diff options
| author |  ENathanLe <Nath.wessix@gmail.com>
| 2024-02-22 23:46:11 -0800 |
|---|---|---|
| committer |  Anshul Gupta <ansg191@anshulg.com>
| 2024-02-23 18:31:47 -0800 |
| commit | 2df5c2edaa3d03f0f0f4ebf29f5b96c7c83c262b (patch) | |
| tree | 9a448b765f742b1147d5c7cf70ef1032c74c8036 | |
| parent | d2854b75fdd15f1313ca3a5cd1c4929ce868fb2f (diff) | |
| download | CS105MiniProject-2df5c2edaa3d03f0f0f4ebf29f5b96c7c83c262b.tar.gz CS105MiniProject-2df5c2edaa3d03f0f0f4ebf29f5b96c7c83c262b.tar.zst CS105MiniProject-2df5c2edaa3d03f0f0f4ebf29f5b96c7c83c262b.zip | |
Minor fixes to remove trailing and starting spaces from index names
| -rw-r--r-- | CS105MiniProject.ipynb | 186 | ||||
| -rw-r--r-- | data.csv | 4 |
2 files changed, 92 insertions, 98 deletions
diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb index b8d92b8..3e15525 100644 --- a/CS105MiniProject.ipynb +++ b/CS105MiniProject.ipynb @@ -2,49 +2,35 @@ "cells": [ { "cell_type": "markdown", + "id": "21abd26c73fd0070", + "metadata": { + "collapsed": false + }, "source": [ "<div>\n", " <h1><center>CS105 Mini-Project</center></h1>\n", " <h2><center>Does who a student is living with effect if and how they work jobs?</center></h2>\n", " <p>By: <b>NAMES HERE</b></p>\n", "</div>" - ], - "metadata": { - "collapsed": false - }, - "id": "21abd26c73fd0070" + ] }, { "cell_type": "markdown", - "source": [ - "# Data Loading & Preprocessing" - ], + "id": "69d8e8ad7c61ba61", "metadata": { "collapsed": false }, - "id": "69d8e8ad7c61ba61" + "source": [ + "# Data Loading & Preprocessing" + ] }, { "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? \\\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>" + "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? \\\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, apartment, 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": {}, @@ -65,7 +51,16 @@ "# Select relevant columns\n", "df = df.iloc[:, [2, 3, 7, 8, 9, 34, 58, 59, 60, 61, 26]]\n", "df" - ] + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-24T02:30:50.385493Z", + "start_time": "2024-02-24T02:30:50.364241Z" + } + }, + "id": "b68b27041fdab1a5", + "execution_count": 1 }, { "cell_type": "markdown", @@ -75,24 +70,15 @@ "metadata": { "collapsed": false }, - "id": "3f7614a5665d55b6" + "id": "f7ee1fc9a8abba2b" }, { "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? \\\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>" + "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n4 Graduate 22 \n.. ... ... \n255 Junior 21 \n256 NaN 21 \n257 Senior 21 \n258 Sophomore 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n0 Neither \n1 Both \n2 Friends \n3 Neither \n4 Neither \n.. ... \n255 Friends \n256 Family \n257 Family \n258 Family \n259 Friends \n\n Do you currently live in a house, apartment, or dorm? \\\n0 House \n1 Apartment \n2 House \n3 Apartment \n4 Apartment \n.. ... \n255 House \n256 Apartment \n257 House \n258 Apartment \n259 Dorm \n\n How many people live in your household? \\\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, apartment, 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": {}, @@ -114,10 +100,10 @@ " .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(\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", + "df['Do you currently live in a house, apartment, or dorm?'] = (\n", + " df['Do you currently live in a house, apartment, 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", @@ -131,30 +117,29 @@ " .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": "2024-02-23T06:53:02.965372Z", - "start_time": "2024-02-23T06:53:02.954441Z" + "end_time": "2024-02-24T02:30:50.398700Z", + "start_time": "2024-02-24T02:30:50.386214Z" } }, + "id": "3f72adcb3bc0285e", + "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? \\\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>" + "text/plain": " What is your current class standing? What is your age? \\\n0 Senior 23+ \n4 Graduate 22 \n8 Junior 20 \n9 Senior 22 \n13 Junior 21 \n.. ... ... \n246 Graduate 23+ \n247 Senior 21 \n252 Junior 20 \n255 Junior 21 \n258 Sophomore 21 \n\n Who do you live with? \\\n0 Neither \n4 Neither \n8 Friends \n9 Family \n13 Family \n.. ... \n246 Family \n247 Friends \n252 Family \n255 Friends \n258 Family \n\n Do you currently live in a house, apartment, or dorm? \\\n0 House \n4 Apartment \n8 House \n9 House \n13 Apartment \n.. ... \n246 House \n247 Apartment \n252 House \n255 House \n258 Apartment \n\n How many people live in your household? \\\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, apartment, 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": {}, @@ -167,24 +152,24 @@ "# 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": "2024-02-23T06:53:02.975332Z", - "start_time": "2024-02-23T06:53:02.968284Z" + "end_time": "2024-02-24T02:30:50.408153Z", + "start_time": "2024-02-24T02:30:50.400240Z" } }, + "id": "285236650ff590d8", + "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? \\\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>" + "text/plain": " What is your current class standing? What is your age? \\\n1 Junior 20 \n2 Junior 23+ \n3 Senior 23+ \n5 Junior 21 \n6 Sophomore 19 \n.. ... ... \n253 Senior 21 \n254 Junior 19 \n256 NaN 21 \n257 Senior 21 \n259 Sophomore 18 \n\n Who do you live with? \\\n1 Both \n2 Friends \n3 Neither \n5 Both \n6 Friends \n.. ... \n253 Family \n254 Family \n256 Family \n257 Family \n259 Friends \n\n Do you currently live in a house, apartment, or dorm? \\\n1 Apartment \n2 House \n3 Apartment \n5 Apartment \n6 Apartment \n.. ... \n253 House \n254 House \n256 Apartment \n257 House \n259 Dorm \n\n How many people live in your household? \\\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, apartment, 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": {}, @@ -193,17 +178,26 @@ ], "source": [ "nw_df" - ] + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2024-02-24T02:30:50.417032Z", + "start_time": "2024-02-24T02:30:50.408722Z" + } + }, + "id": "6516c926e6efd1c3", + "execution_count": 4 }, { "cell_type": "markdown", - "id": "899d85626b77db20", + "source": [ + "# Analysis" + ], "metadata": { "collapsed": false }, - "source": [ - "# Analysis" - ] + "id": "7efd20d58edbb05d" }, { "cell_type": "code", @@ -230,11 +224,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.191673Z", - "start_time": "2024-02-23T06:53:02.976617Z" + "end_time": "2024-02-24T02:30:50.608877Z", + "start_time": "2024-02-24T02:30:50.418071Z" } }, - "id": "6bc50ddc195d88a", + "id": "6deea60d8966fa15", "execution_count": 5 }, { @@ -243,7 +237,7 @@ { "data": { "text/plain": "<Figure size 800x800 with 2 