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authorGravatar Anshul Gupta <ansg191@anshulg.com> 2024-02-23 18:23:57 -0800
committerGravatar Anshul Gupta <ansg191@anshulg.com> 2024-02-23 18:23:57 -0800
commitd2854b75fdd15f1313ca3a5cd1c4929ce868fb2f (patch)
tree6f2a2d49cfd1c377c84cfdb373bd74e1b9a159df /CS105MiniProject.ipynb
parent08f88717cf4f1c8e59240640a664476e6544303d (diff)
downloadCS105MiniProject-d2854b75fdd15f1313ca3a5cd1c4929ce868fb2f.tar.gz
CS105MiniProject-d2854b75fdd15f1313ca3a5cd1c4929ce868fb2f.tar.zst
CS105MiniProject-d2854b75fdd15f1313ca3a5cd1c4929ce868fb2f.zip
Revert "Merge branch 'main' into nlee097_csv_indexing_fixes"
This reverts commit 4abd16460633065cbad176e169144bdd1f2e2e59, reversing changes made to 70eacc7f9ebc996f58f50e967ddaf877a3a41315.
Diffstat (limited to 'CS105MiniProject.ipynb')
-rw-r--r--CS105MiniProject.ipynb278
1 files changed, 210 insertions, 68 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb
index 818356f..b8d92b8 100644
--- a/CS105MiniProject.ipynb
+++ b/CS105MiniProject.ipynb
@@ -12,7 +12,7 @@
"metadata": {
"collapsed": false
},
- "id": "845bdbd833f03cba"
+ "id": "21abd26c73fd0070"
},
{
"cell_type": "markdown",
@@ -22,15 +22,29 @@
"metadata": {
"collapsed": false
},
- "id": "d720609d765d221b"
+ "id": "69d8e8ad7c61ba61"
},
{
"cell_type": "code",
+ "execution_count": 1,
+ "id": "daa13044",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 614
+ },
+ "id": "daa13044",
+ "outputId": "4d440aaa-1ee7-4771-c526-f55e9458ca8a",
+ "ExecuteTime": {
+ "end_time": "2024-02-23T06:53:02.933496Z",
+ "start_time": "2024-02-23T06:53:02.907444Z"
+ }
+ },
"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, 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? \\\n0 6 \n1 4 \n2 4 \n3 1 \n4 1 \n.. ... \n255 5 \n256 North District 4 bed 2 bath \n257 9 \n258 4 \n259 3 (room), 8 (hall), ~70 (building) \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n1 3.7 No \n2 3.75 No \n3 3.81 No \n4 3.23 Yes \n.. ... ... \n255 4 Yes \n256 3.5 No \n257 3.7 No \n258 3 Yes \n259 4 No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 NaN \n2 NaN \n3 NaN \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 NaN \n257 1 - 5 \n258 5 - 10 \n259 NaN \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 No \n4 Yes \n.. ... \n255 No \n256 NaN \n257 No \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 11 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.7</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>North District 4 bed 2 bath</td>\n <td>3.5</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.7</td>\n <td>No</td>\n <td>1 - 5</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3 (room), 8 (hall), ~70 (building)</td>\n <td>4</td>\n <td>No</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 11 columns</p>\n</div>"
},
"execution_count": 1,
"metadata": {},
@@ -49,18 +63,9 @@
"df = pd.read_csv(\"data.csv\")\n",
"\n",
"# Select relevant columns\n",
- "df = df.iloc[:, [2, 3, 7, 8, 9, 58, 59, 60, 61, 26]]\n",
+ "df = df.iloc[:, [2, 3, 7, 8, 9, 34, 58, 59, 60, 61, 26]]\n",
"df"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-23T02:10:58.731970Z",
- "start_time": "2024-02-23T02:10:58.699969Z"
- }
- },
- "id": "3bea6ea662d6c063",
- "execution_count": 1
+ ]
},
{
"cell_type": "markdown",
@@ -70,15 +75,24 @@
"metadata": {
"collapsed": false
},
- "id": "7e69a5a21a9de4ee"
+ "id": "3f7614a5665d55b6"
},
{
"cell_type": "code",
+ "execution_count": 2,
+ "id": "29889175",
+ "metadata": {
+ "id": "29889175",
+ "ExecuteTime": {
+ "end_time": "2024-02-23T06:53:02.952629Z",
+ "start_time": "2024-02-23T06:53:02.936631Z"
+ }
+ },
"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, 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? \\\n0 6 \n1 4 \n2 4 \n3 1 \n4 1 \n.. ... \n255 5 \n256 4 \n257 9 \n258 4 \n259 3 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n1 3.70 No \n2 3.75 No \n3 3.81 No \n4 3.23 Yes \n.. ... ... \n255 4.00 Yes \n256 3.50 No \n257 3.70 No \n258 3.00 Yes \n259 4.00 No \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n1 0 \n2 0 \n3 0 \n4 10 - 20 \n.. ... \n255 10 - 20 \n256 0 \n257 0 \n258 5 - 10 \n259 0 \n\n Do you work on or off campus? \\\n0 Off-campus \n1 NaN \n2 NaN \n3 NaN \n4 Off-campus \n.. ... \n255 On-campus \n256 NaN \n257 Off-campus \n258 On-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n0 No \n1 NaN \n2 NaN \n3 NaN \n4 Yes \n.. ... \n255 No \n256 NaN \n257 NaN \n258 No \n259 NaN \n\n Do you have roommates that are part of your major? \n0 No \n1 Yes \n2 No \n3 No \n4 No \n.. ... \n255 No \n256 No \n257 No \n258 No \n259 Yes \n\n[260 rows x 11 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4.00</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.00</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>260 rows × 11 columns</p>\n</div>"
},
"execution_count": 2,
"metadata": {},
@@ -100,30 +114,47 @@
" .replace('North District 4 bed 2 bath', '4')\n",
" .replace('3 (room), 8 (hall), ~70 (building)', '3')\n",
" .astype(int))\n",
- "df['Who do you live with? '] = df['Who do you live with? '].replace('Family, Friends', 'Both').replace('Family, Friends, Both', 'Both')\n",
+ "df['Who do you live with? '] = df['Who do you live with? '].replace('Family, Friends', 'Both').replace(\n",
+ " 'Family, Friends, Both', 'Both')\n",
+ "df['Do you currently live in a house, apartnment, or dorm? '] = (\n",
+ " df['Do you currently live in a house, apartnment, or dorm? ']\n",
+ " .replace('house (renting)', 'House'))\n",
+ "\n",
+ "df.loc[df['What was your GPA your very first quarter at UCR?'].str.contains(\n",
