From 5a5993d939421f308e202ed214118cac54d3bade Mon Sep 17 00:00:00 2001
From: Marisan13 <96127669+Marisan13@users.noreply.github.com>
Date: Tue, 14 Nov 2023 20:59:01 +0000
Subject: [PATCH] Lab done
---
.../.ipynb_checkpoints/main-checkpoint.ipynb | 1729 +++++++++++++++++
your-code/ages_population.csv | 1001 ++++++++++
your-code/ages_population2.csv | 1001 ++++++++++
your-code/ages_population3.csv | 1001 ++++++++++
your-code/main.ipynb | 1445 ++++++++++++--
your-code/roll_the_dice_hundred.csv | 101 +
your-code/roll_the_dice_thousand.csv | 1001 ++++++++++
7 files changed, 7160 insertions(+), 119 deletions(-)
create mode 100644 your-code/.ipynb_checkpoints/main-checkpoint.ipynb
create mode 100644 your-code/ages_population.csv
create mode 100644 your-code/ages_population2.csv
create mode 100644 your-code/ages_population3.csv
create mode 100644 your-code/roll_the_dice_hundred.csv
create mode 100644 your-code/roll_the_dice_thousand.csv
diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
new file mode 100644
index 0000000..8e8dcf6
--- /dev/null
+++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
@@ -0,0 +1,1729 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Understanding Descriptive Statistics\n",
+ "\n",
+ "Import the necessary libraries here:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.linear_model import LinearRegression\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from pylab import rcParams # library for plotting histograms"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Challenge 1\n",
+ "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
+ "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "\n",
+ "def rolling_dice(times):\n",
+ " \n",
+ " dice = [1,2,3,4,5,6]\n",
+ " result = []\n",
+ " counter = 0\n",
+ " while counter < times:\n",
+ " result.append(random.choices(dice))\n",
+ " counter += 1\n",
+ " result_df = pd.DataFrame(result)\n",
+ " result_df[0] = result_df.rename(columns={0: \"value\"}, inplace=True)\n",
+ " result_df = result_df[[\"value\"]]\n",
+ " return result_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " value \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " value \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotting the value\n",
+ "result.plot(kind='bar', alpha=0.7)\n",
+ "plt.xlabel('roll(index)')\n",
+ "plt.ylabel('value')\n",
+ "plt.title('')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4 4\n",
+ "2 3\n",
+ "6 2\n",
+ "5 1\n",
+ "Name: value, dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# calculating FD\n",
+ "frequency_distribution = result['value'].value_counts()\n",
+ "frequency_distribution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotting FD\n",
+ "frequency_distribution.plot(kind='bar', alpha=0.7)\n",
+ "plt.xlabel('value')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Frequency Distribution')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : Horizontal and vertical axes are interchanged. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Challenge 2\n",
+ "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
+ "\n",
+ "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def calculate_mean(input_series):\n",
+ " count = 0\n",
+ " \n",
+ " for key,value in input_series.items():\n",
+ " count += key*value\n",
+ " return count/sum(input_series.values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4 4\n",
+ "2 3\n",
+ "6 2\n",
+ "5 1\n",
+ "Name: value, dtype: int64"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# calculating FD\n",
+ "frequency_distribution = result['value'].value_counts()\n",
