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authorGravatar ENathanLe <Nath.wessix@gmail.com> 2024-02-22 18:04:31 -0800
committerGravatar ENathanLe <Nath.wessix@gmail.com> 2024-02-22 18:04:31 -0800
commitfb04ae7d101386baca5987e2f39beb5212aacc17 (patch)
tree4aad7ae3c4e6bec0e9304a5a71fd2972e7f55064 /CS105MiniProject.ipynb
parent7d8e90875c0d5cc42b247c121b1baac08eb20fd9 (diff)
downloadCS105MiniProject-fb04ae7d101386baca5987e2f39beb5212aacc17.tar.gz
CS105MiniProject-fb04ae7d101386baca5987e2f39beb5212aacc17.tar.zst
CS105MiniProject-fb04ae7d101386baca5987e2f39beb5212aacc17.zip
Add bar graph and heatmap
Diffstat (limited to 'CS105MiniProject.ipynb')
-rw-r--r--CS105MiniProject.ipynb131
1 files changed, 81 insertions, 50 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb
index 2f86c48..1859cb7 100644
--- a/CS105MiniProject.ipynb
+++ b/CS105MiniProject.ipynb
@@ -29,10 +29,10 @@
"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,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -41,23 +41,26 @@
"%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": "2024-02-23T02:00:29.244799Z",
+ "start_time": "2024-02-23T02:00:28.800749Z"
}
},
"id": "3bea6ea662d6c063",
- "execution_count": 1
+ "execution_count": 7
},
{
"cell_type": "markdown",
@@ -74,10 +77,10 @@
"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>"
},
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+ "execution_count": 8,
"metadata": {},
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@@ -107,22 +110,22 @@
"metadata": {
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"ExecuteTime": {
- "end_time": "2024-02-23T01:12:49.066644Z",
- "start_time": "2024-02-23T01:12:49.047827Z"
+ "end_time": "2024-02-23T02:00:29.279290Z",
+ "start_time": "2024-02-23T02:00:29.247783Z"
}
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"id": "f71f8085d5f66b0",
- "execution_count": 2
+ "execution_count": 8
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{
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- "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,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -137,22 +140,22 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T01:12:49.084475Z",
- "start_time": "2024-02-23T01:12:49.068965Z"
+ "end_time": "2024-02-23T02:00:29.308973Z",
+ "start_time": "2024-02-23T02:00:29.282289Z"
}
},
"id": "6c1d9ee7948e6b9a",
- "execution_count": 3
+ "execution_count": 9
},
{
"cell_type": "code",
"outputs": [
{
"data": {
- "text/plain": " Timestamp What is your current class standing? \\\n1 2/9/2024 20:16:34 Junior \n2 2/9/2024 20:18:55 Junior \n3 2/9/2024 20:24:00 Senior \n5 2/9/2024 20:45:09 Junior \n6 2/9/2024 21:55:59 Sophomore \n.. ... ... \n253 2/14/2024 13:45:45 Senior \n254 2/14/2024 16:26:06 Junior \n256 2/15/2024 0:28:38 NaN \n257 2/15/2024 8:33:45 Senior \n259 2/15/2024 16:14:11 Sophomore \n\n Who do you live with? \\\n1 Both \n2 Friends \n3 Neither \n5 Both \n6 Friends \n.. ... \n253 Family \n254 Family \n256 Family \n257 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n1 Apartment \n2 House \n3 Apartment \n5 Apartment \n6 Apartment \n.. ... \n253 House \n254 House \n256 Apartment \n257 House \n259 Dorm \n\n How many people live in your household? Do you currently work? \\\n1 4 No \n2 4 No \n3 1 No \n5 4 No \n6 4 No \n.. ... ... \n253 6 No \n254 5 No \n256 4 No \n257 9 No \n259 3 No \n\n How many hours do you work per week on average? \\\n1 0 \n2 0 \n3 0 \n5 0 \n6 0 \n.. ... \n253 0 \n254 0 \n256 0 \n257 0 \n259 0 \n\n Do you work on or off campus? