdings, Tus Holdings\n", + "\n", + "\n", + "Bukalapak\n", + "$2.5\n", + "11/16/2017\n", + "Indonesia\n", + "E-commerce & direct-to-consumer\n", + "500 Startups, Batavia Incubator, Emtek Group\n", + "\n", + "\n", + "Celonis\n", + "$2.5\n", + "6/26/2018\n", + "Germany\n", + "Data management & analytics\n", + "Accel, 83North\n", + "\n", + "\n", + "Cambricon\n", + "$2.5\n", + "8/18/2017\n", + "China\n", + "Artificial intelligence\n", + "\n", + "\n", + "Cohesity\n", + "$2.5\n", + "6/11/2018\n", + "United States\n", + "Data management & analytics\n", + "SoftBank Group, Sequoia Capital, Wing Venture Capital\n", + "\n", + "\n", + "Lime\n", + "$2.4\n", + "7/9/2018\n", + "United States\n", + "Auto & transportation\n", + "Andreessen Horowitz, Coatue Management, Uber\n", + "\n", + "\n", + "Carbon\n", + "$2.4\n", + "12/20/2017\n", + "United States\n", + "Hardware\n", + "Google Ventures, Sequoia Capital, Wakefield Group\n", + "\n", + "\n", + "Postmates\n", + "$2.4\n", + "9/18/2018\n", + "United States\n", + "Supply chain, logistics, & delivery\n", + "Founders Fund, Matrix Partners, Tiger Global Management\n", + "\n", + "\n", + "YITU Technology\n", + "$2.37\n", + "3/8/2018\n", + "China\n", + "Artificial intelligence\n", + "Sequoia Capital China, Banyan Capital\n", + "\n", + "\n", + "Collibra\n", + "$2.36\n", + "1/29/2019\n", + "United States\n", + "Data management & analytics\n", + "Index Ventures, Battery Ventures, ICONIQ Capital\n", + "\n", + "\n", + "Dadi Cinema\n", + "$3.2\n", + "5/11/2016\n", + "China\n", + "Other\n", + "Alibaba Pictures Group\n", + "\n", + "\n", + "Uptake\n", + "$2.3\n", + "10/27/2015\n", + "United States\n", + "Artificial intelligence\n", + "Revolution, New Enterprise Associates, Caterpillar\n", + "\n", + "\n", + "OakNorth\n", + "$2.3\n", + "10/12/2017\n", + "United Kingdom\n", + "Fintech\n", + "Clermont Group, Coltrane Asset Management, Toscafund Asset Management\n", + "\n", + "\n", + "Udaan\n", + "$2.3\n", + "9/3/2018\n", + "India\n", + "Supply chain, logistics, & delivery\n", + "DST Global, Lightspeed Venture Partners, Microsoft ScaleUp\n", + "\n", + "\n", + "Skydance Media\n", + "$2.3\n", + "2/11/2020\n", + "United States\n", + "Other\n", + "RedBird Capital Partners, CJ ENM, Tencent Holdings\n", + "\n", + "\n", + "Zume Pizza\n", + "$2.25\n", + "11/1/2018\n", + "United States\n", + "Consumer & retail\n", + "Softbank Group, AME Cloud Ventures, SignalFire\n", + "\n", + "\n", + "FlixBus\n", + "$2.25\n", + "7/18/2019\n", + "Germany\n", + "Auto & transportation\n", + "Holtzbrinck Ventures, Unternehmertum Venture Capital, General Atlantic\n", + "\n", + "\n", + "Via Transportation\n", + "$2.25\n", + "3/30/2020\n", + "United States\n", + "Auto & transportation\n", + "83North, RiverPark Ventures, Pitango Venture Capital\n", + "\n", + "\n", + "NuCom Group\n", + "$2.2\n", + "2/22/2018\n", + "Germany\n", + "Other\n", + "General Atlantic\n", + "\n", + "\n", + "MINISO Life\n", + "$2.2\n", + "9/30/2018\n", + "China\n", + "Consumer & retail\n", + "Hillhouse Capital Management, and Tencent Holdings\n", + "\n", + "\n", + "Viva Republica (Toss)\n", + "$2.2\n", + "12/10/2018\n", + "South Korea\n", + "Fintech\n", + "Bessemer Venture Partners, Qualcomm Ventures, Kleiner Perkins Caufield & Byers\n", + "\n", + "\n", + "Checkr\n", + "$2.2\n", + "9/19/2019\n", + "United States\n", + "Internet software & services\n", + "Y Combinator, Accel, T. Rowe Price\n", + "\n", + "\n", + "Huaqin Telecom Technology\n", + "$2.19\n", + "10/8/2019\n", + "China\n", + "Mobile & telecommunications\n", + "Zhangjiang Haocheng Venture Capital, Walden International, Intel Capital\n", + "\n", + "\n", + "Zomato\n", + "$3.25\n", + "4/10/2015\n", + "India\n", + "Internet software & services\n", + "Sequoia Capital, VY Capital\n", + "\n", + "\n", + "BenevolentAI\n", + "$2.1\n", + "6/2/2015\n", + "United Kingdom\n", + "Artificial intelligence\n", + "Woodford Investment Management\n", + "\n", + "\n", + "Nextdoor\n", + "$2.1\n", + "3/4/2015\n", + "United States\n", + "Internet software & services\n", + "Benchmark Capital, DAG\n", + "Ventures, Insight Venture Partners\n", + "\n", + "\n", + "Perfect Diary\n", + "$2\n", + "9/11/2019\n", + "China\n", + "Other\n", + "Sequoia Capital China, Hillhouse Capital Management, CMC Capital Partners\n", + "\n", + "\n", + "ReNew Power\n", + "$2\n", + "2/14/2017\n", + "India\n", + "Other\n", + "Goldman Sachs, JERA, Asian Development Bank\n", + "\n", + "\n", + "Traveloka\n", + "$2\n", + "7/28/2017\n", + "Indonesia\n", + "Travel\n", + "Global Founders Capital, East Ventures, Expedia Inc.\n", + "\n", + "\n", + "Huimin\n", + "$2\n", + "9/5/2016\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Zheshang Venture Capital, GP Capital, Western Capital Management\n", + "\n", + "Zenefits\n", + "$2\n", + "5/6/2015\n", + "United States\n", + "Fintech\n", + "SV Angel, Institutional Venture Partners, Venrock\n", + "\n", + "\n", + "Avant\n", + "$2\n", + "9/30/2015\n", + "United States\n", + "Artificial intelligence\n", + "RRE Ventures, Tiger Global, August Capital\n", + "\n", + "\n", + "Trendy\n", + "\t Group International\n", + "$2\n", + "2/13/2012\n", + "China\n", + "Consumer & retail\n", + "L Capital Partners\n", + "\n", + "\n", + "Deliveroo\n", + "$2\n", + "9/25/2017\n", + "United Kingdom\n", + "Supply chain, logistics, & delivery\n", + "Accel Partners, General Catalyst, Index Ventures\n", + "\n", + "\n", + "Preferred Networks\n", + "$2\n", + "5/17/2018\n", + "Japan\n", + "Artificial intelligence\n", + "Toyota Motor Corporation, Mizuho Financial Group, FANUC\n", + "\n", + "\n", + "Improbable\n", + "$2\n", + "5/12/2017\n", + "United Kingdom\n", + "Other\n", + "Andreessen Horowitz, SoftBank Group, Temasek Holdings\n", + "\n", + "\n", + "LegalZoom\n", + "$2\n", + "7/31/2018\n", + "United States\n", + "Internet software & services\n", + "K1 Capital, Francisco Partners, Neuberger Berman\n", + "\n", + "\n", + "Lemonade\n", + "$2\n", + "04/11/2019\n", + "United States\n", + "Fintech\n", + "Google Ventures, Thrive Capital, SoftBank Group\n", + "\n", + "\n", + "\n", + "Discord\n", + "$2\n", + "04/20/2018\n", + "United States\n", + "Internet software & services\n", + "Benchmark, Greylock Partners, Tencent Holdings\n", + "\n", + "\n", + "Checkout.com\n", + "$2\n", + "5/2/2019\n", + "United Kingdom\n", + "Fintech\n", + "Insight Partners, DST Global\n", + "\n", + "\n", + "Marqeta\n", + "$2\n", + "5/21/2019\n", + "United States\n", + "Fintech\n", + "83North, Granite Ventures, CommerzVentures\n", + "\n", + "\n", + "Mafengwo\n", + "$2\n", + "5/23/2019\n", + "China\n", + "Travel\n", + "Qiming Venture Partners, Capital Today, General Atlantic\n", + "\n", + "\n", + "Babylon Health\n", + "$2\n", + "8/2/2019\n", + "United Kingdom\n", + "Artificial intelligence\n", + "Kinnevik, Vostok New Ventures, Public Investment Fund of Saudi Arabia\n", + "\n", + "\n", + "Tongdun Technology\n", + "$2\n", + "10/10/2017\n", + "China\n", + "Cybersecurity\n", + "Advantech Capital, Temasek Holdings Ltd., Tiantu Capital Co.\n", + "\n", + "\n", + "Nuvei\n", + "$2\n", + "12/11/2019\n", + "Canada\n", + "Other\n", + "Caisse de depot et placement du Quebec, Novacap Investments, Goldman Sachs\n", + "\n", + "\n", + "Udemy\n", + "$2\n", + "2/19/2020\n", + "United States\n", + "Edtech\n", + "MHS Capital, Insight Partners, Norwest Venture Partners\n", + "\n", + "\n", + "4Paradigm\n", + "$2\n", + "12/19/2018\n", + "China\n", + "Artificial intelligence\n", + "Sequoia Capital China, China Construction Bank, Bank of China\n", + "\n", + "Tubatu.com\n", + "$2\n", + "3/9/2015\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Sequoia Capital China, Matrix Partners China, 58.com\n", + "\n", + "\n", + "Oxford Nanopore Technologies\n", + "$1.96\n", + "7/21/2015\n", + "United Kingdom\n", + "Health\n", + "Illumina, Invesco Perpetual, IP Group\n", + "\n", + "\n", + "eDaili\n", + "$1.9\n", + "2/1/2019\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "K2VC, Lightspeed China Partners, Sky9 Capital\n", + "\n", + "\n", + "monday.com\n", + "$1.9\n", + "7/30/2019\n", + "United States\n", + "Internet software & services\n", + "\tEntree Capital, Genesis Partners, Insight Partners\n", + "\n", + "\n", + "RigUp\n", + "$1.9\n", + "9/30/2019\n", + "United States\n", + "Internet software & services\n", + "Founders Fund, Quantum Energy Partners, Bedrock Capital\n", + "\n", + "\n", + "MUSINSA\n", + "$1.89\n", + "11/11/2019\n", + "South Korea\n", + "E-commerce & direct-to-consumer\n", + "Sequoia Capital\n", + "\n", + "\n", + "Quora\n", + "$2\n", + "4/21/2017\n", + "United States\n", + "Internet software & services\n", + "Y Combinator, Matrix Partners, Benchmark\n", + "\n", + "\n", + "Figma\n", + "$2\n", + "4/30/2020\n", + "United States\n", + "Internet software & services\n", + "Index Ventures, Greylock Partners, Kleiner Perkins Caufield & Byers\n", + "\n", + "\n", + "ENOVATE\n", + "$1.85\n", + "4/15/2019\n", + "China\n", + "Auto & transportation\n", + "Automobile Industry Guidance Fund\n", + "\n", + "\n", + "Zocdoc\n", + "$1.8\n", + "8/20/2015\n", + "United States\n", + "Health\n", + "Founders Fund, Khosla Ventures, Goldman Sachs\n", + "\n", + "\n", + "Sprinklr\n", + "$1.8\n", + "3/31/2015\n", + "United States\n", + "Internet software & services\n", + "Azure Capital Partners,\n", + "\t Battery Ventures, Intel Capital\n", + "\n", + "\n", + "reddit\n", + "$1.8\n", + "7/31/2017\n", + "United States\n", + "Internet software & services\n", + "Y Combinator, Sequoia Capital, Coatue Management\n", + "\n", + "\n", + "Devoted Health\n", + "$1.8\n", + "10/16/2018\n", + "United States\n", + "Health\n", + "Andreessen Horowitz, F-Prime Capital, Venrock\n", + "\n", + "\n", + "Afiniti\n", + "$1.8\n", + "4/14/2017\n", + "United States\n", + "Artificial intelligence\n", + "GAM Holding\n", + "\n", + "\n", + "BillDesk\n", + "$1.8\n", + "11/16/2018\n", + "India\n", + "Fintech\n", + "Temasek Holdings, Visa, March Capital Partners\n", + "\n", + "\n", + "Verkada\n", + "$1.8\n", + "1/29/2020\n", + "United States\n", + "Cybersecurity\n", + "next47, First Round Capital, Sequoia Capital\n", + "\n", + "\n", + "L&P Cosmetic\n", + "$1.19\n", + "1/1/2016\n", + "South Korea\n", + "Consumer & retail\n", + "CDIB Capital\n", + "\n", + "\n", + "wefox Group\n", + "$1.76\n", + "12/11/2019\n", + "Germany\n", + "Fintech\n", + "Salesforce Ventures, Seedcamp, OMERS Ventures\n", + "\n", + "\n", + "Kaseya\n", + "$2\n", + "3/27/2019\n", + "United States\n", + "Cybersecurity\n", + "Insight Partners, TPG Alternative & Renewable Technologies, Ireland Strategic Investment Fund\n", + "\n", + "\n", + "Apus Group\n", + "$1.73\n", + "1/16/2015\n", + "China\n", + "Mobile & telecommunications\n", + "Redpoint Ventures, QiMing Venture Partners, Chengwei Capital\n", + "\n", + "\n", + "\n", + "Xinchao Media\n", + "$1.72\n", + "4/9/2018\n", + "China\n", + "Internet software & services\n", + "JD.com, Baidu, Vision Plus Capital\n", + "\n", + "\n", + "Squarespace\n", + "$1.7\n", + "12/14/2017\n", + "United States\n", + "Internet software & services\n", + "General Atlantic, Index Ventures, Accel Partners\n", + "\n", + "\n", + "Buzzfeed\n", + "$1.7\n", + "8/18/2015\n", + "United States\n", + "Internet software & services\n", + "SV Angel, RRE Ventures, New Enterprise Associates\n", + "\n", + "\n", + "\n", + "XANT\n", + "$1.7\n", + "4/28/2014\n", + "United States\n", + "Artificial intelligence\n", + "Microsoft Ventures, US\n", + "\t Venture Partners, Kleiner Perkins Caufield & Byers\n", + "\n", + "\n", + "Graphcore\n", + "$1.95\n", + "12/18/2018\n", + "United Kingdom\n", + "Artificial intelligence\n", + "Dell Technologies Capital, Pitango Venture Capital, Amadeus Capital Partners\n", + "\n", + "\n", + "Pax Labs\n", + "$1.7\n", + "10/22/2018\n", + "United States\n", + "Consumer & retail\n", + "Tao Capital Partners, Global Asset Capital, Tiger Global Management\n", + "\n", + "\n", + "Carta\n", + "$1.7\n", + "5/6/2019\n", + "United States\n", + "Fintech\n", + "Menlo Ventures, Spark Capital, Union Square Ventures\n", + "\n", + "\n", + "Thumbtack\n", + "$1.7\n", + "9/29/2015\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "Tiger Global, Sequoia Capital, Google Capital\n", + "\n", + "\n", + "\n", + "Scopely\n", + "$1.7\n", + "10/29/2019\n", + "United States\n", + "Mobile & telecommunications\n", + "Greycroft, Sands Capital, Revolution Growth\n", + "\n", + "\n", + "CureVac\n", + "$1.65\n", + "3/5/2015\n", + "Germany\n", + "Health\n", + "dievini Hopp BioTech Holding & Co., Eli Lilly & Co., LBBW Venture Capital\n", + "\n", + "\n", + "Darktrace\n", + "$1.65\n", + "5/15/2018\n", + "United Kingdom\n", + "Artificial intelligence\n", + "KKR, Ten Eleven Ventures, Summit Partners\n", + "\n", + "\n", + "Jusfoun Big Data\n", + "$1.65\n", + "12/19/2018\n", + "China\n", + "Data management & analytics\n", + "Boxin Capital, DT Capital Partners, IDG Capital\n", + "\n", + "\n", + "ServiceTitan\n", + "$1.65\n", + "11/14/2018\n", + "United States\n", + "Internet software & services\n", + "Bessemer Venture Partners, ICONIQ Capital, Battery Ventures\n", + "\n", + "\n", + "Zhubajie\n", + "$1.61\n", + "6/16/2015\n", + "China\n", + "Internet software & services\n", + "Cybernaut Growth Fund, IDG Capital\n", + "\n", + "\n", + "Infinidat\n", + "$1.6\n", + "4/29/2015\n", + "Israel\n", + "Hardware\n", + "TPG Growth, Goldman Sachs\n", + "\n", + "\n", + "BlaBlaCar\n", + "$1.6\n", + "9/16/2015\n", + "France\n", + "Auto & transportation\n", + "Accel Partners, Index Ventures, Insight Venture Partners\n", + "\n", + "\n", + "CAOCAO\n", + "$1.6\n", + "1/17/2018\n", + "China\n", + "Auto & transportation\n", + "People Electrical Appliance Group China, Zhongrong International Trust\n", + "\n", + "\n", + "Gan & Lee Pharmaceuticals\n", + "$1.6\n", + "11/1/2014\n", + "China\n", + "Health\n", + "Qiming Venture Partners, Goldman Sachs, Hillhouse Capital Management\n", + "\n", + "\n", + "Dataminr\n", + "$1.6\n", + "6/4/2018\n", + "United States\n", + "Artificial intelligence\n", + "Venrock, Institutional Venture Partners, Goldman Sachs\n", + "\n", + "\n", + "Sweetgreen\n", + "$1.6\n", + "11/13/2018\n", + "United States\n", + "Consumer & retail\n", + "Red Sea Ventures, Fidelity Investments, Revolution\n", + "\n", + "\n", + "Pine Labs\n", + "$1.6\n", + "1/24/2020\n", + "India\n", + "Fintech\n", + "MasterCard, Temasek, PayPal Ventures\n", + "\n", + "\n", + "Airwallex\n", + "$1.6\n", + "3/25/2019\n", + "Australia\n", + "Fintech\n", + "DST Global, Sequoia Capital China, Tencent Holdings\n", + "\n", + "\n", + "ASR Microelectronics\n", + "$1.6\n", + "4/30/2020\n", + "China\n", + "Hardware\n", + "Shenzhen Capital Group, Sequoia Capital China, Hillhouse Capital Management\n", + "\n", + "\n", + "Podium\n", + "$1.5\n", + "4/7/2020\n", + "United States\n", + "Internet software & services\n", + "Accel, Summit Partners, Google Ventures\n", + "\n", + "\n", + "Delhivery\n", + "$1.5\n", + "2/27/2019\n", + "India\n", + "Supply chain, logistics, & delivery\n", + "Times Internet, Nexus Venture Partners, SoftBank Group\n", + "\n", + "\n", + "Quanergy Systems\n", + "$2\n", + "8/24/2016\n", + "United States\n", + "Auto & transportation\n", + "Delphi Automotive, Samsung Ventures, Motus Ventures\n", + "\n", + "\n", + "AIWAYS\n", + "$1.59\n", + "4/16/2018\n", + "China\n", + "Auto & transportation\n", + "Jiangsu Sha Steel Group, Shanghai Puyin Industry, Funa Yuanchuang Technology\n", + "\n", + "\n", + "Promasidor Holdings\n", + "$1.58\n", + "11/8/2016\n", + "South Africa\n", + "Consumer & retail\n", + "IFC, Ajinomoto\n", + "\n", + "\n", + "Northvolt\n", + "$1.57\n", + "6/12/2019\n", + "Sweden\n", + "Other\n", + "Vattenfall, Volkswagen Group, Goldman Sachs\n", + "\n", + "\n", + "Ximalaya FM\n", + "$1.52\n", + "9/22/2017\n", + "China\n", + "Mobile & telecommunications\n", + "China Creation Ventures, Sierra Ventures, Xingwang Investment Management\n", + "\n", + "\n", + "Mu Sigma\n", + "$1.5\n", + "2/7/2013\n", + "United States\n", + "Data management & analytics\n", + "Sequoia Capital, General Atlantic\n", + "\n", + "\n", + "STX Entertainment\n", + "$1.5\n", + "8/10/2016\n", + "United States\n", + "Other\n", + "Tencent, TPG Growth, Hony Capital\n", + "\n", + "\n", + "Tujia\n", + "$1.5\n", + "6/17/2015\n", + "China\n", + "Travel\n", + "GGV Capital, QiMing Venture Partnersl\n", + "\n", + "\n", + "ironSource\n", + "$1.5\n", + "8/11/2014\n", + "Israel\n", + "Mobile & telecommunications\n", + "Access Industries, Clal\n", + "\t Industries and Investments\n", + "\n", + "\n", + "Asana\n", + "$1.5\n", + "11/29/2018\n", + "United States\n", + "Internet software & services\n", + "8VC, Benchmark, Generation Investment Management\n", + "\n", + "\n", + "Segment\n", + "$1.5\n", + "4/2/2019\n", + "United States\n", + "Data management & analytics\n", + "Accel, Y Combinator, Thrive Capital\n", + "\n", + "\n", + "Cybereason\n", + "$1.5\n", + "8/6/2019\n", + "United States\n", + "Cybersecurity\n", + "SoftBank Group, CRV, Spark Capital\n", + "\n", + "\n", + "PolicyBazaar\n", + "$1.5\n", + "5/6/2019\n", + "India\n", + "Fintech\n", + "Info Edge, Softbank Capital\n", + "\n", + "\n", + "DT Dream\n", + "$1.5\n", + "6/8/2017\n", + "China\n", + "Data management & analytics\n", + "Alibaba Group, China Everbright Investment Management, Yinxinggu Capital\n", + "\n", + "\n", + "JFrog\n", + "$1.5\n", + "10/4/2018\n", + "United States\n", + "Internet software & services\n", + "Gemini Israel Ventures, VMware, Battery Ventures\n", + "\n", + "\n", + "ACV Auctions\n", + "$1.5\n", + "11/12/2019\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "Bessemer Venture Partners, Tribeca Venture Partners, Bain Capital Ventures\n", + "\n", + "\n", + "Gett\n", + "$1.4\n", + "6/7/2018\n", + "Israel\n", + "Auto & transportation\n", + "Volkswagen, Access Industries, Vostok New Ventures\n", + "\n", + "\n", + "Duolingo\n", + "$1.5\n", + "12/4/2019\n", + "United States\n", + "Education\n", + "capitalG, Union Square Ventures, New Enterprise Associates\n", + "\n", + "\n", + "CGTZ\n", + "$1.4\n", + "2/21/2017\n", + "China\n", + "Fintech\n", + "Shunwei Capital Partners, China Media Group, Guangzhou Huiyin Aofeng Equity Investment Fund\n", + "\n", + "\n", + "Coocaa\n", + "$1.45\n", + "3/16/2018\n", + "China\n", + "Hardware\n", + "Baidu, Tencent Holdings\n", + "\n", + "\n", + "Tuya Smart\n", + "$1.44\n", + "7/41/2018\n", + "China\n", + "Internet software & services\n", + "New Enterprise Associates, Quadrille Capital, Global Bridge Capital\n", + "\n", + "\n", + "Koudai\n", + "$1.4\n", + "10/23/2014\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "New Enterprise Associates,\n", + "\t Tiger Global management, Tencent\n", + "\n", + "\n", + "AvidXchange\n", + "$1.4\n", + "6/8/2017\n", + "United States\n", + "Fintech\n", + "Temasek Holdings, Charlotte Angel Partners, TPG Growth\n", + "\n", + "\n", + "Hike\n", + "$1.4\n", + "8/16/2016\n", + "India\n", + "Mobile & telecommunications\n", + "Foxconn, Tiger Global management, Tencent\n", + "\n", + "\n", + "C3\n", + "$1.4\n", + "3/2/2017\n", + "United States\n", + "Artificial intelligence\n", + "Makena Capital Management, TPG Growth, Breyer Capital\n", + "\n", + "\n", + "AppLovin\n", + "$1.4\n", + "1/1/2017\n", + "United States\n", + "Mobile & telecommunications\n", + "Orient Hontai Capital, Webb Investment Network\n", + "\n", + "\n", + "Allbirds\n", + "$1.4\n", + "10/11/2018\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "Lerer Hippeau Ventures, T. Rowe Price, Tiger Global Management\n", + "\n", + "\n", + "Cabify\n", + "$1.4\n", + "1/22/2018\n", + "Spain\n", + "Auto & transportation\n", + "Seaya Ventures, Otter Rock Capital, Rakuten\n", + "\n", + "\n", + " Away\n", + "$1.4\n", + "5/15/2019\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "Global Founders Capital, Comcast Ventures,\tForerunner Ventures\n", + "\n", + "\n", + "Symphony Communication Services\n", + "$1.4\n", + "5/16/2017\n", + "United States\n", + "Internet software & services\n", + "BNP Paribas, Goldman Sachs, Google\n", + "\n", + "\n", + "Dataiku\n", + "$1.4\n", + "12/4/2019\n", + "United States\n", + "Internet software & services\n", + "\tAlven Capital, FirstMark Capital, capitalG\n", + "\n", + "\n", + "Yidian Zixun\n", + "$1.4\n", + "10/17/2017\n", + "China\n", + "Mobile & telecommunications\n", + "Phoenix New Media, Tianjin Haihe Industry Fund\n", + "\n", + "\n", + "Hive Box\n", + "$1.4\n", + "1/23/2018\n", + "China\n", + "Supply chain, logistics, & delivery\n", + "Eastern Bell Capital, SF Holding Co, STO Express\n", + "\n", + "\n", + "GPClub\n", + "$1.32\n", + "10/22/2018\n", + "South Korea\n", + "Other\n", + "Goldman Sachs\n", + "\n", + "\n", + "Zeta Global\n", + "$1.3\n", + "7/15/2015\n", + "United States\n", + "Internet software & services\n", + "GSO Capital Partners, Franklin Square\n", + "\n", + "\n", + "Docker\n", + "$1.3\n", + "4/14/2015\n", + "United States\n", + "Internet software & services\n", + "Greylock Partners,\n", + "\t Lightspeed Venture Partners, Lowercase Capital\n", + "\n", + "\n", + "Trax\n", + "$1.3\n", + "7/22/2019\n", + "Singapore\n", + "Artificial intelligence\n", + "Hopu Investment Management, Boyu Capital, DC Thomson Ventures\n", + "\n", + "\n", + "Wildlife Studios\n", + "$1.3\n", + "12/5/2019\n", + "Brazil\n", + "Other\n", + "Benchmark, Bessemer Venture Partners\n", + "\n", + "\n", + "You & Mr Jones\n", + "$1.3\n", + "11/19/2019\n", + "United States\n", + "Other\n", + "Undisclosed\n", + "\n", + "\n", + "InSightec\n", + "$1.3\n", + "3/6/2020\n", + "Israel\n", + "Health\n", + "York Capital Management, GE Healthcare, Koch Disruptive Technologies\n", + "\n", + "\n", + "Ovo Energy\n", + "$1.28\n", + "2/14/2019\n", + "United Kingdom\n", + "Other\n", + "Mitsubishi Corporation, Mayfair Equity Partners\n", + "\n", + "\n", + "Starry\n", + "$1.27\n", + "3/27/2018\n", + "United States\n", + "Mobile & telecommunications\n", + "Social Capital, Bessemer Venture Partners\n", + "\n", + "\n", + "WTOIP\n", + "$1.27\n", + "4/8/2018\n", + "China\n", + "Internet software & services\n", + "Dark