diff --git a/Data Intake Report_VI.pdf b/Data Intake Report_VI.pdf new file mode 100644 index 00000000..ff5b62e6 Binary files /dev/null and b/Data Intake Report_VI.pdf differ diff --git a/EDA.ipynb b/EDA.ipynb new file mode 100644 index 00000000..69e3daee --- /dev/null +++ b/EDA.ipynb @@ -0,0 +1,1525 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f04ad0b9", + "metadata": {}, + "outputs": [], + "source": [ + "#import important libraries\n", + "\n", + "import pandas as pd\n", + "import matplotlib as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import datetime \n", + "\n", + "%matplotlib inline\n", + "plt.rcParams[\"figure.figsize\"]=[12,5]" + ] + }, + { + "cell_type": "markdown", + "id": "28b85286", + "metadata": {}, + "source": [ + "# Data Imported \n", + "Here I imported cab Data file and next step would be Data cleansing. So I will take out the information of this dataset\n", + "Also I will reflect the dataset with head method for referencing" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "74d6c043", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 359392 entries, 0 to 359391\n", + "Data columns (total 7 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Transaction ID 359392 non-null int64 \n", + " 1 Date of Travel 359392 non-null int64 \n", + " 2 Company 359392 non-null object \n", + " 3 City 359392 non-null object \n", + " 4 KM Travelled 359392 non-null float64\n", + " 5 Price Charged 359392 non-null float64\n", + " 6 Cost of Trip 359392 non-null float64\n", + "dtypes: float64(3), int64(2), object(2)\n", + "memory usage: 19.2+ MB\n" + ] + } + ], + "source": [ + "cab_data=pd.read_csv(\"cab_data.csv\")\n", + "cab_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "9c8f8b6f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Transaction IDDate of TravelCompanyCityKM TravelledPrice ChargedCost of TripProfit
01000001101-09-2016Pink CabATLANTA GA30.45370.95313.63557.315
11000001201-07-2016Pink CabATLANTA GA28.62358.52334.85423.666
21000001301-03-2016Pink CabATLANTA GA9.04125.2097.63227.568
31000001401-08-2016Pink CabATLANTA GA33.17377.40351.60225.798
41000001501-04-2016Pink CabATLANTA GA8.73114.6297.77616.844
\n", + "
" + ], + "text/plain": [ + " Transaction ID Date of Travel Company City KM Travelled \n", + "0 10000011 01-09-2016 Pink Cab ATLANTA GA 30.45 \\\n", + "1 10000012 01-07-2016 Pink Cab ATLANTA GA 28.62 \n", + "2 10000013 01-03-2016 Pink Cab ATLANTA GA 9.04 \n", + "3 10000014 01-08-2016 Pink Cab ATLANTA GA 33.17 \n", + "4 10000015 01-04-2016 Pink Cab ATLANTA GA 8.73 \n", + "\n", + " Price Charged Cost of Trip Profit \n", + "0 370.95 313.635 57.315 \n", + "1 358.52 334.854 23.666 \n", + "2 125.20 97.632 27.568 \n", + "3 377.40 351.602 25.798 \n", + "4 114.62 97.776 16.844 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cab_data.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "41cce0f3", + "metadata": {}, + "source": [ + "# Changing the datatypes\n", + "In some of the columns the datatypes do not reflect the actual one, so here I will change the datatypes of columns as part of the cleansing process for futhur actions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8a839135", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "Casting to unit-less dtype 'datetime64' is not supported. Pass e.g. 'datetime64[ns]' instead.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m start_date \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mdate(\u001b[38;5;241m1899\u001b[39m, \u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m31\u001b[39m)\n\u001b[1;32m 2\u001b[0m cab_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDate of Travel\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m cab_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDate of Travel\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: (start_date \u001b[38;5;241m+\u001b[39m datetime\u001b[38;5;241m.\u001b[39mtimedelta(days\u001b[38;5;241m=\u001b[39mx))\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm-\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m cab_data \u001b[38;5;241m=\u001b[39m \u001b[43mcab_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDate of Travel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdatetime64\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCompany\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstring\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCity\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstring\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m cab_data\u001b[38;5;241m.\u001b[39mdtypes\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/pandas/core/generic.py:6305\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6303\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 6304\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 6305\u001b[0m res_col \u001b[38;5;241m=\u001b[39m \u001b[43mcol\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6306\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[1;32m 6307\u001b[0m ex\u001b[38;5;241m.