+ "frequency_distribution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3.9"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "calculate_mean(frequency_distribution)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
+ "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def calculate_median(input_series):\n",
+ "\n",
+ " values_list = input_series.index.tolist()\n",
+ " values_list.sort()\n",
+ "\n",
+ " n = len(values_list)\n",
+ " if n % 2 == 0:\n",
+ " middle1 = values_list[n // 2 - 1]\n",
+ " middle2 = values_list[n // 2]\n",
+ " median = (middle1 + middle2) / 2\n",
+ " else:\n",
+ " median = values_list[n // 2]\n",
+ "\n",
+ " return median"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4.5"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "calculate_median(frequency_distribution)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def calculate_quartiles(df, column):\n",
+ " \n",
+ " values_list = df[column].to_list()\n",
+ " values_list.sort()\n",
+ " \n",
+ " q75, q50, q25 = np.percentile(values_list, [75, 50, 25])\n",
+ " return q75, q50, q25"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(4.75, 4.0, 2.5)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "calculate_quartiles(result, \"value\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Challenge 3\n",
+ "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
+ "#### 1.- Sort the values and plot them. What do you see?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
+ " Unnamed: 0 \n",
+ " roll \n",
+ " value \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Unnamed: 0 \n",
+ " roll \n",
+ " value \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 47 \n",
+ " 47 \n",
+ " 47 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 56 \n",
+ " 56 \n",
+ " 56 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 73 \n",
+ " 73 \n",
+ " 73 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " 11 \n",
+ " 11 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " 24 \n",
+ " 24 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " 21 \n",
+ " 21 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 99 \n",
+ " 99 \n",
+ " 99 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
100 rows × 3 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotting the value\n",
+ "dice_hundred[\"value\"].plot(kind='bar', alpha=0.7)\n",
+ "plt.xlabel('roll(index)')\n",
+ "plt.ylabel('value')\n",
+ "plt.title('')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : The number of times that each value appears (the frequency) differs by value (ex. 5 less vs. 4 and 6)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3.74"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "frequency_distribution = dice_hundred['value'].value_counts()\n",
+ "calculate_mean(frequency_distribution)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 3.- Now, calculate the frequency distribution.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "6 23\n",
+ "4 22\n",
+ "2 17\n",
+ "3 14\n",
+ "1 12\n",
+ "5 12\n",
+ "Name: value, dtype: int64"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# already calculated in above cell\n",
+ "frequency_distribution"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting into histogram\n",
+ "rcParams['figure.figsize'] = 3, 3 \n",
+ "dice_hundred[\"value\"].hist();\n",
+ "plt.tight_layout() \n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : As larger numbers (4 and 6) appears more frequently compared to smaller values (2 and 3), mean is larger than the median (1 and 5 are found with same frequency, meaning they make no difference)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Unnamed: 0 \n",
+ " roll \n",
+ " value \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting into histogram\n",
