diff options
| author |  Anshul Gupta <ansg191@anshulg.com>
| 2024-02-23 18:33:39 -0800 |
|---|---|---|
| committer |  GitHub <noreply@github.com>
| 2024-02-23 18:33:39 -0800 |
| commit | 7d9962d6e4d95419120e05a29e6462ad2fb83953 (patch) | |
| tree | 9a448b765f742b1147d5c7cf70ef1032c74c8036 /CS105MiniProject.ipynb | |
| parent | 08f88717cf4f1c8e59240640a664476e6544303d (diff) | |
| parent | 2df5c2edaa3d03f0f0f4ebf29f5b96c7c83c262b (diff) | |
| download | CS105MiniProject-7d9962d6e4d95419120e05a29e6462ad2fb83953.tar.gz CS105MiniProject-7d9962d6e4d95419120e05a29e6462ad2fb83953.tar.zst CS105MiniProject-7d9962d6e4d95419120e05a29e6462ad2fb83953.zip | |
Merge pull request #22 from ansg191/fix-lost-cells
Fixes cells lost in #21
Diffstat (limited to 'CS105MiniProject.ipynb')
| -rw-r--r-- | CS105MiniProject.ipynb | 238 |
1 files changed, 187 insertions, 51 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb index 818356f..3e15525 100644 --- a/CS105MiniProject.ipynb +++ b/CS105MiniProject.ipynb @@ -2,35 +2,35 @@ "cells": [ { "cell_type": "markdown", + "id": "21abd26c73fd0070", + "metadata": { + "collapsed": false + }, "source": [ "<div>\n", " <h1><center>CS105 Mini-Project</center></h1>\n", " <h2><center>Does who a student is living with effect if and how they work jobs?</center></h2>\n", " <p>By: <b>NAMES HERE</b></p>\n", "</div>" - ], - "metadata": { - "collapsed": false - }, - "id": "845bdbd833f03cba" + ] }, { "cell_type": "markdown", - "source": [ - "# Data Loading & Preprocessing" - ], + "id": "69d8e8ad7c61ba61", "metadata": { "collapsed": false }, - "id": "d720609d765d221b" + "source": [ + "# Data Loading & Preprocessing" + ] }, { "cell_type": "code", "outputs": [ { "data": { - "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \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>What is your current class standing?</th>\n <th>What is your age?</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>Senior</td>\n <td>23+</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>Junior</td>\n <td>20</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>Junior</td>\n <td>23+</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>Senior</td>\n <td>23+</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>Graduate</td>\n <td>22</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>Junior</td>\n <td>21</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>NaN</td>\n <td>21</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>Senior</td>\n <td>21</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>Sophomore</td>\n <td>21</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>Sophomore</td>\n <td>18</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>" + "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \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, apartment, 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? \\\n0 6 \n1 4 \n2 4 \n3 1 \n4 1 \n.. ... \n255 5 \n256 North District 4 bed 2 bath \n257 9 \n258 4 \n259 3 (room), 8 (hall), ~70 (building) \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n1 3.7 No \n2 3.75 No \n3 3.81 No \n4 3.23 Yes \n.. ... ... \n255 4 Yes \n256 3.5 No \n257 3.7 No \n258 3 Yes \n259 4 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 11 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>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</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>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</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>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.7</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>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</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>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</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>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</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 <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4</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>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>North District 4 bed 2 bath</td>\n <td>3.5</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>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.7</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>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3</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>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3 (room), 8 (hall), ~70 (building)</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 </tbody>\n</table>\n<p>260 rows × 11 columns</p>\n</div>" }, "execution_count": 1, "metadata": {}, @@ -49,17 +49,17 @@ "df = pd.read_csv(\"data.csv\")\n", "\n", "# Select relevant columns\n", - "df = df.iloc[:, [2, 3, 7, 8, 9, 58, 59, 60, 61, 26]]\n", + "df = df.iloc[:, [2, 3, 7, 8, 9, 34, 58, 59, 60, 61, 26]]\n", "df" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T02:10:58.731970Z", - "start_time": "2024-02-23T02:10:58.699969Z" + "end_time": "2024-02-24T02:30:50.385493Z", + "start_time": "2024-02-24T02:30:50.364241Z" } }, - "id": "3bea6ea662d6c063", + "id": "b68b27041fdab1a5", "execution_count": 1 }, { @@ -70,15 +70,15 @@ "metadata": { "collapsed": false }, - "id": "7e69a5a21a9de4ee" + "id": "f7ee1fc9a8abba2b" }, { "cell_type": "code", "outputs": [ { "data": { - "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \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>What is your current class standing?</th>\n <th>What is your age?