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Diffstat (limited to 'CS105MiniProject.ipynb')
| -rw-r--r-- | CS105MiniProject.ipynb | 113 |
1 files changed, 72 insertions, 41 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb index 2f86c48..818356f 100644 --- a/CS105MiniProject.ipynb +++ b/CS105MiniProject.ipynb @@ -29,8 +29,8 @@ "outputs": [ { "data": { - "text/plain": " Timestamp What is your current class standing? \\\n0 2/9/2024 20:12:14 Senior \n1 2/9/2024 20:16:34 Junior \n2 2/9/2024 20:18:55 Junior \n3 2/9/2024 20:24:00 Senior \n4 2/9/2024 20:26:16 Graduate \n.. ... ... \n255 2/14/2024 19:46:28 Junior \n256 2/15/2024 0:28:38 NaN \n257 2/15/2024 8:33:45 Senior \n258 2/15/2024 16:10:40 Sophomore \n259 2/15/2024 16:14:11 Sophomore \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? Do you currently work? \\\n0 6 Yes \n1 4 No \n2 4 No \n3 1 No \n4 1 Yes \n.. ... ... \n255 5 Yes \n256 North District 4 bed 2 bath No \n257 9 No \n258 4 Yes \n259 3 (room), 8 (hall), ~70 (building) No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 NaN \n2 NaN \n3 NaN \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 NaN \n257 1 - 5 \n258 5 - 10 \n259 NaN \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 No \n4 Yes \n.. ... \n255 No \n256 NaN \n257 No \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 10 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Timestamp</th>\n <th>What is your current class standing?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2/9/2024 20:12:14</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2/9/2024 20:16:34</td>\n <td>Junior</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2/9/2024 20:18:55</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2/9/2024 20:24:00</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2/9/2024 20:26:16</td>\n <td>Graduate</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>2/14/2024 19:46:28</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>2/15/2024 0:28:38</td>\n <td>NaN</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>North District 4 bed 2 bath</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>2/15/2024 8:33:45</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>No</td>\n <td>1 - 5</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>2/15/2024 16:10:40</td>\n <td>Sophomore</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>2/15/2024 16:14:11</td>\n <td>Sophomore</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3 (room), 8 (hall), ~70 (building)</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 10 columns</p>\n</div>" + "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": 1, "metadata": {}, @@ -41,19 +41,22 @@ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", "\n", "# Load dataframe from data.csv\n", "df = pd.read_csv(\"data.csv\")\n", "\n", "# Select relevant columns\n", - "df = df.iloc[:, [0, 2, 7, 8, 9, 58, 59, 60, 61, 26]]\n", + "df = df.iloc[:, [2, 3, 7, 8, 9, 58, 59, 60, 61, 26]]\n", "df" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T01:12:49.045312Z", - "start_time": "2024-02-23T01:12:48.152070Z" + "end_time": "2024-02-23T02:10:58.731970Z", + "start_time": "2024-02-23T02:10:58.699969Z" } }, "id": "3bea6ea662d6c063", @@ -74,8 +77,8 @@ "outputs": [ { "data": { - "text/plain": " Timestamp What is your current class standing? \\\n0 2/9/2024 20:12:14 Senior \n1 2/9/2024 20:16:34 Junior \n2 2/9/2024 20:18:55 Junior \n3 2/9/2024 20:24:00 Senior \n4 2/9/2024 20:26:16 Graduate \n.. ... ... \n255 2/14/2024 19:46:28 Junior \n256 2/15/2024 0:28:38 NaN \n257 2/15/2024 8:33:45 Senior \n258 2/15/2024 16:10:40 Sophomore \n259 2/15/2024 16:14:11 Sophomore \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? Do you currently work? \\\n0 6 Yes \n1 4 No \n2 4 No \n3 1 No \n4 1 Yes \n.. ... ... \n255 5 Yes \n256 4 No \n257 9 No \n258 4 Yes \n259 3 No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 0 \n2 0 \n3 0 \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 0 \n257 0 \n258 5 - 10 \n259 0 \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 NaN \n4 Yes \n.. ... \n255 No \n256 NaN \n257 NaN \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 10 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Timestamp</th>\n <th>What is your current class standing?