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 Off-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 NaN \n259 NaN \n\n Do you have roommates that are part of your major? \n1 Yes \n2 No \n3 No \n5 No \n6 No \n.. ... \n253 No \n254 Yes \n256 No \n257 No \n259 Yes \n\n[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,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -163,12 +166,12 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T01:12:49.104996Z",
- "start_time": "2024-02-23T01:12:49.089572Z"
+ "end_time": "2024-02-23T02:00:29.333356Z",
+ "start_time": "2024-02-23T02:00:29.313068Z"
}
},
"id": "34f69a756f513fb7",
- "execution_count": 4
+ "execution_count": 10
},
{
"cell_type": "markdown",
@@ -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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"
+ "image/png": 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"
},
"metadata": {},
"output_type": "display_data"
@@ -221,26 +208,44 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T01:12:49.355506Z",
- "start_time": "2024-02-23T01:12:49.112753Z"
+ "end_time": "2024-02-23T02:00:29.477781Z",
+ "start_time": "2024-02-23T02:00:29.336352Z"
}
},
"id": "da1811cc63b41845",
- "execution_count": 6
+ "execution_count": 11
},
{
"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": "2024-02-23T02:00:29.790484Z",
+ "start_time": "2024-02-23T02:00:29.479779Z"
}
},
"id": "201db70188d3e778",
- "execution_count": 6
+ "execution_count": 12
},
{
"cell_type": "markdown",
@@ -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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Ynmz35JNPavbs2c7IAgAAADeTq8f9zZkzR6tXr9bdd99ttwm1JMXExDgsHAAAAFzLdLG4b98+1atXT5J0+PBhu2N/3a8PAAAAeZvpYjEuLs4ZOQAAAOCGbmuDwF9//VW//vqro7IAAADAzZguFq1Wq8aPH6/AwEBVqFBBFSpUUFBQkF599VVZrVZnZAQAAICLmL4NPXLkSM2ePVuTJk1SZGSkJGnDhg0aO3asrly5ogkTJjg8JAAAAFzDdLE4b948ffjhh+rQoYOtrXbt2ipbtqz69+9PsQgAAJCPmL4NnZycrGrVqt3UXq1aNSUnJzskFAAAANyDqcf9SVLDhg3VsGFDTZs2za59wIAB2rp1q3766SeHBoT7u/G4v9TUVAUEBLg6DgAAcCDTt6EnT56s9u3ba/Xq1WrUqJEkafPmzTp16pS+++47hwcEAACA65i+Dd2sWTMdPnxYDz/8sFJSUpSSkqLOnTvr0KFDuu+++5yREQAAAC5i+jY08FfchgYAIP+6rU25AQAAkL9RLAIAACBbFIsAAADIFsUiAAAAskWxCAAAgGw5tFhs2bKlXn31VaWnpzvysgAAAHARhxaL5cuX15o1a275OEAAAADkPU7ZZzEtLY399goQ9lkEACD/Mj2yOH/+fF29evWm9oyMDM2fP1+SKBgAAADyCdMji56enkpMTFSpUqXs2s+dO6dSpUopKyvLoQHh/hhZBAAg/zI9smgYhiwWy03tv/76qwIDAx0SCgAAAO7BK6cn1q1bVxaLRRaLRa1atZKX1/99NCsrSwkJCYqKinJKSAAAALhGjovFTp06SZJ27dqltm3bqkiRIrZjPj4+CgsL0yOPPOLwgAAAAHAd03MW582bp8cee0x+fn7OyoQ8hjmLAADkX7neOicjI0NJSUmyWq127eXLl3dIMOQdFIsAAORfOb4NfcORI0f01FNPadOmTXbtNxa+sBoaAAAg/zBdLPbu3VteXl5atmyZQkNDb7kyGgAAAPmD6WJx165d2r59O4/0AwAAKABM77NYo0YN/fHHH87IAgAAADdjulh84403NHz4cK1bt07nzp1TWlqa3QsAAAD5h+nV0B4e1+vLv85VZIFLwcVqaAAA8i/Tcxbj4uKckQMAAABuKNf7LAI3MLIIAED+ZXrOoiT9+