</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>Senior</td>\n <td>23+</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>Junior</td>\n <td>20</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>Junior</td>\n <td>23+</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>Senior</td>\n <td>23+</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>Graduate</td>\n <td>22</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>Junior</td>\n <td>21</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>NaN</td>\n <td>21</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>Senior</td>\n <td>21</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>Sophomore</td>\n <td>21</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>Sophomore</td>\n <td>18</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>" + "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \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, apartment, 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? \\\n0 6 \n1 4 \n2 4 \n3 1 \n4 1 \n.. ... \n255 5 \n256 4 \n257 9 \n258 4 \n259 3 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n1 3.70 No \n2 3.75 No \n3 3.81 No \n4 3.23 Yes \n.. ... ... \n255 4.00 Yes \n256 3.50 No \n257 3.70 No \n258 3.00 Yes \n259 4.00 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 11 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>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</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>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</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>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.70</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>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</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>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</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>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</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 <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4.00</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>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</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>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.70</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>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.00</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>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>4.00</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 × 11 columns</p>\n</div>" }, "execution_count": 2, "metadata": {}, @@ -100,7 +100,23 @@ " .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", + "df['Who do you live with?'] = df['Who do you live with?'].replace('Family, Friends', 'Both').replace(\n", + " 'Family, Friends, Both', 'Both')\n", + "df['Do you currently live in a house, apartment, or dorm?'] = (\n", + " df['Do you currently live in a house, apartment, or dorm?']\n", + " .replace('house (renting)', 'House'))\n", + "\n", + "df.loc[df['What was your GPA your very first quarter at UCR?'].str.contains(\n", + " \"I am not sure|idk|I don't know|This is my first quarter|i don't rem|not sure|I never checked. |I dont know\") == True, 'What was your GPA your very first quarter at UCR?'] = np.nan\n", + "df['What was your GPA your very first quarter at UCR?'] = (\n", + " df['What was your GPA your very first quarter at UCR?']\n", + " .replace('Idk, I think 3.2 or something along those lines', '3.2')\n", + " .replace('2.8?', '2.8')\n", + " .replace('3 point something', '3.0')\n", + " .replace('3.67 I think', '3.67')\n", + " .replace('3.0?', '3.0')\n", + " .replace('about 3.0', '3.0')\n", + " .astype(np.float64))\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", @@ -110,11 +126,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T02:10:58.744774Z", - "start_time": "2024-02-23T02:10:58.732815Z" + "end_time": "2024-02-24T02:30:50.398700Z", + "start_time": "2024-02-24T02:30:50.386214Z" } }, - "id": "f71f8085d5f66b0", + "id": "3f72adcb3bc0285e", "execution_count": 2 }, { @@ -122,8 +138,8 @@ "outputs": [ { "data": { - "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n4 Graduate 22 \n8 Junior 20 \n9 Senior 22 \n13 Junior 21 \n.. ... ... \n246 Graduate 23+ \n247 Senior 21 \n252 Junior 20 \n255 Junior 21 \n258 Sophomore 21 \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>What is your current class standing?</th>\n <th>What is your age?</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>Senior</td>\n <td>23+</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>Graduate</td>\n <td>22</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>Junior</td>\n <td>20</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>Senior</td>\n <td>22</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>Junior</td>\n <td>21</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>Graduate</td>\n <td>23+</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>Senior</td>\n <td>21</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>Junior</td>\n <td>20</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>Junior</td>\n <td>21</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>Sophomore</td>\n <td>21</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>" + "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n4 Graduate 22 \n8 Junior 20 \n9 Senior 22 \n13 Junior 21 \n.. ... ... \n246 Graduate 23+ \n247 Senior 21 \n252 Junior 20 \n255 Junior 21 \n258 Sophomore 21 \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, apartment, 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? \\\n0 6 \n4 1 \n8 6 \n9 5 \n13 4 \n.. ... \n246 2 \n247 3 \n252 5 \n255 5 \n258 4 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n4 3.23 Yes \n8 3.40 Yes \n9 NaN Yes \n13 3.50 Yes \n.. ... ... \n246 4.00 Yes \n247 3.60 Yes \n252 3.50 Yes \n255 4.00 Yes \n258 3.00 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 11 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>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</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>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</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>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</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>Junior</td>\n <td>20</td>\n <td>Friends</td>\n <td>House</td>\n <td>6</td>\n <td>3.40</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>Senior</td>\n <td>22</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>NaN</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>Junior</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</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 <td>...