+ "rcParams['figure.figsize'] = 3, 3 \n",
+ "dice_thousand[\"value\"].hist();\n",
+ "plt.tight_layout() \n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comments : The frequency distribution is more consistent for all values. It becomes more stable as the number of rolls increases."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Challenge 4\n",
+ "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
+ "\n",
+ "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " observation \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 68.0 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 12.0 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 45.0 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 38.0 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 49.0 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 995 \n",
+ " 27.0 \n",
+ " \n",
+ " \n",
+ " 996 \n",
+ " 47.0 \n",
+ " \n",
+ " \n",
+ " 997 \n",
+ " 53.0 \n",
+ " \n",
+ " \n",
+ " 998 \n",
+ " 33.0 \n",
+ " \n",
+ " \n",
+ " 999 \n",
+ " 31.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1000 rows × 1 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting into histogram\n",
+ "rcParams['figure.figsize'] = 3, 3 \n",
+ "population['observation'].hist();\n",
+ "plt.tight_layout() \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Assumption\n",
+ "- range of mean : 30-40\n",
+ "- std : 15 "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "36.56"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.mean(population['observation'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "12.81008977329979"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.std(population['observation'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Diffrence vs. assumption\n",
+ "- mean: falls inside the range assumed\n",
+ "- std: lower than assumption"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
+ " observation \n",
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+ " \n",
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+ "
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting into histogram\n",
+ "rcParams['figure.figsize'] = 3, 3 \n",
+ "population_2['observation'].hist();\n",
+ "plt.tight_layout() \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : Distribution is centered in the range of 26-29 and std is assumed to be smaller compared to that of step 2 (meaning most of the values are found between smaller range of values)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "27.155"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.mean(population_2['observation'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.9683286543103704"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.std(population_2['observation'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : Same to above."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Challenge 5\n",
+ "Now is the turn of `ages_population3.csv`.\n",
+ "\n",
+ "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
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1000 rows × 1 columns
\n",
+ "
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting into histogram\n",
+ "rcParams['figure.figsize'] = 3, 3 \n",
+ "population_3['observation'].hist();\n",