OOPevLJJ9W4cWOdPn1akvTRRx9pw4YNDg0HAAAA1zJdLH755Zdq27atChUqpB07dujq1auSpNTUVL3++usODwgAAADXMV0svvbaa5o5c6ZmzZolb29vW3tkZKR27Njh0HAAAABwLdPF4qFDh9S0adOb2gMDA5WSkuKITAAAAHATpovFkJAQHT169Kb2DRs2qGLFig4JBQAAAPdgulh8+umnNWjQIMXHx8tisejMmTP65JNPNHToUD333HPOyAgAAAAXMb3P4ogRI2S1WtWqVSulp6eradOm8vX11dChQzVgwABnZAQAAICL5HqfxYyMDB09elQXL15UjRo1VKRIEUdnQx7BPosAAORfpkcWb/Dx8VGNGjUcmQUAAABuxnSxeOXKFU2fPl1xcXFKSkqS1Wq1O872OQAAAPmH6WKxb9++WrlypR599FE1aNBAFovFGbmQB/12zztK9/STJIXsH+biNAAAwBFMF4vLli3Td999p8jISGfkAQAAgBsxvXVO2bJlVbRoUWdkAQAAgJsxXSxOmTJFL730kk6cOOGMPAAAAHAjpm9D169fX1euXFHFihXl7+9v93xoSUpOTnZYOAAAALiW6WKxW7duOn36tF5//XWVLl2aBS4AAAD5mOlicdOmTdq8ebPq1KnjjDwAAABwI6bnLFarVk2XL192RhYAAAC4GdPF4qRJk/Tiiy9q3bp1OnfunNLS0uxeAAAAyD9M34aOioqSJLVq1cqu3TAMWSwWZWVlOSYZAAAAXM50sRgXF+eMHAAAAHBDpovFZs2aOSMHAAAA3JDpYlGSUlJStGXLFiUlJclqtdod69mzp0OCAQAAwPVMF4vffPONunfvrosXLyogIMBun0WLxUKxCAAAkI+YXg394osv6qmnntLFixeVkpKi8+fP2148vSX3evfuLYvFIovFIm9vb4WHh2v48OG6cuWKq6MBAIACzPTI4unTpzVw4ED5+/s7I0+BFhUVpblz5yozM1Pbt29Xr169ZLFY9MYbb7g6GgAAKKBMjyy2bdtW27Ztc0aWAs/X11chISEqV66cOnXqpNatW2vVqlWSpKtXr2rgwIEqVaqU/Pz81KRJE23dutXu8+vXr1eDBg3k6+ur0NBQjRgxQteuXbMdb968uQYMGKDBgwcrODhYpUuX1qxZs3Tp0iX16dNHRYsWVeXKlbV8+fJ/9XcDAAD3ZbpYbN++vYYNG6axY8fqyy+/1Ndff233gmPs27dPmzZtko+PjyRp+PDh+vLLLzVv3jzt2LFDlStXVtu2bW23/k+fPq127drpnnvu0e7duzVjxgzNnj1br732mt11582bpxIlSmjLli0aMGCAnnvuOXXp0kWNGzfWjh071KZNG/Xo0UPp6en/+m8GAADux2IYhmHmAx4e2deXbMqde71799bHH38sPz8/Xbt2TVevXpWHh4cWLlyoqKgoBQcHKzY2Vk888YQkKTMzU2FhYRo8eLCGDRumkSNH6ssvv9SBAwdsi47+97//6aWXXlJqaqo8PDzUvHlzZWVl6ccff5QkZWVlKTAwUJ07d9b8+fMlSWfPnlVoaKg2b96se++995ZZr169qqtXr9rep6WlqVy5cjp853gV9fSTJIXsH+a0vgIAAP8e0yOLVqs12xeF4u1p0aKFdu3apfj4ePXq1Ut9+vTRI488omPHjikzM1ORkZG2c729vdWgQQMdOHBAknTgwAE1atTIbnV6ZGSkLl68qF9//dXWVrt2bdvfnp6eKl68uCIiImxtpUuXliQlJSVlm3PixIkKDAy0vcqVK3f7Px4AALgl08Xi/Pnz7UaVbsjIyLCNTiF3ChcurMqVK6tOnTqaM2eO4uPjNXv2bId+h7e3t937G6uv//xe0k37Z/7Zyy+/rNTUVNvr1KlTDs0IAADch+lisU+fPkpNTb2p/cKFC+rTp49DQuH67f7//ve/GjVqlCpVqiQfHx9t3LjRdjwzM1Nbt25VjRo1JEnVq1fX5s2b9edZBRs3blTRokV1xx13ODSbr6+vAgIC7F4AACB/Ml0sGoZhd6vzhl9//VWBgYEOCYXrunTpIk9PT82YMUPPPfechg0bphUrVmj//v16+umnlZ6err59+0qS+vfvr1OnTmnAgAE6ePCgli5dqjFjxig6Ovpv55kCAAD8nRzvs1i3bl3bptGtWrWSl9f/fTQrK0sJCQmKiopySsiCysvLSy+88IImT56shIQEWa1W9ejRQxcuXFD9+vX1/fffKzg4WJJUtmxZfffddxo2bJjq1KmjYsWKqW/fvho1apSLfwUAAMjLcrwaety4cbb/fPHFF1WkSBHbMR8fH4WFhemRRx6xbfWCgiMtLU2BgYGshgYAIB/K8cjimDFjlJWVpbCwMLVp00ahoaHOzAUAAAA3YGoym6enp/7zn//wvGIAAIACwvTKh1q1aun48ePOyAIAAAA3Y7pYfO211zR06FAtW7ZMiYmJSktLs3sBAAAg/8jxnMUb2rVrJ0nq0KGD3RY6N7bU4SkuAAAA+YfpYjEuLs4ZOQAAAOCGTBeLzZo1c0YOAAAAuCHTxeIN6enpOnnypDIyMuzaa9eufduhAAAA4B5MF4u///67+vTpo+XLl9/yOHMWAQAA8g/Tq6EHDx6slJQUxcfHq1ChQlqxYoXmzZunKlWq6Ouvv3ZGRgAAALiI6ZHFtWvXaunSpapfv748PDxUoUIF3X///QoICNDEiRPVvn17Z+QEAACAC5geWbx06ZJKlSolSQoODtbvv/8uSYqIiNCOHTscmw4AAAAuZXpksWrVqjp06JDCwsJUp04dvf/++woLC9PMmTN5XnQBV3rrIAUEBLg6BgAAcCDTxeKgQYOUmJgoSRozZoyioqL0ySefyMfHR7GxsY7OBwAAABeyGIZh3M4F0tPTdfDgQZUvX14lSpRwVC7kIWlpaQoMDFRqaiojiwAA5DO53mdRuv6Iv0KFCqlevXqOygMAAAA3YnqBiyTNnj1btWrVkp+fn/z8/FSrVi19+OGHjs4GAAAAFzM9svjKK68oJiZGAwYMUKNGjSRJmzdv1pAhQ3Ty5EmNHz/e4SEBAADgGqbnLJYsWVLTpk1Tt27d7NoXLFigAQMG6I8//nBoQLg/5iwCAJB/mb4NnZmZqfr169/Ufvfdd+vatWsOCQUAAAD3YLpY7NGjh2bMmHFT+wcffKDu3bs7JBQAAADcQ65WQ8+ePVsrV67UvffeK0mKj4/XyZMn1bNnT0VHR9vOi4mJcUxKAAAAuITpOYstWrTI2YUtFq1duzZXoZC3MGcRAID867Y35QYoFgEAyL9ytc8iAAAACoZczVnctm2bFi5cqJMnTyojI8Pu2OLFix0SDAAAAK5nemTxs88+U+PGjXXgwAF99dVXyszM1M8//6y1a9cqMDDQGRkBAADgIqaLxddff11vv/22vvnmG/n4+Oidd97RwYMH1bVrV5UvX94ZGQEAAOAipovFY8eOqX379pIkHx8fXbp0SRaLRUOGDNEHH3zg8IAAAABwHdPFYnBwsC5cuCBJKlu2rPbt2ydJSklJUXp6umPTAQAAwKVML3Bp2rSpVq1apYiICHXp0kWDBg3S2rVrtWrVKrVq1coZGQEAAOAipvdZTE5O1pUrV1SmTBlZrVZNnjxZmzZtUpUqVTRq1CgFBwc7KyvcFPssAgCQf7EpN24bxSIAAPkXm3IDAAAgWxSLAAAAyBbFIgAAALJFsQgAAIBsUSwCAAAgWw4tFp966il99NFHjrwkAAAAXMihxeLx48c1evRo3XXXXY68LAAAAFzE9BNc/s66deskSfv373fkZQEAAOAiTpmzWKNGDWdcFgAAAP+yXBWLH330kSIjI1WmTBmdOHFCkjR16lQtXbrUoeEAAADgWqaLxRkzZig6Olrt2rVTSkqKsrKyJElBQUGaOnWqo/MBAADAhUwXi9OnT9esWbM0cuRIeXp62trr16+vvXv3OjQcAAAAXMt0sZiQkKC6deve1O7r66tLly45JBQAAADcg+liMTw8XLt27bqpfcWKFapevbojMgEAAMBNmN46Jzo6Ws8//7yuXLkiwzC0ZcsWLViwQBMnTtSHH37ojIwAAABwEdPFYr9+/VSoUCGNGjVK6enpeuKJJ1SmTBm98847evzxx52REQAAAC5iMQzDyO2H09PTdfHiRZUqVcqRmZDHpKWlKTAwUAOf+1y+vv6ujgMAuIXJbz/o6gjIo0zPWbx8+bLS09MlSf7+/rp8+bKmTp2qlStXOjwcAAAAXMt0sdixY0fNnz9fkpSSkqIGDRpoypQp6tixo2bMmOHwgAAAAHAd08Xijh07dN9990mSFi1apJCQEJ04cULz58/XtGnTHB4QAAAArmO6WExPT1fRokUlSStXrlTnzp3l4eGhe++91/boPwAAAOQPpovFypUra8mSJTp16pS+//57tWnTRpKUlJSkgIAAhwcEAACA65guFl955RUNHTpUYWFhatiwoRo1aiTp+ijjrZ7sAgAAgLzL9D6Ljz76qJo0aaLExETVqVPH1t6qVSs9/PDDDg0HAAAA1zJdLEpSSEiIQkJC7NoaNGjgkEAAAABwH6aLxRYtWshisWR7fO3atbcVCAAAAO7DdLF411132b3PzMzUrl27tG/fPvXq1ctRuQAAAOAGTBeLb7/99i3bx44dq4sXL952IAAAALgP06uhs/Pkk09qzpw5jrocAAAA3IDDisXNmzfLz8/PUZcDAACAGzB9G7pz58527w3DUGJiorZt26bRo0c7LFh+17t3b6WkpGjJkiV27evWrVOLFi10/vx5BQUFuSQbAADADaaLxcDAQLv3Hh4eqlq1qsaPH297mgsAAADyB9O3oefOnWv3mj17tiZNmkSh6CRffvmlatasKV9fX4WFhWnKlCl2xy0Wy02jk0FBQYqNjZUkZWRk6IUXXlBoaKj8/PxUoUIFTZw40XZuSkqK+vXrp5IlSyogIEAtW7bU7t27nf2zAABAHpGrTbklafv27Tpw4IAkqWbNmjzqzwm2b9+url27auzYsXrssce0adMm9e/fX8WLF1fv3r1zdI1p06bp66+/1sKFC1W+fHmdOnVKp06dsh3v0qWLChUqpOXLlyswMFDvv/++WrVqpcOHD6tYsWK3vObVq1d19epV2/u0tLTb+p0AAMB9mS4Wk5KS9Pjjj2vdunW2OXUpKSlq0aKFPvvsM5UsWdLRGfOtZcuWqUiRInZtWVlZtr9jYmLUqlUr21zQO++8U/v379ebb76Z42Lx5MmTqlKlipo0aSKLxaIKFSrYjm3YsEFbtmxRUlKSfH19JUlvvfWWlixZokWLFumZZ5655TUnTpyocePGmfmpAAAgjzJ9G3rAgAG6cOGCfv75ZyUnJys5OVn79u1TWlqaBg4c6IyM+VaLFi20a9cuu9eHH35oO37gwAFFRkbafSYyMlJHjhyxKyr/Tu/evbVr1y5VrVpVAwcO1MqVK23Hdu/erYsXL6p48eIqUqSI7ZWQkKBjx45le82XX35ZqampttefRyoBAED+YnpkccWKFVq9erWqV69ua6tRo4bee+895i2aVLhwYVWuXNmu7ddffzV1DYvFIsMw7NoyMzNtf9erV08JCQlavny5Vq9era5du6p169ZatGiRLl68qNDQUK1bt+6m6/7dSmxfX1/bSCQAAMjfTBeLVqtV3t7eN7V7e3vLarU6JBSuq169ujZu3GjXtnHjRt15553y9PSUJJUsWVKJiYm240eOHFF6errdZwICAvTYY4/pscce06OPPqqoqCglJyerXr16Onv2rLy8vBQWFub03wMAAPIe07ehW7ZsqUGDBunMmTO2ttOnT2vIkCFq1aqVQ8MVdC+++KLWrFmjV199VYcPH9a8efP07rvvaujQobZzWrZsqXfffVc7d+7Utm3b9Oyzz9oV8zExMVqwYIEOHjyow4cP64svvlBISIiCgoLUunVrNWrUSJ06ddLKlSv1yy+/aNOmTRo5cqS2bdvmip8MAADcjOli8d1331VaWprCwsJUqVIlVapUSeHh4UpLS9P06dOdkbHAqlevnhYuXKjPPvtMtWrV0iuvvKLx48fbLW6ZMmWKypUrp/vuu09PPPGEhg4dKn9/f9vxokWLavLkyapfv77uuece/fLLL/ruu+/k4eEhi8Wi7777Tk2bNlWfPn1055136vHHH9eJEydUunRpF/xiAADgbizGXye85YBhGFq9erUOHjwo6frt0tatWzs8HPKGtLQ0BQYGauBzn8vX1/+fPwAA+NdNfvtBV0dAHpWrfRYtFovuv/9+3X///