</td>\n </tr>\n <tr>\n <th>246</th>\n <td>Graduate</td>\n <td>23+</td>\n <td>Family</td>\n <td>House</td>\n <td>2</td>\n <td>4.00</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>Senior</td>\n <td>21</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>3</td>\n <td>3.60</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>Junior</td>\n <td>20</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>3.50</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>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4.00</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>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.00</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 × 11 columns</p>\n</div>" }, "execution_count": 3, "metadata": {}, @@ -140,11 +156,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T02:10:58.754973Z", - "start_time": "2024-02-23T02:10:58.746452Z" + "end_time": "2024-02-24T02:30:50.408153Z", + "start_time": "2024-02-24T02:30:50.400240Z" } }, - "id": "6c1d9ee7948e6b9a", + "id": "285236650ff590d8", "execution_count": 3 }, { @@ -152,8 +168,8 @@ "outputs": [ { "data": { - "text/plain": " What is your current class standing? What is your age? \\\n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n5 Junior 21 \n6 Sophomore 19 \n.. ... ... \n253 Senior 21 \n254 Junior 19 \n256 NaN 21 \n257 Senior 21 \n259 Sophomore 18 \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[183 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>What is your current class standing?</th>\n <th>What is your age?</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>Junior</td>\n <td>20</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>Junior</td>\n <td>23+</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>Senior</td>\n <td>23+</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>Junior</td>\n <td>21</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>Sophomore</td>\n <td>19</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>Senior</td>\n <td>21</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>Junior</td>\n <td>19</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>NaN</td>\n <td>21</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>Senior</td>\n <td>21</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>Sophomore</td>\n <td>18</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>183 rows × 10 columns</p>\n</div>" + "text/plain": " What is your current class standing? What is your age? \\\n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n5 Junior 21 \n6 Sophomore 19 \n.. ... ... \n253 Senior 21 \n254 Junior 19 \n256 NaN 21 \n257 Senior 21 \n259 Sophomore 18 \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, apartment, 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? \\\n1 4 \n2 4 \n3 1 \n5 4 \n6 4 \n.. ... \n253 6 \n254 5 \n256 4 \n257 9 \n259 3 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n1 3.70 No \n2 3.75 No \n3 3.81 No \n5 4.00 No \n6 4.00 No \n.. ... ... \n253 4.00 No \n254 3.80 No \n256 3.50 No \n257 3.70 No \n259 4.00 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[183 rows x 11 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>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</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>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.70</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>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</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>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</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>Junior</td>\n <td>21</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>4.00</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>Sophomore</td>\n <td>19</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>4</td>\n <td>4.00</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 <td>...</td>\n </tr>\n <tr>\n <th>253</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>6</td>\n <td>4.00</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>Junior</td>\n <td>19</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>3.80</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>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</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>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.70</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>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>4.00</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>183 rows × 11 columns</p>\n</div>" }, "execution_count": 4, "metadata": {}, @@ -166,11 +182,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T02:10:58.763694Z", - "start_time": "2024-02-23T02:10:58.755858Z" + "end_time": "2024-02-24T02:30:50.417032Z", + "start_time": "2024-02-24T02:30:50.408722Z" } }, - "id": "34f69a756f513fb7", + "id": "6516c926e6efd1c3", "execution_count": 4 }, { @@ -181,7 +197,7 @@ "metadata": { "collapsed": false }, - "id": "d5c1424ddd30ca97" + "id": "7efd20d58edbb05d" }, { "cell_type": "code", @@ -208,11 +224,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T02:10:58.965154Z", - "start_time": "2024-02-23T02:10:58.764461Z" + "end_time": "2024-02-24T02:30:50.608877Z", + "start_time": "2024-02-24T02:30:50.418071Z" } }, - "id": "da1811cc63b41845", + "id": "6deea60d8966fa15", "execution_count": 5 }, { @@ -221,30 +237,32 @@ { "data": { "text/plain": "<Figure size 800x800 with 2 Axes>", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "plt.subplots(figsize=(8, 8))\n", "df_2dhist = pd.DataFrame({\n", " x_label: grp['Do you currently work?'].value_counts()\n", - " for x_label, grp in df.groupby('Do you currently live in a house, apartnment, or dorm? ')\n", + " for x_label, grp in df.groupby('Do you currently live in a house, apartment, or dorm?')