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{
"cells": [
{
"cell_type": "markdown",
"source": [
"<div>\n",
" <h1><center>CS105 Mini-Project</center></h1>\n",
" <p>By: <b>NAMES HERE</b></p>\n",
"</div>"
],
"metadata": {
"collapsed": false
},
"id": "845bdbd833f03cba"
},
{
"cell_type": "markdown",
"source": [
"# Data Loading & Preprocessing"
],
"metadata": {
"collapsed": false
},
"id": "d720609d765d221b"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Load dataframe from data.csv\n",
"df = pd.read_csv(\"data.csv\")\n",
"\n",
"# Select relevant columns\n",
"df = df.iloc[:, [0, 2, 7, 8, 9, 58, 59, 60, 61, 26]]\n",
"df"
],
"metadata": {
"collapsed": false
},
"id": "3bea6ea662d6c063"
},
{
"cell_type": "markdown",
"source": [
"## Preprocessing"
],
"metadata": {
"collapsed": false
},
"id": "7e69a5a21a9de4ee"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"# Fixes empty values\n",
"df['Do you currently work?'] = df['Do you currently work?'].fillna('No')\n",
"\n",
"# Replaces custom text answers with appropriate values\n",
"df['How many people live in your household?'] = (df['How many people live in your household?']\n",
" .fillna(0)\n",
" .replace('4 in total', '4')\n",
" .replace('4 (Including me)', '4')\n",
" .replace('at school 4 including me ', '4')\n",
" .replace('3 excluding me', '4')\n",
" .replace('5 including me', '5')\n",
" .replace('North District 4 bed 2 bath', '4')\n",
" .replace('3 (room), 8 (hall), ~70 (building)', '3')\n",
" .astype(int))\n",
"df['Who do you live with? '] = df['Who do you live with? '].replace('Family, Friends', 'Both').replace('Family, Friends, Both', 'Both')\n",
"# Normalizes non-applicable answers\n",
"df.loc[df['Do you currently work?'] == 'No', 'How many hours do you work per week on average?'] = 0\n",
"df.loc[df['Do you currently work?'] == 'No', 'Do you work in a department related to your major?'] = np.nan\n",
"\n",
"df"
],
"metadata": {
"collapsed": false
},
"id": "f71f8085d5f66b0"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"# Working DataFrame\n",
"w_df = df[df['Do you currently work?'] == 'Yes']\n",
"# Not working DataFrame\n",
"nw_df = df[df['Do you currently work?'] == 'No']\n",
"w_df"
],
"metadata": {
"collapsed": false
},
"id": "6c1d9ee7948e6b9a"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"nw_df"
],
"metadata": {
"collapsed": false
},
"id": "34f69a756f513fb7"
},
{
"cell_type": "markdown",
"source": [
"<div>\n",
" <h1>CS105 Project</h2>\n",
" <p>Ali Naqvi, ...</p>\n",
" <p>Topic: Does who a student is living with effect if and how they work jobs?</p>\n",
"</div>\n"
],
"metadata": {
"collapsed": false
},
"id": "d5c1424ddd30ca97"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Assuming 'df' is your DataFrame\n",
"\n",
"# Count the number of people who work and don't work\n",
"work_counts = df['Do you currently work?'].value_counts()\n",
"\n",
"# Plotting a pie chart\n",
"plt.figure(figsize=(8, 8))\n",
"plt.pie(work_counts, labels=work_counts.index, autopct='%1.1f%%', startangle=90, colors=['lightblue', 'lightcoral'])\n",
"plt.title('Distribution of People Who Work and Don\\'t Work')\n",
"plt.show()\n"
],
"metadata": {
"collapsed": false
},
"id": "da1811cc63b41845"
},
{
"cell_type": "code",
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
},
"id": "201db70188d3e778"
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"collapsed": false
},
"id": "8d65fec230193b72"
}
],
"metadata": {
"colab": {
"provenance": []
},
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