Y7OAwAAADeSq2JxzZo1WrNmjZKSkm5a1DJnzhyHBAMAAIDrmS4Wx40bp/Hjx6t+/foKDQ2VxWJxRi4AAAC4AdPF4syZMxUbG6sePXo4Iw8AAADciOnV0BkZGWrcuLEzsgAAAMDNmC4W+/Xrp08//dQZWQAAAOBmcnQbOjo62va31WrVBx98oNWrV6t27do3Pc0lJibGsQkBAADgMjkqFnfu3Gn3/q677pIk7du3z66dxS4AAAD5S46Kxbi4OGfnAAAAgBsyPWcRAAAABQfFIgAAALJFsQgAAIBsWQzDMFwdAnlbWlqaAgMDlZqaqoCAAFfHAQAADsTIIgAAALJl+nF/N+zfv18nT55URkaGXXuHDh1uOxQAAADcg+li8fjx43r44Ye1d+9eWSwW3biLfWOPxaysLMcmBAAAgMuYvg09aNAghYeHKykpSf7+/vr555/1ww8/qH79+lq3bp0TIgIAAMBVTI8sbt68WWvXrlWJEiXk4eEhDw8PNWnSRBMnTtTAgQNvetoLAAAA8i7TI4tZWVkqWrSoJKlEiRI6c+aMJKlChQo6dOiQY9MBAADApUyPLNaqVUu7d+9WeHi4GjZsqMmTJ8vHx0cffPCBKlas6IyMAAAAcBHTxeKoUaN06dIlSdL48eP14IMP6r777lPx4sX1+eefOzwgAAAAXMchm3InJycrODjYtiIaBQubcgMAkH/lep/FPytWrJgjLgMAAAA3wxNcAAAAkC2KRQAAAGSLYhEAAADZolgEAABAtnK1wOXIkSOKi4tTUlKSrFar3bFXXnnFIcEAAADgeqa3zpk1a5aee+45lShRQiEhIXbb5VgsFu3YscPhIeHe2DoHAID8y3SxWKFCBfXv318vvfSSszIhj6FYBAAg/zI9Z/H8+fPq0qWLM7IAAADAzZguFrt06aKVK1c6IwsAAADcjOkFLpUrV9bo0aP1008/KSIiQt7e3nbHBw4c6LBwAAAAcC3TcxbDw8Ozv5jFouPHj992KOQtzFkEACD/Mj2ymJCQ4IwcAAAAcENsyg0AAIBs5WhkMTo6Wq+++qoKFy6s6Ojovz03JibGIcEAAADgejkqFnfu3KnMzEzb39n58wbdAAAAyPtML3AB/ooFLgAA5F/MWQQAAEC2KBYBAACQLYpFAAAAZItiEQAAANmiWAQAAEC2clUsfvTRR4qMjFSZMmV04sQJSdLUqVO1dOlSh4YDAACAa5kuFmfMmKHo6Gi1a9dOKSkpysrKkiQFBQVp6tSpjs4HAAAAFzJdLE6fPl2zZs3SyJEj5enpaWuvX7++9u7d69BwAAAAcC3TxWJCQoLq1q17U7uvr68uXbrkkFAAAABwD6aLxfDwcO3ateum9hUrVqh69eqOyAQAAAA3kaNnQ/9ZdHS0nn/+eV25ckWGYWjLli1asGCBJk6cqA8//NAZGZFHHH02SEV8eD64O7szNsvVEQAAeYzpYrFfv34qVKiQRo0apfT0dD3xxBMqU6aM3nnnHT3++OPOyAgAAAAXsRiGYeT2w+np6bp48aJKlSrlyEzIY9LS0hQYGKjt3SyMLLo5RhYBAGaZnrP42muvKSEhQZLk7+9PoQgAAJCPmS4Wv/jiC1WuXFmNGzfW//73P/3xxx/OyAUAAAA3YLpY3L17t/bs2aPmzZvrrbfeUpkyZdS+fXt9+umnSk9Pd0ZGAAAAuMhtzVmUpI0bN+rTTz/VF198oStXrigtLc1R2ZBHMGcx72DOIgDArFw9G/rPChcurEKFCsnHx0eZmZmOyAQAAAA3katiMSEhQRMmTFDNmjVVv3597dy5U+PGjdPZs2cdnQ8AAAAuZHqfxXvvvVdbt25V7dq11adPH3Xr1k1ly5Z1RjYAAAC4mOlisVWrVpozZ45q1KjhjDwAAABwI6aLxQkTJjgjBwAAANxQjorF6OhovfrqqypcuLCio6P/9tyYmBiHBAMAAIDr5ahY3Llzp22l886dO7M9z2Jh2xQAAID85Lb3WYTj9O7dW/PmzZMkeXl5qVixYqpdu7a6deum3r17y8Pjtnc6cgr2Wcw72GcRAGCWe1YfBVhUVJQSExP1yy+/aPny5WrRooUGDRqkBx98UNeuXcvVNTMyMhycEgAAFBQ5ug3duXPnHF9w8eLFuQ4DydfXVyEhIZKksmXLql69err33nvVqlUrxcbGql+/fjp58qQGDBigNWvWyMPDQ1FRUZo+fbpKly4tSRo7dqyWLFmiF154QRMmTNCJEydktVplsVg0c+ZMffPNN1q7dq0qVKigOXPmqGTJkurXr5+2bt2qOnXq6KOPPlKlSpVc2Q0AAMBN5GhkMTAwMMcvOF7Lli1Vp04dLV68WFarVR07dlRycrLWr1+vVatW6fjx43rsscfsPnP06FF9+eWXWrx4sXbt2mVrf/XVV9WzZ0/t2rVL1apV0xNPPKH//Oc/evnll7Vt2zYZhqEXXnjhb/NcvXpVaWlpdi8AAJA/5Whkce7cuc7OgX9QrVo17dmzR2vWrNHevXuVkJCgcuXKSZLmz5+vmjVrauvWrbrnnnskXb/1PH/+fJUsWdLuOn369FHXrl0lSS+99JIaNWqk0aNHq23btpKkQYMGqU+fPn+bZeLEiRo3bpyjfyIAAHBDzFnMIwzDkMVi0YEDB1SuXDlboShJNWrUUFBQkA4cOGBrq1Chwk2FoiTVrl3b9veN29YRERF2bVeuXPnb0cKXX35ZqampttepU6du67cBAAD3ZXpTbrjGgQMHFB4enuPzCxcufMt2b29v2983tjq6VZvVas322r6+vvL19c1xFgAAkHcxspgHrF27Vnv37tUjjzyi6tWr69SpU3ajefv371dKSgqPYAQAAA7HyKKbuXr1qs6ePausrCz99ttvWrFihSZOnKgHH3xQPXv2lIeHhyIiItS9e3dNnTpV165dU//+/dWsWTPVr1/f1fEBAEA+Y3pk8fjx487Igf9vxYoVCg0NVVhYmKKiohQXF6dp06Zp6dKl8vT0lMVi0dKlSxUcHKymTZuqdevWqlixoj7//HNXRwcAAPmQ6Se4eHh4qFmzZurbt68effRR+fn5OSsb8gie4JJ38AQXAIBZpkcWd+zYodq1ays6OlohISH6z3/+oy1btjgjGwAAAFzMdLF411136Z133tGZM2c0Z84cJSYmqkmTJqpVq5ZiYmL0+++/OyMnAAAAXCDXq6G9vLzUuXNnffHFF3rjjTd09OhRDR06VOXKlVPPnj2VmJjoyJwAAABwgVwXi9u2bVP//v0VGhqqmJgYDR06VMeOHdOqVat05swZdezY0ZE5AQAA4AKmt86JiYnR3LlzdejQIbVr107z589Xu3bt5OFxve4MDw9XbGyswsLCHJ0VAAAA/zLTxeKMGTP01FNPqXfv3goNDb3lOaVKldLs2bNvOxwAAABcy/TWOcBfsXVO3sHWOQAAs3I0srhnz54cX7B27dq5DgMAAAD3kqNi8a677pLFYlF2g5A3jlksFmVlMXIBAACQX+SoWExISHB2DgAAALihHBWLFSpUsP39ww8/qHHjxvLysv/otWvXtGnTJrtzAQAAkLeZ3mexRYsWSk5Ovqk9NTVVLVq0cEgoAAAAuAfTW+fcmJv4V+fOnVPhwoUdEgp5U+WZKQoICHB1DAAA4EA5LhY7d+4s6fpilt69e8vX19d2LCsrS3v27FHjxo0dnxAAAAAuk+NiMTAwUNL1kcWiRYuqUKFCtmM+Pj6699579fTTTzs+IQAAAFwmx8Xi3LlzJUlhYWEaOnQot5wBAAAKAJ7ggtt24wkuqampzFkEACCfMb0a+rffflOPHj1UpkwZeXl5ydPT0+4FAACA/MP0aujevXvr5MmTGj16tEJDQ2+5MhoAAAD5g+liccOGDfrxxx911113OSEOAAAA3Inp29DlypXL9hnRAAAAyF9MF4tTp07ViBEj9MsvvzghDgAAANyJ6dXQwcHBSk9P17Vr1+Tv7y9vb2+747d6FCDyN1ZDAwCQf5meszh16lQnxAAAAIA7Yp9F3DZGFgEAyL9Mz1mUpGPHjmnUqFHq1q2bkpKSJEnLly/Xzz//7NBwAAAAcC3TxeL69esVERGh+Ph4LV68WBcvXpQk7d69W2PGjHF4QAAAALiO6WJxxIgReu2117Rq1Sr5+PjY2lu2bKmffvrJoeEAAADgWqaLxb179+rhhx++qb1UqVL6448/HBIKAAAA7sF0sRgUFKTExMSb2nfu3KmyZcs6JBQAAADcg+li8fHHH9dLL72ks2fPymKxyGq1auPGjRo6dKh69uzpjIwAAABwEdNb52RkZOj5559XbGyssrKy5OXlpaysLD3xxBOKjY2Vp6ens7LCTbF1DgAA+ZepYtEwDJ06dUolS5bUH3/8ob179+rixYuqW7euqlSp4syccGMUiwAA5F+mnuBiGIYqV66sn3/+WVWqVFG5cuWclQsAAABuwNScRQ8PD1WpUkXnzp1zVh4AAAC4EdMLXCZNmqRhw4Zp3759zsgDAAAAN2J6gUtwcLDS09N17do1+fj4qFChQnbHk5OTHRoQ7o85iwAA5F+m5ixK0tSpU50QAwAAAO7IVLGYmZmp9evXa/To0QoPD3dWJgAAALgJU3MWvb299eWXXzorCwAAANyM6QUunTp10pIlS5wQBQAAAO7G9JzFKlWqaPz48dq4caPuvvtuFS5c2O74wIEDHRYOAAAArmV6NfTfzVW0WCw6fvz4bYdC3sJqaAAA8i/TI4sJCQnOyAEAAAA3ZHrOIgAAAAoO0yOLTz311N8enzNnTq7DAAAAwL2YLhbPnz9v9z4zM1P79u1TSkqKWrZs6bBgAAAAcD3TxeJXX311U5vVatVzzz2nSpUqOSQUAAAA3IPp1dDZOXTokJo3b67ExERHXA55CKuhAQDIv0yPLGbn2LFjunbtmqMuhzyo2sdj5FHI19UxAADIN37tM8nVEcwXi9HR0XbvDcNQYmKivv32W/Xq1cthwQAAAOB6povFnTt32r338PBQyZIlNWXKlH9cKQ0AAIC8xXSxGBcX54wcAAAAcEOmN+VOSEjQkSNHbmo/cuSIfvnlF0dkAgAAgJswXSz27t1bmzZtuqk9Pj5evXv3dkQmAAAAuAnTxeLOnTsVGRl5U/u9996rXbt2OSITAAAA3ITpYtFisejChQs3taempiorK8shoQAAAOAeTBeLTZs21cSJE+0Kw6ysLE2cOFFNmjRxaDgAAAC4lunV0G+88YaaNm2qqlWr6r777pMk/fjjj0pLS9PatWsdHhAAAACuY3pksUaNGtqzZ4+6du2qpKQkXbhwQT179tTBgwdVq1YtZ2QEAACAi+TqcX9lypTR66+/7ugsAAAAcDOmRxYBAABQcFAsAgAAIFsUi5I2b94sT09PtW/f/l/9XovFoiVLlvyr3/l3mjdvrsGDB7s6BgAAcCMUi5Jmz56tAQMG6IcfftCZM2ec/n0ZGRlO/w4AAABHKPDF4sWLF/X555/rueeeU/v27RUbG2s7tm7dOlksFn377beqXbu2/Pz8dO+992rfvn22c86dO6du3bqpbNmy8vf3V0REhBYsWGD3Hc2bN9cLL7ygwYMHq0SJEmrbtq3CwsIkSQ8//LAsFovt/dixY3XXXXdpzpw5Kl++vIoUKaL+/fsrKytLkydPVkhIiEqVKqUJEybYfUdKSor69eunkiVLKiAgQC1bttTu3bttx29c96OPPlJYWJgCAwP1+OOP2zZY7927t9avX6933nlHFotFFouFZ30DAADzxWJ4eLgqVqyY7SuvWbhwoapVq6aqVavqySef1Jw5c2QYht05w4YN05QpU7R161aVLFlSDz30kDIzMyVJV65c0d13361vv/1W+/bt0zPPPKMePXpoy5YtdteYN2+efHx8tHHjRs2cOVNbt26VJM2dO1eJiYm295J07NgxLV++XCtWrNCCBQs0e/ZstW/fXr/++qvWr1+vN954Q6NGjVJ8fLztM126dFFSUpKWL1+u7du3q169emrVqpWSk5PtrrtkyRItW7ZMy5Yt0/r16zVp0iRJ0jvvvKNGjRrp6aefVmJiohITE1WuXLlb9tnVq1eVlpZm9wIAAPmT6a1z/jqnLTMzUzt37tSKFSs0bNgwR+X618yePVtPPvmkJCkqKkqpqalav369mjdvbjtnzJgxuv/++yVdL/ruuOMOffXVV+ratavKli2roUOH2s4dMGCAvv/+ey1cuFANGjSwtVepUkWTJ0++6fuDgoIUEhJi12a1WjVnzhwVLVpUNWrUUIsWLXTo0CF999138vDwUNWqVfXGG