Axes>", - "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++v5s2bS0quWte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qhgAAANzg2oyKIQAAACSRGAIAAMCMVjIAAMj16CSnoWIIAAAASVQMAQAAmHxiRsUQAAAAkkgMAQAAYEYrGQAA5Hq0ktNQMQQAAIAkKoYAAADcrsaMiiEAAAAkUTEEAADgGkMzKoYAAACQRGIIAAAAM1rJAAAg16OTnIaKIQAAACRRMQQAAGDyiRkVQwAAAEgiMQQAAIAZrWQAAJDr0UlOQ8UQAAAAkqgYAgAAMPnEjIohAAAAJFExBAAA4BpDMyqGAAAAkERiCAAAADNayQAAINdj8kkaKoYAAACQRMUQAACAySdmVAwBAAAgicQQAAAAZiSGAAAg1zMYDHZbbGE0GvXBBx+oevXqql27tiZPniyTySRJOnjwoDp06CAfHx+1a9dOBw4csPk8kBgCAADkEKNGjdJPP/2kuXPnatKkSVq+fLmWLVumK1euqGfPnvL391dMTIx8fX0VFhamK1eu2DQ+k08AAECulxMmn5w/f17R0dGaN2+evL29JUldu3bVvn37lDdvXjk5OWnAgAEyGAwaMmSI/vvf/2r9+vVq27Ztho9BxRAAACAH2LVrl1xdXRUQEGBZ17NnT40ZM0b79u2Tn5+fpTVtMBhUrVo17d2716ZjkBgCAIBcz57XGBqNRiUnJ1stRqMxXYwJCQkqW7asVq5cqaZNm6px48aaOXOmUlNTlZSUJDc3N6v9ixcvrtOnT9t0HmglAwAA2FFUVJRmzJhhtS48PFy9e/e2WnflyhX9+eefWrp0qcaMGaOkpCQNHz5c+fPnV0pKihwdHa32d3R0vGuCeT8khgAAAHYUFhamLl26WK27M8mTpLx58yo5OVmTJk1S2bJlJUmJiYlasmSJ3N3d0yWBRqNRzs7ONsVCYggAAHI9e04+cXR0vGsieKeSJUvKycnJkhRKUvny5fXXX38pICBAZ8+etdr/7Nmz6drLD8I1hgAAADmAj4+Prl27pmPHjlnWHT16VGXLlpWPj4/27NljuaehyWTS7t275ePjY9MxSAwBAECulxNucF2hQgU1bNhQgwYNUmxsrH744QfNmTNHwcHBatq0qS5evKjRo0crLi5Oo0ePVkpKipo1a2bTeSAxBAAAyCEmTpyop556SsHBwYqIiNArr7yikJAQubq6KioqSrt27VLbtm21b98+zZkzRy4uLjaNzzWGAAAAOUTBggU1fvz4u27z9vbWihUrHml8EkMAAJDr2frM4n8rWskAAACQRMUQAAAgRzwrOTtQMQQAAIAkEkMAAACY0UoGAAC5HpNP0lAxBAAAgCQqhgAAAEw+MaNiCAAAAElUDAEAALjG0IyKIQAAACSRGAIAAMCMVjIAAMj16CSnoWIIAAAASVQMAQAA5EDJUBIVQwAAAJiRGAIAAEASrWQAAAAmn5hRMQQAAIAkGyqG8fHx+uabb5ScnKwmTZrI398/K+MCAADINjz5JE2GKoY//fST2rRpo+3bt+vYsWMKDQ3VpEmTsjo2AAAAZKMMVQwnTZqksLAwhYeHS5J27typt956S3/99Zf69++vUqVKKTU1VadPn1aZMmWyNGAAAIDM5kDBUFIGK4ZHjx5Vq1atLK8DAgK0YMECxcbGqmHDhnrjjTf0zz//qHHjxlkWKAAAALJWhiqGTz75pL777ju9/vrrlnXPP/+8Vq9erYSEBBmNRhUuXFgLFizIskABAACQtTJUMezTp4/Gjx+vbt26KTY21rLeYDDoqaeeUsWKFZUvXz4FBARkWaAAAABZxWAw2G15nGQoMfzPf/6jpUuXqly5ckpNTc3qmAAAAGAHGb5dTZUqVVSlShUlJyffc5/NmzfrhRdeyJTAAAAAsstjVrizG5tvcB0SEqJ//vnHal1CQoJ69Oihvn37ZlZcAAAAyGY2J4blypVTcHCwEhMTZTQa9fHHH6t58+a6evWqoqOjsyJGAAAAZAObn5X88ccfa/To0erUqZMcHR2VmpqqcePGqVmzZlkRHwAAQJYziF6y9BCJocFg0NChQ/XEE09o6tSp+vTTT1WzZs2siA0AAADZKEOJYWBg4D2nU4eFhalEiRKW11u2bMmcyAAAALIJTz5Jk6HEsHfv3lkdBwAAAOwsQ4nhSy+9ZPnzW2+9pffee08eHh5ZFhQAAEB2etxuNG0vNs9K3r17t/LmtfnSRAAAADzmbM7wOnfurHfeeUedOnVSmTJl5OTkZLW9evXqmRYcAAAAso/NieGsWbMkScOHD0+3zWAw6NChQ48eFQAAQDaik5zG5sQwNjY2K+IAAACAnT3UxYJXr17VqlWrFB8fr5s3b6pChQoKCgpSkSJFMjk8AACArOdAyVDSQ0w++eOPP/Tiiy8qMjJSiYmJSkxMVFRUlJo1a6a4uLisiBEAAADZwOaK4ejRo1WnTh2NHDnSMjv5xo0bGjp0qD766CN99tlnmR4kAAAAsp7NFcO9e/eqR48eVresyZs3r3r06KE9e/ZkanAAAADZwWCw3/I4sTkxLFmypE6cOJFu/YkTJ1SgQIFMCQoAAADZz+ZWcqdOnTR06FC9/fbb8vb2liTt27dP06ZNU4cOHTI9QAAAgKzGk0/S2JwYduvWTSkpKZo4caIuXLggSSpRooRCQ0PVtWvXTA8QAAAA2cPmxPDkyZPq3bu3evfurXPnzsnJyUmurq5ZERsAAEC2oGCYxubEsFmzZipbtqzq1aunevXqqUaNGlkRFwAAALKZzYnhzp07tXPnTm3fvl0TJ07UiRMn5OfnZ0kUK1asmBVxAgAAIIvZnBi6uLioYcOGatiwoSTp6NGjmjlzpsaPH6/x48fzrGQAAJDj8OSTNDYnhqdOndLu3bu1e/du7dq1S0ePHlX58uXVsWNH+fv7Z0WMAAAAyAY2J4aNGzeWg4OD6tevr7ffflv+/v4qXLhwVsQGAACQLagXprE5MRw/frx+/fVX/fLLLxo0aJCqVq0qf39/VatWTd7e3nJ0dMyKOAEAAJDFbE4MW7VqpVatWkmSzp07p19//VVbt27VjBkzZDAYtG/fvkwPEgAAAFnP5sRQkpKTk7Vr1y7t2LFDO3bs0OHDh/Xcc8+pbt26mR0fAABAluPJJ2lsTgzbtWunw4cPq0SJEqpTp466deum2rVrq0iRIlkQHgAAALKLzYlhy5YtNX78eHl4eGRFPAAAANnOgYKhJMnB1jeEhoaSFAIAANjBpk2b5OXlZbX06dNHknTw4EF16NBBPj4+ateunQ4cOGDz+A91jSEAAMC/SU65xjAuLk6NGjXSyJEjLeucnJx05coV9ezZUy1bttTYsWO1ZMkShYWFadOmTXJxccnw+DZXDAEAAGAf8fHx8vT0VMmSJS1LoUKFtHbtWjk5OWnAgAHy8PDQkCFDVKBAAa1fv96m8W1ODBMSEmx9CwAAADJBfHy8nn766XTr9+3bJz8/P0vl02AwqFq1atq7d69N49ucGDZt2lQdOnTQ/PnzdebMGVvfDgAA8NgxGOy3GI1GJScnWy1GozFdjCaTSceOHdO2bdvUpEkTvfDCC5o4caKMRqOSkpLk5uZmtX/x4sV1+vRpm86DzdcY/vDDD9qwYYPWrVuniRMnqmrVqgoKClLTpk1VrFgxW4cDAADI1aKiojRjxgyrdeHh4erdu7fVusTERKWkpMjR0VFTp07VyZMnNWrUKF29etWy/naOjo53TTDvx+bEsFixYgoODlZwcLDOnTunjRs3auvWrZo4caJ8fX3VokULNW3aVPnz57d1aAAAALuw5+STsLAwdenSxWrd3R4xXLZsWe3YsUOFCxeWwWDQc889p9TUVPXv318BAQHpkkCj0ShnZ2ebYnmkySdJSUlKSkrS6dOnlZqaqgIFCmj58uVq2LChNm7c+ChDAwAA5AqOjo5ydXW1Wu6WGEpSkSJFrJJYDw8PXbt2TSVLltTZs2et9j179my69vKD2JwYHjp0SFOmTFGTJk3Uvn17HThwQF26dNGPP/6oadOmacmSJXr99dc1bNgwW4cGAADAPfzwww+qUaOGUlJSLOsOHTqkIkWKyM/PT3v27JHJZJKUdj3i7t275ePjY9MxbG4lt23bVn5+fgoNDVXTpk1VtGjRdPv4+fkxexkAAOQYOeHJJ76+vnJyctLQoUPVq1cvJSQkaPz48erevbuaNm2qSZMmafTo0erUqZOWLl2qlJQUNWvWzKZj2JwYfvfdd3riiSfuu0+NGjVUo0YNW4cGAADAPbi6umru3Ln66KOP1K5dOxUoUECdOnVS9+7dZTAYFBUVpffff1/Lly+Xl5eX5syZY9PNraUMJoZ3zpS5n/DwcJsCAAAAsLec8uSTZ555RvPmzbvrNm9vb61YseKRxs9QYrhjx44MDZZTTioAAADSy1BiuGjRIsufExMT9cQTT8jBwXreys2bNxUbG5u50QEAAGQDSltpbJ6V3LhxY50/fz7d+pMnT6pz586ZERMAAADsIEMVwy+//FKzZ8+WlDb9uV27dukqhhcvXpSHh0fmRwgAAIBskaHEsE2bNsqXL59SU1M1ePBgdenSRQULFrRsNxgMyp8/v2rWrJllgQIAAGQVB+ZJSMpgYpgvXz61adNGkvTkk0+qWrVqypvX5jvdAAAA4DFmc3YXEBCg7du367ffftP169ctd9i+hdvVAACAnIaCYRqbE8OxY8dq4cKFevbZZ1WgQAGrbdyuBgAAIOeyOTGMjo7W2LFj1apVq6yIBwAAAHZic2KYJ08eeXt7Z0UsAAAAdkHXM43N9zF85ZVXNH36dF25ciUr4gEAAICd2Fwx3Llzp/bs2aP169erePHiypcvn9X2LVu2ZFpwAAAA2YGCYRqbE8O2bduqbdu2WRELAAAA7MjmxPCll16y/PnChQsqWLCgDAYDvXkAAIAczuZrDE0mkyIjI1WjRg3VqlVLp06dUv/+/TV8+HAZjcasiBEAACBLORgMdlseJzYnhjNnztSqVas0duxYOTo6SkqrIv74448aP358pgcIAACA7GFzYrhixQp9+OGHatSokaV9XKdOHY0bN07r1q3L9AABAACymsFgv+VxYnNieO7cObm5uaVbX6hQIW5hAwAAkIPZnBjWrFlTc+fOtVqXnJysyZMnq0aNGpkWGAAAQHa5NZHWHsvjxObEcMSIETp48KDq1Kmja9eu6a233lKDBg106tQpDR06NCtiBAAAQDaw+XY1hQoV0ldffaXt27fr6NGjunHjhsqXL6+6devKwcHmPBMAAACPCZsTwxYtWmjGjBmqVauWatWqlRUxPbIx096zdwjIRpXKFrJ3CMhGA9s+Z+8QkI0u/e+SvUPIdgaDVKJEQZ09e0kmk72jyV7OJQva7diUttLYfB4cHBx0/fr1rIgFAAAAdmRzxbBhw4bq0qWLGjVqpLJly1ruZXhLeHh4pgUHAACQHR63SSD2YnNiePjwYT3//PP6+++/9ffff1tt46QCAADkXDYnhh07dlSdOnVUtGjRrIgHAAAAdmLzNYYffPCBzp8/nwWhAAAA2IeDwX7L48TmxLBGjRpavXq1jEZjVsQDAAAAO7G5lXzu3DnNmjVLs2fPVrFixeTk5GS1fcuWLZkWHAAAQHZ43Cp39mJzYvjyyy/r5ZdfzopYAAAAYEc2J4YvvfRSVsQBAABgN9xZJY3NiWFISMh9T97ChQsfKSAAAADYh82JYY0aNaxe37hxQwkJCdq6davefPPNTAsMAAAA2cvmxPBeTzaJiYnRxo0b1a1bt0cOCgAAIDsx+SRNpj0zunr16tq+fXtmDQcAAIBsZnPFMDExMd26y5cva+7cuSpbtmymBAUAAJCdmHuSxubEMDAwUAaDQSaTyTIJxWQyqXTp0vroo48yPUAAAABkD5sTwztvYG0wGJQvXz6VKFGCqd4AAAA5mM3XGJYtW1bff/+99uzZo7Jly6pMmTL64IMPtHTp0qyIDwAAIMs5GAx2Wx4nNieGU6ZMUWRkpFxcXCzrAgICNGvWLM2cOTNTgwMAAED2sTkxjI6O1tSpUxUYGGhZ99prr2nixIlatmxZpgYHAACQHRzsuDxObI4nJSVFrq6u6dYXLVpUly5dypSgAAAAkP1sTgzr1aun0aNHW9225syZMxo3bpzq1q2bqcEBAABkB4PBfsvjxObEcPjw4bp+/boaN26smjVrqmbNmmrYsKFSU1P1/vvvZ0WMAAAAyAY2366mWLFiWrp0qQ4fPqxjx44pb968evrpp1WxYsWsiA8AAADZxObE8BYvLy95eXllZiwAAAB28bjdNsZeHrfJMAAAALCTh64YAgAA/FtQMExDxRAAAACSHqJi+Msvv9x3e/Xq1R86GAAAANiPzYlhSEjIXdc7OjqqZMmS2rJlyyMHBQAAkJ0caCVLeojEMDY21ur1zZs3deLECY0cOVItW7bMtMAAAACQvR75GsM8efKofPnyGjhwoD7++OPMiAkAACBbORgMdlseJ5k2+eTcuXO6ePFiZg0HAACAbGZzK3nQoEHp1l2+fFk//fSTmjZtmilBAQAAZKfHrHBnN5lyH8MiRYooIiJCrVu3zozhAAAAYAc2J4ZjxozJijgAAABgZw91jeHmzZvVqVMnBQQEyM/PT+3bt9fKlSszOTQAAIDs4WCw3/KwevbsqYEDB1peHzx4UB06dJCPj4/atWunAwcO2H4ebH3D0qVL1b9/f1WvXl1jx47VuHHjFBAQoA8++EBffvmlzQEAAADANmvWrNHWrVstr69cuaKePXvK399fMTEx8vX1VVhYmK5cuWLTuDa3kj/99FO9//77atOmjWXdCy+8oGeeeUazZ89Whw4dbB0SAADArgzKObNPzp8/r/Hjx6tKlSqWdWvXrpWTk5MGDBggg8GgIUOG6L///a/Wr1+vtm3bZnhsmyuG586dU9WqVdOt9/X11V9//WXrcAAAALDBuHHj1Lp1a1WsWNGybt++ffLz85PBPL3aYDCoWrVq2rt3r01j25wYPvfcc3e9nnDFihVWAQIAAODBjEajkpOTrRaj0XjXfbdv365ff/1Vb731ltX6pKQkubm5Wa0rXry4Tp8+bVMsNreS+/fvr9DQUO3YsUM+Pj6SpL179yo2NlazZ8+2dTgAAAC7s+ezkqOiojRjxgyrdeHh4erdu7fVumvXrun999/X8OHD5ezsbLUtJSVFjo6OVuscHR3vmWDei82Joa+vr2JiYrR8+XLFx8fLyclJ1atX15QpU1S6dGlbhwMAAMjVwsLC1KVLF6t1dyZ5kjRjxgxVrlxZ9erVS7fNyckpXRJoNBrTJZAPYnNiGB0drSZNmtz1CSgAAAA5kT0rho6OjndNBO+0Zs0anT17Vr6+vpJkSQQ3bNigFi1a6OzZs1b7nz17Nl17+UFsTgznz5+vDz74QHXq1FGLFi0UGBio/Pnz2zoMAAAAbLBo0SLduHHD8nrixImSpH79+umXX37RJ598IpPJJIPBIJPJpN27d+uNN96w6Rg2Tz5ZvXq1VqxYoeeff16RkZGqXbu2+vbtq02bNtncxwYAAHgcGAwGuy0ZVbZsWbm7u1uWAgUKqECBAnJ3d1fTpk118eJFjR49WnFxcRo9erRSUlLUrFkzm87DQz35xMPDQ+Hh4frmm2/01Vdf6amnnlL//v1Vu3ZtDRo0SLt3736YYQEAAPAQXF1dFRUVpV27dqlt27bat2+f5syZIxcXF5vGsbmVfMuZM2e0YcMGbdy4UXv37