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| author |  Anshul Gupta <ansg191@anshulg.com>
| 2024-02-22 22:19:24 -0800 |
|---|---|---|
| committer | Anshul Gupta <ansg191@anshulg.com>
| 2024-02-22 22:19:24 -0800 |
| commit | 9b066744bfcc7992917f93d6d0c2cea2bd6afb6d (patch) | |
| tree | 16f1aa6e9a365df6876d5646f7056be49c147ba3 /CS105MiniProject.ipynb | |
| parent | 53863f0c1c5bf6f31c50b1efb2c4d00df811a095 (diff) | |
| download | CS105MiniProject-9b066744bfcc7992917f93d6d0c2cea2bd6afb6d.tar.gz CS105MiniProject-9b066744bfcc7992917f93d6d0c2cea2bd6afb6d.tar.zst CS105MiniProject-9b066744bfcc7992917f93d6d0c2cea2bd6afb6d.zip | |
Formatting
Diffstat (limited to 'CS105MiniProject.ipynb')
| -rw-r--r-- | CS105MiniProject.ipynb | 1339 |
1 files changed, 149 insertions, 1190 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb index 35ebf16..0663bc0 100644 --- a/CS105MiniProject.ipynb +++ b/CS105MiniProject.ipynb @@ -26,312 +26,27 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 1, "id": "daa13044", "metadata": { - "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.396867Z", - "start_time": "2024-02-23T01:01:40.758392Z" - }, "colab": { "base_uri": "https://localhost:8080/", "height": 614 }, "id": "daa13044", - "outputId": "4d440aaa-1ee7-4771-c526-f55e9458ca8a" + "outputId": "4d440aaa-1ee7-4771-c526-f55e9458ca8a", + "ExecuteTime": { + "end_time": "2024-02-23T06:18:35.652667Z", + "start_time": "2024-02-23T06:18:35.617295Z" + } }, "outputs": [ { "data": { - "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", - 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"0 2/9/2024 20:12:14 Senior \n", - "1 2/9/2024 20:16:34 Junior \n", - "2 2/9/2024 20:18:55 Junior \n", - "3 2/9/2024 20:24:00 Senior \n", - "4 2/9/2024 20:26:16 Graduate \n", - ".. ... ... \n", - "255 2/14/2024 19:46:28 Junior \n", - "256 2/15/2024 0:28:38 NaN \n", - "257 2/15/2024 8:33:45 Senior \n", - "258 2/15/2024 16:10:40 Sophomore \n", - "259 2/15/2024 16:14:11 Sophomore \n", - "\n", - " Who do you live with? \\\n", - "0 Neither \n", - "1 Both \n", - "2 Friends \n", - "3 Neither \n", - "4 Neither \n", - ".. ... \n", - "255 Friends \n", - "256 Family \n", - "257 Family \n", - "258 Family \n", - "259 Friends \n", - "\n", - " Do you currently live in a house, apartnment, or dorm? \\\n", - "0 House \n", - "1 Apartment \n", - "2 House \n", - "3 Apartment \n", - "4 Apartment \n", - ".. ... \n", - "255 House \n", - "256 Apartment \n", - "257 House \n", - "258 Apartment \n", - "259 Dorm \n", - "\n", - " How many people live in your household? Do you currently work? \\\n", - "0 6 Yes \n", - "1 4 No \n", - "2 4 No \n", - "3 1 No \n", - "4 1 Yes \n", - ".. ... ... \n", - "255 5 Yes \n", - "256 North District 4 bed 2 bath No \n", - "257 9 No \n", - "258 4 Yes \n", - "259 3 (room), 8 (hall), ~70 (building) No \n", - "\n", - " How many hours do you work per week on average? \\\n", - "0 5 - 10 \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 10 - 20 \n", - ".. ... \n", - "255 10 - 20 \n", - "256 NaN \n", - "257 1 - 5 \n", - "258 5 - 10 \n", - "259 NaN \n", - "\n", - " Do you work on or off campus? \\\n", - "0 Off-campus \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 Off-campus \n", - ".. ... \n", - "255 On-campus \n", - "256 NaN \n", - "257 Off-campus \n", - "258 On-campus \n", - "259 NaN \n", - "\n", - " Do you work in a department related to your major? \\\n", - "0 No \n", - "1 NaN \n", - "2 NaN \n", - "3 No \n", - "4 Yes \n", - ".. ... \n", - "255 No \n", - "256 NaN \n", - "257 No \n", - "258 No \n", - "259 NaN \n", - "\n", - " Do you have roommates that are part of your major? \n", - "0 No \n", - "1 Yes \n", - "2 No \n", - "3 No \n", - "4 No \n", - ".. ... \n", - "255 No \n", - "256 No \n", - "257 No \n", - "258 No \n", - "259 Yes \n", - "\n", - "[260 rows x 10 columns]" - ] + "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>" }, - "execution_count": 54, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -364,307 +79,22 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 2, "id": "29889175", "metadata": { + "id": "29889175", "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.409516Z", - "start_time": "2024-02-23T01:01:41.398267Z" - }, - "id": "29889175" + "end_time": "2024-02-23T06:18:35.665240Z", + "start_time": "2024-02-23T06:18:35.654717Z" + } }, "outputs": [ { "data": { - "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", - 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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 - 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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>" }, - "execution_count": 56, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -1013,307 +158,22 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 