+ " \"I am not sure|idk|I don't know|This is my first quarter|i don't rem|not sure|I never checked. |I dont know\") == True, 'What was your GPA your very first quarter at UCR?'] = np.nan\n",
+ "df['What was your GPA your very first quarter at UCR?'] = (\n",
+ " df['What was your GPA your very first quarter at UCR?']\n",
+ " .replace('Idk, I think 3.2 or something along those lines', '3.2')\n",
+ " .replace('2.8?', '2.8')\n",
+ " .replace('3 point something', '3.0')\n",
+ " .replace('3.67 I think', '3.67')\n",
+ " .replace('3.0?', '3.0')\n",
+ " .replace('about 3.0', '3.0')\n",
+ " .astype(np.float64))\n",
+ "\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",
"\n",
"df"
- ],
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "de4448fd64205d85",
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:58.744774Z",
- "start_time": "2024-02-23T02:10:58.732815Z"
+ "end_time": "2024-02-23T06:53:02.965372Z",
+ "start_time": "2024-02-23T06:53:02.954441Z"
}
},
- "id": "f71f8085d5f66b0",
- "execution_count": 2
- },
- {
- "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, 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? \\\n0 6 \n4 1 \n8 6 \n9 5 \n13 4 \n.. ... \n246 2 \n247 3 \n252 5 \n255 5 \n258 4 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n0 2.73 Yes \n4 3.23 Yes \n8 3.40 Yes \n9 NaN Yes \n13 3.50 Yes \n.. ... ... \n246 4.00 Yes \n247 3.60 Yes \n252 3.50 Yes \n255 4.00 Yes \n258 3.00 Yes \n\n How many hours do you work per week on average? \\\n0 5 - 10 \n4 10 - 20 \n8 10 - 20 \n9 1 - 5 \n13 10 - 20 \n.. ... \n246 10 - 20 \n247 20 - 40 \n252 20 - 40 \n255 10 - 20 \n258 5 - 10 \n\n Do you work on or off campus? \\\n0 Off-campus \n4 Off-campus \n8 On-campus \n9 On-campus \n13 Off-campus \n.. ... \n246 On-campus \n247 Off-campus \n252 Off-campus \n255 On-campus \n258 On-campus \n\n Do you work in a department related to your major? \\\n0 No \n4 Yes \n8 No \n9 No \n13 No \n.. ... \n246 Yes \n247 No \n252 No \n255 No \n258 No \n\n Do you have roommates that are part of your major? \n0 No \n4 No \n8 No \n9 No \n13 No \n.. ... \n246 No \n247 Yes \n252 No \n255 No \n258 No \n\n[77 rows x 11 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>House</td>\n <td>6</td>\n <td>2.73</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Graduate</td>\n <td>22</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.23</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Junior</td>\n <td>20</td>\n <td>Friends</td>\n <td>House</td>\n <td>6</td>\n <td>3.40</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Senior</td>\n <td>22</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>NaN</td>\n <td>Yes</td>\n <td>1 - 5</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Junior</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>246</th>\n <td>Graduate</td>\n <td>23+</td>\n <td>Family</td>\n <td>House</td>\n <td>2</td>\n <td>4.00</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>Yes</td>\n <td>No</td>\n </tr>\n <tr>\n <th>247</th>\n <td>Senior</td>\n <td>21</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>3</td>\n <td>3.60</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>252</th>\n <td>Junior</td>\n <td>20</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>3.50</td>\n <td>Yes</td>\n <td>20 - 40</td>\n <td>Off-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>255</th>\n <td>Junior</td>\n <td>21</td>\n <td>Friends</td>\n <td>House</td>\n <td>5</td>\n <td>4.00</td>\n <td>Yes</td>\n <td>10 - 20</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n <tr>\n <th>258</th>\n <td>Sophomore</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.00</td>\n <td>Yes</td>\n <td>5 - 10</td>\n <td>On-campus</td>\n <td>No</td>\n <td>No</td>\n </tr>\n </tbody>\n</table>\n<p>77 rows × 11 columns</p>\n</div>"
},
"execution_count": 3,
"metadata": {},
@@ -136,24 +167,24 @@
"# Not working DataFrame\n",
"nw_df = df[df['Do you currently work?'] == 'No']\n",
"w_df"
- ],
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "5fe8ec7f22878e60",
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:58.754973Z",
- "start_time": "2024-02-23T02:10:58.746452Z"
+ "end_time": "2024-02-23T06:53:02.975332Z",
+ "start_time": "2024-02-23T06:53:02.968284Z"
}
},
- "id": "6c1d9ee7948e6b9a",
- "execution_count": 3
- },
- {
- "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, 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? \\\n1 4 \n2 4 \n3 1 \n5 4 \n6 4 \n.. ... \n253 6 \n254 5 \n256 4 \n257 9 \n259 3 \n\n What was your GPA your very first quarter at UCR? Do you currently work? \\\n1 3.70 No \n2 3.75 No \n3 3.81 No \n5 4.00 No \n6 4.00 No \n.. ... ... \n253 4.00 No \n254 3.80 No \n256 3.50 No \n257 3.70 No \n259 4.00 No \n\n How many hours do you work per week on average? \\\n1 0 \n2 0 \n3 0 \n5 0 \n6 0 \n.. ... \n253 0 \n254 0 \n256 0 \n257 0 \n259 0 \n\n Do you work on or off campus? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 Off-campus \n259 NaN \n\n Do you work in a department related to your major? \\\n1 NaN \n2 NaN \n3 NaN \n5 NaN \n6 NaN \n.. ... \n253 NaN \n254 NaN \n256 NaN \n257 NaN \n259 NaN \n\n Do you have roommates that are part of your major? \n1 Yes \n2 No \n3 No \n5 No \n6 No \n.. ... \n253 No \n254 Yes \n256 No \n257 No \n259 Yes \n\n[183 rows x 11 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>What is your current class standing?</th>\n <th>What is your age?</th>\n <th>Who do you live with?</th>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th>How many people live in your household?