pW3t7eCgoKUlJSkN998Uy+//LLee++9hx0eAAAA9zF27Fir197e3lqxYsUjjflQ1xhu2LBB+/btk6enp5o3b64JEyZYzUh++umn9eGHH5IYAgCAHMGek08eJzYnhkuWLFHz5s01atQoeXh43HWfSpUqaejQoY8cHAAAALKPzYnhhg0bHriPl5eXvLy8HiogAACA7GbDHJB/tYeafAIAAIB/HxJDAAAASHqEWckAAAD/Fg70kiU9QmJ4/PhxxcfHKzU1VeXLl1fFihUzMy4AAABkM5sTw4sXL2rQoEHasmWLChcurJs3b+ry5cuqXr26Zs6cqYIFC2ZFnAAAAFmG29Wksfkaw1GjRun06dNau3atduzYoV9//VWrV6/WlStXNGbMmKyIEQAAANnA5orht99+q3nz5qlChQqWdRUrVtTw4cPVo0ePTA0OAAAgO3CJYRqbK4ZOTk5ycEj/NoPBoJs3b2ZKUAAAAMh+NieGgYGB+uCDD3TixAnLuuPHj2vUqFFq0KBBpgYHAACA7GNzK7l///7q1auXXnzxRRUuXFhS2oSUevXqadiwYZkeIAAAQFZzEL1k6SESw0KFCmnRokU6fPiw4uPj5eTkpPLly1tdcwgAAICcx+bEMCEhQXFxcbp8+bJcXV31zDPPqGzZslkRGwAAQLZg8kmaDCeG27dv15gxY3TkyBGZTCbLeoPBoOeff14DBw6Uv79/lgQJAACArJehySfbtm1T9+7d9eyzz2rRokX6+eef9fvvv2vHjh2aP3++KlSooC5dumjPnj1ZHS8AAACySIYqhjNnzlRoaKj69+9vtb5w4cKqUaOGatSoocKFCysyMlJz5szJkkABAACyCk8+SZOhimFsbKxeeuml++7ToUMHHTx4MFOCAgAAQPbLUMXw6tWrllvT3EvRokX1zz//ZEpQAAAA2cmB2SeSMlgxNJlMd33aye0MBoPVpBQAAADkLBmelbxu3Tq5urrec/ulS5cyJSAAAADYR4YSwzJlyuizzz574H6lS5d+5IAAAACyG53kNBlKDL/99tusjgMAAAB2ZvOTTwAAAP5tmHySJkOTTwAAAPDvR8UQAADkehQM01AxBAAAgCQSQwAAAJjRSgYAALkelbI0nAcAAABIomIIAAAgA7NPJFExBAAAgBmJIQAAACTRSgYAABCN5DRUDAEAACCJiiEAAADPSjajYggAAABJVAwBAAC4xtCMiiEAAAAkkRgCAADAjFYyAADI9Zh7koaKIQAAACRRMQQAAOBZyWZUDAEAACCJxBAAAABmtJIBAECuR6UsDecBAAAAkqgYAgAAMPnEjIohAAAAJFExBAAA4FnJZlQMAQAAIInEEAAAAGa0kgEAQK7H5JM0VAwBAAAgiYohAAAAlTIzzgMAAEAO8eeff6pbt27y9fVVw4YN9emnn1q2JSQkKDQ0VFWrVlVQUJC2bdtm8/gkhgAAADlAamqqevbsqaJFi2rFihX64IMPFBkZqdWrV8tkMqlXr14qUaKEoqOj1bp1a4WHhysxMdGmY9BKBgAAuV5OmHxy9uxZPffccxoxYoRcXV319NNPq1atWtq1a5dKlCihhIQELV26VC4uLvLw8ND27dsVHR2t3r17Z/gYVAwBAAByADc3N02dOlWurq4ymUzatWuXfvnlFwUEBGjfvn2qVKmSXFxcLPv7+flp7969Nh2DxBAAAOR6BjsuRqNRycnJVovRaLxvvIGBgercubN8fX3VpEkTJSUlyc3NzWqf4sWL6/Tp0zadBxJDAAAAO4qKipKfn5/VEhUVdd/3TJs2TbNnz9ahQ4c0ZswYpaSkyNHR0WofR0fHByaYd+IaQwAAkOvZ8xLDsLAwdenSxWrdnUnenapUqSJJunbtmvr166d27dopJSXFah+j0ShnZ2ebYqFiCAAAYEeOjo5ydXW1Wu6WGJ49e1abN2+2WlexYkVdv35dJUuW1NmzZ9Ptf2d7+UFIDAEAAHKAkydPKjw8XGfOnLGsO3DggIoVKyY/Pz/9/vvvunr1qmXbrl275OPjY9MxSAwBAECu5yCD3ZaMqlKlip5//nkNHjxYcXFx2rp1qyZMmKA33nhDAQEBKl26tAYNGqQjR45ozpw52r9/v9q3b2/jeQAAAMBjL0+ePJo1a5by58+vjh07asiQIQoJCdFrr71m2ZaUlKS2bdtq1apVmjlzpsqUKWPTMZh8AgAAcr0ccH9rSVKpUqU0Y8aMu25zd3fX4sWLH2l8KoYAAACQRGIIAAAAM1rJAAAg1zPYMAnk34yKIQAAACRRMQQAAMgxk0+yGhVDAAAASKJiCAAAYNONpv/NqBgCAABAEokhAAAAzGglAwCAXI/JJ2moGAIAAEASFUMAAAAqhmZUDAEAACCJxBAAAABmtJIBAECux7OS01AxBAAAgCQqhgAAAHKgYCiJiiEAAADMqBgCAIBcj2sM01AxBAAAgCQSQwAAAJjRSgYAALkeTz5JQ8UQAAAAkqgYAgAAMPnEjIohAAAAJJEYAgAAwIxWMgAAyPV48kkaKoYAAACQ9JhWDP/55x8VLVpUBuaOAwCAbMDkkzR2rxieOXNG77zzjg4dOqRr167p1VdfVZ06dRQYGKjY2Fh7hwcAAJBr2D0xHDFihP755x8VKVJEMTEx+uOPP7R06VIFBgZq5MiR9g4PAAAg17B7K/nnn39WTEyMSpcurc2bN6tx48by8fFRsWLF1KJFC3uHBwAAcgGuXktj94qhk5OTrl27pgsXLmjHjh1q2LChJOnkyZMqXLiwfYMDAADIRexeMXzhhRfUt29fOTs7q3DhwmrYsKHWrl2rjz76SC+99JK9wwMAALkABcM0dk8MR4wYocWLF+vUqVPq2LGjnJycZDQa9cYbb+iVV16xd3gAAAC5ht0Tw7x58yo0NFSSdOHCBaWmpqp169bcqgYAAGQbB/IOSY/BNYYmk0mRkZGqUaOGatWqpVOnTql///4aPny4jEajvcMDAADINeyeGM6cOVOrVq3S2LFj5ejoKEl66aWX9OOPP2r8+PF2jg4AYE8GQ+5ccutnh/3ZvZW8YsUKjR07VtWrV7e0j+vUqaNx48bp7bff1tChQ+0cIQDAXkqUKGjvEOymePHc+9ntgbw0jd0Tw3PnzsnNzS3d+kKFCunKlSt2iAgA8Lg4e/aSvUPIdgZDWlJ47twlmUz2jiZ75eZ/CDwu7N5KrlmzpubOnWu1Ljk5WZMnT1aNGjXsFBUA4HFgMuXOJbd+drsy2HF5jNglMezcubPi4+Mlpd2u5uDBg6pTp46uXbumt956Sw0aNNCpU6doIwMAAGQju7SSnZyc1KZNG3Xt2lW9evXSV199pe3bt+vo0aO6ceOGypcvr7p168rBwe4FTQAAgFzDLonhvHnztHHjRo0dO1br1q3TiBEjVLt2bdWqVcse4QAAgFzO8Lj1dO3EbpNPXnzxRTVo0ECffPKJwsPDFRgYqF69esnJyclqvzJlytgpQgAAgNzFrrOSnZycFB4ermeffVZ9+/bVmjVrLNtMJpMMBoMOHTpkxwgBAEBuwH0U09g1MTx16pTGjx+vTZs2qUWLFgoLC5Ozs7M9QwIAAMi17JIYXrt2TbNnz9a8efNUrlw5LVy4UP7+/vYIBQAAgCsMzeySGDZp0kTJycnq27evQkJClCdPHnuEAQAAgNvYJTH08/NTRETEXZ94AgAAAPuwS2I4adIkexwWAADg7uglS3oMHokHAACAx4NdZyUDAAA8DrjBdRoqhgAAAJBEYggAAAAzWskAACDX48knaagYAgAA5BBnzpxRnz59FBAQoHr16mnMmDG6du2aJCkhIUGhoaGqWrWqgoKCtG3bNpvHJzEEAAC5nsGOS0aZTCb16dNHKSkp+vzzzzVlyhR99913mjp1qkwmk3r16qUSJUooOjparVu3Vnh4uBITE206D7SSAQAAcoCjR49q7969+vHHH1WiRAlJUp8+fTRu3DjVr19fCQkJWrp0qVxcXOTh4aHt27crOjpavXv3zvAxSAwBAABywDWGJUuW1KeffmpJCm9JTk7Wvn37VKlSJbm4uFjW+/n5ae/evTYdg1YyAABADlCoUCHVq1fP8jo1NVWLFy9WzZo1lZSUlO5Rw8WLF9fp06dtOgaJIQAAgB0ZjUYlJydbLUaj8YHvmzBhgg4ePKh33nlHKSkpcnR0tNru6OiYoXFuR2IIAAByPYMd/4uKipKfn5/VEhUVdd94J0yYoAULFmjChAny9PSUk5NTuiTQaDTK2dnZpvPANYYAAAB2FBYWpi5dulitu7P6d7uRI0dqyZIlmjBhgpo0aSJJKlWqlOLi4qz2O3v2bLr28oNQMQQAALmewWC/xdHRUa6urlbLvRLDGTNmaOnSpZo8ebKaN29uWe/j46Pff/9dV69etazbtWuXfHx8bDoPJIYAAAA5QHx8vGbNmqUePXrIz89PSUlJliUgIEClS5fWoEGDdOTIEc2ZM0f79+9X+/btbToGrWQAAIAcYMuWLbp586YiIyMVGRlpte3w4cOaNWuWhgwZorZt28rd3V0zZ85UmTJlbDoGiSEAAMj1csBtDNWzZ0/17Nnzntvd3d21ePHiRzoGrWQAAABIomIIAACQM0qG2YCKIQAAACRRMQQAAJCBkqEkKoYAAAAwIzEEAACAJFrJAAAAMtBJlkTFEAAAAGZUDAEAQK5HwTANFUMAAABIIjEEAACAGa1kAAAAesmSqBgCAADAjIohAADI9XjySRoqhgAAAJBExRAAAIAbXJtRMQQAAIAkEkMAAACY0UoGAAC5Hp3kNFQMAQAAIImKIQAAACVDMyqGAAAAkERiCAAAADNayQAAINfjySdpqBgCAABAEhVDAAAAnnxiRsUQAAAAkqgYAgAAcIWhGRVDAAAASCIxBAAAgBmtZAAAAHrJkqgYAgAAwIyKIQAAyPW4wXUaKoYAAACQRGIIAAAAM1rJAAAg1+PJJ2moGAIAAEASFUMAAACmnphRMQQAAIAkEkMAAACY0UoGAACglyyJiiEAAADMqBgCAIBcjyefpKFiCAAAAElUDAEAALjBtRkVQwAAAEgiMQQAAIAZrWQAAJDr0UlOQ8UQAAAAkqgYAgAAUDI0o2IIAAAASSSGAAAAMKOVDAAAcj2efJKGiiEAAAAkUTEEAADgySdmVAwBAAAgiYohAAAAVxiaUTEEAADIYYxGo1q0aKEdO3ZY1iUkJCg0NFRVq1ZVUFCQtm3bZvO4JIYAAAA5yLVr1/Tuu+/qyJEjlnUmk0m9evVSiRIlFB0drdatWys8PFyJiYk2jU0rGQAA5Ho5ZfJJXFyc3nvvPZlMJqv1P//8sxISErR06VK5uLjIw8ND27dvV3R0tHr37p3h8akYAgAA5BA7d+5UjRo1tGzZMqv1+/btU6VKleTi4mJZ5+fnp71799o0PhVDAAAAO04/MRqNMhqNVuscHR3l6OiYbt/OnTvfdYykpCS5ublZrStevLhOnz5tUyxUDAEAAOwoKipKfn5+VktUVJRNY6SkpKRLJB0dHdMlnA9CxRAAAMCOwsLC1KVLF6t1d6sW3o+Tk5POnz9vtc5oNMrZ2dmmcUgMAQBArmfPySf3ahvbolSpUoqLi7Nad/bs2XTt5QehlQwAAJDD+fj46Pfff9fVq1ct63bt2iUfHx+bxiExBAAAuZ7BjktmCAgIUOnSpTVo0CAdOXJEc+bM0f79+9W+fXubxiExBAAAyOHy5MmjWbNmKSkpSW3bttWqVas0c+ZMlSlTxqZxuMYQAADkejnlBte3O3z4sNVrd3d3LV68+JHGpGIIAAAASSSGAAAAMKOVDAAAcj2DHZ988jihYggAAABJVAwBAADs+ajkxwoVQwAAAEgiMQQAAIAZrWQAAJDr0UlOQ8UQAAAAkqgYAgAA5Mgnn2QFKoYAAACQRMUQAACAG1ybUTEEAACAJBJDAAAAmNFKBgAAoJMsiYohAAAAzKgYAgCAXI+CYRoqhgAAAJBEYggAAAAzWskAACDX48knaagYAgAAQBIVQwAAAJ58YkbFEAAAAJKoGAIAAHCNoRkVQwAAAEgiMQQAAIAZiSEAAAAkkRgCAADAjMknAAAg12PySRoqhgAAAJBEYggAAAAzWskAACDX48knaagYAgAAQBIVQwAAACafmFExBAAAgCQqhgAAAFxhaEbFEAAAAJJIDAEAAGBGKxkAAIBesiQqhgAAADCjYggAAHI9bnCdhoohAAAAJJEYAgAAwIxWMgAAyPV48kkaKoYAAACQRMUQAACAqSdmVAwBAAAgicQQAAAAZrSSAQAA6CVLomIIAAAAMyqGAAAg1+PJJ2moGAIAAEASFUMAAABucG1GxRAAAACSSAwBAABgZjCZTCZ7BwEAAAD7o2IIAAAASSSGAAAAMCMxBAAAgCQSQwAAAJiRGAIAAEASiSEAAADMSAwBAAAgicQQAAAAZiSGAAAAkERimKViYmLk5eWlL7/8MkuPc+7cOa1bty5Lj3EvCQkJ2rp1q12OndMFBgbKy8tLXl5eevbZZ+Xr66tOnTrphx9+sHdoyCSBgYGKiYlJtz4mJkaBgYF2iAjZ4fbv9q3vd0BAgN5880399ddf9g4PuC8Swyy0Zs0aPfXUU/r666+z9DgTJ060W3I2ePBg7d+/3y7H/jcYPHiwtm3bpq1bt2rZsmWqVq2awsLC9NNPP9k7NACP4NZ3+9b3e8qUKTpy5IgiIiLsHRpwXySGWeTcuXPavn27evXqpV9//VUJCQlZdiwed51zFSxYUCVLllSpUqXk6empAQMGqHnz5hozZoy9QwPwCG59t299v+vUqaM+ffpox44dunTpkr3DA+6JxDCLrF+/XgULFlSrVq3k5uZmVTUMDAzU/Pnz1bJlS1WtWlU9e/ZUUlKSZfuWLVvUpk0bValSRf7+/nr33Xd1+fJlSdL06dP11ltv6ZVXXlFAQIBCQkK0YsUKrVixwtKa8vLy0rp169SsWTP5+Pjo3XffVUJCgl577TX5+Pioc+fOOnPmjOV4mzZtUlBQkHx8fNS+fXvt3LnTsi0kJESRkZHq1q2bvL291aRJE0urc+DAgdq5c6dmzJihkJCQLD2fuUnHjh31xx9/6M8//9SFCxc0bNgw1a5dW35+furfv78uXLggSdqxY4cCAwP1/vvvy8/PT3PmzNHAgQM1YcIE9e3bVz4+PgoKCtLBgwc1ZcoU+fv7q379+na77AB3d/r0ab399tsKCAhQjRo1NGrUKBmNRkl3bzmHhIRo+vTpkqTExER17dpVvr6+qlWrlkaOHKnr169LSvsH48yZM1W3bl35+/vrjTfeUGJiYvZ+OFhxdHSUJDk4ONz3uy1J8fHx6tatm6pVq6Z69eppxowZSk1NlZT2e2DAgAEaOXKkfH19FRgYqG3btmnx4sWqXbu2atasqYULF9rlMyLnIzHMImvWrFHDhg3l4OCgwMBArVy50qqyN336dHXv3l3Lli1TSkqKevfuLUk6ceKE3n77bXXu3Fnr1q3T1KlT9dNPP2n58uWW927ZskUtWrTQggULFBkZqWbNmqlZs2b66quvLPtMmzZNY8eOVVRUlDZu3Kjg4GAFBwdr6dKlSkpK0ieffCJJio2NVUREhN58802tWrVKrVq1Uo8ePfTnn39axpo9e7aaN2+ub775Rs8++6yGDRum1NRUDRkyRL6+vuratavlFxUenYeHhyQpLi5O4eHhOnTokGbPnq158+YpPj5eAwcOtOx76tQpGY1GxcTEqEWLFpKkBQsWKCAgQKtWrVKRIkX0+uuv69y5c1q2bJklkbz1Cwb2ZTQa9frrryslJUWLFi3S1KlT9f3332v8+PEZev/IkSPl4uKilStXaubMmdqwYYPl/xWLFy/W6tWrNWnSJC1btkzFixdX165dLYkjsteJEyc0Z84c1atXTwUKFLjvd/uff/5R586d5ebmpi+//FLvv/++Fi9ebJXsrV27VgULFtTXX38tb29v9e3bV9u2bdOiRYsUEhKicePG6Z9//rHXx0VOZkKmS0xMNHl5eZk2btxoMplMph9//NHk6elp+uWXX0wmk8nUqFEj0+jRoy37nzhxwuTp6Wk6fPiw6dixY6YlS5ZYjffOO++YBg0aZDKZTKZp06aZateubbU9IiLCFBERYXnt6elpWrp0qeV1+/btTf3797e8Hj9+vKlr164mk8lk6tevn2nMmDFW44WHh1vWvfrqq6bevXtbth06dMjk6elpOn36tGX7tGnTbDk9MGvUqJEpOjo63frr16+bPD09TdOnTzd5enqajh49atkWFxdn8vT0NMXHx5t+/vlnk6enpykuLs6yPSIiwtSxY0fL688//9z0/PPPm1JSUqzef+bMmSz8ZLilUaNGpsqVK5uqVq1qtVSuXNnUqFEj0+bNm00+Pj6m8+fPW96zdetWU6VKlUzJycmm6OhoU6NGjazGvP0717JlS9PAgQNNRqPRZDKZTL///rspISHBZDKZTPXr1zdt2bLF8r4bN26YatasabUOWePOv/fKlSubfH19Tf369TP9888/lv+P3uu7vWDBAlODBg1M169ft2z/4osvTHXq1DGZTGm/B+rWrWtKTU01mUwm0/fff2/y9PQ0nThxwmQymUwpKSkmT09P0+7du7PxU+PfIq+9E9N/ozVr1sjJyUl169aVJAUEBKhw4cJasWKF/P39JUnVqlWz7F+uXDkVKVJE8fHxatasmRwdHRUZGakjR47oyJEjiouLU+vWrS37ly1b9oExlCtXzvJnZ2dnq/c4OztbWlXx8fFat26dli1bZtl+/fp1S+yS9PTTT1v+7OrqKkm6ceNGhs4FbJecnCwp7e+5UKFCKl++vGWbh4eHChcurKNHj6pgwYKSpCeffNLq/be/dnZ2VokSJeTs7CxJcnJykiTL3z+yXp8+ffTiiy9ardu4caOWLFmi+Ph4Pf300ypcuLBlW7Vq1XTjxg2dOHHigWN3795dgwcP1qZNm1S/fn0FBQWpUqVKunz5sk6fPq133nlHDg7/3xi6evWqjh8/nmmfDfd26+/98uXLmj59uk6dOqX33ntPRYsW1fbt2+/73Y6Pj9fzzz+vvHn//1e0r6+vkpKSdPHiRUlp33ODwSBJlu/3rf/P33rN9xwPg8QwC6xZs0ZXr16Vn5+fZd3Nmze1fv16DRs2TJKsvvC3tjs4OCg2NlbBwcEKDAyUv7+/QkNDtWDBAqt9b/1yv588efJYvb79l8Odx+3Ro4fatGljtf7W/1gkKV++fOneZ2LCS5Y5fPiwJFl+Adzp5s2bunnzpuX1nT8Pd/5s3evvHtmjePHicnd3T7dOuvt3+dbf7c2bNy2/+G93+z/KWrVqpVq1amnz5s36/vvv1adPH/Xo0UPdunWTJH388cdWyYckqyQUWef2v/ePP/5Y7du311tvvaVly5ZZrjW8063v9t1+Lm5d/nHr5+PO77nEdx2Zg5+iTHbs2DEdPHhQQ4cO1cqVKy3LlClTlJycrE2bNklKu7bvlj///FOXLl2Sl5eXvv76a1WvXl2TJk1S586d5e3trT///PO+idjdfnlkVPny5XXy5Em5u7tblmXLlum///3vQ4+JRxMdHa3nn39edevW1cWLF3X06FHLtri4OCUnJ6f7ZY+cqXz58jp+/LjOnz9vWbd3717lzZtXTz31lPLly2eZeCal/YPs5MmTltdTpkzRuXPnFBwcrKioKPXt21cbN25UoUKFVLx4cSUlJVm+16VLl9aECRN07Nix7PyIUNqkk1GjRunQoUOaP3++ypcvf9/vdvny5fX7779bXQ+6Z88eFStWTEWKFLHDJ0BuQmKYydasWaMiRYqoY8eO8vT0tCxBQUGqWLGiVq5cKUlauHChtmzZotjYWA0ePFh16tTR008/rSJFiujw4cPav3+/jh07prFjx+q33367b0sgf/78OnXqlNVM44wKDQ3V2rVrtXDhQp04cULz58/X/PnzrdrH9+Pi4qLjx4/r3LlzNh8b0qVLl5SUlKS///5bhw8f1ujRo7V27VoNHDhQHh4eql+/viIiIrR//37t379fERERql69ujw9Pe0dOjJBnTp1VK5cOQ0YMECHDx/Wzz//rJEjR6pFixYqVKiQKleurPPnz2vRokVKSEjQmDFjrGauHj16VB9++KFiY2N15MgRbd26VZUqVZKU9t2eOnWqvv32Wx0/flxDhw7V7t27VaFCBXt93FzN29tb7du316xZs+Tq6nrf73bLli1lNBo1fPhwxcfHa/PmzZo+fbqCg4MfqRAAZASJYSZbs2aNWrZseddWQXBwsH766SedOXNGL730kiZPnqzg4GCVLFlSU6ZMkZR2K4qqVasqNDRUnTt3VmJionr16qWDBw/e85itW7fWsWPH1KpVK5tbvFWrVtX48eP1xRdfKCgoSMuXL9ekSZNUvXr1DL2/Q4cO+uGHH9S9e3ebjos0H330kerWrav69eurS5cuOnbsmObPn6+AgABJ0rhx41SuXDmFhoaqW7dueuaZZzRz5kw7R43MkidPHs2aNUuS9PLLL+vdd99V48aN9eGHH0pKu743IiJCkZGRatOmjUwmk5o0aWJ5/4gRI1SiRAmFhITo5Zdflpubm4YMGSJJ6tatm9q3b6/hw4erTZs2SkxM1Ny5c2kl29E777yjfPnyacKECff9bru6uurTTz/ViRMn1KZNG40cOVKvv/66wsPD7fwJkBsYTFwslu0CAwMVHh6utm3b2jsUAAAACyqGAAAAkERiCAAAADNayQAAAJBExRAAAABmJIYAAACQRGIIAAAAMxJDAAAASCIxRAYFBgbKy8tLXl5eevbZZ+Xr66tOnTrphx9+sHdoOZ7RaNTy5cstr0NCQjR9+nSbx7n9fQMHDtTAgQMzLcZbAgMDFRMTk+nj3mn69OkKCQnJ8uPkdIcOHdLu3bvtcuzt27crPj7eLseWHv578jBSU1M1c+ZMNWrUSP7+/urRo4f+/PPPbDk2kN1IDJFhgwcP1rZt27R161YtW7ZM1apVU1hYmH766Sd7h5ajrVmzRrNnz87UMYcMGWJ5AkZm+uqrrxQUFJTp4+Lh9OrVS8ePH7fLsUNDQ3X27Fm7HDu7LVy4UPPnz9ewYcO0dOlS5cmTR927d9fVq1ftHRqQ6UgMkWEFCxZUyZIlVapUKXl6emrAgAFq3ry5xowZY+/QcrSsuGNUwYIFVbBgwUwft1ixYnJ2ds70cYHH2bJly9S1a1cFBgaqYsWKmjhxok6fPm23ai2QlUgM8Ug6duyoP/74w9JWuXDhgoYNG6batWvLz89P/fv314ULF+763hdffFHz5s2zWteyZUt9+eWXkqQ9e/YoODhYVatWVWBgoJYsWWLZ726tUi8vL+3YseOuxzp37pz69u2ratWqqU6dOpo8ebJMJpNOnjwpLy8vnTx50rLv7W3MmJgYderUSb169ZKfn59WrVqlkJAQjRw5Uo0bN1bDhg2VnJysv/76S2+88YZ8fHwUGBioGTNm6ObNm5YxQkJCNG3aNNWoUUP+/v4aM2aMTCaTduzYoUGDBunUqVPp4vjrr7/07LPP6vfff7f6HJUqVXpgG+vW+bl06ZKqVKmin3/+2bItOTlZVapU0a+//ipJ2rRpk4KCguTj46P27dtr586d9xz39lZySEiIIiMj1a1bN3l7e6tJkyb3vbRg165dCg4Olo+Pj6pWraoePXro77//vuf+169f1wcffKBq1aqpdu3aVj8rqamp+vTTT9W4cWN5e3srJCREhw8ftmy/82chJiZGgYGBlteTJ09W3bp1Le89cuSIZduvv/6qtm3bytvbWy1bttSGDRvuGeOdzpw5oz59+qh69eqqXLmyXnrpJe3atUuSLD9rq1evVr169eTv769Ro0bpxo0bktL+gTB79mwFBgaqcuXKqlu3rmbMmGEZ+86fu7Zt2+rUqVMaNGiQBg4cqB07digwMFBfffWV6tSpo+rVq+uTTz7RL7/8oqZNm8rX11cDBgxQamqq5XgzZ85U3bp15e/vrzfeeEOJiYlW5/Drr79WixYtVLlyZXXu3FkJCQmSZDmXr732WobbuTExMWrWrJm8vb3Vtm1b/fLLL5ZtgYGBmjBhgurWrWt5JvSdNm3apCZNmqhq1ar68MMPLd+vhxn/559/tulcDRw40OoRpvnz51fevHmVnJycoc8O5CQkhngkHh4ekqS4uDhJUnh4uA4dOqTZs2dr3rx5io+Pv+e1bs2bN7f6pRsfH69jx47pxRdfVHx8vF5//XVVr15dMTEx6t27t8aNG6dNmzY9VJy9evVSUlKSFi9erKlTpyomJkaff/55ht67Z88eVaxYUcuXL1fdunUlpf0SmjBhgmbMmKECBQooPDxcxYsX14oVKzRmzBitXr3aqj28Z88eHTt2TEuWLNGwYcO0cOFC/fTTT/L19dXgwYP1xBNPaNu2bSpdurTlPaVLl5afn5/VOdqwYYOee+45ubu7Zyj2ggULql69elbn7fvvv1exYsXk5+en2NhYRURE6M0339SqVavUqlUrm66fmj17tpo3b65vvvlGzz77rIYNG2b5ZXq7S5cuKSwsTHXq1NE333yjuXPn6sSJE5ozZ849x96zZ4/y5cunlStXqmfPnho7dqzlmraZM2fqs88+0+DBg7VixQqVLVtW3bt315UrVx4Y86ZNm7Rs2TJNnTpV33zzjUqUKKFBgwZJkpKSkhQWFqa2bdtq9erV6t69uwYOHGhJoh+kX79+unnzppYuXaqVK1eqVKlSGjFihNU+M2bM0JQpUzRjxgxt3LjRklitXLlSCxYs0OjRo7V+/Xr16tVL06dPt/qHwe0/d5999pmeeOIJDR482HLZwN9//63Nmzdr0aJFeuONNzR58mR99NFHGjt2rCZPnqy1a9dqy5YtkqTFixdr9erVmjRpkpYtW6bixYura9euun79uuV406dP15AhQxQTE6P//e9/mjp1qqS0Swpube/atesDz0tMTIxGjhypsLAwrVy5UrVr11bPnj115swZyz6rV6/W3LlzNXbsWBkMBqv3x8XFqW/fvgoODlZ0dLRu3LhhSbgfdnxbzlWDBg1UqlQpy1hz5sxR3rx5VaNGjQd+diCnITHEI7nVrrx8+bJiY2O1c+dOTZgwQd7e3vL29taECRP07bff6ujRo+ne26JFC+3du1enT5+WJK1bt05169ZV4cKFtXz5clWqVEnvvvuuKlSooJdeekmvvvqqPv30U5tjjI2N1Z49ezR27FhVqlRJ1atX14gRI1SoUKEMvd9gMOjNN9+Uh4eHihUrJklq2LChqlWrpsqVK+vnn39WYmKiRo4cqQoVKqhGjRqKiIjQwoULLWPcvHnTsr1169Z69tln9dtvv8nR0VEFCxZUnjx5VLJkSeXJk8fq2M2bN9f69estr9etW6fmzZvb9PmbN2+uTZs2WaowGzZsULNmzWQwGDR37ly9/PLLatmypdzd3fXaa6+pfv36VtXZ+2nQoIHatm2rp556Sm+++ab++usvJSUlpdvv6tWreuutt9SrVy+VK1dOfn5+evHFF60qdXcqVaqUBg0apKeeekqhoaEqVKiQDh8+LJPJpMWLF+vtt99W48aN5eHhoZEjRypPnjxatWrVA2M+deqU8uXLpzJlyuipp57SsGHDLP94+fzzz1W7dm29+uqrcnd3V+vWrdWxY0ctWLDggeOaTCa98MILGjZsmDw8PFSxYkW98sorln803dK/f3/5+/urZs2aevvtt7V8+XKZTCaVLl1aY8aMUa1atfTkk08qODhYJUuWtDpHt//cFSlSRHny5LG6bOD69euKiIhQhQoV9Morryg1NVWvvPKKqlatqkaNGum5556zfBc//fRTDRgwQDVq1JCHh4c+/PBDXbhwwarq26VLF9WqVUuenp4KDg7WgQMHJMnyPShcuLAKFCjwwHOzaNEihYSEqE2bNqpQoYL69esnT09PLV682LJPq1atLJPb7hQdHS1/f3+FhobKw8NDw4YNk5ub2yONb8u5ut3y5cs1Y8YMjR07VoULF37gZwdymrz2DgA5261Wiqurq44ePapChQqpfPnylu0eHh4qXLiwjh49qgoVKli918PDQ15eXlq/fr1CQ0O1bt06hYWFSUqrHnp7e1vt7+vrq6VLl9oc47Fjx1SkSBGVK1fOsu6FF16QJKvW7b0UL1483XV1ZcuWtfw5Pj5e58+fl5+fn2Vdamqqrl69qv/973+WMVxdXS3bXV1dLS3E+2natKlGjx6tQ4cOqWTJktq9e7cmTJjwwPfdrlGjRhoyZIj27dsnLy8v/fDDD5akNT4+XuvWrdOyZcss+1+/ft1SGX2Qp59+2uozSbrr5ypZsqTatGmj+fPn69ChQ4qLi9Phw4dVrVq1e4795JNPWlWOChYsqGvXruncuXM6f/68fHx8LNvy5cunypUrZ2iWbPPmzbV48