FinalProject_group038.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Project\n",
    "Imported libraries. The dataset we're using is OSMI 2016 Mental Health Tech Survey."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Names\n",
    "- XXXXXXXXX Caroline Hogan\n",
    "- XXXXXXXXX Adriana Ramirez\n",
    "- XXXXXXXXX Gabriela Gutierrez\n",
    "- XXXXXXXXX Abhiram Iyer\n",
    "- XXXXXXXXX Cang Le"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction and Background \n",
    "\n",
    "Many [tech entrepreneurs have realized that mental health](https://www.statnews.com/2018/07/20/tech-developers-tackle-mental-health/) is becoming a more relevant issue in the tech industry, as it’s a high-pressure environment and often unreasonable work hours negatively impact the health of many employees. As a result, many companies and startups are now focusing on addressing these problems, with new technologies like chatbots that help provide assistance to those who need it. However, as many have realized, a technological solution to solving this specific problem might be counterintuitive - forcing many to use apps or similar solutions to helping relieve anxiety might only spur greater agitation. Thus, while many startups are trying to conquer the “mental health space” with new ideas, not many attempts to understand the root causes and reasons for tech-related mental health issues. We hope to find and analyze some of these fundamental causes ourselves so we can draw meaningful conclusions ourselves.\n",
    "\n",
    "The dataset we are working with attempts to describe various employees’ reactions and attitudes towards mental health in the tech workplace. Specifically, each employee discusses how they believe their company prioritizes mental health, and the general attitude and environment in the workplace regarding mental health. The dataset covers “tangible” features that the employees responded to, like whether their employer provides mental health benefits or not (e.g. a concrete question without much room for subjectivity). The dataset also covers more subjective questions, like whether the employee would feel comfortable raising mental health related topics with their manager or supervisor. We can correlate the objective and subjective viewpoints provided by each employee’s survey, and then be able to extrapolate some specific trends.\n",
    "\n",
    "Our greater inquiry we are attempting to investigate is how certain qualities of tech companies affect the mental health of their employees. Due to largely subjective nature of the data on this topic, we’ve decided to center our data analysis around two quantitative variables of each response, their company size (number of employees) and the participant's age. This will allow us to find correlation between our other variables, which are largely yes or no questions, and objective variables with more variation. These two variables may also shed light on how size and bureaucracy affects employee well-being and how the industry experience is changing for the newest generation of tech employees.\n",
    "\n",
    "The dataset is procured courtesy of OSMI (Open Source Mental Illness), and their datasets can be found here: https://osmihelp.org/research. The organization attempts to study mental health in the tech workplace every year and is considered a pioneer in this field. Similar proposals and research have never attempted to collect data on the matter, but have instead tried to propose solutions to raise awareness about mental health. For example, [Prompt](https://mhprompt.org) is a project that attempts to raise awareness about mental health in the tech workplace specifically, and even tries to distribute resources/help to those who need it. Prompt also tries to organize public sessions and talks regarding mental health in the tech industry in order to destigmatize the topic. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research Question\n",
    "\n",
    "Is there a correlation between the size of tech companies and the number of their employees who face mental health issues? How is their experience in their workplace correlated with employee beliefs on how mental health is addressed in their workplace?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis\n",
    "\n",
    "We expect that their will be a negative correlation between company size and representations of good mental health. Such representations may include low occurrence of mental illness, high awareness of available mental health resources, and beliefs that mental illness will not inhibit their professional growth.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset(s)\n",