</th>\n <th>What was your GPA your very first quarter at UCR?</th>\n <th>Do you currently work?</th>\n <th>How many hours do you work per week on average?</th>\n <th>Do you work on or off campus?</th>\n <th>Do you work in a department related to your major?</th>\n <th>Do you have roommates that are part of your major?</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>Junior</td>\n <td>20</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Junior</td>\n <td>23+</td>\n <td>Friends</td>\n <td>House</td>\n <td>4</td>\n <td>3.75</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Senior</td>\n <td>23+</td>\n <td>Neither</td>\n <td>Apartment</td>\n <td>1</td>\n <td>3.81</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Junior</td>\n <td>21</td>\n <td>Both</td>\n <td>Apartment</td>\n <td>4</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Sophomore</td>\n <td>19</td>\n <td>Friends</td>\n <td>Apartment</td>\n <td>4</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>253</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>6</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>254</th>\n <td>Junior</td>\n <td>19</td>\n <td>Family</td>\n <td>House</td>\n <td>5</td>\n <td>3.80</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n <tr>\n <th>256</th>\n <td>NaN</td>\n <td>21</td>\n <td>Family</td>\n <td>Apartment</td>\n <td>4</td>\n <td>3.50</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Senior</td>\n <td>21</td>\n <td>Family</td>\n <td>House</td>\n <td>9</td>\n <td>3.70</td>\n <td>No</td>\n <td>0</td>\n <td>Off-campus</td>\n <td>NaN</td>\n <td>No</td>\n </tr>\n <tr>\n <th>259</th>\n <td>Sophomore</td>\n <td>18</td>\n <td>Friends</td>\n <td>Dorm</td>\n <td>3</td>\n <td>4.00</td>\n <td>No</td>\n <td>0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Yes</td>\n </tr>\n </tbody>\n</table>\n<p>183 rows × 11 columns</p>\n</div>"
},
"execution_count": 4,
"metadata": {},
@@ -162,26 +193,17 @@
],
"source": [
"nw_df"
- ],
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-23T02:10:58.763694Z",
- "start_time": "2024-02-23T02:10:58.755858Z"
- }
- },
- "id": "34f69a756f513fb7",
- "execution_count": 4
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "# Analysis"
- ],
+ "id": "899d85626b77db20",
"metadata": {
"collapsed": false
},
- "id": "d5c1424ddd30ca97"
+ "source": [
+ "# Analysis"
+ ]
},
{
"cell_type": "code",
@@ -208,11 +230,11 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:58.965154Z",
- "start_time": "2024-02-23T02:10:58.764461Z"
+ "end_time": "2024-02-23T06:53:03.191673Z",
+ "start_time": "2024-02-23T06:53:02.976617Z"
}
},
- "id": "da1811cc63b41845",
+ "id": "6bc50ddc195d88a",
"execution_count": 5
},
{
@@ -221,18 +243,20 @@
{
"data": {
"text/plain": "<Figure size 800x800 with 2 Axes>",
- "image/png": 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"
+ "image/png": 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NcY9hMqclhocOHXLWqQEAAHAXDKgDAABAUiaYlQwAAOBsfPNJMiqGAAAAkETFEAAAgMknVlQMAQAAIInEEAAAAFYMJQMAgGyPoeRkVAwBAAAgiYohAAAAFUMrKoYAAACQRMUQAACAiqEVFUMAAABIIjEEAACAFUPJAAAg2+O7kpNRMQQAAIAkKoYAAABMPrGiYggAAABJJIYAAACwYigZAABkewwlJ6NiCAAAAElUDAEAAKgYWlExBAAAgCQqhgAAAFQMragYAgAAQBKJIQAAAKwYSgYAANmewVCyJCqGAAAAsKJiCAAAsj2LqBhKVAwBAABgRWIIAAAASQwlAwAAsI6hFRVDAAAASKJiCAAAwHI1VlQMAQAAIImKIQAAAPcYWlExBAAAgCQSQwAAAFgxlAwAALI9Jp8ko2IIAAAASVQMAQAAmHxiRcUQAAAAkkgMAQAAYEViCAAAsj3DcN7mCLPZrA8//FDVqlVTrVq1NHHiRBnWTg4cOKC2bdvKx8dHrVu31v79+x2+DiSGAAAAWcSIESP0888/a/bs2ZowYYKWLl2qJUuW6OrVq+rRo4f8/f0VFRUlX19fhYSE6OrVqw71z+QTAACQ7VmU+SefXLx4UZGRkZozZ468vb0lSa+//rr27t2rnDlzytXVVe+//75MJpMGDRqkH3/8UWvXrlWrVq1SfQ4qhgAAAFnAzp075e7uroCAAFtbjx49NGrUKO3du1d+fn4ymZITXJPJpKpVq2rPnj0OnYPEEAAAZHuGYXLaZjablZCQYLeZzeYUMcbGxqp06dJavny5GjVqpAYNGmj69OmyWCyKi4tT8eLF7Y4vUqSIzp4969B1YCgZAADAiSIiIjRt2jS7ttDQUPXq1cuu7erVqzpx4oQWL16sUaNGKS4uTkOHDlWePHmUmJgoFxcXu+NdXFzummDeD4khAACAE4WEhKhLly52bXcmeZKUM2dOJSQkaMKECSpdurQk6cyZM1q0aJHKli2bIgk0m83KnTu3Q7GQGAIAgGzPmd984uLictdE8E7FihWTq6urLSmUJA8PD/31118KCAhQfHy83fHx8fEphpcfhHsMAQAAsgAfHx9dv35dx44ds7UdPXpUpUuXlo+Pj3bv3m1b09AwDO3atUs+Pj4OnYPEEAAAZHtZYYHrJ554QvXq1dOAAQN06NAhbdmyRbNmzVKHDh3UqFEjXb58WSNHjlR0dLRGjhypxMRENW7c2KHrQGIIAACQRYwfP16PP/64OnTooLCwML388svq1KmT3N3dFRERoZ07d6pVq1bau3evZs2aJTc3N4f65x5DAACALCJfvnwaO3bsXfd5e3tr2bJl/6p/EkMAAJDtGU6cfJKZMJQMAAAASVQMAQAAqBhaUTEEAACAJBJDAAAAWDGUDAAAsj1nfvNJZkLFEAAAAJKoGAIAADj0DST/ZVQMAQAAIImKIQAAAMvVWFExBAAAgCQSQwAAAFgxlAwAALI9hpKTUTEEAACAJCqGAAAAYrWaZFQMAQAAIInEEAAAAFYMJQMAgGyPySfJqBgCAABAkgMVw5iYGH377bdKSEhQw4YN5e/vn55xAQAAZBxmn0hKZcXw559/VsuWLbVt2zYdO3ZMnTt31oQJE9I7NgAAAGSgVFUMJ0yYoJCQEIWGhkqSfvnlF7311lv666+/1K9fP5UoUUIWi0Vnz55VqVKl0jVgAACAtMY9hslSVTE8evSomjdvbnscEBCgefPm6dChQ6pXr57eeOMNXbhwQQ0aNEi3QAEAAJC+UlUxfOyxx/T999/rtddes7U9/fTTWrlypWJjY2U2m1WgQAHNmzcv3QIFAABA+kpVxbB3794aO3asunbtqkOHDtnaTSaTHn/8cVWoUEG5cuVSQEBAugUKAACQXgzDeVtmkqrE8Pnnn9fixYtVpkwZWSyW9I4JAAAATpDq5WoqV66sypUrKyEh4Z7HbNy4Uc8991yaBAYAAJBRmHySzOEFrjt16qQLFy7YtcXGxqp79+7q06dPWsUFAACADOZwYlimTBl16NBBZ86ckdls1ieffKImTZro2rVrioyMTI8YAQAAkAEc/q7kTz75RCNHjlT79u3l4uIii8WiMWPGqHHjxukRHwAAQPpjKFnSQySGJpNJgwcP1qOPPqrJkyfrs88+U40aNdIjNgAAAGSgVCWGQUFBMpnunkmHhISoaNGitsebNm1Km8gAAAAySGZbNsZZUpUY9urVK73jAAAAgJOlKjF88cUXbX9/66239N5776l8+fLpFhQAAECGomIo6SFmJe/atUs5czp8ayIAAAAyOYczvI4dO+qdd95R+/btVapUKbm6utrtr1atWpoFBwAAgIzjcGI4Y8YMSdLQoUNT7DOZTDp48OC/jwoAACAD8c0nyRxODA8dOpQecQAAAMDJHupmwWvXrmnFihWKiYlRUlKSnnjiCQUHB6tgwYJpHB4AAEAGYPKJpIeYfPLnn3/qhRdeUHh4uM6cOaMzZ84oIiJCjRs3VnR0dHrECAAAgAzgcMVw5MiRCgwM1PDhw22zk2/evKnBgwfr448/1ueff57mQQIAACD9OVwx3LNnj7p37263ZE3OnDnVvXt37d69O02DAwAAyAiGYXLalpk4nBgWK