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author | 2024-02-22 16:53:07 -0800 | |
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committer | 2024-02-22 17:10:07 -0800 | |
commit | 11052b28b124233d26303750944b838f83b0cb0e (patch) | |
tree | 8ecc85fe26259e63691f83fce6f3f31b7f98c1ba /CS105MiniProject.ipynb | |
parent | e70c4343c56137100a786b2d1f1b7f8b5487c3da (diff) | |
download | CS105MiniProject-11052b28b124233d26303750944b838f83b0cb0e.tar.gz CS105MiniProject-11052b28b124233d26303750944b838f83b0cb0e.tar.zst CS105MiniProject-11052b28b124233d26303750944b838f83b0cb0e.zip |
Adds some formatting
Diffstat (limited to 'CS105MiniProject.ipynb')
-rw-r--r-- | CS105MiniProject.ipynb | 184 |
1 files changed, 75 insertions, 109 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb index ffb4fa4..c783024 100644 --- a/CS105MiniProject.ipynb +++ b/CS105MiniProject.ipynb @@ -1,65 +1,66 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, - "id": "daa13044", + "cell_type": "markdown", + "source": [ + "<div>\n", + " <h1><center>CS105 Mini-Project</center></h1>\n", + " <p>By: <b>NAMES HERE</b></p>\n", + "</div>" + ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 614 - }, - "id": "daa13044", - "outputId": "4d440aaa-1ee7-4771-c526-f55e9458ca8a", - "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.396867Z", - "start_time": "2024-02-23T01:01:40.758392Z" - } + "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": " Timestamp What is your current class standing? \\\n0 2/9/2024 20:12:14 Senior \n1 2/9/2024 20:16:34 Junior \n2 2/9/2024 20:18:55 Junior \n3 2/9/2024 20:24:00 Senior \n4 2/9/2024 20:26:16 Graduate \n.. ... ... \n255 2/14/2024 19:46:28 Junior \n256 2/15/2024 0:28:38 NaN \n257 2/15/2024 8:33:45 Senior \n258 2/15/2024 16:10:40 Sophomore \n259 2/15/2024 16:14:11 Sophomore \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? Do you currently work? \\\n0 6 Yes \n1 4 No \n2 4 No \n3 1 No \n4 1 Yes \n.. ... ... \n255 5 Yes \n256 North District 4 bed 2 bath No \n257 9 No \n258 4 Yes \n259 3 (room), 8 (hall), ~70 (building) No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 NaN \n2 NaN \n3 NaN \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 NaN \n257 1 - 5 \n258 5 - 10 \n259 NaN \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 No \n4 Yes \n.. ... \n255 No \n256 NaN \n257 No \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 10 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Timestamp</th>\n <th>What is your current class standing?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2/9/2024 20:12:14</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2/9/2024 20:16:34</td>\n <td>Junior</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2/9/2024 20:18:55</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2/9/2024 20:24:00</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2/9/2024 20:26:16</td>\n <td>Graduate</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>2/14/2024 19:46:28</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>2/15/2024 0:28:38</td>\n <td>NaN</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>North District 4 bed 2 bath</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>2/15/2024 8:33:45</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>No</td>\n <td>1 - 5</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>2/15/2024 16:10:40</td>\n <td>Sophomore</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>2/15/2024 16:14:11</td>\n <td>Sophomore</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3 (room), 8 (hall), ~70 (building)</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 10 columns</p>\n</div>" - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } + "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": "code", - "execution_count": 2, - "id": "29889175", + "cell_type": "markdown", + "source": [ + "## Preprocessing" + ], "metadata": { - "id": "29889175", - "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.409516Z", - "start_time": "2024-02-23T01:01:41.398267Z" - } + "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": " Timestamp What is your current class standing? \\\n0 2/9/2024 20:12:14 Senior \n1 2/9/2024 20:16:34 Junior \n2 2/9/2024 20:18:55 Junior \n3 2/9/2024 20:24:00 Senior \n4 2/9/2024 20:26:16 Graduate \n.. ... ... \n255 2/14/2024 19:46:28 Junior \n256 2/15/2024 0:28:38 NaN \n257 2/15/2024 8:33:45 Senior \n258 2/15/2024 16:10:40 Sophomore \n259 2/15/2024 16:14:11 Sophomore \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? Do you currently work? \\\n0 6 Yes \n1 4 No \n2 4 No \n3 1 No \n4 1 Yes \n.. ... ... \n255 5 Yes \n256 4 No \n257 9 No \n258 4 Yes \n259 3 No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 0 \n2 0 \n3 0 \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 0 \n257 0 \n258 5 - 10 \n259 0 \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 NaN \n4 Yes \n.. ... \n255 No \n256 NaN \n257 NaN \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 10 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Timestamp</th>\n <th>What is your current class standing?