\n", "})\n", - "sns.heatmap(df_2dhist, cmap='viridis')\n", - "plt.xlabel('Do you currently live in a house, apartnment, or dorm? ')\n", + "\n", + "# Plot heatmap\n", + "plt.subplots(figsize=(8, 8))\n", + "sns.heatmap(df_2dhist, cmap=\"Blues\")\n", + "plt.xlabel('Do you currently live in a house, apartment, or dorm?')\n", "_ = plt.ylabel('Do you currently work?')" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T02:10:59.277945Z", - "start_time": "2024-02-23T02:10:58.967433Z" + "end_time": "2024-02-24T02:30:50.805764Z", + "start_time": "2024-02-24T02:30:50.611563Z" } }, - "id": "201db70188d3e778", + "id": "c6372820e5ee501f", "execution_count": 6 }, { @@ -253,7 +271,7 @@ "metadata": { "collapsed": false }, - "id": "8d65fec230193b72" + "id": "3ef5084b2abd603e" }, { "cell_type": "code", @@ -261,25 +279,143 @@ { "data": { "text/plain": "<Figure size 640x480 with 1 Axes>", - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "df.groupby('Do you currently live in a house, apartnment, or dorm? ').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n", - "plt.gca().spines[['top', 'right',]].set_visible(False)" + "df.groupby('Do you currently live in a house, apartment, or dorm?').size().plot(kind='barh',\n", + " color=sns.palettes.mpl_palette(\n", + " 'Dark2'))\n", + "plt.gca().spines[['top', 'right', ]].set_visible(False)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T02:10:59.485432Z", - "start_time": "2024-02-23T02:10:59.281076Z" + "end_time": "2024-02-24T02:30:50.904997Z", + "start_time": "2024-02-24T02:30:50.807674Z" } }, - "id": "5e460707e32c4a2a", + "id": "67df9b48e43a5307", "execution_count": 7 + }, + { + "cell_type": "code", + "outputs": [ + { + "data": { + "text/plain": "<Figure size 640x480 with 1 Axes>", + "image/png": 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JX39OVLOoh7OnjiAhORW3bwXAZdjPSEpOQ4MG9li2bAkUCgWCg29hwIBBSrf7+msT8f/6+vpQKBQAAFPTKihRQhubN69HeHgYHj0Kx8OH4UrHez+nYcPGuHEjfY/M69evMHz4GKxbl35o5Pr1qxg2bDQAoEOHzjh37jT++usAIiIe4d69EADpb861almgaVM5xo8fhSpVqkIub4GuXbtDVzfzi2Xp0qXRpElT+PmdRP36Njh16rj4ghYR8RAWFnWU9kbUq2eNV69e4f379znankqVvsr2uow3xPfv34tvZK1atYGNTfoA4L17dyE09IHSbTZu3AEg/c1SX19fae/UyZPHoK2tLQY1W1s7lC5tgL//Ppzl3pa2bdvDx2c73r59g6dPn+Dt2zdwcJAjNvY13r17i7p164nzamlpoXbtuoiIeJSj7U7f9srZXte6dTtMnfozkpOTcetWEJKTk9G4sQPi4+Px8uULzJo1VenTcVJSktIhg4/v12rVqsPMrBZOnTqBXr364tSpE+jf/8cc1/kxQ8NySm+qLi4/wtt7Bf78cz+aNpWjfftO2QaYj0VEPIKJiSn09P79EFKvXn2cP382R3U0b94S165dxvLlSxAZ+Qj376c/vlNT/31eV66c/f0bEfEQFy6cU3rTTklJUdrj8bnbGxmVR8eOXeDjsx0PHtzHo0cPERp6P9tA/SktLa1sfzsmLS1NfE4ZGJTB+/fvcrRM4N/H/6NHD/H77x5wdGyBnj17ZznvwoW/4euvTfDLL3Py/SSSDCFEuWRmYYk3sTF4FHoPb17HwKx2+htPjRpmKFVKH0FBAXj4MEx8Y8zw6e7TjF3+Dx7cx8iRQyCXNxf3VuzenfPDF40aNcGOHVthaVkPlpb1YWNji4iIR4iIeISoqMfip+N582bh1q2b6NChE7p37wUjo/IYPjz9jUcmk2HhwqW4cycY58+fxdmzfjhwYC9WrlyLWrUsMq2zTZv2WLHiDwwa5Irr16/ip58mAUg/1vyptLRU8d+sXtg+fpPIbhkZLCzqQFNTE8HBQXB0bAkAKFVKH6VKpX/LJSOkfCyrPQsZTpw4iqSkJLRv30KpHj+/Exg/flKmT6y1alnAxMQU586dRmRkJBwdm0NHRwfJyclZLj8tLVXc/k99ut0AxD0FWbGxaQA9vZK4du0Krly5iObNW6JEiRLiOJG5cxcofSsCgNKejU/v1zZt2sHP7yTs7Zvg2bMn4h4cVX263H79BsLJqS3OnvXDhQvnMG7cCLi5TUfXrt0/u5z0wKt8GOzj04L/12NnzZqVOHToIDp16ooOHTrj55+nZBpD8rmfh0hNTUW7dh3Rv7/yh4ePA/Xnbh8d/RJDhrjAwqIO7O0b45tvvsXFi+dx+/atbG/zsdKlS2c7DuPDhzjo66d/48vCog58fLZBEIRM98nJk8dx5cpFTJs2S5yW8fg3MTFFmTJlMGrUUFSoUBF9+vRTum1ycjKuX7+CZctWZxpvlR84JoQol3R09VClei2cPX4I1WrWFt+oZDIZbGxs8b//HYKpadUsdzln5ehRX9jY2GLWrHn49tteqFPHElFRkWJI+a9PJPb2jRAW9gCXLl2AtbUNDAzKoEqVqtiwYQ2srGygp6eHDx/icPz4EcyZ8xsGDx6GFi1a4f379PEegiAgIuIRvLyWom7denB1HYmtW3fD2NgYV65cynKdcnlzvH//Hjt3boWZWS1xL0+VKlVx795dpbEIwcG3ULasIQwMyqBEiRJKg03j4+OzPW9CVgwNDdG8eSts3bpJaR0ZYmKic7ysyMgI3L9/Dz/9NBEbN24X/3799Td8+PABZ86czvJ2bdt2wIUL53Dp0nm0bt0eQPqerXLljJTecFJSUnDvXogYDNK3PV68PmPwcE5paGigVas2uHjxPM6dO4M2bdLXXbp0aRgalsPr1zEwMTGFiYkpjI0rYeXKZYiMjMh2eW3adEBwcDCOHPnfP+NaSqpUT1aSkpKwdOnvKFGiBPr06Yfly73xzTffiuNPPvdYrlatBh4/jhTHNwHAgwf3xP9raWkp3X+CIODZs6fi5T//3Ifx490wYsQYtG7dLsdjcTKYmlZFVNRj8T5MD5tncOzY3zm6/dmzfihdugwWLlyK3r37wtraVqnH//U8rlmzFj58+KB0rg8gfQ/Ghw8fUKuWOQDAyakN3r17h+PHjyrNl5qail27tn12u62srPHtt72wdu0qpYHxQPqYliZNmmYKsvmFIYTUWs2K+rD8yiDf/2pW1M9VfbXq1Me1CydhYWmjNN3W1g7nz5/JNB7kc8qUKYOwsFDcuROMyMgILF++BHfv3kFycvrhmozd0yEhd5GUlJTF7cuiVi1zHD9+RBxgZm1ti1OnjouHGbS1daCrq4fTp0/h2bOnuHLlEhYvTv+GS3JyMvT19XHw4F5s2rQOT58+wcWL5/Hs2VOYm9fOsmYdHV04OrbArl3blY4tt2vXEcnJyVi40B2PHj3EuXOnsWGDN779thdkMhlq166L0NAHOHXqBCIjI7BwoTs0NDRzfF8BwPjxk/Du3TuMGTMMly6l1xkYGIAZMybj0KGDSoPsPifjWwbffNMDNWrUFP9at26HatVq4MiRw1nerk2b9rhy5TJevXql9BXG7777HuvXe+P8+bN49OghFiyYB4UiCU5O6fdP7dp1sW+fDx4/jsT582fg66v6+SJat26Ho0d9oVAoxD1cGetes2YVzp8/i8ePIzF//lzcuhUkjgHJSqVKlWBpWQ+7d+9EmzaZxwfkho6ODm7eDMSSJZ6IjHyEkJA7CAq6AXPz9L1purq6ePbsKaKjX2a6bYMG9qhSpRo8PH7Fw4fh+Pvvwzh69N9z8tSuXRfv3r3F3r278ORJFJYvX4x37/49LGFgUAYXLpzFkydRCAoKxNy5MwFAPOyZFT09PTx8GIa4uDj06OGMkJC7WLNmJR4/jsSxY0ewZs2Kzx4i+5iBQRm8ePEc169fxZMnUdi2bRPOnDklrl9XN/15HBp6XylMZTA2rgRHxxaYM2cGAgKu49mzp7h69TJmz56O1q3bokKFigDSD9n9+ONQzJ8/Fz4+2/H4cSTu3AnGjBluePIkCsOHj/5snUOGjICeXkksX774k/oNMHnyjCz3JuYHHo4htZScmobk1DT80Sfrbz/k5zpVUauOFY4f2g3zLEJIYmIibG1zHkJ69erzzyfyUdDW1oaNjS1+/HEoTpxI/6RTo0ZNNGzYGCNGDMLs2e5ZjlpP/5bMA9SpYwkgfXDmwYP70KRJeggpUaIEZs6cAy+vpdi7dxcqV/4aAwYMwtq1q3D/fgjatu0Ad3dPrFq1HFu2bIShoSGGDRv92fMEODm1xfHjR5RG+pcsWQqLFi3DH38swqBBP6BsWUM4O/eFi0v6YR97+0bo0+cHLFzoDk1NDXz33Q8q7b0AgHLljLBu3Rbs2LEFy5YtxosXz1GyZEnUr2+L5cu9c3yCuJMnj6Fdu45ZHv759tue+OOPRYiOfim++GcwMTFFtWrVYWFRW2lXfZ8+/fDhwwcsXOiODx/iUK+eNZYv9xb3iI0fPwnz589D//7foXbtuhg8eLhK3/4AgHr1rFC2bFk0buygtO6+fV0QHx8PT093fPjwAbVr18XixcszDTT9VJs27RAa+gAODnKV6vicOXM8sHjxAgwZMgCamppwcmqDgQMHAwDat++MadN+xsCBfXH48AmlvQMymQzu7p5YsGAuBg/uBwuL2ujQobM4rsXUtApGjfoJmzdvwNq1q9Cp0zdo1erf58LUqTOxaNF8uLh8hwoVKqBr1+7Q1NTEgwf30KRJ0yxr7dWrD1asWIYnT6IwduzPWLBgMVatWo6dO7eifPmKGD36J7Rr1zFH2+3k1BZBQTcwY8ZkyGQy1KlTF6NH/4T1672hUChQtmxZtG/fETNnTsWIEWPQu/f3mZYxc+Y8eHuvwLx5sxAb+xqGhuXQunU7DBkyXGm+/v0HoWJFY+zd64P169dAR0cH9etbY/Xq9Upjz7JSunRpDB8+CvPnz8O1a1fQsGFjAOmnG3B2/gbLlq1WCrj5RSao+VljYmLeQ70r/DyZDChfvnSh3478lJyswKtXz1Cx4leIiE0WB4bqlNBEiQIcoJqcmoakHA5KzY5eCU3UMtYvBD/FnXf++usAjh37G15ea1S6XeH4yfLspaWloVevrpgx49cCebHOT2vXrsSLFy8wY8av/z2zBNav98aNG/4qP8YKo8LyvMh43TYyqqw0Zgf4930vJ7gnhNRWUnLqF4cCyj9RUY8REnIHmzevh6vrSKnLKVAXL57H1auXoK2to9IhN3UTGvoADx7cw/79ezB//hKpy6FiqNiHEA0NmUo/XJZb+fmV07Q0IUenGifKS8+ePcH8+XPh6Ngy01kbi7qdO7ciMjICc+Z4ZHmyqMIiJOQOli71RM+evWFtbSN1OVQMFevDMVL8SFp+UOWH19RRdodjCqPieDgmtwrLbufigL1QH4WlFzwckwcK+kfS8oOqP7xGRESkLop1CMmQ8SNpRERE9N/y6iAKQwhJLuOYekpK1mebpIKnqamBfD5bM4D0Xc/5SRCAVBW/dk1E/02hSD9XUcavUecWQwhJTkNDEzo6unj37g2QVhIy9R6m9HlpaVAokpCaWoi3AUBamgzP3iZCUQiOTWdHW0sDlcvoFvpeFIS0NBnvJzWh7r0QBAEKRRLi4mKhp6f/xQOzGUJIcjKZDIaG5fHq1XPEvYtGiho/Af9LqpYMMYJutj9AVVhoaGgg9n0iklMKby9KaMlQIrnw96IgaGho8H5SE4WlF3p6+jAwyPmv+GaHIYTUgpZWCZibm8N93UWERxfOQcJA+kBhb5e6ePs2Xq0/zXyOpqYMZcqUxMKt1wvtgG2gaPSiIMhkgKFhKcTGfuAJFSVWWHqhqamVZ19NZwghtaGhoYHXiQKevC+8X9EtW1qArq4uEhJSC8XX7LKipaUBXV1dxCSwF8WBTJb+Wy4lSiSr9RtfcVAce1G4T5BBREREhRb3hBAREeVQQZxlOz/PsA2o11m2GUKIiIhyoKDOsm1oWCpfl69OZ9lWOYRERERgzpw5CAgIQJkyZdCvXz8MGTIky3lHjBiBU6dOKU1bvXo1WrVqlbtqiYiIJMKzbOc9lUJIWloaXF1dYWVlhQMHDiAiIgITJkyAsbExunbtmmn+sLAweHp6wsHBQZxWpkyZL6+aiIhIIjzLdt5RKYTExMSgTp06mD17NvT19VGtWjU4ODjA398/UwhRKBSIioqClZUVKlSokKdFExERUeGn0oGtihUrYunSpdDX14cgCPD398e1a9fQqFGjTPOGh4dDJpPB1NQ0z4olIiKioiPXA1OdnJzw9OlTtGrVCu3bt890fXh4OPT19eHm5oarV6+iUqVKGDNmDFq0aKHSegri9yuKCt5X6oX9UB/sRfYy7hveR8VPfvVcleXmOoQsW7YMMTExmD17Njw8PDBjxgyl68PDw5GYmAi5XA5XV1ccP34cI0aMgI+PD6ysrHK8HiOj0rktsVjJ79HUpBr2Q32wFznD19riRV2eF7kOIRlBIikpCRMnToSbmxu0tbXF60eOHAkXFxdxIGrt2rVx+/Zt7N69W6UQ8urV+3w7c5ympobaNOJLxcZ+KNS/FlqUegEU7n6wF+pDQ0MGgzL5/5XQgpCSmoZ3b9Xja6G5VZSeG/n5vJDJch5qVR6YGhgYiDZt2ojTatasieTkZMTFxaFcuX9/zEZDQyPTN2Fq1KiB0NBQVVYJQUCxOX3tl+L9pF7YD/VRWHshkxX+r4QC/34tVCaTQSiszSiC1KEVKoWQqKgojB49GmfOnIGxsTEAIDg4GOXKlVMKIAAwZcoUyGQyeHh4iNNCQkJgbm6eB2UTERUf/EooFVUq7eOzsrKCpaUlpk2bhtDQUJw5cwaenp4YPnw4ACA6OhqJiYkA0geuHjp0CAcPHkRERAS8vLzg7++Pfv365f1WEBERUaGjUgjR1NTEypUroaenh++++w7Tp0+Hi4sL+vfvDwCQy+Xw9fUFALRr1w6zZs3CqlWr0KVLF5w6dQrr1q2DiYlJ3m8FERERFToqD0w1NjaGl5dXltfdu3dP6bKzszOcnZ1zVxkREREVaYV/yDUREREVSgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSYAghIiIiSTCEEBERkSQYQoiIiEgSDCFEREQkCYYQIiIikgRDCBEREUmCIYSIiIgkwRBCREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKShMohJCIiAoMHD4atrS1atmyJdevWZTvvnTt34OzsDGtra/Ts2RPBwcFfVCwREREVHSqFkLS0NLi6usLQ0BAHDhzAr7/+ilWrVuHQoUOZ5o2Pj4erqyvs7e2xf/9+2NraYtiwYYiPj8+z4omIiKjwUimExMTEoE6dOpg9ezaqVauGFi1awMHBAf7+/pnm9fX1hY6ODtzc3GBmZobp06ejVKlSOHLkSJ4VT0RERIWXSiGkYsWKWLp0KfT19SEIAvz9/XHt2jU0atQo07xBQUGws7ODTCYDAMhkMjRo0ACBgYF5UjgREREVblq5vaGTkxOePn2KVq1aoX379pmuj46ORs2aNZWmGRkZ4cGDByqt558MQznA+0q9sB/qg71QH+yF+sivXqiy3FyHkGXLliEmJgazZ8+Gh4cHZsyYoXR9QkICtLW1laZpa2tDoVCotB4jo9K5LbFYMTQsJXUJ9BH2Q32wF+qDvVAf6tKLXIcQKysrAEBSUhImTpwINzc3pdCho6OTKXAoFAro6uqqtJ5Xr95DEHJb5edpamqoTSO+VGzsB6SmpkldRq4VpV4Ahbsf7IX6YC/US1HqR372QibL+Q4ElUJITEwMAgMD0aZNG3FazZo1kZycjLi4OJQrV06cbmxsjJiYmEy3r1ixoiqrhCAg30JIUcP7Sb2wH+qDvVAf7IX6UIdeqDQwNSoqCqNHj8aLFy/EacHBwShXrpxSAAEAa2tr3LhxA8I/WykIAgICAmBtbZ0HZRMREVFhp1IIsbKygqWlJaZNm4bQ0FCcOXMGnp6eGD58OID0waiJiYkAgA4dOuDdu3dwd3dHaGgo3N3dkZCQgI4dO+b9VhAREVGho1II0dTUxMqVK6Gnp4fvvvsO06dPh4uLC/r37w8AkMvl8PX1BQDo6+vD29sb/v7+6NGjB4KCgrBmzRqULFky77eCiIiICh2VB6YaGxvDy8sry+vu3bundLl+/fo4cOBA7iojIiKiIo0/YEdERESSYAghIiIiSTCEEBERkSQYQoiIiEgSDCFEREQkCYYQIiIikgRDCBEREUmCIYSIiIgkwRBCREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSYAghIiIiSTCEEBERkSQYQoiIiEgSDCFEREQkCYYQIiIikgRDCBEREUmCIYSIiIgkwRBCREREkmAIISIiIkmoHEJevHiBsWPHolGjRnB0dISHhweSkpKynHfEiBGwsLBQ+vPz8/vioomIiKjw01JlZkEQMHbsWBgYGGD79u14+/Ytpk2bBg0NDUyePDnT/GFhYfD09ISDg4M4rUyZMl9eNRERERV6KoWQ8PBwBAYG4sKFCyhfvjwAYOzYsViwYEGmEKJQKBAVFQUrKytUqFAh7yomIiKiIkGlwzEVKlTAunXrxACSIS4uLtO84eHhkMlkMDU1/bIKiYiIqEhSaU+IgYEBHB0dxctpaWnYtm0bmjRpkmne8PBw6Ovrw83NDVevXkWlSpUwZswYtGjRQqUCZTKVZi/WeF+pF/ZDfbAX6oO9UB/51QtVlqtSCPmUp6cn7ty5g71792a6Ljw8HImJiZDL5XB1dcXx48cxYsQI+Pj4wMrKKsfrMDIq/SUlFhuGhqWkLoE+wn6oD/ZCfbAX6kNdepHrEOLp6YnNmzdjyZIlMDc3z3T9yJEj4eLiIg5ErV27Nm7fvo3du3erFEJevXoPQchtlZ+nqamhNo34UrGxH5CamiZ1GblWlHoBFO5+sBfqg71QL0WpH/nZC5ks5zsQchVC5s6di507d8LT0xPt27fPch4NDY1M34SpUaMGQkNDVVqXICDfQkhRw/tJvbAf6oO9UB/shfpQh16ofJ4QLy8v7Nq1C4sXL0bnzp2znW/KlCmYOnWq0rSQkBDUqFFD9SqJiIioyFEphISFhWHlypUYOnQo7OzsEB0dLf4BQHR0NBITEwEATk5OOHToEA4ePIiIiAh4eXnB398f/fr1y/utICIiokJHpcMxJ0+eRGpqKlatWoVVq1YpXXfv3j3I5XJ4eHigR48eaNeuHWbNmoVVq1bh6dOnqFWrFtatWwcTE5M83QAiIiIqnFQKIa6urnB1dc32+nv37ilddnZ2hrOzc+4qIyIioiKNP2BHREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSYAghIiIiSTCEEBERkSQYQoiIiEgSDCFEREQkCYYQIiIikgRDCBEREUmCIYSIiIgkwRBCREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJFQKIS9evMDYsWPRqFEjODo6wsPDA0lJSVnOe+fOHTg7O8Pa2ho9e/ZEcHBwnhRMRERERUOOQ4ggCBg7diwSEhKwfft2LFmyBH5+fli6dGmmeePj4+Hq6gp7e3vs378ftra2GDZsGOLj4/OydiIiIirEchxCwsPDERgYCA8PD9SqVQv29vYYO3YsDh8+nGleX19f6OjowM3NDWZmZpg+fTpKlSqFI0eO5GnxREREVHjlOIRUqFAB69atQ/ny5ZWmx8XFZZo3KCgIdnZ2kMlkAACZTIYGDRogMDDwy6olIiKiIkMrpzMaGBjA0dFRvJyWloZt27ahSZMmmeaNjo5GzZo1laYZGRnhwYMHKhf4T46hHOB9pV7YD/XBXqgP9kJ95FcvVFlujkPIpzw9PXHnzh3s3bs303UJCQnQ1tZWmqatrQ2FQqHyeoyMSue2xGLF0LCU1CXQR9gP9cFeqA/2Qn2oSy9yFUI8PT2xefNmLFmyBObm5pmu19HRyRQ4FAoFdHV1VV7Xq1fvIQi5qfK/aWpqqE0jvlRs7AekpqZJXUauFaVeAIW7H+yF+mAv1EtR6kd+9kImy/kOBJVDyNy5c7Fz5054enqiffv2Wc5jbGyMmJgYpWkxMTGoWLGiqquDICDfQkhRw/tJvbAf6oO9UB/shfpQh16odJ4QLy8v7Nq1C4sXL0bnzp2znc/a2ho3btyA8M8WCoKAgIAAWFtbf1m1REREVGTkOISEhYVh5cqVGDp0KOzs7BAdHS3+AemDURMTEwEAHTp0wLt37+Du7o7Q0FC4u7sjISEBHTt2zJ+tICIiokInxyHk5MmTSE1NxapVqyCXy5X+AEAul8PX1xcAoK+vD29vb/j7+6NHjx4ICgrCmjVrULJkyfzZCiIiIip0cjwmxNXVFa6urtlef+/ePaXL9evXx4EDB3JfGRERERVp/AE7IiIikgRDCBEREUmCIYSIiIgkwRBCREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSYAghIiIiSTCEEBERkSQYQoiIiEgSDCFEREQkCYYQIiIikgRDCBEREUmCIYSIiIgkwRBCREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJItchRKFQoEuXLrhy5Uq284wYMQIWFhZKf35+frldJRERERUhWrm5UVJSEn7++Wc8ePDgs/OFhYXB09MTDg4O4rQyZcrkZpVERERUxKgcQkJDQ/Hzzz9DEITPzqdQKBAVFQUrKytUqFAh1wUSERFR0aTy4ZirV6+icePG8PHx+ex84eHhkMlkMDU1zXVxREREVHSpvCfk+++/z9F84eHh0NfXh5ubG65evYpKlSphzJgxaNGihcpFEhERUdGTqzEhOREeHo7ExETI5XK4urri+PHjGDFiBHx8fGBlZZXj5chk+VVh0cP7Sr2wH+qDvVAf7IX6yK9eqLLcfAshI0eOhIuLizgQtXbt2rh9+zZ2796tUggxMiqdXyUWKYaGpaQugT7CfqgP9kJ9sBfqQ116kW8hRENDI9M3YWrUqIHQ0FCVlvPq1Xv8xxjYXNPU1FCbRnyp2NgPSE1Nk7qMXCtKvQAKdz/YC/XBXqiXotSP/OyFTJbzHQj5FkKmTJkCmUwGDw8PcVpISAjMzc1VWo4gIN9CSFHD+0m9sB/qg71QH+yF+lCHXuTpGVOjo6ORmJgIAHBycsKhQ4dw8OBBREREwMvLC/7+/ujXr19erpKIiIgKqTwNIXK5HL6+vgCAdu3aYdasWVi1ahW6dOmCU6dOYd26dTAxMcnLVRIREVEh9UWHY+7du/fZy87OznB2dv6SVRAREVERxR+wIyIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSYAghIiIiSTCEEBERkSQYQoiIiEgSDCFEREQkCYYQIiIikgRDCBEREUmCIYSIiIgkwRBCREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBK5DiEKhQJdunTBlStXsp3nzp07cHZ2hrW1NXr27Ing4ODcro6IiIiKmFyFkKSkJEyYMAEPHjzIdp74+Hi4urrC3t4e+/fvh62tLYYNG4b4+PhcF0tERERFh8ohJDQ0FL1790ZkZORn5/P19YWOjg7c3NxgZmaG6dOno1SpUjhy5EiuiyUiIqKiQ+UQcvXqVTRu3Bg+Pj6fnS8oKAh2dnaQyWQAAJlMhgYNGiAwMDBXhRIREVHRoqXqDb7//vsczRcdHY2aNWsqTTMyMvrsIZys/JNhKAd4X6kX9kN9sBfqg71QH/nVC1WWq3IIyamEhARoa2srTdPW1oZCoVBpOUZGpfOyrCLL0LCU1CXQR9gP9cFeqA/2Qn2oSy/yLYTo6OhkChwKhQK6uroqLefVq/cQhLys7F+amhpq04gvFRv7AampaVKXkWtFqRdA4e4He6E+2Av1UpT6kZ+9kMlyvgMh30KIsbExYmJilKbFxMSgYsWKKi1HEJBvIaSo4f2kXtgP9cFeqA/2Qn2oQy/y7WRl1tbWuHHjBoR/tlIQBAQEBMDa2jq/VklERESFSJ6GkOjoaCQmJgIAOnTogHfv3sHd3R2hoaFwd3dHQkICOnbsmJerJCIiokIqT0OIXC6Hr68vAEBfXx/e3t7w9/dHjx49EBQUhDVr1qBkyZJ5uUoiIiIqpL5oTMi9e/c+e7l+/fo4cODAl6yCiIiIiij+gB0RERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSYAghIiIiSTCEEBERkSQYQoiIiEgSDCFEREQkCYYQIiIikgRDCBEREUmCIYSIiIgkwRBCREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSUDmEJCUlYdq0abC3t4dcLseGDRuynXfEiBGwsLBQ+vPz8/uigomIiKho0FL1BgsXLkRwcDA2b96Mp0+fYvLkyfjqq6/QoUOHTPOGhYXB09MTDg4O4rQyZcp8WcVERERUJKgUQuLj47Fnzx6sXbsWlpaWsLS0xIMHD7B9+/ZMIUShUCAqKgpWVlaoUKFCnhZNREREhZ9Kh2NCQkKQkpICW1tbcZqdnR2CgoKQlpamNG94eDhkMhlMTU3zplIiIiIqUlTaExIdHQ1DQ0Noa2uL08qXL4+kpCS8efMG5cqVE6eHh4dDX18fbm5uuHr1KipVqoQxY8agRYsWKhUok6k0e7HG+0q9sB/qg71QH+yF+sivXqiyXJVCSEJCglIAASBeVigUStPDw8ORmJgIuVwOV1dXHD9+HCNGjICPjw+srKxyvE4jo9KqlFhsGRqWkroE+gj7oT7YC/XBXqgPdemFSiFER0cnU9jIuKyrq6s0feTIkXBxcREHotauXRu3b9/G7t27VQohr169hyCoUmXOaWpqqE0jvlRs7Aekpqb994xqqij1Aijc/WAv1Ad7oV6KUj/ysxcyWc53IKgUQoyNjREbG4uUlBRoaaXfNDo6Grq6ujAwMFCaV0NDI9M3YWrUqIHQ0FBVVglBQL6FkKKG95N6YT/UB3uhPtgL9aEOvVBpYGqdOnWgpaWFwMBAcZq/vz+srKygoaG8qClTpmDq1KlK00JCQlCjRo3cV0tERERFhkohRE9PD927d8fs2bNx8+ZNnDhxAhs2bED//v0BpO8VSUxMBAA4OTnh0KFDOHjwICIiIuDl5QV/f3/069cv77eCiIiICh2Vz5g6depUWFpaYsCAAfj1118xZswYtGvXDgAgl8vh6+sLAGjXrh1mzZqFVatWoUuXLjh16hTWrVsHExOTvN0CIiIiKpRUPmOqnp4eFixYgAULFmS67t69e0qXnZ2d4ezsnPvqiIiIqMjiD9gRERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSYAghIiIiSTCEEBERkSQYQoiIiEgSDCFEREQkCYYQIiIikgRDCBEREUmCIYSIiIgkwRBCREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJJgCCEiIiJJMIQQERGRJBhCiIiISBIMIURERCQJhhAiIiKSBEMIERERSYIhhIiIiCTBEEJERESSYAghIiIiSTCEEBERkSQYQoiIiEgSKoeQpKQkTJs2Dfb29pDL5diwYUO28965cwfOzs6wtrZGz549ERwc/EXFEhERUdGhcghZuHAhgoODsXnzZsyaNQteXl44cuRIpvni4+Ph6uoKe3t77N+/H7a2thg2bBji4+PzpHAiIiIq3FQKIfHx8dizZw+mT58OS0tLtG3bFkOGDMH27dszzevr6wsdHR24ubnBzMwM06dPR6lSpbIMLERERFT8qBRCQkJCkJKSAltbW3GanZ0dgoKCkJaWpjRvUFAQ7OzsIJPJAAAymQwNGjRAYGDgl1dNREREhZ6WKjNHR0fD0NAQ2tra4rTy5csjKSkJb968Qbly5ZTmrVmzptLtjYyM8ODBA5UK1NAABEGlm6jM8isD6Glr5u9K8kmN8qXE/2sUgWHGhbkXQNHqB3uhPtgL9VKY+1EQvfhn30OOqBRCEhISlAIIAPGyQqHI0byfzvdfypUrrdL8ubGwl3W+ryO/GRqW+u+ZCoGi0AugaPSDvVAf7IV6KQr9UJdeqJSDdHR0MoWIjMu6uro5mvfT+YiIiKh4UimEGBsbIzY2FikpKeK06Oho6OrqwsDAINO8MTExStNiYmJQsWLFLyiXiIiIigqVQkidOnWgpaWlNLjU398fVlZW0Pjk4JK1tTVu3LgB4Z8BHYIgICAgANbWhX83FhEREX05lUKInp4eunfvjtmzZ+PmzZs4ceIENmzYgP79+wNI3yuSmJgIAOjQoQPevXsHd3d3hIaGwt3dHQkJCejYsWPebwUREREVOjJBUO27JwkJCZg9ezaOHTsGfX19DB48GAMHDgQAWFhYwMPDAz169AAA3Lx5E7NmzUJYWBgsLCzw66+/om7dunm+EURERFT4qBxCiIiIiPJCEfjGNhERERVGDCFEREQkCYYQIiIikgRDCBEREUmCISQfxMbG4sWLF3j37p3UpdA/oqOjERwcjPfv30tdChER/UOl346h7B07dgzbtm3DzZs3kZSUJE7X1dVFvXr1MGDAALRp00bCCouHXr16Yf369ShTpgwAIC4uDpMnT8bJkycBAFpaWvjuu+8wZcoUlChRQspSi4XExEQcOXIEN27cwIsXL8SfbqhQoQJsbGzQsWNH/pRDAbp27Vq2vWjUqJHU5RUbz58/x969exEYGJhlL3r16oVKlSpJXWaB4Fd088DGjRvh5eWFIUOGwM7ODkZGRuKP9cXExOD69evYuHEjxo0bBxcXF6nLLdJq166NCxcuwMjICAAwc+ZM3LhxA/Pnz4eZmRnu3LmDmTNnomnTppg2bZrE1RZtt2/fxrBhw1CqVCk0aNAg0/MiICAACQkJWLt2LWrXri11uUXa48ePMWrUKDx58gR169ZF+fLllXpx584dVKlSBV5eXvj666+lLrdIu3DhAkaPHg0bG5ss3y/8/f1x69YtrFixAk2aNJG63Pwn0BeTy+XC8ePHPzvP8ePHhebNmxdQRcWXhYWFEBMTI15u2bKlcPHiRaV5rl27JjRu3LigSyt2evXqJcybN++z88ydO1fo3bt3AVVUfA0YMED46aefhISEhCyvj4+PF8aNGycMGjSogCsrfjp37ix4e3t/dh5vb2+hS5cuBVSRtDgmJA8kJibCxMTks/MYGxtzPEIBkMlkkMlk4mV9ff1MP65YpkwZ8TeNKP88ePAAffv2/ew8ffv2xb179wqoouIrMDAQo0ePzvbQl56eHkaPHo2AgIACrqz4efLkyX8emndyckJkZGQBVSQthpA80LZtW0yZMgXXr19X+oVhAEhLS0NAQACmTZuG9u3bS1Rh8SEIAoYMGYKJEyfCy8sLVapUwfLly5Gamgog/QXAw8MDjRs3lrjSos/c3Bz79u377Dw+Pj6oUaNGAVVUfJmamuLcuXOfncfPzw/GxsYFVFHxZWNjA29vb6Wxgx9TKBRYuXIl6tevX8CVSYNjQvKAQqHAggULsHfvXqSmpqJs2bLiMb43b95AS0sL3bp1w9SpUzkIL5/dvXsXoaGhCAsLE/8iIyNx5coVcWxCtWrVsGrVKr7g5rM7d+7A1dUVenp6sLOzQ8WKFcXnRXR0NG7cuIH3799j9erVsLKykrrcIu3ixYsYNWoUrKys0LBhw0y9CAgIQEBAAJYvXw5HR0epyy3SoqKiMGrUKDx+/BiWlpaZenHnzh1UrlwZK1euhKmpqdTl5juGkDyUkJCAkJAQREdHIyEhATo6OjA2NkadOnUYPiSUkpICLa30L4KFhYWhRo0aSodsKP8kJCTgf//7H27evImXL18iMTFRfF5YW1ujffv20NfXl7rMYuHZs2fYs2cPgoKCsuxFz549OSi1AF26dAk3b97M9H5hbW2NRo0aQUOjeByoYAghIiIiSRSPqEVEktixYwe+//57dO3aFR4eHnj9+rXS9a9fv0br1q0lqo4AoGvXrnj+/LnUZVAxxZOVEVG+8Pb2xubNmzFw4EAAwO7du3Ho0CGsWrUK1tbWANIHbj99+lTCKosHLy+vbK97+PCh0gn+Ro8eXVBlFUvXrl3L8bwNGzbMx0rUA0MIEeWLPXv2YP78+WjevDkAYMCAAZg8eTIGDhyItWvXwt7eXuIKi4/jx4/j/v37qFmzJsqWLat0XVpaGoKCgqCjo8OxUgVgzpw5CA0NBYDPnipAJpPh7t27BVWWZDgmhIqFrl27Ys2aNahcubLUpRQbdnZ22LdvH6pVqyZOEwQBEydOhJ+fH9avXw9TU1M4OjoWixdbKaWmpmL9+vXYunUrxo4dC2dnZ/E6W1tb/PXXX8XimxjqQKFQYMKECYiKioKPjw90dHSkLklSHBNCxUJUVFSmc7hQ/rKxscHatWuV7neZTIaFCxeiadOmGDJkCPz8/CSssPjQ1NSEq6srtm3bhv/973/4/vvvERYWJnVZxZK2tjYWL14MAFi6dKm0xagBhhAiyhfTp0/HlStX0LRpU6Xj4Jqamli6dCnatWuHX375RcIKi5+qVati06ZN6NmzJwYMGIAlS5ZIXVKxpK2