+ "plt.tight_layout() \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "41.989"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.mean(population_3['observation'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "16.136631587788084"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.std(population_3['observation'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : Mean is larger than mode and it is assume to be positively skewed. Distribution is varied in wider range resulting in larger std."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(53.0, 40.0, 30.0)"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "calculate_quartiles(population_3, \"observation\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : mean is slightly larger than median and the difference between median(q2) and q3 is larger than that of q1 and median, meaning it is positive distribution."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([22., 67.])"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "values_list = population_3[\"observation\"].to_list()\n",
+ "values_list.sort()\n",
+ " \n",
+ "np.percentile(values_list, [10, 90])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : If we see the 10th and 90th percentile and compared the difference between the median, we find that the difference vs. 90th(67-40=23) is larger than that vs. 10th (40-22=18) , meaning it is positively skewed."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Bonus challenge\n",
+ "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\"\"\"\n",
+ "your comments here\n",
+ "\"\"\""
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/ages_population.csv b/your-code/ages_population.csv
new file mode 100644
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new file mode 100644
index 0000000..00860cb
--- /dev/null
+++ b/your-code/ages_population2.csv
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new file mode 100644
index 0000000..6339a1d
--- /dev/null
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diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..8e8dcf6 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,11 +11,15 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
- "# Libraries"
+ "from sklearn.linear_model import LinearRegression\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from pylab import rcParams # library for plotting histograms"
]
},
{
@@ -29,11 +33,122 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
- "# your code here"
+ "import random\n",
+ "\n",
+ "def rolling_dice(times):\n",
+ " \n",
+ " dice = [1,2,3,4,5,6]\n",
+ " result = []\n",
+ " counter = 0\n",
+ " while counter < times:\n",
+ " result.append(random.choices(dice))\n",
+ " counter += 1\n",
+ " result_df = pd.DataFrame(result)\n",
+ " result_df[0] = result_df.rename(columns={0: \"value\"}, inplace=True)\n",
+ " result_df = result_df[[\"value\"]]\n",
+ " return result_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " value \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " value \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotting the value\n",
+ "result.plot(kind='bar', alpha=0.7)\n",
+ "plt.xlabel('roll(index)')\n",
+ "plt.ylabel('value')\n",
+ "plt.title('')\n",
+ "plt.show()"
]
},
{
@@ -61,22 +291,62 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4 4\n",
+ "2 3\n",
+ "6 2\n",
+ "5 1\n",
+ "Name: value, dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "# calculating FD\n",
+ "frequency_distribution = result['value'].value_counts()\n",
+ "frequency_distribution"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotting FD\n",
+ "frequency_distribution.plot(kind='bar', alpha=0.7)\n",
+ "plt.xlabel('value')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.title('Frequency Distribution')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "\"\"\"\n",
- "your comments here\n",
- "\"\"\""
+ "- Comment : Horizontal and vertical axes are interchanged. "
]
},
{
@@ -91,11 +361,16 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
- "# your code here"
+ "def calculate_mean(input_series):\n",
+ " count = 0\n",
+ " \n",