\u001b[39margs \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 6308\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mex\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: Error while type casting for column \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcol_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 6309\u001b[0m )\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/pandas/core/generic.py:6324\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6317\u001b[0m results \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 6318\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miloc[:, i]\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m 6319\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns))\n\u001b[1;32m 6320\u001b[0m ]\n\u001b[1;32m 6322\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 6323\u001b[0m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 6324\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6325\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6327\u001b[0m \u001b[38;5;66;03m# GH 33113: handle empty frame or series\u001b[39;00m\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/pandas/core/internals/managers.py:451\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 449\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 451\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 455\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 456\u001b[0m \u001b[43m \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/pandas/core/internals/managers.py:352\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 350\u001b[0m applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 351\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 352\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 353\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m 355\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/pandas/core/internals/blocks.py:511\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors, using_cow)\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 492\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[1;32m 493\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[1;32m 508\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 509\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m--> 511\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 513\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m 515\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/pandas/core/dtypes/astype.py:242\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 239\u001b[0m dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 242\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m 244\u001b[0m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/pandas/core/dtypes/astype.py:187\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 184\u001b[0m values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 187\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/pandas/core/dtypes/astype.py:116\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m 114\u001b[0m dti \u001b[38;5;241m=\u001b[39m to_datetime(arr\u001b[38;5;241m.\u001b[39mravel())\n\u001b[1;32m 115\u001b[0m dta \u001b[38;5;241m=\u001b[39m dti\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39mreshape(arr\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdta\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m_ndarray\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_timedelta64_dtype(dtype):\n\u001b[1;32m 119\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstruction\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ensure_wrapped_if_datetimelike\n", + "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/pandas/core/arrays/datetimes.py:694\u001b[0m, in \u001b[0;36mDatetimeArray.astype\u001b[0;34m(self, dtype, copy)\u001b[0m\n\u001b[1;32m 682\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 683\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use .astype to convert from timezone-aware dtype to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 684\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimezone-naive dtype. Use obj.tz_localize(None) or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 685\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobj.tz_convert(\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUTC\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m).tz_localize(None) instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 686\u001b[0m )\n\u001b[1;32m 688\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m (\n\u001b[1;32m 689\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtz \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 690\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m is_datetime64_dtype(dtype)\n\u001b[1;32m 691\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m dtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype\n\u001b[1;32m 692\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m is_unitless(dtype)\n\u001b[1;32m 693\u001b[0m ):\n\u001b[0;32m--> 694\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 695\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCasting to unit-less dtype \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdatetime64\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not supported. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 696\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPass e.g. \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdatetime64[ns]\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 697\u001b[0m )\n\u001b[1;32m 699\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_period_dtype(dtype):\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_period(freq\u001b[38;5;241m=\u001b[39mdtype\u001b[38;5;241m.\u001b[39mfreq)\n", + "\u001b[0;31mTypeError\u001b[0m: Casting to unit-less dtype 'datetime64' is not supported. Pass e.g. 'datetime64[ns]' instead." + ] + } + ], + "source": [ + "start_date = datetime.date(1899, 12, 31)\n", + "cab_data['Date of Travel'] = cab_data['Date of Travel'].apply(lambda x: (start_date + datetime.timedelta(days=x)).strftime(\"%m-%d-%Y\"))\n", + "cab_data = cab_data.astype({'Date of Travel':'datetime64', 'Company':'string','City':'string'})\n", + "cab_data.dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "294d36d5", + "metadata": {}, + "source": [ + "# Importing Other files and checking the null values\n", + "I will import the other files to understand the relations between them" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cd5794a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CityPopulationUsers