Horse Technology Group, Hopu Investment Management, Kefa Capital\n", + "\n", + "\n", + "Intercom\n", + "$1.29\n", + "7/3/2018\n", + "United States\n", + "Internet software & services\n", + "FirstMark Capital, Tiger Global Management\n", + "\n", + "\n", + "Atom Bank\n", + "$1.25\n", + "3/7/2018\n", + "United Kingdom\n", + "Fintech\n", + "Toscafund Asset Management, Woodford Investment Management, BBVA\n", + "\n", + "\n", + "Butterfly Network\n", + "$1.25\n", + "7/13/2018\n", + "United States\n", + "Artificial intelligence\n", + "Bill & Melinda Gates Foundation, Aeris Capital,\n", + "\n", + "\n", + "\n", + "ezCater\n", + "$1.25\n", + "4/2/2019\n", + "United States\n", + "Supply chain, logistics, & delivery\n", + "Insight Venture Partners, ICONIQ Capital, Launchpad Venture Group\n", + "\n", + "\n", + "Infi\n", + "$1.25\n", + "5/1/2018\n", + "Israel\n", + "Artificial intelligence\n", + "Pacific Century Group\n", + "\n", + "\n", + "KeepTruckin\n", + "$1.2\n", + "4/23/2019\n", + "United States\n", + "Supply chain, logistics, & delivery\n", + "Google Ventures, Index Ventures, Scale Venture Partners\n", + "\n", + "\n", + "Ten-X\n", + "$1.2\n", + "3/5/2014\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "Stone Point Capital, Google Capital\n", + "\n", + "\n", + "Clover Health\n", + "$1.2\n", + "5/10/2017\n", + "United States\n", + "Health\n", + "Google Ventures, Sequoia Capital, First Round Capital\n", + "\n", + "\n", + "Warby\n", + "\t Parker\n", + "$1.2\n", + "4/30/2015\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "BoxGroup, Felicis Ventures,\n", + "\t First Round Capital\n", + "\n", + "\n", + "OfferUp\n", + "$1.2\n", + "9/8/2016\n", + "United States\n", + "Mobile & telecommunications\n", + "Andreessen Horowitz, GGV Capital, T. Rowe Price\n", + "\n", + "\n", + "Yiguo (易果生鲜)\n", + "$1.2\n", + "11/9/2016\n", + "China\n", + "Supply chain, logistics, & delivery\n", + "Alibaba Group, KKR, Goldman Sachs\n", + "\n", + "\n", + "Glossier\n", + "$1.2\n", + "3/19/2019\n", + "United States\n", + "Consumer & retail\n", + "Forerunner Ventures, Institutional Venture Partners, Thrive Capital\n", + "\n", + "\n", + "Zipline International\n", + "$1.2\n", + "5/20/2019\n", + "United States\n", + "Supply chain, logistics, & delivery\n", + "Sequoia Capital, Baillie Gifford & Co., Google Ventures\n", + "\n", + "\n", + "SmartNews\n", + "$1.2\n", + "8/5/2019\n", + "Japan\n", + "Mobile & telecommunications\n", + "Japan Post Capital, Globis Capital Partners, Atomico\n", + "\n", + "\n", + "Fair\n", + "$1.2\n", + "12/20/2018\n", + "United States\n", + "Auto & transportation\n", + "CreditEase Fintech Investment Fund, BMW i Ventures, SoftBank Group\n", + "\n", + "\n", + "Rapyd\n", + "$1.2\n", + "12/3/2019\n", + "United Kingdom\n", + "Fintech\n", + "Target Global, General Catalyst, Durable Capital Partners\n", + "\n", + "\n", + "Figure Technologies\n", + "$1.2\n", + "11/5/2019\n", + "United States\n", + "Fintech\n", + "DCM Ventures, Ribbit Capital, RPM Ventures\n", + "\n", + "\n", + "FirstCry\n", + "$1.2\n", + "2/7/2020\n", + "India\n", + "E-commerce & direct-to-consumer\n", + "SoftBank Group, SAIF Partners India, Valiant Capital Partners\n", + "\n", + "\n", + "VAST Data\n", + "$1.2\n", + "4/16/2020\n", + "United States\n", + "Data management & analytics\n", + "Norwest Venture Partners, Goldman Sachs, Dell Technologies Capital\n", + "\n", + "\n", + "HeartFlow\n", + "$1.5\n", + "12/4/2017\n", + "United States\n", + "Health\n", + "BlueCross BlueShield Venture Partners, US Venture Partners\n", + "\n", + "\n", + "Luoji Siwei\n", + "$1.17\n", + "7/20/2017\n", + "China\n", + "Edtech\n", + "Sequoia Capital China, Qiming Venture Partners, Tencent Holdings\n", + "\n", + "\n", + "Yimidida\n", + "$1.17\n", + "7/1/2019\n", + "China\n", + "Supply chain, logistics, & delivery\n", + "Source Code Capital, Global Logistic Properties, K2VC\n", + "\n", + "\n", + "\n", + "Lyell Immunopharma\n", + "$1.16\n", + "2/8/2019\n", + "United States\n", + "Health\n", + "ARCH Venture Partners, Foresite Capital, Altitude Life Science Ventures\n", + "\n", + "\n", + "Deezer\n", + "$1.16\n", + "8/2/2018\n", + "France\n", + "Internet software & services\n", + "Orange Digital Ventures, Access Industries\n", + "\n", + "\n", + "LIfeMiles\n", + "$1.15\n", + "7/13/2015\n", + "Colombia\n", + "Other\n", + "Advent International\n", + "\n", + "\n", + "BrewDog\n", + "$1.15\n", + "4/10/2017\n", + "United Kingdom\n", + "Consumer & retail\n", + "TSG Consumer Partners, Crowdcube\n", + "\n", + "\n", + "Doctolib\n", + "$1.14\n", + "3/19/2019\n", + "France\n", + "Health\n", + "BPI France, Kerala Ventures, Accel\n", + "\n", + "\n", + "Deposit Solutions\n", + "$1.12\n", + "9/18/2019\n", + "Germany\n", + "Fintech\n", + "e.ventures, Greycroft, FinLab\n", + "\n", + "\n", + "TELD\n", + "$1.12\n", + "12/16/2019\n", + "China\n", + "Fintech\n", + "China Reform Fund, Gaopeng Capital, Jinhui Xingye\n", + "\n", + "\n", + "\n", + "Actifio\n", + "$1.1\n", + "3/24/2014\n", + "United States\n", + "Data management & analytics\n", + "Greylock Partners, North\n", + "\t Bridge Venture Partners, Technology Crossover Ventures\n", + "\n", + "\n", + "TangoMe\n", + "$1.1\n", + "3/20/2014\n", + "United States\n", + "Mobile & telecommunications\n", + "Draper Fisher Jurtson,\n", + "\t Qualcomm Ventures, Alibaba Group\n", + "\n", + "\n", + "Tuhu\n", + "$1.16\n", + "9/15/2018\n", + "China\n", + "Auto & transportation\n", + "Qiming Venture Partners, Yaxia Automobile, Far East Horizon\n", + "\n", + "\n", + "OVH\n", + "$1.1\n", + "7/3/2015\n", + "France\n", + "Other\n", + "KKR, TowerBrook Capital Partners\n", + "\n", + "\n", + "Tradeshift\n", + "$1.1\n", + "5/30/2018\n", + "United States\n", + "Fintech\n", + "Notion Capital, Scentan Ventures, Kite Ventures\n", + "\n", + "\n", + "Yijiupi (易久批)\n", + "$1.1\n", + "9/20/2018\n", + "China\n", + "Consumer & retail\n", + "Source Code Capital, Meituan Dianping, Tencent Holdings\n", + "\n", + "\n", + "Outreach\n", + "$1.1\n", + "4/16/2019\n", + "United States\n", + "Artificial intelligence\n", + "Mayfield Fund, M12, Trinity Ventures\n", + "\n", + "\n", + "Ivalua\n", + "$1.1\n", + "5/21/2019\n", + "United States\n", + "Fintech\n", + "Ardian, Tiger Global Management, KKR\n", + "\n", + "\n", + "Sonder\n", + "$1.1\n", + "7/11/2019\n", + "United States\n", + "Travel\n", + "Structure Capital, Spark Capital, Greylock Partners\n", + "\n", + "\n", + "Vinted\n", + "$1.1\n", + "11/27/2019\n", + "Lithuania\n", + "E-commerce & direct-to-consumer\n", + "\tAccel, Insight Partners, Burda Principal Investments\n", + "\n", + "\n", + "Coveo\n", + "$1.1\n", + "11/06/2019\n", + "Canada\n", + "Artificial intelligence\n", + "Fonds de Solidarite FTQ, Propulsion Ventures, BDC Venture Capital\n", + "\n", + "\n", + "Course Hero\n", + "$1.1\n", + "2/12/2020\n", + "United States\n", + "Edtech\n", + "NewView Capital, Maveron, Ridge Ventures\n", + "\n", + "\n", + "SentinelOne\n", + "$1.1\n", + "2/19/2020\n", + "United States\n", + "Cybersecurity\n", + "Granite Hill Capital Partners, Data Collective, Tiger Global Management\n", + "\n", + "\n", + "Linklogis\n", + "$1.05\n", + "10/15/2018\n", + "China\n", + "Fintech\n", + "Bertelsmann Asia Investments, Loyal Valley Capital, Tencent Holdings\n", + "\n", + "\n", + "Instabase\n", + "$1.05\n", + "10/21/2019\n", + "United States\n", + "Data management & analytics\n", + "New Enterprise Associates, Greylock Partners, Andreessen Horowitz\n", + "\n", + "\n", + "Aprogen\n", + "$1.04\n", + "5/31/2019\n", + "South Korea\n", + "Health\n", + "\tLindeman Asia Investment, Nichi-Iko Pharmaceutical\n", + "\n", + "\n", + "Miaoshou Doctor\n", + "$1.02\n", + "6/27/2019\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Sequoia Capital China, Qiming Venture Partners, Tencent Holdings\n", + "\n", + "\n", + "TuSimple\n", + "$1\n", + "2/13/2019\n", + "United States\n", + "Artificial intelligence\n", + "Sina Weibo Fund, Zhiping Capital, Composite Capital Partners\n", + "\n", + "\n", + "Radius Payment Solutions\n", + "$1.07\n", + "11/27/2017\n", + "United Kingdom\n", + "Fintech\n", + "Inflexion Private Equity\n", + "\n", + "\n", + "Formlabs\n", + "$1.06\n", + "8/1/2018\n", + "United States\n", + "Hardware\n", + "Pitango Venture Capital, DFJ Growth Fund, Foundry Group\n", + "\n", + "\n", + "Jiuxian\n", + "$1.05\n", + "7/30/2015\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Sequoia Capital China, Rich Land Capital, Merrysunny Wealth\n", + "\n", + "\n", + "AppDirect\n", + "$1.04\n", + "10/7/2015\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "Mithril, iNovia Capital, Foundry Group\n", + "\n", + "\n", + "Judo Capital\n", + "$1.04\n", + "7/29/2019\n", + "Australia\n", + "Other\n", + "Credit Suisse, OPTrust, Ironbridge Capital\n", + "\n", + "\n", + "Kendra Scott\n", + "$1\n", + "12/21/2016\n", + "United States\n", + "Consumer & retail\n", + "Berkshire Partners, Norwest Venture Partners\n", + "\n", + "\n", + "Avaloq Group\n", + "$1.01\n", + "3/22/2017\n", + "Switzerland\n", + "Fintech\n", + "Warbug Pincus\n", + "\n", + "\n", + "Leap Motor\n", + "$1.01\n", + "8/5/2019\n", + "China\n", + "Auto & transportation\n", + "Sequoia Capital China, Gopher Asset Management, Shanghai Electric Group\n", + "\n", + "\n", + "Dianrong\n", + "$1\n", + "8/2/2017\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Standard Chartered, FinSight Ventures, Affirma Capital\n", + "\n", + "\n", + "DotC United Group\n", + "$1\n", + "7/25/2017\n", + "China\n", + "Mobile & telecommunications\n", + "Chengwei Capital, Lightspeed China Partners, Morningside Venture Capital\n", + "\n", + "\n", + "Katerra\n", + "$1\n", + "4/13/2017\n", + "United States\n", + "Supply chain, logistics, & delivery\n", + "Foxconn Technology Company, Khosla Ventures, Moore Capital Management\n", + "\n", + "\n", + "Womai\n", + "$1\n", + "10/12/2015\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "SAIF Partners China, Baidu, IDG Capital\n", + "\n", + "\n", + "Procore Technologies\n", + "$3\n", + "12/8/2016\n", + "United States\n", + "Internet software & services\n", + "Bessemer Venture Partners, O'Connor Ventures, Iconiq Capital\n", + "\n", + "\n", + "Lookout\n", + "$1\n", + "8/13/2014\n", + "United States\n", + "Cybersecurity\n", + "Accel Partners, Greylock\n", + "\t Partners, Lowercase Capital\n", + "\n", + "\n", + "TechStyle Fashion Group\n", + "$1\n", + "8/29/2014\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "Matrix Partners, Passport\n", + "\t Capital, Rho Ventures\n", + "\n", + "\n", + "Proteus Digital Health\n", + "$1.5\n", + "6/2/2014\n", + "United States\n", + "Health\n", + "Novartis, Essex Woodlands, The Carlyle Group\n", + "\n", + "\n", + "Desktop Metal\n", + "$1.5\n", + "7/17/2017\n", + "United States\n", + "Hardware\n", + "Australian Future Fund, GE Ventures, Data Collective\n", + "\n", + "\n", + "Lenskart\n", + "$1.5\n", + "12/20/2019\n", + "India\n", + "E-commerce & direct-to-consumer\n", + "Chiratae Ventures, PremjiInvest, Softbank\n", + "\n", + "\n", + "Illumio\n", + "$1\n", + "4/14/2014\n", + "United States\n", + "Cybersecurity\n", + "Data Collective, Formation\n", + "\t 8, General Catalyst Partners\n", + "\n", + "\n", + "BeiBei\n", + "$1\n", + "1/22/2015\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Banyan Capital, New Horizon\n", + "\t Capital, IDG Capital Partners\n", + "\n", + "\n", + "InMobi\n", + "$1\n", + "12/2/2014\n", + "India\n", + "Mobile & telecommunications\n", + "Kleiner Perkins Caufield\n", + "\t & Byers, Softbank Corp., Sherpalo Ventures\n", + "\n", + "MarkLogic\n", + "$1\n", + "5/12/2015\n", + "United States\n", + "Data management & analytics\n", + "Sequoia Capital, Tenaya Capital, Northgate Capital\n", + "\n", + "\n", + "Zhaogang\n", + "$1\n", + "6/29/2017\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "K2 Ventures, Matrix Partners China, IDG Capital\n", + "\n", + "\n", + "Vox Media\n", + "$1\n", + "8/12/2015\n", + "United States\n", + "Internet software & services\n", + "Accel Partners, Comcast Ventures, General Atlantic\n", + "\n", + "\n", + "Kabbage\n", + "$1\n", + "10/14/2015\n", + "United States\n", + "Fintech\n", + "BlueRun Ventures, SV Angel, Mohr Davidow Ventures\n", + "\n", + "\n", + "iTutorGroup\n", + "$1\n", + "11/18/2015\n", + "China\n", + "Edtech\n", + "QiMing Venture Partners, Temasek Holdings, Silverlink Capital\n", + "\n", + "\n", + "Cell C\n", + "$1\n", + "8/8/2017\n", + "South Africa\n", + "Mobile & telecommunications\n", + "Blue Label Telecoms, Net1 UEPS Technologies\n", + "\n", + "MindMaze\n", + "$1\n", + "2/17/2016\n", + "Switzerland\n", + "Health\n", + "Hinduja Group\n", + "\n", + "\n", + "Mia.com\n", + "$1\n", + "9/8/2015\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Sequoia Capital China, ZhenFund, K2 Ventures\n", + "\n", + "\n", + "iCarbonX\n", + "$1\n", + "4/12/2016\n", + "China\n", + "Artificial intelligence\n", + "Tencent, Vcanbio\n", + "\n", + "\n", + "Age of Learning\n", + "$1\n", + "5/3/2016\n", + "United States\n", + "Edtech\n", + "Iconiq Capital\n", + "\n", + "\n", + "SMS Assist\n", + "$1\n", + "6/13/2016\n", + "United States\n", + "Internet software & services\n", + "Goldman Sachs, Insights Venture Partners, Pritzker Group Venture Capital\n", + "\n", + "\n", + "Mofang Living\n", + "$1\n", + "4/13/2016\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Warburg Pincus, Aviation Industry Corporation of China\n", + "\n", + "\n", + "HuJiang\n", + "$1\n", + "10/29/2015\n", + "China\n", + "Edtech\n", + "China Minsheng Investment, Baidu, Wanxin Media\n", + "\n", + "\n", + "Rubicon Global\n", + "$1\n", + "8/25/2017\n", + "United States\n", + "Other\n", + "Goldman Sachs, Leonardo DiCaprio, Promecap \n", + "\n", + "\n", + "YH Global\n", + "$1\n", + "9/21/2017\n", + "China\n", + "Supply chain, logistics, & delivery\n", + "Co-Energy Finance, Grandland\n", + "\n", + "\n", + "Rocket Lab\n", + "$1\n", + "3/21/2017\n", + "United States\n", + "Other\n", + "Lockheed Martin, Khosla Ventures, Bessemer Venture Partners\n", + "\n", + "\n", + "Zhuan Zhuan\n", + "$1\n", + "4/18/2017\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "58.com, Tencent Holdings\n", + "\n", + "\n", + "Supreme\n", + "$1\n", + "10/9/2017\n", + "United States\n", + "Consumer & retail\n", + "The Carlyle Group\n", + "\n", + "\n", + "XiaoZhu\n", + "$1\n", + "11/1/2017\n", + "China\n", + "Travel\n", + "Morningside Ventures, Capital Today, JOY Capital\n", + "\n", + "\n", + "WeLab\n", + "$1\n", + "11/8/2017\n", + "Hong Kong\n", + "Fintech\n", + "Sequoia Capital China, ING,\tAlibaba Entrepreneurs Fund\n", + "\n", + "\n", + "Payoneer\n", + "$1\n", + "12/5/2017\n", + "United States\n", + "Fintech\n", + "Susquehanna Growth Equity, 83North, China Broadband Capital\n", + "\n", + "\n", + "100credit\n", + "$1\n", + "4/18/2018\n", + "China\n", + "Fintech\n", + "Sequoia Capital China, China Reform Fund, Hillhouse Capital Management\n", + "\n", + "\n", + "Rani Therapeutics\n", + "$1\n", + "2/8/2018\n", + "United States\n", + "Health\n", + "Google Ventures, VentureHealth, InCube Ventures\n", + "\n", + "\n", + "OrCam Technologies\n", + "$1\n", + "02/21/2018\n", + "Israel\n", + "Artificial intelligence\n", + "Intel Capital, Aviv Venture Capital\n", + "\n", + "\n", + "Lalamove\n", + "$1\n", + "02/21/2018\n", + "Hong Kong\n", + "Supply chain, logistics, & delivery\n", + "MindWorks Ventures, Shunwei Capital Partners, \tXiang He Capital\n", + "\n", + "\n", + "17zuoye \n", + "$1\n", + "03/7/2018\n", + "China\n", + "Edtech\n", + "DST Global, Temasek Holdings\n", + "\n", + "\n", + "Dxy.cn\n", + "$1\n", + "04/10/2018\n", + "China\n", + "Health\n", + "Tencent Holdings, DCM Ventures\n", + "\n", + "\n", + "Soundhound\n", + "$1\n", + "05/03/2018\n", + "United States\n", + "Artificial intelligence\n", + "Tencent Holdings, Walden Venture Capital, Global Catalyst Partnera\n", + "\n", + "\n", + "Huike Group\n", + "$1\n", + "05/24/2018\n", + "China\n", + "Edtech\n", + "Fosun RZ Capital, Oceanwide Holdings, Shenzhen Qianhe Capital Management Co.\n", + "\n", + "\n", + "JOLLY Information Technology\n", + "$1\n", + "05/29/2018\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Legend Capital, CDH Investments, Sequoia Capital China\n", + "\n", + "\n", + "Bolt\n", + "$1\n", + "05/30/2018\n", + "Estonia\n", + "Auto & transportation\n", + "Didi Chuxing, Diamler, TMT Investments\n", + "\n", + "\n", + "Dada-JD Daojia\n", + "$1\n", + "12/31/2015\n", + "China\n", + "Supply chain, logistics, & delivery\n", + "DST Global, Sequoia Capital China\n", + "\n", + "\n", + "OutSystems\n", + "$1\n", + "6/5/2018\n", + "Portugal\n", + "Internet software & services\n", + "KKR, ES Ventures, North Bridge Growth Equity\n", + "\n", + "\n", + "MediaMath\n", + "$1\n", + "7/10/2018\n", + "United States\n", + "Internet software & services\n", + "Silicon Valley Bank, QED Investors, European Founders Fund\n", + "\n", + "\n", + "About You\n", + "$1\n", + "7/19/2018\n", + "Germany\n", + "E-commerce & direct-to-consumer\n", + "German Media Pool, Seven Venture Capital\n", + "\n", + "\n", + "Revolution Precrafted\n", + "$1\n", + "10/23/2017\n", + "Philippines\n", + "Other\n", + "K2 Global, 500 Startups\n", + "\n", + "\n", + "Klook\n", + "$1\n", + "8/7/2018\n", + "Hong Kong\n", + "Travel\n", + "Sequoia Capital China, Goldman Sachs, Matrix Partners China\n", + "\n", + "\n", + "Shansong Express (FlashEx)\n", + "$1\n", + "8/27/2018\n", + "China\n", + "Supply chain, logistics, & delivery\n", + "Prometheus Capital, Matrix Partners China, JD Capital Management\n", + "\n", + "\n", + "Rappi\n", + "$1\n", + "8/31/2018\n", + "Colombia\n", + "Supply chain, logistics, & delivery\n", + "DST Global, Andreessen Horowitz, Sequoia Capital, Redpoint e.ventures\n", + "\n", + "\n", + "Aijia Life\n", + "$1\n", + "9/17/2018\n", + "China\n", + "Other\n", + "Tiantu Capital, Fortune Capital, Zhenghedao Fund\n", + "\n", + "\n", + "Nxin (农信互联)\n", + "$1\n", + "9/18/2018\n", + "China\n", + "Internet software & services\n", + "Beijing Juneng Hesheng Industry Investment Fund, Beijing Shuju Xinrong Fund\n", + "\n", + "\n", + "WalkMe\n", + "$1\n", + "9/20/2018\n", + "United States\n", + "Internet software & services\n", + "Gemini Israel Ventures, Insight Venture Partners, Giza Venture Capital\n", + "\n", + "\n", + "ZipRecruiter\n", + "$1\n", + "10/4/2018\n", + "United States\n", + "Artificial intelligence\n", + "Basepoint Ventures,Industry Ventures, and Institutional Venture Partners\n", + "\n", + "\n", + "Medlinker\n", + "$1\n", + "7/31/2018\n", + "China\n", + "Health\n", + "China Health Industry Investment Fund, China Renaissance, and Sequoia Capital China\n", + "\n", + "\n", + "Momenta\n", + "$1\n", + "10/17/2018\n", + "China\n", + "Artificial intelligence\n", + "Sinovation Ventures, Tencent Holdings, Sequoia Capital China\n", + "\n", + "\n", + "Bitfury\n", + "$1\n", + "11/6/2018\n", + "Netherlands\n", + "Hardware\n", + "Georgian Co-Investment Fund, iTech Capital, Galaxy Digital\n", + "\n", + "\n", + "Airtable\n", + "$1.1\n", + "11/15/2018\n", + "United States\n", + "Internet software & services\n", + "Caffeinated Capital, CRV, Founder Collective\n", + "\n", + "\n", + "LinkDoc Technology\n", + "$1\n", + "7/5/2018\n", + "China\n", + "Health\n", + "China Investment Corporation, New Enterprise Associates\n", + "\n", + "\n", + "Banma Network Technologies\n", + "$1\n", + "9/13/2018\n", + "China\n", + "Auto & transportation\n", + "Yunfeng Capital, SDIC Innovation Investment Management, Shang Qi Capital\n", + "\n", + "\n", + "TalkDesk\n", + "$1\n", + "10/3/2018\n", + "United States\n", + "Internet software & services\n", + "DJF, Salesforce Ventures, Storm Ventures\n", + "\n", + "\n", + "Geek+\n", + "$1\n", + "11/21/2018\n", + "China\n", + "Hardware\n", + "Volcanics Ventures, Vertex Ventures China, Warburg Pincus\n", + "\n", + "\n", + "Pat McGrath Labs\n", + "$1\n", + "7/13/2018\n", + "United States\n", + "Consumer & retail\n", + "One Luxury Group, Eurazeo\n", + "\n", + "\n", + "Seismic\n", + "$1\n", + "12/18/2018\n", + "United States\n", + "Internet software & services\n", + "Jackson Square Ventures, General Atlantic, Lightspeed Venture Partners\n", + "\n", + "\n", + "iFood\n", + "$1\n", + "11/13/2018\n", + "Brazil\n", + "Supply chain, logistics, & delivery\n", + "Movile, Just Eat, Naspers\n", + "\n", + "\n", + "Omio\n", + "$1\n", + "10/23/2018\n", + "Germany\n", + "Travel\n", + "Lakestar, Battery Ventures, New Enterprise Associates\n", + "\n", + "\n", + "Zhangmen\n", + "$1\n", + "12/26/2017\n", + "United States\n", + "Edtech\n", + "Shunwei Capital Partners, QingSong Fund, Warburg Pincus\n", + "\n", + "\n", + "Calm\n", + "$1\n", + "2/6/2019\n", + "United States\n", + "Consumer & retail\n", + "Insight Venture Partners, TPG Growth, Sound Ventures\n", + "\n", + "\n", + "58 Daojia\n", + "$1\n", + "2/18/2016\n", + "China\n", + "Internet software & services\n", + "KKR, Alibaba Group, Ping An Insurance\n", + "\n", + "\n", + "LinkSure Network\n", + "$1\n", + "1/1/2015\n", + "China\n", + "Mobile & telecommunications\n", + "N/A\n", + "\n", + "\n", + "China Cloud\n", + "$1\n", + "6/11/2018\n", + "China\n", + "Hardware\n", + "V Star Capital, GF Xinde Investment Management Co., Haitong Leading Capital Management\n", + "\n", + "\n", + "Hosjoy\n", + "$1\n", + "10/18/2018\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "U.S.