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authorGravatar ENathanLe <Nath.wessix@gmail.com> 2024-02-22 23:46:11 -0800
committerGravatar ENathanLe <Nath.wessix@gmail.com> 2024-02-22 23:46:11 -0800
commitddae73badb4685b5a9b7b0b2013587ba54d342b9 (patch)
treee5c2ab27dbc685fef9a5aaddd07bd7ef7667cde8 /CS105MiniProject.ipynb
parent63562ce5c494bc00b969cdb5f58d5bfad2864ef7 (diff)
downloadCS105MiniProject-ddae73badb4685b5a9b7b0b2013587ba54d342b9.tar.gz
CS105MiniProject-ddae73badb4685b5a9b7b0b2013587ba54d342b9.tar.zst
CS105MiniProject-ddae73badb4685b5a9b7b0b2013587ba54d342b9.zip
Minor fixes to remove trailing and starting spaces from index names
Diffstat (limited to 'CS105MiniProject.ipynb')
-rw-r--r--CS105MiniProject.ipynb102
1 files changed, 51 insertions, 51 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb
index 818356f..371a9bc 100644
--- a/CS105MiniProject.ipynb
+++ b/CS105MiniProject.ipynb
@@ -29,10 +29,10 @@
"outputs": [
{
"data": {
- "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? Do you currently work? \\\n0 6 Yes \n1 4 No \n2 4 No \n3 1 No \n4 1 Yes \n.. ... ... \n255 5 Yes \n256 North District 4 bed 2 bath No \n257 9 No \n258 4 Yes \n259 3 (room), 8 (hall), ~70 (building) No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 NaN \n2 NaN \n3 NaN \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 NaN \n257 1 - 5 \n258 5 - 10 \n259 NaN \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 No \n4 Yes \n.. ... \n255 No \n256 NaN \n257 No \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 10 columns]",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>North District 4 bed 2 bath</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>No</td>\n <td>1 - 5</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3 (room), 8 (hall), ~70 (building)</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 10 columns</p>\n</div>"
+ "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? 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, apartment, 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": 38,
"metadata": {},
"output_type": "execute_result"
}
@@ -55,12 +55,12 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:58.731970Z",
- "start_time": "2024-02-23T02:10:58.699969Z"
+ "end_time": "2024-02-23T07:21:06.785993Z",
+ "start_time": "2024-02-23T07:21:06.759204Z"
}
},
- "id": "3bea6ea662d6c063",
- "execution_count": 1
+ "id": "e976ece77ff13d3a",
+ "execution_count": 38
},
{
"cell_type": "markdown",
@@ -70,17 +70,17 @@
"metadata": {
"collapsed": false
},
- "id": "7e69a5a21a9de4ee"
+ "id": "80c951c9acd81289"
},
{
"cell_type": "code",
"outputs": [
{
"data": {
- "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? Do you currently work? \\\n0 6 Yes \n1 4 No \n2 4 No \n3 1 No \n4 1 Yes \n.. ... ... \n255 5 Yes \n256 4 No \n257 9 No \n258 4 Yes \n259 3 No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 0 \n2 0 \n3 0 \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 0 \n257 0 \n258 5 - 10 \n259 0 \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 NaN \n4 Yes \n.. ... \n255 No \n256 NaN \n257 NaN \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 10 columns]",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 10 columns</p>\n</div>"
+ "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? 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, apartment, 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,
+ "execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
@@ -100,7 +100,7 @@
" .replace('North District 4 bed 2 bath', '4')\n",
" .replace('3 (room), 8 (hall), ~70 (building)', '3')\n",
" .astype(int))\n",
- "df['Who do you live with? '] = df['Who do you live with? '].replace('Family, Friends', 'Both').replace('Family, Friends, Both', 'Both')\n",
+ "df['Who do you live with?'] = df['Who do you live with?'].replace('Family, Friends', 'Both').replace('Family, Friends, Both', 'Both')\n",
"# Normalizes non-applicable answers\n",
"df.loc[df['Do you currently work?'] == 'No', 'How many hours do you work per week on average?'] = 0\n",
"df.loc[df['Do you currently work?'] == 'No', 'Do you work in a department related to your major?'] = np.nan\n",
@@ -110,22 +110,22 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:58.744774Z",
- "start_time": "2024-02-23T02:10:58.732815Z"
+ "end_time": "2024-02-23T07:21:06.895899Z",
+ "start_time": "2024-02-23T07:21:06.879767Z"
}
},
- "id": "f71f8085d5f66b0",
- "execution_count": 2
+ "id": "928d5052c0a0c0ea",
+ "execution_count": 39
},
{
"cell_type": "code",
"outputs": [
{
"data": {