0NEW YORK NY8,405,837302,149
1CHICAGO IL1,955,130164,468
2LOS ANGELES CA1,595,037144,132
3MIAMI FL1,339,15517,675
4SILICON VALLEY1,177,60927,247
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" + ], + "text/plain": [ + " City Population Users\n", + "0 NEW YORK NY 8,405,837 302,149 \n", + "1 CHICAGO IL 1,955,130 164,468 \n", + "2 LOS ANGELES CA 1,595,037 144,132 \n", + "3 MIAMI FL 1,339,155 17,675 \n", + "4 SILICON VALLEY 1,177,609 27,247 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "city = pd.read_csv(\"City.csv\")\n", + "city.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e27a356f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Customer IDGenderAgeIncome (USD/Month)
029290Male2810813
127703Male279237
228712Male5311242
328020Male2323327
427182Male338536
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" + ], + "text/plain": [ + " Customer ID Gender Age Income (USD/Month)\n", + "0 29290 Male 28 10813\n", + "1 27703 Male 27 9237\n", + "2 28712 Male 53 11242\n", + "3 28020 Male 23 23327\n", + "4 27182 Male 33 8536" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_id = pd.read_csv(\"Customer_ID.csv\")\n", + "customer_id.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d421e188", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Transaction IDCustomer IDPayment_Mode
01000001129290Card
11000001227703Card
21000001328712Cash
31000001428020Cash
41000001527182Card
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" + ], + "text/plain": [ + " Transaction ID Customer ID Payment_Mode\n", + "0 10000011 29290 Card\n", + "1 10000012 27703 Card\n", + "2 10000013 28712 Cash\n", + "3 10000014 28020 Cash\n", + "4 10000015 27182 Card" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transaction_id = pd.read_csv(\"Transaction_ID.csv\")\n", + "transaction_id.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4e4722db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 359392 entries, 0 to 359391\n", + "Data columns (total 7 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Transaction ID 359392 non-null int64 \n", + " 1 Date of Travel 359392 non-null object \n", + " 2 Company 359392 non-null object \n", + " 3 City 359392 non-null object \n", + " 4 KM Travelled 359392 non-null float64\n", + " 5 Price Charged 359392 non-null float64\n", + " 6 Cost of Trip 359392 non-null float64\n", + "dtypes: float64(3), int64(1), object(3)\n", + "memory usage: 19.2+ MB\n" + ] + } + ], + "source": [ + "cab_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8760f8c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of Cab data (359392, 7)\n" + ] + } + ], + "source": [ + "print(f'Shape of Cab data {cab_data.shape}')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "47cf321b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Transaction IDKM TravelledPrice ChargedCost of Trip
count3.593920e+05359392.000000359392.000000359392.000000
mean1.022076e+0722.567254423.443311286.190113
std1.268058e+0512.233526274.378911157.993661
min1.000001e+071.90000015.60000019.000000
25%1.011081e+0712.000000206.437500151.200000
50%1.022104e+0722.440000386.360000282.480000
75%1.033094e+0732.960000583.660000413.683200
max1.044011e+0748.0000002048.030000691.200000
\n", + "
" + ], + "text/plain": [ + " Transaction ID KM Travelled Price Charged Cost of Trip\n", + "count 3.593920e+05 359392.000000 359392.000000 359392.000000\n", + "mean 1.022076e+07 22.567254 423.443311 286.190113\n", + "std 1.268058e+05 12.233526 274.378911 157.993661\n", + "min 1.000001e+07 1.900000 15.600000 19.000000\n", + "25% 1.011081e+07 12.000000 206.437500 151.200000\n", + "50% 1.022104e+07 22.440000 386.360000 282.480000\n", + "75% 1.033094e+07 32.960000 583.660000 413.683200\n", + "max 1.044011e+07 48.000000 2048.030000 691.200000" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cab_data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5ce9d14c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Transaction ID 0\n", + "Date of Travel 0\n", + "Company 0\n", + "City 0\n", + "KM Travelled 0\n", + "Price Charged 0\n", + "Cost of Trip 0\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cab_data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f36c54cc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20 entries, 0 to 19\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 City 20 non-null object\n", + " 1 Population 20 non-null object\n", + " 2 Users 20 non-null object\n", + "dtypes: object(3)\n", + "memory usage: 608.0+ bytes\n" + ] + } + ], + "source": [ + "city.info() " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7bc0bb06", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "City 0\n", + "Population 0\n", + "Users 0\n", + "dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "city.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c1339a53", + "metadata": {}, + "outputs": [], + "source": [ + "city= city.astype({\"City\":\"string\",\"Population\":\"string\",\"Users\":\"string\"})\n", + "\n", + "city['Population']= city['Population'].apply(lambda x:x.replace(',',\"\"))\n", + "city['Users']= city['Users'].apply(lambda x: x.replace(',',\"\"))\n", + "\n", + "city = city.astype({\"Population\":\"int\",\"Users\":\"int\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a38c3839", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PopulationUsers