+ " for key,value in input_series.items():\n",
+ " count += key*value\n",
+ " return count/sum(input_series.values)"
]
},
{
@@ -107,11 +382,48 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4 4\n",
+ "2 3\n",
+ "6 2\n",
+ "5 1\n",
+ "Name: value, dtype: int64"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "# calculating FD\n",
+ "frequency_distribution = result['value'].value_counts()\n",
+ "frequency_distribution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3.9"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "calculate_mean(frequency_distribution)"
]
},
{
@@ -124,11 +436,44 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
- "# your code here"
+ "def calculate_median(input_series):\n",
+ "\n",
+ " values_list = input_series.index.tolist()\n",
+ " values_list.sort()\n",
+ "\n",
+ " n = len(values_list)\n",
+ " if n % 2 == 0:\n",
+ " middle1 = values_list[n // 2 - 1]\n",
+ " middle2 = values_list[n // 2]\n",
+ " median = (middle1 + middle2) / 2\n",
+ " else:\n",
+ " median = values_list[n // 2]\n",
+ "\n",
+ " return median"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4.5"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "calculate_median(frequency_distribution)"
]
},
{
@@ -140,11 +485,37 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
- "# your code here"
+ "def calculate_quartiles(df, column):\n",
+ " \n",
+ " values_list = df[column].to_list()\n",
+ " values_list.sort()\n",
+ " \n",
+ " q75, q50, q25 = np.percentile(values_list, [75, 50, 25])\n",
+ " return q75, q50, q25"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(4.75, 4.0, 2.5)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "calculate_quartiles(result, \"value\")"
]
},
{
@@ -158,22 +529,251 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Unnamed: 0 \n",
+ " roll \n",
+ " value \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Unnamed: 0 \n",
+ " roll \n",
+ " value \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 47 \n",
+ " 47 \n",
+ " 47 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 56 \n",
+ " 56 \n",
+ " 56 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 73 \n",
+ " 73 \n",
+ " 73 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " 11 \n",
+ " 11 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " 24 \n",
+ " 24 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " 21 \n",
+ " 21 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 99 \n",
+ " 99 \n",
+ " 99 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
100 rows × 3 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plotting the value\n",
+ "dice_hundred[\"value\"].plot(kind='bar', alpha=0.7)\n",
+ "plt.xlabel('roll(index)')\n",
+ "plt.ylabel('value')\n",
+ "plt.title('')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : The number of times that each value appears (the frequency) differs by value (ex. 5 less vs. 4 and 6)"
]
},
{
@@ -185,11 +785,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3.74"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "frequency_distribution = dice_hundred['value'].value_counts()\n",
+ "calculate_mean(frequency_distribution)"
]
},
{
@@ -201,11 +813,29 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "6 23\n",
+ "4 22\n",
+ "2 17\n",
+ "3 14\n",
+ "1 12\n",
+ "5 12\n",
+ "Name: value, dtype: int64"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "# already calculated in above cell\n",
+ "frequency_distribution"
]
},
{