WI1btxYVatW1QsvvKD27dtLko4eParvvvtOvr6+lv2vX79u9TN9LwaDQcHBwVq7dq12796tY8eO6cCBA+kqqLd/5sqVK+uff/7R//73P9WsWVP79u3TpEmTFB8fr0OHDikpKcnq/bf/3N3LrZ/zWz+zt7/H2dlZRqNRly9f1unTp/XOO+/IweH/6wNXr161msxye2Xa1dXVqppoi/j4ePXq1ctqXdWqVa3+vu732eLj4/Xcc89ZXufLl8/q9cOOn5FzdbtTp05p5MiRGjp0qBo3bnzPeIGcjMQQj+TWdV3PPPOM1TVet7t582a664Fuad68uTZu3Kh69erp5MmTlv/ZOjk5pds3NTXVMo7BYLC6Dul+SVa+fPnuue3OltXdxrpbLLevu3HjhipUqKBZs2al2+9WJcfR0THdtoxMOilWrJhq1aqlDRs2yM3NTT4+PnriiSce+L7bubi4qFGjRtqwYYPOnDmjEiVKWJLumzdvqkePHmrTpo3VezI6weRu5/Zun+vMmTNq166dnn/+edWuXVsvv/yyvv/+e+3bt++eY99ZPb019t3+PqS0z3K3NvatbbeULFlS69at048//qjvvvtOc+fO1fLly7Vy5UrduHFDLVu21BtvvGH1/rx5H/y/ytTUVHXt2lUXL15UUFCQAgMDdf36dYWHh1vtd/s5uxWvwWDQl19+qY8++kgdOnTQiy++qIiICL322mtW773XZ79frLcnfrfcOh8ff/xxuqT39irY/b47trhb3Hf+fT3os935c3V7bA87fkbO1e1++uknubm5KTg4+L77ATkZrWQ8kujoaD3//PMqV66cypcvr4sXL1q1X+Li4pScnHzPikuLFi20b98+rVy5Ug0aNLC0pcqXL58uadizZ49lnHz58uny5cuWbbcuir8bd3d3nT9/Xn/99Zdl3cKFC/XWW29ZfrncPlZGqoi3K1++vBITE1WsWDG5u7vL3d1dJ0+e1LRp0+6aeN7pQfu0aNFC3333nbZu3WpzG/mW5s2ba+vWrdq8ebPV7WbKly+vkydPWuJ2d3fXsmXL9N///vehjnMvmzZtUuHChRUVFaXXX39d/v7+SkhIeKgZ2QULFlSJEiW0d+9ey7rr16/r999/z9DPx/fff68vv/xSDRs21AcffKCvv/5ax48f1x9//KHy5cvrzz//tDofW7Zs0erVqx8YV1xcnH755RfNnz9fb7zxhho2bGiZXHP75zx06JDlzwcOHJCbm5uKFi2qJUuWqFevXho8eLDatGmjokWL6ty5c1kya71QoUIqXry4kpKSLJ+zdOnSmjBhgo4dO5bpx7vb93nfvn0ZqsRKaf/w/O233yyvU1NTFRsbm2njZ5SLi4uaNWuWqWMCjxsSQ2TYpUuXlJSUpL///luHDx/W6NGjtXbtWsv1WR4eHqpfv74iIiK0f/9+7d+/XxEREapevbo8PT3vOmaZMmXk7e2tBQsWWCU9nTt31qFDhzR58mQdO3ZMK1as0BdffKFXXnlFklSlShX9+OOP2r59u/744w99+OGH96xuPPPMM6pZs6aGDBmiw4cPa8eOHZozZ47q1KmjEiVKqHTp0po7d64SEhIUExOj77//3qbzUrduXZUtW1b9+/fX4cOH9euvv2rYsGHKnz//Xated8qfP78uXLig48eP37Xy+cILL+j48ePauXOnmjZtalNst9SvX99ysf3tiWFoaKjWrl2rhQsX6sSJE5o/f77mz59v1SLODEWKFFFiYqK2b9+uhIQEzZkzRxs3bkzXqsuo0NBQTZs2Td9++63i4+M1bNgwXbt2zfLZqlSposWLF+v48ePasmWL1U25U1NTNX78eG3atEknT55UTEyM8ufPr6efflqdO3fWgQMHNGXKFB0/flyrV6/W5MmTVaZMGUlpNyNPSkq6awW8UKFCcnBw0Jo1a3Tq1CmtX7/eMrHk9s85evRo/fbbb/rpp5/08ccfW36mixYtqu3bt1ta0O+8846uX79+33Pk4uKio0eP6vz58w91DqdOnapvv/1Wx48f19ChQ7V79+50l3zc79hHjhzRpUuXJKVN3LnXff1CQ0O1ePFirVy5UseOHdPEiRMVGxtraeE/yMsvv6wDBw4oMjJSR48e1bhx46xmUD/q+BnVsGHDDE22AXIyEkNk2EcffaS6deuqfv366tKli44dO6b58+crICDAss+4ceNUrlw5hYaGqlu3bnrmmWc0c+bM+44bFBSkvHnzqmHDhpZ1ZcqUUVRUlH744Qe1bNlSkZGRGjhwoNq1aydJat26tZo0aaK33npL3bt3V4sWLawuRr/ThAkTlD9/fnXs2FHvvfeeOnbsqM6dO8vBwUGjR4/W/v37FRQUpPXr16drIz5Injx5FBkZqdTUVL388svq3bu3GjRooKFDh2bo/TVr1pS7u7tatmxpVU26xdXVVfXr11fVqlVVvHhxm2K7xdHRUS+88IKeeOIJq4v7q1atqvHjx+uLL75QUFCQli9frkmTJql69eoPdZx7adasmVq1aqU+ffqoXbt22rFjhyIiIhQfH/9QyWHXrl3VoUMHDRs2TG3bttXp06e1aNEiy6SIYcOG6fz582rRooU+/fRT9enTx/LewMBA9enTR2PGjFGzZs20du1azZo1S4ULF1bZsmU1e/Zs/fDDD2rRooWmTp2qgQMHqlWrVpLSqtZ169a1qj7f8sQTT2jEiBH65JNP1KJFC82ZM0dDhw5V3rx5dfDgQct+QUFBCgsL07vvvqsOHTqoZ8+ektJuIJ+cnKzWrVurd+/e8vLy0n/+85+7/kzcEhwcrM8//zzDP2u369atm9q3b6/hw4erTZs2SkxM1Ny5czM8oSIkJETjx4+3JL9169bV2rVr77pvUFCQ3nnnHU2bNk2tWrXSzp079dlnn1nuavAg7u7uioyM1Jo1a9SmTRslJSWpQYMGmTZ+Rn322WeZnmwCjxuDKSv6FIANpkyZotOnT2vcuHH2DuWx1alTJ3Xo0MGSGMN+IiIi1K9fP5UsWdKm9926hnbLli168sknsyg6+4mOjpazs/NDX+4A4PHA5BPYTWxsrA4dOqQvvvhCkZGR9g7nsfTzzz9r9+7dio+Pf+g2MjLPiRMn9M8//9icFP7bpaamasWKFZb7HALIuUgMYTcHDhzQqFGj1LlzZ/n7+9s7nMfS119/rS1btujDDz/M0P3ikLXKlSvHP2LuwsHBQfPmzcu0WcwA7IdWMgAAACQx+QQAAABmJIYAAACQRGIIAAAAMxJDAAAASCIxBAAAgBmJIQAAACSRGAIAAMCMxBAAAACSSAwBAABg9n8+C3Nl6E+VxwAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" @@ -252,23 +246,23 @@ "source": [ "df_2dhist = pd.DataFrame({\n", " x_label: grp['Do you currently work?'].value_counts()\n", - " for x_label, grp in df.groupby('Do you currently live in a house, apartnment, or dorm? ')\n", + " for x_label, grp in df.groupby('Do you currently live in a house, apartment, or dorm?')\n", "})\n", "\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", + "sns.heatmap(df_2dhist, cmap=\"Blues\")\n", + "plt.xlabel('Do you currently live in a house, apartment, or dorm?')\n", "_ = plt.ylabel('Do you currently work?')" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.404019Z", - "start_time": "2024-02-23T06:53:03.194598Z" + "end_time": "2024-02-24T02:30:50.805764Z", + "start_time": "2024-02-24T02:30:50.611563Z" } }, - "id": "15f1e14311b1b17f", + "id": "c6372820e5ee501f", "execution_count": 6 }, { @@ -277,7 +271,7 @@ "metadata": { "collapsed": false }, - "id": "2b499b750ea3aec9" + "id": "3ef5084b2abd603e" }, { "cell_type": "code", @@ -285,14 +279,14 @@ { "data": { "text/plain": "<Figure size 640x480 with 1 Axes>", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "df.groupby('Do you currently live in a house, apartnment, or dorm? ').size().plot(kind='barh',\n", + "df.groupby('Do you currently live in a house, apartment, or dorm?').size().plot(kind='barh',\n", " color=sns.palettes.mpl_palette(\n", " 'Dark2'))\n", "plt.gca().spines[['top', 'right', ]].set_visible(False)" @@ -300,11 +294,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.516283Z", - "start_time": "2024-02-23T06:53:03.408298Z" + "end_time": "2024-02-24T02:30:50.904997Z", + "start_time": "2024-02-24T02:30:50.807674Z" } }, - "id": "a3d9a4a3b5eba149", + "id": "67df9b48e43a5307", "execution_count": 7 }, { @@ -327,11 +321,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.640403Z", - "start_time": "2024-02-23T06:53:03.518082Z" + "end_time": "2024-02-24T02:30:51.009476Z", + "start_time": "2024-02-24T02:30:50.906524Z" } }, - "id": "36727f07413da341", + "id": "1163d27db8106025", "execution_count": 8 }, { @@ -342,7 +336,7 @@ "metadata": { "collapsed": false }, - "id": "4df3824f641fb18b" + "id": "8f2599d399d14333" }, { "cell_type": "markdown", @@ -352,15 +346,15 @@ "metadata": { "collapsed": false }, - "id": "796d474b4650e712" + "id": "85d89eaa6a2c8057" }, { "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>" + "text/plain": "Do you work in a department related to your major? No Yes Total\nDo you currently live in a house, apartment, or... \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, apartment, 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": {}, @@ -374,11 +368,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.657146Z", - "start_time": "2024-02-23T06:53:03.642872Z" + "end_time": "2024-02-24T02:30:51.029851Z", + "start_time": "2024-02-24T02:30:51.010794Z" } }, - "id": "2ee7f39b5d8df8de", + "id": "5cbb7ab4d38de9ef", "execution_count": 9 }, { @@ -416,11 +410,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-02-23T06:53:03.664072Z", - "start_time": "2024-02-23T06:53:03.659800Z" + "end_time": "2024-02-24T02:30:51.035050Z", + "start_time": "2024-02-24T02:30:51.030607Z" } }, - "id": "957406c164cf2ef1", + "id": "2fbaac2d0722a7e3", "execution_count": 10 } ], @@ -1,4 +1,4 @@ -Timestamp,What class are you filling out this survey for? Select all that apply.,What is your current class standing?, What is your age?,What is your ethnicity?,What gender do you identify as?,Select all that you identify with:,Who do you live with? ,"Do you currently live in a house, apartnment, or dorm? ",How many people live in your household?,What is your preferred domain of interest?,What is your second most preferred domain of interest?,How much free time would you say you have a day on average?,What hobbies/activities outside of general education did you enjoy before you entered college? Choose