count2.000000e+0120.000000
mean1.231592e+0664520.650000
std1.740127e+0683499.375289
min2.489680e+053643.000000
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75%1.067041e+0691766.000000
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" + ], + "text/plain": [ + " Population Users\n", + "count 2.000000e+01 20.000000\n", + "mean 1.231592e+06 64520.650000\n", + "std 1.740127e+06 83499.375289\n", + "min 2.489680e+05 3643.000000\n", + "25% 6.086372e+05 11633.250000\n", + "50% 7.845590e+05 23429.000000\n", + "75% 1.067041e+06 91766.000000\n", + "max 8.405837e+06 302149.000000" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "city.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "253c092c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Customer IDAgeIncome (USD/Month)
count49171.00000049171.00000049171.000000
mean28398.25228335.36312115015.631856
std17714.13733312.5990668002.208253
min1.00000018.0000002000.000000
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" + ], + "text/plain": [ + " Customer ID Age Income (USD/Month)\n", + "count 49171.000000 49171.000000 49171.000000\n", + "mean 28398.252283 35.363121 15015.631856\n", + "std 17714.137333 12.599066 8002.208253\n", + "min 1.000000 18.000000 2000.000000\n", + "25% 12654.500000 25.000000 8289.500000\n", + "50% 27631.000000 33.000000 14656.000000\n", + "75% 43284.500000 42.000000 21035.000000\n", + "max 60000.000000 65.000000 35000.000000" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_id.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f28e516c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 49171 entries, 0 to 49170\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Customer ID 49171 non-null int64 \n", + " 1 Gender 49171 non-null string\n", + " 2 Age 49171 non-null int64 \n", + " 3 Income (USD/Month) 49171 non-null int64 \n", + "dtypes: int64(3), string(1)\n", + "memory usage: 1.5 MB\n" + ] + } + ], + "source": [ + "customer_id = customer_id.astype({\"Gender\":\"string\"})\n", + "customer_id.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1599cc04", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 440098 entries, 0 to 440097\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Transaction ID 440098 non-null int64 \n", + " 1 Customer ID 440098 non-null int64 \n", + " 2 Payment_Mode 440098 non-null object\n", + "dtypes: int64(2), object(1)\n", + "memory usage: 10.1+ MB\n" + ] + } + ], + "source": [ + "transaction_id.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5f5d35a3", + "metadata": {}, + "outputs": [], + "source": [ + "transaction_id = transaction_id.astype({'Payment_Mode':'string'})" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0c5b19fc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Transaction IDCustomer ID
count4.400980e+05440098.000000
mean1.022006e+0723619.513120
std1.270455e+0521195.549816
min1.000001e+071.000000
25%1.011004e+073530.000000
50%1.022006e+0715168.000000
75%1.033008e+0743884.000000
max1.044011e+0760000.000000
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" + ], + "text/plain": [ + " Transaction ID Customer ID\n", + "count 4.400980e+05 440098.000000\n", + "mean 1.022006e+07 23619.513120\n", + "std 1.270455e+05 21195.549816\n", + "min 1.000001e+07 1.000000\n", + "25% 1.011004e+07 3530.000000\n", + "50% 1.022006e+07 15168.000000\n", + "75% 1.033008e+07 43884.000000\n", + "max 1.044011e+07 60000.000000" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transaction_id.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "2caf86d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Company\n", + "Yellow Cab 274681\n", + "Pink Cab 84711\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cab_data[\"Company\"].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "798fbbd3", + "metadata": {}, + "source": [ + "# Conclusion-1\n", + "From the above output, we can see that yellow cab has marginally higer customer base than pink cab. Although, it cannot say the current scenario because pink cab is the newcommer in the market and has been operating for last couple of years which is less than yellow cab." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "faaeadea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Price charge by Yellow cab: \n", + "count 274681.000000\n", + "mean 458.181990\n", + "std 288.386166\n", + "min 20.730000\n", + "25% 226.680000\n", + "50% 425.060000\n", + "75% 633.880000\n", + "max 2048.030000\n", + "Name: Price Charged, dtype: float64\n", + "\n", + "\n", + "Price charge by Pink cab: \n", + "count 84711.000000\n", + "mean 310.800856\n", + "std 181.995661\n", + "min 15.600000\n", + "25% 159.970000\n", + "50% 298.060000\n", + "75% 441.505000\n", + "max 1623.480000\n", + "Name: Price Charged, dtype: float64\n" + ] + } + ], + "source": [ + "print(f'Price charge by Yellow cab: \\n{cab_data[cab_data[\"Company\"] == \"Yellow Cab\"][\"Price Charged\"].describe()}')\n", + "print('\\n')\n", + "print(f'Price charge by Pink cab: \\n{cab_data[cab_data[\"Company\"] == \"Pink Cab\"][\"Price Charged\"].describe()}')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "9fc19715", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(data=cab_data,x=\"Price Charged\",y=\"Company\",palette=[\"purple\",\"yellow\"]).set_title('Price charged by Companies');" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "6670092b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(data=cab_data,x=\"Cost of Trip\",y='Company',palette=[\"purple\",\"yellow\"]).set_title('Cost of Trip for Companies');" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "9663cade", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cab_data['Profit']= cab_data['Price Charged']- cab_data['Cost of Trip']\n", + "cab_data[\"Profit(%)\"] = cab_data['Profit']*100/cab_data['Price Charged']\n", + "sns.boxplot(data=cab_data, y=\"Company\",x=\"Profit(%)\",palette=[\"purple\",\"yellow\"]).set_title('Profit(%) distribution for both Companies');" + ] + }, + { + "cell_type": "markdown", + "id": "8937050e", + "metadata": {}, + "source": [ + "# Conclusion-2\n", + "From above two graphs, we can interprete that Yellow cab has higher profit margin than pink cab." + ] + }, + { + "cell_type": "markdown", + "id": "d57233b9", + "metadata": {}, + "source": [ + "# Merging the datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0dd4633a", + "metadata": {}, + "outputs": [], + "source": [ + "masterdata = cab_data.merge(city,on=\"City\").merge(transaction_id,on=\"Transaction ID\").merge(customer_id, on=\"Customer ID\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6abe9eb7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 359392 entries, 0 to 359391\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Transaction ID 359392 non-null int64 \n", + " 1 Date of Travel 359392 non-null datetime64[ns]\n", + " 2 Company 359392 non-null string \n", + " 3 City 359392 non-null string \n", + " 4 KM Travelled 359392 non-null float64 \n", + " 5 Price Charged 359392 non-null float64 \n", + " 6 Cost of Trip 359392 non-null float64 \n", + " 7 Profit 359392 non-null float64 \n", + " 8 Population 359392 non-null int64 \n", + " 9 Users 359392 non-null int64 \n", + " 10 Customer ID 359392 non-null int64 \n", + " 11 Payment_Mode 359392 non-null string \n", + " 12 Gender 359392 non-null string \n", + " 13 Age 359392 non-null int64 \n", + " 14 Income (USD/Month) 359392 non-null int64 \n", + "dtypes: datetime64[ns](1), float64(4), int64(6), string(4)\n", + "memory usage: 41.1 MB\n" + ] + } + ], + "source": [ + "\n", + "masterdata=masterdata.astype({\"Date of Travel\":\"datetime64[ns]\",\"Company\":\"string\",\"City\":'string'})\n", + "masterdata.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d9f4624c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(data=masterdata,x=\"Company\",hue=\"Gender\").set_title(\"Gender comparison by Companies\");" + ] + }, + { + "cell_type": "markdown", + "id": "aac92b3e", + "metadata": {}, + "source": [ + "# Conclusion -3\n", + "Number of male users are more in the both cab brands over the years." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "e7ffd7e3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(data=masterdata,x=\"Company\",hue='Payment_Mode',palette=[\"purple\",\"yellow\"]).set_title(\"Mode of payment by the Customer\");" + ] + }, + { + "cell_type": "markdown", + "id": "e56f5e50", + "metadata": {}, + "source": [ + "# Conclusion - 4\n", + "Customer prefers the Card option more for the mode of payment." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c748cd33", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(data=city,y=\"City\",x=\"Users\").set_title('Number of Users in US cities');" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "38cfc395", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 443 ms, sys: 20.4 ms, total: 463 ms\n", + "Wall time: 471 ms\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "sns.countplot(data=masterdata,y=\"City\",hue=\"Company\", palette=[\"purple\",\"yellow\"]).set_title('Number of Users of the company in US cities');" + ] + }, + { + "cell_type": "markdown", + "id": "50b63cb7", + "metadata": {}, + "source": [ + "# Conclusion -5\n", + "From above graph, it can iterate that Yellow cab has captured the top cities like New york, Chicago, Washington where there are higher number of users. Pink cab has dominance in some cities like San Diego, Sacramento but it is not that significant. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "80bff000", + "metadata": {}, + "outputs": [], + "source": [ + "masterdata[\"Profit Year\"]= masterdata['Date of Travel'].dt.year" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "87d47028", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lineplot(data=masterdata,x='Profit Year',y=\"Profit\",hue=\"Company\",palette=[\"purple\",\"yellow\"]).set_title('Profit of the company over the years');" + ] + }, + { + "cell_type": "markdown", + "id": "a5e8657c", + "metadata": {}, + "source": [ + "# Conclusion 6\n", + "We can argue that Pink Cab profit has incresed over the years and gasp the market firmly. While, Yellow cab profit has been hampered over the years and declining significantly." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "35feb21a", + "metadata": {}, + "outputs": [], + "source": [ + "masterdata.to_csv(\"Merged_Data.csv\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/G2M.pdf b/G2M.pdf new file mode 100644 index 00000000..c16ee170 Binary files /dev/null and b/G2M.pdf differ