4, "id": "5fe8ec7f22878e60", "metadata": { + "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T01:01:41.427847Z", - "start_time": "2024-02-23T01:01:41.419852Z" - }, - "collapsed": false + "end_time": "2024-02-23T06:18:35.679365Z", + "start_time": "2024-02-23T06:18:35.673583Z" + } }, "outputs": [ { "data": { - "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", - 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"254 Family \n", - "256 Family \n", - "257 Family \n", - "259 Friends \n", - "\n", - " Do you currently live in a house, apartnment, or dorm? \\\n", - "1 Apartment \n", - "2 House \n", - "3 Apartment \n", - "5 Apartment \n", - "6 Apartment \n", - ".. ... \n", - "253 House \n", - "254 House \n", - "256 Apartment \n", - "257 House \n", - "259 Dorm \n", - "\n", - " How many people live in your household? 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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>" }, - "execution_count": 57, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1334,7 +194,16 @@ }, { "cell_type": "code", - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "<Figure size 800x800 with 1 Axes>", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Count the number of people who work and don't work\n", "work_counts = df['Do you currently work?'].value_counts()\n", @@ -1346,13 +215,27 @@ "plt.show()" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-23T06:18:35.781584Z", + "start_time": "2024-02-23T06:18:35.680024Z" + } }, - "id": "6bc50ddc195d88a" + "id": "6bc50ddc195d88a", + "execution_count": 5 }, { "cell_type": "code", - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "<Figure size 800x800 with 2 Axes>", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_2dhist = pd.DataFrame({\n", " x_label: grp['Do you currently work?'].value_counts()\n", @@ -1366,9 +249,14 @@ "_ = plt.ylabel('Do you currently work?')" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-23T06:18:36.021224Z", + "start_time": "2024-02-23T06:18:35.783111Z" + } }, - "id": "15f1e14311b1b17f" + "id": "15f1e14311b1b17f", + "execution_count": 6 }, { "cell_type": "markdown", @@ -1380,25 +268,91 @@ }, { "cell_type": "code", - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "<Figure size 640x480 with 1 Axes>", + "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',\n", " color=sns.palettes.mpl_palette(\n", " 'Dark2'))\n", - "plt.gca().spines[['top', 'right', ]].set_visible(False)\n", - "roommates_major_table = pd.crosstab(df.iloc[:, 3], df.iloc[:, 9], margins=True, margins_name='Total')\n", - "\n", - "# Print the table\n", - "print(roommates_major_table)\n" + "plt.gca().spines[['top', 'right', ]].set_visible(False)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-23T06:18:36.170073Z", + "start_time": "2024-02-23T06:18:36.024936Z" + } + }, + "id": "a3d9a4a3b5eba149", + "execution_count": 7 + }, + { + "cell_type": "markdown", + "source": [ + "## Hypotheses" ], "metadata": { "collapsed": false }, - "id": "2ee7f39b5d8df8de" + "id": "4df3824f641fb18b" + }, + { + "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": "796d474b4650e712" }, { "cell_type": "code", - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "Do you have roommates that are part of your major? No Yes Total\nDo you currently live in a house, apartnment, o... \nApartment 83 44 127\nDorm 17 11 28\nHouse 78 21 99\nRoom 1 0 1\nTotal 179 76 255", + "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 have roommates that are part of 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, apartnment, 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>83</td>\n <td>44</td>\n <td>127</td>\n </tr>\n <tr>\n <th>Dorm</th>\n <td>17</td>\n <td>11</td>\n <td>28</td>\n </tr>\n <tr>\n <th>House</th>\n <td>78</td>\n <td>21</td>\n <td>99</td>\n </tr>\n <tr>\n <th>Room</th>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>Total</th>\n <td>179</td>\n <td>76</td>\n <td>255</td>\n </tr>\n </tbody>\n</table>\n</div>" + }, + "execution_count": 8, + "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": "2024-02-23T06:18:36.201284Z", + "start_time": "2024-02-23T06:18:36.179437Z" + } + }, + "id": "2ee7f39b5d8df8de", + "execution_count": 8 + }, + { + "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chi-squared Value: 6.54402786926266\n", + "Degrees of Freedom: 8\n" + ] + } + ], "source": [ "# Extract the observed values from the contingency table\n", "observed_values = roommates_major_table.iloc[:-1, :-1].values\n", @@ -1417,12 +371,17 @@ "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}\")\n" + "print(f\"Chi-squared Value: {chi2_statistic}\\nDegrees of Freedom: {degrees_of_freedom}\")" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-23T06:18:36.219063Z", + "start_time": "2024-02-23T06:18:36.205767Z" + } }, - "id": "957406c164cf2ef1" + "id": "957406c164cf2ef1", + "execution_count": 9 } ], "metadata": { |