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2/9/2024 20:12:14</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2/9/2024 20:16:34</td>\n <td>Junior</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2/9/2024 20:18:55</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2/9/2024 20:24:00</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2/9/2024 20:26:16</td>\n <td>Graduate</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>2/14/2024 19:46:28</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>2/15/2024 0:28:38</td>\n <td>NaN</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>2/15/2024 8:33:45</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>2/15/2024 16:10:40</td>\n <td>Sophomore</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>2/15/2024 16:14:11</td>\n <td>Sophomore</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 10 columns</p>\n</div>" + "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>" }, "execution_count": 2, "metadata": {}, @@ -107,8 +110,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T01:12:49.066644Z", - "start_time": "2024-02-23T01:12:49.047827Z" + "end_time": "2024-02-23T02:10:58.744774Z", + "start_time": "2024-02-23T02:10:58.732815Z" } }, "id": "f71f8085d5f66b0", @@ -119,8 +122,8 @@ "outputs": [ { "data": { - "text/plain": " Timestamp What is your current class standing? \\\n0 2/9/2024 20:12:14 Senior \n4 2/9/2024 20:26:16 Graduate \n8 2/9/2024 22:02:49 Junior \n9 2/9/2024 22:08:43 Senior \n13 2/9/2024 22:15:13 Junior \n.. ... ... \n246 2/13/2024 19:37:02 Graduate \n247 2/13/2024 21:39:14 Senior \n252 2/14/2024 9:48:12 Junior \n255 2/14/2024 19:46:28 Junior \n258 2/15/2024 16:10:40 Sophomore \n\n Who do you live with? \\\n0 Neither \n4 Neither \n8 Friends \n9 Family \n13 Family \n.. ... \n246 Family \n247 Friends \n252 Family \n255 Friends \n258 Family \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n4 Apartment \n8 House \n9 House \n13 Apartment \n.. ... \n246 House \n247 Apartment \n252 House \n255 House \n258 Apartment \n\n How many people live in your household? Do you currently work? \\\n0 6 Yes \n4 1 Yes \n8 6 Yes \n9 5 Yes \n13 4 Yes \n.. ... ... \n246 2 Yes \n247 3 Yes \n252 5 Yes \n255 5 Yes \n258 4 Yes \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n4 10 - 20 \n8 10 - 20 \n9 1 - 5 \n13 10 - 20 \n.. ... \n246 10 - 20 \n247 20 - 40 \n252 20 - 40 \n255 10 - 20 \n258 5 - 10 \n\n Do you work on or off campus? \\\n0 Off-campus \n4 Off-campus \n8 On-campus \n9 On-campus \n13 Off-campus \n.. ... \n246 On-campus \n247 Off-campus \n252 Off-campus \n255 On-campus \n258 On-campus \n\n Do you work in a department related to your major? \\\n0 No \n4 Yes \n8 No \n9 No \n13 No \n.. ... \n246 Yes \n247 No \n252 No \n255 No \n258 No \n\n Do you have roommates that are part of your major? \n0 No \n4 No \n8 No \n9 No \n13 No \n.. ... \n246 No \n247 Yes \n252 No \n255 No \n258 No \n\n[77 rows x 10 columns]", - "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Timestamp</th>\n <th>What is your current class standing?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2/9/2024 20:12:14</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2/9/2024 20:26:16</td>\n <td>Graduate</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2/9/2024 22:02:49</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2/9/2024 22:08:43</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>1 - 5</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2/9/2024 22:15:13</td>\n <td>Junior</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>246</th>\n <td>2/13/2024 19:37:02</td>\n <td>Graduate</td>\n <td>Family</td>\n <td>House</td>\n <td>2</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>247</th>\n <td>2/13/2024 21:39:14</td>\n <td>Senior</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>3</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>252</th>\n <td>2/14/2024 9:48:12</td>\n <td>Junior</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>255</th>\n <td>2/14/2024 19:46:28</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>2/15/2024 16:10:40</td>\n <td>Sophomore</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n </tbody>\n</table>\n<p>77 rows × 10 columns</p>\n</div>" + "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>" }, "execution_count": 