- "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n4 Graduate 22 \n8 Junior 20 \n9 Senior 22 \n13 Junior 21 \n.. ... ... \n246 Graduate 23+ \n247 Senior 21 \n252 Junior 20 \n255 Junior 21 \n258 Sophomore 21 \n\n Who do you live with? \\\n0 Neither \n4 Neither \n8 Friends \n9 Family \n13 Family \n.. ... \n246 Family \n247 Friends \n252 Family \n255 Friends \n258 Family \n\n Do you currently live in a house, apartnment, or dorm? \\\n0 House \n4 Apartment \n8 House \n9 House \n13 Apartment \n.. ... \n246 House \n247 Apartment \n252 House \n255 House \n258 Apartment \n\n How many people live in your household? Do you currently work? \\\n0 6 Yes \n4 1 Yes \n8 6 Yes \n9 5 Yes \n13 4 Yes \n.. ... ... \n246 2 Yes \n247 3 Yes \n252 5 Yes \n255 5 Yes \n258 4 Yes \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n4 10 - 20 \n8 10 - 20 \n9 1 - 5 \n13 10 - 20 \n.. ... \n246 10 - 20 \n247 20 - 40 \n252 20 - 40 \n255 10 - 20 \n258 5 - 10 \n\n Do you work on or off campus? \\\n0 Off-campus \n4 Off-campus \n8 On-campus \n9 On-campus \n13 Off-campus \n.. ... \n246 On-campus \n247 Off-campus \n252 Off-campus \n255 On-campus \n258 On-campus \n\n Do you work in a department related to your major? \\\n0 No \n4 Yes \n8 No \n9 No \n13 No \n.. ... \n246 Yes \n247 No \n252 No \n255 No \n258 No \n\n Do you have roommates that are part of your major? \n0 No \n4 No \n8 No \n9 No \n13 No \n.. ... \n246 No \n247 Yes \n252 No \n255 No \n258 No \n\n[77 rows x 10 columns]",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Junior</td>\n <td>20</td>\n <td>Friends</td>\n <td>House</td>\n <td>6</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Senior</td>\n <td>22</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>1 - 5</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Junior</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>246</th>\n <td>Graduate</td>\n <td>23+</td>\n <td>Family</td>\n <td>House</td>\n <td>2</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>247</th>\n <td>Senior</td>\n <td>21</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>3</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>252</th>\n <td>Junior</td>\n <td>20</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n </tbody>\n</table>\n<p>77 rows × 10 columns</p>\n</div>"
+ "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n4 Graduate 22 \n8 Junior 20 \n9 Senior 22 \n13 Junior 21 \n.. ... ... \n246 Graduate 23+ \n247 Senior 21 \n252 Junior 20 \n255 Junior 21 \n258 Sophomore 21 \n\n Who do you live with? \\\n0 Neither \n4 Neither \n8 Friends \n9 Family \n13 Family \n.. ... \n246 Family \n247 Friends \n252 Family \n255 Friends \n258 Family \n\n Do you currently live in a house, apartment, or dorm? \\\n0 House \n4 Apartment \n8 House \n9 House \n13 Apartment \n.. ... \n246 House \n247 Apartment \n252 House \n255 House \n258 Apartment \n\n How many people live in your household? 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, apartment, 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": 40,
"metadata": {},
"output_type": "execute_result"
}
@@ -140,22 +140,22 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:58.754973Z",
- "start_time": "2024-02-23T02:10:58.746452Z"
+ "end_time": "2024-02-23T07:21:06.909991Z",
+ "start_time": "2024-02-23T07:21:06.896917Z"
}
},
- "id": "6c1d9ee7948e6b9a",
- "execution_count": 3
+ "id": "a071281e62789311",
+ "execution_count": 40
},
{
"cell_type": "code",
"outputs": [
{
"data": {
- "text/plain": " What is your current class standing? What is your age? \\\n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n5 Junior 21 \n6 Sophomore 19 \n.. ... ... \n253 Senior 21 \n254 Junior 19 \n256 NaN 21 \n257 Senior 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n1 Both \n2 Friends \n3 Neither \n5 Both \n6 Friends \n.. ... \n253 Family \n254 Family \n256 Family \n257 Family \n259 Friends \n\n Do you currently live in a house, apartnment, or dorm? \\\n1 Apartment \n2 House \n3 Apartment \n5 Apartment \n6 Apartment \n.. ... \n253 House \n254 House \n256 Apartment \n257 House \n259 Dorm \n\n How many people live in your household? Do you currently work? \\\n1 4 No \n2 4 No \n3 1 No \n5 4 No \n6 4 No \n.. ... ... \n253 6 No \n254 5 No \n256 4 No \n257 9 No \n259 3 No \n\n How many hours do you work per week on average? \\\n1 0 \n2 0 \n3 0 \n5 0 \n6 0 \n.. ... \n253 0 \n254 0 \n256 0 \n257 0 \n259 0 \n\n Do you work on or off campus? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 Off-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 NaN \n259 NaN \n\n Do you have roommates that are part of your major? \n1 Yes \n2 No \n3 No \n5 No \n6 No \n.. ... \n253 No \n254 Yes \n256 No \n257 No \n259 Yes \n\n[183 rows x 10 columns]",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Junior</td>\n <td>21</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Sophomore</td>\n <td>19</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>253</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>6</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>254</th>\n <td>Junior</td>\n <td>19</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>183 rows × 10 columns</p>\n</div>"
+ "text/plain": " What is your current class standing? What is your age? \\\n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n5 Junior 21 \n6 Sophomore 19 \n.. ... ... \n253 Senior 21 \n254 Junior 19 \n256 NaN 21 \n257 Senior 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n1 Both \n2 Friends \n3 Neither \n5 Both \n6 Friends \n.. ... \n253 Family \n254 Family \n256 Family \n257 Family \n259 Friends \n\n Do you currently live in a house, apartment, or dorm? \\\n1 Apartment \n2 House \n3 Apartment \n5 Apartment \n6 Apartment \n.. ... \n253 House \n254 House \n256 Apartment \n257 House \n259 Dorm \n\n How many people live in your household? 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, apartment, 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": 41,