1ZMJ0+eTNF+8uRJ5c2bN02CAgAAQMZzeCi5ffv2Gjx4sN5++215e3tLkvbu3aspU6aobdu2aR4gAABAumPyiaSHSAy7du2qxMREjR8/XpcuXZIkFS1aVJ07d9brr7+e5gECAAAgYzicGJ46dUq9evVSr169dP78ebm6usrd3T09YgMAAMggmeteP2dxODFs3LixSpcurTp16qhOnTqqXr16esQFAACADOZwYvjLL7/ol19+0bZt2zR+/HidPHlSfn5+tkSxQoUK6REnAAAA0pnDiaGbm5vq1aunevXqSZKOHj2q6dOna+zYsRo7dizflQwAALIeJp9IeojE8PTp09q1a5d27dqlnTt36ujRo/Lw8FC7du3k7++fHjECAAAgAzicGDZo0EA5cuRQ3bp19fbbb8vf318FChRIj9gAAAAyBhVDSQ+RGI4dO1a//fabfv31Vw0YMEBVqlSRv7+/qlatKm9vb7m4uKRHnAAAAEhnDieGzZs3V/PmzSVJ58+f12+//abNmzdr2rRpMplM2rt3b5oHCQAAgPTncGIoSQkJCdq5c6d27NihHTt26PDhw3rqqadUu3bttI4PAAAg/WWy7yx2FocTw9atW+vw4cMqWrSoAgMD1bVrV9WqVUsFCxZMh/AAAACQURxODJs1a6axY8eqfPny6REPAABAhjOYfCJJyuHoEzp37kxSCAAA4AQbNmyQl5eX3da7d29J0oEDB9S2bVv5+PiodevW2r9/v8P9P9Q9hgAAAP8pWaRiGB0drfr162v48OG2NldXV129elU9evRQs2bNNHr0aC1atEghISHasGGD3NzcUt2/wxVDAAAAOEdMTIw8PT1VrFgx25Y/f36tXr1arq6uev/991W+fHkNGjRIefPm1dq1ax3q3+HEMDY21tGnAAAAIA3ExMSoXLlyKdr37t0rPz8/mUzJs6tNJpOqVq2qPXv2ONS/w4lho0aN1LZtW82dO1fnzp1z9OkAAACZj2Fy2mY2m5WQkGC3mc3mlCEaho4dO6atW7eqYcOGeu655zR+/HiZzWbFxcWpePHidscXKVJEZ8+edegyOHyP4ZYtW7Ru3TqtWbNG48ePV5UqVRQcHKxGjRqpcOHCjnYHAACQrUVERGjatGl2baGhoerVq5dd25kzZ5SYmCgXFxdNnjxZp06d0ogRI3Tt2jVb++1cXFzummDej8OJYeHChdWhQwd16NBB58+f1/r167V582aNHz9evr6+atq0qRo1aqQ8efI42jUAAIBTmJw4+SQkJERdunSxa7vbVwyXLl1aO3bsUIECBWQymfTUU0/JYrGoX79+CggISJEEms1m5c6d26FY/tXkk7i4OMXFxens2bOyWCzKmzevli5dqnr16mn9+vX/pmsAAIBswcXFRe7u7nbb3RJDSSpYsKDtPkJJKl++vK5fv65ixYopPj7e7tj4+PgUw8sP4nBiePDgQU2aNEkNGzZUmzZttH//fnXp0kU//fSTpkyZokWLFum1117TkCFDHO0aAAAA97BlyxZVr15diYmJtraDBw+qYMGC8vPz0+7du2VYV+o2DEO7du2Sj4+PQ+dweCi5VatW8vPzU+fOndWoUSMVKlQoxTF+fn7MXgYAAFlHFljH0NfXV66urho8eLB69uyp2NhYjR07Vt26dVOjRo00YcIEjRw5Uu3bt9fixYuVmJioxo0bO3QOhxPD77//Xo8++uh9j6levbqqV6/uaNcAAAC4B3d3d82ePVsff/yxWrdurbx586p9+/bq1q2bTCaTIiIiNGzYMC1dulReXl6aNWuWQ4tbS6lMDO+cKXM/oaGhDgUAAADgdIbpwcdkAk8++aTmzJlz133e3t5atmzZv+o/VYnhjh07UtXZ7TdDAgAAIGtJVWK4YMEC29/PnDmjRx99VDly2M9bSUpK0qFDh9I2OgAAgIyQBe4xzAgOz0pu0KCBLl68mKL91KlT6tixY1rEBAAAACdIVcXwq6++0syZMyUlT39u3bp1iorh5cuXVb58+bSPEAAAABkiVYlhy5YtlStXLlksFg0cOFBdunRRvnz5bPtNJpPy5MmjGjVqpFugAAAA6YahZEmpTAxz5cqlli1bSpIee+wxVa1aVTlzOrzSDQAAADIxh7O7gIAAbdu2Tb///rtu3LhhW2H7FparAQAAWQ4VQ0kPkRiOHj1a8+fPV8WKFZU3b167fSxXAwAAkHU5nBhGRkZq9OjRat68eXrEAwAAACdxODF85JFH5O3tnR6xAAAAOEcW+eaT9ObwOoYvv/yypk6dqqtXr6ZHPAAAAHAShyuGv/zyi3bv3q21a9eqSJEiypUrl93+TZs2pVlwAAAAGcHE5BNJD5EYtmrVSq1atUqPWAAAAOBEDieGL774ou3vly5dUr58+WQymZiRDAAAkMU5fI+hYRgKDw9X9erVVbNmTZ0+fVr9+vXT0KFDZTab0yNGAACA9GU4cctEHE4Mp0+frhUrVmj06NFycXGRlFxF/OmnnzR27Ng0DxAAAAAZw+HEcNmyZfroo49Uv3592/BxYGCgxowZozVr1qR5gAAAAMgYDieG58+fV/HixVO058+fnyVsAAAAsjCHE8MaNWpo9uzZdm0JCQmaOHGiqlevnmaBAQAAZBST4bwtM3E4Mfzggw904MABBQYG6vr163rrrbf07LPP6vTp0xo8eHB6xAgAAIAM4PByNfnz59fXX3+tbdu26ejRo7p586Y8PDxUu3Zt5cjhcJ4JAACATMLhxLBp06aaNm2aatasqZo1a6ZHTP/aM9PedHYIyECPf3fA2SEgAx0rU8nZISADbTo3ytkhZDiTSSpaNJ/i46/IyGTDjP9pfFeypIcYSs6RI4du3LiRHrEAAADAiRyuGNarV09dunRR/fr1Vbp0adtahreEhoamWXAAAAAZguqspIdIDA8fPqynn35af//9t/7++2+7fXwtHgAAQNblcGLYrl07BQYGqlChQukRDwAAAJzE4XsMP/zwQ128eDEdQgEAAHASvitZ0kMkhtWrV9fKlStlNpvTIx4AAAA4icNDyefPn9eMGTM0c+ZMFS5cWK6urnb7N23alGbBAQAAZITM9g0kzuJwYvjSSy/ppZdeSo9YAAAA4EQOJ4YvvvhiesQBAADgPFQMJT1EYtipU6f7Lkszf/78fxUQAAAAnMPhxLB69ep2j2/evKnY2Fht3rxZb77JV9EBAABkVQ4nhvf6ZpOoqCitX79eXbt2/ddBAQAAZCiGkiU9xHI191KtWjVt27YtrboDAABABnO4YnjmzJkUbf/8849mz56t0qVLp0lQAAAAGYnlapI5nBgGBQXJZDLJMAzbJBTDMFSyZEl9/PHHaR4gAAAAMobDieGdC1ibTCblypVLRYsWve9sZQAAAGRuDt9jWLp0af3www/avXu3SpcurVKlSunDDz/U4sWL0yM+AACA9GeYnLdlIg4nhpMmTVJ4eLjc3NxsbQEBAZoxY4amT5+epsEBAAAg4zicGEZGRmry5MkKCgqytb366qsaP368lixZkqbBAQAAZAjDiVsm4nBimJiYKHd39xTthQoV0pUrV9IkKAAAAGQ8hxPDOnXqaOTIkXbL1pw7d05jxoxR7dq10zQ4AACAjGAynLdlJg4nhkOHDtWNGzfUoEED1ahRQzVq1FC9evVksVg0bNiw9IgRAAAAGcDh5WoKFy6sxYsX6/Dhwzp27Jhy5sypcuXKqUKFCukRHwAAADKIw4nhLV5eXvLy8krLWAAAAJwjkw3pOkuafVcyAAAAsraHrhgCAAD8V2S2SSDOQsUQAAAAkh6iYvjrr7/ed3+1atUeOhgAAAA4j8OJYadOne7a7uLiomLFimnTpk3/OigAAIAMxVCypIdIDA8dOmT3OCkpSSdPntTw4cPVrFmzNAsMAAAAGetf32P4yCOPyMPDQ/3799cnn3ySFjEBAABkLL4rWVIaTj45f/68Ll++nFbdAQAAIIM5PJQ8YMCAFG3//POPfv75ZzVq1ChNggIAAMhILFeTLE3WMSxYsKDCwsLUokWLtOgOAAAATuBwYjhq1Kj0iAMAAABO9lD3GG7cuFHt27dXQECA/Pz81KZNGy1fvjyNQwMAAMC99OjRQ/3797c9PnDggNq2bSsfHx+1bt1a+/fvd7hPhxPDxYsXq1+/fqpWrZpGjx6tMWPGKCAgQB9++KG++uorhwMAAACAY1atWqXNmzfbHl+9elU9evSQv7+/oqKi5Ovrq5CQEF29etWhfh0eSv7ss880bNgwtWzZ0tb23HPP6cknn9TMmTPVtm1bR7sEAABwriw0+eTixYsaO3asKleubGtbvXq1XF1d9f7778tkMmnQoEH68ccftXbtWrVq1SrVfTtcMTx//ryqVKmSot3X11d//fWXo90BAADAAWPGjFGLFi1UoUIFW9vevXvl5+cnk8kkSTKZTKpatar27NnjUN8OJ4ZPPfXUXe8nXLZsmV2AAAAAeDCz2ayEhAS7zWw23/XYbdu26bffftNbb71l1x4XF6fixYvbtRUpUkRnz551KBaHh5L79eunzp07a8eOHfLx8ZEk7dmzR4cOHdLMmTMd7Q4AAMDpnLmOYUREhKZNm2bXFhoaql69etm1Xb9+XcOGDdPQoUOVO3duu32JiYlycXGxa3NxcblngnkvDieGvr6+ioqK0tKlSxUTEyNXV1dVq1ZNkyZNUsmSJR3tDgAAIFsLCQlRly5d7NruTPIkadq0aXrmmWdUp06dFPtcXV1TJIFmszlFAvkgDieGkZGRatiw4V2/AQUAACBLcmLF0MXF5a6J4J1WrVql+Ph4+fr6SpItEVy3bp2aNm2q+Ph4u+Pj4+NTDC8/iMOJ4dy5c/Xhhx8qMDBQTZs2VVBQkPLkyeNoNwAAAHDAggULdPPmTdvj8ePHS5L69u2rX3/9VZ9++qkMw5DJZJJhGNq1a5feeOMNh87h8OSTlStXatmyZXr66acVHh6uWrVqqU+fPtqwYYPD49gAAACZguHELZVKly6tsmXL2ra8efMqb968Klu2rBo1aqTLly9r5MiRio6O1siRI5WYmKjGjRs7dBke6ptPypcvr9DQUH377bf6+uuv9fjjj6tfv36qVauWBgwYoF27dj1MtwAAAHgI7u7uioiI0M6dO9WqVSvt3btXs2bNkpubm0P9ODyUfMu5c+e0bt06rV+/Xnv27JG3t7eCg4MVFxenN998Uy+99JLee++9h+0eAAAA9zF69Gi7x97e3lq2bNm/6vOh7jFct26d9u7dK09PTzVp0kTjxo2zm5Fcrlw5ffTRRySGAAAgS3DmcjWZicOJ4aJFi9SkSRONGDFC5cuXv+sxlSpV0uDBg/91cAAAAMg4DieG69ate+AxXl5e8vLyeqiAAAAAMhwVQ0kPOfkEAAAA/z0khgAAAJD0L2YlAwAA/Fcw+STZQyeGx48fV0xMjCwWizw8PFShQoW0jAsAAAAZzOHE8PLlyxowYIA2bdqkAgUKKCkpSf/884+qVaum6dOnK1++fOkRJwAAQPqhYijpIe4xHDFihM6ePavVq1drx44d+u2337Ry5UpdvXpVo0aNSo8YAQAAkAEcrhh+9913mjNnjp544glbW4UKFTR06FB17949TYMDAADIEFQMJT1ExdDV1VU5cqR8mslkUlJSUpoEBQAAgIzncGIYFBSkDz/8UCdPnrS1HT9+XCNGjNCzzz6bpsEBAAAg4zg8lNyvXz/17NlTL7zwggoUKCApeUJKnTp1NGTIkDQPEAAAIL2xXE0yhxPD/Pnza8GCBTp8+LBiYmLk6uoqDw8Pu3sOAQAAkPU4nBjGxsYqOjpa//zzj9zd3fXkk0+qdOnS6REbAABAxqBiKMmBxHDbtm0aNWqUjhw5IsP4/6tnMpn09NNPq3///vL390+XIAEAAJD+UjX5ZOvWrerWrZsqVqyoBQsWaPv27frjjz+0Y8cOzZ07V0888YS6dOmi3bt3p3e8AAAASCepqhhOnz5dnTt3Vr9+/ezaCxQooOrVq6t69eoqUKCAwsPDNWvWrHQJFAAAIN0wlCwplRXDQ4cO6cUXX7zvMW3bttWBAwfSJCgAAABkvFRVDK9du2ZbmuZeChUqpAsXLqRJUAAAABmJ5WqSpapiaBjGXb/t5HYmk8luUgoAAACyllTPSl6zZo3c3d3vuf/KlStpEhAAAACcI1WJYalSpfT5558/8LiSJUv+64AAAAAyHIOeklKZGH733XfpHQcAAACczOFvPgEAAPivYfJJslRNPgEAAMB/HxVDAAAAKoaSqBgCAADAisQQAAAAkhhKBgAAYCjZioohAAAAJFExBAAAkMnZAWQSVAwBAAAgicQQAAAAVgwlAwAAMPlEEhVDAAAAWFExBAAA2R7flZyMiiEAAAAkUTEEAADgHkMrKoYAAACQRGIIAAAAK4aSAQAAGEqWRMUQAAAAVlQMAQBAtsdyNcmoGAIAAEASiSEAAACsGEoGAABgKFkSFUMAAABYUTEEAADZHpNPklExBAAAgCQqhgAAANxjaEXFEAAAAJJIDAEAAGDFUDIAAMj2mHySjIohAAAAJFExBAAAYPKJFRVDAACALOLEiRPq2rWrfH19Va9ePX322We2fbGxsercubOqVKmi4OBgbd261eH+SQwBAACyAIvFoh49eqhQoUJatmyZPvzwQ4WHh2vlypUyDEM9e/ZU0aJFFRkZqRYtWig0NFRnzpxx6BwMJQMAAGSBoeT4+Hg99dRT+uCDD+Tu7q5y5cqpZs2a2rlzp4oWLarY2FgtXrxYbm5uKl++vLZt26bIyEj16tUr1eegYggAAJAFFC9eXJMnT5a7u7sMw9DOnTv166+/KiAgQHv37lWlSpXk5uZmO97Pz0979uxx6BxUDAEAQLbnzOVqzGazzGazXZuLi4tcXFzu+ZygoCCdOXNG9evXV8OGDfXxxx+rePHidscUKVJEZ8+edSgWKoYAAABOFBERIT8/P7stIiLivs+ZMmWKZs6cqYMHD2rUqFFKTExMkUi6uLikSDgfhIohAACAEyuGISEh6tKli13b/aqFklS5cmVJ0vXr19W3b1+1bt1aiYmJdseYzWblzp3boVioGAIAADiRi4uL3N3d7ba7JYbx8fHauHGjXVuFChV048YNFStWTPHx8SmOv3N4+UFIDAEAALKAU6dOKTQ0VOfOnbO17d+/X4ULF5afn5/++OMPXbt2zbZv586d8vHxcegcJIYAACDbMxmG07bUqly5sp5++mkNHDhQ0dHR2rx5s8aNG6c33nhDAQEBKlmypAYMGKAjR45o1qxZ2rdvn9q0aePQdSAxBAAAyAIeeeQRzZgxQ3ny5FG7du00aNAgderUSa+++qptX1xcnFq1aqUVK1Zo+vTpKlWqlEPnYPIJAABAFljgWpJKlCihadOm3XVf2bJltXDhwn/VPxVDAAAASCIxBAAAgBVDyQAAINtz5jefZCZUDAEAACCJiiEAAECWmXyS3qgYAgAAQBIVQwAAAO4xtKJiCAAAAEkkhgAAALBiKBkAAIChZElUDAEAAGBFxRAAAGR7TD5JRsUQAAAAkkgMAQAAYMVQMgAAAEPJkqgYAgAAwIqKIQAAyPaYfJKMiiEAAAAkUTEEAACQDEqGEhVDAAAAWJEYAgAAQBJDyQAAAEw+saJiCAAAAElUDAEAAFjg2oqKIQAAACSRGAIAAMCKoWQAAJDtmSzOjiBzoGIIAAAASZk0Mbxw4YIMViAHAAAZxXDilok4PTE8d+6c3nnnHR08eFDXr1/XK6+8osDAQAUFBenQoUPODg8AACDbcHpi+MEHH+jChQsqWLCgoqKi9Oeff2rx4sUKCgrS8OHDnR0eAABAtuH0ySfbt29XVFSUSpYsqY0bN6pBgwby8fFR4cKF1bRpU2eHBwAAsgG++SSZ0yuGrq6uun79ui5duqQdO3aoXr16kqRTp06pQIECzg0OAAAgG3F6xfC5555Tnz59lDt3bhUoUED16tXT6tWr9fHHH+vFF190dngAACA7YNKrpEyQGH7wwQdauHChTp8+rXbt2snV1VVms1lvvPGGXn75ZWeHBwAAkG04PTHMmTOnOnfuLEm6dOmSLBaLWrRoIZPJ5NzAAABAtsE9hsmcfo+hYRgKDw9X9erVVbNmTZ0+fVr9+vXT0KFDZTabnR0eAABAtuH0xHD69OlasWKFRo8eLRcXF0nSiy++qJ9++kljx451cnQAAGcymbLnll1fO5zP6UPJy5Yt0+jRo1WtWjXb8HFgYKDGjBmjt99+W4MHD3ZyhAAAZylaNJ+zQ3CaIkWy72t3CoaSJWWCxPD8+fMqXrx4ivb8+fPr6tWrTogIAJBZxMdfcXYIGc5kSk4Kz5+/ku0mymbn/whkFk4fSq5Ro4Zmz55t15aQkKCJEyeqevXqTooKAJAZGEb23LLra3cmk+G8LTNxSmLYsWNHxcTESEperubAgQMKDAzU9evX9dZbb+nZZ5/V6dOnGUYGAADIQE4ZSnZ1dVXLli31+uuvq2fPnvr666+1bds2HT16VDdv3pSHh4dq166tHDmcXtAEAADINpySGM6ZM0fr16/X6NGjtWbNGn3wwQeqVauWatas6YxwAABAdufssexMwmmTT1544QU9++yz+vTTTxUaGqqgoCD17NlTrq6udseVKlXKSRECAABkL06dlezq6qrQ0FBVrFhRffr00apVq2z7DMOQyWTSwYMHnRghAADIDjLbJBBncWpiePr0aY0dO1YbNmxQ06ZNFRISoty5czszJAAAgGzLKYnh9evXNXPmTM2ZM0dlypTR/Pnz5e/v74xQAAAAWODayimJYcOGDZWQkKA+ffqoU6dOeuSRR5wRBgAAAG7jlMTQz89PYWFhd/3GEwAAADiHUxLDCRMmOOO0AAAAd8Xkk2SsIA0AAABJTp6VDAAAkClYKBlKVAwBAABgRWIIAAAASQwlAwAAsI6hFRVDAACALOLcuXPq3bu3AgICVKdOHY0aNUrXr1+XJMXGxqpz586qUqWKgoODtXXrVof7JzEEAADZnslw3pZahmGod+/eSkxM1BdffKFJkybp+++/1+TJk2UYhnr27KmiRYsqMjJSLVq0UGhoqM6cOePQdWAoGQAAIAs4evSo9uzZo59++klFixaVJPXu3VtjxoxR3bp1FRsbq8WLF8vNzU3ly5fXtm3bFBkZqV69eqX6HCSGAAAARua/ybBYsWL67LPPbEnhLQkJCdq7d68qVaokNzc3W7ufn5/27Nnj0DkYSgYAAMgC8ufPrzp16tgeWywWLVy4UDVq1FBcXFyKrxouUqSIzp4969A5SAwBAACcyGw2KyEhwW4zm80PfN64ceN04MABvfPOO0pMTJSLi4vdfhcXl1T1czsSQwAAkO05c/JJRESE/Pz87LaIiIj7xjtu3DjNmzdP48aNk6enp1xdXVMkgWazWblz53boOnCPIQAAgBOFhISoS5cudm13Vv9uN3z4cC1atEjjxo1Tw4YNJUklSpRQdHS03XHx8fEphpcfhIohAACA4bzNxcVF7u7udtu9EsNp06Zp8eLFmjhxopo0aWJr9/Hx0R9//KFr167Z2nbu3CkfHx+HLgOJIQAAQBYQExOjGTNmqHv37vLz81NcXJxtCwgIUMmSJTVgwAAdOXJEs2bN0r59+9SmTRuHzsFQMgAAQBawadMmJSUlKTw8XOHh4Xb7Dh8+rBkzZmjQoEFq1aqVypYtq+nTp6tUqVIOnYPEEAAAZHumLLCOYY8ePdSjR4977i9btqwWLlz4r87BUDIAAAAkUTEEAACQLM4OIHOgYggAAABJVAwBAACyxD2GGYGKIQAAACSRGAIAAMCKoWQAAABGkiVRMQQAAIAVFUMAAAAmn0iiYggAAAArEkMAAABIYigZAABAJkaSJVExBAAAgBUVQwAAACafSKJiCAAAACsqhgAAINszWZwdQeZAxRAAAACSSAwBAABgxVAyAAAAk08kUTEEAACAFRVDAAAACoaSqBgCAADAisQQAAAAkhhKBgAAkInJJ5KoGAIAAMCKiiEAAAAVQ0lUDAEAAGBFxRAAAIDvSpZExRAAAABWJIYAAACQxFAyAAAAy9VYUTEEAACAJCqGAAAALFdjRcUQAAAAkkgMAQAAYMVQMgAAAEPJkqgYAgAAwIqKIQAAAN98IomKIQAAAKxIDAEAACCJoWQAAAC++cSKiiEAAAAkUTEEAABguRorKoYAAACQRMUQAACAiqEVFUMAAABIIjEEAACAFUPJAAAADCVLomIIAAAAKyqGAAAAfFeyJCqGAAAAsCIxBAAAgCSGkgEAAPiuZCsqhgAAAJBExRAAAIDlaqyoGAIAAEASFUMAAADJQsVQomIIAACQ5ZjNZjVt2lQ7duywtcXGxqpz586qUqWKgoODtXXrVof7JTEEAADIQq5fv653331XR44csbUZhqGePXuqaNGiioyMVIsWLRQaGqozZ8441DdDyQAAAFlk8kl0dLTee+89GXfEu337dsXGxmrx4sVyc3NT+fLltW3bNkVGRqpXr16p7p+KIQAAQBbxyy+/qHr16lqyZIld+969e1WpUiW5ubnZ2vz8/LRnzx6H+qdiCAAA4MSKodlsltlstmtzcXGRi4tLimM7dux41z7i4uJUvHhxu7YiRYro7NmzDsVCxRAAAMCJIiIi5OfnZ7dFREQ41EdiYmKKRNLFxSVFwvkgVAwBAACcKCQkRF26dLFru1u18H5cXV118eJFuzaz2azcuXM71A+JIQAAgBOHku81bOyIEiVKKDo62q4tPj4+xfDygzCUDAAAkMX5+Pjojz/+0LVr12xtO3fulI+Pj0P9kBgCAABYDOdtaSAgIEAlS5bUgAEDdOTIEc2aNUv79u1TmzZtHOqHxBAAACCLe+SRRzRjxgzFxcWpVatWWrFihaZPn65SpUo51A/3GAIAABgWZ0fgsMOHD9s9Llu2rBYuXPiv+qRiCAAAAEkkhgAAALBiKBkAACCLfFdyeqNiCAAAAElUDAEAANJs2ZisjoohAAAAJJEYAgAAwIqhZAAAACafSKJiCAAAACsqhgAAAFQMJVExBAAAgBUVQwAAACqGkqgYAgAAwIrEEAAAAJIYSgYAAJAsFmdHkClQMQQAAIAkKoYAAABMPrGiYggAAABJJIYAAACwYigZAACAoWRJVAwBAABgRcUQAADAQsVQomIIAAAAKyqGAAAg2zMMFriWqBgCAADAisQQAAAAkhhKBgAAYPKJFRVDAAAASKJiCAAAwALXVlQMAQAAIInEEAAAAFYMJQMAAFhYx1CiYggAAAArKoYAAABMPpFExRAAAABWVAwBAEC2Z3CPoSQqhgAAALAiMQQAAIAkhpIBAACYfGJFxRAAAACSqBgCAABIFiqGEhVDAAAAWJEYAgAAQBJDyQAAAJLBOoYSFUMAAABYUTEEAADZnsHkE0lUDAEAAGBFYggAAABJDCUDAAAw+cSKiiEAAAAkUTEEAABg8okVFUMAAABIomIIAADAPYZWVAwBAAAgicQQAAAAVibDMLjbEgAAAFQMAQAAkIzEEAAAAJJIDAEAAGBFYggAAABJJIYAAACwIjEEAACAJBJDAAAAWJEYAgAAQBKJIQAAAKxIDNNRVFSUvLy89NVXX6Xrec6fP681a9ak6znuJTY2Vps3b3bKubO6oKAgeXl5ycvLSxUrVpSvr6/at2+vLVu2ODs0pJGgoCBFRUWlaI+KilJQUJATIkJGuP2zfevzHRAQoDfffFN//fWXs8MD7ovEMB2tWrVKjz/+uL755pt0Pc/48eOdlpwNHDhQ+/btc8q5/wsGDhyorVu3avPmzVqyZImqVq2qkJAQ/fzzz84ODcC/cOuzfevzPWnSJB05ckRhYWHODg24LxLDdHL+/Hlt27ZNPXv21G+//abY2Nh0Oxdfd5115cuXT8WKFVOJEiXk6emp999/X02aNNGoUaOcHRqAf+HWZ/vW5zswMFC9e/fWjh07dOXKFWeHB9wTiWE6Wbt2rfLly6fmzZurePHidlXDoKAgzZ07V82aNVOVKlXUo0cPxcXF2fZv2rRJLVu2VOXKleXv7693331X//zzjyRp6tSpeuutt/Tyyy8rICBAnTp10rJly7Rs2TLb0JSXl5fWrFmjxo0by8fHR++++65iY2P16quvysfHRx07dtS5c+ds59uwYYOCg4Pl4+OjNm3a6JdffrHt69Spk8LDw9W1a1d5e3urYcOGtqHO/v3765dfftG0adPUqVOndL2e2Um7du30559/6sSJE7p06ZKGDBmiWrVqyc/PT/369dOlS5ckSTt27FBQUJCGDRsmPz8/zZo1S/3799e4cePUp08f+fj4KDg4WAcOHNCkSZPk7++vunXrOu22A9zd2bNn9fbbbysgIEDVq1fXiBEjZDabJd19yLlTp06aOnWqJOnMmTN6/fXX5evrq5o1a2r48OG6ceOGpOT/ME6fPl21a9eWv7+/3njjDZ05cyZjXxzsuLi4SJJy5Mhx38+2JMXExKhr166qWrWq6tSpo2nTpslisUhK/j3w/vvva/jw4fL19VVQUJC2bt2qhQsXqlatWqpRo4bmz5/vlNeIrI/EMJ2sWrVK9erVU44cORQUFKTly5fbVfamTp2qbt26acmSJUpMTFSvXr0kSSdPntTbb7+tjh07as2aNZo8ebJ+/vlnLV261PbcTZs2qWnTppo3b57Cw8PVuHFjNW7cWF9//bXtmClTpmj06NGKiIjQ+vXr1aFDB3Xo0EGLFy9WXFycPv30U0nSoUOHFBYWpjfffFMrVqxQ8+bN1b17d504ccLW18yZM9WkSRN9++23qlixooYMGSKLxaJBgwbJ19dXr7/+uu0XFf698uXLS5Kio6MVGhqqgwcPaubMmZozZ45iYmLUv39/27GnT5+W2WxWVFSUmjZtKkmaN2+eAgICtGLFChUsWFCvvfaazp8/ryVLltgSyVu/YOBcZrNZr732mhITE7VgwQJNnjxZP/zwg8aOHZuq5w8fPlxubm5avny5pk+frnXr1tn+rVi4cKFWrlypCRMmaMmSJSpSpIhef/11W+KIjHXy5EnNmjVLderUUd68ee/72b5w4YI6duyo4sWL66uvvtKwYcO0cOFCu2Rv9erVypcvn7755ht5e3urT58+2rp1qxYsWKBOnTppzJgxunDhgrNeLrIyA2nuzJkzhpeXl7F+/XrDMAzjp59+Mjw9PY1ff/3VMAzDqF+/vjFy5Ejb8SdPnjQ8PT2Nw4cPG8eOHTMWLVpk198777xjDBgwwDAMw5gyZYpRq1Ytu/1hYWFGWFiY7bGnp6exePFi2+M2bdoY/fr1sz0eO3as8frrrxuGYRh9+/Y1Ro0aZddfaGiore2VV14xevXqZdt38OBBw9PT0zh79qxt/5QpUxy5PLCqX7++ERkZmaL9xo0bhqenpzF16lTD09PTOHr0qG1fdHS04enpacTExBjbt283PD09jejoaNv+sLAwo127drbHX3zxhfH0008biYmJds8/d+5cOr4y3FK/fn3jmWeeMapUqWK3PfPMM0b9+vWNjRs3Gj4+PsbFixdtz9m8ebNRqVIlIyEhwYiMjDTq169v1+ftn7lmzZoZ/fv3N8xms2EYhvHHH38YsbGxhmEYRt26dY1NmzbZnnfz5k2jRo0adm1IH3f+3J955hnD19fX6Nu3r3HhwgXbv6P3+mzPmzfPePbZZ40bN27Y9n/55ZdGYGCgYRjJvwdq165tWCwWwzAM44cffjA8PT2NkydPGoZhGImJiYanp6exa9euDHzV+K/I6ezE9L9o1apVcnV1Ve3atSVJAQEBKlCggJYtWyZ/f39JUtWqVW3HlylTRgULFlRMTIwaN24sFxcXhYeH68iRIzpy5Iiio6PVokUL2/GlS5d+YAxlypSx/T137tx2z8mdO7dtqComJkZr1qzRkiVLbPtv3Lhhi12SypUrZ/u7u7u7JOnmzZupuhZwXEJCgqTkn3P+/Pnl4eFh21e+fHkVKFBAR48eVb58+SRJjz32mN3zb3+cO3duFS1aVLlz55Ykubq6SpLt54/017t3b73wwgt2bevXr9eiRYsUExOjcuXKqUCBArZ9VatW1c2bN3Xy5MkH9t2tWzcNHDhQGzZsUN26dRUcHKxKlSrpn3/+0dmzZ/XOO+8oR47/Hxi6du2ajh8/nmavDfd26+f+zz//aOrUqTp9+rTee+89FSpUSNu2bbvvZzsmJkZPP/20cub8/1/Rvr6+iouL0+XLlyUlf85NJpMk2T7ft/6dv/WYzzkeBolhOli1apWuXbsmPz8/W1tSUpLWrl2rIUOGSJLdB/7W/hw5cujQoUPq0KGDgoKC5O/vr86dO2vevHl2x9765X4/jzzyiN3j23853Hne7t27q2XLlnbtt/5hkaRcuXKleJ7BhJd0c/jwYUmy/QK4U1JSkpKSkmyP73w/3PneutfPHhmjSJEiKlu2bIo26e6f5Vs/26SkJNsv/tvd/p+y5s2bq2bNmtq4caN++OEH9e7dW927d1fXrl0lSZ988old8iHJLglF+rn95/7JJ5+oTZs2euutt7RkyRLbvYZ3uvXZvtv74tbtH7feH3d+ziU+60gbvIvS2LFjx3TgwAENHjxYy5cvt22TJk1SQkKCNmzYICn53r5bTpw4oStXrsjLy0vffPONqlWrpgkTJqhjx47y9vbWiRMn7puI3e2XR2p5eHjo1KlTKlu2rG1bsmSJfvzxx4fuE/9OZGSknn76adWuXVuXL1/W0aNHbfuio6OVkJCQ4pc9siYPDw8dP35cFy9etLXt2bNHOXPm1OOPP65cuXLZJp5Jyf8hO3XqlO3xpEmTdP78eXXo0EERERHq06eP1q9fr/z586tIkSKKi4uzfa5LliypcePG6dixYxn5EqHkSScjRozQwYMHNXfuXHl4eNz3s+3h4aE//vjD7n7Q3bt3q3DhwipYsKATXgGyExLDNLZq1SoVLFhQ7dq1k6enp20LDg5WhQoVtHz5cknS/PnztWnTJh06dEgDBw5UYGCgypUrp4IFC+rw4cPat2+fjh07ptGjR+v333+/75BAnjx5dPr0abuZxqnVuXNnrV69WvPnz9fJkyc1d+5czZ071274+H7c3Nx0/PhxnT9/3uFzQ7py5Yri4uL0999/6/Dhwxo5cqRWr16t/v37q3z58qpbt67CwsK0b98+7du3T2FhYapWrZo8PT2dHTrSQGBgoMqUKaP3339fhw8f1vbt2zV8+HA1bdpU+fPn1zPPPKOLFy9qwYIFio2N1ahRo+xmrh49elQfffSRDh06pCNHjmjz5s2qVKmSpOTP9uTJk/Xdd9/p+PHjGjx4sHbt2qUnnnjCWS83W/P29labNm00Y8YMubu73/ez3axZM5nNZg0dOlQxMTHauHGjpk6dqg4dOvyrQgCQGiSGaWzVqlVq1qzZXYcKOnTooJ9//lnnzp3Tiy++qIkTJ6pDhw4qVqyYJk2aJCl5KYoqVaqoc+fO6tixo86cOaOePXvqwIED9zxnixYtdOzYMTVv3tzhId4qVapo7Nix+vLLLxUcHKylS5dqwoQJqlatWqqe37ZtW23ZskXdunVz6LxI9vHHH6t27dqqW7euunTpomPHjmnu3LkKCAiQJI0ZM0ZlypRR586d1bVrVz355JOaPn26k6NGWnnkkUc0Y8YMSdJLL72kd999Vw0aNNBHH30kKfn+3rCwMIWHh6tly5YyDEMNGza0Pf+DDz5Q0aJF1alTJ7300ksqXry4Bg0aJEnq2rWr2rRpo6FDh6ply5Y6c+aMZs+ezVCyE73zzjvKlSuXxo0bd9/Ptru7uz777DOdPHlSLVu21PDhw/Xaa68pNDTUya8A2YHJ4GaxDBcUFKTQ0FC1atXK2aEAAADYUDEEAACAJBJDAAAAWDGUDAAAAElUDAEAAGBFYggAAABJJIYAAACwIjEEAACAJBJD3CEoKEheXl7y8vJSxYoV5evrq/bt22vLli3ODi3LM5vNWrp0qe1xp06dNHXqVIf7uf15/fv3V//+/dMsxluCgoIUFRWV5v3eaerUqerUqVO6nyerO3jwoHbt2uXsMFIwDENffPGFU2Pw8vLSjh07MuRcFy9eVFhYmGrUqKHatWtr2LBhSkhIyJBzAxmFxBApDBw4UFu3btXmzZu1ZMkSVa1aVSEhIfr555+dHVqWtmrVKs2cOTNN+xw0aJDtmy7S0tdff63g4OA07xcPp2fPnjp+/Lizw0jh119/tX1LS3YwYMAAHTp0SJ9++qk++eQTbd++XcOGDXN2WECaIjFECvny5VOxYsVUokQJeXp66v3331eTJk00atQoZ4eWpaXHylD58uVTvnz50rzfwoULK3fu3GneL/5bstNqZ/Hx8fruu+80dOhQVa5cWX5+fhoyZIjWrFlz3++yB7IaEkOkSrt27fTnn3/qxIkTkqRLly5pyJAhqlWrlvz8/NSvXz9dunTprs994YUXNGfOHLu2Zs2a6auvvpIk7d69Wx06dFCVKlUUFBSkRYsW2Y6721Dp/YaOzp8/rz59+qhq1aoKDAzUxIkTZRiGTp06JS8vL506dcp27O3DmFFRUWrfvr169uwpPz8/rVixQp06ddLw4cPVoEED1atXTwkJCfrrr7/0xhtvyMfHR0FBQZo2bZqSkpJsfXTq1ElTpkxR9erV5e/vr1GjRskwDO3YsUMDBgzQ6dOnU8Tx119/qWLFivrjjz/sXkelSpVs1/tebl2fK1euqHLlytq+fbttX0JCgipXrqzffvtNkrRhwwYFBwfLx8dHbdq00S+//HLPfm8fSu7UqZPCw8PVtWtXeXt7q2HDhve9tWDnzp3q0KGDfHx8VKVKFXXv3l1///33PY+/ceOGPvzwQ1WtWlW1atWye69YLBZ99tlnatCggby9vdWpUycdPnzYtv/O90JUVJSCgoJsjydOnKjatWvbnnvkyBHbvt9++02tWrWSt7e3mjVrpnXr1t0zxjudO3dOvXv3VrVq1fTMM8/oxRdf1M6dOyXJ9l5buXKl6tSpI39/f40YMUI3b96UlJxMzZw5U0FBQXrmmWdUu3ZtTZs2zdb3ne+7Vq1a6fTp0xowYID69++vHTt2KCgoSF9++aXq1KmjKlWqqF+/frbkZOrUqXr//fc1fPhw+fr6KigoSFu3btXChQtVq1Yt1ahRQ/Pnz7ed7/Lly+rXr5+qVq2q2rVra/jw4bp27Zok3fdcp06d0quvvnrXn8O9XL9+XePGjdOzzz6rKlWq6I033tBff/1ld92mT5+uatWq3bMSOW3aNNWsWVPVq1e3/RvysP07cq1y586tKVOmqEqVKrbzubu7KykpSf/8888DXzuQVZAYIlXKly8vSYqOjpYkhYaG6uDBg5o5c6bmzJmjmJiYe97r1qRJE7tfujExMTp27JheeOEFxcTE6LXXXlO1atUUFRWlXr16acyYMdqwYcNDxdmzZ0/FxcVp4cKFmjx5sqKiolJ9D9Tu3btVoUIFLV26VLVr15aUnGiMGzdO06ZNU968eRUaGqoiRYpo2bJlGjVqlFauXGk3PLx7924dO3ZMixYt0pAhQzR//nz9/PPP8vX11cCBA/Xoo49q69atKlmypO05JUuWlJ+fn901WrdunZ566imVLVs2VbHny5dPderUsbtuP/zwgwoXLiw/Pz8dOnRIYWFhevPNN7VixQo1b95c3bt3f2DiecvMmTPVpEkTffvtt6pYsaKGDBkii8WS4rgrV64oJCREgYGB+vbbbzV79mydPHlSs2bNumffu3fvVq5cubR8+XL16NFDo0ePVkxMjCRp+vTp+vzzzzVw4EAtW7ZMpUuXVrdu3XT16tUHxrxhwwYtWbJEkydP1rfffquiRYtqwIABkqS4uDiFhISoVatWWrlypbp166b+/fvbkugH6du3r5KSkrR48WItX75cJUqU0AcffGB3zLRp0zRp0iRNmzZN69evt90Xunz5cs2bN08jR47U2rVr1bNnT02dOtXuPwa3v+8+//xzPfrooxo4cKDttoG///5b69at02effaapU6dq/fr1Wr58ue35q1evVr58+fTNN9/I29tbffr00datW7VgwQJ16tRJY8aM0YULFyQl345w5coVLVq0SDNmzNDvv/9ul5Td61wlS5a0vaatW7fK19f3gddt2LBh2rBhg8aMGaPFixfr5s2beuutt+zeS7t27VJkZKQt6bzdkiVLNH/+fH388ceaO3euIiMj/3X/qb1W7u7uatiwoR555BFJ0s2bNzVt2jQFBASoUKFCD3ztQJZhALepX7++ERkZmaL9xo0bhqenp/HNN98YBw8eNDw9PY2jR4/a9kdHRxuenp5GTExMiudGR0cbXl5exl9//WUYhmFMnTrVCAkJMQzDMD7++GOjXbt2dsePGzfOeOmllwzDMIywsDAjLCzMbr+np6exffv2FOe5FdfJkydtbRs2bDC++eYbIzY21vD09DRiY2Nt+6ZMmWK88sorhmEYRmRkpOHl5WUkJiba9r/yyitGnz59bI9//vlno0aNGkZSUpKtbdOmTUZAQICtj4oVKxpXrlyx7W/ZsqURHh5u21+/fn27/qdMmWIYhmF88cUXxvPPP2+3b/bs2Sle453Pu/36fPvtt0adOnUMi8ViGIZhhIaGGqNGjTIMwzD69u1r+/stt++/0+3vg1deecXo1auXbd+t63z27NkUz/v777+N2bNn22IwDMMYP3688eqrr971PFOmTLGL2TAMw9/f31i1apVhsViMgIAAY/HixbZ9ZrPZePbZZ41FixYZhpHyvXD7NZ4zZ44RGBhonD592jAMwzh//rzx66+/GoZhGJMmTTJCQ0PtYhk1alSKtruxWCzG3Llzbe9nwzCMH3/80ahYsaJhGIbtvbZhwwbb/q+//tqoUaOGYbFYjG3bthnff/+9XZ+BgYHGsmXLDMNI+b4zDPufx/bt2w1PT0/jzz//tO3v2bOnMXjwYMMwkq9p7dq1bdf0hx9+sPtcJCYmGp6ensauXbuMEydOGBUrVjQuX75s6+vQoUO2tged69b+1Lh48aJRsWJFY8uWLba2//3vf4aPj4/x448/2q7b5s2b79lHq1atjGnTptkeHzlyxPYeeJj+HblWt7NYLEbfvn2NWrVq2f17A/wX5HR2Yoqs4dbMO3d3dx09elT58+eXh4eHbX/58uVVoEABHT16VE888YTdc8uXLy8vLy+tXbtWnTt31po1axQSEiIpuXro7e1td7yvr68WL17scIzHjh1TwYIFVaZMGVvbc889J0l2Q7f3UqRIkRT31ZUuXdr295iYGF28eFF+fn62NovFomvXrul///ufrQ93d3fbfnd3d9sQ4v00atRII0eO1MGDB1WsWDHt2rVL48aNe+Dzble/fn0NGjRIe/fulZeXl7Zs2WIbBouJidGaNWu0ZMkS2/E3btywVUYfpFy5cnavSdJdX1exYsXUsmVLzZ07VwcPHlR0dLQOHz6sqlWr3rPvxx57TCaTyfY4X758un79us6fP6+LFy/Kx8fHti9Xrlx65plnbBXF+2nSpIkWLlyoBg0aqEqVKnruuefUpk0bSdLRo0f1/fff21W5bty4YfeevheTyaQOHTpo9erV2rVrl44dO6b9+/enqKDe/pqfeeYZXbhwQf/73/9Uo0YN7d27VxMmTFBMTIwOHjyouLg4u+ff/r67l9uryXe+z26/prfe07f6vPXYbDYrJiZGFotFdevWtevbYrHYVZPvd67UOn78uCwWi93Ps2DBgvLw8FBMTIzt2t/vtcfExKhnz562xxUqVJCbm9u/6j+11+p2q1ev1vr167Vo0SK7f2+A/wISQ6TKrfu6nnzySbt7vG6XlJRku9/uTk2aNNH69etVp04dnTp1Sg0aNJAkubq6pjjWYrHY+jGZTHY3uN/vF1KuXLnuue/2xONefd0tltvbbt68qSeeeEIzZsxIcdytCSAuLi4p9hmpuEG/cOHCqlmzptatW6fixYvLx8dHjz766AOfdzs3NzfVr19f69at07lz51S0aFFb0p2UlKTu3burZcuWds9J7QSTu13bu72uc+fOqXXr1nr66adVq1YtvfTSS/rhhx+0d+/ee/Z9a2juzr7v9vOQkl/L3Yaxb+27pVixYlqzZo1++uknff/995o9e7aWLl2q5cuX6+bNm2rWrJneeOMNu+fnzPngfxItFotef/11Xb58WcHBwQoKCtKNGzcUGhpqd9zt1+xWvCaTSV999ZU+/vhjtW3bVi+88ILCwsJSDJve67Xf7s732u0/j7u9jhw5Ut45lJSUpHz58qUYkpWkEiVK2H5u9ztXaqX25/mg137nuW+91oftP7XX6nbff/+9goODValSpfseB2RF3GOIVImMjNTTTz+tMmXKyMPDQ5cvX9bRo0dt+6Ojo5WQkHDPikvTpk21d+9eLV++XM8++6zy5s0rSfLw8EiRNOzevdvWT65cuexu7I6Njb1njGXLltXFixdtN5tL0vz58/XWW2/Zfknf3ldqqoi38/Dw0JkzZ1S4cGGVLVtWZcuW1alTpzRlypS7Jp53etAxTZs21ffff6/NmzerSZMmDsV2S5MmTbR582Zt3LjRbrkZDw8PnTp1yhZ32bJltWTJEv34448PdZ572bBhgwoUKKCIiAi99tpr8vf3V2xs7EMlEvny5VPRokW1Z88eW9uNGzf0xx9/pOr98cMPP+irr75SvXr19OGHH+qbb77R8ePH9eeff8rDw0MnTpywux6bNm3SypUrHxhXdHS0fv31V82dO1dvvPGG6tWrZ5tcc/vrPHjwoO3v+/fvV/HixVWoUCEtWrRIPXv21MCBA9WyZUsVKlRI58+fd8oMXw8PD125ckUmk8l2Ha5du6axY8emaqZtat73t5QpU0Y5c+a0+3n+73//04kTJ1JVqZWS/2P6+++/2x6fOnVKly9fTrP+U6t06dKqWbNmmvYJZBYkhkjhypUriouL099//63Dhw9r5MiRWr16tW1ySfny5VW3bl2FhYVp37592rdvn8LCwlStWjV5enretc9SpUrJ29tb8+bNs0t6OnbsqIMHD2rixIk6duyYli1bpi+//FIvv/yyJKly5cr66aeftG3bNv3555/66KOP7lkZfPLJJ1WjRg0NGjRIhw8f1o4dOzRr1iwFBgaqaNGiKlmypGbPnq3Y2FhFRUXphx9+cOi61K5dW6VLl1a/fv10+PBh/fbbbxoyZIjy5Mlz16rXnfLkyaNLly7p+PHjd618Pvfcczp+/Lh++eUXNWrUyKHYbqlbt67+/vvvFIlh586dtXr1as2fP18nT57U3LlzNXfuXLsh4rRQsGBBnTlzRtu2bVNsbKxmzZql9evXP/RyHp07d9aUKVP03XffKSYmRkOGDNH169dtr61y5cpauHChjh8/rk2bNtktym2xWDR27Fht2LBBp06dUlRUlPLkyaNy5cqpY8eO2r9/vyZNmqTjx49r5cqVmjhxokqVKiUpeegwLi7urhXw/PnzK0eOHFq1apVOnz6ttWvX2iZh3P46R44cqd9//10///yzPvnkE9t7ulChQtq2bZttCPqdd97RjRs37nuN3NzcdPToUV28ePGhruO9lC9fXnXq1FHfvn21b98+/fHHHxowYICuXr2q/PnzP/D5efLkkZSc+F6/fv2+1y1v3rxq27athg8frh07dujQoUPq16+fHn30UQUGBqYq3ldeeUXz58/XunXr9Oeff2rQoEG26l5a9J9aXbp0Uf369dO0TyCzIDFECh9//LFq166tunXrqkuXLjp27Jjmzp2rgIAA2zFjxoxRmTJl1LlzZ3Xt2lVPPvmkpk+fft9+g4ODlTNnTtWrV8/WVqpUKUVERGjLli1q1qyZwsPD1b9/f7Vu3VqS1KJFCzVs2FBvvfWWunXrpqZNm6p48eL3PMe4ceOUJ08etWvXTu+9957atWunjh07KkeOHBo5cqT27dun4OBgrV27NsUw4oM88sgjCg8Pl8Vi0UsvvaRevXrp2Wef1eDBg1P1/Bo1aqhs2bJq1qyZXTXpFnd3d9WtW1dVqlRRkSJFHIrtFhcXFz333HN69NFHVbFiRVt7lSpVNHbsWH355ZcKDg7W0qVLNWHCBFWrVu2hznMvjRs3VvPmzdW7d2+1bt1aO3bsUFhYmGJiYh4qOXz99dfVtm1bDRkyRK1atdLZs2e1YMECFS5cWJI0ZMgQXbx4UU2bNtVnn32m3r17254bFBSk3r17a9SoUWrcuLFWr16tGTNmqECBAipdurRmzpypLVu2qGnTppo8ebL69++v5s2bS0quWteuXduu+nzLo48+qg8++ECffvqpmjZtqlmzZmnw4MHKmTOnDhw4YDsuODhYISEhevfdd9W2bVv16NFDUvIC8gkJCWrRooV69eolLy8vPf/883d9T9zSoUMHffHFF6l+rzli7Nixeuyxx9S5c2d16dJFHh4emjhxYqqe6+XlpcDAQLVv316bN2++73WTpLCwMNWqVUu9e/dWhw4d5Orqqrlz5971Foy7adGihXr37q3hw4erY8eOCgwMtEtg/23/qdWrVy+NHDkyTfsEMguT4YzxC2RLkyZN0tmzZzVmzBhnh5JptW/fXm3btrUlxnCesLAw9e3bV8WKFXPoebfuod20aZMee+yxdIou83rY6wYgc2DyCdLdoUOHdPDgQX355ZcKDw93djiZ0vbt27Vr1y7FxMQ89DAy0s7Jkyd14cIFkhsHcd2ArI/EEOlu//79GjFihDp27Ch/f39nh5MpffPNN9q0aZM++ugj28QcOE+ZMmX4T8xD4LoBWR9DyQAAAJDE5BMAAABYkRgCAABAEokhAAAArEgMAQAAIInEEAAAAFYkhgAAAJBEYggAAAArEkMAAABIIjEEAACA1f8B3yPn6EBYLBUAAAAASUVORK5CYII="