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2/9/2024 20:12:14</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2/9/2024 20:16:34</td>\n <td>Junior</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2/9/2024 20:18:55</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2/9/2024 20:24:00</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2/9/2024 20:26:16</td>\n <td>Graduate</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>2/14/2024 19:46:28</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>2/15/2024 0:28:38</td>\n <td>NaN</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>2/15/2024 8:33:45</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>2/15/2024 16:10:40</td>\n <td>Sophomore</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>2/15/2024 16:14:11</td>\n <td>Sophomore</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 10 columns</p>\n</div>" - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "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", @@ -70,67 +71,43 @@ " .replace('North District 4 bed 2 bath', '4')\n", " .replace('3 (room), 8 (hall), ~70 (building)', '3')\n", " .astype(int))\n", - "df.loc[df['Do you currently work?'] == 'No', 'How many hours do you work per week on average?'] = 0\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", - "execution_count": 3, - "id": "de4448fd64205d85", - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.418974Z", - "start_time": "2024-02-23T01:01:41.410787Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": " Timestamp What is your current class standing? \\\n0 2/9/2024 20:12:14 Senior \n4 2/9/2024 20:26:16 Graduate \n8 2/9/2024 22:02:49 Junior \n9 2/9/2024 22:08:43 Senior \n13 2/9/2024 22:15:13 Junior \n.. ... ... \n246 2/13/2024 19:37:02 Graduate \n247 2/13/2024 21:39:14 Senior \n252 2/14/2024 9:48:12 Junior \n255 2/14/2024 19:46:28 Junior \n258 2/15/2024 16:10:40 Sophomore \n\n Who do you live with? \\\n0 Neither \n4 Neither \n8 Friends \n9 Family \n13 Family \n.. ... \n246 Family \n247 Friends \n252 Family \n255 Friends \n258 Family \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n4 Apartment \n8 House \n9 House \n13 Apartment \n.. ... \n246 House \n247 Apartment \n252 House \n255 House \n258 Apartment \n\n How many people live in your household? Do you currently work? \\\n0 6 Yes \n4 1 Yes \n8 6 Yes \n9 5 Yes \n13 4 Yes \n.. ... ... \n246 2 Yes \n247 3 Yes \n252 5 Yes \n255 5 Yes \n258 4 Yes \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n4 10 - 20 \n8 10 - 20 \n9 1 - 5 \n13 10 - 20 \n.. ... \n246 10 - 20 \n247 20 - 40 \n252 20 - 40 \n255 10 - 20 \n258 5 - 10 \n\n Do you work on or off campus? \\\n0 Off-campus \n4 Off-campus \n8 On-campus \n9 On-campus \n13 Off-campus \n.. ... \n246 On-campus \n247 Off-campus \n252 Off-campus \n255 On-campus \n258 On-campus \n\n Do you work in a department related to your major? \\\n0 No \n4 Yes \n8 No \n9 No \n13 No \n.. ... \n246 Yes \n247 No \n252 No \n255 No \n258 No \n\n Do you have roommates that are part of your major? \n0 No \n4 No \n8 No \n9 No \n13 No \n.. ... \n246 No \n247 Yes \n252 No \n255 No \n258 No \n\n[77 rows x 10 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Timestamp</th>\n <th>What is your current class standing?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2/9/2024 20:12:14</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2/9/2024 20:26:16</td>\n <td>Graduate</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2/9/2024 22:02:49</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2/9/2024 22:08:43</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>1 - 5</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2/9/2024 22:15:13</td>\n <td>Junior</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>246</th>\n <td>2/13/2024 19:37:02</td>\n <td>Graduate</td>\n <td>Family</td>\n <td>House</td>\n <td>2</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>247</th>\n <td>2/13/2024 21:39:14</td>\n <td>Senior</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>3</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>252</th>\n <td>2/14/2024 9:48:12</td>\n <td>Junior</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>255</th>\n <td>2/14/2024 19:46:28</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>2/15/2024 16:10:40</td>\n <td>Sophomore</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n </tbody>\n</table>\n<p>77 rows × 10 columns</p>\n</div>" - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "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": [ - { - "data": { - "text/plain": " Timestamp What is your current class standing? \\\n1 2/9/2024 20:16:34 Junior \n2 2/9/2024 20:18:55 Junior \n3 2/9/2024 20:24:00 Senior \n5 2/9/2024 20:45:09 Junior \n6 2/9/2024 21:55:59 Sophomore \n.. ... ... \n253 2/14/2024 13:45:45 Senior \n254 2/14/2024 16:26:06 Junior \n256 2/15/2024 0:28:38 NaN \n257 2/15/2024 8:33:45 Senior \n259 2/15/2024 16:14:11 Sophomore \n\n Who do you live with? \\\n1 Both \n2 Friends \n3 Neither \n5 Both \n6 Friends \n.. ... \n253 Family \n254 Family \n256 Family \n257 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n1 Apartment \n2 House \n3 Apartment \n5 Apartment \n6 Apartment \n.. ... \n253 House \n254 House \n256 Apartment \n257 House \n259 Dorm \n\n How many people live in your household? Do you currently work? \\\n1 4 No \n2 4 No \n3 1 No \n5 4 No \n6 4 No \n.. ... ... \n253 6 No \n254 5 No \n256 4 No \n257 9 No \n259 3 No \n\n How many hours do you work per week on average? \\\n1 0 \n2 0 \n3 0 \n5 0 \n6 0 \n.. ... \n253 0 \n254 0 \n256 0 \n257 0 \n259 0 \n\n Do you work on or off campus? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 Off-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 NaN \n259 NaN \n\n Do you have roommates that are part of your major? \n1 Yes \n2 No \n3 No \n5 No \n6 No \n.. ... \n253 No \n254 Yes \n256 No \n257 No \n259 Yes \n\n[176 rows x 10 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Timestamp</th>\n <th>What is your current class standing?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>2/9/2024 20:16:34</td>\n <td>Junior</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2/9/2024 20:18:55</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2/9/2024 20:24:00</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2/9/2024 20:45:09</td>\n <td>Junior</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2/9/2024 21:55:59</td>\n <td>Sophomore</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>253</th>\n <td>2/14/2024 13:45:45</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>6</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>254</th>\n <td>2/14/2024 16:26:06</td>\n <td>Junior</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>256</th>\n <td>2/15/2024 0:28:38</td>\n <td>NaN</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>2/15/2024 8:33:45</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>2/15/2024 16:14:11</td>\n <td>Sophomore</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>176 rows × 10 columns</p>\n</div>" - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nw_df" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.427847Z", - "start_time": "2024-02-23T01:01:41.419852Z" - } + "collapsed": false }, - "id": "5fe8ec7f22878e60", - "execution_count": 4 + "id": "34f69a756f513fb7" }, { "cell_type": "markdown", @@ -144,20 +121,11 @@ "metadata": { "collapsed": false }, - "id": "899d85626b77db20" + "id": "d5c1424ddd30ca97" }, { "cell_type": "code", - "outputs": [ - { - "data": { - "text/plain": "<Figure size 800x800 with 1 Axes>", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -173,28 +141,26 @@ "plt.show()\n" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.526696Z", - "start_time": "2024-02-23T01:01:41.430135Z" - } + "collapsed": false }, - "id": "bfa40c9e9693481d", - "execution_count": 5 + "id": "da1811cc63b41845" }, { "cell_type": "code", "outputs": [], "source": [], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.532148Z", - "start_time": "2024-02-23T01:01:41.528825Z" - } + "collapsed": false + }, + "id": "201db70188d3e778" + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false }, - "id": "9c830283e9b26466", - "execution_count": 5 + "id": "8d65fec230193b72" } ], "metadata": { |