-China Green Fund, Founder H Fund, Richland Equities\n", + "\n", + "\n", + "Unisound\n", + "$1.19\n", + "7/19/2018\n", + "China\n", + "Artificial intelligence\n", + "Qiming Venture Partners, China Internet Investment Fund, Qualcomm Ventures\n", + "\n", + "\n", + "Tresata\n", + "$1\n", + "10/10/2018\n", + "United States\n", + "Fintech\n", + "GCP Capital Partners\n", + "\n", + "\n", + "Globality\n", + "$1\n", + "1/22/2019\n", + "United States\n", + "Artificial intelligence\n", + "SoftBank Group\n", + "\n", + "\n", + "Rent the Runway\n", + "$1\n", + "3/21/2019\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "Bain Capital Ventures, Kleiner Perkins Caufield & Byers, Highland Capital Partners\n", + "\n", + "\n", + "Intellifusion\n", + "$1\n", + "3/22/2019\n", + "China\n", + "Artificial intelligence\n", + "BOC International, TopoScend Capital, Hongxiu VC\n", + "\n", + "\n", + "Hims\n", + "$1.1\n", + "1/29/2019\n", + "United States\n", + "Health\n", + "Forerunner Ventures, Thrive Capital, Redpoint Ventures\n", + "\n", + "\n", + "\n", + "Liquid\n", + "$1\n", + "4/3/2019\n", + "Japan\n", + "Fintech\n", + "JAFCO Co, Bitmain Technologies, IDG Capital\n", + "\n", + "\n", + "Red Ventures\n", + "$1\n", + "1/7/2015\n", + "United States\n", + "Other\n", + "Silver Lake Partners, General Atlantic\n", + "\n", + "\n", + "Terminus Technologies\n", + "$1\n", + "10/25/2018\n", + "China\n", + "Hardware\n", + "China Everbright Limited, IDG Capital, iFLYTEK\n", + "\n", + "\n", + "Sila Nanotechnologies\n", + "$1\n", + "4/16/2019\n", + "United States\n", + "Other\n", + "Bessemer Venture Partners, Sutter Hill Ventures, Matrix Partners\n", + "\n", + "\n", + "Dream11\n", + "$1\n", + "4/9/2019\n", + "India\n", + "Internet software & services\n", + "Kaalari Capital, Tencent Holdings, Steadview Capital\n", + "\n", + "\n", + "Coursera\n", + "$1\n", + "4/25/2019\n", + "United States\n", + "Edtech\n", + "New Enterprise Associates, Kleiner Perkins Caufield & Byers, GSV Capital, Learn Capital\n", + "\n", + "\n", + "Poizon\n", + "$1\n", + "4/29/2019\n", + "China\n", + "Mobile & telecommunications\n", + "DST Global, Sequoia Capital China, Gaorong Capital\n", + "\n", + "\n", + "BigBasket\n", + "$1\n", + "5/6/2019\n", + "India\n", + "Supply chain, logistics, & delivery\n", + "\tAlibaba Group, Bessemer Venture Partners, Helion Venture Partners\n", + "\n", + "\n", + "VTS\n", + "$1\n", + "5/7/2019\n", + "United States\n", + "Internet software & services\n", + "Trinity Ventures, Fifth Wall Ventures, OpenView Venture Partners\n", + "\n", + "\n", + "Sumo Logic\n", + "$1\n", + "5/8/2019\n", + "United States\n", + "Data management & analytics\n", + "Greylock Partners, Sutter Hill Ventures, Accel\n", + "\n", + "\n", + "GetYourGuide\n", + "$1\n", + "5/16/2019\n", + "Germany\n", + "Travel\n", + "\tSpark Capital, Highland Europe, Sunstone Capital\n", + "\n", + "\n", + "Auth0\n", + "$1\n", + "5/20/2019\n", + "United States\n", + "Cybersecurity\n", + "Bessemer Venture Partners, K9 Ventures, Trinity Ventures\n", + "\n", + "\n", + "OCSiAl\n", + "$1\n", + "3/4/2019\n", + "Luxembourg\n", + "Other\n", + "\tA&NN, Rusnano\n", + "\n", + "\n", + "KnowBox\n", + "$1\n", + "5/30/2019\n", + "China\n", + "Edtech\n", + "TAL Education Group, Legend Star, Alibaba Group\n", + "\n", + "\n", + "Loggi\n", + "$1\n", + "6/05/2019\n", + "Brazil\n", + "Supply chain, logistics, & delivery\n", + "Qualcomm Ventures, SoftBank Group. Monashees+\n", + "\n", + "\n", + "Yanolja\n", + "$1\n", + "6/11/2019\n", + "South Korea\n", + "Travel\n", + "SBI Investment Korea, Partners Investment, GIC\n", + "\n", + "\n", + "KnowBe4\n", + "$1\n", + "6/12/2019\n", + "United States\n", + "Cybersecurity\n", + "Elephant Venture Capital, KKR, Ten Eleven Ventures\n", + "\n", + "\n", + "Meero\n", + "$1\n", + "6/18/2019\n", + "France\n", + "Artificial intelligence\n", + "Aglae Ventures, Global Founders Capital, Alven Capital\n", + "\n", + "\n", + "Druva\n", + "$1\n", + "6/19/2019\n", + "United States\n", + "Data management & analytics\t\n", + "Nexus Venture Partners, Tenaya Capital, Sequoia Capital\n", + "\n", + "\n", + "StockX\n", + "$1\n", + "6/26/2019\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "Google Ventures, Battery Ventures, DST Global\n", + "\n", + "\n", + "Branch\n", + "$1\n", + "9/10/2018\n", + "United States\n", + "Mobile & telecommunications\n", + "New Enterprise Associates, Pear, Cowboy Ventures\n", + "\n", + "\n", + "Ola Electric Mobility\n", + "$1\n", + "7/2/2019\n", + "India\n", + "Auto & transportation\n", + "SoftBank Group, Tiger Global Management, Matrix Partners India\n", + "\n", + "\n", + "Rivigo\n", + "$1.07\n", + "7/11/2019\n", + "India\n", + "Supply chain, logistics, & delivery\n", + "SAIF Partners India, Warburg Pincus, Trifecta Capital Advisors\n", + "\n", + "\n", + "Icertis\n", + "$1\n", + "7/17/2019\n", + "United States\n", + "Artificial intelligence\n", + "Eight Roads Ventures, Greycroft, Ignition Partners\n", + "\n", + "\n", + "Turo\n", + "$1\n", + "7/17/2019\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "August Capital, Google Ventures, Shasta Ventures\n", + "\n", + "\n", + "Hippo\n", + "$1\n", + "7/24/2019\n", + "United States\n", + "Fintech\n", + "Propel Venture Partners, Horizons Ventures, Comcast Ventures\n", + "\n", + "\n", + "Gympass\n", + "$1\n", + "6/12/2019\n", + "United States\n", + "Internet software & services\n", + "General Atlantic, SoftBank Group, Atomico\n", + "\n", + "\n", + "DataRobot\n", + "$1\n", + "7/29/2019\n", + "United States\n", + "Artificial intelligence\n", + "New Enterprise Associates, Accomplice, IA Ventures\n", + "\n", + "\n", + "Lightricks\n", + "$1\n", + "7/31/2019\n", + "Israel\n", + "Artificial intelligence\n", + "Viola Ventures, Insight Partners, ClalTech, Goldman Sachs\n", + "\n", + "\n", + "Scale AI\n", + "$1\n", + "8/5/2019\n", + "United States\n", + "Artificial intelligence\n", + "Accel, Y Combinator, Index Ventures\n", + "\n", + "\n", + "Ibotta\n", + "$1\n", + "8/6/2019\n", + "United States\n", + "Fintech\n", + "Koch Disruptive Technologies, Teamworthy Ventures, GGV Capital\n", + "\n", + "\n", + "C2FO\n", + "$1\n", + "8/7/2019\n", + "United States\n", + "Fintech\n", + "Union Square Ventures, Summerhill Venture Partners, Mithril Capital Management\n", + "\n", + "\n", + "Numbrs\n", + "$1\n", + "8/22/2019\n", + "Switzerland\n", + "Fintech\n", + "Investment Corporation of Dubai, Centralway\n", + "\n", + "\n", + "InVision\n", + "$1\n", + "11/1/2017\n", + "United States\n", + "Internet software & services\n", + "FirstMark Capital, Tiger Global Management, ICONIQ Capital\n", + "\n", + "\n", + "ThoughtSpot\n", + "$1\n", + "5/8/2018\n", + "United States\n", + "Internet software & services\n", + "Lightspeed Venture Partners, Khosla Ventures, Geodesic Capital\n", + "\n", + "\n", + "Knotel\n", + "$1\n", + "8/21/2019\n", + "United States\n", + "Other\n", + "\tBloomberg Beta, Newmark Knight Frank, Norwest Venture Partners\n", + "\n", + "\n", + "Grove Collaborative\n", + "$1\n", + "9/06/2019\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "MHS Capital, NextView Ventures, Mayfield Fund\n", + "\n", + "\n", + "QuintoAndar\n", + "$1\n", + "9/10/2019\n", + "Brazil\n", + "E-commerce & direct-to-consumer\n", + "Kaszek Ventures, General Atlantic, SoftBank Group\n", + "\n", + "\n", + "Anduril\n", + "$1\n", + "9/11/2019\n", + "United States\n", + "Artificial intelligence\n", + "Andreessen Horowitz, Founders Fund, Revolution Ventures\n", + "\n", + "\n", + "CMR Surgical\n", + "$1\n", + "9/17/2019\n", + "United Kingdom\n", + "Health\n", + "Cambridge Innovation Capital, LGT Capital Partners, Escala Capital\n", + "\n", + "\n", + "Acronis\n", + "$1\n", + "9/18/2019\n", + "Switzerland\n", + "Cybersecurity\n", + "Goldman Sachs, VebVentures, Insight Partners\n", + "\n", + "\n", + "Dave\n", + "$1\n", + "9/30/2019\n", + "United States\n", + "Fintech\n", + "Section 32, SV Angel, Norwest Venture Partners\n", + "\n", + "\n", + "Next Insurance\n", + "$1\n", + "10/7/2019\n", + "United States\n", + "Fintech\n", + "Zeev Ventures, Ribbit Capital, TLV Partners\n", + "\n", + "\n", + "Grammarly\n", + "$1\n", + "10/10/2019\n", + "United States\n", + "Internet software & services\n", + "General Catalyst, Institutional Venture Partners, Breyer Capital\n", + "\n", + "\n", + "EBANX\n", + "$1\n", + "10/16/2019\n", + "Brazil\n", + "Fintech\n", + "FTV Capital, Endeavor\n", + "\n", + "\n", + "Pendo\n", + "$1\n", + "10/17/2019\n", + "United States\n", + "Internet software & services\n", + "Contour Venture Partners, Battery Ventures, Core Capital Partners\n", + "\n", + "\n", + "KK Group\n", + "$1\n", + "10/23/2019\n", + "China\n", + "E-commerce & direct-to-consumer\n", + "Matrix Partners China, Bright Venture Capita, Shenzhen Capital Group\n", + "\n", + "\n", + "Kujiale\n", + "$1\n", + "10/25/2019\n", + "China\n", + "Internet software & services\n", + "GGV Capital, IDG Capital, Linear Venture\n", + "\n", + "\n", + "Vacasa\n", + "$1\n", + "10/29/2019\n", + "United States\n", + "Travel\n", + "Level Equity, NewSpring Holdings, Riverwood Capital\n", + "\n", + "\n", + "Faire\n", + "$1\n", + "10/30/2019\n", + "United States\n", + "Artificial intelligence\n", + "Khosla Ventures, Forerunner Ventures, Sequoia Capital\n", + "\n", + "Riskified\n", + "$1\n", + "11/05/2019\n", + "United States\n", + "Cybersecurity\n", + "Entree Capital, Genesis Partners, Qumra Capital\n", + "\n", + "\n", + "Guild Education\n", + "$1\n", + "11/13/2019\n", + "United States\n", + "Internet software & services\n", + "Redpoint Ventures, Harrison Metal, Bessemer Venture Partners\n", + "\n", + "\n", + "Wacai\n", + "$1\n", + "7/18/2018\n", + "China\n", + "Mobile & telecommunications\n", + "Qiming Venture Partners, China Broadband Capital, CDH Investments\n", + "\n", + "\n", + "Vroom\n", + "$1\n", + "12/6/2019\n", + "United States\n", + "E-commerce & direct-to-consumer\n", + "L Catterton, General Catalyst, T. Rowe Price\n", + "\n", + "\n", + "Bright Health\n", + "$1\n", + "12/17/2019\n", + "United States\n", + "Health\n", + "New Enterprise Associates, Bessemer Venture Partners, Flare Capital Partners\n", + "\n", + "\n", + "\n", + "Glovo\n", + "$1\n", + "12/19/2019\n", + "Spain\n", + "Supply chain, logistics, & delivery\n", + "IDInvest Partners, Seaya Ventures, Lakestar\n", + "\n", + "\n", + "Loft\n", + "$1\n", + "1/3/2020\n", + "Brazil\n", + "E-commerce & direct-to-consumer\n", + "Monashees+, Andreessen Horowitz, QED Investors\n", + "\n", + "\n", + "HighRadius\n", + "$1\n", + "1/7/2020\n", + "United States\n", + "Fintech\n", + "Susquehanna Growth Equity, Citi Ventures, ICONIQ Capital\n", + "\n", + "\n", + "ClassPass\n", + "$1\n", + "1/8/2020\n", + "United States\n", + "Internet software & services\n", + "General Catalyst, L Catterton, Acequia Capital\n", + "\n", + "\n", + "Sisense\n", + "$1\n", + "1/9/2020\n", + "United States\n", + "Data management & analytics\n", + "Opus Capital, Genesis Partners, Battery Ventures\n", + "\n", + "\n", + "Snyk\n", + "$1\n", + "1/21/2020\n", + "United Kingdom\n", + "Cybersecurity\n", + "BOLDstart Ventures, Google Ventures, Accel\n", + "\n", + "\n", + "AppsFlyer\n", + "$1.6\n", + "1/21/2020\n", + "United States\n", + "Mobile & telecommunications\n", + "\tMagma Venture Partners, Pitango Venture Capital, Qumra Capital\n", + "\n", + "\n", + "Maimai\n", + "$1\n", + "11/15/2017\n", + "China\n", + "Mobile & telecommunications\n", + "Morningside Venture Capital, IDG Capital, DCM Ventures\n", + "\n", + "\n", + "Orbbec Technology\n", + "$1\n", + "5/21/2018\n", + "China\n", + "Hardware\n", + "R-Z Capital, Green Pine Capital Partners, SAIF Partners China\n", + "\n", + "\n", + "Alto Pharmacy\n", + "$1\n", + "1/30/2020\n", + "United States\n", + "Health\n", + "Jackson Square Ventures, Greenoaks Capital Management, Softbank Group\n", + "\n", + "\n", + "Flywire\n", + "$1\n", + "2/13/2020\n", + "United States\n", + "Fintech\n", + "Spark Capital, F-Prime Capital, Kibo Ventures\n", + "\n", + "\n", + "Headspin\n", + "$1.16\n", + "2/25/2020\n", + "United States\n", + "Mobile & telecommunications\n", + "ICONIQ Capital, Dell Technologies Capital, Tiger Global Management\n", + "\n", + "\n", + "o9 Solutions\n", + "$1\n", + "4/28/2020\n", + "United States\n", + "Artificial intelligence\n", + "KKR\n", + "\n", + "\n", + "Emerging Markets Property Group\n", + "$1\n", + "4/28/2020\n", + "United Arab Emirates\n", + "Other\n", + "KKR\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "

What is a Unicorn Startup?

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A unicorn startup or unicorn company is a private company with a valuation over $1 billion. As of April 2020, there are more than 400 unicorns around the world. Variants include a decacorn, valued at over $10 billion, and a hectocorn, valued at over $100 billion.

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© 2020 CB Insights

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CompanyValuation ($B)Date JoinedCountryIndustrySelect Investors
Toutiao (Bytedance)$754/7/2017ChinaArtificial intelligenceSequoia Capital China, SIG Asia Investments, Sina Weibo, Softbank Group
Didi Chuxing\n", + " \t $5612/31/2014ChinaAuto & transportationMatrix Partners, Tiger\n", + " \t Global Management, Softbank Corp.,
Stripe$361/23/2014United StatesFintechKhosla Ventures, LowercaseCapital, capitalG
SpaceX$33.312/1/2012United StatesOtherFounders Fund, Draper\n", + " \t Fisher Jurvetson, Rothenberg Ventures
Airbnb$187/26/2011United StatesTravelGeneral Catalyst Partners,\n", + " \t Andreessen Horowitz, ENIAC Ventures
Kuaishou$181/1/2015ChinaMobile & telecommunicationsMorningside Venture Capital, Sequoia Capital, Baidu
One97 Communications$165/12/2015IndiaFintechIntel Capital, Sapphire\n", + " \t Ventures, Alibaba Group
Epic Games$1510/26/2018United StatesOtherTencent Holdings, KKR, Smash Ventures
DJI Innovations$155/6/2015ChinaHardwareAccel Partners, Sequoia\n", + " \t Capital
Grab$14.312/4/2014SingaporeAuto & transportationGGV Capital, Vertex Venture\n", + " \t Holdings, Softbank Group
Beike Zhaofang$147/18/2019ChinaInternet software & servicesTencent Holdings, Hillhouse Capital Management, Source Code Capital
DoorDash$12.63/1/2018United StatesSupply chain, logistics, & deliverySoftbank Group, Sequoia Capital, Khosla Ventures
Snowflake Computing$12.41/25/2018United StatesData management & analyticsRedpoint Ventures,Iconiq Capital, Madrona Venture Group
Palantir Technologies$12.185/5/2011United StatesData management & analyticsRRE Ventures, Founders\n", + " \t Fund, In-Q-Tel
JUUL Labs$1212/20/2017United StatesConsumer & retailTiger Global Management
Bitmain Technologies$127/6/2018ChinaHardwareCoatue Management, Sequoia Capital China, IDG Capital
Samumed$128/6/2018United StatesHealthVickers Venture Partners, IKEA GreenTech
Wish$11.25/18/2015United StatesE-commerce & direct-to-consumerFounders Fund, GGV Capital, Digital Sky Technologies
Global Switch$11.0812/22/2016United KingdomHardwareAviation Industry Corporation of China, Essence Financial, Jiangsu Sha Steel Group
Go-Jek$108/4/2016IndonesiaSupply chain, logistics, & deliveryFormation Group, Sequoia Capital India, Warburg Pincus
Nubank$103/1/2018BrazilFintechSequoia Capital, Redpoint e.ventures, Kaszek Ventures
Oyo Rooms$109/25/2018IndiaTravelSoftBank Group, Sequoia Capital India,Lightspeed India Partners
Ripple$1012/20/2019United StatesFintechIDG Capital, Venture51, Lightspeed Venture Partners
Coupang$95/28/2014South KoreaE-commerce & direct-to-consumerSequoia Capital, Founder\n", + " \t Collective, Wellington Management
Guazi (Chehaoduo)$93/12/2016ChinaE-commerce & direct-to-consumerSequoia Capital China, GX Capital
Coinbase$88/10/2017United StatesFintechY Combinator, Union Square Ventures, DFJ Growth
BYJU'S$87/25/2017IndiaEdtechTencent Holdings, Lightspeed India Partners, Sequoia Capital India
Robinhood$84/26/2017United StatesFintechGoogle Ventures, Andreessen Horowitz, DST Global
Yuanfudao$7.85/31/2017ChinaEdtechTencent Holdings, Warbug Pincus, IDG Capital
Instacart$7.612/30/2014United StatesSupply chain, logistics, & deliveryKhosla Ventures, Kleiner\n", + " \t Perkins Caufield & Byers, Collaborative Fund
SenseTime$7.57/11/2017ChinaArtificial intelligenceStar VC, IDG Capital, Infore Capital, Alibaba Group
Snapdeal$75/21/2014IndiaE-commerce & direct-to-consumerSoftBankGroup, Blackrock, Alibaba Group
Roivant Sciences$711/13/2018United StatesHealthSoftBankGroup, Founders Fund
Tokopedia$712/12/2018IndonesiaE-commerce & direct-to-consumerSoftBankGroup, Alibaba Group, Sequoia Capital India
Argo AI$707/12/2019United StatesArtificial intelligenceVolkswagen Group, Ford Autonomous Vehicles
Automation Anywhere$6.87/2/2018United StatesArtificial intelligenceGeneral Atlantic, Goldman Sachs, New Enterprise Associates
Tanium$6.73/31/2015United StatesCybersecurityAndreessen Horowitz,\n", + " \t Nor-Cal Invest, TPG Growth
Ziroom$6.61/17/2018ChinaE-commerce & direct-to-consumerSequoia Capital China, Warburg Pincus, General Catalyst
UiPath$6.43/2/2018United StatesArtificial intelligenceAccel, capitalG, Earlybrid Venture Capital, Seedcamp
Compass$6.48/31/2016United StatesE-commerce & direct-to-consumerFounders Fund, Thrive Capital, Wellington Management
Magic Leap$6.310/21/2014United StatesHardwareObvious Ventures, Qualcomm Ventures, Andreessen Horowitz
Samsara Networks$6.33/22/2018United StatesHardwareAndreessen Horowitz, General Catalyst
Ola Cabs$6.3210/27/2014IndiaAuto & transportationAccel Partners, SoftBank Group, Sequoia Capital
Databricks$6.22/5/2019United StatesData management & analyticsAndreessen Horowitz, New Enterprise Associates, Battery Ventures
Manbang Group$64/24/2018ChinaSupply chain, logistics, & deliverySoftbank Group, CapitalG
Unity Technologies$67/13/2016United StatesOtherSequoia Capital, iGlobe Partners, DFJ Growth
Revolut$5.54/26/2018United KingdomFintechindex Ventures, DST Global, Ribbit Capital
Lianjia (Homelink)$5.84/8/2016ChinaE-commerce & direct-to-consumerTencent, Baidu, Huasheng Capital
Chime$5.83/5/2019United StatesFintechForerunner Ventures, Crosslink Capital, Homebrew
EasyHome$5.72/12/2018ChinaConsumer & retailAlibaba Group, Boyu Capital, Borui Capital
Vice Media$5.78/17/2013United StatesInternet software & servicesTechnology Crossover Ventures, A&E Television Networks
Intarcia\n", + " \t Therapeutics$5.54/1/2014United StatesHealthNew Enterprise Associates,\n", + " \t New Leaf Venture Partners, Charter Venture Capital
Klarna$5.512/12/2011SwedenFintechInstitutional Venture\n", + " \t Partners, Sequoia Capital, General Atlantic
GuaHao (We Doctor)$5.59/22/2015ChinaHealthTencent, Morningside Group
HashiCorp$5.111/1/2018United StatesInternet software & servicesRedpoint Ventures, True Ventures, Mayfield Fund
United Imaging Healthcare$59/14/2017ChinaHealthChina Life Insurance, China Development Bank Capital, CITIC Securities International
UBTECH Robotics$57/26/2016ChinaHardwareCDH Investments, Goldstone Investments, Qiming Venture Partners
Krafton Game Union$58/9/2018South KoreaOtherTencent Holdings, Stonebridge Capital, IMM Investment
Machine Zone$57/16/2014United StatesMobile & telecommunicationsJ.P. Morgan Chase & Co., Menlo Ventures
WM Motor$53/8/2019ChinaAuto & transportationBaidu Capital, Linear Venture, Tencent
Royole Corporation$58/18/2015ChinaHardwareWarmsun Holding, IDG Capital Partners
Hello TransTech$56/01/2018ChinaAuto & transportationAnt Financial Services Group, GGV Capital
Tempus$53/21/2018United StatesHealthNew Enterprise Associates, T. Rowe Associates, Lightbank
Toast$4.97/10/2018United StatesFintechBessemer Venture Partners, Tiger Global Management, Google Ventures
Meizu Technology$4.587/23/2014ChinaHardwareTelling Telecommunication Holding Co., Alibaba Group
Fanatics$4.56/6/2012United StatesE-commerce & direct-to-consumerSoftBank Group, Andreessen Horowitz, Temasek Holdings
SoFi$4.52/3/2015United StatesFintechBaseline Ventures, DCM Ventures, Institutional Venture Partners
Vipkid$4.58/23/2017ChinaEdtechSequoia Capital China, Tencent Holdings, Sinovation Ventures
Confluent$4.51/23/2019United StatesData management & analyticsBenchmark, Sequoia Capital, Index Ventures
Ginkgo BioWorks$4.212/14/2017United StatesHealthY Combinator, Data Collective, MassVentures
Yello\n", + " \t Mobile$411/11/2014South KoreaMobile & telecommunicationsFormation 8
Houzz$49/30/2014United StatesE-commerce & direct-to-consumerNew Enterprise Associates,\n", + " \t Sequoia Capital, Comcast Ventures
Face++ (Megvii)$410/31/2017ChinaArtificial intelligenceAnt Financial Services Group, Russia-China Investment Fund, Foxconn Technology Company
Roblox$49/4/2018United StatesInternet software & servicesAtlos Ventures, Index Ventures, First Round Capital
Impossible Foods$45/13/2019United StatesConsumer & retailKhosla Ventures, Horizons Ventures, Temasek Holdings
TripActions$411/8/2018United StatesTravelAndreessen Horowitz, Lightspeed Venture Partners, Zeev Ventures
XPeng Motors$48/2/2018ChinaAuto & transportationMorningside Venture Capital, Foxconn Technology Company, Alibaba Group
OpenDoor Labs$3.811/30/2016United StatesE-commerce & direct-to-consumerNorwest Venture Partners, New Enterprise Associates, Khosla Ventures
]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "soup.find_all('table')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 3)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m Company Names = [elem.find_all('td')[0].getText() for elem in result]\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "result = Unicorn_Tracker.select('tbody tr')\n", + "\n", + "Company Names = [elem.find_all('td')[0].getText() for elem in result]\n", + "summary" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"page = requests.get(url)\\n\\nsoup = BeautifulSoup(page.content, 'html.parser')\\nweather_2weeks = soup.find(id='wt-ext')\\ntable_rows= weather_2weeks.select('table tbody tr')\\n\\nfor elem in table_rows: # we expect all rows\\n temp_string = elem.find_all('td')[1].getText()\\n print(temp_string)\"" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''page = requests.get(url)\n", + "\n", + "soup = BeautifulSoup(page.content, 'html.parser')\n", + "weather_2weeks = soup.find(id='wt-ext')\n", + "table_rows= weather_2weeks.select('table tbody tr')\n", + "\n", + "for elem in table_rows: # we expect all rows\n", + " temp_string = elem.find_all('td')[1].getText()\n", + " print(temp_string)'''" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "html_string = Unicorn_Tracker.prettify()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CompanyValuation ($B)Date JoinedCountryIndustrySelect Investors
0Toutiao (Bytedance)$754/7/2017ChinaArtificial intelligenceSequoia Capital China, SIG Asia Investments, S...