all that apply.,What hobbies/activities outside of general education do you enjoy during college? Choose all that apply.,Approximately how many hours a week on average do you spend on these hobbies/this hobby? ,What is your major? ,"After what event did you first think that you may want to major in computer science? (For example: After taking an AP CS class in high school, taking part in a coding competition, etc.)",When did you take your first CS or CS-related Class?,How many AP classes did you take during your high school?,What was your (unweighted) high school GPA?,What is your SAT score?,What is your ACT Score?,How many units are you taking this quarter?,What was the language that you wrote your first program in?, What is your range of cumulative GPA at UCR?,Do you have roommates that are part of your major?,I attended higher education because... [Most of my friends did as well],I attended higher education because... [It was the best choice for my career],I attended higher education because... [I wanted a career with a higher than average salary],I attended higher education because... [I wanted to have more college experiences],I attended higher education because... [I love learning],I attended higher education because... [Other],Did you attend a public or private high school?,What was your GPA your very first quarter at UCR?,What influenced your decision to choose your current major/department at UCR?,What was the most important thing when you considered attending UCR?,Did you complete an iEval evaluation last quarter(Fall or last quarter you attended)? Choose one that applies:,Do you leave comments on an iEval? Select all that apply:,"When filling out an iEval, which of the following statements resonate with your experience and approach? Select all that apply.",How much change do you believe iEvals will lead to in your class? ,Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Math],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Biology],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Chemistry],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Physics],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Computer Science and Programming],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Language and Literature],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [History and Social Studies],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Art and Music],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Buisness and Economics],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Engineering and Technology],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Health and Physical Education],Did you ever feel that using ChatGPT for school/course work prevents you from learning or understanding the course material? ,How many of your course instructors have explicitly allowed the use of ChatGPT for assignments? (Enter a number),Do you believe that tools like ChatGPT will become an essential part of education in the future?,What are your career plans right after graduation?,What are your long-term career plans?,Are you considering work-study or co-op programs as part of your educational plan?,Do you currently work?,How many hours do you work per week on average?,Do you work on or off campus?,Do you work in a department related to your major?,Do you have family members who have careers related to your career aspirations?,How confident are you in being able to secure a position in the tech industry or some field related to computer science?,How often if at all do you consider switching majors? ,"How many hours in a week do you prepare for your future career? (Leetcode, Personal Projects, Applications etc.)",How many internship/job applications have you sent out so far?,"Have you been accepted to any of these? If so, how many? If not, reply N/A",What is your total screen time per day?,At what age did you have your own personal smartphone?,How many of your friends use social media?,How many hours do you spend on social media on a typical day?,How often do you post content on your social media pages?,"How much do you agree with the following statement: +Timestamp,What class are you filling out this survey for? Select all that apply.,What is your current class standing?,What is your age?,What is your ethnicity?,What gender do you identify as?,Select all that you identify with:,Who do you live with?,"Do you currently live in a house, apartment, or dorm?",How many people live in your household?,What is your preferred domain of interest?,What is your second most preferred domain of interest?,How much free time would you say you have a day on average?,What hobbies/activities outside of general education did you enjoy before you entered college? Choose all that apply.,What hobbies/activities outside of general education do you enjoy during college? Choose all that apply.,Approximately how many hours a week on average do you spend on these hobbies/this hobby? ,What is your major? ,"After what event did you first think that you may want to major in computer science? (For example: After taking an AP CS class in high school, taking part in a coding competition, etc.)",When did you take your first CS or CS-related Class?,How many AP classes did you take during your high school?,What was your (unweighted) high school GPA?,What is your SAT score?,What is your ACT Score?,How many units are you taking this quarter?,What was the language that you wrote your first program in?, What is your range of cumulative GPA at UCR?,Do you have roommates that are part of your major?,I attended higher education because... [Most of my friends did as well],I attended higher education because... [It was the best choice for my career],I attended higher education because... [I wanted a career with a higher than average salary],I attended higher education because... [I wanted to have more college experiences],I attended higher education because... [I love learning],I attended higher education because... [Other],Did you attend a public or private high school?,What was your GPA your very first quarter at UCR?,What influenced your decision to choose your current major/department at UCR?,What was the most important thing when you considered attending UCR?,Did you complete an iEval evaluation last quarter(Fall or last quarter you attended)? Choose one that applies:,Do you leave comments on an iEval? Select all that apply:,"When filling out an iEval, which of the following statements resonate with your experience and approach? Select all that apply.",How much change do you believe iEvals will lead to in your class? ,Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Math],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Biology],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Chemistry],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Physics],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Computer Science and Programming],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Language and Literature],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [History and Social Studies],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Art and Music],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Buisness