3, "metadata": {}, @@ -137,8 +140,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T01:12:49.084475Z", - "start_time": "2024-02-23T01:12:49.068965Z" + "end_time": "2024-02-23T02:10:58.754973Z", + "start_time": "2024-02-23T02:10:58.746452Z" } }, "id": "6c1d9ee7948e6b9a", @@ -149,8 +152,8 @@ "outputs": [ { "data": { - "text/plain": " Timestamp What is your current class standing? \\\n1 2/9/2024 20:16:34 Junior \n2 2/9/2024 20:18:55 Junior \n3 2/9/2024 20:24:00 Senior \n5 2/9/2024 20:45:09 Junior \n6 2/9/2024 21:55:59 Sophomore \n.. ... ... \n253 2/14/2024 13:45:45 Senior \n254 2/14/2024 16:26:06 Junior \n256 2/15/2024 0:28:38 NaN \n257 2/15/2024 8:33:45 Senior \n259 2/15/2024 16:14:11 Sophomore \n\n Who do you live with? \\\n1 Both \n2 Friends \n3 Neither \n5 Both \n6 Friends \n.. ... \n253 Family \n254 Family \n256 Family \n257 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n1 Apartment \n2 House \n3 Apartment \n5 Apartment \n6 Apartment \n.. ... \n253 House \n254 House \n256 Apartment \n257 House \n259 Dorm \n\n How many people live in your household? Do you currently work? \\\n1 4 No \n2 4 No \n3 1 No \n5 4 No \n6 4 No \n.. ... ... \n253 6 No \n254 5 No \n256 4 No \n257 9 No \n259 3 No \n\n How many hours do you work per week on average? \\\n1 0 \n2 0 \n3 0 \n5 0 \n6 0 \n.. ... \n253 0 \n254 0 \n256 0 \n257 0 \n259 0 \n\n Do you work on or off campus? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 Off-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 NaN \n259 NaN \n\n Do you have roommates that are part of your major? \n1 Yes \n2 No \n3 No \n5 No \n6 No \n.. ... \n253 No \n254 Yes \n256 No \n257 No \n259 Yes \n\n[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>Timestamp</th>\n <th>What is your current class standing?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>2/9/2024 20:16:34</td>\n <td>Junior</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2/9/2024 20:18:55</td>\n <td>Junior</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2/9/2024 20:24:00</td>\n <td>Senior</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2/9/2024 20:45:09</td>\n <td>Junior</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2/9/2024 21:55:59</td>\n <td>Sophomore</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>253</th>\n <td>2/14/2024 13:45:45</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>6</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>254</th>\n <td>2/14/2024 16:26:06</td>\n <td>Junior</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>256</th>\n <td>2/15/2024 0:28:38</td>\n <td>NaN</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>2/15/2024 8:33:45</td>\n <td>Senior</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>2/15/2024 16:14:11</td>\n <td>Sophomore</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>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, 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": 4, "metadata": {}, @@ -163,8 +166,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T01:12:49.104996Z", - "start_time": "2024-02-23T01:12:49.089572Z" + "end_time": "2024-02-23T02:10:58.763694Z", + "start_time": "2024-02-23T02:10:58.755858Z" } }, "id": "34f69a756f513fb7", @@ -182,27 +185,11 @@ }, { "cell_type": "code", - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-02-23T01:12:49.110581Z", - "start_time": "2024-02-23T01:12:49.107274Z" - } - }, - "id": "39571411a9ea92e0", - "execution_count": 5 - }, - { - "cell_type": "code", "outputs": [ { "data": { "text/plain": "<Figure size 800x800 with 1 Axes>", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -221,22 +208,40 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T01:12:49.355506Z", - "start_time": "2024-02-23T01:12:49.112753Z" + "end_time": "2024-02-23T02:10:58.965154Z", + "start_time": "2024-02-23T02:10:58.764461Z" } }, "id": "da1811cc63b41845", - "execution_count": 6 + "execution_count": 5 }, { "cell_type": "code", - "outputs": [], - "source": [], + "outputs": [ + { + "data": { + "text/plain": "<Figure size 800x800 with 2 Axes>", + "image/png": 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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", + "})\n", + "sns.heatmap(df_2dhist, cmap='viridis')\n", + "plt.xlabel('Do you currently live in a house, apartnment, or dorm? ')\n", + "_ = plt.ylabel('Do you currently work?')" + ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T01:12:49.360434Z", - "start_time": "2024-02-23T01:12:49.357193Z" + "end_time": "2024-02-23T02:10:59.277945Z", + "start_time": "2024-02-23T02:10:58.967433Z" } }, "id": "201db70188d3e778", @@ -249,6 +254,32 @@ "collapsed": false }, "id": "8d65fec230193b72" + }, + { + "cell_type": "code", + "outputs": [ + { + "data": { + "text/plain": "<Figure size 640x480 with 1 Axes>", + "image/png": 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eprNJl2W3Z+MnW5vgKQ/7vhPMKHOYk3PMyDlm5JynzCg0NMDpe0xf3GGz2XTy5MkMxe/QoUPKmTOn2c0hi0pKSnZ8brcblD4AALIB08Wvc+fOGjx4sHr06KEHH3xQhmFo9+7dmjdvnvr37++KjHADu90uu93dKQAAgJVMF79+/fopNDRUixYt0qxZsyRJDzzwgEaOHKlWrVpZHhAAAADWMF38JOnJJ5/Uk08+aXUWAAAAuJDpR7YBAADg3kTxAwAA8BAUPwAAAA9huviNGTNGR44ccUUWAAAAuJDp4vf555/LZrO5IgsAAABcyPRVvVFRUXrttdcUFRWlwoULZ7hpc+HChS0LBwAAAOuYLn6TJ0+WJG3YsMGxzGazyTAM2Ww27dmzx7p0AAAAsIzp4rdu3TpX5AAAAICLmS5+YWFhkqRNmzYpPj5edrtd4eHhqlWrlnx8fCwPCAAAAGuYLn5//PGH+vbtq4MHDyo8PFxpaWk6fPiwChcurPfff18FCxZ0RU4AAADcIdNX9Y4ePVr58+fXN998oyVLluizzz7T119/rcKFC2vs2LGuyAgAAAALmC5+3333nQYNGqSgoCDHsuDgYL3yyivatGmTpeEAAABgHdPFLygoSOfOncuw/Pz585zjBwAAkIWZLn7NmzdXTEyMtmzZoosXL+rixYvatGmTRowYoWbNmrkiIwAAACxg+uKOF198UYmJierZs6cMw5AkeXt7q0OHDho8eLDlAQEAAGAN08XP19dX48eP17Bhw3To0CH5+vrq/vvvV+7cuV2RDwAAABYxXfxuCAwMVIUKFazMAgAAABcyfY4fAAAA7k0UPwAAAA9B8QMAAPAQlhU/u92uEydOKDEx0apNAgAAwEK3fXHH3505c0aRkZFq27atzpw5oxkzZsjLix2KAAAAWYVlxS8oKEjz5s1TRESEjh49SukDAADIYky3s+3btys1NTXDcsMwdP78eUlS0aJF7zwZAAAALGW6+HXr1s1R8P5q//79GjhwoCWhAAAAYL1MHer96KOP9Nprr8lms8kwDNWuXfum76tVq5al4QAAAGCdTBW/zp0764EHHpDdblf37t01efJkBQUFOdbbbDb5+fmpdOnSLgsKAACAO5PpizuqV68uSVq3bp0KFy4sm83mslAAAACwnumregsUKKDFixdr9+7dSk1NlWEY6dbHxcVZFg4AAADWMX1xx/DhwzV27FglJSVlKH0AAADIukzv8VuzZo2mTZt2yws8AAAAkDWZLn4BAQEqWLCgK7IgC/Hy8tKNe3Db7YbsdvbuAgBwrzN9qLdPnz4aO3as4uPjb3ojZ2QPwcF5HB/58uaWlxcX8wAAcK8zvcfv3Xff1Z9//qkWLVrcdP2ePXvuOBTc7+yIVUr99aRylMyvvBNbyMvLxl4/AADucaaL3/jx412RA1lM2sEzSt3zp7tjAAAAC5kufhEREZKkixcv6siRIypVqpRSUlLk7+9veTgAAABYx/Q5fikpKYqJiVFERISeeOIJnTx5UkOHDlXPnj117tw5V2QEAACABUwXv4kTJ+rAgQNaunSpcubMKUnq37+/kpKSNGbMGMsDAgAAwBqmi9/q1as1fPhwlSlTxrGsTJkyio2N1X/+8x9LwwEAAMA6potfcnKy/Pz8Miy32+1KS0uzJBQAAACsZ7r4RUZG6q233tLFixcdy44ePaoxY8aofv36loYDAACAdUwXv5EjR8rLy0sRERG6fPmy2rdvr8aNGyswMFAjRoxwRUYAAABY4LYe2TZlyhQdPXrU8fSO8PBwlSxZ0hX5AAAAYBHTxU+SkpKSdObMGeXJk0eSdObMGZ05c0aSVL16devSAQAAwDKmi9/8+fM1YcKEmz6n12az8cg2AACALMp08Zs+fbr69u2rnj17Ou7jh5uLjIzU8ePHHa9tNpsCAwNVtWpVjRw5UoUKFXJjOgAA4GlMX9zh5eWlJk2aUPoyadiwYdq4caM2btyob7/9Vm+99Zb279+vIUOGuDsaAADwMKaLX9++fTVp0qR0e7JwawEBAQoNDVVoaKgKFiyo2rVr64UXXtDWrVt14cIFd8cDAAAexPSh3uLFi+vtt99Wo0aNbrqec/yc8/X1lXR97+m5c+f0+uuva926dbp69aoiIyMVExOjoKAgSVJ8fLzGjRunnTt3Kk+ePHryySfVt29feXl5Oa6uDggI0JIlSxQcHKzXXntNhw4d0vTp02W329W3b19169bNnT8uAADIIkwXv5EjR6pOnTpq27atcuXK5YpM2dqRI0f0zjvvqG7dusqTJ4+6du2qy5cva+bMmZKkV199VUOHDtWMGTN05swZde7cWZGRkVq0aJEOHjyomJgY+fv7KyoqSpL05ZdfqlevXvrss8/05ptvasCAAapWrZrmz5+vVatWacKECWrRooXy5cvnxp8aAABkBaaL35kzZzRw4EAVLVrUFXmynVGjRik2NlaSlJqaKh8fHzVs2FDDhg3T3r17tW3bNq1atUrh4eGSpEmTJqlZs2ZKSEjQxo0b5efnp9jYWOXIkUMlS5bUqVOnNG3aNEfxCw4O1osvviibzaa2bdtq5cqVGj58uIoWLaqePXtq8uTJOnz4sCXFz2a7401kCzfmwDxujRllDnNyjhk5x4ycY0b/Y7r4tW/fXsuWLVP//v1dkSfbeeGFF9S4cWMlJydrypQpOn78uF5++WUFBwdry5YtCgwMdJQ+SSpZsqSCgoKUkJCg+Ph4lStXTjly/O+PqXLlyjp16pTOnz8vSSpSpIhs//2bfGMPbFhYWLrXKSkpd/xzBAfnueNtZDf58we4O0KWx4wyhzk5x4ycY0bOMaPbKH4XLlzQwoULtXjxYhUpUkTe3t7p1s+bN8+ycNlB/vz5VaxYMUnSv/71Lz3xxBPq27evFi5c6DjX7+/S0tKUlpZ20yun7Xa74z2S0pXCG7y8TF+z41RSUrLS0uyWb/deZLNd/+WRmHhBhuHuNFkTM8oc5uQcM3KOGTnnKTMKCXFebE0Xv6JFi+rZZ5+9rUCeztfXV2PGjNGTTz6puXPnKjIyUufPn1dCQoJKlCghSTpw4IAuXryo8PBwnTlzRqtXr9a1a9fk4+MjSdq5c6fy5cunvHnz3vX82flfltthGMzEGWaUOczJOWbkHDNyjhndRvHr16+fK3J4jAoVKuiJJ57Q9OnT1apVK9WrV09DhgzRiBEjJEmjR49W9erVVbp0aRUuXFhTpkzRyJEj1atXLx08eFBTpkxR586dHYd3AQAAMst08bt27ZqWLVum3bt3KzU1VcbfqnNcXJxl4bKrl156SV999ZUmTZqkCRMmaMyYMYqKipK3t7caNmyo6OhoSZK/v79mz56tsWPHqk2bNsqXL5+6d+/OHlcAAHBbbMbfm5sTgwcP1urVq1W3bl35+/tnWE/xyx4Sn/pI13YcV46HCijk0+5KSkpWairn+EnXzxUJCQnQ6dPZ+1yRO8GMMoc5OceMnGNGznnKjEJDXXCO35o1azRt2jTVrl37tkIBAADAPUxf/hkQEKCCBQu6IgsAAABcyHTx69Onj8aOHav4+Hilpqa6IhMAAABcwPSh3nfffVd//vmnWrRocdP1PKsXAAAgazJd/MaPH++KHAAAAHAx08Vv6dKlGj58eIYres+dO6cRI0YoIiLCsnAAAACwTqaK386dO3X48GFJ0rJly