1Didi Chuxing$5612/31/2014ChinaAuto & transportationMatrix Partners, Tiger Global Management, Sof...
2Stripe$361/23/2014United StatesFintechKhosla Ventures, LowercaseCapital, capitalG
3SpaceX$33.312/1/2012United StatesOtherFounders Fund, Draper Fisher Jurvetson, Rothe...
4Airbnb$187/26/2011United StatesTravelGeneral Catalyst Partners, Andreessen Horowit...
.....................
72Face++ (Megvii)$410/31/2017ChinaArtificial intelligenceAnt Financial Services Group, Russia-China Inv...
73Roblox$49/4/2018United StatesInternet software & servicesAtlos Ventures, Index Ventures, First Round Ca...
74Impossible Foods$45/13/2019United StatesConsumer & retailKhosla Ventures, Horizons Ventures, Temasek Ho...
75TripActions$411/8/2018United StatesTravelAndreessen Horowitz, Lightspeed Venture Partne...
76XPeng Motors$48/2/2018ChinaAuto & transportationMorningside Venture Capital, Foxconn Technolog...
\n", + "

77 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined Country \\\n", + "0 Toutiao (Bytedance) $75 4/7/2017 China \n", + "1 Didi Chuxing $56 12/31/2014 China \n", + "2 Stripe $36 1/23/2014 United States \n", + "3 SpaceX $33.3 12/1/2012 United States \n", + "4 Airbnb $18 7/26/2011 United States \n", + ".. ... ... ... ... \n", + "72 Face++ (Megvii) $4 10/31/2017 China \n", + "73 Roblox $4 9/4/2018 United States \n", + "74 Impossible Foods $4 5/13/2019 United States \n", + "75 TripActions $4 11/8/2018 United States \n", + "76 XPeng Motors $4 8/2/2018 China \n", + "\n", + " Industry \\\n", + "0 Artificial intelligence \n", + "1 Auto & transportation \n", + "2 Fintech \n", + "3 Other \n", + "4 Travel \n", + ".. ... \n", + "72 Artificial intelligence \n", + "73 Internet software & services \n", + "74 Consumer & retail \n", + "75 Travel \n", + "76 Auto & transportation \n", + "\n", + " Select Investors \n", + "0 Sequoia Capital China, SIG Asia Investments, S... \n", + "1 Matrix Partners, Tiger Global Management, Sof... \n", + "2 Khosla Ventures, LowercaseCapital, capitalG \n", + "3 Founders Fund, Draper Fisher Jurvetson, Rothe... \n", + "4 General Catalyst Partners, Andreessen Horowit... \n", + ".. ... \n", + "72 Ant Financial Services Group, Russia-China Inv... \n", + "73 Atlos Ventures, Index Ventures, First Round Ca... \n", + "74 Khosla Ventures, Horizons Ventures, Temasek Ho... \n", + "75 Andreessen Horowitz, Lightspeed Venture Partne... \n", + "76 Morningside Venture Capital, Foxconn Technolog... \n", + "\n", + "[77 rows x 6 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfs = pd.read_html(html_string)\n", + "df = dfs[0]\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(r'data/cbinsights_unicorntracker.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Project 5 - Scraping CB Insights2.ipynb b/your-project/code/Project 5 - Scraping CB Insights2.ipynb new file mode 100644 index 0000000..093c364 --- /dev/null +++ b/your-project/code/Project 5 - Scraping CB Insights2.ipynb @@ -0,0 +1,3177 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Try 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from bs4 import BeautifulSoup\n", + "import pandas as pd\n", + "import requests\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fname = 'vicky_table.html'\n", + "HtmlFile = open(fname, 'r', encoding='utf-8')\n", + "source_code = HtmlFile.read() " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "soup = BeautifulSoup(source_code, 'html.parser')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "rows = soup.find_all('tr')\n", + "columns = rows[0].getText().strip().split('\\n')\n", + "\n", + "list_of_rows = []\n", + "list_to_clean = []\n", + "\n", + "for row in rows:\n", + " list_of_cell = []\n", + " company = row.getText().strip('\\t').split('\\n')\n", + " for i in company:\n", + " i = re.sub('\\t', '', i)\n", + " i = re.sub(' ', '', i)\n", + " i = re.sub(' ', '', i)\n", + " if i:\n", + " list_of_cell.append(i)\n", + " if len(list_of_cell) == 6:\n", + " list_of_rows.append(list_of_cell)\n", + " elif len(list_of_cell) > 6:\n", + " list_to_clean.append(list_of_cell)\n", + " \n", + "del list_of_rows[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'dflist_to_clean' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdflist_to_clean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'dflist_to_clean' is not defined" + ] + } + ], + "source": [ + "dflist_to_clean" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(data=list_of_rows, columns= columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CompanyValuation ($B)Date JoinedCountryIndustrySelect Investors
0Toutiao(Bytedance)$754/7/2017ChinaArtificialintelligenceSequoiaCapitalChina,SIGAsiaInvestments,SinaWei...
1Stripe$361/23/2014UnitedStatesFintechKhoslaVentures,LowercaseCapital,capitalG
2Kuaishou$181/1/2015ChinaMobile&telecommunicationsMorningsideVentureCapital,SequoiaCapital,Baidu
3EpicGames$1510/26/2018UnitedStatesOtherTencentHoldings,KKR,SmashVentures
4BeikeZhaofang$147/18/2019ChinaInternetsoftware&servicesTencentHoldings,HillhouseCapitalManagement,Sou...
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" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined Country \\\n", + "0 Toutiao(Bytedance) $75 4/7/2017 China \n", + "1 Stripe $36 1/23/2014 UnitedStates \n", + "2 Kuaishou $18 1/1/2015 China \n", + "3 EpicGames $15 10/26/2018 UnitedStates \n", + "4 BeikeZhaofang $14 7/18/2019 China \n", + "\n", + " Industry \\\n", + "0 Artificialintelligence \n", + "1 Fintech \n", + "2 Mobile&telecommunications \n", + "3 Other \n", + "4 Internetsoftware&services \n", + "\n", + " Select Investors \n", + "0 SequoiaCapitalChina,SIGAsiaInvestments,SinaWei... \n", + "1 KhoslaVentures,LowercaseCapital,capitalG \n", + "2 MorningsideVentureCapital,SequoiaCapital,Baidu \n", + "3 TencentHoldings,KKR,SmashVentures \n", + "4 TencentHoldings,HillhouseCapitalManagement,Sou... " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "columns.append('extra1')\n", + "columns.append('extra2')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CompanyValuation ($B)Date JoinedCountryIndustrySelect Investors_new
0DidiChuxing$5612/31/2014ChinaAuto&transportationMatrixPartners,TigerGlobalManagement,SoftbankC...
1SpaceX$33.312/1/2012UnitedStatesOtherFoundersFund,DraperFisherJurvetson,RothenbergV...
2Airbnb$187/26/2011UnitedStatesTravelGeneralCatalystPartners,AndreessenHorowitz,ENI...
3One97Communications$165/12/2015IndiaFintechIntelCapital,SapphireVentures,AlibabaGroup
4DJIInnovations$155/6/2015ChinaHardwareAccelPartners,SequoiaCapital
5Grab$14.312/4/2014SingaporeAuto&transportationGGVCapital,VertexVentureHoldings,SoftbankGroup
6PalantirTechnologies$12.185/5/2011UnitedStatesDatamanagement&analyticsRREVentures,FoundersFund,In-Q-Tel
7Coupang$95/28/2014SouthKoreaE-commerce&direct-to-consumerSequoiaCapital,FounderCollective,WellingtonMan...
8Instacart$7.612/30/2014UnitedStatesSupplychain,logistics,&deliveryKhoslaVentures,KleinerPerkinsCaufield&Byers,Co...
9Tanium$6.73/31/2015UnitedStatesCybersecurityAndreessenHorowitz,Nor-CalInvest,TPGGrowth
11Klarna$5.512/12/2011SwedenFintechInstitutionalVenturePartners,SequoiaCapital,Ge...
13Houzz$49/30/2014UnitedStatesE-commerce&direct-to-consumerNewEnterpriseAssociates,SequoiaCapital,Comcast...
15Automattic$35/27/2013UnitedStatesInternetsoftware&servicesInsightVenturePartners,LowercaseCapital,Polari...
16VANCL$312/14/2010ChinaE-commerce&direct-to-consumerCeyuanVentures,QiMingVenturePartners,TemasekHo...
17Nextdoor$2.13/4/2015UnitedStatesInternetsoftware&servicesBenchmarkCapital,DAGVentures,InsightVenturePar...
19Sprinklr$1.83/31/2015UnitedStatesInternetsoftware&servicesAzureCapitalPartners,BatteryVentures,IntelCapital
20XANT$1.74/28/2014UnitedStatesArtificialintelligenceMicrosoftVentures,USVenturePartners,KleinerPer...
21ironSource$1.58/11/2014IsraelMobile&telecommunicationsAccessIndustries,ClalIndustriesandInvestments
22Koudai$1.410/23/2014ChinaE-commerce&direct-to-consumerNewEnterpriseAssociates,TigerGlobalmanagement,...
23Docker$1.34/14/2015UnitedStatesInternetsoftware&servicesGreylockPartners,LightspeedVenturePartners,Low...
25Actifio$1.13/24/2014UnitedStatesDatamanagement&analyticsGreylockPartners,NorthBridgeVenturePartners,Te...
26TangoMe$1.13/20/2014UnitedStatesMobile&telecommunicationsDraperFisherJurtson,QualcommVentures,AlibabaGroup
27Lookout$18/13/2014UnitedStatesCybersecurityAccelPartners,GreylockPartners,LowercaseCapital
28TechStyleFashionGroup$18/29/2014UnitedStatesE-commerce&direct-to-consumerMatrixPartners,PassportCapital,RhoVentures
29Illumio$14/14/2014UnitedStatesCybersecurityDataCollective,Formation8,GeneralCatalystPartners
30BeiBei$11/22/2015ChinaE-commerce&direct-to-consumerBanyanCapital,NewHorizonCapital,IDGCapitalPart...
31InMobi$112/2/2014IndiaMobile&telecommunicationsKleinerPerkinsCaufield&Byers,SoftbankCorp.,She...
\n", + "
" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined Country \\\n", + "0 DidiChuxing $56 12/31/2014 China \n", + "1 SpaceX $33.3 12/1/2012 UnitedStates \n", + "2 Airbnb $18 7/26/2011 UnitedStates \n", + "3 One97Communications $16 5/12/2015 India \n", + "4 DJIInnovations $15 5/6/2015 China \n", + "5 Grab $14.3 12/4/2014 Singapore \n", + "6 PalantirTechnologies $12.18 5/5/2011 UnitedStates \n", + "7 Coupang $9 5/28/2014 SouthKorea \n", + "8 Instacart $7.6 12/30/2014 UnitedStates \n", + "9 Tanium $6.7 3/31/2015 UnitedStates \n", + "11 Klarna $5.5 12/12/2011 Sweden \n", + "13 Houzz $4 9/30/2014 UnitedStates \n", + "15 Automattic $3 5/27/2013 UnitedStates \n", + "16 VANCL $3 12/14/2010 China \n", + "17 Nextdoor $2.1 3/4/2015 UnitedStates \n", + "19 Sprinklr $1.8 3/31/2015 UnitedStates \n", + "20 XANT $1.7 4/28/2014 UnitedStates \n", + "21 ironSource $1.5 8/11/2014 Israel \n", + "22 Koudai $1.4 10/23/2014 China \n", + "23 Docker $1.3 4/14/2015 UnitedStates \n", + "25 Actifio $1.1 3/24/2014 UnitedStates \n", + "26 TangoMe $1.1 3/20/2014 UnitedStates \n", + "27 Lookout $1 8/13/2014 UnitedStates \n", + "28 TechStyleFashionGroup $1 8/29/2014 UnitedStates \n", + "29 Illumio $1 4/14/2014 UnitedStates \n", + "30 BeiBei $1 1/22/2015 China \n", + "31 InMobi $1 12/2/2014 India \n", + "\n", + " Industry \\\n", + "0 Auto&transportation \n", + "1 Other \n", + "2 Travel \n", + "3 Fintech \n", + "4 Hardware \n", + "5 Auto&transportation \n", + "6 Datamanagement&analytics \n", + "7 E-commerce&direct-to-consumer \n", + "8 Supplychain,logistics,&delivery \n", + "9 Cybersecurity \n", + "11 Fintech \n", + "13 E-commerce&direct-to-consumer \n", + "15 Internetsoftware&services \n", + "16 E-commerce&direct-to-consumer \n", + "17 Internetsoftware&services \n", + "19 Internetsoftware&services \n", + "20 Artificialintelligence \n", + "21 Mobile&telecommunications \n", + "22 E-commerce&direct-to-consumer \n", + "23 Internetsoftware&services \n", + "25 Datamanagement&analytics \n", + "26 Mobile&telecommunications \n", + "27 Cybersecurity \n", + "28 E-commerce&direct-to-consumer \n", + "29 Cybersecurity \n", + "30 E-commerce&direct-to-consumer \n", + "31 Mobile&telecommunications \n", + "\n", + " Select Investors_new \n", + "0 MatrixPartners,TigerGlobalManagement,SoftbankC... \n", + "1 FoundersFund,DraperFisherJurvetson,RothenbergV... \n", + "2 GeneralCatalystPartners,AndreessenHorowitz,ENI... \n", + "3 IntelCapital,SapphireVentures,AlibabaGroup \n", + "4 AccelPartners,SequoiaCapital \n", + "5 GGVCapital,VertexVentureHoldings,SoftbankGroup \n", + "6 RREVentures,FoundersFund,In-Q-Tel \n", + "7 SequoiaCapital,FounderCollective,WellingtonMan... \n", + "8 KhoslaVentures,KleinerPerkinsCaufield&Byers,Co... \n", + "9 AndreessenHorowitz,Nor-CalInvest,TPGGrowth \n", + "11 InstitutionalVenturePartners,SequoiaCapital,Ge... \n", + "13 NewEnterpriseAssociates,SequoiaCapital,Comcast... \n", + "15 InsightVenturePartners,LowercaseCapital,Polari... \n", + "16 CeyuanVentures,QiMingVenturePartners,TemasekHo... \n", + "17 BenchmarkCapital,DAGVentures,InsightVenturePar... \n", + "19 AzureCapitalPartners,BatteryVentures,IntelCapital \n", + "20 MicrosoftVentures,USVenturePartners,KleinerPer... \n", + "21 AccessIndustries,ClalIndustriesandInvestments \n", + "22 NewEnterpriseAssociates,TigerGlobalmanagement,... \n", + "23 GreylockPartners,LightspeedVenturePartners,Low... \n", + "25 GreylockPartners,NorthBridgeVenturePartners,Te... \n", + "26 DraperFisherJurtson,QualcommVentures,AlibabaGroup \n", + "27 AccelPartners,GreylockPartners,LowercaseCapital \n", + "28 MatrixPartners,PassportCapital,RhoVentures \n", + "29 DataCollective,Formation8,GeneralCatalystPartners \n", + "30 BanyanCapital,NewHorizonCapital,IDGCapitalPart... \n", + "31 KleinerPerkinsCaufield&Byers,SoftbankCorp.,She... " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = pd.DataFrame(data=list_to_clean, columns= columns)\n", + "df2['Select Investors_new'] = df2[['Select Investors','extra1']].apply(lambda x: ''.join(x), axis=1)\n", + "df2.drop(columns=['Select Investors','extra1', 'extra2'], inplace = True)\n", + "\n", + "# There were still three rows that are messed up so I decided to drop them.\n", + "df2.drop(index=[10, 12, 14, 18, 24], inplace = True)\n", + "\n", + "df2.head(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "complete_df = pd.concat([df, df2], ignore_index = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(463, 7)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "complete_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CompanyValuation ($B)Date JoinedCountryIndustrySelect InvestorsSelect Investors_new
0Toutiao(Bytedance)$754/7/2017ChinaArtificialintelligenceSequoiaCapitalChina,SIGAsiaInvestments,SinaWei...NaN
1Stripe$361/23/2014UnitedStatesFintechKhoslaVentures,LowercaseCapital,capitalGNaN
2Kuaishou$181/1/2015ChinaMobile&telecommunicationsMorningsideVentureCapital,SequoiaCapital,BaiduNaN
3EpicGames$1510/26/2018UnitedStatesOtherTencentHoldings,KKR,SmashVenturesNaN
4BeikeZhaofang$147/18/2019ChinaInternetsoftware&servicesTencentHoldings,HillhouseCapitalManagement,Sou...NaN
5DoorDash$12.63/1/2018UnitedStatesSupplychain,logistics,&deliverySoftbankGroup,SequoiaCapital,KhoslaVenturesNaN
6SnowflakeComputing$12.41/25/2018UnitedStatesDatamanagement&analyticsRedpointVentures,IconiqCapital,MadronaVentureG...NaN
7JUULLabs$1212/20/2017UnitedStatesConsumer&retailTigerGlobalManagementNaN
8BitmainTechnologies$127/6/2018ChinaHardwareCoatueManagement,SequoiaCapitalChina,IDGCapitalNaN
9Samumed$128/6/2018UnitedStatesHealthVickersVenturePartners,IKEAGreenTechNaN
10Wish$11.25/18/2015UnitedStatesE-commerce&direct-to-consumerFoundersFund,GGVCapital,DigitalSkyTechnologiesNaN
11GlobalSwitch$11.0812/22/2016UnitedKingdomHardwareAviationIndustryCorporationofChina,EssenceFina...NaN
12Go-Jek$108/4/2016IndonesiaSupplychain,logistics,&deliveryFormationGroup,SequoiaCapitalIndia,WarburgPincusNaN
13Nubank$103/1/2018BrazilFintechSequoiaCapital,Redpointe.ventures,KaszekVenturesNaN
14OyoRooms$109/25/2018IndiaTravelSoftBankGroup,SequoiaCapitalIndia,LightspeedIn...NaN
15Ripple$1012/20/2019UnitedStatesFintechIDGCapital,Venture51,LightspeedVenturePartnersNaN
16Guazi(Chehaoduo)$93/12/2016ChinaE-commerce&direct-to-consumerSequoiaCapitalChina,GXCapitalNaN
17Coinbase$88/10/2017UnitedStatesFintechYCombinator,UnionSquareVentures,DFJGrowthNaN
18BYJU'S$87/25/2017IndiaEdtechTencentHoldings,LightspeedIndiaPartners,Sequoi...NaN
19Robinhood$84/26/2017UnitedStatesFintechGoogleVentures,AndreessenHorowitz,DSTGlobalNaN
20Yuanfudao$7.85/31/2017ChinaEdtechTencentHoldings,WarbugPincus,IDGCapitalNaN
21SenseTime$7.57/11/2017ChinaArtificialintelligenceStarVC,IDGCapital,InforeCapital,AlibabaGroupNaN
22Snapdeal$75/21/2014IndiaE-commerce&direct-to-consumerSoftBankGroup,Blackrock,AlibabaGroupNaN
23RoivantSciences$711/13/2018UnitedStatesHealthSoftBankGroup,FoundersFundNaN
24Tokopedia$712/12/2018IndonesiaE-commerce&direct-to-consumerSoftBankGroup,AlibabaGroup,SequoiaCapitalIndiaNaN
25ArgoAI$707/12/2019UnitedStatesArtificialintelligenceVolkswagenGroup,FordAutonomousVehiclesNaN
26AutomationAnywhere$6.87/2/2018UnitedStatesArtificialintelligenceGeneralAtlantic,GoldmanSachs,NewEnterpriseAsso...NaN
27Ziroom$6.61/17/2018ChinaE-commerce&direct-to-consumerSequoiaCapitalChina,WarburgPincus,GeneralCatalystNaN
28UiPath$6.43/2/2018UnitedStatesArtificialintelligenceAccel,capitalG,EarlybridVentureCapital,SeedcampNaN
29Compass$6.48/31/2016UnitedStatesE-commerce&direct-to-consumerFoundersFund,ThriveCapital,WellingtonManagementNaN
30MagicLeap$6.310/21/2014UnitedStatesHardwareObviousVentures,QualcommVentures,AndreessenHor...NaN
31SamsaraNetworks$6.33/22/2018UnitedStatesHardwareAndreessenHorowitz,GeneralCatalystNaN
32OlaCabs$6.3210/27/2014IndiaAuto&transportationAccelPartners,SoftBankGroup,SequoiaCapitalNaN
33Databricks$6.22/5/2019UnitedStatesDatamanagement&analyticsAndreessenHorowitz,NewEnterpriseAssociates,Bat...NaN
34ManbangGroup$64/24/2018ChinaSupplychain,logistics,&deliverySoftbankGroup,CapitalGNaN
35UnityTechnologies$67/13/2016UnitedStatesOtherSequoiaCapital,iGlobePartners,DFJGrowthNaN
36Revolut$5.54/26/2018UnitedKingdomFintechindexVentures,DSTGlobal,RibbitCapitalNaN
37Lianjia(Homelink)$5.84/8/2016ChinaE-commerce&direct-to-consumerTencent,Baidu,HuashengCapitalNaN
38Chime$5.83/5/2019UnitedStatesFintechForerunnerVentures,CrosslinkCapital,HomebrewNaN
39EasyHome$5.72/12/2018ChinaConsumer&retailAlibabaGroup,BoyuCapital,BoruiCapitalNaN
40ViceMedia$5.78/17/2013UnitedStatesInternetsoftware&servicesTechnologyCrossoverVentures,A&ETelevisionNetworksNaN
41GuaHao(WeDoctor)$5.59/22/2015ChinaHealthTencent,MorningsideGroupNaN
42HashiCorp$5.111/1/2018UnitedStatesInternetsoftware&servicesRedpointVentures,TrueVentures,MayfieldFundNaN
43UnitedImagingHealthcare$59/14/2017ChinaHealthChinaLifeInsurance,ChinaDevelopmentBankCapital...NaN
44UBTECHRobotics$57/26/2016ChinaHardwareCDHInvestments,GoldstoneInvestments,QimingVent...NaN
45KraftonGameUnion$58/9/2018SouthKoreaOtherTencentHoldings,StonebridgeCapital,IMMInvestmentNaN