28oLi5ODRs21IYNG7RlyxYlJSXJ19dXkvTWW29pyZIlWrRokZ555hnbdWNjY1W0aFFJUo8ePbRmzRpNmDBBgYGB8vHxkb+//015/mrixIkaN25cTrsYAADkYaaLxUGDBt2y/b333tO2bdtuO9C/6dChQ9qyZYu++uorSZKXl5cee+wxzZ49265YbNSoke3vYsWKqWrVqjpw4ICk6486fP3117Vw4UKdPn1aGRkZunr1qvz9/e2+6+67785xrrCwMFtBJ0mlS5eWp6enPDw87NqSkpIkSbt379bFixdVvHhxu+tcvnxZx44dy/a6oaGhtmuY8fLLLys6Otr2Pi0tLdtRSAAAkLflalPuW3nggQf08ssva+7cuY66pNPNnj1b165dU5kyZWxthmHI19dX7777bo6u8eabb+qdd97R1KlTFRERocKFC2vw4ME3LWIpXLhwjnN5e3vbvbdYLLdss1qtkq7PuwwNDdW6detuulZQUNDfXvfGNczw9fW1jWACAID8zWHF4qJFi1SsWDFHXc7prl27pvnz52vKlClq06aN3bFOnTppwYIFqlatmiTpp59+Uvny5SVJ58+f1+HDh1W9enVJ0saNG9WxY0fbrWyr1arDhw+rRo0a/5jB29tbWVlZt/1b6tWrp7Nnz8rLy8u2UCY3fHx8HJIHAADkH6aLxbp168pisdjeG4ahs2fP6vfff9f//vc/h4ZzpmXLlun8+fPq27evAgMD7Y498sgjmj17tt58801J0vjx41W8eHGVLl1aI0eOVIkSJdSpUydJ1+ciLlq0SJs2bVJwcLBiYmL022+/5ahYDAsL05o1axQZGSlfX18FBwfn6re0bt1ajRo1UqdOnTR58mTdeeedOnPmjL799ls9/PDDql+/fo6uExYWpvj4eP3yyy8qUqSIihUrZnfrGwAAFDymi8UbRdINHh4eKlmypJo3b24bicsLZs+erdatW99UKErXi8XJkydrz549kqRJkyZp0KBBOnLkiO666y5988038vHxkSSNGjVKx48fV9u2beXv769nnnlGnTp1Umpq6j9mmDJliqKjozVr1iyVLVs211vVWCwWfffddxo5cqT69Omj33//XSEhIWratKlKly6d4+sMHTpUvXr1Uo0aNXT58mUlJCTc1kglAADI+yzGX/eJgc26devUokULnT9/3m7uH+ylpaUpMDBQoe8Nlkch5jICAOAov/aZ5OoIuZuzmJWVpSVLlthWBNesWVMdOnSQp6enQ8MBAADAtUwXi0ePHlW7du10+vRpVa1aVdL1fffKlSunb7/9VpUqVXJ4SAAAALiG6dULAwcOVKVKlXTq1Cnt2LFDO3bs0MmTJxUeHq6BAwc6I6PLNG/eXIZhcAsaAAAUWKZHFtevX6+ffvrJbpuc4sWLa9KkSYqMjHRoOAAAALiW6ZFFX19fXbhw4ab2ixcv2lYIAwAAIH8wXSw++OCDeuaZZxQfHy/DMGQYhn766Sc9++yz6tChgzMyAgAAwEVMF4vTpk1TpUqV1KhRI/n5+cnPz0+RkZGqXLmy3nnnHWdkBAAAgIuYnrMYFBSkpUuX6siRIzp48KAkqXr16qpcubLDwwEAAMC1TBeLcXFxatGihapUqaIqVao4IxMAAADchOnb0FFRUapUqZJee+01nTp1yhmZAAAA4CZMF4unT5/WCy+8oEWLFqlixYpq27atFi5cqIyMDGfkAwAAgAuZLhZLlCihIUOGaNeuXYqPj9edd96p/v37q0yZMho4cKB2797tjJwAAABwAYthGMbtXODMmTP64IMPNGnSJHl5eenKlStq1KiRZs6cqZo1azoqJ9xYWlqaAgMDlZqaqoCAAFfHAQAADmR6ZFGSMjMztWjRIrVr104VKlTQ999/r3fffVe//fabjh49qgoVKqhLly6OzgoAAIB/memRxQEDBmjBggUyDEM9evRQv379VKtWLbtzzp49qzJlyshqtTo0LNwTI4sAAORfprfO2b9/v6ZPn67OnTvL19f3lueUKFFCcXFxtx0OAAAArnXbcxYBRhYBAMi/cjVnEQAAAAUDxSIAAACyRbEIAACAbFEsAgAAIFumV0PfsH37dh04cECSVKNGDdWrV89hoQAAAOAeTBeLSUlJevzxx7Vu3ToFBQVJklJSUtSiRQt99tlnKlmypKMzAgAAwEVM34YeMGCALly4oJ9//lnJyclKTk7Wvn37lJaWpoEDBzojIwAAAFzE9D6LgYGBWr16te655x679i1btqhNmzZKSUlxZD7kAeyzCABA/mV6ZNFqtcrb2/umdm9vbx7vBwAAkM+YLhZbtmypQYMG6cyZM7a206dPa8iQIWrVqpVDwwEAAMC1TBeL7777rtLS0hQWFqZKlSqpUqVKCg8PV1pamqZPn+6MjAAAAHCRXD0b2jAMrV69WgcPHpQkVa9eXa1bt3Z4OOQNzFkEACD/ylWxCPxZamqqgoKCdOrUKYpFAADymKJFi8pisWR73NQ+i1arVbGxsVq8eLF++eUXWSwWhYeH69FHH1WPHj3+9ouQf507d06SVK5cORcnAQAAZv3TncEcjywahqGHHnpI3333nerUqaNq1arJMAwdOHBAe/fuVYcOHbRkyRJH5UYekpKSouDgYJ08eVKBgYGujuOW0tLSVK5cOUZf/wZ9lDP00z+jj/4ZffTPClIfOWxkMTY2Vj/88IPWrFmjFi1a2B1bu3atOnXqpPnz56tnz565T4s8ycPj+jqpwMDAfP8P1O0KCAigj/4BfZQz9NM/o4/+GX30z+gjE6uhFyxYoP/+9783FYrS9e10RowYoU8++cSh4QAAAOBaOS4W9+zZo6ioqGyPP/DAA9q9e7dDQgEAAMA95LhYTE5OVunSpbM9Xrp0aZ0/f94hoZC3+Pr6asyYMfL19XV1FLdFH/0z+ihn6Kd/Rh/9M/ron9FH/yfHC1w8PT119uxZlSxZ8pbHf/vtN5UpU0ZZWVkODQgAAADXyfECF8Mw1Lt372wr7KtXrzosFAAAANxDjovFXr16/eM5rIQGAADIX3iCCwAAALKV4wUuAAAAKHgoFnFb3nvvPYWFhcnPz08NGzbUli1bXB3JZSZOnKh77rlHRYsWValSpdSpUycdOnTI7pwrV67o+eefV/HixVWkSBE