@@ -217,22 +847,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAASIAAAEiCAYAAABdvt+2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAASuUlEQVR4nO3df2zU9R3H8de37e0rdXeYgv0Vuq6amrnVX6MOB05KtlYbY4L4K2u2gZlOY2HDi2EwQjymtgvJCFmITdgfqH808I8ocWh7ZlpUgkI3o6JTjFU7pGMg65XWHAf33R+kN2u7tle+X970fD6S/nHffve59312ffrttT0cz/M8AYChPOsBAIAQATBHiACYI0QAzBEiAOYIEQBzhAiAOUIEwFyB9QBflU6n9dlnnykcDstxHOtxAEyR53kaGBhQeXm58vLGv+Y570L02WefqaKiwnoMAD7p7e3VnDlzxj3nvAtROByWdGb4SCRiPI3/UqmUOjs71dDQoFAoZD1OzmF/g5XN/iYSCVVUVGS+psdz3oVo+NuxSCSSsyEqLCxUJBLhCyUA7G+wprK/k3mJhRerAZgjRADMESIA5ggRAHOECIA5QgTAHCECYI4QATBHiACYO+9+sxqAP769+i++r+nme9rwA9+X5YoIgD1CBMAcIQJgjhABMEeIAJgjRADMESIA5ggRAHOECIA5frMaOakm1qHkaX//OaqP/3Czr+vhf7giAmCOEAEwR4gAmCNEAMwRIgDmCBEAc4QIgDlCBMAcIQJgjhABMEeIAJgjRADMESIA5ggRAHOECIC5rELU2tqqa6+9VuFwWMXFxVq8eLHef//9Eed4nqdYLKby8nLNmDFDdXV1OnDggK9DA8gtWYWoq6tLzc3N2rt3r+LxuE6dOqWGhgYNDg5mztmwYYM2btyozZs3a9++fSotLVV9fb0GBgZ8Hx5AbsjqHRpfeOGFEbe3bt2q4uJidXd364YbbpDnedq0aZPWrl2rJUuWSJKefPJJlZSUqL29Xffdd59/kwPIGWf1VrH9/f2SpKKiIklST0+P+vr61NDQkDnHdV0tXLhQe/bsGTNEyWRSyWQyczuRSEiSUqmUUqnU2Yx3Xhp+TLn42M4Hw/vq5nmBrT1duPn+78Hwvk5mL7LZrymHyPM8RaNRXX/99aqpqZEk9fX1SZJKSkpGnFtSUqJPPvlkzHVaW1u1fv36Ucc7OztVWFg41fHOe/F43HqEnPZIbdr3NXft2uX7mkHa8IPg1p7M83doaGjS6005RMuXL9dbb72lV199ddTnHGfkm5Z7njfq2LA1a9YoGo1mbicSCVVUVKihoUGRSGSq4523UqmU4vG46uvrFQqFrMfJOcP7u25/npJpf988/53Yjb6uF7SaWIfva7p5nh6pTU/q+Tv83c1kTClEK1as0M6dO7V7927NmTMnc7y0tFTSmSujsrKyzPEjR46Mukoa5rquXNcddTwUCuX0F2quPz5rybTj+7/iMd3+//L78X/ZZJ6/2exXVj818zxPy5cv19NPP62//vWvqqqqGvH5qqoqlZaWjrhsO3nypLq6ujR//vxs7grA10hWV0TNzc1qb2/Xs88+q3A4nHlNaObMmZoxY4Ycx9HKlSvV0tKi6upqVVdXq6WlRYWFhWpqagrkAQCY/rIKUVtbmySprq5uxPGtW7dq2bJlkqRVq1bpiy++0AMPPKDjx49r3rx56uzsVDgc9mVgALknqxB53sQ/DnQcR7FYTLFYbKozAfia4W/NAJgjRADMESIA5ggRAHOECIA5QgTAHCECYI4QATBHiACYI0QAzBEiAOYIEQBzhAiAOUIEwBwhAmCOEAEwR4gAmCNEAMwRIgDmCBEAc4QIgDlCBMAcIQJgjhABMJfVP7AI/9TEOpQ87fi65sd/uNnX9YBzhSsiAOYIEQBzhAiAOUIEwBwhAmCOEAEwR4gAmCNEAMwRIgDmCBEAc4QIgDlCBMAcIQJgjhABMEeIAJjLOkS7d+/WLbfcovLycjmOo2eeeWbE55ctWybHcUZ8XHfddX7NCyAHZR2iwcFBXXXVVdq8efP/Peemm27S4cOHMx+7du06qyEB5Las36GxsbFRjY2N457juq5KS0unPBSAr5dA3ir25ZdfVnFxsS666CItXLhQjz32mIqLi8c8N5lMKplMZm4nEglJUiqVUiqVCmI8U8OPyc3zAlv764z9/R833/89GN7XyexFNvvleJ435Wkdx9GOHTu0ePHizLHt27frm9/8piorK9XT06N169bp1KlT6u7uluu6o9aIxWJav379qOPt7e0qLCyc6mgAjA0NDampqUn9/f2KRCLjnut7iL7q8OHDqqys1LZt27RkyZJRnx/riqiiokJHjx6dcPjpKJVKKR6Pa93+PCXT/r55/juxG31dbzpif/+nJtbh+5punqdHatOqr69XKBQa99xEIqHZs2dPKkSB/yseZWVlqqys1MGDB8f8vOu6Y14phUKhCR/odJZMO77/Kx65vF/ZYn/l++P/ssl8fWazX4H/HtGxY8fU29ursrKyoO8KwDSV9RXRiRMn9OGHH2Zu9/T06M0331RRUZGKiooUi8V02223qaysTB9//LF+97vfafbs2br11lt9HRxA7sg6RPv379eiRYsyt6PRqCRp6dKlamtr09tvv62nnnpK//nPf1RWVqZFixZp+/btCofD/k0NIKdkHaK6ujqN9/p2R4f/L5AByG38rRkAc4QIgDlCBMAcIQJgLvBfaMT09+3Vfwlk3Y//cHMg62L64YoIgDlCBMAcIQJgjhABMEeIAJgjRADMESIA5ggRAHOECIA5QgTAHCECYI4QATBHiACYI0QAzBEiAOYIEQBzhAiAOUIEwBwhAmBu2r9ndVDvpyzxnso4N4J8Dk8XXBEBMEeIAJgjRADMESIA5ggRAHOECIA5QgTAHCECYI4QATBHiACYI0QAzBEiAOYIEQBzhAiAOUIEwFzWIdq9e7duueUWlZeXy3EcPfPMMyM+73meYrGYysvLNWPGDNXV1enAgQN+zQsgB2UdosHBQV111VXavHnzmJ/fsGGDNm7cqM2bN2vfvn0qLS1VfX29BgYGznpYALkp63dobGxsVGNj45if8zxPmzZt0tq1a7VkyRJJ0pNPPqmSkhK1t7frvvvuO7tpAeQkX98qtqenR319fWpoaMgcc11XCxcu1J49e8YMUTKZVDKZzNxOJBKSpFQqpVQqNeF9uvmeD5OPbTL3P9U13Tz/5w5iXim4PWZ/zwjyOey34X2dzF5ks1+O53lT3gXHcbRjxw4tXrxYkrRnzx4tWLBAhw4dUnl5eea8X/3qV/rkk0/U0dExao1YLKb169ePOt7e3q7CwsKpjgbA2NDQkJqamtTf369IJDLuuYG8eb7jOCNue5436tiwNWvWKBqNZm4nEglVVFSooaFhwuElqSY2Om5+eSd2o+9rplIpxeNxrdufp2R67D2ZqiDmlYLbY/b3jCCfw35z8zw9UptWfX29QqHQuOcOf3czGb6GqLS0VJLU19ensrKyzPEjR46opKRkzP+N67pyXXfU8VAoNOEDlaTkaX+fbF+dISjJtOP77EHNG9Qes79nBPkcDspkvj6z2S9ff4+oqqpKpaWlisfjmWMnT55UV1eX5s+f7+ddAcghWV8RnThxQh9++GHmdk9Pj958800VFRXpW9/6llauXKmWlhZVV1erurpaLS0tKiwsVFNTk6+DA8gdWYdo//79WrRoUeb28Os7S5cu1RNPPKFVq1bpiy++0AMPPKDjx49r3rx56uzsVDgc9m9qADkl6xDV1dVpvB+0OY6jWCymWCx2NnMB+Brhb80AmCNEAMwRIgDmCBEAc4QIgDlCBMAcIQJgjhABMEeIAJgjRADMESIA5ggRAHOECIA5QgTAHCECYI4QATBHiACYI0QAzBEiAOYIEQBzhAiAOUIEwBwhAmCOEAEwR4gAmCNEAMwRIgDmCBEAc4QIgDlCBMAcIQJgjhABMEeIAJgjRADMESIA5ggRAHOECIA5QgTAHCECYI4QATDne4hisZgcxxnxUVpa6vfdAMghBUEs+r3vfU8vvvhi5nZ+fn4QdwMgRwQSooKCAq6CAExaICE6ePCgysvL5bqu5s2bp5aWFl1yySVjnptMJpVMJjO3E4mEJCmVSimVSk14X26+58/QY5jM/U91TTfP/7mDmFcKbo/Z3zOCfA77bXhfJ7MX2eyX43mer7vw/PPPa2hoSJdddpn+9a9/6dFHH9U//vEPHThwQLNmzRp1fiwW0/r160cdb29vV2FhoZ+jATiHhoaG1NTUpP7+fkUikXHP9T1EXzU4OKhLL71Uq1atUjQaHfX5sa6IKioqdPTo0QmHl6SaWIev837ZO7EbfV8zlUopHo9r3f48JdOOr2sHMa8U3B6zv2cE+Rz2m5vn6ZHatOrr6xUKhcY9N5FIaPbs2ZMKUSDfmn3ZhRdeqCuuuEIHDx4c8/Ou68p13VHHQ6HQhA9UkpKn/X2yfXWGoCTTju+zBzVvUHvM/p4R5HM4KJP5+sxmvwL/PaJkMqn33ntPZWVlQd8VgGnK9xA99NBD6urqUk9Pj15//XXdfvvtSiQSWrp0qd93BSBH+P6t2T//+U/99Kc/1dGjR3XxxRfruuuu0969e1VZWen3XQHIEb6HaNu2bX4vCSDH8bdmAMwRIgDmCBEAc4QIgDlCBMAcIQJgjhABMEeIAJgjRADMESIA5ggRAHOECIA5QgTAHCECYI4QATBHiACYI0QAzBEiAOYIEQBzhAiAOUIEwBwhAmCOEAEwR4gAmCNEAMwRIgDmCBEAc4QIgDlCBMAcIQJgjhABMEeIAJgjRADMESIA5ggRAHOECIA5QgTAHCECYI4QATBHiACYCyxEjz/+uKqqqnTBBRdo7ty5euWVV4K6KwDTXCAh2r59u1auXKm1a9fq73//u370ox+psbFRn376aRB3B2CaCyREGzdu1C9/+Uvdc889uvzyy7Vp0yZVVFSora0tiLsDMM0V+L3gyZMn1d3drdWrV4843tDQoD179ow6P5lMKplMZm739/dLkj7//HOlUqkJ76/g1OBZTvz/HTt2zPc1U6mUhoaGVJDK0+m04+vaQcwrBbfH7O8ZQT6H/VaQ9jQ0lNaxY8cUCoXGPXdgYECS5HnexAt7Pjt06JAnyXvttddGHH/ssce8yy67bNT5Dz/8sCeJDz74yNGP3t7eCbvh+xXRMMcZ+V8jz/NGHZOkNWvWKBqNZm6n02l9/vnnmjVr1pjnT3eJREIVFRXq7e1VJBKxHifnsL/BymZ/Pc/TwMCAysvLJ1zX9xDNnj1b+fn56uvrG3H8yJEjKikpGXW+67pyXXfEsYsuusjvsc47kUiEL5QAsb/Bmuz+zpw5c1Lr+f5i9Te+8Q3NnTtX8Xh8xPF4PK758+f7fXcAckAg35pFo1H9/Oc/V21trX74wx9qy5Yt+vTTT3X//fcHcXcAprlAQnTXXXfp2LFj+v3vf6/Dhw+rpqZGu3btUmVlZRB3N624rquHH3541Lej8Af7G6yg9tfxvMn8bA0AgsPfmgEwR4gAmCNEAMwRIgDmCNE50NraqmuvvVbhcFjFxcVavHix3n//feuxclZra6scx9HKlSutR8kphw4d0s9+9jPNmjVLhYWFuvrqq9Xd3e3L2oToHOjq6lJzc7P27t2reDyuU6dOqaGhQYOD0+ePHaeLffv2acuWLbryyiutR8kpx48f14IFCxQKhfT888/r3Xff1R//+Eff/gqCH98b+Pe//63i4mJ1dXXphhtusB4nZ5w4cULf//739fjjj+vRRx/V1VdfrU2bNlmPlRNWr16t1157LbA3OOSKyMDwW50UFRUZT5JbmpubdfPNN+snP/mJ9Sg5Z+fOnaqtrdUdd9yh4uJiXXPNNfrzn//s2/qE6BzzPE/RaFTXX3+9ampqrMfJGdu2bdPf/vY3tba2Wo+Skz766CO1tbWpurpaHR0duv/++/XrX/9aTz31lC/rB/Y2IBjb8uXL9dZbb+nVV1+1HiVn9Pb26je/+Y06Ozt1wQUXWI+Tk9LptGpra9XS0iJJuuaaa3TgwAG1tbXpF7/4xVmvzxXRObRixQrt3LlTL730kubMmWM9Ts7o7u7WkSNHNHfuXBUUFKigoEBdXV3605/+pIKCAp0+fdp6xGmvrKxM3/3ud0ccu/zyy317H3quiM4Bz/O0YsUK7dixQy+//LKqqqqsR8opP/7xj/X222+POHb33XfrO9/5jn77298qPz/faLLcsWDBglG/cvLBBx/49ofshOgcaG5uVnt7u5599lmFw+HMm8bNnDlTM2bMMJ5u+guHw6Neb7vwwgs1a9YsXofzyYMPPqj58+erpaVFd955p9544w1t2bJFW7Zs8WV9fnx/Dvy/t7zdunWrli1bdm6H+Zqoq6vjx/c+e+6557RmzRodPHhQVVVVikajuvfee31ZmxABMMeL1QDMESIA5ggRAHOECIA5QgTAHCECYI4QATBHiACYI0QAzBEiAOYIEQBzhAiAuf8CkE34rsQ/UD0AAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "# your code here"
+ "# Plotting into histogram\n",
+ "rcParams['figure.figsize'] = 3, 3 \n",
+ "dice_hundred[\"value\"].hist();\n",
+ "plt.tight_layout() \n",
+ "plt.show() "
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "\"\"\"\n",
- "your comments here\n",
- "\"\"\""
+ "- Comment : As larger numbers (4 and 6) appears more frequently compared to smaller values (2 and 3), mean is larger than the median (1 and 5 are found with same frequency, meaning they make no difference)."
]
},
{
@@ -244,22 +885,119 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Unnamed: 0 \n",
+ " roll \n",
+ " value \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "\"\"\"\n",
- "your comments here\n",
- "\"\"\""
+ "# Plotting into histogram\n",
+ "rcParams['figure.figsize'] = 3, 3 \n",
+ "dice_thousand[\"value\"].hist();\n",
+ "plt.tight_layout() \n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comments : The frequency distribution is more consistent for all values. It becomes more stable as the number of rolls increases."