46MachineZone$57/16/2014UnitedStatesMobile&telecommunicationsJ.P.MorganChase&Co.,MenloVenturesNaN
47WMMotor$53/8/2019ChinaAuto&transportationBaiduCapital,LinearVenture,TencentNaN
48RoyoleCorporation$58/18/2015ChinaHardwareWarmsunHolding,IDGCapitalPartnersNaN
49HelloTransTech$56/01/2018ChinaAuto&transportationAntFinancialServicesGroup,GGVCapitalNaN
\n", + "
" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined Country \\\n", + "0 Toutiao(Bytedance) $75 4/7/2017 China \n", + "1 Stripe $36 1/23/2014 UnitedStates \n", + "2 Kuaishou $18 1/1/2015 China \n", + "3 EpicGames $15 10/26/2018 UnitedStates \n", + "4 BeikeZhaofang $14 7/18/2019 China \n", + "5 DoorDash $12.6 3/1/2018 UnitedStates \n", + "6 SnowflakeComputing $12.4 1/25/2018 UnitedStates \n", + "7 JUULLabs $12 12/20/2017 UnitedStates \n", + "8 BitmainTechnologies $12 7/6/2018 China \n", + "9 Samumed $12 8/6/2018 UnitedStates \n", + "10 Wish $11.2 5/18/2015 UnitedStates \n", + "11 GlobalSwitch $11.08 12/22/2016 UnitedKingdom \n", + "12 Go-Jek $10 8/4/2016 Indonesia \n", + "13 Nubank $10 3/1/2018 Brazil \n", + "14 OyoRooms $10 9/25/2018 India \n", + "15 Ripple $10 12/20/2019 UnitedStates \n", + "16 Guazi(Chehaoduo) $9 3/12/2016 China \n", + "17 Coinbase $8 8/10/2017 UnitedStates \n", + "18 BYJU'S $8 7/25/2017 India \n", + "19 Robinhood $8 4/26/2017 UnitedStates \n", + "20 Yuanfudao $7.8 5/31/2017 China \n", + "21 SenseTime $7.5 7/11/2017 China \n", + "22 Snapdeal $7 5/21/2014 India \n", + "23 RoivantSciences $7 11/13/2018 UnitedStates \n", + "24 Tokopedia $7 12/12/2018 Indonesia \n", + "25 ArgoAI $7 07/12/2019 UnitedStates \n", + "26 AutomationAnywhere $6.8 7/2/2018 UnitedStates \n", + "27 Ziroom $6.6 1/17/2018 China \n", + "28 UiPath $6.4 3/2/2018 UnitedStates \n", + "29 Compass $6.4 8/31/2016 UnitedStates \n", + "30 MagicLeap $6.3 10/21/2014 UnitedStates \n", + "31 SamsaraNetworks $6.3 3/22/2018 UnitedStates \n", + "32 OlaCabs $6.32 10/27/2014 India \n", + "33 Databricks $6.2 2/5/2019 UnitedStates \n", + "34 ManbangGroup $6 4/24/2018 China \n", + "35 UnityTechnologies $6 7/13/2016 UnitedStates \n", + "36 Revolut $5.5 4/26/2018 UnitedKingdom \n", + "37 Lianjia(Homelink) $5.8 4/8/2016 China \n", + "38 Chime $5.8 3/5/2019 UnitedStates \n", + "39 EasyHome $5.7 2/12/2018 China \n", + "40 ViceMedia $5.7 8/17/2013 UnitedStates \n", + "41 GuaHao(WeDoctor) $5.5 9/22/2015 China \n", + "42 HashiCorp $5.1 11/1/2018 UnitedStates \n", + "43 UnitedImagingHealthcare $5 9/14/2017 China \n", + "44 UBTECHRobotics $5 7/26/2016 China \n", + "45 KraftonGameUnion $5 8/9/2018 SouthKorea \n", + "46 MachineZone $5 7/16/2014 UnitedStates \n", + "47 WMMotor $5 3/8/2019 China \n", + "48 RoyoleCorporation $5 8/18/2015 China \n", + "49 HelloTransTech $5 6/01/2018 China \n", + "\n", + " Industry \\\n", + "0 Artificialintelligence \n", + "1 Fintech \n", + "2 Mobile&telecommunications \n", + "3 Other \n", + "4 Internetsoftware&services \n", + "5 Supplychain,logistics,&delivery \n", + "6 Datamanagement&analytics \n", + "7 Consumer&retail \n", + "8 Hardware \n", + "9 Health \n", + "10 E-commerce&direct-to-consumer \n", + "11 Hardware \n", + "12 Supplychain,logistics,&delivery \n", + "13 Fintech \n", + "14 Travel \n", + "15 Fintech \n", + "16 E-commerce&direct-to-consumer \n", + "17 Fintech \n", + "18 Edtech \n", + "19 Fintech \n", + "20 Edtech \n", + "21 Artificialintelligence \n", + "22 E-commerce&direct-to-consumer \n", + "23 Health \n", + "24 E-commerce&direct-to-consumer \n", + "25 Artificialintelligence \n", + "26 Artificialintelligence \n", + "27 E-commerce&direct-to-consumer \n", + "28 Artificialintelligence \n", + "29 E-commerce&direct-to-consumer \n", + "30 Hardware \n", + "31 Hardware \n", + "32 Auto&transportation \n", + "33 Datamanagement&analytics \n", + "34 Supplychain,logistics,&delivery \n", + "35 Other \n", + "36 Fintech \n", + "37 E-commerce&direct-to-consumer \n", + "38 Fintech \n", + "39 Consumer&retail \n", + "40 Internetsoftware&services \n", + "41 Health \n", + "42 Internetsoftware&services \n", + "43 Health \n", + "44 Hardware \n", + "45 Other \n", + "46 Mobile&telecommunications \n", + "47 Auto&transportation \n", + "48 Hardware \n", + "49 Auto&transportation \n", + "\n", + " Select Investors Select Investors_new \n", + "0 SequoiaCapitalChina,SIGAsiaInvestments,SinaWei... NaN \n", + "1 KhoslaVentures,LowercaseCapital,capitalG NaN \n", + "2 MorningsideVentureCapital,SequoiaCapital,Baidu NaN \n", + "3 TencentHoldings,KKR,SmashVentures NaN \n", + "4 TencentHoldings,HillhouseCapitalManagement,Sou... NaN \n", + "5 SoftbankGroup,SequoiaCapital,KhoslaVentures NaN \n", + "6 RedpointVentures,IconiqCapital,MadronaVentureG... NaN \n", + "7 TigerGlobalManagement NaN \n", + "8 CoatueManagement,SequoiaCapitalChina,IDGCapital NaN \n", + "9 VickersVenturePartners,IKEAGreenTech NaN \n", + "10 FoundersFund,GGVCapital,DigitalSkyTechnologies NaN \n", + "11 AviationIndustryCorporationofChina,EssenceFina... NaN \n", + "12 FormationGroup,SequoiaCapitalIndia,WarburgPincus NaN \n", + "13 SequoiaCapital,Redpointe.ventures,KaszekVentures NaN \n", + "14 SoftBankGroup,SequoiaCapitalIndia,LightspeedIn... NaN \n", + "15 IDGCapital,Venture51,LightspeedVenturePartners NaN \n", + "16 SequoiaCapitalChina,GXCapital NaN \n", + "17 YCombinator,UnionSquareVentures,DFJGrowth NaN \n", + "18 TencentHoldings,LightspeedIndiaPartners,Sequoi... NaN \n", + "19 GoogleVentures,AndreessenHorowitz,DSTGlobal NaN \n", + "20 TencentHoldings,WarbugPincus,IDGCapital NaN \n", + "21 StarVC,IDGCapital,InforeCapital,AlibabaGroup NaN \n", + "22 SoftBankGroup,Blackrock,AlibabaGroup NaN \n", + "23 SoftBankGroup,FoundersFund NaN \n", + "24 SoftBankGroup,AlibabaGroup,SequoiaCapitalIndia NaN \n", + "25 VolkswagenGroup,FordAutonomousVehicles NaN \n", + "26 GeneralAtlantic,GoldmanSachs,NewEnterpriseAsso... NaN \n", + "27 SequoiaCapitalChina,WarburgPincus,GeneralCatalyst NaN \n", + "28 Accel,capitalG,EarlybridVentureCapital,Seedcamp NaN \n", + "29 FoundersFund,ThriveCapital,WellingtonManagement NaN \n", + "30 ObviousVentures,QualcommVentures,AndreessenHor... NaN \n", + "31 AndreessenHorowitz,GeneralCatalyst NaN \n", + "32 AccelPartners,SoftBankGroup,SequoiaCapital NaN \n", + "33 AndreessenHorowitz,NewEnterpriseAssociates,Bat... NaN \n", + "34 SoftbankGroup,CapitalG NaN \n", + "35 SequoiaCapital,iGlobePartners,DFJGrowth NaN \n", + "36 indexVentures,DSTGlobal,RibbitCapital NaN \n", + "37 Tencent,Baidu,HuashengCapital NaN \n", + "38 ForerunnerVentures,CrosslinkCapital,Homebrew NaN \n", + "39 AlibabaGroup,BoyuCapital,BoruiCapital NaN \n", + "40 TechnologyCrossoverVentures,A&ETelevisionNetworks NaN \n", + "41 Tencent,MorningsideGroup NaN \n", + "42 RedpointVentures,TrueVentures,MayfieldFund NaN \n", + "43 ChinaLifeInsurance,ChinaDevelopmentBankCapital... NaN \n", + "44 CDHInvestments,GoldstoneInvestments,QimingVent... NaN \n", + "45 TencentHoldings,StonebridgeCapital,IMMInvestment NaN \n", + "46 J.P.MorganChase&Co.,MenloVentures NaN \n", + "47 BaiduCapital,LinearVenture,Tencent NaN \n", + "48 WarmsunHolding,IDGCapitalPartners NaN \n", + "49 AntFinancialServicesGroup,GGVCapital NaN " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "complete_df.head(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CompanyValuation ($B)Date JoinedCountryIndustrySelect InvestorsSelect Investors_new
413KKGroup$110/23/2019ChinaE-commerce&direct-to-consumerMatrixPartnersChina,BrightVentureCapita,Shenzh...NaN
414Kujiale$110/25/2019ChinaInternetsoftware&servicesGGVCapital,IDGCapital,LinearVentureNaN
415Vacasa$110/29/2019UnitedStatesTravelLevelEquity,NewSpringHoldings,RiverwoodCapitalNaN
416Faire$110/30/2019UnitedStatesArtificialintelligenceKhoslaVentures,ForerunnerVentures,SequoiaCapitalNaN
417Riskified$111/05/2019UnitedStatesCybersecurityEntreeCapital,GenesisPartners,QumraCapitalNaN
418GuildEducation$111/13/2019UnitedStatesInternetsoftware&servicesRedpointVentures,HarrisonMetal,BessemerVenture...NaN
419Wacai$17/18/2018ChinaMobile&telecommunicationsQimingVenturePartners,ChinaBroadbandCapital,CD...NaN
420Vroom$112/6/2019UnitedStatesE-commerce&direct-to-consumerLCatterton,GeneralCatalyst,T.RowePriceNaN
421BrightHealth$112/17/2019UnitedStatesHealthNewEnterpriseAssociates,BessemerVenturePartner...NaN
422Glovo$112/19/2019SpainSupplychain,logistics,&deliveryIDInvestPartners,SeayaVentures,LakestarNaN
423Loft$11/3/2020BrazilE-commerce&direct-to-consumerMonashees+,AndreessenHorowitz,QEDInvestorsNaN
424HighRadius$11/7/2020UnitedStatesFintechSusquehannaGrowthEquity,CitiVentures,ICONIQCap...NaN
425ClassPass$11/8/2020UnitedStatesInternetsoftware&servicesGeneralCatalyst,LCatterton,AcequiaCapitalNaN
426Sisense$11/9/2020UnitedStatesDatamanagement&analyticsOpusCapital,GenesisPartners,BatteryVenturesNaN
427Snyk$11/21/2020UnitedKingdomCybersecurityBOLDstartVentures,GoogleVentures,AccelNaN
428AppsFlyer$1.61/21/2020UnitedStatesMobile&telecommunicationsMagmaVenturePartners,PitangoVentureCapital,Qum...NaN
429Maimai$111/15/2017ChinaMobile&telecommunicationsMorningsideVentureCapital,IDGCapital,DCMVenturesNaN
430OrbbecTechnology$15/21/2018ChinaHardwareR-ZCapital,GreenPineCapitalPartners,SAIFPartne...NaN
431AltoPharmacy$11/30/2020UnitedStatesHealthJacksonSquareVentures,GreenoaksCapitalManageme...NaN
432Flywire$12/13/2020UnitedStatesFintechSparkCapital,F-PrimeCapital,KiboVenturesNaN
433Headspin$1.162/25/2020UnitedStatesMobile&telecommunicationsICONIQCapital,DellTechnologiesCapital,TigerGlo...NaN
434o9Solutions$14/28/2020UnitedStatesArtificialintelligenceKKRNaN
435EmergingMarketsPropertyGroup$14/28/2020UnitedArabEmiratesOtherKKRNaN
436DidiChuxing$5612/31/2014ChinaAuto&transportationNaNMatrixPartners,TigerGlobalManagement,SoftbankC...
437SpaceX$33.312/1/2012UnitedStatesOtherNaNFoundersFund,DraperFisherJurvetson,RothenbergV...
438Airbnb$187/26/2011UnitedStatesTravelNaNGeneralCatalystPartners,AndreessenHorowitz,ENI...
439One97Communications$165/12/2015IndiaFintechNaNIntelCapital,SapphireVentures,AlibabaGroup
440DJIInnovations$155/6/2015ChinaHardwareNaNAccelPartners,SequoiaCapital
441Grab$14.312/4/2014SingaporeAuto&transportationNaNGGVCapital,VertexVentureHoldings,SoftbankGroup
442PalantirTechnologies$12.185/5/2011UnitedStatesDatamanagement&analyticsNaNRREVentures,FoundersFund,In-Q-Tel
443Coupang$95/28/2014SouthKoreaE-commerce&direct-to-consumerNaNSequoiaCapital,FounderCollective,WellingtonMan...
444Instacart$7.612/30/2014UnitedStatesSupplychain,logistics,&deliveryNaNKhoslaVentures,KleinerPerkinsCaufield&Byers,Co...
445Tanium$6.73/31/2015UnitedStatesCybersecurityNaNAndreessenHorowitz,Nor-CalInvest,TPGGrowth
446Klarna$5.512/12/2011SwedenFintechNaNInstitutionalVenturePartners,SequoiaCapital,Ge...
447Houzz$49/30/2014UnitedStatesE-commerce&direct-to-consumerNaNNewEnterpriseAssociates,SequoiaCapital,Comcast...
448Automattic$35/27/2013UnitedStatesInternetsoftware&servicesNaNInsightVenturePartners,LowercaseCapital,Polari...
449VANCL$312/14/2010ChinaE-commerce&direct-to-consumerNaNCeyuanVentures,QiMingVenturePartners,TemasekHo...
450Nextdoor$2.13/4/2015UnitedStatesInternetsoftware&servicesNaNBenchmarkCapital,DAGVentures,InsightVenturePar...
451Sprinklr$1.83/31/2015UnitedStatesInternetsoftware&servicesNaNAzureCapitalPartners,BatteryVentures,IntelCapital
452XANT$1.74/28/2014UnitedStatesArtificialintelligenceNaNMicrosoftVentures,USVenturePartners,KleinerPer...
453ironSource$1.58/11/2014IsraelMobile&telecommunicationsNaNAccessIndustries,ClalIndustriesandInvestments
454Koudai$1.410/23/2014ChinaE-commerce&direct-to-consumerNaNNewEnterpriseAssociates,TigerGlobalmanagement,...
455Docker$1.34/14/2015UnitedStatesInternetsoftware&servicesNaNGreylockPartners,LightspeedVenturePartners,Low...
456Actifio$1.13/24/2014UnitedStatesDatamanagement&analyticsNaNGreylockPartners,NorthBridgeVenturePartners,Te...
457TangoMe$1.13/20/2014UnitedStatesMobile&telecommunicationsNaNDraperFisherJurtson,QualcommVentures,AlibabaGroup
458Lookout$18/13/2014UnitedStatesCybersecurityNaNAccelPartners,GreylockPartners,LowercaseCapital
459TechStyleFashionGroup$18/29/2014UnitedStatesE-commerce&direct-to-consumerNaNMatrixPartners,PassportCapital,RhoVentures
460Illumio$14/14/2014UnitedStatesCybersecurityNaNDataCollective,Formation8,GeneralCatalystPartners
461BeiBei$11/22/2015ChinaE-commerce&direct-to-consumerNaNBanyanCapital,NewHorizonCapital,IDGCapitalPart...
462InMobi$112/2/2014IndiaMobile&telecommunicationsNaNKleinerPerkinsCaufield&Byers,SoftbankCorp.,She...
\n", + "
" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined \\\n", + "413 KKGroup $1 10/23/2019 \n", + "414 Kujiale $1 10/25/2019 \n", + "415 Vacasa $1 10/29/2019 \n", + "416 Faire $1 10/30/2019 \n", + "417 Riskified $1 11/05/2019 \n", + "418 GuildEducation $1 11/13/2019 \n", + "419 Wacai $1 7/18/2018 \n", + "420 Vroom $1 12/6/2019 \n", + "421 BrightHealth $1 12/17/2019 \n", + "422 Glovo $1 12/19/2019 \n", + "423 Loft $1 1/3/2020 \n", + "424 HighRadius $1 1/7/2020 \n", + "425 ClassPass $1 1/8/2020 \n", + "426 Sisense $1 1/9/2020 \n", + "427 Snyk $1 1/21/2020 \n", + "428 AppsFlyer $1.6 1/21/2020 \n", + "429 Maimai $1 11/15/2017 \n", + "430 OrbbecTechnology $1 5/21/2018 \n", + "431 AltoPharmacy $1 1/30/2020 \n", + "432 Flywire $1 2/13/2020 \n", + "433 Headspin $1.16 2/25/2020 \n", + "434 o9Solutions $1 4/28/2020 \n", + "435 EmergingMarketsPropertyGroup $1 4/28/2020 \n", + "436 DidiChuxing $56 12/31/2014 \n", + "437 SpaceX $33.3 12/1/2012 \n", + "438 Airbnb $18 7/26/2011 \n", + "439 One97Communications $16 5/12/2015 \n", + "440 DJIInnovations $15 5/6/2015 \n", + "441 Grab $14.3 12/4/2014 \n", + "442 PalantirTechnologies $12.18 5/5/2011 \n", + "443 Coupang $9 5/28/2014 \n", + "444 Instacart $7.6 12/30/2014 \n", + "445 Tanium $6.7 3/31/2015 \n", + "446 Klarna $5.5 12/12/2011 \n", + "447 Houzz $4 9/30/2014 \n", + "448 Automattic $3 5/27/2013 \n", + "449 VANCL $3 12/14/2010 \n", + "450 Nextdoor $2.1 3/4/2015 \n", + "451 Sprinklr $1.8 3/31/2015 \n", + "452 XANT $1.7 4/28/2014 \n", + "453 ironSource $1.5 8/11/2014 \n", + "454 Koudai $1.4 10/23/2014 \n", + "455 Docker $1.3 4/14/2015 \n", + "456 Actifio $1.1 3/24/2014 \n", + "457 TangoMe $1.1 3/20/2014 \n", + "458 Lookout $1 8/13/2014 \n", + "459 TechStyleFashionGroup $1 8/29/2014 \n", + "460 Illumio $1 4/14/2014 \n", + "461 BeiBei $1 1/22/2015 \n", + "462 InMobi $1 12/2/2014 \n", + "\n", + " Country Industry \\\n", + "413 China E-commerce&direct-to-consumer \n", + "414 China Internetsoftware&services \n", + "415 UnitedStates Travel \n", + "416 UnitedStates Artificialintelligence \n", + "417 UnitedStates Cybersecurity \n", + "418 UnitedStates Internetsoftware&services \n", + "419 China Mobile&telecommunications \n", + "420 UnitedStates E-commerce&direct-to-consumer \n", + "421 UnitedStates Health \n", + "422 Spain Supplychain,logistics,&delivery \n", + "423 Brazil E-commerce&direct-to-consumer \n", + "424 UnitedStates Fintech \n", + "425 UnitedStates Internetsoftware&services \n", + "426 UnitedStates Datamanagement&analytics \n", + "427 UnitedKingdom Cybersecurity \n", + "428 UnitedStates Mobile&telecommunications \n", + "429 China Mobile&telecommunications \n", + "430 China Hardware \n", + "431 UnitedStates Health \n", + "432 UnitedStates Fintech \n", + "433 UnitedStates Mobile&telecommunications \n", + "434 UnitedStates Artificialintelligence \n", + "435 UnitedArabEmirates Other \n", + "436 China Auto&transportation \n", + "437 UnitedStates Other \n", + "438 UnitedStates Travel \n", + "439 India Fintech \n", + "440 China Hardware \n", + "441 Singapore Auto&transportation \n", + "442 UnitedStates Datamanagement&analytics \n", + "443 SouthKorea E-commerce&direct-to-consumer \n", + "444 UnitedStates Supplychain,logistics,&delivery \n", + "445 UnitedStates Cybersecurity \n", + "446 Sweden Fintech \n", + "447 UnitedStates E-commerce&direct-to-consumer \n", + "448 UnitedStates Internetsoftware&services \n", + "449 China E-commerce&direct-to-consumer \n", + "450 UnitedStates Internetsoftware&services \n", + "451 UnitedStates Internetsoftware&services \n", + "452 UnitedStates Artificialintelligence \n", + "453 Israel Mobile&telecommunications \n", + "454 China E-commerce&direct-to-consumer \n", + "455 UnitedStates Internetsoftware&services \n", + "456 UnitedStates Datamanagement&analytics \n", + "457 UnitedStates Mobile&telecommunications \n", + "458 UnitedStates Cybersecurity \n", + "459 UnitedStates E-commerce&direct-to-consumer \n", + "460 UnitedStates Cybersecurity \n", + 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MagmaVenturePartners,PitangoVentureCapital,Qum... \n", + "429 MorningsideVentureCapital,IDGCapital,DCMVentures \n", + "430 R-ZCapital,GreenPineCapitalPartners,SAIFPartne... \n", + "431 JacksonSquareVentures,GreenoaksCapitalManageme... \n", + "432 SparkCapital,F-PrimeCapital,KiboVentures \n", + "433 ICONIQCapital,DellTechnologiesCapital,TigerGlo... \n", + "434 KKR \n", + "435 KKR \n", + "436 NaN \n", + "437 NaN \n", + "438 NaN \n", + "439 NaN \n", + "440 NaN \n", + "441 NaN \n", + "442 NaN \n", + "443 NaN \n", + "444 NaN \n", + "445 NaN \n", + "446 NaN \n", + "447 NaN \n", + "448 NaN \n", + "449 NaN \n", + "450 NaN \n", + "451 NaN \n", + "452 NaN \n", + "453 NaN \n", + "454 NaN \n", + "455 NaN \n", + "456 NaN \n", + "457 NaN \n", + "458 NaN \n", + "459 NaN \n", + "460 NaN \n", + "461 NaN \n", + "462 NaN \n", + "\n", + " Select Investors_new \n", + "413 NaN \n", + "414 NaN \n", + "415 NaN \n", + "416 NaN \n", + "417 NaN \n", + "418 NaN \n", + "419 NaN \n", + "420 NaN \n", + "421 NaN \n", 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0Toutiao(Bytedance)75.04/7/2017ChinaArtificialintelligenceSequoiaCapitalChina,SIGAsiaInvestments,SinaWei...NaN
1DidiChuxing56.012/31/2014ChinaAuto&transportationNaNMatrixPartners,TigerGlobalManagement,SoftbankC...
2Stripe36.01/23/2014UnitedStatesFintechKhoslaVentures,LowercaseCapital,capitalGNaN
3SpaceX33.312/1/2012UnitedStatesOtherNaNFoundersFund,DraperFisherJurvetson,RothenbergV...