tjUWLFqFKlSpSlyI5jgmhYuHrr78WzxVCBaNGjRo4fPgwrl27lumsqFpaWvDw8ECXLl1w7NgxiSosvnr27ImWLVvC3d0dCQkJUpdTLJmZmcHMzEzqMiTHMSFEREQkCR6OISIiIkkwhBAREZEkGEKIiIhIEgwhRFSgZs+enen07SQN9kJ9uLq64uXLl1KXUeAYQoioQP3111/48OGD1GUQ2At1cu3aNSQlJUldRoHjdxapSOHvMqg/fiFPfbAX6qO4njKfIYSKFP4uAxEVRsU1EPI8IVSk8HcZ1N+zZ89QsWJFaGpqSl1KscdekNQYQqjIUSgU6N27NxwcHDB58mSpyyEiomxwYCoVOfxdBiKiwoF7QoiIiEgS3BNCREQkMUEQEBsbK3UZBY7fjiGifPP8+XPs3bsXgYGBePHiBRQKBXR1dVGhQgXY2NigV69eqFSpktRlFgvshXoYN24c3N3doa+vDwBITk6Gp6cndu/ejaSkJJQtWxZDhw7FoEGDJK60YPBwDBHliwsXLmD06NGwsbGBnZ0djIyMoK2tDYVCgZiYGPj7++PWrVtYsWIFmjRpInW5RRp7oT7q1KmD8+fPw8jICACwaNEiHDp0CNOmTYOZmRnu3LkDT09P9OnTByNHjpS42vzHEEJE+aJLly745ptv4Orqmu08a9aswaFDh3Do0KECrKz4YS/UR+3atXHhwgUxhLRt2xaTJ09GmzZtxHnOnDmDX375BWfPnpWqzALDMSFElC+ePHmi9MKaFScnJ0RGRhZQRcUXe6E+ZDKZ0tlRNTQ0YGJiojRPlSpVis3p9BlCiChf2NjYwNvbO9vfw1AoFFi5ciXq169fwJUVP+yF+hAEATNmzMCSJUtw8OBB1KtXD1u2bBGvT0pKwooVK2BjYyNdkQWIh2OIKF9ERUVh1KhRePz4MSwtLVGxYkVxHEJ0dDTu3LmDypUrY+XKlTA1NZW63CKNvVAfJ06cQGhoKMLCwhAWFoaHDx8iMTERV65cgYGBARo3bgw9PT2sX78eZmZmUpeb7xhCiChfXbp0CTdv3kR0dDQSEhKgo6MDY2NjWFtbo1GjRtDQ4A7ZgsJeqKenT5/iq6++AgCcP38etra2KFWqlMRVFQyGECIiIpIEYy8RURGnUCjg6emJFi1aoEGDBhg9ejTCwsKU5omJiUGdOnUkqpCKK56sjIjyxbVr13I8b8OGDfOxElq8eDH8/Pzg5uYGQRCwbds29OzZE7///rvSt2a4Y5wKGg/HEFG+6Nq1K0JDQwF8/s1NJpPh7t27BVVWsdSiRQssXrwYdnZ2ANL7sXDhQmzduhWenp7o2LEjYmJi4OjoyF5QgeKeECLKF/v27cOECRMQFRUFHx8f6OjoSF1SsZWYmIiyZcuKl2UyGSZPngwNDQ1MmjQJWlpasLW1la5AKrY4JoSI8oW2tjYWL14MAFi6dKm0xRRzjRs3xsKFC/H69Wul6ZMmTcJ3332H8ePHY8eOHRJVR8UZQwgR5RttbW0sWrQIVapUkbqUYm369Ol48+YNmjVrhgsXLihd98svv2D48OHw9vaWqDoqzjgmhIiomAgPD0eFChVQunTpTNeFhYXh5MmTn/19GaK8xhBCREREkuDhGCIiIpIEQwgRERFJgiGEiIiIJMEQUkw5OTlh//79mabv378fTk5OElQkvSlTpmDKlClSl5EjxblPQPaP36LKxcUFy5cvl7qMQu3+/fvo378/bG1t0bZtW2zatIlniFUDPFkZEREVaXFxcRg6dCgaNmyIffv24e7du5gxYwZKliyJ3r17S11escYQQkRERdrZs2cRFxeHefPmQVdXFzVq1MC9e/dw4MABhhCJ8XAMfdbz588xbtw4NGrUCI0bN8a8efOgUCgQGxuLOnXq4P79+wCA5ORk2NjYYNmyZeJtf/75ZyxZsiTTMpcvXw43NzfMnTsXtra2cHJywvnz57Ft2zY0bdoUTZo0wZYtW8T5Q0NDMXjwYNja2sLKygrff/+9+AugV65cgZOTE3bs2AFHR0fY2Nhg0qRJUCgUSExMRIMGDXDs2DFxWcnJyWjcuDEuXbqU5fbGxcVh/PjxsLa2RsuWLXHo0CHxuqSkJPGXSG1sbDB8+HA8e/YMABAVFQULCwtERUUpbaeLiwuA9MMnffr0wahRo2BnZ4e//voLISEh6NOnD6ytreHo6AgvL69s+/DixQsMGTIENjY2+PbbbxEZGal0fVhYGAYPHowGDRqIy0pLS8u0nGfPnqF27dq4ffu2OO3Vq1eoW7cuIiIixFo7duyI+vXro0ePHko/RPfpYZArV67AwsIi03rS0tLQqFEj+Pn5idPatWuHyZMni5cXL16MiRMn/mf9y5cvx8iRI/HDDz+gUaNGuHr1qtK6goKCYGtri71792aqI6Mvhw4dgqOjI+zt7TFv3jykpKSI8xw/fhydOnWCtbU1evXqpbR8FxcXzJ07F61bt0bLli0RFxeX6228f/8+XFxcUL9+fbRv3x7bt29XqvVzdXwsMjISTZs2VXqufezt27f45Zdf0LRpU9jZ2WHSpEl4+/YtgM8/X7KiUCjg4eEBR0dHWFpawsnJCT4+PgCAnTt3Zjok6OPjg3bt2om3nTdvHho3bozGjRtj4sSJePPmDYB/+7JixQo0bNgQc+bMgSAIWL16NZycnFCvXj3I5XKl50RaWhp+//13cXkrV65E27ZtceXKFQDAu3fvMGnSJDRo0AByuRxz585FYmIiAKB+/fpYtmwZdHV1xeXp6+sr9ZOkwRBC2VIoFBgwYAASEhKwdetWLF26FKdPn8bChQthaGgIS0tL8YXy1q1bSExMREBAAID0H8i6dOkSHB0ds1y2r68vSpcujT///BP169fHTz/9hPPnz2Pr1q1wcXHBggUL8Pr1a6SlpWH48OH4+uuv8eeff2LXrl1ITU2Fp6enuKyXL1/i6NGjWLduHZYvX45jx47h4MGD0NXVRZs2bXD06FFx3osXL0JLSwuNGjXKsq7jx4/D0tIShw8fRseOHTFt2jS8f/8eADBr1iwcP34cCxYswK5du5CSkoKRI0dm+WaflRs3bqBmzZrYvXs35HI53NzcUKdOHRw+fBju7u5Yt24dzpw5k+Vtx40bh7S0NOzZswdDhw7F5s2bxetev36N77//HhUrVsSePXswa9YsbNu2TSnIZahcuTLs7OyU7pOjR4+iTp06qFq1Kvbv34+5c+di2LBhOHjwIJo2bQpXV1e8ePEiR9uYQUNDAw4ODuLj48WLF4iMjBQfHwBw4cIFODo65qj+kydPokuXLti8eTPq168vTn/48CGGDRuGMWPGoFevXtnW4+XlhSVLlsDLywvHjh0Tx1eEhIRg8uTJGDFiBP766y988803GDp0qBjIgPRQ5unpCS8vL+jr6+dqGxMTEzF06FAxgE6ePBkrV67EwYMHc1wHkN7rwYMHo2PHjhg7dmyW2zp69GjcvXsXq1evxsaNGxEWFqY01im750tW1qxZg9OnT2P58uU4cuQIunfvjrlz5yImJgbt27fHixcvEBwcLM5/7NgxdOzYEUB6AAsODsbatWuxZcsWxMXFYdy4cUrLDwgIwL59+9C/f38cPHgQmzdvhru7O44cOYJRo0Zh+fLlYmD29vbGwYMHsWjRImzcuBGnT5/G48ePxWVNnz4d79+/x86dO7Fy5UrcunULc+bMAQCYmJigWbNm4rzR0dHYsWOHGJhIQgIVS61atRLq1asn2NjYKP3Vq1dPaNWqlSAIgnDixAnB2tpaePPmjXi7M2fOCHXr1hXi4uKExYsXC2PGjBEEQRC8vb2FoUOHCjY2NkJKSopw9+5dwc7OTkhOTs607mXLlglyuVxIS0sTBEEQTp8+LZibmwuRkZGCIAhCQkKCYG5uLgQEBAgfPnwQ1q5dK3z48EG8/c6dO4XWrVsLgiAIly9fFszNzYX79++L148aNUqYMWOGuGxbW1shMTFREARBmDJlijBnzpws75PJkycL3333nXj53bt3grm5uRAYGCi8efNGqF27tnDu3Dnx+tjYWMHa2lo4e/as8PjxY8Hc3Fx4/Pix0nb269dPEARB2Ldvn2BhYSEkJCSI1zdo0EBYunSpkJqaKgiCIAQEBAgvX77MVNf9+/cFc3Nz4cmTJ+K0BQsWiH3avHmz0KJFC6X7eseOHUKzZs2y3M7t27cLbdu2FS/369dPWL9+vSAIgtC9e3dh0aJFSvP37t1b+P333wVBSH/c7Nu3T7wu4/7Pyu7du4Vvv/1WEARBOHz4sDBo0CChbt26QnR0tPDmzRuhbt26wqtXr/6z/mXLlglNmzZVWnarVq0Eb29vwcnJKVO9H8voy/Hjx8Vpe/fuFZo0aSKkpaUJEydOFDw8PJRuM3r0aHFav379hJ9++inb5ed0Gz+eL8OWLVvEaTmpY/78+ULPnj2FCRMmiM+dT929e1cwNzcXwsPDxWmhoaGCubm5EBYW9p/Pl08dP35cuHbtmng5KSlJMDc3F6cNGjRIvP/fvHkjWFpaCiEhIUJ8fLz4/wxv374VateuLYSEhIh9OXPmjHj9pUuXBD8/P6X1N2vWTDhw4IAgCIIgl8uFPXv2iNeFhYUJ5ubmwuXLl4WIiAihdu3awrt378TrQ0JCMk3LqKNLly5C3759haSkpCy3mwoOx4QUY2PHjs30SeDYsWPYuXMngPRd5NWqVUOZMmXE6xs0aICUlBRERkbC0dERu3fvhiAIuHbtGnr27ImgoCDcvXsXV69eRdOmTaGllfVDzMTEBDKZDADEXaRff/210mWFQoGSJUuib9++OHjwIIKDgxEeHo47d+6gfPnySsurWrWq+H99fX1xd3uzZs2gra2Nc+fOoUWLFjhx4gRWr16d7X1iamoq/j/j1NZJSUl49OgR0tLSYG1tLV5ftmxZVK9eHWFhYahevXq2y8xgZGSktDt42LBhWLx4MXx8fNCyZUt069YNFSpUyHS70NBQlC1bFl999ZU4zcrKCkeOHAGQ3idLS0ul+9rW1hbR0dF49+4dDAwMlJbXoUMHuLu74+7du6hQoQICAgLEPUthYWEYNWqU0vw2Njbi4S9VyOVyzJo1C+/fv8e1a9fQrFkzxMbGwt/fHwBgYWGBcuXK/Wf9wL+PjY8tW7YMKSkpqFSp0n/W0qBBA/H/9erVw+vXrxEbG4uwsDD8/fff4iEGIP2QnVwuFy9ntW5VtzE8PBwhISFKv1SbmpoKTU1NAMhRHVu3bkVKSgoaN24sPnc+FR4eDgMDA6XHo5mZGcqUKYPw8HDxMZ3d8+VTbdq0wYULFzB//nzxuZdROwB07twZa9aswYQJE3Dy5ElUrVoVFhYWuH//PpKTk9GnTx+l5aWlpeHRo0ewtLTMdN82adIEQUFBWLRoEcLCwnD37l1ER0cjLS0Nr1+/xsuXL2FlZSXOX6NGDfG1KSwsDGlpaWjevHmm9UVERKBevXritN9//x0pKSlYvXo1tLW1s9xuKjgMIcWYkZGR0otRxrQMWf30esaLT2pqKmxsbJCUlIR79+4hICAAHh4eaNCgAQICAnDp0qXP7urMKpxoaGQ+Ovjhwwf06tULhoaGcHJyQpcuXRAeHo4NGzYozffpi4nwz1fvtLS00L59exw9ehQlSpSAvr6+0hvSpzLeFD5dVnY/Q5+amoq0tLQs3xQ+fWH/dBmurq7o2LEjTpw4gVOnTmHAgAGYO3cunJ2ds6zhYyVKlMh2uQDEQ0QZ/fpYuXLl4ODggKNHj6JixYqwtrYW38iz63l2h5yyWn6GypUro2rVqrh+/TquX7+Ob7/9Fk+fPkVAQACSkpLEQ3U5qT+reVq2bIlGjRph6dKl6NChA8qVK5dtLR/fXxnLlslkSE1NxdChQ9G9e3el+T8Oi9n1XpVtTElJgYODA2bOnJnlcnJSh6WlJQYOHIhJkyahR48eMDMzy7Sc7N5UU1NTlXqV3fPlU0uWLMGePXvQo0cPdO/eHbNmzVIaB9K2bVvMmjULDx48UDoUk7GuHTt2oGTJkkrLNDIyEseGfHzf7tmzB7/99hucnZ3FsTX9+/cH8O/rxad1ZlxOTU1F6dKlsW/fvkzbYGxsrHT59OnTmDBhQqZwTtLgmBDKVvXq1fHo0SPxBQMAAgMDoaWlhSpVqkBLSwtNmjTB9u3bUb58eZQvXx729va4dOkSrl27lu14EFVcvXoVL1++xJYtWzBkyBA0bdoUT58+Ven7/V27dsXZs2dx6tQpdOjQIdtPkZ9jamoKLS0tBAYGitNiY2MRERGB6tWri29yHz58EK//eJDqp5KSkjBv3jxoa2vjxx9/xNatW9G7d2+lsRoZzM3N8fbtW6XxAXfv3hX/X716ddy+fRvJycnitBs3bqBcuXIoW7Zsluvv0qUL/Pz8cObMGXTu3FlpWUFBQUrzBgUFiZ+sS5QoobSNHx+Tz4pcLseJEyfw5MkT1K1bF/b29vD398f58+fFx0du6gfSB8n+8MMPMDY2VhojlJWP76/g4GBUrFgRhoaGqF69OqKiolC1alXxz8fHB2fPnv3s8nKzjQ8fPoSJiYm4nsDAQGzdulW8/r/qkMvl6NixIxwcHMSxDp+qXr063r17h/DwcHFaaGgo4uLicrS37lO7du3CL7/8gokTJ6JTp05ISEgA8O+bf+nSpeHo6Ii///4bFy9eFB9Lpqam0NTUxJs3b8Tt0dfXh4eHB169epXlunbu3IlRo0Zh2rRp6N69OwwNDfHq1SsIggADAwNUrFhRaUD148ePxT1l1atXx/v37yGTycT1JSYmYuHChZkG3datWxe1a9dW+b6g/MEQQtlq1qwZTE1N4ebmhnv37uHy5cuYO3cuunTpIn6KaNasGQ4cOCDuXbC3t4efnx9MTExytJv8v5QtWxbx8fE4ceIEoqKisGfPHmzfvj3b0fxZsbOzg56eHg4cOKD0hquKUqVKwdnZGXPnzsWVK1cQEhKCSZMmoVKlSmjWrBnKly+PypUrY/369Xj8+DH279+P06dPZ7s8HR0dBAQEYO7cuQgPD8etW7dw/fp11K1bN9O8ZmZmcHBwwLRp0xASEoITJ05g27Zt4vVdu3aFQqHAzJkzERYWhhMnTmD58uXo27dvtoGrTZs2ePToEa5evYoOHTqI0wcOHIht27bh4MGDePjwIX7//XeEhISIgz6trKywd+9e3L9/H1euXMm0R+pTcrkcf/75J6ysrFCiRAnY29vj9u3bePPmDWxsbHJdfwZNTU3MmDEDBw4cwI0bN7Kdz93dHbdu3cLFixfxxx9/4IcffhC319fXF1u2bEFkZCQ2bdqETZs2oVq1ap9dr6rb+M033yAxMVHcxjNnzsDd3V3c86hKHdOmTYO/vz/+97//ZbrOzMwMzZs3x+TJk3Hz5k3cvHkTkydPRsOGDWFubp7jbcpQtmxZ+Pn54fHjx7h+/Trc3NwAQOn517lzZ2zcuBE1atQQg46+vj6cnZ0xe/ZsXLlyBaGhoXBzc0NERARMTEyyXJehoSEuXbqEhw8fIjg4GOPHj0dycrK4LhcXFyxbtgyXLl1CSEgIpk6dCiB9j5aZmRkcHR0xceJE3Lx5E7dv38bUqVMRHx+faY/H3LlzVeov5S+GEMqWpqYmVq5cCQDo3bs3JkyYgNatWyt9CnN0dERycjLs7OwApH/K0NXVzZO9IED62IBRo0bh119/xTfffIP9+/dj5syZePXqVY6/sSGTydChQwdUqlRJ6diwqiZPnoymTZti7Nix6Nu3L3R0dLBp0yZoa2tDQ0MD7u7uuHnzJjp16oQjR45g+PDhn13ekiVLkJCQgF69emHw4MGwt7fHyJEjs53X0NAQffr0weLFi8Wv/gLpL/jr1q1DZGSk+O2FAQMGYPTo0dmuW19fH82bN4eNjY3SIbhOnTph/PjxWLZsGb755htcvXoVGzZsEHf9//TTTzAwMECPHj3g7u6e6dsOn2rUqBFkMpn4+ChfvjyqVKmiNF4oN/V/rHHjxmjXrh3mzJmT7eGhTp06YdiwYZgwYQKcnZ3Fn6u3sbHBwoULsWPHDnTq1Am7d+/GokWL0LBhwxytW5VtXLt2LR49eoTu3btjxowZ+OGHHzBs2DCV66hevTpcXFwwf/78LL9iumDBApiammLgwIEYPHgwatWqhRUrVuR4ez7222+/4e7du+jcuTOmTp2KDh06oH79+kp7llq1agVBENCpUyel206ZMgUODg4YO3YsevfuDS0tLaxZsybLQ55AeriKi4tDt27dMGbMGFhYWKBt27biugYNGoS2bdtizJgxGDBgAFq1agWZTCbuhVy4cCFMTEwwcOBA/Pjjj6hevToWL16caT1yuRy+vr65uj8o78kEVfZrExVSP//8M6pWrZrt1xqLoz59+sDZ2Rk9e/aUupR8ExUVhdatW+PkyZPZfgKnwuHs2bOoV6+eOPbn9evXcHBwYG8LOQ5MpSItMDAQt2/fxsmTJ3H48GGpy1ELly9fRkBAAMLCwpQOxRCpMx8fH+zYsQMTJ06ETCbDH3/8ASsrKwaQQo6HY6hIO3fuHH7//XeMHz+eL1b/+PPPP7Fp0ybMmTMHpUqVkrocohyZOXMmNDQ00KdPH/Tu3RtpaWm5PsxE6oOHY4iIiEgS3BNCREREkmAIISIiIkkwhBAREZEkGEKIiIhIEgwhREREJAmGECIiIpIEQwgRERFJgiGEiIiIJMEQQkRERJL4P0PVo4wgXtJgAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataTable1 = pd.pivot_table(data=df, values='What was your GPA your very first quarter at UCR?',\n", + " index='How many hours do you work per week on average?', aggfunc='mean')\n", + "_ = dataTable1.plot(kind='bar')" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-24T02:30:51.009476Z", + "start_time": "2024-02-24T02:30:50.906524Z" + } + }, + "id": "1163d27db8106025", + "execution_count": 8 + }, + { + "cell_type": "markdown", + "source": [ + "## Hypotheses" + ], + "metadata": { + "collapsed": false + }, + "id": "8f2599d399d14333" + }, + { + "cell_type": "markdown", + "source": [ + "### Hypothesis 2: Students who live on-campus are more likely to have roommates of the same major." + ], + "metadata": { + "collapsed": false + }, + "id": "85d89eaa6a2c8057" + }, + { + "cell_type": "code", + "outputs": [ + { + "data": { + "text/plain": "Do you work in a department related to your major? No Yes Total\nDo you currently live in a house, apartment, or... \nApartment 22 16 38\nDorm 4 1 5\nHouse 27 7 34\nTotal 53 24 77", + "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>Do you work in a department related to your major?</th>\n <th>No</th>\n <th>Yes</th>\n <th>Total</th>\n </tr>\n <tr>\n <th>Do you currently live in a house, apartment, or dorm?</th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Apartment</th>\n <td>22</td>\n <td>16</td>\n <td>38</td>\n </tr>\n <tr>\n <th>Dorm</th>\n <td>4</td>\n <td>1</td>\n <td>5</td>\n </tr>\n <tr>\n <th>House</th>\n <td>27</td>\n <td>7</td>\n <td>34</td>\n </tr>\n <tr>\n <th>Total</th>\n <td>53</td>\n <td>24</td>\n <td>77</td>\n </tr>\n </tbody>\n</table>\n</div>" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roommates_major_table = pd.crosstab(df.iloc[:, 3], df.iloc[:, 9], margins=True, margins_name='Total')\n", + "roommates_major_table" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-24T02:30:51.029851Z", + "start_time": "2024-02-24T02:30:51.010794Z" + } + }, + "id": "5cbb7ab4d38de9ef", + "execution_count": 9 + }, + { + "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chi-squared Value: 4.183390044200403\n", + "Degrees of Freedom: 6\n" + ] + } + ], + "source": [ + "# Extract the observed values from the contingency table\n", + "observed_values = roommates_major_table.iloc[:-1, :-1].values\n", + "\n", + "# Calculate expected values\n", + "row_totals = roommates_major_table.iloc[:-1, -1].values\n", + "col_totals = roommates_major_table.iloc[-1, :-1].values\n", + "total = np.sum(row_totals)\n", + "\n", + "expected_values = np.outer(row_totals, col_totals) / total\n", + "\n", + "# Calculate chi-squared statistic\n", + "chi2_statistic = np.sum((observed_values - expected_values) ** 2 / expected_values)\n", + "\n", + "# Degrees of freedom\n", + "degrees_of_freedom = (roommates_major_table.shape[0] - 1) * (roommates_major_table.shape[1] - 1)\n", + "\n", + "# Print results\n", + "print(f\"Chi-squared Value: {chi2_statistic}\\nDegrees of Freedom: {degrees_of_freedom}\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-24T02:30:51.035050Z", + "start_time": "2024-02-24T02:30:51.030607Z" + } + }, + "id": "2fbaac2d0722a7e3", + "execution_count": 10 } ], "metadata": { |