},
"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",
+ "\n",
+ "# Plot heatmap\n",
+ "plt.subplots(figsize=(8, 8))\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?')"
@@ -240,11 +264,11 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:59.277945Z",
- "start_time": "2024-02-23T02:10:58.967433Z"
+ "end_time": "2024-02-23T06:53:03.404019Z",
+ "start_time": "2024-02-23T06:53:03.194598Z"
}
},
- "id": "201db70188d3e778",
+ "id": "15f1e14311b1b17f",
"execution_count": 6
},
{
@@ -253,7 +277,7 @@
"metadata": {
"collapsed": false
},
- "id": "8d65fec230193b72"
+ "id": "2b499b750ea3aec9"
},
{
"cell_type": "code",
@@ -261,25 +285,143 @@
{
"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",
- "plt.gca().spines[['top', 'right',]].set_visible(False)"
+ "df.groupby('Do you currently live in a house, apartnment, or dorm? ').size().plot(kind='barh',\n",
+ " color=sns.palettes.mpl_palette(\n",
+ " 'Dark2'))\n",
+ "plt.gca().spines[['top', 'right', ]].set_visible(False)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-02-23T02:10:59.485432Z",
- "start_time": "2024-02-23T02:10:59.281076Z"
+ "end_time": "2024-02-23T06:53:03.516283Z",
+ "start_time": "2024-02-23T06:53:03.408298Z"
}
},
- "id": "5e460707e32c4a2a",
+ "id": "a3d9a4a3b5eba149",
"execution_count": 7
+ },
+ {
+ "cell_type": "code",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "<Figure size 640x480 with 1 Axes>",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dataTable1 = pd.pivot_table(data=df, values='What was your GPA your very first quarter at UCR?',\n",
+ " index='How many hours do you work per week on average?', aggfunc='mean')\n",
+ "_ = dataTable1.plot(kind='bar')"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-02-23T06:53:03.640403Z",
+ "start_time": "2024-02-23T06:53:03.518082Z"
+ }
+ },
+ "id": "36727f07413da341",
+ "execution_count": 8
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Hypotheses"
+ ],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "4df3824f641fb18b"
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Hypothesis 2: Students who live on-campus are more likely to have roommates of the same major."
+ ],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "796d474b4650e712"
+ },
+ {
+ "cell_type": "code",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "Do you work in a department related to your major? No Yes Total\nDo you currently live in a house, apartnment, o... \nApartment 22 16 38\nDorm 4 1 5\nHouse 27 7 34\nTotal 53 24 77",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>Do you work in a department related to your major?</th>\n <th>No</th>\n <th>Yes</th>\n <th>Total</th>\n </tr>\n <tr>\n <th>Do you currently live in a house, apartnment, or dorm?</th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Apartment</th>\n <td>22</td>\n <td>16</td>\n <td>38</td>\n </tr>\n <tr>\n <th>Dorm</th>\n <td>4</td>\n <td>1</td>\n <td>5</td>\n </tr>\n <tr>\n <th>House</th>\n <td>27</td>\n <td>7</td>\n <td>34</td>\n </tr>\n <tr>\n <th>Total</th>\n <td>53</td>\n <td>24</td>\n <td>77</td>\n </tr>\n </tbody>\n</table>\n</div>"
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "roommates_major_table = pd.crosstab(df.iloc[:, 3], df.iloc[:, 9], margins=True, margins_name='Total')\n",
+ "roommates_major_table"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-02-23T06:53:03.657146Z",
+ "start_time": "2024-02-23T06:53:03.642872Z"
+ }
+ },
+ "id": "2ee7f39b5d8df8de",
+ "execution_count": 9
+ },
+ {
+ "cell_type": "code",
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Chi-squared Value: 4.183390044200403\n",
+ "Degrees of Freedom: 6\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Extract the observed values from the contingency table\n",
+ "observed_values = roommates_major_table.iloc[:-1, :-1].values\n",
+ "\n",
+ "# Calculate expected values\n",
+ "row_totals = roommates_major_table.iloc[:-1, -1].values\n",
+ "col_totals = roommates_major_table.iloc[-1, :-1].values\n",
+ "total = np.sum(row_totals)\n",
+ "\n",
+ "expected_values = np.outer(row_totals, col_totals) / total\n",
+ "\n",
+ "# Calculate chi-squared statistic\n",
+ "chi2_statistic = np.sum((observed_values - expected_values) ** 2 / expected_values)\n",
+ "\n",
+ "# Degrees of freedom\n",
+ "degrees_of_freedom = (roommates_major_table.shape[0] - 1) * (roommates_major_table.shape[1] - 1)\n",
+ "\n",
+ "# Print results\n",
+ "print(f\"Chi-squared Value: {chi2_statistic}\\nDegrees of Freedom: {degrees_of_freedom}\")"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-02-23T06:53:03.664072Z",
+ "start_time": "2024-02-23T06:53:03.659800Z"
+ }
+ },
+ "id": "957406c164cf2ef1",
+ "execution_count": 10
}
],
"metadata": {
generated by cgit v1.2.3 (git 2.39.1) at 2025-07-13 15:51:21 +0000