4Airbnb18.07/26/2011UnitedStatesTravelNaNGeneralCatalystPartners,AndreessenHorowitz,ENI...
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" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined Country \\\n", + "0 Toutiao(Bytedance) 75.0 4/7/2017 China \n", + "1 DidiChuxing 56.0 12/31/2014 China \n", + "2 Stripe 36.0 1/23/2014 UnitedStates \n", + "3 SpaceX 33.3 12/1/2012 UnitedStates \n", + "4 Airbnb 18.0 7/26/2011 UnitedStates \n", + "\n", + " Industry Select Investors \\\n", + "0 Artificialintelligence SequoiaCapitalChina,SIGAsiaInvestments,SinaWei... \n", + "1 Auto&transportation NaN \n", + "2 Fintech KhoslaVentures,LowercaseCapital,capitalG \n", + "3 Other NaN \n", + "4 Travel NaN \n", + "\n", + " Select Investors_new \n", + "0 NaN \n", + "1 MatrixPartners,TigerGlobalManagement,SoftbankC... \n", + "2 NaN \n", + "3 FoundersFund,DraperFisherJurvetson,RothenbergV... \n", + "4 GeneralCatalystPartners,AndreessenHorowitz,ENI... " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_complete_df = sorted_complete_df.reset_index(drop=True)\n", + "sorted_complete_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_complete_df.to_csv(r'data/cbinsights_entire_unicorn_tracker_sorted.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "unicorns_final = sorted_complete_df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CompanyValuation ($B)Date JoinedCountryIndustrySelect InvestorsSelect Investors_new
0Toutiao(Bytedance)75.04/7/2017ChinaArtificialintelligenceSequoiaCapitalChina,SIGAsiaInvestments,SinaWei...NaN
1DidiChuxing56.012/31/2014ChinaAuto&transportationMatrixPartners,TigerGlobalManagement,SoftbankC...MatrixPartners,TigerGlobalManagement,SoftbankC...
2Stripe36.01/23/2014UnitedStatesFintechKhoslaVentures,LowercaseCapital,capitalGNaN
3SpaceX33.312/1/2012UnitedStatesOtherFoundersFund,DraperFisherJurvetson,RothenbergV...FoundersFund,DraperFisherJurvetson,RothenbergV...
4Airbnb18.07/26/2011UnitedStatesTravelGeneralCatalystPartners,AndreessenHorowitz,ENI...GeneralCatalystPartners,AndreessenHorowitz,ENI...
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" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined Country \\\n", + "0 Toutiao(Bytedance) 75.0 4/7/2017 China \n", + "1 DidiChuxing 56.0 12/31/2014 China \n", + "2 Stripe 36.0 1/23/2014 UnitedStates \n", + "3 SpaceX 33.3 12/1/2012 UnitedStates \n", + "4 Airbnb 18.0 7/26/2011 UnitedStates \n", + "\n", + " Industry Select Investors \\\n", + "0 Artificialintelligence SequoiaCapitalChina,SIGAsiaInvestments,SinaWei... \n", + "1 Auto&transportation MatrixPartners,TigerGlobalManagement,SoftbankC... \n", + "2 Fintech KhoslaVentures,LowercaseCapital,capitalG \n", + "3 Other FoundersFund,DraperFisherJurvetson,RothenbergV... \n", + "4 Travel GeneralCatalystPartners,AndreessenHorowitz,ENI... \n", + "\n", + " Select Investors_new \n", + "0 NaN \n", + "1 MatrixPartners,TigerGlobalManagement,SoftbankC... \n", + "2 NaN \n", + "3 FoundersFund,DraperFisherJurvetson,RothenbergV... \n", + "4 GeneralCatalystPartners,AndreessenHorowitz,ENI... " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unicorns_final['Select Investors'].fillna(unicorns_final['Select Investors_new'], inplace=True)\n", + "unicorns_final.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "unicorns_final = unicorns_final.drop(columns=['Select Investors_new'])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CompanyValuation ($B)Date JoinedCountryIndustrySelect Investors
0Toutiao(Bytedance)75.04/7/2017ChinaArtificialintelligenceSequoiaCapitalChina,SIGAsiaInvestments,SinaWei...
1DidiChuxing56.012/31/2014ChinaAuto&transportationMatrixPartners,TigerGlobalManagement,SoftbankC...
2Stripe36.01/23/2014UnitedStatesFintechKhoslaVentures,LowercaseCapital,capitalG
3SpaceX33.312/1/2012UnitedStatesOtherFoundersFund,DraperFisherJurvetson,RothenbergV...
4Airbnb18.07/26/2011UnitedStatesTravelGeneralCatalystPartners,AndreessenHorowitz,ENI...
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" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined Country \\\n", + "0 Toutiao(Bytedance) 75.0 4/7/2017 China \n", + "1 DidiChuxing 56.0 12/31/2014 China \n", + "2 Stripe 36.0 1/23/2014 UnitedStates \n", + "3 SpaceX 33.3 12/1/2012 UnitedStates \n", + "4 Airbnb 18.0 7/26/2011 UnitedStates \n", + "\n", + " Industry Select Investors \n", + "0 Artificialintelligence SequoiaCapitalChina,SIGAsiaInvestments,SinaWei... \n", + "1 Auto&transportation MatrixPartners,TigerGlobalManagement,SoftbankC... \n", + "2 Fintech KhoslaVentures,LowercaseCapital,capitalG \n", + "3 Other FoundersFund,DraperFisherJurvetson,RothenbergV... \n", + "4 Travel GeneralCatalystPartners,AndreessenHorowitz,ENI... " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unicorns_final.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "unicorns_final.to_csv(r'data/cbinsights_entire_unicorn_tracker_sorted.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Project 5 - Success 2.ipynb b/your-project/code/Project 5 - Success 2.ipynb new file mode 100644 index 0000000..6c0b623 --- /dev/null +++ b/your-project/code/Project 5 - Success 2.ipynb @@ -0,0 +1,5732 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Success by funding amount" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0object_idnamecategory_codestatusfounded_atclosed_atacquired_atcountry_codestate_codecityregionfunding_total_usdyear_foundedyear_closedmonth_closeddurationyear_acquiredmonth_acquiredt_unt_acqterm_codeprice_amountprice_currency_codeidipo_idvaluation_amountvaluation_currency_coderaised_amountraised_currency_codepublic_at
00c:1Wetpaintweboperating2005-10-17NaNNaNUSAWASeattleSeattle39750000.02005.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11c:10Flektorgames_videoacquiredNaNNaN2007-05-30USACACulver CityLos Angeles0.0NaNNaNNaNNaN2007.05.0NaNNaN20000000.0USDNaNNaNNaNNaNNaNNaNNaN
22c:100Theregames_videoacquiredNaNNaN2005-05-29USACASan MateoSF Bay0.0NaNNaNNaNNaN2005.05.0NaNcash0.0USDNaNNaNNaNNaNNaNNaNNaN
33c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
44c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " Unnamed: 0 object_id name category_code status \\\n", + "0 0 c:1 Wetpaint web operating \n", + "1 1 c:10 Flektor games_video acquired \n", + "2 2 c:100 There games_video acquired \n", + "3 3 c:10000 MYWEBBO network_hosting operating \n", + "4 4 c:10001 THE Movie Streamer games_video operating \n", + "\n", + " founded_at closed_at acquired_at country_code state_code city \\\n", + "0 2005-10-17 NaN NaN USA WA Seattle \n", + "1 NaN NaN 2007-05-30 USA CA Culver City \n", + "2 NaN NaN 2005-05-29 USA CA San Mateo \n", + "3 2008-07-26 NaN NaN NaN NaN NaN \n", + "4 2008-07-26 NaN NaN NaN NaN NaN \n", + "\n", + " region funding_total_usd year_founded year_closed month_closed \\\n", + "0 Seattle 39750000.0 2005.0 NaN NaN \n", + "1 Los Angeles 0.0 NaN NaN NaN \n", + "2 SF Bay 0.0 NaN NaN NaN \n", + "3 unknown 0.0 2008.0 NaN NaN \n", + "4 unknown 0.0 2008.0 NaN NaN \n", + "\n", + " duration year_acquired month_acquired t_unt_acq term_code price_amount \\\n", + "0 NaN NaN NaN NaN NaN NaN \n", + "1 NaN 2007.0 5.0 NaN NaN 20000000.0 \n", + "2 NaN 2005.0 5.0 NaN cash 0.0 \n", + "3 NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN \n", + "\n", + " price_currency_code id ipo_id valuation_amount valuation_currency_code \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 USD NaN NaN NaN NaN \n", + "2 USD NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " raised_amount raised_currency_code public_at \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "success_master_merged_slimmed = pd.read_csv(r'data/success_master_merged_slimmed.csv')\n", + "pd.options.display.max_columns = None\n", + "success_master_merged_slimmed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "success_master_merged_slimmed.founded_at = success_master_merged_slimmed.founded_at.astype('datetime64')\n", + "success_master_merged_slimmed.public_at = success_master_merged_slimmed.public_at.astype('datetime64')\n", + "\n", + "success_master_merged_slimmed['t_unt_public'] = success_master_merged_slimmed['public_at'] - success_master_merged_slimmed['founded_at']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# how many get funded by industry and country" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "funding_probability_industry = success_master_merged_slimmed[['object_id', 'country_code', 'category_code', 'funding_total_usd', 't_unt_acq', 'price_amount', 'valuation_amount' , 'raised_amount', 'public_at']]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], + "source": [ + "funding_probability_industry['funding_total_usd'] = funding_probability_industry['funding_total_usd'].replace(0, np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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object_idcountry_codecategory_codefunding_total_usd
0c:1USAweb39750000.0
1c:10USAgames_videoNaN
2c:100USAgames_videoNaN
3c:10000NaNnetwork_hostingNaN
4c:10001NaNgames_videoNaN
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" + ], + "text/plain": [ + " object_id country_code category_code funding_total_usd\n", + "0 c:1 USA web 39750000.0\n", + "1 c:10 USA games_video NaN\n", + "2 c:100 USA games_video NaN\n", + "3 c:10000 NaN network_hosting NaN\n", + "4 c:10001 NaN games_video NaN" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_industry.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Group by industry" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codeobject_idcountry_codefunding_total_usd
0advertising597942871081
1analytics1019879621
2automotive27315169
3biotech423039773036
4cleantech18621512967
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" + ], + "text/plain": [ + " category_code object_id country_code funding_total_usd\n", + "0 advertising 5979 4287 1081\n", + "1 analytics 1019 879 621\n", + "2 automotive 273 151 69\n", + "3 biotech 4230 3977 3036\n", + "4 cleantech 1862 1512 967" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_industry = funding_probability_industry.groupby(['category_code']).count().reset_index()\n", + "funding_probability_industry.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "funding_probability_industry.rename(columns = {'object_id' : 'number', 'funding_total_usd' : 'number_funded'} , inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "funding_probability_industry['percent_funded'] = funding_probability_industry['number_funded'] / funding_probability_industry['number']" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codenumbercountry_codenumber_fundedpercent_funded
0advertising5979428710810.180799
1analytics10198796210.609421
2automotive273151690.252747
3biotech4230397730360.717730
4cleantech186215129670.519334
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category_codenumbercountry_codenumber_fundedpercent_funded
0nanotech7068550.785714
1biotech4230397730360.717730
2messaging2942512080.707483
3semiconductor6365954090.643082
4analytics10198796210.609421
5medical11297996510.576616
6cleantech186215129670.519334
7manufacturing6044743070.508278
8nonprofit173146820.473988
9social13077965370.410865
10fashion5383662000.371747
11pets5743200.350877
12finance13219054550.344436
13hardware272920149300.340784
14health16258855530.340308
15enterprise4290321114300.333333
16security11168403610.323477
17hospitality7374142350.318860
18real_estate4342921250.288018
19music5772571640.284229
20mobile6813463118190.266990
21automotive273151690.252747
22news7363701860.252717
23photo_video5372571330.247672
24software173501334941410.238674
25travel9054311960.216575
26government352970.200000
27network_hosting229715974390.191119
28advertising5979428710810.180799
29sports6442141030.159938
30education281414564490.159559
31design270151430.159259
32transportation467177740.158458
33web15033938723600.156988
34games_video7426350211590.156073
35ecommerce8870569713240.149267
36public_relations271919293480.127988
37search215511672650.122970
38consulting478433352710.056647
39legal921547520.056460
40local716300330.046089
41other1272565885200.040864
\n", + "
" + ], + "text/plain": [ + " category_code number country_code number_funded percent_funded\n", + "0 nanotech 70 68 55 0.785714\n", + "1 biotech 4230 3977 3036 0.717730\n", + "2 messaging 294 251 208 0.707483\n", + "3 semiconductor 636 595 409 0.643082\n", + "4 analytics 1019 879 621 0.609421\n", + "5 medical 1129 799 651 0.576616\n", + "6 cleantech 1862 1512 967 0.519334\n", + "7 manufacturing 604 474 307 0.508278\n", + "8 nonprofit 173 146 82 0.473988\n", + "9 social 1307 796 537 0.410865\n", + "10 fashion 538 366 200 0.371747\n", + "11 pets 57 43 20 0.350877\n", + "12 finance 1321 905 455 0.344436\n", + "13 hardware 2729 2014 930 0.340784\n", + "14 health 1625 885 553 0.340308\n", + "15 enterprise 4290 3211 1430 0.333333\n", + "16 security 1116 840 361 0.323477\n", + "17 hospitality 737 414 235 0.318860\n", + "18 real_estate 434 292 125 0.288018\n", + "19 music 577 257 164 0.284229\n", + "20 mobile 6813 4631 1819 0.266990\n", + "21 automotive 273 151 69 0.252747\n", + "22 news 736 370 186 0.252717\n", + "23 photo_video 537 257 133 0.247672\n", + "24 software 17350 13349 4141 0.238674\n", + "25 travel 905 431 196 0.216575\n", + "26 government 35 29 7 0.200000\n", + "27 network_hosting 2297 1597 439 0.191119\n", + "28 advertising 5979 4287 1081 0.180799\n", + "29 sports 644 214 103 0.159938\n", + "30 education 2814 1456 449 0.159559\n", + "31 design 270 151 43 0.159259\n", + "32 transportation 467 177 74 0.158458\n", + "33 web 15033 9387 2360 0.156988\n", + "34 games_video 7426 3502 1159 0.156073\n", + "35 ecommerce 8870 5697 1324 0.149267\n", + "36 public_relations 2719 1929 348 0.127988\n", + "37 search 2155 1167 265 0.122970\n", + "38 consulting 4784 3335 271 0.056647\n", + "39 legal 921 547 52 0.056460\n", + "40 local 716 300 33 0.046089\n", + "41 other 12725 6588 520 0.040864" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_industry.sort_values(by='percent_funded', ascending=False).reset_index().drop(columns = 'index')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# funding_probability_industry.to_csv(r'data/funding_general_probability_industry.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Try Correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slope: -6593.698635273976\n", + "Intercept: 4855.2986355256035\n", + "rvalue: -0.3126823887315767\n", + "pvalue: 0.043786709837793286\n", + "stderr: 3167.0450601021307\n" + ] + }, + { + "data": { + "image/png": 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category_codenumbercountry_codenumber_fundedpercent_funded
0advertising5979428710810.180799
1analytics10198796210.609421
2automotive273151690.252747
3biotech4230397730360.717730
4cleantech186215129670.519334
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" + ], + "text/plain": [ + " category_code number country_code number_funded percent_funded\n", + "0 advertising 5979 4287 1081 0.180799\n", + "1 analytics 1019 879 621 0.609421\n", + "2 automotive 273 151 69 0.252747\n", + "3 biotech 4230 3977 3036 0.717730\n", + "4 cleantech 1862 1512 967 0.519334" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_industry.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "# funding by geography" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "funding_probability_industry_geo = success_master_merged_slimmed[['object_id', 'country_code', 'category_code', 'funding_total_usd']]" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], + "source": [ + "funding_probability_industry_geo['funding_total_usd'] = funding_probability_industry_geo['funding_total_usd'].replace(0, np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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object_idcountry_codecategory_codefunding_total_usd
0c:1USAweb39750000.0
1c:10USAgames_videoNaN
2c:100USAgames_videoNaN
3c:10000NaNnetwork_hostingNaN
4c:10001NaNgames_videoNaN
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" + ], + "text/plain": [ + " object_id country_code category_code funding_total_usd\n", + "0 c:1 USA web 39750000.0\n", + "1 c:10 USA games_video NaN\n", + "2 c:100 USA games_video NaN\n", + "3 c:10000 NaN network_hosting NaN\n", + "4 c:10001 NaN games_video NaN" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_geo = funding_probability_industry_geo.copy()\n", + "funding_probability_geo.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " country_code object_id category_code funding_total_usd\n", + "0 AFG 8 8 0\n", + "1 AGO 2 2 0\n", + "2 AIA 1 0 0\n", + "3 ALB 10 10 2\n", + "4 AND 1 1 0" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_geo = funding_probability_geo.groupby(['country_code']).count().reset_index()\n", + "funding_probability_geo.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "funding_probability_geo.rename(columns = {'object_id' : 'number', 'funding_total_usd' : 'number_funded'} , inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "funding_probability_geo['percent_funded'] = funding_probability_geo['number_funded'] / funding_probability_geo['number']" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.....................
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" + ], + "text/plain": [ + " index country_code number category_code number_funded percent_funded\n", + "0 109 NER 1 1 1 1.0\n", + "1 142 SOM 1 1 1 1.0\n", + "2 116 NRU 1 1 1 1.0\n", + "3 84 KHM 1 1 1 1.0\n", + "4 60 GIN 1 1 1 1.0\n", + ".. ... ... ... ... ... ...\n", + "167 87 LAO 2 2 0 0.0\n", + "168 1 AGO 2 2 0 0.0\n", + "169 83 KGZ 2 1 0 0.0\n", + "170 81 KAZ 3 3 0 0.0\n", + "171 171 ZWE 4 4 0 0.0\n", + "\n", + "[172 rows x 6 columns]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_geo.sort_values(by='percent_funded', ascending=False).reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# funding_probability_geo.to_csv(r'data/funding_general_probability_geo.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# funding by industry and geography" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4c:10001NaNgames_videoNaN
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" + ], + "text/plain": [ + " object_id country_code category_code funding_total_usd\n", + "0 c:1 USA web 39750000.0\n", + "1 c:10 USA games_video NaN\n", + "2 c:100 USA games_video NaN\n", + "3 c:10000 NaN network_hosting NaN\n", + "4 c:10001 NaN games_video NaN" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_industry_geo.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "funding_probability_industry_geo_sum = funding_probability_industry_geo.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " category_code country_code object_id funding_total_usd\n", + "0 advertising ARE 14 2\n", + "1 advertising ARG 37 8\n", + "2 advertising AUS 83 10\n", + "3 advertising AUT 5 0\n", + "4 advertising AZE 1 1" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_industry_geo = funding_probability_industry_geo.groupby(['category_code', 'country_code']).count().reset_index()\n", + "funding_probability_industry_geo.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "funding_probability_industry_geo.rename(columns = {'object_id' : 'number', 'funding_total_usd' : 'number_funded'} , inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " category_code country_code funding_total_usd\n", + "0 advertising ARE 3950000.0\n", + "1 advertising ARG 7127394.0\n", + "2 advertising AUS 16331000.0\n", + "3 advertising AUT 0.0\n", + "4 advertising AZE 100000.0" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_industry_geo_sum = funding_probability_industry_geo_sum.groupby(['category_code', 'country_code']).sum().reset_index()\n", + "funding_probability_industry_geo_sum.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " category_code country_code number number_funded funding_total_usd\n", + "0 advertising ARE 14 2 3950000.0\n", + "1 advertising ARG 37 8 7127394.0\n", + "2 advertising AUS 83 10 16331000.0\n", + "3 advertising AUT 5 0 0.0\n", + "4 advertising AZE 1 1 100000.0" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_industry_geo_sum = pd.merge(funding_probability_industry_geo, funding_probability_industry_geo_sum)\n", + "funding_probability_industry_geo_sum.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "funding_probability_industry_geo_sum['average_funding'] = funding_probability_industry_geo_sum['funding_total_usd'] / funding_probability_industry_geo_sum['number_funded']" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "funding_probability_industry_geo_sum['percent_funded'] = funding_probability_industry_geo_sum['number_funded'] / funding_probability_industry_geo_sum['number']" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " category_code country_code number number_funded funding_total_usd \\\n", + "0 advertising ARE 14 2 3950000.0 \n", + "1 advertising ARG 37 8 7127394.0 \n", + "2 advertising AUS 83 10 16331000.0 \n", + "3 advertising AUT 5 0 0.0 \n", + "4 advertising AZE 1 1 100000.0 \n", + "\n", + " average_funding percent_funded \n", + "0 1975000.00 0.142857 \n", + "1 890924.25 0.216216 \n", + "2 1633100.00 0.120482 \n", + "3 NaN 0.000000 \n", + "4 100000.00 1.000000 " + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_probability_industry_geo_sum.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# funding_probability_industry_geo_sum.to_csv(r'data/funding_probability_industry_geo_sum.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### First: funding rounds in itself - assuming you get funding" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idfunding_round_idobject_idfunded_atfunding_round_typefunding_round_coderaised_amount_usdraised_amountraised_currency_codepre_money_valuation_usdpre_money_valuationpre_money_currency_codepost_money_valuation_usdpost_money_valuationpost_money_currency_codeparticipantsis_first_roundis_last_roundsource_urlsource_descriptioncreated_bycreated_atupdated_at
011c:42006-12-01series-bb8500000.08500000.0USD0.00.0NaN0.00.0NaN200http://www.marketingvox.com/archives/2006/12/2...NaNinitial-importer2007-07-04 04:52:572008-02-27 23:14:29
122c:52004-09-01angelangel500000.0500000.0USD0.00.0USD0.00.0USD201NaNNaNinitial-importer2007-05-27 06:08:182013-06-28 20:07:23
233c:52005-05-01series-aa12700000.012700000.0USD115000000.0115000000.0USD0.00.0USD300http://www.techcrunch.com/2007/11/02/jim-breye...Jim Breyer: Extra $500 Million Round For Faceb...initial-importer2007-05-27 06:09:102013-06-28 20:07:23
344c:52006-04-01series-bb27500000.027500000.0USD525000000.0525000000.0USD0.00.0USD400http://www.facebook.com/press/info.php?factsheetFacebook Fundinginitial-importer2007-05-27 06:09:362013-06-28 20:07:24
455c:72992006-05-01series-bb10500000.010500000.0USD0.00.0NaN0.00.0NaN200http://www.techcrunch.com/2006/05/14/photobuck...PhotoBucket Closes $10.5M From Trinity Venturesinitial-importer2007-05-29 11:05:592008-04-16 17:09:12
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" + ], + "text/plain": [ + " id funding_round_id object_id funded_at funding_round_type \\\n", + "0 1 1 c:4 2006-12-01 series-b \n", + "1 2 2 c:5 2004-09-01 angel \n", + "2 3 3 c:5 2005-05-01 series-a \n", + "3 4 4 c:5 2006-04-01 series-b \n", + "4 5 5 c:7299 2006-05-01 series-b \n", + "\n", + " funding_round_code raised_amount_usd raised_amount raised_currency_code \\\n", + "0 b 8500000.0 8500000.0 USD \n", + "1 angel 500000.0 500000.0 USD \n", + "2 a 12700000.0 12700000.0 USD \n", + "3 b 27500000.0 27500000.0 USD \n", + "4 b 10500000.0 10500000.0 USD \n", + "\n", + " pre_money_valuation_usd pre_money_valuation pre_money_currency_code \\\n", + "0 0.0 0.0 NaN \n", + "1 0.0 0.0 USD \n", + "2 115000000.0 115000000.0 USD \n", + "3 525000000.0 525000000.0 USD \n", + "4 0.0 0.0 NaN \n", + "\n", + " post_money_valuation_usd post_money_valuation post_money_currency_code \\\n", + "0 0.0 0.0 NaN \n", + "1 0.0 0.0 USD \n", + "2 0.0 0.0 USD \n", + "3 0.0 0.0 USD \n", + "4 0.0 0.0 NaN \n", + "\n", + " participants is_first_round is_last_round \\\n", + "0 2 0 0 \n", + "1 2 0 1 \n", + "2 3 0 0 \n", + "3 4 0 0 \n", + "4 2 0 0 \n", + "\n", + " source_url \\\n", + "0 http://www.marketingvox.com/archives/2006/12/2... \n", + "1 NaN \n", + "2 http://www.techcrunch.com/2007/11/02/jim-breye... \n", + "3 http://www.facebook.com/press/info.php?factsheet \n", + "4 http://www.techcrunch.com/2006/05/14/photobuck... \n", + "\n", + " source_description created_by \\\n", + "0 NaN initial-importer \n", + "1 NaN initial-importer \n", + "2 Jim Breyer: Extra $500 Million Round For Faceb... initial-importer \n", + "3 Facebook Funding initial-importer \n", + "4 PhotoBucket Closes $10.5M From Trinity Ventures initial-importer \n", + "\n", + " created_at updated_at \n", + "0 2007-07-04 04:52:57 2008-02-27 23:14:29 \n", + "1 2007-05-27 06:08:18 2013-06-28 20:07:23 \n", + "2 2007-05-27 06:09:10 2013-06-28 20:07:23 \n", + "3 2007-05-27 06:09:36 2013-06-28 20:07:24 \n", + "4 2007-05-29 11:05:59 2008-04-16 17:09:12 " + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_rounds = pd.read_csv(r'data/initial/funding_rounds.csv')\n", + "funding_rounds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " object_id funding_round_id funding_round_type funding_round_code \\\n", + "0 c:4 1 series-b b \n", + "1 c:5 2 angel angel \n", + "2 c:5 3 series-a a \n", + "3 c:5 4 series-b b \n", + "4 c:7299 5 series-b b \n", + "\n", + " raised_amount_usd pre_money_valuation_usd \n", + "0 8500000.0 0.0 \n", + "1 500000.0 0.0 \n", + "2 12700000.0 115000000.0 \n", + "3 27500000.0 525000000.0 \n", + "4 10500000.0 0.0 " + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_rounds_slimmed = funding_rounds[['object_id', 'funding_round_id', 'funding_round_type', 'funding_round_code', 'raised_amount_usd', 'pre_money_valuation_usd']]\n", + "funding_rounds_slimmed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4c:52.0angelangel500000.00.0socialUSA2.425700e+09