98sgj+u2331yU2PUmTZoki8WiwYMH29roo+tOnz6tJ598UsWLF1ehQoUUERGhbdu22Y4bhqFXXnlFoaGhKlSokFq3bq0jR464MPG/KysrS6NHj1Z4eLgKFSqkSpUq6dVXX9Wfb5YVtD764Ycf9NBDD6lMmTKyWCw3PVUtJ/2RnJys7t27KyAgQEFBQerbt68uXrz4L/4K5/u7fsrMzNRLL72kiIgIFS5cWGXKlFHPnj115swZu2sUhH76M4pF5Nrnn3+u6OhojRkzRjt27FCdOnXUtm1bJSUluTqaS6xfv17PP/+8fvrpJ61atUqZmZlq06aNLl26ZDtnyJAh+uabb/TFF19o/fr1OnPmjDp37uzC1K6zdetWvf/++6pdu7ZdO30knT9/XpGRkfL29tby5cu1f/9+TZkyRcHBwbZzJk+erGnTpmnmzJmKj49X4cKF1bZtW125csWFyf89b7zxhmbMmKF3331XBw4c0BtvvKHJkydr+vTptnMKWh9dunRJderU0XvvvXfL4znpj+7du+vnn3/WqlWrtGzZMv3www965pln/q2f8K/4u35KT0/Xjh07NHr0aO3YsUOLFy/WoUOH1KFDB7vzCkI/2TGAXGrQoIHx/PPP295nZWUZZcqUMSZOnOjCVO4jKSnJkGSsX7/eMAzDSElJMby9vY0vvvjCds6BAwcMScbmzZtdFdMlLly4YFSpUsVYtWqV0axZM2PQoEGGYdBHN7z00ktGkyZNsj1utVqNkJAQ480337S1paSkGL6+vsaCBQv+jYgu1759e+Opp56ya+vcubPRvXt3wzDoI0nGV199ZXufk/7Yv3+/IcnYunWr7Zzly5cbFovFOH369L+W/d/01366lS1bthiSjBMnThiGUTD7iZFF5EpGRoa2b9+u1q1b29o8PDzUunVrbd682YXJ3EdqaqokqVixYpKk7du3KzMz067PqlWrpvLlyxe4Pnv++efVvn17u76Q6KMbvv76a9WvX19dunRRqVKlVLduXc2aNct2PCEhQWfPnrXrp8DAQDVs2LDA9FPjxo21Zs0aHT58WJK0e/dubdiwQQ888IAk+uivctIfmzdvVlBQkOrXr287p3Xr1vLw8FB8fPy/ntldpKamymKxKCgoSFLB7Kccb50D/Nkff/yhrKysm57qU7p0aR08eNBFqdyH1WrV4MGDFRkZqVq1akmSzp49Kx8fH9u/cG4oXbq0zp4964KUrvHZZ59px44d2rp1603H6KPrjh8/rhkzZig6Olr//e9/tXXrVg0cOFA+Pj7q1auXrS9u9c9fQemnESNGKC0tTdWqVZOnp6eysrI0YcIEde/eXZLoo7/ISX+cPXtWpUqVsjvu5eWlYsWKFcg+k67PoX7ppZfUrVs3BQQESCqY/USxCDjB888/r3379mnDhg2ujuJWTp06pUGDBmnVqlXy8/NzdRy3ZbVaVb9+fb3++uuSpLp162rfvn2aOXNmjva8LQgWLlyoTz75RJ9++qlq1qypXbt2afDgwSpTpgx9BIfIzMxU165dZRiGZsyY4eo4LsVtaORKiRIl5OnpedMq1d9++00hISEuSuUeXnjhBS1btkxxcXG64447bO0hISHKyMhQSkqK3fkFqc+2b9+upKQk1atXT15eXvLy8tL69es1bdo0eXl5qXTp0gW+jyQpNDRUNWrUsGurXr26Tp48KUm2vijI//wNGzZMI0aM0OOPP66IiAj16NFDQ4YM0cSJEyXRR3+Vk/4ICQm5aYHitWvXlJycXOD67EaheOLECa1atco2qigVzH6iWESu+Pj46O6779aaNWtsbVarVWvWrFGjRo1cmMx1DMPQCy+8oK+++kpr165VeHi43fG7775b3t7edn126NAhnTx5ssD0WatWrbR3717t2rXL9qpfv766d+9u+7ug95EkRUZG3rTt0uHDh1WhQgVJUnh4uEJCQuz6KS0tTfHx8QWmn9LT0+XhYf8/YZ6enrJarZLoo7/KSX80atRIKSkp2r59u+2ctWvXymq1qmHDhv96Zle5USgeOXJEq1evVvHixe2OF8h+cvUKG+Rdn332meHr62vExsYa+/fvN5555hkjKCjIOHv2rKujucRzzz1nBAYGGuvWrTMSExNtr/T0dNs5zz77rFG+fHlj7dq1xrZt24xGjRoZjRo1cmFq1/vzamjDoI8M4/rqSy8vL2PChAnGkSNHjE8++cTw9/c3Pv74Y9s5kyZNMoKCgoylS5cae/bsMTp27GiEh4cbly9fdmHyf0+vXr2MsmXLGsuWLTMSEhKMxYsXGyVKlDCGDx9uO6eg9dGFCxeMnTt3Gjt37jQkGTExMcbOnTttq3hz0h9RUVFG3bp1jfj4eGPDhg1GlSpVjG7durnqJznF3/VTRkaG0aFDB+OOO+4wdu3aZffv8qtXr9quURD66c8oFnFbpk+fbpQvX97w8fExGjRoYPz000+ujuQykm75mjt3ru2cy5cvG/379zeCg4MNf39/4+GHHzYSExNdF9oN/LVYpI+u++abb4xatWoZvr6+RrVq1YwPPvjA7rjVajVGjx5tlC5d2vD19TVatWplHDp0yEVp/31paWnGoEGDjPLlyxt+fn5GxYoVjZEjR9r9D3pB66O4uLhb/juoV69ehmHkrD/OnTtndOvWzShSpIgREBBg9OnTx7hw4YILfo3z/F0/JSQkZPvv8ri4ONs1CkI//RnPhgYAAEC2mLMIAACAbFEsAgAAIFsUiwAAAMgWxSIAAACyRbEIAACAbFEsAgAAIFsUiwAAAMgWxSIAAACyRbEIAACAbFEsAgAAIFsUiwAAAMgWxSIAAACy9f8AeJDrj5vBpAEAAAAASUVORK5CYII="
+ },
+ "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": "2024-02-23T02:00:30.015262Z",
+ "start_time": "2024-02-23T02:00:29.792483Z"
+ }
+ },
+ "id": "5e460707e32c4a2a",
+ "execution_count": 13
}
],
"metadata": {
generated by cgit v1.2.3 (git 2.39.1) at 2025-07-14 08:11:55 +0000