"metadata": {},
"output_type": "execute_result"
}
@@ -166,12 +166,12 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:58.763694Z",
- "start_time": "2024-02-23T02:10:58.755858Z"
+ "end_time": "2024-02-23T07:21:06.926581Z",
+ "start_time": "2024-02-23T07:21:06.910994Z"
}
},
- "id": "34f69a756f513fb7",
- "execution_count": 4
+ "id": "5cd2d7babaed1257",
+ "execution_count": 41
},
{
"cell_type": "markdown",
@@ -181,7 +181,7 @@
"metadata": {
"collapsed": false
},
- "id": "d5c1424ddd30ca97"
+ "id": "4f875c798f9898d8"
},
{
"cell_type": "code",
@@ -189,7 +189,7 @@
{
"data": {
"text/plain": "<Figure size 800x800 with 1 Axes>",
- "image/png": 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"
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"
},
"metadata": {},
"output_type": "display_data"
@@ -208,12 +208,12 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:58.965154Z",
- "start_time": "2024-02-23T02:10:58.764461Z"
+ "end_time": "2024-02-23T07:21:07.012140Z",
+ "start_time": "2024-02-23T07:21:06.927582Z"
}
},
- "id": "da1811cc63b41845",
- "execution_count": 5
+ "id": "ac92d008940aa879",
+ "execution_count": 42
},
{
"cell_type": "code",
@@ -221,7 +221,7 @@
{
"data": {
"text/plain": "<Figure size 800x800 with 2 Axes>",
- "image/png": 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"
+ "image/png": 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KKoYAAACQRMUQAACAC1xbUDEEAACAJBJDAAAAWDCUDAAAsj1GklNQMQQAAIAkKoYAAABMPrGgYggAAABJJIYAAACwYCgZAABkewwlp6BiCAAAAElUDAEAALhcjQUVQwAAAEiiYggAAMA5hhZUDAEAACCJxBAAAAAWDCUDAIBsj5HkFFQMAQAAIImKIQAAAJNPLKgYAgAAQBKJIQAAACwYSgYAANkeI8kpqBgCAABAEhVDAAAAJp9YUDEEAACAJCqGAAAAnGNoQcUQAAAAkkgMAQAAYMFQMgAAyPaYfJKCiiEAAAAkUTEEAABg8okFFUMAAABIIjEEAACABYkhAADI9kwmk8MWe5jNZo0aNUo1atRQnTp1NHXqVBmGIUnav3+/OnfuLG9vb3Xs2FH79u2z+ziQGAIAAGQRY8aM0fbt2zV//nxNmTJFy5cv17Jly3T58mX16tVLfn5+ioyMlI+Pj0JCQnT58mW7+mfyCQAAyPaywuST8+fPKyIiQp988om8vLwkSd27d1d0dLRy5colZ2dnvf322zKZTBo2bJh++OEHrV+/Xh06dEjzPqgYAgAAZAE7d+6Ui4uL/P39rW29evXSuHHjFB0dLV9fX+vQtMlkUvXq1bVnzx679kFiCAAAsj1HnmNoNpuVmJhos5jN5lQxxsXFqWzZslq5cqWaN2+uJk2aaPbs2UpOTlZCQoJKlixps32xYsV0+vRpu44DQ8kAAAAOFB4erlmzZtm09e3bV/369bNpu3z5so4fP66lS5dq3LhxSkhI0DvvvKO8efMqKSlJTk5ONts7OTndMcG8FxJDAAAABwoJCVG3bt1s2m5P8iQpV65cSkxM1JQpU1S2bFlJUnx8vJYsWSJXV9dUSaDZbFaePHnsioXEEAAAZHuOnHzi5OR0x0TwdiVKlJCzs7M1KZQkNzc3/fnnn/L399fZs2dttj979myq4eX74RxDAACALMDb21tXr17V0aNHrW1HjhxR2bJl5e3trd27d1uvaWgYhnbt2iVvb2+79kFiCAAAsr2scIHr8uXLq1GjRhoyZIhiYmK0detWzZs3T4GBgWrevLkuXryosWPH6vDhwxo7dqySkpLUokULu44DiSEAAEAWMXnyZD3xxBMKDAxUaGioXnjhBQUFBcnFxUXh4eHauXOnOnTooOjoaM2bN0/58uWzq3/OMQQAAMgiChQooIkTJ95xnZeXl1asWPFQ/ZMYAgCAbM/eexb/VzGUDAAAAElUDAEAALLEvZIzAxVDAAAASCIxBAAAgAVDyQAAINtj8kkKKoYAAACQRMUQAACAyScWVAwBAAAgiYohAAAA5xhaUDEEAACAJBJDAAAAWDCUDAAAsj1GklNQMQQAAIAkKoYAAADKQclQEhVDAAAAWJAYAgAAQBJDyQAAAEw+saBiCAAAAEl2VAxjY2P19ddfKzExUc2aNZOfn19GxgUAAJBpuPNJijRVDLdv36727dsrKipKR48eVXBwsKZMmZLRsQEAACATpaliOGXKFIWEhKhv376SpJ9//lmvvfaa/vzzTw0aNEilSpVScnKyTp8+rTJlymRowAAAAOktBwVDSWmsGB45ckRt27a1Pvb399fChQsVExOjRo0aqXfv3vr777/VpEmTDAsUAAAAGStNFcPHH39c3333nV5++WVrW+XKlbV69WrFxcXJbDarUKFCWrhwYYYFCgAAgIyVpoph//79NXHiRPXo0UMxMTHWdpPJpCeeeEIVKlRQ7ty55e/vn2GBAgAAZBSTyeSw5VGSpsTwf//7n5YuXapy5copOTk5o2MCAACAA6T5cjVVq1ZV1apVlZiYeNdtNm3apKZNm6ZLYAAAAJnlESvcOYzdF7gOCgrS33//bdMWFxenV155RQMGDEivuAAAAJDJ7E4My5Urp8DAQMXHx8tsNuuDDz5Qq1atdOXKFUVERGREjAAAAMgEdt8r+YMPPtDYsWP1/PPPy8nJScnJyZowYYJatGiREfEBAABkOJMYS5YeIDE0mUwaPny4HnvsMU2fPl0fffSRatWqlRGxAQAAIBOlKTEMCAi463TqkJAQFS9e3Pp48+bN6RMZAABAJuHOJynSlBj269cvo+MAAACAg6UpMXz22Wet/3/ttdf01ltvyd3dPcOCAgAAyEyP2oWmHcXuWcm7du1Srlx2n5oIAACAR5zdGV7Xrl31xhtv6Pnnn1eZMmXk7Oxss75GjRrpFhwAAAAyj92J4Zw5cyRJ77zzTqp1JpNJBw4cePioAAAAMhEjySnsTgxjYmIyIg4AAAA42AOdLHjlyhWtWrVKsbGxunHjhsqXL6+WLVuqcOHC6RweAABAxstByVDSA0w++eOPP/TMM88oLCxM8fHxio+PV3h4uFq0aKHDhw9nRIwAAADIBHZXDMeOHau6detq9OjR1tnJ169f1/Dhw/X+++/r448/TvcgAQAAkPHsrhju2bNHr7zyis0la3LlyqVXXnlFu3fvTtfgAAAAMoPJ5LjlUWJ3YliiRAmdOHEiVfuJEyeUP3/+dAkKAAAAmc/uoeTnn39ew4cP1+uvvy4vLy9JUnR0tGbMmKHOnTune4AAAAAZjTufpLA7MezRo4eSkpI0efJkXbhwQZJUvHhxBQcHq3v37ukeIAAAADKH3YnhyZMn1a9fP/Xr10/nzp2Ts7OzXFxcMiI2AACATEHBMIXdiWGLFi1UtmxZ1a9fX/Xr11fNmjUzIi4AAABkMrsTw59//lk///yzoqKiNHnyZJ04cUK+vr7WRLFChQoZEScAAAAymN2JYb58+dSoUSM1atRIknTkyBHNnj1bEydO1MSJE7lXMgAAyHK480kKuxPDU6dOadeuXdq1a5d27typI0eOyM3NTV26dJGfn19GxAgAAIBMYHdi2KRJE+XIkUMNGjTQ66+/Lj8/PxUqVCgjYgMAAMgU1AtT2J0YTpw4Ub/++qt++eUXDRkyRNWqVZOfn5+qV68uLy8vOTk5ZUScAAAAyGB2J4Zt27ZV27ZtJUnnzp3Tr7/+qi1btmjWrFkymUyKjo5O9yABAACQ8exODCUpMTFRO3fu1I4dO7Rjxw4dPHhQTz/9tOrVq5fe8QEAAGQ47nySwu7EsGPHjjp48KCKFy+uunXrqkePHqpTp44KFy6cAeEBAAAgs9idGLZp00YTJ06Uu7t7RsQDAACQ6XJQMJQk5bD3CcHBwSSFAAAADrBx40Z5enraLP3795ck7d+/X507d5a3t7c6duyoffv22d3/A51jCAAA8F+SVc4xPHz4sBo3bqzRo0db25ydnXX58mX16tVLbdq00fjx47VkyRKFhIRo48aNypcvX5r7t7tiCAAAAMeIjY2Vh4eHSpQoYV0KFiyotWvXytnZWW+//bbc3d01bNgw5c+fX+vXr7erf7sTw7i4OHufAgAAgHQQGxurJ598MlV7dHS0fH19rZVPk8mk6tWra8+ePXb1b3di2Lx5c3Xu3FkLFizQmTNn7H06AADAI8dkctxiNpuVmJhos5jN5lQxGoaho0ePatu2bWrWrJmaNm2qyZMny2w2KyEhQSVLlrTZvlixYjp9+rRdx8Hucwy3bt2qb775RuvWrdPkyZNVrVo1tWzZUs2bN1fRokXt7Q4AACBbCw8P16xZs2za+vbtq379+tm0xcfHKykpSU5OTpo+fbpOnjypMWPG6MqVK9b2Wzk5Od0xwbwXuxPDokWLKjAwUIGBgTp37pw2bNigLVu2aPLkyfLx8VHr1q3VvHlz5c2b196uAQAAHMKRk09CQkLUrVs3m7Y73WK4bNmy2rFjhwoVKiSTyaSnn35aycnJGjRokPz9/VMlgWazWXny5LErloeafJKQkKCEhASdPn1aycnJyp8/v5YvX65GjRppw4YND9M1AABAtuDk5CQXFxeb5U6JoSQVLlzYJol1d3fX1atXVaJECZ09e9Zm27Nnz6YaXr4fuxPDAwcOaNq0aWrWrJk6deqkffv2qVu3bvrxxx81Y8YMLVmyRC+//LJGjBhhb9cAAAC4i61bt6pmzZpKSkqyth04cECFCxeWr6+vdu/eLcMwJKWcj7hr1y55e3vbtQ+7h5I7dOggX19fBQcHq3nz5ipSpEiqbXx9fZm9DAAAsoyscOcTHx8fOTs7a/jw4erTp4/i4uI0ceJE9ezZU82bN9eUKVM0duxYPf/881q6dKmSkpLUokULu/Zhd2L43Xff6bHHHrvnNjVr1lTNmjXt7RoAAAB34eLiovnz5+v9999Xx44dlT9/fj3//PPq2bOnTCaTwsPD9e6772r58uXy9PTUvHnz7Lq4tZTGxPD2mTL30rdvX7sCAAAAcLSscueTp556Sp988skd13l5eWnFihUP1X+aEsMdO3akqbOsclABAACQWpoSw08//dT6//j4eD322GPKkcN23sqNGzcUExOTvtEBAABkAkpbKeyeldykSROdP38+VfvJkyfVtWvX9IgJAAAADpCmiuEXX3yhuXPnSkqZ/tyxY8dUFcOLFy/K3d09/SMEAABApkhTYti+fXvlzp1bycnJGjp0qLp166YCBQpY15tMJuXNm1e1atXKsEABAAAySg7mSUhKY2KYO3dutW/fXpL0+OOPq3r16sqVy+4r3QAAAOARZnd25+/vr6ioKP3222+6du2a9QrbN3G5GgAAkNVQMExhd2I4fvx4LVq0SBUrVlT+/Plt1nG5GgAAgKzL7sQwIiJC48ePV9u2bTMiHgAAADiI3Ylhzpw55eXllRGxAAAAOASjninsvo7hCy+8oJkzZ+ry5csZEQ8AAAAcxO6K4c8//6zdu3dr/fr1KlasmHLnzm2zfvPmzekWHAAAQGagYJjC7sSwQ4cO6tChQ0bEAgAAAAeyOzF89tlnrf+/cOGCChQoIJPJxNg8AABAFmf3OYaGYSgsLEw1a9ZU7dq1derUKQ0aNEjvvPOOzGZzRsQIAACQoXKYTA5bHiV2J4azZ8/WqlWrNH78eDk5OUlKqSL++OOPmjhxYroHCAAAgMxhd2K4YsUKvffee2rcuLF1+Lhu3bqaMGGC1q1bl+4BAgAAZDSTyXHLo8TuxPDcuXMqWbJkqvaCBQtyCRsAAIAszO7EsFatWpo/f75NW2JioqZOnaqaNWumW2AAAACZ5eZEWkcsjxK7E8ORI0dq//79qlu3rq5evarXXntNDRs21KlTpzR8+PCMiBEAAACZwO7L1RQsWFBffvmloqKidOTIEV2/fl1ubm6qV6+ecuSwO88EAADAI8LuxLB169aaNWuWateurdq1a2dETA9t3Iy3HB0CLCqVLejoEHCLt9tXdHQIsLj0zyVHh/BQTCapePECOnv2kgzD0dHgv/J+5ClRwGH7prSVwu7jkCNHDl27di0jYgEAAIAD2V0xbNSokbp166bGjRurbNmy1msZ3tS3b990Cw4AACAzPGqTQBzF7sTw4MGDqly5sv766y/99ddfNus4qAAAAFmX3Ylhly5dVLduXRUpUiQj4gEAAICD2H2O4ahRo3T+/PkMCAUAAMAxcpgctzxK7E4Ma9asqdWrV8tsNmdEPAAAAHAQu4eSz507pzlz5mju3LkqWrSonJ2dbdZv3rw53YIDAADIDI9a5c5R7E4Mn3vuOT333HMZEQsAAAAcyO7E8Nlnn82IOAAAAByGK6uksDsxDAoKuufBW7Ro0UMFBAAAAMewOzGsWbOmzePr168rLi5OW7Zs0auvvppugQEAACBz2Z0Y3u3OJpGRkdqwYYN69Ojx0EEBAABkJiafpEi3e0bXqFFDUVFR6dUdAAAAMpndFcP4+PhUbf/++6/mz5+vsmXLpktQAAAAmYm5JynsTgwDAgJkMplkGIZ1EophGCpdurTef//9dA8QAAAAmcPuxPD2C1ibTCblzp1bxYsXZ6o3AABAFmb3OYZly5bV999/r927d6ts2bIqU6aMRo0apaVLl2ZEfAAAABkuh8nksOVRYndiOG3aNIWFhSlfvnzWNn9/f82ZM0ezZ89O1+AAAACQeexODCMiIjR9+nQFBARY21566SVNnjxZy5YtS9fgAAAAMkMOBy6PErvjSUpKkouLS6r2IkWK6NKlS+kSFAAAADKf3Ylh/fr1NXbsWJvL1pw5c0YTJkxQvXr10jU4AACAzGAyOW55lNidGL7zzju6du2amjRpolq1aqlWrVpq1KiRkpOT9e6772ZEjAAAAMgEdl+upmjRolq6dKkOHjyoo0ePKleuXHryySdVoUKFjIgPAAAAmcTuxPAmT09PeXp6pmcsAAAADvGoXTbGUR61yTAAAABwkAeuGAIAAPxXUDBMQcUQAAAAkh6gYvjLL7/cc32NGjUeOBgAAAA4jt2JYVBQ0B3bnZycVKJECW3evPmhgwIAAMhMORhKlvQAiWFMTIzN4xs3bujEiRMaPXq02rRpk26BAQAAIHM99DmGOXPmlJubmwYPHqwPPvggPWICAADIVDlMJoctj5J0m3xy7tw5Xbx4Mb26AwAAQCazeyh5yJAhqdr+/fdfbd++Xc2bN0+XoAAAADLTI1a4c5h0uY5h4cKFFRoaqnbt2qVHdwAAAHAAuxPDcePGZUQcAAAAcLAHOsdw06ZNev755+Xv7y9fX1916tRJK1euTOfQAAAAMkcOk+OWB9WrVy8NHjzY+nj//v3q3LmzvL291bFjR+3bt8/+42DvE5YuXapBgwapRo0aGj9+vCZMmCB/f3+NGjVKX3zxhd0BAAAAwD5r1qzRli1brI8vX76sXr16yc/PT5GRkfLx8VFISIguX75sV792DyV/9NFHevfdd9W+fXtrW9OmTfXUU09p7ty56ty5s71dAgAAOJRJWWf2yfnz5zVx4kRVrVrV2rZ27Vo5Ozvr7bfflslk0rBhw/TDDz9o/fr16tChQ5r7trtieO7cOVWrVi1Vu4+Pj/788097uwMAAIAdJkyYoHbt2qlChQrWtujoaPn6+spkmV5tMplUvXp17dmzx66+7U4Mn3766TueT7hixQqbAAEAAHB/ZrNZiYmJNovZbL7jtlFRUfr111/12muv2bQnJCSoZMmSNm3FihXT6dOn7YrF7qHkQYMGKTg4WDt27JC3t7ckac+ePYqJidHcuXPt7Q4AAMDhHHmv5PDwcM2aNcumrW/fvurXr59N29WrV/Xuu+/qnXfeUZ48eWzWJSUlycnJyabNycnprgnm3didGPr4+CgyMlLLly9XbGysnJ2dVaNGDU2bNk2lS5e2tzsAAIBsLSQkRN26dbNpuz3Jk6RZs2apSpUqql+/fqp1zs7OqZJAs9mcKoG8H7sTw4iICDVr1uyOd0ABAADIihxZMXRycrpjIni7NWvW6OzZs/Lx8ZEkayL4zTffqHXr1jp79qzN9mfPnk01vHw/dieGCxYs0KhRo1S3bl21bt1aAQEByps3r73dAAAAwA6ffvqprl+/bn08efJkSdLAgQP1yy+/6MMPP5RhGDKZTDIMQ7t27VLv3r3t2ofdk09Wr16tFStWqHLlygoLC1OdOnU0YMAAbdy40e5xbAAAgEeByWRy2JJWZcuWlaurq3XJnz+/8ufPL1dXVzVv3lwXL17U2LFjdfjwYY0dO1ZJSUlq0aKFXcfhge584u7urr59++rrr7/Wl19+qSeeeEKDBg1SnTp1NGTIEO3atetBugUAAMADcHFxUXh4uHbu3KkOHTooOjpa8+bNU758+ezqx+6h5JvOnDmjb775Rhs2bNCePXvk5eWlli1bKiEhQa+++qqee+45vfXWWw/aPQAAAO5h/PjxNo+9vLy0YsWKh+rzgc4x/OabbxQdHS0PDw+1atVKkyZNspmR/OSTT+q9994jMQQAAFmCIyefPErsTgyXLFmiVq1aacyYMXJ3d7/jNpUqVdLw4cMfOjgAAABkHrsTw2+++ea+23h6esrT0/OBAgIAAMhsdswB+U97oMknAAAA+O8hMQQAAICkh5iVDAAA8F+Rg7FkSQ+RGB47dkyxsbFKTk6Wm5ubKlSokJ5xAQAAIJPZnRhevHhRQ4YM0ebNm1WoUCHduHFD//77r2rUqKHZs2erQIECGREnAABAhuFyNSnsPsdwzJgxOn36tNauXasdO3bo119/1erVq3X58mWNGzcuI2IEAABAJrC7Yvjtt9/qk08+Ufny5a1tFSpU0DvvvKNXXnklXYMDAADIDJximMLuiqGzs7Ny5Ej9NJPJpBs3bqRLUAAAAMh8dieGAQEBGjVqlE6cOGFtO3bsmMaMGaOGDRuma3AAAADIPHYPJQ8aNEh9+vTRM888o0KFCklKmZBSv359jRgxIt0DBAAAyGg5xFiy9ACJYcGCBfXpp5/q4MGDio2NlbOzs9zc3GzOOQQAAEDWY3diGBcXp8OHD+vff/+Vi4uLnnrqKZUtWzYjYgMAAMgUTD5JkebEMCoqSuPGjdOhQ4dkGIa13WQyqXLlyho8eLD8/PwyJEgAAABkvDRNPtm2bZt69uypihUr6tNPP9VPP/2k33//XTt27NCCBQtUvnx5devWTbt3787oeAEAAJBB0lQxnD17toKDgzVo0CCb9kKFCqlmzZqqWbOmChUqpLCwMM2bNy9DAgUAAMgo3PkkRZoqhjExMXr22WfvuU3nzp21f//+dAkKAAAAmS9NFcMrV65YL01zN0WKFNHff/+dLkEBAABkphzMPpGUxoqhYRh3vNvJrUwmk82kFAAAAGQtaZ6VvG7dOrm4uNx1/aVLl9IlIAAAADhGmhLDMmXK6OOPP77vdqVLl37ogAAAADIbI8kp0pQYfvvttxkdBwAAABzM7jufAAAA/Ncw+SRFmiafAAAA4L+PiiEAAMj2KBimoGIIAAAASSSGAAAAsGAoGQAAZHtUylJwHAAAACCJiiEAAIBMzD6RRMUQAAAAFiSGAAAAkMRQMgAAgBhITkHFEAAAAJKoGAIAAHCvZAsqhgAAAJBExRAAAIBzDC2oGAIAAEASiSEAAAAsGEoGAADZHnNPUlAxBAAAgCQqhgAAANwr2YKKIQAAACSRGAIAAMCCoWQAAJDtUSlLwXEAAACAJCqGAAAATD6xoGIIAAAASVQMAQAAuFeyBRVDAAAASCIxBAAAgAVDyQAAINtj8kkKKoYAAACQRMUQAACASpkFxwEAACCLOH78uHr06CEfHx81atRIH330kXVdXFycgoODVa1aNbVs2VLbtm2zu38SQwAAgCwgOTlZvXr1UpEiRbRixQqNGjVKYWFhWr16tQzDUJ8+fVS8eHFFRESoXbt26tu3r+Lj4+3aB0PJAAAg28sKk0/Onj2rp59+WiNHjpSLi4uefPJJ1a5dWzt37lTx4sUVFxenpUuXKl++fHJ3d1dUVJQiIiLUr1+/NO+DiiEAAEAWULJkSU2fPl0uLi4yDEM7d+7UL7/8In9/f0VHR6tSpUrKly+fdXtfX1/t2bPHrn2QGAIAgGzP5MDFbDYrMTHRZjGbzfeMNyAgQF27dpWPj4+aNWumhIQElSxZ0mabYsWK6fTp03YdBxJDAAAABwoPD5evr6/NEh4efs/nzJgxQ3PnztWBAwc0btw4JSUlycnJyWYbJyen+yaYt+McQwAAkO058hTDkJAQdevWzabt9iTvdlWrVpUkXb16VQMHDlTHjh2VlJRks43ZbFaePHnsioWKIQAAgAM5OTnJxcXFZrlTYnj27Flt2rTJpq1ChQq6du2aSpQoobNnz6ba/vbh5fshMQQAAMgCTp48qb59++rMmTPWtn379qlo0aLy9fXV77//ritXrljX7dy5U97e3nbtg8QQAABkezlkctiSVlWrVlXlypU1dOhQHT58WFu2bNGkSZPUu3dv+fv7q3Tp0hoyZIgOHTqkefPmae/everUqZOdxwEAAACPvJw5c2rOnDnKmzevunTpomHDhikoKEgvvfSSdV1CQoI6dOigVatWafbs2SpTpoxd+2DyCQAAyPaywPWtJUmlSpXSrFmz7rjO1dVVixcvfqj+qRgCAABAEokhAAAALBhKBgAA2Z7Jjkkg/2VUDAEAACCJiiEAAECWmXyS0agYAgAAQBIVQwAAALsuNP1fRsUQAAAAkkgMAQAAYMFQMgAAyPaYfJKCiiEAAAAkUTEEAACgYmhBxRAAAACSSAwBAABgwVAyAADI9rhXcgoqhgAAAJBExRAAAEA5KBhKomIIAAAACyqGAAAg2+McwxRUDAEAACCJxBAAAAAWDCUDAIBsjzufpKBiCAAAAElUDAEAAJh8YkHFEAAAAJJIDAEAAGDBUDIAAMj2uPNJCiqGAAAAkPSIVgz//vtvFSlSRCbmjgMAgEzA5JMUDq8YnjlzRm+88YYOHDigq1ev6sUXX1TdunUVEBCgmJgYR4cHAACQbTg8MRw5cqT+/vtvFS5cWJGRkfrjjz+0dOlSBQQEaPTo0Y4ODwAAINtw+FDyTz/9pMjISJUuXVqbNm1SkyZN5O3traJFi6p169aODg8AAGQDnL2WwuEVQ2dnZ129elUXLlzQjh071KhRI0nSyZMnVahQIccGBwAAkI04vGLYtGlTDRgwQHny5FGhQoXUqFEjrV27Vu+//76effZZR4cHAACyAQqGKRyeGI4cOVKLFy/WqVOn1KVLFzk7O8tsNqt379564YUXHB0eAABAtuHwxDBXrlwKDg6WJF24cEHJyclq164dl6oBAACZJgd5h6RH4BxDwzAUFhammjVrqnbt2jp16pQGDRqkd955R2az2dHhAQAAZBsOTwxnz56tVatWafz48XJycpIkPfvss/rxxx81ceJEB0cHABnDZMr6y3/ldfxXlv/C+wHHc/hQ8ooVKzR+/HjVqFFDN4eP69atqwkTJuj111/X8OHDHRwhAKS/4sULODqEdFGs2H/jdfxX8H48OPLSFA5PDM+dO6eSJUumai9YsKAuX77sgIgAIOOdPXvJ0SE8FJMpJQk5d+6SDMPR0eC/8n78V/5gysocnhjWqlVL8+fP13vvvWdtS0xM1NSpU1WzZk0HRgYAGScrf3nfyjD+O6/lv4D34yFQMpTkoHMMu3btqtjYWEkpl6vZv3+/6tatq6tXr+q1115Tw4YNderUKYaRAQAAMpFDKobOzs5q3769unfvrj59+ujLL79UVFSUjhw5ouvXr8vNzU316tVTjhwOnxsDAACQbTgkMfzkk0+0YcMGjR8/XuvWrdPIkSNVp04d1a5d2xHhAACAbM7EWLIkB55j+Mwzz6hhw4b68MMP1bdvXwUEBKhPnz5ydna22a5MmTIOihAAACB7cejkE2dnZ/Xt21cVK1bUgAEDtGbNGus6wzBkMpl04MABB0YIAACyA66jmMKhieGpU6c0ceJEbdy4Ua1bt1ZISIjy5MnjyJAAAACyLYckhlevXtXcuXP1ySefqFy5clq0aJH8/PwcEQoAAABnGFo4JDFs1qyZEhMTNWDAAAUFBSlnzpyOCAMAAAC3cEhi6Ovrq9DQ0Dve8QQAAACO4ZDEcMqUKY7YLQAAwJ0xlizJQXc+AQAAwKPH4fdKBgAAcDQucJ2CiiEAAAAkkRgCAADAgqFkAACQ7XHnkxRUDAEAALKIM2fOqH///vL391f9+vU1btw4Xb16VZIUFxen4OBgVatWTS1bttS2bdvs7p/EEAAAZHsmBy5pZRiG+vfvr6SkJH322WeaNm2avvvuO02fPl2GYahPnz4qXry4IiIi1K5dO/Xt21fx8fF2HQeGkgEAALKAI0eOaM+ePfrxxx9VvHhxSVL//v01YcIENWjQQHFxcVq6dKny5csnd3d3RUVFKSIiQv369UvzPkgMAQAAssA5hiVKlNBHH31kTQpvSkxMVHR0tCpVqqR8+fJZ2319fbVnzx679sFQMgAAQBZQsGBB1a9f3/o4OTlZixcvVq1atZSQkJDqVsPFihXT6dOn7doHiSEAAIADmc1mJSYm2ixms/m+z5s0aZL279+vN954Q0lJSXJycrJZ7+TklKZ+bkViCAAAsj2TA/+Fh4fL19fXZgkPD79nvJMmTdLChQs1adIkeXh4yNnZOVUSaDablSdPHruOA+cYAgAAOFBISIi6detm03Z79e9Wo0eP1pIlSzRp0iQ1a9ZMklSqVCkdPnzYZruzZ8+mGl6+HyqGAAAg2zOZHLc4OTnJxcXFZrlbYjhr1iwtXbpUU6dOVatWrazt3t7e+v3333XlyhVr286dO+Xt7W3XcSAxBAAAyAJiY2M1Z84cvfLKK/L19VVCQoJ18ff3V+nSpTVkyBAdOnRI8+bN0969e9WpUye79sFQMgAAQBawefNm3bhxQ2FhYQoLC7NZd/DgQc2ZM0fDhg1Thw4d5OrqqtmzZ6tMmTJ27YPEEAAAZHtZ4DKG6tWrl3r16nXX9a6urlq8ePFD7YOhZAAAAEiiYggAAJA1SoaZgIohAAAAJFExBAAAkImSoSQqhgAAALAgMQQAAIAkhpIBAABkYiRZEhVDAAAAWFAxBAAA2R4FwxRUDAEAACCJxBAAAAAWDCUDAAAwliyJiiEAAAAsqBgCAIBsjzufpKBiCAAAAElUDAEAALjAtQUVQwAAAEgiMQQAAIAFQ8kAACDbYyQ5BRVDAAAASKJiCAAAQMnQgoohAAAAJJEYAgAAwIKhZAAAkO1x55MUVAwBAAAgiYohAAAAdz6xoGIIAAAASVQMAQAAOMPQgoohAAAAJJEYAgAAwIKhZAAAAMaSJVExBAAAgAUVQwAAkO1xgesUVAwBAAAgicQQAAAAFgwlAwCAbI87n6SgYggAAABJVAwBAACYemJBxRAAAACSSAwBAABgwVAyAAAAY8mSqBgCAADAgoohAADI9rjzSQoqhgAAAJBExRAAAIALXFtQMQQAAIAkEkMAAABYMJQMAACyPUaSU1AxBAAAgCQqhgAAAJQMLagYAgAAQBKJIQAAACwYSgYAANkedz5JQcUQAAAAkqgYAgAAcOcTCyqGAAAAkETFEAAAgDMMLagYAgAAZDFms1mtW7fWjh07rG1xcXEKDg5WtWrV1LJlS23bts3ufkkMAQAAspCrV6/qzTff1KFDh6xthmGoT58+Kl68uCIiItSuXTv17dtX8fHxdvXNUDIAAMj2ssrkk8OHD+utt96SYRg27T/99JPi4uK0dOlS5cuXT+7u7oqKilJERIT69euX5v6pGAIAAGQRP//8s2rWrKlly5bZtEdHR6tSpUrKly+ftc3X11d79uyxq38qhgAAAA6cfmI2m2U2m23anJyc5OTklGrbrl273rGPhIQElSxZ0qatWLFiOn36tF2xUDEEAABwoPDwcPn6+tos4eHhdvWRlJSUKpF0cnJKlXDeDxVDAAAABwoJCVG3bt1s2u5ULbwXZ2dnnT9/3qbNbDYrT548dvVDYggAALI9R04+uduwsT1KlSqlw4cP27SdPXs21fDy/TCUDAAAkMV5e3vr999/15UrV6xtO3fulLe3t139kBgCAIBsz+TAJT34+/urdOnSGjJkiA4dOqR58+Zp79696tSpk139kBgCAABkcTlz5tScOXOUkJCgDh06aNWqVZo9e7bKlCljVz+cYwgAALK9rHKB61sdPHjQ5rGrq6sWL178UH1SMQQAAIAkEkMAAABYMJQMAACyPZMD73zyKKFiCAAAAElUDAEAABx5q+RHChVDAAAASCIxBAAAgAVDyQAAINtjJDkFFUMAAABIomIIAACQJe98khGoGAIAAEASFUMAAAAucG1BxRAAAACSSAwBAABgwVAyAAAAI8mSqBgCAADAgoohAADI9igYpqBiCAAAAEkkhgAAALBgKBkAAGR73PkkBRVDAAAASKJiCAAAwJ1PLKgYAgAAQBIVQwAAAM4xtKBiCAAAAEkkhgAAALAgMQQAAIAkEkMAAABYMPkEAABke0w+SUHFEAAAAJJIDAEAAGDBUDIAAMj2uPNJCiqGAAAAkETFEAAAgMknFlQMAQAAIImKIQAAAGcYWlAxBAAAgCQSQwAAAFgwlAwAAMBYsiQqhgAAALCgYggAALI9LnCdgoohAAAAJJEYAgAAwIKhZAAAkO1x55MUVAwBAAAgiYohAAAAU08sqBgCAABAEokhAAAALBhKBgAAYCxZEhVDAAAAWFAxBAAA2R53PklBxRAAAACSqBgCAABwgWsLKoYAAACQRGIIAAAAC5NhGIajgwAAAIDjUTEEAACAJBJDAAAAWJAYAgAAQBKJIQAAACxIDAEAACCJxBAAAAAWJIYAAACQRGIIAAAACxJDAAAASCIxtEtkZKQ8PT31xRdfZOh+zp07p3Xr1mXoPu4mLi5OW7Zscci+M1JAQIA8PT3l6empihUrysfHR88//7y2bt3q6NCyhYCAAEVGRqZqj4yMVEBAgAMiwq2fiZufC39/f7366qv6888/HR2ew5w8eVKenp46efKko0NJkx9//FEDBw7MkL7NZrOWL19ufRwUFKSZM2emS7/PPvuszp0799B9If2RGNphzZo1euKJJ/TVV19l6H4mT57ssORs6NCh2rt3r0P2ndGGDh2qbdu2acuWLVq2bJmqV6+ukJAQbd++3dGhAQ5x8zNx83Mxbdo0HTp0SKGhoY4ODWlgNps1ZswY9evXL0P6X7NmjebOnWt9PHPmTHXv3v2h+3VyctKLL76oSZMmPXRfSH8khml07tw5RUVFqU+fPvr1118VFxeXYfvi9tUZo0CBAipRooRKlSolDw8Pvf3222rVqpXGjRvn6NAAh7j5mbj5uahbt6769++vHTt26NKlS44OD/exdu1alSlTRq6urhnS/+3fRYULF1b+/PnTpe82bdro22+/1alTp9KlP6QfEsM0Wr9+vQoUKKC2bduqZMmSNlXDgIAALViwQG3atFG1atXUq1cvJSQkWNdv3rxZ7du3V9WqVeXn56c333xT//77r6SUv8Bee+01vfDCC/L391dQUJBWrFihFStWWIfYPD09tW7dOrVo0ULe3t568803FRcXp5deekne3t7q2rWrzpw5Y93fxo0b1bJlS3l7e6tTp076+eefreuCgoIUFhamHj16yMvLS82aNbMOpw4ePFg///yzZs2apaCgoAw9no+KLl266I8//tDx48d14cIFjRgxQnXq1JGvr68GDRqkCxcuSJJ27NihgIAAvfvuu/L19dW8efM0ePBgTZo0SQMGDJC3t7datmyp/fv3a9q0afLz81ODBg0cdkpAVnT69Gm9/vrr8vf3V82aNTVmzBiZzWZJdx5yvnVYKz4+Xt27d5ePj49q166t0aNH69q1a5JSvtxmz56tevXqyc/PT71791Z8fHzmvrgsxMnJSZKUI0eOe34mJCk2NlY9evRQ9erVVb9+fc2aNUvJycmSUn63vf322xo9erR8fHwUEBCgbdu2afHixapTp45q1aqlRYsWOeQ1psWmTZvUtGlTeXt7q3fv3jave/fu3QoMDFS1atUUEBCgJUuWWNcNHjxYgwcPtunL09NTO3bskCRFRUWpXbt2qlq1qpo0aaKlS5dat7t48aIGDRqk6tWrq169eho9erSuXLly1xiXLFmipk2bWh8HBQVp9OjRatKkiRo1aqTExET9+eef6t27t7y9vRUQEKBZs2bpxo0bklI+V0FBQZoxY4Zq1qwpPz8/jRs3ToZhaMeOHRoyZIhOnTplHVq/9TM3ePBgjRs3zvr7r2HDhlq5cqU1litXrmjYsGHy9fVV/fr19cUXX6hSpUrWIXonJyfVqVNHy5Yts/etQQYjMUyjNWvWqFGjRsqRI4cCAgK0cuVKm7+mZs6cqZ49e2rZsmVKSkqylvZPnDih119/XV27dtW6des0ffp0bd++3ea8jc2bN6t169ZauHChwsLC1KJFC7Vo0UJffvmldZsZM2Zo/PjxCg8P14YNGxQYGKjAwEAtXbpUCQkJ+vDDDyVJMTExCg0N1auvvqpVq1apbdu2euWVV3T8+HFrX3PnzlWrVq309ddfq2LFihoxYoSSk5M1bNgw+fj4qHv37ulyHklW4O7uLkk6fPiw+vbtqwMHDmju3Ln65JNPFBsba/ML/tSpUzKbzYqMjFTr1q0lSQsXLpS/v79WrVqlwoUL6+WXX9a5c+e0bNkyayJ584sSd2c2m/Xyyy8rKSlJn376qaZPn67vv/9eEydOTNPzR48erXz58mnlypWaPXu2vvnmG+tnbPHixVq9erWmTJmiZcuWqVixYurevbs1ccT/O3HihObNm6f69esrf/789/xM/P333+ratatKliypL774Qu+++64WL15sk+ytXbtWBQoU0FdffSUvLy8NGDBA27Zt06effqqgoCBNmDBBf//9t6Ne7j2tWLFCU6dO1aJFi/T7779bf8fGxsbq5ZdfVo0aNRQZGal+/fppwoQJ2rhx4337vHHjhgYMGKDmzZtr3bp1ev311zVq1CgdPnxYkjRs2DBdunRJS5Ys0Zw5c/Tbb7/pvffeu2NfFy5cUHR0tOrWrWvTHhkZqUmTJmnWrFnW97BYsWJasWKFxo0bp9WrV9sMD+/evVtHjx7VkiVLNGLECC1atEjbt2+Xj4+Phg4dqscee0zbtm1T6dKlU8Xw2WefqXLlyvr666/1zDPP6N1337VWmseMGaPdu3dr/vz5mjZtmj766CNrQnpT3bp1Oc/7UWTgvuLj4w1PT09jw4YNhmEYxo8//mh4eHgYv/zyi2EYhtG4cWNj7Nix1u1PnDhheHh4GAcPHjSOHj1qLFmyxKa/N954wxgyZIhhGIYxY8YMo06dOjbrQ0NDjdDQUOtjDw8PY+nSpdbHnTp1MgYNGmR9PHHiRKN79+6GYRjGwIEDjXHjxtn017dvX2vbiy++aPTr18+67sCBA4aHh4dx+vRp6/oZM2bYc3iyhMaNGxsRERGp2q9du2Z4eHgYM2fONDw8PIwjR45Y1x0+fNjw8PAwYmNjjZ9++snw8PAwDh8+bF0fGhpqdOnSxfr4s88+MypXrmwkJSXZPP/MmTMZ+MqyhsaNGxtVqlQxqlWrZrNUqVLFaNy4sbFp0ybD29vbOH/+vPU5W7ZsMSpVqmQkJiYaERERRuPGjW36vPVntU2bNsbgwYMNs9lsGIZh/P7770ZcXJxhGIbRoEEDY/PmzdbnXb9+3ahVq5ZNW3Z0+3tSpUoVw8fHxxg4cKDx999/W3833O0zsXDhQqNhw4bGtWvXrOs///xzo27duoZhpPxuq1evnpGcnGwYhmF8//33hoeHh3HixAnDMAwjKSnJ8PDwMHbt2pWJr/r+4uLiDA8PD2Pr1q3WtrFjxxo9evQwDMMw3n//fZvPvWEYxqRJk4znnnvOMIzUv78NI+V3+E8//WT8888/hoeHh7F8+XLruqioKOP8+fPG8ePHjYoVKxoXL160rouJiUnVduvzqlSpYty4ccPa9uKLLxoDBgywPt6+fbtRq1Ytm202b95s+Pv7G4ZhGBEREUbFihWNS5cuWde3b9/eCAsLs66/9XN362cuNDTU6NChg3XdpUuXDA8PD2Pnzp1GYmKiUblyZWP79u3W9T/88IPh4eFh/VwahmHs3LnTqFixonH9+vVUrw+Ok8vRiWlWsGbNGjk7O6tevXqSJH9/fxUqVEgrVqyQn5+fJKl69erW7cuVK6fChQsrNjZWLVq0kJOTk8LCwnTo0CEdOnRIhw8fVrt27azbly1b9r4xlCtXzvr/PHny2DwnT5481iG32NhYrVu3zqY8f+3aNWvskvTkk09a/+/i4iJJun79epqOxX9NYmKipJT3oGDBgnJzc7Ouc3d3V6FChXTkyBEVKFBAkvT444/bPP/Wx3ny5FHx4sWVJ08eSZKzs7MkWd+b7K5///565plnbNo2bNigJUuWKDY2Vk8++aQKFSpkXVe9enVdv35dJ06cuG/fPXv21NChQ7Vx40Y1aNBALVu2VKVKlfTvv//q9OnTeuONN5Qjx/8PkFy5ckXHjh1Lt9eWVd18T/7991/NnDlTp06d0ltvvaUiRYooKirqnp+J2NhYVa5cWbly/f/XiI+PjxISEnTx4kVJKZ8Pk8kkSdbPxc3fXTcfP6qfjyeeeML6/wIFCujq1auSUn7Henl52Wzr4+NjMyR8N4ULF1ZgYKCGDx+uOXPmqHHjxurYsaMKFSqkXbt2KTk5WQ0aNLB5TnJyso4fP64qVarYtP/9998qVKiQzc+1ZPt9Ehsbq/Pnz8vX19emvytXruiff/6RJBUrVsz6PSClfCek9fvgbt8lR44c0bVr11S1alXreh8fnzsej+TkZJ0/f17FihVL0z6R8UgM02DNmjW6cuWKzYfrxo0bWr9+vUaMGCFJNr8cb67PkSOHYmJiFBgYqICAAPn5+Sk4OFgLFy602fZmAnEvOXPmtHl8+y+DW/f7yiuvqH379jbtN38JS1Lu3LlTPc/IphNeDh48KEnWL7Lb3bhxw2b44/b36vb3/W7vC1K+gG4/Sf7ml8GdPgM3j/uNGzesycWtbv3yatu2rWrXrq1Nmzbp+++/V//+/fXKK6+oR48ekqQPPvjAJsGRZJOEZle3vicffPCBOnXqpNdee03Lli2znmt4u5ufiTu9ZzdPm7j53t3++ZCyzmfkbnHe7XXffM0mk8nm9+ntSdbIkSP1wgsvaNOmTdq0aZOWLVumOXPm6MaNGypQoIAiIiJS9V+qVKlUbSaT6Y6nqdwa3/Xr11W+fHnNmTMn1XY3/9i90/uc1u+Du32X3Ol9v1OfN9vu9PmG42SNT6gDHT16VPv379fw4cO1cuVK6zJt2jQlJiZazyuJiYmxPuf48eO6dOmSPD099dVXX6lGjRqaMmWKunbtKi8vLx0/fvyeH7yH+ZC4ubnp5MmTcnV1tS7Lli3TDz/88MB9/pdFRESocuXKqlevni5evKgjR45Y1x0+fFiJiYmpEgqkPzc3Nx07dkznz5+3tu3Zs0e5cuXSE088ody5c1snbEkpXyi3Xmdu2rRpOnfunAIDAxUeHq4BAwZow4YNKliwoIoVK6aEhATr56F06dKaNGmSjh49mpkv8ZHn5OSkMWPG6MCBA1qwYIHc3Nzu+Zlwc3PT77//bnOu5u7du1W0aFEVLlzYAa8gc7i5uSk6Otqmbffu3dbfE7f/rN56BYuEhASNGjVKrq6uevXVVxUREaFatWrp22+/lZubmy5duiSTyWT9Wb1y5YomTpx4x6pq8eLFdfHixXt+l7i5uSk+Pl5Fixa19nny5EnNmDEjTd8zD/pddPMzu2/fPmvbrf+/6Z9//lGuXLlUpEiRB9oPMgaJ4X2sWbNGhQsXVpcuXeTh4WFdWrZsqQoVKlhnYS1atEibN29WTEyMhg4dqrp16+rJJ59U4cKFdfDgQe3du1dHjx7V+PHj9dtvv91z+