]
},
{
@@ -274,11 +1012,142 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " observation \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 68.0 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 12.0 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 45.0 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 38.0 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 49.0 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 995 \n",
+ " 27.0 \n",
+ " \n",
+ " \n",
+ " 996 \n",
+ " 47.0 \n",
+ " \n",
+ " \n",
+ " 997 \n",
+ " 53.0 \n",
+ " \n",
+ " \n",
+ " 998 \n",
+ " 33.0 \n",
+ " \n",
+ " \n",
+ " 999 \n",
+ " 31.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1000 rows × 1 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting into histogram\n",
+ "rcParams['figure.figsize'] = 3, 3 \n",
+ "population['observation'].hist();\n",
+ "plt.tight_layout() \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Assumption\n",
+ "- range of mean : 30-40\n",
+ "- std : 15 "
]
},
{
@@ -290,22 +1159,51 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 25,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "36.56"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "np.mean(population['observation'])"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 26,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "12.81008977329979"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "\"\"\"\n",
- "your comments here\n",
- "\"\"\""
+ "np.std(population['observation'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Diffrence vs. assumption\n",
+ "- mean: falls inside the range assumed\n",
+ "- std: lower than assumption"
]
},
{
@@ -317,11 +1215,133 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 27,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " observation \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 25.0 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 31.0 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 29.0 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 31.0 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 29.0 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 995 \n",
+ " 26.0 \n",
+ " \n",
+ " \n",
+ " 996 \n",
+ " 22.0 \n",
+ " \n",
+ " \n",
+ " 997 \n",
+ " 21.0 \n",
+ " \n",
+ " \n",
+ " 998 \n",
+ " 19.0 \n",
+ " \n",
+ " \n",
+ " 999 \n",
+ " 28.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1000 rows × 1 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting into histogram\n",
+ "rcParams['figure.figsize'] = 3, 3 \n",
+ "population_2['observation'].hist();\n",
+ "plt.tight_layout() \n",
+ "plt.show()"
]
},
{
@@ -332,14 +1352,10 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "\"\"\"\n",
- "your comments here\n",
- "\"\"\""
+ "- Comment : Distribution is centered in the range of 26-29 and std is assumed to be smaller compared to that of step 2 (meaning most of the values are found between smaller range of values)."
]
},
{
@@ -351,22 +1367,49 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 29,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "27.155"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "np.mean(population_2['observation'])"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 30,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.9683286543103704"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "\"\"\"\n",
- "your comments here\n",
- "\"\"\""
+ "np.std(population_2['observation'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : Same to above."
]
},
{
@@ -381,11 +1424,133 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 31,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " observation \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 21.0 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 21.0 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 24.0 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 31.0 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 54.0 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 995 \n",
+ " 16.0 \n",
+ " \n",
+ " \n",
+ " 996 \n",
+ " 55.0 \n",
+ " \n",
+ " \n",
+ " 997 \n",
+ " 30.0 \n",
+ " \n",
+ " \n",
+ " 998 \n",
+ " 35.0 \n",
+ " \n",
+ " \n",
+ " 999 \n",
+ " 43.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1000 rows × 1 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting into histogram\n",
+ "rcParams['figure.figsize'] = 3, 3 \n",
+ "population_3['observation'].hist();\n",
+ "plt.tight_layout() \n",
+ "plt.show()"
]
},
{
@@ -397,22 +1562,49 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 33,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "41.989"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "np.mean(population_3['observation'])"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 34,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "16.136631587788084"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "\"\"\"\n",
- "your comments here\n",
- "\"\"\""
+ "np.std(population_3['observation'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Comment : Mean is larger than mode and it is assume to be positively skewed. Distribution is varied in wider range resulting in larger std."
]
},
{
@@ -424,22 +1616,29 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 35,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(53.0, 40.0, 30.0)"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "calculate_quartiles(population_3, \"observation\")"
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "\"\"\"\n",
- "your comments here\n",
- "\"\"\""
+ "- Comment : mean is slightly larger than median and the difference between median(q2) and q3 is larger than that of q1 and median, meaning it is positive distribution."
]
},
{
@@ -451,22 +1650,32 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 36,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([22., 67.])"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# your code here"
+ "values_list = population_3[\"observation\"].to_list()\n",
+ "values_list.sort()\n",
+ " \n",
+ "np.percentile(values_list, [10, 90])"
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "\"\"\"\n",
- "your comments here\n",
- "\"\"\""
+ "- Comment : If we see the 10th and 90th percentile and compared the difference between the median, we find that the difference vs. 90th(67-40=23) is larger than that vs. 10th (40-22=18) , meaning it is positively skewed."
]
},
{
@@ -479,7 +1688,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
@@ -487,10 +1696,8 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
"\"\"\"\n",
"your comments here\n",
@@ -500,9 +1707,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "ironhack-3.7",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "ironhack-3.7"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -514,7 +1721,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.11.3"
}
},
"nbformat": 4,
diff --git a/your-code/roll_the_dice_hundred.csv b/your-code/roll_the_dice_hundred.csv
new file mode 100644
index 0000000..50975a2
--- /dev/null
+++ b/your-code/roll_the_dice_hundred.csv
@@ -0,0 +1,101 @@
+,roll,value
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diff --git a/your-code/roll_the_dice_thousand.csv b/your-code/roll_the_dice_thousand.csv
new file mode 100644
index 0000000..f820dbb
--- /dev/null
+++ b/your-code/roll_the_dice_thousand.csv
@@ -0,0 +1,1001 @@
+,roll,value
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