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" + ], + "text/plain": [ + " object_id funding_round_id funding_round_type funding_round_code \\\n", + "0 c:4 1.0 series-b b \n", + "1 c:4 85.0 series-a a \n", + "2 c:4 3503.0 series-c+ c \n", + "3 c:4 24136.0 series-c+ d \n", + "4 c:5 2.0 angel angel \n", + "\n", + " raised_amount_usd pre_money_valuation_usd category_code country_code \\\n", + "0 8500000.0 0.0 news USA \n", + "1 2800000.0 0.0 news USA \n", + "2 28700000.0 0.0 news USA \n", + "3 5000000.0 0.0 news USA \n", + "4 500000.0 0.0 social USA \n", + "\n", + " funding_total_usd \n", + "0 4.500000e+07 \n", + "1 4.500000e+07 \n", + "2 4.500000e+07 \n", + "3 4.500000e+07 \n", + "4 2.425700e+09 " + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "funding_rounds_merged = pd.merge(funding_rounds_slimmed, industry_country, on = 'object_id', how='outer')\n", + "funding_rounds_merged.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# total funding by industry nochmal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# total funding by country nochmal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# merge " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Second: Aquisitions following funding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# see funding at all" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " category_code number percent_funded percent_acquired percent_public\n", + "0 advertising 5979 0.180799 0.052183 0.004349\n", + "1 analytics 1019 0.609421 0.039254 0.002944\n", + "2 automotive 273 0.252747 0.018315 0.010989\n", + "3 biotech 4230 0.717730 0.097400 0.056028\n", + "4 cleantech 1862 0.519334 0.057465 0.024705" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acquisition_probability_industry_slimmed = acquisition_probability_industry[['category_code', 'object_id', 'percent_funded', 'percent_acquired', 'percent_public']]\n", + "acquisition_probability_industry_slimmed.rename(columns = {'object_id' : 'number'}, inplace = True)\n", + "acquisition_probability_industry_slimmed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "# acquisition_probability_industry_slimmed.to_csv(r'data/fund_acq_ipo_prob_ind.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# correlation amount funding and being acquired" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# filter by acquisition" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0object_idnamecategory_codestatusfounded_atclosed_atacquired_atcountry_codestate_codecityregionfunding_total_usdyear_foundedyear_closedmonth_closeddurationyear_acquiredmonth_acquiredt_unt_acqterm_codeprice_amountprice_currency_codeidipo_idvaluation_amountvaluation_currency_coderaised_amountraised_currency_codepublic_att_unt_public
00c:1Wetpaintweboperating2005-10-17NaNNaNUSAWASeattleSeattle39750000.02005.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaT
11c:10Flektorgames_videoacquiredNaTNaN2007-05-30USACACulver CityLos Angeles0.0NaNNaNNaNNaN2007.05.0NaNNaN20000000.0USDNaNNaNNaNNaNNaNNaNNaTNaT
22c:100Theregames_videoacquiredNaTNaN2005-05-29USACASan MateoSF Bay0.0NaNNaNNaNNaN2005.05.0NaNcash0.0USDNaNNaNNaNNaNNaNNaNNaTNaT
33c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaT
44c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaT
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" + ], + "text/plain": [ + " Unnamed: 0 object_id name category_code status \\\n", + "0 0 c:1 Wetpaint web operating \n", + "1 1 c:10 Flektor games_video acquired \n", + "2 2 c:100 There games_video acquired \n", + "3 3 c:10000 MYWEBBO network_hosting operating \n", + "4 4 c:10001 THE Movie Streamer games_video operating \n", + "\n", + " founded_at closed_at acquired_at country_code state_code city \\\n", + "0 2005-10-17 NaN NaN USA WA Seattle \n", + "1 NaT NaN 2007-05-30 USA CA Culver City \n", + "2 NaT NaN 2005-05-29 USA CA San Mateo \n", + "3 2008-07-26 NaN NaN NaN NaN NaN \n", + "4 2008-07-26 NaN NaN NaN NaN NaN \n", + "\n", + " region funding_total_usd year_founded year_closed month_closed \\\n", + "0 Seattle 39750000.0 2005.0 NaN NaN \n", + "1 Los Angeles 0.0 NaN NaN NaN \n", + "2 SF Bay 0.0 NaN NaN NaN \n", + "3 unknown 0.0 2008.0 NaN NaN \n", + "4 unknown 0.0 2008.0 NaN NaN \n", + "\n", + " duration year_acquired month_acquired t_unt_acq term_code price_amount \\\n", + "0 NaN NaN NaN NaN NaN NaN \n", + "1 NaN 2007.0 5.0 NaN NaN 20000000.0 \n", + "2 NaN 2005.0 5.0 NaN cash 0.0 \n", + "3 NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN \n", + "\n", + " price_currency_code id ipo_id valuation_amount valuation_currency_code \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 USD NaN NaN NaN NaN \n", + "2 USD NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " raised_amount raised_currency_code public_at t_unt_public \n", + "0 NaN NaN NaT NaT \n", + "1 NaN NaN NaT NaT \n", + "2 NaN NaN NaT NaT \n", + "3 NaN NaN NaT NaT \n", + "4 NaN NaN NaT NaT " + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "success_master_merged_slimmed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "192719\n", + "9060\n" + ] + }, + { + "data": { + "text/html": [ + "
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Unnamed: 0object_idnamecategory_codestatusfounded_atclosed_atacquired_atcountry_codestate_codecityregionfunding_total_usdyear_foundedyear_closedmonth_closeddurationyear_acquiredmonth_acquiredt_unt_acqterm_codeprice_amountprice_currency_codeidipo_idvaluation_amountvaluation_currency_coderaised_amountraised_currency_codepublic_att_unt_public
11c:10Flektorgames_videoacquiredNaTNaN2007-05-30USACACulver CityLos Angeles0.0NaNNaNNaNNaN2007.05.0NaNNaN20000000.0USDNaNNaNNaNNaNNaNNaNNaTNaT
22c:100Theregames_videoacquiredNaTNaN2005-05-29USACASan MateoSF Bay0.0NaNNaNNaNNaN2005.05.0NaNcash0.0USDNaNNaNNaNNaNNaNNaNNaTNaT
1313c:1001FriendFeedwebacquired2007-10-01NaN2009-08-10USACAMountain ViewSF Bay5000000.02007.0NaNNaNNaN2009.08.0679 days 00:00:00.000000000cash_and_stock47500000.0USDNaNNaNNaNNaNNaNNaNNaTNaT
1818c:10014Mobclixmobileacquired2008-03-01NaN2010-09-30USACAPalo AltoSF Bay0.02008.0NaNNaNNaN2010.09.0943 days 00:00:00.000000000NaN0.0USDNaNNaNNaNNaNNaNNaNNaTNaT
4242c:100265Coastal Supply CompanyNaNacquiredNaTNaN2011-09-06NaNNaNNaNunknown0.0NaNNaNNaNNaN2011.09.0NaNNaN0.0USDNaNNaNNaNNaNNaNNaNNaTNaT
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" + ], + "text/plain": [ + " Unnamed: 0 object_id name category_code status \\\n", + "1 1 c:10 Flektor games_video acquired \n", + "2 2 c:100 There games_video acquired \n", + "13 13 c:1001 FriendFeed web acquired \n", + "18 18 c:10014 Mobclix mobile acquired \n", + "42 42 c:100265 Coastal Supply Company NaN acquired \n", + "\n", + " founded_at closed_at acquired_at country_code state_code city \\\n", + "1 NaT NaN 2007-05-30 USA CA Culver City \n", + "2 NaT NaN 2005-05-29 USA CA San Mateo \n", + "13 2007-10-01 NaN 2009-08-10 USA CA Mountain View \n", + "18 2008-03-01 NaN 2010-09-30 USA CA Palo Alto \n", + "42 NaT NaN 2011-09-06 NaN NaN NaN \n", + "\n", + " region funding_total_usd year_founded year_closed month_closed \\\n", + "1 Los Angeles 0.0 NaN NaN NaN \n", + "2 SF Bay 0.0 NaN NaN NaN \n", + "13 SF Bay 5000000.0 2007.0 NaN NaN \n", + "18 SF Bay 0.0 2008.0 NaN NaN \n", + "42 unknown 0.0 NaN NaN NaN \n", + "\n", + " duration year_acquired month_acquired t_unt_acq \\\n", + "1 NaN 2007.0 5.0 NaN \n", + "2 NaN 2005.0 5.0 NaN \n", + "13 NaN 2009.0 8.0 679 days 00:00:00.000000000 \n", + "18 NaN 2010.0 9.0 943 days 00:00:00.000000000 \n", + "42 NaN 2011.0 9.0 NaN \n", + "\n", + " term_code price_amount price_currency_code id ipo_id \\\n", + "1 NaN 20000000.0 USD NaN NaN \n", + "2 cash 0.0 USD NaN NaN \n", + "13 cash_and_stock 47500000.0 USD NaN NaN \n", + "18 NaN 0.0 USD NaN NaN \n", + "42 NaN 0.0 USD NaN NaN \n", + "\n", + " valuation_amount valuation_currency_code raised_amount \\\n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "18 NaN NaN NaN \n", + "42 NaN NaN NaN \n", + "\n", + " raised_currency_code public_at t_unt_public \n", + "1 NaN NaT NaT \n", + "2 NaN NaT NaT \n", + "13 NaN NaT NaT \n", + "18 NaN NaT NaT \n", + "42 NaN NaT NaT " + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acquired_fund_rel = success_master_merged_slimmed.copy()\n", + "idx = acquired_fund_rel[acquired_fund_rel['status'] != 'acquired'].index\n", + "acquired_fund_rel.drop(idx , inplace=True)\n", + "print(len(success_master_merged_slimmed))\n", + "print(len(acquired_fund_rel))\n", + "acquired_fund_rel.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "acquired_fund_rel_slimmed = acquired_fund_rel[['object_id', 'category_code', 'country_code', 'funding_total_usd', 'founded_at', 'acquired_at', 'price_amount']]" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], + "source": [ + "acquired_fund_rel_slimmed['funding_total_usd'] = acquired_fund_rel_slimmed['funding_total_usd'].replace(0, np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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object_idcategory_codecountry_codefunding_total_usdfounded_atacquired_atprice_amount
1c:10games_videoUSANaNNaT2007-05-3020000000.0
2c:100games_videoUSANaNNaT2005-05-290.0
13c:1001webUSA5000000.02007-10-012009-08-1047500000.0
18c:10014mobileUSANaN2008-03-012010-09-300.0
42c:100265NaNNaNNaNNaT2011-09-060.0
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" + ], + "text/plain": [ + " object_id category_code country_code funding_total_usd founded_at \\\n", + "1 c:10 games_video USA NaN NaT \n", + "2 c:100 games_video USA NaN NaT \n", + "13 c:1001 web USA 5000000.0 2007-10-01 \n", + "18 c:10014 mobile USA NaN 2008-03-01 \n", + "42 c:100265 NaN NaN NaN NaT \n", + "\n", + " acquired_at price_amount \n", + "1 2007-05-30 20000000.0 \n", + "2 2005-05-29 0.0 \n", + "13 2009-08-10 47500000.0 \n", + "18 2010-09-30 0.0 \n", + "42 2011-09-06 0.0 " + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acquired_fund_rel_slimmed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "#grouped by industry\n", + "acq_fund_rel_ind = acquired_fund_rel_slimmed.groupby(['category_code']).count().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "acq_fund_rel_ind.rename(columns = {'object_id' : 'number_byind', 'funding_total_usd' : 'number_funded', 'price_amount':'number_acq'} , inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codenumber_byindnumber_fundednumber_acq
0advertising312118312
1analytics403540
2automotive535
3biotech412158412
4cleantech10755107
\n", + "
" + ], + "text/plain": [ + " category_code number_byind number_funded number_acq\n", + "0 advertising 312 118 312\n", + "1 analytics 40 35 40\n", + "2 automotive 5 3 5\n", + "3 biotech 412 158 412\n", + "4 cleantech 107 55 107" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acq_fund_rel_ind = acq_fund_rel_ind[['category_code', 'number_byind', 'number_funded', 'number_acq']]\n", + "acq_fund_rel_ind.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codefunding_total_usdprice_amount
0advertising1.849381e+092.147999e+10
1analytics3.924261e+085.430000e+08
2automotive4.603000e+077.643000e+09
3biotech6.124936e+091.391689e+11
4cleantech4.273230e+091.461245e+10
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" + ], + "text/plain": [ + " category_code funding_total_usd price_amount\n", + "0 advertising 1.849381e+09 2.147999e+10\n", + "1 analytics 3.924261e+08 5.430000e+08\n", + "2 automotive 4.603000e+07 7.643000e+09\n", + "3 biotech 6.124936e+09 1.391689e+11\n", + "4 cleantech 4.273230e+09 1.461245e+10" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sum\n", + "acq_fund_rel_ind_sum = acquired_fund_rel_slimmed.groupby(['category_code']).sum().reset_index()\n", + "acq_fund_rel_ind_sum.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codenumberpercent_fundedpercent_acquiredpercent_public
0advertising59790.1807990.0521830.004349
1analytics10190.6094210.0392540.002944
2automotive2730.2527470.0183150.010989
3biotech42300.7177300.0974000.056028
4cleantech18620.5193340.0574650.024705
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" + ], + "text/plain": [ + " category_code number percent_funded percent_acquired percent_public\n", + "0 advertising 5979 0.180799 0.052183 0.004349\n", + "1 analytics 1019 0.609421 0.039254 0.002944\n", + "2 automotive 273 0.252747 0.018315 0.010989\n", + "3 biotech 4230 0.717730 0.097400 0.056028\n", + "4 cleantech 1862 0.519334 0.057465 0.024705" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acquisition_probability_industry_slimmed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [], + "source": [ + "acq_fund_ind = pd.merge(acq_fund_rel_ind, acq_fund_rel_ind_sum, on = 'category_code')" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "acq_fund_ind['number'] = acquisition_probability_industry_slimmed['number']\n", + "acq_fund_ind['percent_funded_total'] = acquisition_probability_industry_slimmed['percent_funded']\n", + "acq_fund_ind['percent_acquired_total'] = acquisition_probability_industry_slimmed['percent_acquired']\n", + "\n", + "acq_fund_ind['percent_public'] = acquisition_probability_industry_slimmed['percent_public']" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [], + "source": [ + "acq_fund_ind['percent_funded&acq'] = acq_fund_ind['number_funded'] / acq_fund_ind['number'] \n", + "acq_fund_ind['percent_acquired'] = acq_fund_ind['number_acq'] / acq_fund_ind['number'] \n", + "acq_fund_ind['percent_acq_fund'] = acq_fund_ind['number_funded'] / acq_fund_ind['number_acq'] \n", + "acq_fund_ind['avg_funding_acq'] = acq_fund_ind['funding_total_usd'] / acq_fund_ind['number_funded']\n", + "acq_fund_ind['avg_price_acq'] = acq_fund_ind['price_amount'] / acq_fund_ind['number_acq']\n", + "acq_fund_ind['nr_total_funded'] = acquisition_probability_industry_slimmed['number'] * acquisition_probability_industry_slimmed['percent_funded']\n", + "acq_fund_ind['percent_of_fund_acq'] = acq_fund_ind['number_acq'] / acq_fund_ind['nr_total_funded']\n", + "acq_fund_ind['increase_prob_acq_fund'] = acq_fund_ind['percent_of_fund_acq'] - acq_fund_ind['percent_acq_fund']" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codenumber_byindnumber_fundednumber_acqfunding_total_usdprice_amountnumberpercent_funded_totalpercent_acquired_totalpercent_publicpercent_funded&acqpercent_acquiredpercent_acq_fundavg_funding_acqavg_price_acqnr_total_fundedpercent_of_fund_acqincrease_prob_acq_fund
0advertising3121183121.849381e+092.147999e+1059790.1807990.0521830.0043490.0197360.0521830.3782051.567272e+076.884612e+071081.00.2886220.089583
1analytics4035403.924261e+085.430000e+0810190.6094210.0392540.0029440.0343470.0392540.8750001.121218e+071.357500e+07621.00.0644120.810588
2automotive5354.603000e+077.643000e+092730.2527470.0183150.0109890.0109890.0183150.6000001.534333e+071.528600e+0969.00.0724640.527536
3biotech4121584126.124936e+091.391689e+1142300.7177300.0974000.0560280.0373520.0974000.3834953.876542e+073.377885e+083036.00.1357050.247790
4cleantech107551074.273230e+091.461245e+1018620.5193340.0574650.0247050.0295380.0574650.5140197.769510e+071.365650e+08967.00.1106510.403367
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" + ], + "text/plain": [ + " category_code number_byind number_funded number_acq funding_total_usd \\\n", + "0 advertising 312 118 312 1.849381e+09 \n", + "1 analytics 40 35 40 3.924261e+08 \n", + "2 automotive 5 3 5 4.603000e+07 \n", + "3 biotech 412 158 412 6.124936e+09 \n", + "4 cleantech 107 55 107 4.273230e+09 \n", + "\n", + " price_amount number percent_funded_total percent_acquired_total \\\n", + "0 2.147999e+10 5979 0.180799 0.052183 \n", + "1 5.430000e+08 1019 0.609421 0.039254 \n", + "2 7.643000e+09 273 0.252747 0.018315 \n", + "3 1.391689e+11 4230 0.717730 0.097400 \n", + "4 1.461245e+10 1862 0.519334 0.057465 \n", + "\n", + " percent_public percent_funded&acq percent_acquired percent_acq_fund \\\n", + "0 0.004349 0.019736 0.052183 0.378205 \n", + "1 0.002944 0.034347 0.039254 0.875000 \n", + "2 0.010989 0.010989 0.018315 0.600000 \n", + "3 0.056028 0.037352 0.097400 0.383495 \n", + "4 0.024705 0.029538 0.057465 0.514019 \n", + "\n", + " avg_funding_acq avg_price_acq nr_total_funded percent_of_fund_acq \\\n", + "0 1.567272e+07 6.884612e+07 1081.0 0.288622 \n", + "1 1.121218e+07 1.357500e+07 621.0 0.064412 \n", + "2 1.534333e+07 1.528600e+09 69.0 0.072464 \n", + "3 3.876542e+07 3.377885e+08 3036.0 0.135705 \n", + "4 7.769510e+07 1.365650e+08 967.0 0.110651 \n", + "\n", + " increase_prob_acq_fund \n", + "0 0.089583 \n", + "1 0.810588 \n", + "2 0.527536 \n", + "3 0.247790 \n", + "4 0.403367 " + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acq_fund_ind.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [], + "source": [ + "acq_fund_ind.to_csv(r'data/funding_acquisition_prob.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slope: 554174783.0721252\n", + "Intercept: 991433602.7657511\n", + "rvalue: 0.295620385276551\n", + "pvalue: 0.06402363322474158\n", + "stderr: 290511185.48998517\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set(style=\"white\", context=\"talk\")\n", + "fig, ax = plt.subplots(figsize=(16,10)) \n", + "x = acq_fund_ind['percent_acquired']\n", + "y = acq_fund_ind['funding_total_usd']\n", + "ax = sns.regplot(x, y, \n", + " data = acq_fund_ind, scatter_kws = {\"s\": 250},\n", + " marker = \"o\", color = 'g')\n", + "ax.set(xlabel = \"Percentage of Companies acquired in an industry (%)\", ylabel = \"Total funding amount by industry\")\n", + "result = stats.linregress(x, y)\n", + "print(\"Slope: \", result.slope)\n", + "print(\"Intercept: \", result.intercept)\n", + "print(\"rvalue: \", result.rvalue)\n", + "print(\"pvalue: \", result.pvalue)\n", + "print(\"stderr: \", result.stderr)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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country_codeobject_idcategory_codefunding_total_usdfounded_atacquired_atprice_amount
0AIA100011
1ANT110111
2ARE220122
3ARG1211491212
4AUS78545297878
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" + ], + "text/plain": [ + " country_code object_id category_code funding_total_usd founded_at \\\n", + "0 AIA 1 0 0 0 \n", + "1 ANT 1 1 0 1 \n", + "2 ARE 2 2 0 1 \n", + "3 ARG 12 11 4 9 \n", + "4 AUS 78 54 5 29 \n", + "\n", + " acquired_at price_amount \n", + "0 1 1 \n", + "1 1 1 \n", + "2 2 2 \n", + "3 12 12 \n", + "4 78 78 " + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# grouped by country\n", + "acq_fund_rel_geo = acquired_fund_rel_slimmed.groupby(['country_code']).count().reset_index()\n", + "acq_fund_rel_geo.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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country_codefunding_total_usdprice_amount
0AIA0.00.000000e+00
1ANT0.06.006000e+07
2ARE0.06.000000e+05
3ARG26240000.05.900000e+07
4AUS15150000.01.844808e+09
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" + ], + "text/plain": [ + " country_code funding_total_usd price_amount\n", + "0 AIA 0.0 0.000000e+00\n", + "1 ANT 0.0 6.006000e+07\n", + "2 ARE 0.0 6.000000e+05\n", + "3 ARG 26240000.0 5.900000e+07\n", + "4 AUS 15150000.0 1.844808e+09" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acq_fund_rel_geo_sum = acquired_fund_rel_slimmed.groupby(['country_code']).sum().reset_index()\n", + "acq_fund_rel_geo_sum.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# grouped by both\n", + "funding_probability_industry = funding_probability_industry.groupby(['category_code']).count().reset_index()\n", + "funding_probability_industry.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# filter by IPO" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0object_idnamecategory_codestatusfounded_atclosed_atacquired_atcountry_codestate_codecityregionfunding_total_usdyear_foundedyear_closedmonth_closeddurationyear_acquiredmonth_acquiredt_unt_acqterm_codeprice_amountprice_currency_codeidipo_idvaluation_amountvaluation_currency_coderaised_amountraised_currency_codepublic_att_unt_public
00c:1Wetpaintweboperating2005-10-17NaNNaNUSAWASeattleSeattle39750000.02005.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaT
11c:10Flektorgames_videoacquiredNaTNaN2007-05-30USACACulver CityLos Angeles0.0NaNNaNNaNNaN2007.05.0NaNNaN20000000.0USDNaNNaNNaNNaNNaNNaNNaTNaT
22c:100Theregames_videoacquiredNaTNaN2005-05-29USACASan MateoSF Bay0.0NaNNaNNaNNaN2005.05.0NaNcash0.0USDNaNNaNNaNNaNNaNNaNNaTNaT
33c:10000MYWEBBOnetwork_hostingoperating2008-07-26NaNNaNNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaT
44c:10001THE Movie Streamergames_videooperating2008-07-26NaNNaNNaNNaNNaNunknown0.02008.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaT
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" + ], + "text/plain": [ + " Unnamed: 0 object_id name category_code status \\\n", + "0 0 c:1 Wetpaint web operating \n", + "1 1 c:10 Flektor games_video acquired \n", + "2 2 c:100 There games_video acquired \n", + "3 3 c:10000 MYWEBBO network_hosting operating \n", + "4 4 c:10001 THE Movie Streamer games_video operating \n", + "\n", + " founded_at closed_at acquired_at country_code state_code city \\\n", + "0 2005-10-17 NaN NaN USA WA Seattle \n", + "1 NaT NaN 2007-05-30 USA CA Culver City \n", + "2 NaT NaN 2005-05-29 USA CA San Mateo \n", + "3 2008-07-26 NaN NaN NaN NaN NaN \n", + "4 2008-07-26 NaN NaN NaN NaN NaN \n", + "\n", + " region funding_total_usd year_founded year_closed month_closed \\\n", + "0 Seattle 39750000.0 2005.0 NaN NaN \n", + "1 Los Angeles 0.0 NaN NaN NaN \n", + "2 SF Bay 0.0 NaN NaN NaN \n", + "3 unknown 0.0 2008.0 NaN NaN \n", + "4 unknown 0.0 2008.0 NaN NaN \n", + "\n", + " duration year_acquired month_acquired t_unt_acq term_code price_amount \\\n", + "0 NaN NaN NaN NaN NaN NaN \n", + "1 NaN 2007.0 5.0 NaN NaN 20000000.0 \n", + "2 NaN 2005.0 5.0 NaN cash 0.0 \n", + "3 NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN \n", + "\n", + " price_currency_code id ipo_id valuation_amount valuation_currency_code \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 USD NaN NaN NaN NaN \n", + "2 USD NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " raised_amount raised_currency_code public_at t_unt_public \n", + "0 NaN NaN NaT NaT \n", + "1 NaN NaN NaT NaT \n", + "2 NaN NaN NaT NaT \n", + "3 NaN NaN NaT NaT \n", + "4 NaN NaN NaT NaT " + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "success_master_merged_slimmed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['operating', 'acquired', 'closed', 'ipo', nan], dtype=object)" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "success_master_merged_slimmed.status.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ipo_probability_ind = success_master_merged_slimmed.copy()\n", + "idx = ipo_probability_ind['status']\n", + "\n", + "idx = comps_closed_ann[comps_closed_ann['year_closed'] < 1998].index\n", + "comps_closed_ann.drop(idx , inplace=True)\n", + "comps_closed_ann.year_closed.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [], + "source": [ + "ipo_probability_ind = success_master_merged_slimmed.copy()\n", + "ipo_probability_ind = ipo_probability_ind[['status', 'object_id', 'category_code', 'country_code', 'funding_total_usd', 'ipo_id', 'valuation_amount', 'raised_amount', 't_unt_public']]" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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category_codestatusobject_idfunding_total_usdvaluation_amountraised_amount
0advertisingacquired3121.849381e+090.000000e+000.0
1advertisingclosed952.386023e+080.000000e+000.0
2advertisingipo231.120431e+091.673322e+09611000000.0
3advertisingoperating55499.107126e+090.000000e+000.0
4analyticsacquired403.924261e+080.000000e+000.0
.....................