CRv3rw6deqUzUzjtAoODtbatWu1aNEinThxQgsWLNCCBQtsSv73ki9fPh07duw/eeHRS5cuKSEhQX/99ZcOHjyosWPHau3atRo8eLDc3d3VoEEDhYaGau/evdq7d69CQ0NVo0YNeXh4ODr0/7y6deuqXLlyevvtt3Xw4EH99NNPGj16tFq3bq2CBQuqSpUqOn/+vD799FPFxcVp3LhxNrNEjxw5ovfee08xMTE6dOiQtmzZokqVKklK+UxMnz5d3377rY4dO6bhw4dr165dKl++vKNe7iPLy8tLnTp10pw5c+Ti4nLPz0SbNm1kNpv1zjvvKDY2Vps2bdLMmTMVGBj4n64Ade3aVQcOHNDUqVN19OhRrVixQp9//rleeOEFSVLVqlX1448/KioqSn/88Yfee+89a2WtUKFC2rhxo95//32dOHFCv/zyi2JiYlSpUiW5u7urfv36GjhwoPbu3avff/9dQ4YM0eXLl1WwYMFUcXh6eio5OVmxsbF3jbVevXoqW7asBg0apIMHD+rXX3/ViBEjlDdv3lSjUHeSN29eXbhwQceOHbPrdKP8+fOrQ4cOGjt2rKKjo7Vnzx6NHTtWkm2yefDgQT399NP/6Z+XrIjE8D7WrFmjNm3a3LHcHhgYqO3bt+vMmTN69tlnNXXqVAUGBqpEiRKaNm2apJTLB1SrVk3BwcHq2rWr4uPj1adPH+3fv/+u+2zXrp2OHj2qtm3b2j3EW61aNU2cOFGff/65WrZsqeXLl2vKlCmqUaNGmp7fuXNnbd26VT179rRrv1nB+++/r3r16qlBgwbq1q2bjh49qgULFsjf31+SNGHCBJUrV07BwcHq0aOHnnrqKc2ePdvBUWcPOXPmtA53Pffcc3rzzTfVpEkT64zMJ598UqGhoQoLC1P79u1lGIaaNWtmff7IkSNVvHhxBQUF6bnnnlPJkiU1bNgwSVKPHj3UqVMnvfPOO2rfvr3i4+M1f/58hpLv4o033lDu3Lk1adKke34mXFxc9NFHH+nEiRNq3769Ro8erZdffll9+/Z18CvIWGXKlFF4eLi2bt2qNm3aKCwsTIMHD1bHjh0lpfz+btasmV577TX17NlTrVu3VsmSJSWlVGXnzJmjmJgYtW3bVgMGDFCnTp3UuXNnSdLEiRP1+OOPKzg4WN26dZObm5umTp16xzgKFiwoLy8v7dy5866x5syZU2FhYUpOTtZzzz2nfv36qWHDhho+fHiaXmutWrXk6uqqNm3a6MCBA/YcJoWGhsrT01PBwcHq16+f9UoOtw4/79y5M9U5lXA8k5FdTy5LRwEBAerbt686dOjg6FAAANlEZGSkVq5c+UheD3LTpk2qXbu29YLYe/fuVdeuXbV7927lzp1bly9fVoMGDbRy5cpUk/rgWFQMAQDIglq3bq34+Hib80AfFbNmzdL777+v48ePa//+/Zo0aZICAgKsFcPVq1erUaNGJIWPIBJDAACyICcnJ40YMeKRPOVl8uTJOnnypNq3b69u3brp8ccft55naDab9dlnn3FP7kcUQ8kAAACQRMUQAAAAFiSGAAAAkERiCAAAAAsSQwAAAEgiMUQaBQQEyNPTU56enqpYsaJ8fHz0/PPPa+vWrY4OLcszm81avny59XFQUJBmzpxpdz+3Pm/w4MEaPHhwusV4U0BAgCIjI9O939vNnDlTQUFBGb6frO7AgQPatWuXQ/YdFRV1z7tuZLQH/Zw8iOTkZM2ePVuNGzeWn5+fXnnlFR0/fjxT9g1kNhJDpNnQoUO1bds2bdmyRcuWLVP16tUVEhKi7du3Ozq0LG3NmjWaO3duuvY5bNgw650/0tOXX36pli1bpnu/eDB9+vTRsWPHHLLv4OBgnT171iH7zmyLFi3SggULNGLECC1dulQ5c+ZUz549deXKFUeHBqQ7EkOkWYECBVSiRAmVKlVKHh4eevvtt9WqVSuNGzfO0aFlaRlxxagCBQqoQIEC6d5v0aJFlSdPnnTvF3iULVu2TN27d1dAQIAqVKigyZMn6/Tp0w6r1gIZicQQD6VLly76448/rMMqFy5c0IgRI1SnTh35+vpq0KBBunDhwh2f+8wzz+iTTz6xaWvTpo2++OILSdLu3bsVGBioatWqKSAgQEuWLLFud6ehUk9PT+3YseOO+zp37pwGDBig6tWrq27dupo6daoMw9DJkyfl6empkydPWre9dRgzMjJSzz//vPr06SNfX1+tWrVKQUFBGj16tJo0aaJGjRopMTFRf/75p3r37i1vb28FBARo1qxZunHjhrWPoKAgzZgxQzVr1pSfn5/GjRsnwzC0Y8cODRkyRKdOnUoVx59//qmKFSvq999/t3kdlSpVuu8w1s3jc+nSJVWtWlU//fSTdV1iYqKqVq2qX3/9VZK0ceNGtWzZUt7e3urUqZN+/vnnu/Z761ByUFCQwsLC1KNHD3l5ealZs2b3PLVg586dCgwMlLe3t6pVq6ZXXnlFf/311123v3btmkaNGqXq1aurTp06Nj8rycnJ+uijj9SkSRN5eXkpKChIBw8etK6//WchMjJSAQEB1sdTp05VvXr1rM89dOiQdd2vv/6qDh06yMvLS23atNE333xz1xhvd+bMGfXv3181atRQlSpV9Oyzz1rvZXvzZ2316tWqX7++/Pz8NGbMGF2/fl1Syh8Ic+fOVUBAgKpUqaJ69epp1qxZ1r5v/7nr0KGDTp06pSFDhmjw4MHasWOHAgIC9OWXX6pu3bqqUaOGPvzwQ/3yyy9q3ry5fHx89Pbbbys5Odm6v9mzZ6tevXry8/NT7969FR8fb3MMv/rqK7Vu3VpVqlRR165dFRcXJ0nWY/nSSy+leTg3MjJSLVq0kJeXlzp06KBffvnFui4gIECTJk1SvXr1rPfCvt3GjRvVrFkzVatWTe+995718/Ug/f/00092HavBgwfb3PI0b968ypUrlxITE9P02oGshMQQD8Xd3V2SdPjwYUlS3759deDAAc2dO1effPKJYmNj73quW6tWrWy+dGNjY3X06FE988wzio2N1csvv6waNWooMjJS/fr104QJE7Rx48YHirNPnz5KSEjQ4sWLNX36dEVGRuqzzz5L03N3796tChUqaPny5apXr56klC+hSZMmadasWcqfP7/69u2rYsWKacWKFRo3bpxWr15tMzy8e/duHT16VEuWLNGIESO0aNEibd++XT4+Pho6dKgee+wxbdu2TaVLl7Y+p3Tp0vL19bU5Rt98842efvppubq6pin2AgUKqH79+jbH7fvvv1fRokXl6+urmJgYhYaG6tVXX9WqVavUtm1bu86fmjt3rlq1aqWvv/5aFStW1IgRI6xfpre6dOmSQkJCVLduXX399deaP3++Tpw4oXnz5t2175v3VF25cqV69eql8ePHW89pmz17tj7++GMNHTpUK1asUNmyZdWzZ09dvnz5vjFv3LhRy5Yt0/Tp0/X111+rePHiGjJkiCQpISFBISEh6tChg1avXq2ePXtq8ODB1iT6fgYOHKgbN25o6dKlWrlypUqVKqWRI0fabDNr1ixNmzZNs2bN0oYNG6yJ1cqVK7Vw4UKNHTtW69evV58+fTRz5kybPwxu/bn7+OOP9dhjj2no0KHW0wb++usvbdq0SZ9++ql69+6tqVOn6v3339f48eM1depUrV27Vps3b5YkLV68WKtXr9aUKVO0bNkyFStWTN27d9e1a9es+5s5c6aGDRumyMhI/fPPP5o+fbqklFMKbq7v3r37fY9LZGSkRo8erZCQEK1cuVJ16tRRr169dObMGes2q1ev1vz58zV+/HiZTCab5x8+fFgDBgxQYGCgIiIidP36dWvC/aD923OsGjZsqFKlSln7mjdvnnLlyqWaNWve97UDWQ2JIR7KzeHKf//9VzExMfr55581adIkeXl5ycvLS5MmTdK33357x3t5tm7dWnv27NHp06clSevWrVO9evVUqFAhLV++XJUqVdKbb76p8uXL69lnn9WLL76ojz76yO4YY2JitHv3bo0fP16VKlVSjRo1NHLkSBUsWDBNzzeZTHr11Vfl7u6uokWLSpIaNWqk6tWrq0qVKvrpp58UHx+v0aNHq3z58qpZs6ZCQ0Ntbmx/48YN6/p27dqpYsWK+u233+Tk5KQCBQooZ86cKlGihHLmzGmz71atWmn9+vXWx+vWrVOrVq3sev2tWrXSxo0brVWYb775Ri1atJDJZNL8+fP13HPPqU2bNnJ1ddVLL72kBg0a2FRn76Vhw4bq0KGDnnjiCb366qv6888/lZCQkGq7K1eu6LXXXlOfPn1Urlw5+fr66plnnrGp1N2uVKlSGjJkiJ544gkFBwerYMGCOnjwoAzD0OLFi/X666+rSZMmcnd31+jRo5UzZ06tWrXqvjGfOnVKuXPnVpkyZfTEE09oxIgR1j9ePvvsM9WpU0cvvviiXF1d1a5dO3Xp0kULFy68b7+GYahp06YaMWKE3N3dVaFCBb3wwgvWP5puGjRokPz8/FSrVi29/vrrWr58uQzDUOnSpTVu3DjVrl1bjz/+uAIDA1WiRAmbY3Trz13hwoWVM2dOm9MGrl27ptDQUJUvX14vvPCCkpOT9cILL6hatWpq3Lixnn76aetn8aOPPtLbb7+tmjVryt3dXe+9954uXLhgU/Xt1q2bateuLQ8PDwUGBmrfvn2SZP0cFCpUSPnz57/vsfn0008VFBSk9u3bq3z58ho4cKA8PDy0ePFi6zZt27a1Tm67XUREhPz8/BQcHCx3d3eNGDFCJUuWfKj+7TlWt1q+fLlmzZql8ePHq1ChQvd97UBWk8vRASBruzmU4uLioiNHjqhgwYJyc3Ozrnd3d1ehQoV05MgRlS9f3ua57u7u8vT01Pr16xUcHKx169YpJCREUkr10MvLy2Z7Hx8fLV261O4Yjx49qsKFC6tcuXLWtqZNm0qSzdDt3RQrVizVeXVly5a1/j82Nlbnz5+Xr6+vtS05OVlXrlzRP//8Y+3DxcXFut7FxcU6hHgvzZs319ixY3XgwAGVKFFCu3bt0qRJk+77vFs1btxYw4YNU3R0tDw9PbV161Zr0hobG6t169Zp2bJl1u2vXbtmrYzez5NPPmnzmiTd8XWVKFFC7du314IFC3TgwAEdPnxYBw8eVPXq1e/a9+OPP25TOSpQoICuXr2qc+fO6fz58/L29rauy507