and Economics],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Engineering and Technology],Have you found ChatGPT to be more helpful for certain subjects or types of assignments? (Rank from least to most helpful) [Health and Physical Education],Did you ever feel that using ChatGPT for school/course work prevents you from learning or understanding the course material? ,How many of your course instructors have explicitly allowed the use of ChatGPT for assignments? (Enter a number),Do you believe that tools like ChatGPT will become an essential part of education in the future?,What are your career plans right after graduation?,What are your long-term career plans?,Are you considering work-study or co-op programs as part of your educational plan?,Do you currently work?,How many hours do you work per week on average?,Do you work on or off campus?,Do you work in a department related to your major?,Do you have family members who have careers related to your career aspirations?,How confident are you in being able to secure a position in the tech industry or some field related to computer science?,How often if at all do you consider switching majors? ,"How many hours in a week do you prepare for your future career? (Leetcode, Personal Projects, Applications etc.)",How many internship/job applications have you sent out so far?,"Have you been accepted to any of these? If so, how many? If not, reply N/A",What is your total screen time per day?,At what age did you have your own personal smartphone?,How many of your friends use social media?,How many hours do you spend on social media on a typical day?,How often do you post content on your social media pages?,"How much do you agree with the following statement: Seeing other people's posts makes me feel like my own life is not interesting enough.","How much do you agree with the following statement: @@ -163,7 +163,7 @@ I have a friend that I feel like truly cares about me.","In the last week, how m 2/11/2024 18:01:55,CS 10C,Freshman,22,Asian,Male,First Generation Student,Family,House,5,Machine learning and Data mining,Software Engineering,4 - 6 hours,"Gaming, Cooking","Gaming, Cooking",0 - 10 hours,CEN,,College,2,3.5 - 4.0,1400-1600,Took SAT,8,C++,Prefer not to say,No,2,1 (Most Important),1 (Most Important),1 (Most Important),2,,Public school,3,Interest/Passion,Academic Caliber,"Yes. Class Incentive (e.g., extra credit or reward).","When something is wrong, When something is worth reporting","I would prefer a more convenient and user-friendly iEval process., I give the highest evaluation to liked professors (all 5s).",Minimal Change,4,3,3,4,3,3,3,3,3,3,3,No,0,Yes,Get into the Job Industry,Get into the Job Industry,Not sure,Yes,10 - 20,Off-campus,No,No family in related fields/careers,3,2,None,0,N/A,0 - 4 hrs per day,Middle School,All,<1 hour,A few times a year,2,4,2,3,"Social media, Entertainment (e.g., streaming videos, gaming), Communication (e.g., messaging, calls)",(0 - 20)%,(21-40)%,Occasionally,3,Occasionally,Occasionally,Yes,3,3,3,Strongly agree,Personal car,45,40,Does not apply,2 - 4,4,4,,,,,,,Almost every day,0 ounces (I don't drink coffee),Green tea (and drinks primarily made with green tea),5 serving (25oz),Several times a week,5 - 8 servings (25 - 40oz),Yes,2 - 3 times a week,5 (Least Frequent),2,2,2,1 (Most Frequent),3,Cook some of your meals (about ⅓ of your meals),Parents,"yes, 20 hours",Yes,5,20,No,No,5,Less than 5 hours,1 (Most likely to participate),2,1 (Most likely to participate),3,Alone,Yes,Yes,"Come to class, Taking notes","Condescending attitude, Doesn't take criticism well, Doesn't listen to your ideas, Distracting behavior (on their phone, talking loudly, ect)",Hot latte or Cappuccino,No,0,Yes,"Hams War, War in Ukraine",30,Yes,Yes,Yes 2/11/2024 19:50:27,CS 10C,Sophomore,20,White,Male,Second Generation Student,Family,Apartment,2,Software Engineering,Software Engineering,<= 3 hours,,,0 - 10 hours,CS,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 2/11/2024 20:09:19,CS105,Sophomore,19,Asian,Male,"Second Generation Student, In-State Student",Family,House,4,Data Science,Machine learning and Data Mining,<= 3 hours,working out ,Playing/Doing some kind of sport or martial art,10 - 20 hours,Data Science,AP Stats,High School,7,3.5 - 4.0,1200-1400,Took SAT,17,C++,3.5 - 4.0,Yes,6 (Least Important),1 (Most Important),2,3,1 (Most Important),,Private school,3.8,Interest/Passion,Academic Caliber,"Yes, I wanted to voice my opinion on professor/course.",Always,I take my time to fill out the iEvals,Some Change,3,1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),3,4,2,1 (Least Helpful),3,3,3,No,1,Yes,Attend Grad School,Get into the Job Industry,Yes,Yes,10 - 20,Off-campus,No,2 or more in my immediate family (parents/legal guardians or siblings),2,5,1 - 3 hours,9,N/A,4 - 8 hrs per day,Middle School,All,1 - 3 hours,A few times a year,2,3,2,5,"Social media, Entertainment (e.g., streaming videos, gaming), Communication (e.g., messaging, calls)",(0 - 20)%,(0 - 20)%,Almost always,4,Frequently,Frequently,Yes,3,1,3,Neutral,Walking,10,0.5,8+ hours,Does not apply,4,3,3,1,1,1,NEVER,NEVER,Once a week,More than 24 ounces,Black Tea (and drinks primarily made with black tea),1 serving (5oz),Daily,0 - 2 servings (0 - 10oz),Yes,2 - 3 times a week,1 (Most Frequent),1 (Most Frequent),5 (Least Frequent),1 (Most Frequent),5 (Least Frequent),2,Cook some of your meals (about ⅓ of your meals),Parents,Yes 10,Yes,2,60,Yes,No,5,Less than 5 hours,1 (Most likely to participate),3,3,1 (Most likely to participate),Alone,Yes,Yes,"Come to class, Go to Office hours, Raise their hands during class, Taking notes",Asking a lot of questions,Black coffee with creamer or milk,No,0,Yes,,,Prefer not to answer,Prefer not to answer,Prefer not to answer -2/11/2024 20:13:33,CS 10C,Sophomore,Prefer not to say,"Asian, South Asian/Bangladeshi-Bengali",Female,International Student,Family,Apartment,3 excluding me,"Web Development, Software Engineering, Computer Graphics (UX/UI-Design), Artificial Intelligence for good",Machine learning and Data Mining,<= 3 hours,"Reading, Art (Music, Dance, Creative art)",Reading,0 - 10 hours,CS,After reading the book “ Invisible Women: Exposing Data Bias in a World Designed for Men” and being fairly good at Math and problem solving,College,None because I’m international,3.5 - 4.0,1400-1600,Took SAT,18,C++,3.0 - 3.5,No,6 (Least Important),1 (Most Important),1 (Most Important),1 (Most Important),1 (Most Important),,Semi-private in Bangladesh,3.25,Interest/Passion,Academic Caliber,"Yes, I wanted to voice my opinion on professor/course.",When something is worth reporting,I would prefer fewer iEval questions.,Minimal Change,1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),3,3,1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),Unsure,0,Yes,Attend Grad School,Get into the Job Industry,Yes,Yes,5 - 10,On-campus,Yes,No family in related fields/careers,2,1,1 - 3 hours,25,N/A,4 - 8 hrs per day,After high school,All,1 - 3 hours,A few times a month,1,3,2,2,"Communication (e.g., messaging, calls)",(0 - 20)%,(0 - 20)%,Occasionally,2,Rarely,Rarely,Yes,4,2,4,Strongly agree,"Public Transport (bus, train, ect)",20,Do not know,8+ hours,8+,2,5,4,,,,CS10B,None,Once