1SuXLkMxS8hIUEbN260PiEAAAAskani5+fnpylTpsgwDBmGodmzZ6d7HqzNZlPu3Ln1yiuvuCwoAAAA7kymit+DDz6odevWSZLatWunuXPnKjAw0KXBAAAAYC3Tt3NJSkrSsWPHXJEFAAAALmS6+Hl7e+vatWuuyAIAAAAXMn1Vb4MGDdSjRw89+uijCgsLk6+vb7r1/fr1sywc3Mc7PJ+My9eUo2R+d0cBAAAWMV389u3bp3LlyunPP//Un3/+mW6dzWazLBjcK29sE8fnRppddns2fqo1AAAewnTxmz9/vityIItJSkp2fG63GxQ/AACyAdPFT7r+WLb9+/fLbrdLkgzDUEpKin799VeNHj3a0oBwD7vdrv/+8QIAgGzCdPGbOnWqpk6dqpCQECUmJqpgwYI6ffq00tLS9Nhjj7kiIwAAACxg+qrehQsXavTo0dq4caMKFSqk+fPna/PmzapVq5buv/9+V2QEAACABW7rPn5169aVJD300EPauXOnAgMD9dJLL+nLL7+0PCAAAACsYbr4FSxYUEePHpUklSxZUr/++qskyd/fX2fOnLE2HQAAACxj+hy/Dh06aODAgRo3bpwaNWqkqKgoFShQQJs3b9aDDz7oiowAAACwgOni99xzz+m+++5Trly5VKFCBUVHR+vjjz9W3rx5NW7cOFdkBAAAgAVu63Yubdq0kXT9fL/GjRurQ4cOVmYCAACAC5gufna7XZMnT9aiRYsc5/QVKFBAXbp00TPPPGN5QAAAAFjDdPGLi4vT6tWr9fLLL+vhhx+W3W7X7t27NXnyZKWkpPCsXgAAgCzKdPH77LPPNHXqVEVERDiWPfjggwoLC9Mrr7xC8QMAAMiiTN/OJVeuXPLx8cmwPDAwUDabzZJQAAAAsJ7p4jd48GANGzZMX3/9tc6ePauLFy/q+++/14gRI9S9e3edOHHC8QEAAICsw2YYhmHmC/56r74be/j+ugmbzSbDMGSz2bRnzx6LYuJuS0y8ILvd3SmyJptNCgkJ0OnTF2Tu3x7PwYwyhzk5x4ycY0bOecqMQkMDnL7H9Dl+69atu60wAAAAcC/TxS8sLOymy1NSUrRnzx5VrFjxjkMBAADAeqaL344dOzR69GgdOHBA9r8dC/T29tbPP/9sWTgAAABYx/TFHWPGjFFYWJhmzpwpPz8/TZkyRTExMcqbN68mTpzoiowAAACwgOk9fvv379ekSZNUsmRJlStXTj4+PurSpYvy58+vd999V82aNXNFTgAAANwh03v8/Pz85O3tLUkqUaKE9u3bJ0mqUKGCDh48aG06AAAAWMZ08atZs6beeOMNnTx5UpUrV9aXX36ps2fPav369QoMDHRFRgAAAFjAdPEbPny4zp07p9WrV6t58+by9/dXzZo1FRcXp+eff94VGQEAAGAB0+f4FSxYUPPmzXO8nj9/vg4cOKDAwEAVLFjQ0nAAAACwjuni93c2m00PPPCAFVkAAADgQqYP9QIAAODeRPEDAADwEBQ/AAAAD2FZ8bPb7Tpx4oQSExOt2iQAAAAsdMcXd9xw5swZRUZGqm3btjpz5oxmzJghLy92KAIAAGQVlhW/oKAgzZs3TxERETp69CilDwAAIIuxrJ35+PgoIiJCklS0aFGrNgsAAACL3Fbx++GHH/TCCy+odevW+v333/XOO+9oxYoVVmcDAACAhUwXv9WrV+uZZ55RWFiYDh48qNTUVOXIkUNDhw7VRx995IqMAAAAsIDp4jd16lS9+uqrGjJkiLy9vSVJTz/9tMaNG6f333/f8oAAAACwhumLOw4fPqxKlSplWF6hQgWdPHnSikzIAry8vMT1Of/M25sBOcOMMoc5OceM0rPbDdnthrtj4B5kuviVKlVKGzZsUOfOndMtX7p0qUqVKmVZMLhXcHAed0fI8piRc8woc5iTc8wovbQ0u86evUT5g2mmi190dLSee+45fffdd7p27Zpmzpypw4cP6+eff9aMGTNckRFusOjjXTpx/Jy7YwAA/qZAgQB16lpZXl42ih9MM138qlWrppUrVzou5Dh79qwqVaqkiRMnqnDhwpYHhHucPnVRx4+dd3cMAABgodu6gXNoaKhefPFFSdKVK1f022+/KSAgwNJgAAAAsJbps2UPHDigjh07aseOHTp//rzatm2rjh07ql69evruu+9ckREAAAAWMF38Ro8eraJFi6p48eJavHixLly4oI0bN+q5557ThAkTXJERAAAAFjBd/H766ScNGDBA+fLl09q1a/XYY48pJCRELVq0UEJCgisyAgAAwAKmi19AQIBOnz6t33//XT/++KMaNGggSdqzZ4/y589vdT4AAABYxPTFHe3atVOfPn3k6+urIkWKqE6dOlqwYIEmTpzouOADAAAAWY/p4jdw4ECVL19ex48fV4sWLeTt7a3ChQvrzTff1KOPPuqKjAAAALDAbd3O5bHHHkv3un79+paEAQAAgOuYLn6RkZGy2Wy3XL9u3bo7CgQAAADXMF38+vfvn+51amqqjh49qiVLlnCOHwAAQBZmuvi1bdv2pssrVqyoOXPmqEOHDncc6l4WGRmpfv36qV27dumWL1myRFOnTtX69evdlAwAAHg607dzuZVSpUpp9+7dVm0OAAAAFjO9x2/79u0ZliUnJ2v+/Pl64IEHLAkFAAAA65kufl27ds2wzMfHR+XLl9eYMWMsCZXd/fHHH4qLi9OWLVtks9nUsmVLDR48WL6+vjc9JNy1a1dFRESof//+OnHihGJiYrRz507lypVLzZo109ChQ+Xj4yPDMDR9+nQtWLBAV65cUbVq1TRy5EgVLlzYjT8tAADIKkwXv71797oih8dISUlR9+7dVaxYMc2fP19nzpzRiBEjJEkxMTFOvz42Nla5c+fWsmXLlJiYqBdeeEElSpRQly5d9O9//1vLly/XG2+8oZCQEM2ZM0dPP/20li9fLh8fH1f/aACAu8xmu/5x43PcHDP6n9u6j9+VK1f0+eefKz4+XmlpaSpRooSaNm2q4OBgq/Pdk0aNGqXY2Nh0y1JTUxUaGqoNGzbo5MmT+uSTTxQUFCRJGjlypPr06aOXXnrJ6baPHz+ucuXKqXDhwipWrJjeeecdBQYGSpJmz56tUaNGqUaNGpKk1157TXXq1NGGDRsUGRlp8U8JAHCn4OA86V7nzx/gpiT3DmZ0G8Xvt99+U69eveTt7a2HH35YaWlpWrNmjaZMmaL58+erVKlSrsh5T3nhhRfUuHHjdMtWr16tBQsWKD4+XsWLF3eUPkmqUqWKUlNTdeTIEafb7tWrl4YNG6Y1a9aoXr16atasmcqWLavk5GT98ccfeumll+Tl9b9rdq5cuaJDhw5Z9rMBALKGpKRkpaXZZbNdLzSJiRdkGO5OlTV5yoxCQpwXW9PFb+zYsapdu7ZiY2OVI8f1L09NTVVMTIzGjRunOXPmmE+azeTPn1/FihXLsEyScubMmeH9aWlpjn/e7ObYqampjs9btWqlRx55RGvXrtU333yjF154Qb1791bPnj0lSf/6178UHh6e7uv/WjIBANnHX0uMYShblxorMKPbuJ3Ljz/+qN69eztKnyTlyJFDvXv31s6dOy0Nlx2Fh4fr0KFDOnv2rGPZjz/+qBw5cuj++++Xj4+PkpOTHesMw9CxY8ccr9966y0lJiaqU6dOmjVrlgYMGKDVq1crMDBQ+fPn16lTp1SsWDEVK1ZMhQoV0qRJk3Tw4MG7+SMCAIAsynTxCw0NvekhySNHjihPnjw3+Qr8Ve3atVW0aFENHjxY+/bt03fffafY2Fi1aNFCgYGBevjhh3X27FnNnz9fR48eVVxcnM6dO+f4+oSEBL322mvau3ev9u/fr2+//VZly5aVJEVFRentt9/W+vXrdejQIcXExGjHjh0qUaKEu35cAACQhZgufv/3f/+nmJgYLVq0SPv27dO+ffv0ySefaMSIER7/1I7M8Pb21vTp0yVJHTt21MCBA9WwYUO99tprkqTixYtryJAhmjFjhtq0aSPDMPT44487vv7VV19VSEiIunbtqo4dO6pAgQIaPny4JKlnz5564oknNHLkSLVp00YnTpzQe++9x6FeAAAgSbIZhrmj3YZhaOrUqfr3v//t2BMVEhKiqKgoPf300+kuLMC9a8aUTTqYkOTuGACAvwkrEqgXX66npKRkpaZev7gjJCRAp09n7wsX7oSnzCg01AUXd9hsNvXv31/9+/dXYmKicubMKX9//9sKCAAAgLvntu7jl5CQoH379unq1asZ1rVp0+ZOMwEAAMAFTBe/uXPnavz48QoMDMywp89ms1H8AAAAsijTxe/dd9/V0KFDFRUV5YI4AAAAcBXTV2JcuXJFDRs2dEUWAAAAuJDp4te6dWt99NFHrsgCAAAAF8rUod6uXbs6HiV27do17dy5UytXrlSRIkUy3L5l3rx51qcEAADAHctU8atRo0a617Vr13ZJGAAAALhOpopfv379XJ0DAAAALsZjNgAAADwExQ8AAMBD3NaTO5D9hYT6KyUlzd0xAAB/U6CA8+exArdiafG7du2afHx8rNwk3KTD/1V0dwQAwC2kpdlltxvujoF7kOnid/r0ac2aNUsHDhxQWtr1PUKGYejatWuKj4/X9u3bLQ+Juy8pKdndEbK04OA8zMgJZpQ5zMk5ZpSR3W5Q/HBbTBe/YcOG6ciRI2rcuLHmzJmjHj166MiRI1qzZo2GDh3qioxwA7vdLrvd3Smypv/e0lJpaXYZ/N69KWaUOczJOWYEWMt08du+fbvmzJmjypUra9OmTWrQoIGqVq2qd955R//5z3/UrVs3V+QEAADAHTJ9Va9hGCpYsKAkqVSpUvr1118lSU2bNtXu3butTQcAAADLmC5+ZcuW1WeffSZJeuihh7Rp0yZJ0rFjx6xNBgAAAEuZPtT78ssv67nnnpOfn59at26t2bNnq2XLljpx4oRatWrliowAAACwgOniV7VqVX399de6cuWKgoOD9emnn2rt2rXKmzevmjZt6oqMAAAAsMBt3cfP399f/v7+kqSCBQuqS5culoYCAACA9XhkGwAAgIeg+AEAAHgIih8AAICHuO1n9Z46dUqpqaky/nYr9cKFC99xKAAAAFjPdPHbuHGjRo4cqd9//z3dcsMwZLPZtGfPHsvCAQAAwDqmi19sbKwqVKigGTNmOK7sBQAAQNZnuvj98ccfmj17tooWLeqKPAAAAHAR0xd3VKtWTT/88IMrsgAAAMCFTO/xq169ukaPHq1vvvlGxYoVk4+PT7r1/fr1sywcAAAArGO6+G3atEkPP/ywEhMTlZiYmG6dzWazLBgAAACsZbr4zZ8/3xU5AAAA4GKZKn7Lli1Ts2bN5Ovrq2XLlv3je9u0aWNBLAAAAFgtU8Vv8uTJql+/vnx9fTV58uRbvs9ms1H8AAAAsqhMFb/169ff9HMAAADcO3hWLwAAgIeg+AEAAHgIih8AAICHoPgBAAB4iNsqfkePHtWECRPUt29f/fnnn1q8eLG+//57q7MBAADAQqaL3/bt29WqVSsdP35cGzZs0NWrV5WQkKCoqCitXr3aFRkBAABgAdPFb9KkSXr55Zc1efJk5chx/W4wgwcP1iuvvPKP9/gDAACAe5kufr/99pvq16+fYXnDhg115MgRS0IBAADAeqaLX1hYmHbv3p1h+TfffKOwsDBLQgEAAMB6mXpyx18NGDBAQ4cO1e7du5WWlqZly5bp2LFjWrFihSZOnOiKjAAAALCA6T1+jz32mD788EMlJibqgQce0Lp165SSkqIPP/xQzZo1c0VGAAAAWMD0Hr8dO3aoSpUq7N0DAAC4x5guflFRUcqfP7+aNm2q5s2bq1y5cq7IBQAAAIuZLn5btmzR119/rdWrV+upp55SaGiomjZtqmbNmqlMmTKuyAg38PLykhfPdflH3t7uHZDdbshuN9yaAQBwb7EZhnHb/+W4cuWK/vOf/2jt2rVat26dChUqpC+++MLKfABuwZ6WqqSzV7Jk+bPZpJCQAJ0+fUG3/xsm+2NOzjEj55iRc54yo9DQAKfvMb3H769+++037dq1S7/88ou8vLxUvnz5O9kcspA/5vTW1cM73R0Dt+Bb+CEVena+vLxsWbL4AQCyJtPFb9u2bVq9erXWrl2rc+fO6dFHH9VLL72kevXqydfX1xUZ4QYpf/xG8QMAIJsxXfx69eqlunXravDgwXr00Ufl5+fnilwAAACwmOnit3nzZvn7+7siCwAAAFwoU8UvOjpaw4cPl7+/v8aOHfuP742Li7MkGAAAAKzFDTsAAAA8RKb2+P11Lx579AAAAO5NmSp+U6dOzfQG+/Xrd9thAAAA4DqZKn5bt27N1MZsNtsdhQEAAIDrZKr4zZ8/39U5AAAA4GJc3AEAAOAhKH4AAAAe4o6e1YvMi4yM1PHjxyVdPxfSz89PZcqU0fPPP6+6deu6OR0AAPAEpvf4HT161BU5PMKwYcO0ceNGffvtt1q4cKGqVKmiZ599Vps3b3Z3NAAA4AFM7/Fr0qSJypYtq+bNm6tp06YqWLCgK3JlSwEBAQoNDZUkFSxYUIMHD9apU6cUFxen5cuXuzkdAADI7kwXvw0bNuirr77SypUr9frrr6tSpUpq1qyZmjRponz58rkiY7b25JNPqkuXLjp8+LDy5s2r119/XevWrdPVq1cVGRmpmJgYBQUFaevWrYqOjlbdunX1xRdf6Nlnn1VCQoLy58+v48eP6+uvv1ZYWJhef/11ffXVV/rwww+VO3duRUdHq2nTpu7+MQEAQBZg+lBvvnz51KlTJ82bN0/ffvutmjdvrv/85z9q1KiRevbsqaVLl+ry5cuuyJotlSxZUpJ04MAB9evXT3v27NHMmTP1/vvvKz4+XkOHDnW89/jx40pJSdGSJUvUokULSdIHH3ygiIgIff7558qbN6+6d++uxMRELVy4UJGRkRo1apTsdrtbfjbcHTZb1vzIytmy0gdzYkbMiBlZ+TM6c0cXd5w6dUqnTp3SH3/8Ibvdrjx58uiTTz7R+PHjFRsbq8aNG9/J5j1CQECAJGnPnj3atm2bVq1apfDwcEnSpEmT1KxZMyUkJDje36tXLxUrVszx+uGHH1bnzp0lSS1atNC4ceMUExOjXLlyqWvXrlqwYIFOnz6tAgUK3MWfCndLcHAed0f4R/nzB7g7wj2BOTnHjJxjRs4xo9sofnv27NGqVau0atUqHT9+XLVq1VKPHj3UqFEj5clz/T9C06dP14gRIyh+mXDx4kVJUlhYmAIDAx2lT7q+NzAoKEgJCQmOglikSJF0X//X17ly5VJISIhy5colScqZM6ckKSUlxaU/A9wnKSlZaWlZb4+uzXb9F2xi4gUZhrvTZF3MyTlm5Bwzcs5TZhQS4rzYmi5+7dq1U9WqVRUVFaUmTZooODg4w3uqVq3K1b+ZtG/fPknS+fPnb7o+LS1NaWlpjtc3ytwNOXKk/yP08uLWjJ4mK/8SM4ysnS+rYE7OMSPnmJFzzOg2it/XX3+t++677x/fU6NGDdWoUeO2Q3mSTz/9VOXKlVOdOnU0btw4JSQkqESJEpKun/d38eJFhYeHKykpyc1JAQDAvS5TxW/q1KmZ3mC/fv1uO0x2d+HCBZ06dUqGYSgpKUmLFy/Wl19+qTlz5qhkyZKqV6+ehgwZohEjRkiSRo8ererVq6t06dLaunWrm9MDAIB7XaaKX2ZLhy2zl5R4qHHjxmncuHGy2WzKly+fypYtq7lz56patWqSpAkTJmjMmDGKioqSt7e3GjZsqOjoaDenBgAA2YXNMMwd7T5x4oTuu+++DOeSpaWlae/evSpXrpylAeEeR8bV15XfNro7Bm4hZ7HKKjb6eyUlJSs1NWte3BESEqDTp7P3idR3ijk5x4ycY0bOecqMQkOdX9xh+kqAhg0b6uzZsxmWHzt2zHFbEQAAAGQ9mTrUu2jRIs2cOVOSZBiG2rdvn2GP3/nz5x03IwYAAEDWk6ni16ZNG/n4+Mhut2vYsGHq0aOH475y0vVz+/z8/FSzZk2XBQUAAMCdyVTx8/HxUZs2bSRdv2FwlSpVMtw/DgAAAFmb6fYWERGhLVu2aPfu3bp27Zr+fm0It3MBAADImkwXv/Hjx2vevHl68MEHHY9ou4HbuQAAAGRdpovfp59+qvHjx6tVq1auyAMAAAAXMX07F29vb1WoUMEVWQAAAOBCpotfly5dNGXKFF26dMkVeQAAAOAipg/1btu2TTt37tSqVauUP39++fj4pFu/bt06y8IBAADAOqaLX7t27dSuXTtXZAEAAIALmS5+bdu2dXx+7tw5BQQEyGazcUVvNuN7X2kZV5PdHQO34Fv4IXdHAADcg0wXP8MwNHPmTM2dO1cXLlzQV199pX/961/KnTu3YmJi5Ovr64qcuMvue/pdd0eAE/a0VNnt2fhp4wAAy5kuftOmTdOKFSs0fvx4vfTSS5Ku7wUcOXKkJk6cqJiYGMtD4u5LSmJv3z8JDs7j9hnZ7QbFDwBgiunit3TpUo0fP17Vq1d3HN6tXbu2JkyYoBdffJHil03Y7XbZ7e5OkTXdOKshLc0ug94FALiHmL6dS2JiogoUKJBheWBgILd4AQAAyMJMF7+aNWvqvffeS7fs4sWLevPNN1WjRg3LggEAAMBapovfq6++ql9//VW1a9fW1atX1bdvX9WvX1/Hjx/nMC8AAEAWZvocv8DAQC1evFhbtmxRQkKCUlNTFR4erjp16sjLy3SPBAAAwF1iuvi1aNFCU6dO1SOPPKJHHnnEFZkAAADgAqZ30Xl5eenatWuuyAIAAAAXMr3Hr0GDBurRo4ceffRRhYWFZbhhc79+/SwLBwAAAOuYLn779u1TuXLl9Oeff+rPP/9Mt47HtgEAAGRdpovfk08+qdq1ays4ONgVeQAAAOAips/xGz16tM6ePeuCKAAAAHAl08WvRo0aWr58uVJSUlyRBwAAAC5i+lBvYmKipk+frpkzZypfvnzKmTNnuvXr1q2zLBwAAACsY7r4dezYUR07dnRFFgAAALiQ6eLXtm1bV+QAAACAi5kufl27dv3H27bMmzfvjgIBAADANUwXvxo1aqR7nZqaqqNHj+rbb79Vnz59LAsGAAAAa5kufrd6MseSJUu0evVq9ezZ845DAQAAwHqmb+dyK9WrV9eWLVus2hwAAAAsZnqP34kTJzIsS05O1nvvvaewsDBLQgEAAMB6potfZGSkbDabDMNwXORhGIYKFSqkcePGWR4QAAAA1jBd/P5+g2abzSYfHx+FhIT849W+AAAAcC/T5/iFhYXpm2++0c6dOxUWFqbChQtr9OjR+vjjj12RDwAAABYxXfzeeustzZgxQ7lz53Ysi4iI0PTp0zVt2jRLwwEAAMA6povfp59+qrfffluRkZGOZd26ddPrr7+uhQsXWhoOAAAA1jFd/C5fvix/f/8My4ODg3XhwgVLQgEAAMB6potf3bp1NXbs2HS3dTl58qQmTJigOnXqWBoOAAAA1jFd/EaOHKlr166pYcOGqlmzpmrWrKkGDRrIbrdr1KhRrsgIAAAAC5i+nUu+fPn08ccfa9++fTp48KBy5Mih4sWLq1SpUq7IBwAAAIuYLn43lClTRmXKlLEyCwAAAFzIsmf1AgAAIGuj+AEAAHgIih8AAICHMH2O3/bt2/9xffXq1W87DAAAAFzHdPHr2rXrTZf7+voqNDRU69atu+NQcD8vLy95sT/4H3l7MyBnmFHmMCfnmJFzzMg5d8/IbjdktxtuzWAzDOOOEqSlpenIkSOKjY1Vy5Yt1bZtW6uyAQAAZBupdrvOJV1yWfkLDQ1w+p47Ln43/Pbbb3rmmWf0zTffWLE5uNmgjYu1+8wJ528EAABOPRBUQFPq/5+SkpKVmmp3yffITPG77fv4/V1iYqLOnz9v1ebgZvHnT+vnRIofAADZieniFx0dnWFZcnKyNm/erCZNmlgSCgAAANazZI9f3rx5NWTIELVu3dqKzQEAAMAFTBe/uLg4V+QAAACAi93WHr+1a9dq9uzZSkhIUFpamsLDw/XUU0+pTZs2FscDAACAVUwXv48//lgTJkzQU089pWeeeUZ2u107duzQ6NGjde3aNXXo0MEVOQEAAHCHTBe/2bNna9SoUen27jVq1EgPPPCAZs6cSfEDAADIokzfwjoxMVGVKlXKsLxy5cr6/fffrcgEAAAAFzBd/B566CEtW7Ysw/KlS5eqVKlSVmQCAACAC5g+1Dto0CBFRUVp69atqlixoiTpxx9/1N69ezVz5kzLAwIAAMAapvf4Va5cWUuWLFGFChUUHx+vY8eOqXr16lq5cqVq1qzpiowAAACwgOk9fp9++qkef/zxmz7B416zZMkSRUdHa8yYMS69KCUxMVHbtm1T06ZNXfY9buXo0aNKSEhQ/fr17/r3BgAAWYvpPX5z585VrVq11KdPH61YsUKXL192Ra67YsWKFbr//vv12WefufT7vP766/r2229d+j1uZdiwYfrpp5/c8r0BAEDWYrr4LV++XEuXLlW5cuU0Y8YM1apVSwMGDNCaNWuUkpLiiowukZiYqC1btuj555/X999/r6NHj7rsexmG4bJtAwAAZJbp4idJJUuWVL9+/fTFF19o8eLFuv/++zVo0CDVqlVL0dHR2rFjh9U5Lbdq1SoFBASoVatWKlCgQLq9fpGRkZo7d65atmypSpUq6ZlnntGpU6cc69etW6c2bdqofPnyqlatmgYOHKjk5GRJ0pQpU9S3b1916dJFERER6tq1q5YuXaqlS5cqMjJSklSmTBmtXLlSTZs2VcWKFTVw4EAdPXpU3bp1U8WKFdW5c2edPHnS8f3WrFmjZs2aqWLFinriiSe0bds2x7quXbtqxowZ6tmzpypUqKDHH39cGzZskCQNHTpU27Zt09SpU9W1a1eXzhMAAGR9t1X8JOnkyZOaN2+eRo0apTlz5qhs2bIaMGCAChQooD59+uiNN96wMqflVqxYoQYNGsjLy0uRkZFatmxZuj1zU6ZMUa9evbRw4UJdvnxZ/fv3lyQdOXJEL774ojp37qyVK1fq7bff1ubNm/XJJ584vnbdunVq0aKFPvjgA82YMUNNmzZV06ZNtXjxYsd7Jk+erPHjx2vWrFlavXq1OnXqpE6dOunjjz/WqVOn9O6770qS9u7dqyFDhqhPnz76/PPP1apVK/Xu3VuHDx92bGvmzJlq3ry5vvjiCz344IMaMWKE7Ha7hg8frsqVK+vpp5/WlClTXD1SAACQCTabaz4yw/TFHXPnztVXX32lXbt2qXTp0mrevLkmTZqkQoUKOd5TvHhxvfbaa3r55ZfNbv6u+P3337Vjxw716NFDktS4cWMtWLBAP/zwg6pVqyZJat++vVq3bi1JGjdunBo1aqTffvtNvr6+iomJUceOHSVJRYoUUa1atbR//37H9kNCQtSpUyfH61y5ckmS8uXL51gWFRXluB3OQw89pPDwcMfFH40bN9bevXslSe+99546duyoli1bSpK6deum7du3a8GCBRo6dKgkqX79+mrXrp0kqU+fPmrdurVOnTqlggULysfHR7lz51bevHktnCAAALgdwcF53Pr9TRe/BQsWqHnz5hozZoxKlix50/eULVtWMTExdxzOVVasWKGcOXOqTp06kqSIiAgFBQVp6dKljuJXpUoVx/uLFi2qvHnzKj4+Xk2bNpWvr69mzJih/fv3a//+/Tpw4ICjJEpSWFiY0wxFixZ1fJ4rV650X5MrVy7H+ZLx8fFauXKlFi5c6Fh/7do1R3bpetG+wd/fX5KUmpqaqVkAAIC7JykpWWlpdpdsOyQkwOl7TBe/r776yul7ypQpozJlypjd9F2zYsUKXblyRVWrVnUsS0tL06pVqzRixAhJUo4c6UeTlpYmLy8v7d27V506dVJkZKSqVaumqKgoffDBB+nemzNnTqcZvL2907328rr5Ufe0tDT17t073bORpf/tRZQkHx+fDF/HBSUAAGRN7vxPtOnid687ePCgfv31V8XExKhGjRqO5QcOHNBLL72kNWvWSLp+bl2jRo0kSYcPH9aFCxdUpkwZLVy4UNWrV093DuPhw4dvufdTkmw2220XsfDwcB07dkzFihVzLJs4caLCw8Ndeu9BAACQ/Xhc8VuxYoXy5s2rJ598Ur6+vo7lpUuX1rRp0xzPIZ43b54eeughhYWFKTY2VrVr11bx4sWVN29e7du3Tz/99JMCAgK0cOFC7d69O92h27/z8/PT/v37dfLkSRUsWNBU3qioKHXp0kXly5dXgwYNtH79es2dOzfDXsZbyZ07tw4dOqTExETlz5/f1PcGAADZy21f1XuvWrFihVq2bJmu9N3QqVMnbd68WSdPnlTbtm315ptvqlOnTgoNDdVbb70l6frtUypVqqSoqCh17txZJ06c0PPPP69ff/31lt+zdevWOnjwoFq1amV6z1+lSpU0ceJEffTRR2rWrJk++eQTvfHGG6pevXqmvr5Dhw7asGGDevXqZer7AgCA7Mdm3OYxyEOHDik+Pl52u13h4eEqVaqU1dncJjIyUv369XNcKeuJ2n05U9tOHnJ3DAAAsoWH8xfWqlYvKCkpWamprrm4IzTUBRd3nD9/XtHR0Vq3bp2CgoKUlpam5ORkVa9eXdOmTVNAgPNvCgAAgLvP9KHeMWPG6I8//tCXX36prVu36vvvv9fy5ct16dIlxcXFuSIjAAAALGB6j9/69ev1/vvvq0SJEo5lpUqV0siRI9W7d29Lw7nL+vXr3R0BAADAcqb3+OXMmfOm95yz2WxKS0uzJBQAAACsZ7r4RUZGavTo0Tpy5Ihj2aFDhzRmzBjVr1/f0nAAAACwjulDvYMGDdLzzz+vxo0bKygoSNL1Cz7q1q3reOoFAAAAsh7TxS8wMFDz58/Xvn37FB8fr5w5cyo8PDzdOX8AAADIekwXv6NHj+rAgQNKTk6Wv7+/HnjgAYWFhbkiGwAAACyU6eK3ZcsWxcXFaf/+/emePmGz2VSuXDkNHTpU1apVc0lIAAAA3LlMXdyxceNG9erVSw8++KDmz5+v7777Tr/88ou2bt2quXPnqkSJEurRo4d27tzp6rwAAAC4TZna4zdt2jRFRUVp0KBB6ZYHBQWpRo0aqlGjhoKCgjRjxgy98847LgkKAACAO5Op4rd3717Fxsb+43s6dOigp59+2pJQcL+SgSG6lJri7hgAAGQLDwQVcHcESZksfleuXHHcuuVWgoODdebMGUtCwf0m1XnC3REAAMhWUu122e2G8ze6UKaKn2EYN31ax1/ZbLZ0F33g3paUlOzuCFlacHAeZuQEM8oc5uQcM3KOGTmXFWZktxv3RvGTpJUrV8rf3/+W6y9cuGBJIGQNdrtddru7U2RNNtv1f6al2cX/69wcM8oc5uQcM3KOGTnHjP4nU8WvcOHCmjNnjtP3FSpU6I4DAQAAwDUyVfzWr1/v6hwAAABwsUzdxw8AAAD3PoofAACAh6D4AQAAeAiKHwAAgIeg+AEAAHgIih8AAICHoPgBAAB4CIofAACAh6D4AQAAeAiKHwAAgIeg+AEAAHgIih8AAICHyOHuAMiabLbrH8joxlyYz60xo8xhTs4xI+eYkXPM6H9shmEY7g4BAAAA1+NQLwAAgIeg+AEAAHgIih8AAICHoPgBAAB4CIofAACAh6D4AQAAeAiKHwAAgIeg+AEAAHgIih8AAICHoPjB4erVqxo2bJiqVaumOnXqaM6cOe6O5HYnT57UCy+8oIiICNWtW1dxcXG6evWqJOno0aOKiopSpUqV1KxZM23cuNHNad3vmWee0dChQx2vf/31V3Xo0EEVK1ZU+/bt9fPPP7sxnfukpKRo9OjRql69umrVqqU333xTNx6axIz+5/fff9ezzz6rKlWqKDIyUnPnznWs8/Q5paSkqEWLFtq6datjmbPfQZs3b1aLFi1UsWJFdevWTUePHr3bse+qm83oxx9/1P/93/+pcuXKevzxx7Vo0aJ0X+NpM5IofviLiRMn6ueff9YHH3ygUaNGaerUqVq1apW7Y7mNYRh64YUXdPnyZX344Yd666239PXXX+vtt9+WYRh6/vnnFRISok8//VStW7dWv379dOLECXfHdpsVK1bo22+/dby+dOmSnnnmGVWrVk1LlixR5cqV9eyzz+rSpUtuTOkeY8aM0ebNm/Xee+/pjTfe0CeffKKFCxcyo78ZMGCAcufOrSVLlmjYsGF6++23tWbNGo+f09WrVzVw4EDt37/fsczZ76ATJ07o+eefV7t27bR48WLly5dPffv2VXZ9SuvNZnTq1Cn17t1bERERWrp0qV544QXFxsbqm2++keR5M3IwAMMwkpOTjfLlyxvfffedY9m0adOMp556yo2p3OvAgQNG6dKljVOnTjmWLV++3KhTp46xefNmo1KlSkZycrJjXffu3Y3Jkye7I6rbJSUlGfXq1TPat29vDBkyxDAMw1i0aJERGRlp2O12wzAMw263G4899pjx6aefujPqXZeUlGSULVvW2Lp1q2PZrFmzjKFDhzKjvzh79qxRunRpY9++fY5l/fr1M0aPHu3Rc9q/f7/RqlUro2XLlkbp0qUdv6Od/Q56++230/3+vnTpklG5cuV0v+Ozi1vN6KOPPjKaNGmS7r0jRowwBg4caBiGZ83or9jjB0nS3r17lZqaqsqVKzuWVa1aVbt27ZLdbndjMvcJDQ3V7NmzFRISkm75xYsXtWvXLpUtW1a5c+d2LK9atap+/PHHu5wya5gwYYJat26tUqVKOZbt2rVLVatWlc1mkyTZbDZVqVLF42b0ww8/yN/fXxEREY5lzzzzjOLi4pjRX+TKlUt+fn5asmSJrl27poSEBO3YsUMPPfSQR89p27ZtqlGjhhYuXJhuubPfQbt27VK1atUc6/z8/FSuXLlsObNbzejG6Tl/d/HiRUmeNaO/ovhB0vVd4sHBwfL19XUsCwkJ0dWrV3X27Fn3BXOjwMBA1a1b1/Habrfr3//+t2rWrKlTp06pQIEC6d6fP39+/fHHH3c7pttt2bJF33//vfr27ZtuOTO67ujRowoLC9OyZcvUpEkTNWzYUNOmTZPdbmdGf5EzZ06NHDlSCxcuVMWKFdW0aVPVq1dPHTp08Og5de7cWcOGDZOfn1+65c5m4kkzu9WMihQpokqVKjleJyYmasWKFXrkkUckedaM/iqHuwMga7h8+XK60ifJ8TolJcUdkbKcSZMm6ddff9XixYs1d+7cm87L02Z19epVjRo1SiNHjlSuXLnSrbvV3ylPm9GlS5d0+PBhffzxx4qLi9OpU6c0cuRI+fn5MaO/iY+P16OPPqoePXpo//79io2N1SOPPMKcbsLZTJhZeleuXFH//v0VEhKiJ598UpLnzojiB0nX/2/773/Zb7z++3/QPdGkSZP0wQcf6K233lLp0qWVM2fODHtCU1JSPG5WU6dO1cMPP5xuz+gNt/o75WkzypEjhy5evKg33nhDYWFhkq6fVL5gwQIVK1aMGf3Xli1btHjxYn377bfKlSuXypcvr5MnT2rGjBkqWrQoc/obZ7+DbvXvX2Bg4N2KmGUkJyerb9++OnTokD766CPHnkFPnRGHeiFJKliwoJKSkpSamupYdurUKeXKlSvb/0vgTGxsrN5//31NmjRJjz/+uKTr8zp9+nS6950+fTrDYYPsbsWKFVq7dq0qV66sypUra/ny5Vq+fLkqV67MjP4rNDRUOXPmdJQ+SQoPD9fvv//OjP7i559/VrFixdKVubJly+rEiRPM6SaczeRW60NDQ+9axqzg4sWL6tmzp/bv368PPvhAxYsXd6zz1BlR/CBJeuihh5QjR450J7X+8MMPKl++vLy8PPevydSpU/Xxxx/rzTffVPPmzR3LK1asqF9++UVXrlxxLPvhhx9UsWJFd8R0m/nz52v58uVatmyZli1bpsjISEVGRmrZsmWqWLGidu7c6bg1gmEY2rFjh8fNqGLFirp69aoOHjzoWJaQkKCwsDBm9BcFChTQ4cOH0+2BSUhIUJEiRZjTTTj7HVSxYkX98MMPjnWXL1/Wr7/+6lEzs9vt6tevn44dO6b58+frgQceSLfeU2fkuf9FRzp+fn5q06aNXn31Vf30009au3at5syZo27durk7mtvEx8dr+vTp6t27t6pWrapTp045PiIiIlSoUCFFR0dr//79euedd/TTTz/piSeecHfsuyosLEzFihVzfOTJk0d58uRRsWLF1KRJE50/f15jx47VgQMHNHbsWF2+fFlNmzZ1d+y7qkSJEmrQoIGio6O1d+9ebdiwQe+88446derEjP4iMjJSPj4+iomJ0cGDB7V+/XrNnDlTXbt2ZU434ex3UPv27bVjxw6988472r9/v6Kjo1WkSBHVqFHDzcnvnsWLF2vr1q0aM2aMAgMDHb+/bxwi99gZufNeMshaLl26ZAwePNioVKmSUadOHeP99993dyS3mjVrllG6dOmbfhiGYRw6dMjo0qWL8fDDDxvNmzc3Nm3a5ObE7jdkyBDHffwMwzB27dpltGnTxihfvrzxxBNPGL/88osb07nP+fPnjUGDBhmVKlUyHnnkEWPKlCmOe9Ixo//Zv3+/ERUVZVSpUsVo1KiR8f777zOnv/jrPeoMw/nvoG+++cZo3LixUaFCBaN79+7GkSNH7nbku+6vM3r66adv+vv7r/fu88QZ2Qwju9+iGgAAABKHegEAADwGxQ8AAMBDUPwAAAA8BMUPAADAQ1D8AAAAPATFDwAAwENQ/AAAADwExQ8AAMBDUPwAAAA8BMUPAADAQ1D8AAAAPATFDwAAwEP8PxQrLK4/9QpoAAAAAElFTkSuQmCC" + }, + "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)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-23T02:10:59.485432Z", + "start_time": "2024-02-23T02:10:59.281076Z" + } + }, + "id": "5e460707e32c4a2a", + "execution_count": 7 } ], "metadata": { |