t6pUqZKmWbKtWrXS4sWL1aRJE1WrVk1NmzZVp06dJElHjhzRd999Jx8fH+v2165ds/mZvhuTyaTAwECtXbtWu3bt0tGjR7Vv375UFdRbX3OVKlX0999/659//lGtWrUUHR2tKVOmKDY2VgcOHFBCQoLN82/9ububmz/nN39mb31Onjx5ZDab9e+//+r06dN64403lCPH/9cHrly5YjOZ5dbKtIuLi0010R6xsbHq06ePTVu1atVs3q97vbbY2Fg9/fTT1se5c+e2efyg/aflWN3q1KlTGj16tIYPH64mTZrcNV4gKyMxxEO5eV7XU089ZXOO161u3LiR6nygm1q1aqUNGzaofv36OnnypPWXrbOzc6ptk5OTrf2YTCab85DulWTlzp37rutuH7K6U193iuXWtuvXr6t8+fKaM2dOqu1uVnKcnJxSrUvLpJOiRYuqdu3a+uabb1SyZEl5e3vrscceu+/zbpUvXz41btxY33zzjc6cOaPixYtbk+4bN27olVdeUfv27W2ek9YJJnc6tnd6XWfOnFHHjh1VuXJl1alTR88995y+//57RUdH37Xv26unN/u+0/shpbyWOw1j31x3U4kSJbRu3Tr9+OOP+u677zR//nwtX75cK1eu1PXr19WmTRv17t3b5vm5ct3/V2VycrK6d++uixcvqmXLlgoICNC1a9fUt29fm+1uPWY34zWZTPriiy/0/vvvq3PnznrmmWcUGhqql156yea5d3vt94r11sTvppvH44MPPkiV9N5aBbvXZ8ced4r79vfrfq/t9p+rW2N70P7TcqxutX37dpUsWVKBgYH33A7IyhhKxkOJiIhQ5cqVVa5cObm5uenixYs2wy+HDx9WYmLiXSsurVu3VnR0tFauXKmGDRtah6Xc3NxSJQ27d++29pM7d279+++/1nU3T4q/E1dXV50/f15//vmntW3RokV67bXXrF8ut/aVlirirdzc3BQfH6+iRYvK1dVVrq6uOnnypGbMmHHHxPN299umdevW+u6777Rlyxa7h5FvatWqlbZs2aJNmzbZXG7Gzc1NJ0+etMbt6uqqZcuW6Ycffnig/dzNxo0bVahQIYWHh+vll1+Wn5+f4uLiHmhGdoECBVS8eHHt2bPH2nbt2jX9/vvvafr5+P777/XFF1+oUaNGGjVqlL766isdO3ZMf/zxh9zc3HT8+HGb47F582atXr36vnEdPnxYv/zyixYsWKDevXurUaNG1sk1t77OAwcOWP+/b98+lSxZUkWKFNGSJUvUp08fDR06VO3bt1eRIkV07ty5DJm1XrBgQRUrVkwJCQnW11m6dGlNmjRJR48eTff93enzHB0dnaZKrJTyh+dvv/1mfZycnKyYmJh06z+t8uXLpxYtWqRrn8CjhsQQaXbp0iUlJCTor7/+0sGDBzV27FitXbvWen6Wu7u7GjRooNDQUO3du1d79+5VaGioatSoIQ8Pjzv2WaZMGXl5eWnhwoU2SU/Xrl114MABTZ06VUePHtWKFSv0+eef64UXXpAkVa1aVT/++KOioqL0xx9/6L333rtrdeOpp55SrVq1NGzYMB08eFA7duzQvHnzVLduXRUvXlylS5fW/PnzFRcXp8jISH3//fd2HZd69eqpbNmyGjRokA4ePKhff/1VI0aMUN68ee9Y9bpd3rx5deHCBR07duyOlc+mTZvq2LFj+vnnn9W8eXO7YrupQYMG1pPtb00Mg4ODtXbtWi1atEgnTpzQggULtGDBApsh4vRQuHBhxcfHKyoqSnFxcZo3b542bNiQaqgurYKDgzVjxgx9++23io2N1YgRI3T16lXra6tataoWL16sY8eOafPmzTYX5U5OTtbEiRO1ceNGnTx5UpGRkcqbN6+efPJJde3aVfv27dO0adN07NgxrV69WlOnTlWZMmUkpVyMPCEh4Y4V8IIFCypHjhxas2aNTp06pfXr11snltz6OseOHavffvtN27dv1wcffGD9mS5SpIiioqKsQ9BvvPGGrl27ds9jlC9fPh05ckTnz59/oGM4ffp0ffvttzp27JiGDx+uXbt2pTrl4177PnTokC5duiQpZeLO3a7rFxwcrMWLF2vlypU6evSoJk+erJiYGOsQ/v0899xz2rdvn8LCwnTkyBFNmDDBZgb1w/afVo0aNUrTZBsgKyMxRJq9//77qlevnho0aKBu3brp6NGjWrBggfz9/a3bTJgwQeXKlVNwcLB69Oihp556SrNnz75nvy1btlSuXLnUqFEja1uZMmUUHh6urVu3qk2bNgoLC9PgwYPVsWNHSVK7du3UrFkzvfbaa+rZs6dat25tczL67SZNmqS8efOqS5cueuutt9SlSxd17dpVOXLk0NixY7V37161bNlS69evTzWMeD85c+ZUWFiYkpOT9dxzz6lfv35q2LChhg8fnqbn16pVS66urmrTpo1NNekmFxcXNWjQQNWqVVOxYsXsiu0mJycnNW3aVI899pjNyf3VqlXTxIkT9fnnn6tly5Zavny5pkyZoho1ajzQfu6mRYsWatu2rfr376+OHTtqx44dCg0NVWxs7AMlh927d1fnzp01YsQIdejQQadPn9ann35qnRQxYsQInT9/Xq1bt9ZHH32k/v37W58bEBCg/v37a9y4cWrRooXWrl2rOXPmqFChQipbtqzmzp2rrVu3qnXr1po+fboGDx6stm3bSkqpWterV8+m+nzTY489ppEjR+rDDz9U69atNW/ePA0fPly5cuXS/v37rdu1bNlSISEhevPNN9W5c2f16tVLUsoF5BMTE9WuXTv169dPnp6e+t///nfHn4mbAgMD9dlnn6X5Z+1WPXr0UKdOnfTOO++offv2io+P1/z589M8oSIoKEgTJ060Jr/16tXT2rVr77hty5Yt9cYbb2jGjBlq27atfv75Z3388cfWqxrcj6urq8LCwrRmzRq1b99eCQkJatiwYbr1n1Yff/xxuiebwKPGZGTEOAVgh2nTpun06dOaMGGCo0N5ZD3//PPq3LmzNTGG44SGhmrgwIEqUaKEXc+7eQ7t5s2b9fjjj2dQdI4TERGhPHnyPPDpDgAeDUw+gcPExMTowIED+vzzzxUWFubocB5JP/30k3bt2qXY2NgHHkZG+jlx4oT+/vtvu5PC/7rk5GStWLHCep1DAFkXiSEcZt++fRozZoy6du0qPz8/R4fzSPrqq6+0efNmvffee2m6XhwyVrly5fgj5g5y5MihTz75JN1mMQNwHIaSAQAAIInJJwAAALAgMQQAAIAkEkMAAABYkBgCAABAEokhAAAALEgMAQAAIInEEAAAABYkhgAAAJBEYggAAACL/wNfQ71QjUk5hwAAAABJRU5ErkJggg=="
},
"metadata": {},
"output_type": "display_data"
@@ -231,21 +231,21 @@
"plt.subplots(figsize=(8, 8))\n",
"df_2dhist = pd.DataFrame({\n",
" x_label: grp['Do you currently work?'].value_counts()\n",
- " for x_label, grp in df.groupby('Do you currently live in a house, apartnment, or dorm? ')\n",
+ " for x_label, grp in df.groupby('Do you currently live in a house, apartment, or dorm?')\n",
"})\n",
- "sns.heatmap(df_2dhist, cmap='viridis')\n",
- "plt.xlabel('Do you currently live in a house, apartnment, or dorm? ')\n",
+ "sns.heatmap(df_2dhist, cmap=\"Blues\")\n",
+ "plt.xlabel('Do you currently live in a house, apartment, or dorm?')\n",
"_ = plt.ylabel('Do you currently work?')"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:59.277945Z",
- "start_time": "2024-02-23T02:10:58.967433Z"
+ "end_time": "2024-02-23T07:21:07.323350Z",
+ "start_time": "2024-02-23T07:21:07.026106Z"
}
},
- "id": "201db70188d3e778",
- "execution_count": 6
+ "id": "49c9c5566ee982e1",
+ "execution_count": 43
},
{
"cell_type": "markdown",
@@ -253,7 +253,7 @@
"metadata": {
"collapsed": false
},
- "id": "8d65fec230193b72"
+ "id": "b9cfc986eba2ac99"
},
{
"cell_type": "code",
@@ -261,25 +261,25 @@
{
"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"
+ "image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
- "df.groupby('Do you currently live in a house, apartnment, or dorm? ').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
+ "df.groupby('Do you currently live in a house, apartment, 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"
+ "end_time": "2024-02-23T07:21:07.527959Z",
+ "start_time": "2024-02-23T07:21:07.324344Z"
}
},
- "id": "5e460707e32c4a2a",
- "execution_count": 7
+ "id": "9ddd30c1b2128081",
+ "execution_count": 44
}
],
"metadata": {
generated by cgit v1.2.3 (git 2.39.1) at 2025-07-13 16:11:24 +0000