or twice a month,0 ounces (I don't drink coffee),I don’t drink,N/A,Several times a week,2 - 5 servings (10 - 25oz),No,Never,5 (Least Frequent),5 (Least Frequent),1 (Most Frequent),5 (Least Frequent),3,4,Cook all meals when possible,Parents and a merit scholarship,8-10,Yes,5,15,Not sure,Yes,1,Less than 5 hours,3,2,3,2,With a random partner,Yes,No,My behaviors are not influenced by classmates,"Condescending attitude, Doesn't take criticism well",Don’t drink coffee,Yes,9 in total,Yes,"The Israel-Palestine genocide, Berkeley active shooting, Texas shooting, Russia-Ukraine conflict, The recent Bangladeshi national elections",1,No,,No +2/11/2024 20:13:33,CS 10C,Sophomore,Prefer not to say,"Asian, South Asian/Bangladeshi-Bengali",Female,International Student,Family,Apartment,4,"Web Development, Software Engineering, Computer Graphics (UX/UI-Design), Artificial Intelligence for good",Machine learning and Data Mining,<= 3 hours,"Reading, Art (Music, Dance, Creative art)",Reading,0 - 10 hours,CS,After reading the book “ Invisible Women: Exposing Data Bias in a World Designed for Men” and being fairly good at Math and problem solving,College,None because I’m international,3.5 - 4.0,1400-1600,Took SAT,18,C++,3.0 - 3.5,No,6 (Least Important),1 (Most Important),1 (Most Important),1 (Most Important),1 (Most Important),,Semi-private in Bangladesh,3.25,Interest/Passion,Academic Caliber,"Yes, I wanted to voice my opinion on professor/course.",When something is worth reporting,I would prefer fewer iEval questions.,Minimal Change,1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),3,3,1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),Unsure,0,Yes,Attend Grad School,Get into the Job Industry,Yes,Yes,5 - 10,On-campus,Yes,No family in related fields/careers,2,1,1 - 3 hours,25,N/A,4 - 8 hrs per day,After high school,All,1 - 3 hours,A few times a month,1,3,2,2,"Communication (e.g., messaging, calls)",(0 - 20)%,(0 - 20)%,Occasionally,2,Rarely,Rarely,Yes,4,2,4,Strongly agree,"Public Transport (bus, train, ect)",20,Do not know,8+ hours,8+,2,5,4,,,,CS10B,None,Once or twice a month,0 ounces (I don't drink coffee),I don’t drink,N/A,Several times a week,2 - 5 servings (10 - 25oz),No,Never,5 (Least Frequent),5 (Least Frequent),1 (Most Frequent),5 (Least Frequent),3,4,Cook all meals when possible,Parents and a merit scholarship,8-10,Yes,5,15,Not sure,Yes,1,Less than 5 hours,3,2,3,2,With a random partner,Yes,No,My behaviors are not influenced by classmates,"Condescending attitude, Doesn't take criticism well",Don’t drink coffee,Yes,9 in total,Yes,"The Israel-Palestine genocide, Berkeley active shooting, Texas shooting, Russia-Ukraine conflict, The recent Bangladeshi national elections",1,No,,No 2/11/2024 20:36:55,CS105,Senior,22,Asian,Male,In-State Student,Family,House,5,Software Engineering,Web Development,4 - 6 hours,"Gaming, Reading","Gaming, Reading, Playing/Doing some kind of sport or martial art",10 - 20 hours,CS,AP Class,High School,1,3.5 - 4.0,1200-1400,Took SAT,16,C++,3.0 - 3.5,No,1 (Most Important),1 (Most Important),1 (Most Important),2,3,4,Public school,3.4,Job prospects,Location,"Yes, I wanted to voice my opinion on professor/course.",When something is good,"I fill out the form with neutral bias., I take my time to fill out the iEvals",Some Change,1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),5,2,1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),1 (Least Helpful),Yes,0,No,Get into the Job Industry,Get into the Job Industry,No,No,1 - 5,On-campus,No,"Extended family (Aunts, uncles, cousins)",3,1,1 - 3 hours,0,N/A,4 - 8 hrs per day,High School,All,3 - 5 hours,Once or twice a year,4,2,2,5,"Social media, Work-related tasks",(0 - 20)%,(0 - 20)%,Rarely or never,5,Frequently,Occasionally,Yes,2,4,4,Agree,Personal car,23,14,5-6 hours,4 - 6,1,3,3,4,5,,CS153,None,Once a week,0 ounces (I don't drink coffee),Black Tea (and drinks primarily made with black tea),2 serving (10oz),Several times a week,5 - 8 servings (25 - 40oz),Yes,Once a week,5 (Least Frequent),2,2,4,5 (Least Frequent),2,Not applicable (parents/others cook meals for you),Parents,no,Yes,4,120,Yes,Yes,4,Less than 5 hours,1 (Most likely to participate),2,2,2,With a random partner,Yes,Yes,"Come to class, Raise their hands during class",,Hot latte or Cappuccino,Yes,7,No,yes,630,Yes,No,Yes 2/11/2024 20:38:30,CS105,Senior,21,"Asian, White",Male,In-State Student,Neither,Apartment,1,Software Engineering,Game Development,<= 3 hours,"Gaming, Reading","Gaming, Reading",10 - 20 hours,CS,Taking computer science principles in High School,Elementary School,6,3.5 - 4.0,1400-1600,Took SAT,16,Java,3.5 - 4.0,No,2,2,2,3,4,4,Public school,4,Interest/Passion,Location,"Yes. Class Incentive (e.g., extra credit or reward).","Always, When something is wrong",I would prefer fewer iEval questions.,Minimal Change,3,3,3,3,3,3,3,2,3,3,2,Yes,1,Unsure,Get into the Job Industry,Get into the Job Industry,Not sure,No,1 - 5,Off-campus,No,"Extended family (Aunts, uncles, cousins)",2,2,1 - 3 hours,10,N/A,4 - 8 hrs per day,Middle School,Few,1 - 3 hours,Never,1,1,1,3,"Entertainment (e.g., streaming videos, gaming), Communication (e.g., messaging, calls)",(0 - 20)%,(0 - 20)%,Frequently,4,Occasionally,Frequently,Yes,3,3,4,Agree,Personal car,15,3,5-6 hours,4 - 6,3,3,3,4,4,3,Discrete Structures,CS 180,Once a week,1 - 8 ounces,Energy Drink,1 serving (5oz),Daily,5 - 8 servings (25 - 40oz),Yes,2 - 3 times a week,5 (Least Frequent),5 (Least Frequent),3,2,5 (Least Frequent),2,Cook some of your meals (about ⅓ of your meals),Parents,No,Yes,3,10,Yes,No,5,Less than 5 hours,3,3,3,3,Alone,Yes,Yes,"Come to class, Go to Office hours, Raise their hands during class, Taking notes","Condescending attitude, Doesn't take criticism well, Doesn't listen to your ideas, Distracting behavior (on their phone, talking loudly, ect)",Frappucino,Yes,4,Yes,yes,0,Not sure,Yes,Yes 2/11/2024 20:55:13,CS105,Junior,21,Asian,,"Second Generation Student, In-State Student",Family,House,4,Software Engineering,Game Development,<= 3 hours,Poker,Gaming,0 - 10 hours,CS,AP CS in high school,High School,,3.5 - 4.0,1400-1600,Took SAT,16,Python,3.0 - 3.5,No,3,4,4,4,2,2,Public school,4,Parents,Location,No. Lack of incentives.,"When something is worth reporting, Never, I never fill out an iEval",I would prefer fewer iEval questions.,No Change,,,,,,,,,,,,No,None,Yes,Get into the Job Industry,Get into the Job Industry,No,Yes,10 - 20,Off-campus,No,2 or more in my immediate family (parents/legal guardians or siblings),3,1,1 - 3 hours,5,N/A,4 - 8 hrs per day,Middle School,All,3 - 5 hours,A few times a month,1,1,1,3,"Social media, Communication (e.g., messaging, calls)",(21-40)%,(0 - 20)%,Occasionally,2,Occasionally,Occasionally,Yes,3,4,3,Neutral,Personal car,30-40,40,Does not apply,2 - 4,1,1,1,1,1,1,Never,None,Once or twice a month,0 ounces (I don't drink coffee),I don’t drink,1 serving (5oz),Daily,0 - 2 servings (0 - 10oz),No,Once a week,4,4,3,2,4,3,Cook some of your meals (about ⅓ of your meals),Parents,25,No,1,30,No,Not sure,3,Less than 5 hours,3,3,3,3,With a random group,Yes,Yes,My behaviors are not influenced by classmates,"Distracting behavior (on their phone, talking loudly, ect)",Frappucino,No,0,No,"The flood, Superbowl",30?,Yes,Yes,Yes |