152traveloperating8711.845505e+090.000000e+000.0
153webacquired9883.470549e+097.341540e+09417000000.0
154webclosed6118.806721e+080.000000e+000.0
155webipo392.264049e+092.568000e+10127000000.0
156weboperating133951.138250e+100.000000e+000.0
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157 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " category_code status object_id funding_total_usd valuation_amount \\\n", + "0 advertising acquired 312 1.849381e+09 0.000000e+00 \n", + "1 advertising closed 95 2.386023e+08 0.000000e+00 \n", + "2 advertising ipo 23 1.120431e+09 1.673322e+09 \n", + "3 advertising operating 5549 9.107126e+09 0.000000e+00 \n", + "4 analytics acquired 40 3.924261e+08 0.000000e+00 \n", + ".. ... ... ... ... ... \n", + "152 travel operating 871 1.845505e+09 0.000000e+00 \n", + "153 web acquired 988 3.470549e+09 7.341540e+09 \n", + "154 web closed 611 8.806721e+08 0.000000e+00 \n", + "155 web ipo 39 2.264049e+09 2.568000e+10 \n", + "156 web operating 13395 1.138250e+10 0.000000e+00 \n", + "\n", + " raised_amount \n", + "0 0.0 \n", + "1 0.0 \n", + "2 611000000.0 \n", + "3 0.0 \n", + "4 0.0 \n", + ".. ... \n", + "152 0.0 \n", + "153 417000000.0 \n", + "154 0.0 \n", + "155 127000000.0 \n", + "156 0.0 \n", + "\n", + "[157 rows x 6 columns]" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ipo_probability_industry = ipo_probability_ind.groupby(['category_code', 'status']).agg({'object_id' : 'count', 'funding_total_usd': 'sum', 'valuation_amount': 'sum', 'raised_amount':'sum'})\n", + "ipo_probability_industry.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ipo_probability_industry['perc_fund_ipo'] = ipo_probability_industry['perc_fund_ipo']\n", + "ipo_probability_industry['ave_fund_status']\n", + "ipo_probability_industry['average_ipo_raised']\n", + "ipo_probability_industry['average_ipo_valuation']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Project 5 - Unicorns.ipynb b/your-project/code/Project 5 - Unicorns.ipynb new file mode 100644 index 0000000..e075db0 --- /dev/null +++ b/your-project/code/Project 5 - Unicorns.ipynb @@ -0,0 +1,981 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unicorns" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CompanyValuation ($B)Date JoinedCountryIndustrySelect Investors
0Toutiao(Bytedance)75.004/7/2017ChinaArtificialintelligenceSequoiaCapitalChina,SIGAsiaInvestments,SinaWei...
1DidiChuxing56.0012/31/2014ChinaAuto&transportationMatrixPartners,TigerGlobalManagement,SoftbankC...
2Stripe36.001/23/2014UnitedStatesFintechKhoslaVentures,LowercaseCapital,capitalG
3SpaceX33.3012/1/2012UnitedStatesOtherFoundersFund,DraperFisherJurvetson,RothenbergV...
4Airbnb18.007/26/2011UnitedStatesTravelGeneralCatalystPartners,AndreessenHorowitz,ENI...
5Kuaishou18.001/1/2015ChinaMobile&telecommunicationsMorningsideVentureCapital,SequoiaCapital,Baidu
6One97Communications16.005/12/2015IndiaFintechIntelCapital,SapphireVentures,AlibabaGroup
7DJIInnovations15.005/6/2015ChinaHardwareAccelPartners,SequoiaCapital
8EpicGames15.0010/26/2018UnitedStatesOtherTencentHoldings,KKR,SmashVentures
9Grab14.3012/4/2014SingaporeAuto&transportationGGVCapital,VertexVentureHoldings,SoftbankGroup
10BeikeZhaofang14.007/18/2019ChinaInternetsoftware&servicesTencentHoldings,HillhouseCapitalManagement,Sou...
11DoorDash12.603/1/2018UnitedStatesSupplychain,logistics,&deliverySoftbankGroup,SequoiaCapital,KhoslaVentures
12SnowflakeComputing12.401/25/2018UnitedStatesDatamanagement&analyticsRedpointVentures,IconiqCapital,MadronaVentureG...
13PalantirTechnologies12.185/5/2011UnitedStatesDatamanagement&analyticsRREVentures,FoundersFund,In-Q-Tel
14JUULLabs12.0012/20/2017UnitedStatesConsumer&retailTigerGlobalManagement
15Samumed12.008/6/2018UnitedStatesHealthVickersVenturePartners,IKEAGreenTech
16BitmainTechnologies12.007/6/2018ChinaHardwareCoatueManagement,SequoiaCapitalChina,IDGCapital
17Wish11.205/18/2015UnitedStatesE-commerce&direct-to-consumerFoundersFund,GGVCapital,DigitalSkyTechnologies
18GlobalSwitch11.0812/22/2016UnitedKingdomHardwareAviationIndustryCorporationofChina,EssenceFina...
19Ripple10.0012/20/2019UnitedStatesFintechIDGCapital,Venture51,LightspeedVenturePartners
20Go-Jek10.008/4/2016IndonesiaSupplychain,logistics,&deliveryFormationGroup,SequoiaCapitalIndia,WarburgPincus
21OyoRooms10.009/25/2018IndiaTravelSoftBankGroup,SequoiaCapitalIndia,LightspeedIn...
22Nubank10.003/1/2018BrazilFintechSequoiaCapital,Redpointe.ventures,KaszekVentures
23Guazi(Chehaoduo)9.003/12/2016ChinaE-commerce&direct-to-consumerSequoiaCapitalChina,GXCapital
24Coupang9.005/28/2014SouthKoreaE-commerce&direct-to-consumerSequoiaCapital,FounderCollective,WellingtonMan...
25Robinhood8.004/26/2017UnitedStatesFintechGoogleVentures,AndreessenHorowitz,DSTGlobal
26Coinbase8.008/10/2017UnitedStatesFintechYCombinator,UnionSquareVentures,DFJGrowth
27BYJU'S8.007/25/2017IndiaEdtechTencentHoldings,LightspeedIndiaPartners,Sequoi...
28Yuanfudao7.805/31/2017ChinaEdtechTencentHoldings,WarbugPincus,IDGCapital
29Instacart7.6012/30/2014UnitedStatesSupplychain,logistics,&deliveryKhoslaVentures,KleinerPerkinsCaufield&Byers,Co...
30SenseTime7.507/11/2017ChinaArtificialintelligenceStarVC,IDGCapital,InforeCapital,AlibabaGroup
31Snapdeal7.005/21/2014IndiaE-commerce&direct-to-consumerSoftBankGroup,Blackrock,AlibabaGroup
32RoivantSciences7.0011/13/2018UnitedStatesHealthSoftBankGroup,FoundersFund
33Tokopedia7.0012/12/2018IndonesiaE-commerce&direct-to-consumerSoftBankGroup,AlibabaGroup,SequoiaCapitalIndia
34ArgoAI7.0007/12/2019UnitedStatesArtificialintelligenceVolkswagenGroup,FordAutonomousVehicles
35AutomationAnywhere6.807/2/2018UnitedStatesArtificialintelligenceGeneralAtlantic,GoldmanSachs,NewEnterpriseAsso...
36Tanium6.703/31/2015UnitedStatesCybersecurityAndreessenHorowitz,Nor-CalInvest,TPGGrowth
37Ziroom6.601/17/2018ChinaE-commerce&direct-to-consumerSequoiaCapitalChina,WarburgPincus,GeneralCatalyst
38Compass6.408/31/2016UnitedStatesE-commerce&direct-to-consumerFoundersFund,ThriveCapital,WellingtonManagement
39UiPath6.403/2/2018UnitedStatesArtificialintelligenceAccel,capitalG,EarlybridVentureCapital,Seedcamp
40OlaCabs6.3210/27/2014IndiaAuto&transportationAccelPartners,SoftBankGroup,SequoiaCapital
41MagicLeap6.3010/21/2014UnitedStatesHardwareObviousVentures,QualcommVentures,AndreessenHor...
42SamsaraNetworks6.303/22/2018UnitedStatesHardwareAndreessenHorowitz,GeneralCatalyst
43Databricks6.202/5/2019UnitedStatesDatamanagement&analyticsAndreessenHorowitz,NewEnterpriseAssociates,Bat...
44UnityTechnologies6.007/13/2016UnitedStatesOtherSequoiaCapital,iGlobePartners,DFJGrowth
45ManbangGroup6.004/24/2018ChinaSupplychain,logistics,&deliverySoftbankGroup,CapitalG
46Chime5.803/5/2019UnitedStatesFintechForerunnerVentures,CrosslinkCapital,Homebrew
47Lianjia(Homelink)5.804/8/2016ChinaE-commerce&direct-to-consumerTencent,Baidu,HuashengCapital
48EasyHome5.702/12/2018ChinaConsumer&retailAlibabaGroup,BoyuCapital,BoruiCapital
49ViceMedia5.708/17/2013UnitedStatesInternetsoftware&servicesTechnologyCrossoverVentures,A&ETelevisionNetworks
\n", + "
" + ], + "text/plain": [ + " Company Valuation ($B) Date Joined Country \\\n", + "0 Toutiao(Bytedance) 75.00 4/7/2017 China \n", + "1 DidiChuxing 56.00 12/31/2014 China \n", + "2 Stripe 36.00 1/23/2014 UnitedStates \n", + "3 SpaceX 33.30 12/1/2012 UnitedStates \n", + "4 Airbnb 18.00 7/26/2011 UnitedStates \n", + "5 Kuaishou 18.00 1/1/2015 China \n", + "6 One97Communications 16.00 5/12/2015 India \n", + "7 DJIInnovations 15.00 5/6/2015 China \n", + "8 EpicGames 15.00 10/26/2018 UnitedStates \n", + "9 Grab 14.30 12/4/2014 Singapore \n", + "10 BeikeZhaofang 14.00 7/18/2019 China \n", + "11 DoorDash 12.60 3/1/2018 UnitedStates \n", + "12 SnowflakeComputing 12.40 1/25/2018 UnitedStates \n", + "13 PalantirTechnologies 12.18 5/5/2011 UnitedStates \n", + "14 JUULLabs 12.00 12/20/2017 UnitedStates \n", + "15 Samumed 12.00 8/6/2018 UnitedStates \n", + "16 BitmainTechnologies 12.00 7/6/2018 China \n", + "17 Wish 11.20 5/18/2015 UnitedStates \n", + "18 GlobalSwitch 11.08 12/22/2016 UnitedKingdom \n", + "19 Ripple 10.00 12/20/2019 UnitedStates \n", + "20 Go-Jek 10.00 8/4/2016 Indonesia \n", + "21 OyoRooms 10.00 9/25/2018 India \n", + "22 Nubank 10.00 3/1/2018 Brazil \n", + "23 Guazi(Chehaoduo) 9.00 3/12/2016 China \n", + "24 Coupang 9.00 5/28/2014 SouthKorea \n", + "25 Robinhood 8.00 4/26/2017 UnitedStates \n", + "26 Coinbase 8.00 8/10/2017 UnitedStates \n", + "27 BYJU'S 8.00 7/25/2017 India \n", + "28 Yuanfudao 7.80 5/31/2017 China \n", + "29 Instacart 7.60 12/30/2014 UnitedStates \n", + "30 SenseTime 7.50 7/11/2017 China \n", + "31 Snapdeal 7.00 5/21/2014 India \n", + "32 RoivantSciences 7.00 11/13/2018 UnitedStates \n", + "33 Tokopedia 7.00 12/12/2018 Indonesia \n", + "34 ArgoAI 7.00 07/12/2019 UnitedStates \n", + "35 AutomationAnywhere 6.80 7/2/2018 UnitedStates \n", + "36 Tanium 6.70 3/31/2015 UnitedStates \n", + "37 Ziroom 6.60 1/17/2018 China \n", + "38 Compass 6.40 8/31/2016 UnitedStates \n", + "39 UiPath 6.40 3/2/2018 UnitedStates \n", + "40 OlaCabs 6.32 10/27/2014 India \n", + "41 MagicLeap 6.30 10/21/2014 UnitedStates \n", + "42 SamsaraNetworks 6.30 3/22/2018 UnitedStates \n", + "43 Databricks 6.20 2/5/2019 UnitedStates \n", + "44 UnityTechnologies 6.00 7/13/2016 UnitedStates \n", + "45 ManbangGroup 6.00 4/24/2018 China \n", + "46 Chime 5.80 3/5/2019 UnitedStates \n", + "47 Lianjia(Homelink) 5.80 4/8/2016 China \n", + "48 EasyHome 5.70 2/12/2018 China \n", + "49 ViceMedia 5.70 8/17/2013 UnitedStates \n", + "\n", + " Industry \\\n", + "0 Artificialintelligence \n", + "1 Auto&transportation \n", + "2 Fintech \n", + "3 Other \n", + "4 Travel \n", + "5 Mobile&telecommunications \n", + "6 Fintech \n", + "7 Hardware \n", + "8 Other \n", + "9 Auto&transportation \n", + "10 Internetsoftware&services \n", + "11 Supplychain,logistics,&delivery \n", + "12 Datamanagement&analytics \n", + "13 Datamanagement&analytics \n", + "14 Consumer&retail \n", + "15 Health \n", + "16 Hardware \n", + "17 E-commerce&direct-to-consumer \n", + "18 Hardware \n", + "19 Fintech \n", + "20 Supplychain,logistics,&delivery \n", + "21 Travel \n", + "22 Fintech \n", + "23 E-commerce&direct-to-consumer \n", + "24 E-commerce&direct-to-consumer \n", + "25 Fintech \n", + "26 Fintech \n", + "27 Edtech \n", + "28 Edtech \n", + "29 Supplychain,logistics,&delivery \n", + "30 Artificialintelligence \n", + "31 E-commerce&direct-to-consumer \n", + "32 Health \n", + "33 E-commerce&direct-to-consumer \n", + "34 Artificialintelligence \n", + "35 Artificialintelligence \n", + "36 Cybersecurity \n", + "37 E-commerce&direct-to-consumer \n", + "38 E-commerce&direct-to-consumer \n", + "39 Artificialintelligence \n", + "40 Auto&transportation \n", + "41 Hardware \n", + "42 Hardware \n", + "43 Datamanagement&analytics \n", + "44 Other \n", + "45 Supplychain,logistics,&delivery \n", + "46 Fintech \n", + "47 E-commerce&direct-to-consumer \n", + "48 Consumer&retail \n", + "49 Internetsoftware&services \n", + "\n", + " Select Investors \n", + "0 SequoiaCapitalChina,SIGAsiaInvestments,SinaWei... \n", + "1 MatrixPartners,TigerGlobalManagement,SoftbankC... \n", + "2 KhoslaVentures,LowercaseCapital,capitalG \n", + "3 FoundersFund,DraperFisherJurvetson,RothenbergV... \n", + "4 GeneralCatalystPartners,AndreessenHorowitz,ENI... \n", + "5 MorningsideVentureCapital,SequoiaCapital,Baidu \n", + "6 IntelCapital,SapphireVentures,AlibabaGroup \n", + "7 AccelPartners,SequoiaCapital \n", + "8 TencentHoldings,KKR,SmashVentures \n", + "9 GGVCapital,VertexVentureHoldings,SoftbankGroup \n", + "10 TencentHoldings,HillhouseCapitalManagement,Sou... \n", + "11 SoftbankGroup,SequoiaCapital,KhoslaVentures \n", + "12 RedpointVentures,IconiqCapital,MadronaVentureG... \n", + "13 RREVentures,FoundersFund,In-Q-Tel \n", + "14 TigerGlobalManagement \n", + "15 VickersVenturePartners,IKEAGreenTech \n", + "16 CoatueManagement,SequoiaCapitalChina,IDGCapital \n", + "17 FoundersFund,GGVCapital,DigitalSkyTechnologies \n", + "18 AviationIndustryCorporationofChina,EssenceFina... \n", + "19 IDGCapital,Venture51,LightspeedVenturePartners \n", + "20 FormationGroup,SequoiaCapitalIndia,WarburgPincus \n", + "21 SoftBankGroup,SequoiaCapitalIndia,LightspeedIn... \n", + "22 SequoiaCapital,Redpointe.ventures,KaszekVentures \n", + "23 SequoiaCapitalChina,GXCapital \n", + "24 SequoiaCapital,FounderCollective,WellingtonMan... \n", + "25 GoogleVentures,AndreessenHorowitz,DSTGlobal \n", + "26 YCombinator,UnionSquareVentures,DFJGrowth \n", + "27 TencentHoldings,LightspeedIndiaPartners,Sequoi... \n", + "28 TencentHoldings,WarbugPincus,IDGCapital \n", + "29 KhoslaVentures,KleinerPerkinsCaufield&Byers,Co... \n", + "30 StarVC,IDGCapital,InforeCapital,AlibabaGroup \n", + "31 SoftBankGroup,Blackrock,AlibabaGroup \n", + "32 SoftBankGroup,FoundersFund \n", + "33 SoftBankGroup,AlibabaGroup,SequoiaCapitalIndia \n", + "34 VolkswagenGroup,FordAutonomousVehicles \n", + "35 GeneralAtlantic,GoldmanSachs,NewEnterpriseAsso... \n", + "36 AndreessenHorowitz,Nor-CalInvest,TPGGrowth \n", + "37 SequoiaCapitalChina,WarburgPincus,GeneralCatalyst \n", + "38 FoundersFund,ThriveCapital,WellingtonManagement \n", + "39 Accel,capitalG,EarlybridVentureCapital,Seedcamp \n", + "40 AccelPartners,SoftBankGroup,SequoiaCapital \n", + "41 ObviousVentures,QualcommVentures,AndreessenHor... \n", + "42 AndreessenHorowitz,GeneralCatalyst \n", + "43 AndreessenHorowitz,NewEnterpriseAssociates,Bat... \n", + "44 SequoiaCapital,iGlobePartners,DFJGrowth \n", + "45 SoftbankGroup,CapitalG \n", + "46 ForerunnerVentures,CrosslinkCapital,Homebrew \n", + "47 Tencent,Baidu,HuashengCapital \n", + "48 AlibabaGroup,BoyuCapital,BoruiCapital \n", + "49 TechnologyCrossoverVentures,A&ETelevisionNetworks " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unicorns = pd.read_csv(r'data/cbinsights_entire_unicorn_tracker_sorted.csv').drop(columns = 'Unnamed: 0')\n", + "unicorns.head(50) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# By Industry / Average Valuation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Artificialintelligence', 'Auto&transportation', 'Fintech',\n", + " 'Other', 'Travel', 'Mobile&telecommunications', 'Hardware',\n", + " 'Internetsoftware&services', 'Supplychain,logistics,&delivery',\n", + " 'Datamanagement&analytics', 'Consumer&retail', 'Health',\n", + " 'E-commerce&direct-to-consumer', 'Edtech', 'Cybersecurity',\n", + " 'Education'], dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unicorns.Industry.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "unicorns_by_industry = unicorns.copy()\n", + "unicorns_by_industry = unicorns_by_industry.groupby(['Industry']).agg({'Company':'count','Valuation ($B)':'mean'})\n", + "unicorns_by_industry = unicorns_by_industry.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IndustryNumberValuation ($B)Percent of Total
0Fintech613.2857380.131749
1Internetsoftware&services581.9993100.125270
2E-commerce&direct-to-consumer542.5874070.116631
3Artificialintelligence463.7126090.099352
4Health312.4983870.066955
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" + ], + "text/plain": [ + " Industry Number Valuation ($B) Percent of Total\n", + "0 Fintech 61 3.285738 0.131749\n", + "1 Internetsoftware&services 58 1.999310 0.125270\n", + "2 E-commerce&direct-to-consumer 54 2.587407 0.116631\n", + "3 Artificialintelligence 46 3.712609 0.099352\n", + "4 Health 31 2.498387 0.066955" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unicorns_by_industry.rename(columns = {'Company' : 'Number'} , inplace = True)\n", + "unicorns_by_industry['Percent of Total'] = unicorns_by_industry['Number'] / len(unicorns.Company)\n", + "unicorns_by_industry = unicorns_by_industry.sort_values(by = \"Percent of Total\", ascending = False).reset_index(drop=True)\n", + "unicorns_by_industry.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# unicorns_by_industry.to_csv(r'unicorns_by_industry.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# By Country / Average Valuation" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryNumberValuation ($B)Percent of Total
0UnitedStates2222.8401800.479482
1China1183.6609320.254860
2UnitedKingdom242.5916670.051836
3India213.5257140.045356
4Germany122.0850000.025918
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" + ], + "text/plain": [ + " Country Number Valuation ($B) Percent of Total\n", + "0 UnitedStates 222 2.840180 0.479482\n", + "1 China 118 3.660932 0.254860\n", + "2 UnitedKingdom 24 2.591667 0.051836\n", + "3 India 21 3.525714 0.045356\n", + "4 Germany 12 2.085000 0.025918" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unicorns_by_country = unicorns.copy()\n", + "unicorns_by_country = unicorns_by_country.groupby(['Country']).agg({'Company':'count','Valuation ($B)':'mean'})\n", + "unicorns_by_country = unicorns_by_country.reset_index()\n", + "unicorns_by_country.rename(columns = {'Company' : 'Number'} , inplace = True)\n", + "unicorns_by_country['Percent of Total'] = unicorns_by_country['Number'] / len(unicorns.Company)\n", + "unicorns_by_country = unicorns_by_country.sort_values(by = \"Percent of Total\", ascending = False).reset_index(drop=True)\n", + "unicorns_by_country.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# unicorns_by_country.to_csv(r'unicorns_by_country.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/images/Companies by Industry.png b/your-project/images/Companies by Industry.png new file mode 100644 index 0000000..5e336b5 Binary files /dev/null and b/your-project/images/Companies by Industry.png differ diff --git a/your-project/images/Companies founded by year.png b/your-project/images/Companies founded by year.png new file mode 100644 index 0000000..416ad8b Binary files /dev/null and b/your-project/images/Companies founded by year.png differ diff --git a/your-project/images/Company founded development ver time.png b/your-project/images/Company founded development ver time.png new file mode 100644 index 0000000..ad7dae5 Binary files /dev/null and b/your-project/images/Company founded development ver time.png differ diff --git a/your-project/images/Correlation 1.png b/your-project/images/Correlation 1.png new file mode 100644 index 0000000..e59bd9b Binary files /dev/null and b/your-project/images/Correlation 1.png differ diff --git a/your-project/images/Correlation 2.png b/your-project/images/Correlation 2.png new file mode 100644 index 0000000..39fb6e3 Binary files /dev/null and b/your-project/images/Correlation 2.png differ diff --git a/your-project/images/Correlation 3.png b/your-project/images/Correlation 3.png new file mode 100644 index 0000000..c13c03e Binary files /dev/null and b/your-project/images/Correlation 3.png differ diff --git a/your-project/images/Growth rates by sector in a 95% Interval.png b/your-project/images/Growth rates by sector in a 95% Interval.png new file mode 100644 index 0000000..a1d2065 Binary files /dev/null and b/your-project/images/Growth rates by sector in a 95% Interval.png differ diff --git a/your-project/images/Probability Acquisition.png b/your-project/images/Probability Acquisition.png new file mode 100644 index 0000000..f8af0ab Binary files /dev/null and b/your-project/images/Probability Acquisition.png differ diff --git a/your-project/images/Unicorns Geography.png b/your-project/images/Unicorns Geography.png new file mode 100644 index 0000000..f4c84d8 Binary files /dev/null and b/your-project/images/Unicorns Geography.png differ diff --git a/your-project/images/Unicorns Sector.png b/your-project/images/Unicorns Sector.png new file mode 100644 index 0000000..660b34b Binary files /dev/null and b/your-project/images/Unicorns Sector.png differ diff --git a/your-project/images/companies by country excl USA.png b/your-project/images/companies by country excl USA.png new file mode 100644 index 0000000..5096c83 Binary files /dev/null and b/your-project/images/companies by country excl USA.png differ diff --git a/your-project/images/companies by country incl USA.png b/your-project/images/companies by country incl USA.png new file mode 100644 index 0000000..2bc30e7 Binary files /dev/null and b/your-project/images/companies by country incl USA.png differ diff --git a/your-project/images/rise in Education.png b/your-project/images/rise in Education.png new file mode 100644 index 0000000..314ee30 Binary files /dev/null and b/your-project/images/rise in Education.png differ