    "The [2016 OSMI Mental Health in Tech survey](https://www.kaggle.com/osmi/mental-health-in-tech-2016) contains responses from 1,580 individuals. It has 60 variables, which are a mix of quantitative (i.e. age, location, company size) and qualitative (i.e. attitudes/beliefs about their employer/workplace) questions related to their personal history and experience in their workplace. The main variables we’ll be working with are company size and age as they relate to the yes-or-no evaluations such as “Have you heard of or observed negative consequences for co-workers who have been open about mental health issues in your workplace?” and “Have you been diagnosed with a mental health condition by a medical professional?”."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statistics as stats\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data wrangling\n",
    "\n",
    "data = pd.read_csv('2016_survey.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "There is a function to turn yes/no into numerical values. We renamed the columns into much shorter names and removing columns with data that we don't want to include. We removed all survey entries that are not tech employees, people outside of the age range of 18 or 70, and creating a categorical columns of age ranges in age ranges of 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(label):\n",
    "# YOUR CODE HERE\n",
    "    if label == 'Yes':\n",
    "        return 1.0\n",
    "    elif label == 'No':\n",
    "        return -1.0\n",
    "    else:\n",
    "        return 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# DATA CLEANING\n",
    "\n",
    "# rename important columns\n",
    "data = data.rename(columns={\"How many employees does your company or organization have?\" : \"company_size\",\n",
    "                     \"Is your employer primarily a tech company/organization?\" : \"is_tech\",\n",
    "                     \"Does your employer provide mental health benefits as part of healthcare coverage?\" : \"mental_health_care\",\n",
    "                     \"Has your employer ever formally discussed mental health (for example, as part of a wellness campaign or other official communication)?\" : \"formal_discussion\",\n",
    "                     \"Does your employer offer resources to learn more about mental health concerns and options for seeking help?\" : \"resources_provided\",\n",
    "                     \"Is your anonymity protected if you choose to take advantage of mental health or substance abuse treatment resources provided by your employer?\" : \"anonymity_protected\",\n",
    "                     \"If a mental health issue prompted you to request a medical leave from work, asking for that leave would be:\" : \"ease_of_leave\",\n",
    "                     \"Do you think that discussing a mental health disorder with your employer would have negative consequences?\" : \"mental_discussion_negative\",\n",
    "                     \"Do you think that discussing a physical health issue with your employer would have negative consequences?\" : \"physical_discussion_negative\",\n",
    "                     \"Would you feel comfortable discussing a mental health disorder with your coworkers?\" : \"coworker_safe_space\",\n",
    "                     \"Would you feel comfortable discussing a mental health disorder with your direct supervisor(s)?\" : \"boss_safe_space\",\n",
    "                     \"Do you feel that your employer takes mental health as seriously as physical health?\" : \"mental_as_serious_as_physical\",\n",
    "                     \"Have you heard of or observed negative consequences for co-workers who have been open about mental health issues in your workplace?\" : \"observed_negative_consequences\",\n",
    "                     \"Would you bring up a mental health issue with a potential employer in an interview?\" : \"discuss_mental_health_interview\",\n",
    "                     \"Why or why not?.1\" : \"interview_why\",\n",
    "                     \"Do you feel that being identified as a person with a mental health issue would hurt your career?\" : \"mental_health_hurt_career\",\n",
    "                     \"Do you think that team members/co-workers would view you more negatively if they knew you suffered from a mental health issue?\" : \"negative_coworker_perception\",\n",
    "                     \"How willing would you be to share with friends and family that you have a mental illness?\" : \"family_openess\",\n",
    "                     \"Have you observed or experienced an unsupportive or badly handled response to a mental health issue in your current or previous workplace?\" : \"bad_environment\",\n",
    "                     \"Do you have a family history of mental illness?\" : \"family_history\",\n",
    "                     \"Have you had a mental health disorder in the past?\" : \"mental_disorder_past\",\n",
    "                     \"Have you been diagnosed with a mental health condition by a medical professional?\" : \"mental_disorder\",\n",
    "                     \"If so, what condition(s) were you diagnosed with?\" : \"condition\",\n",
    "                     \"Have you ever sought treatment for a mental health issue from a mental health professional?\" : \"sought_treatment\",\n",
    "                     \"If you have a mental health issue, do you feel that it interferes with your work when being treated effectively?\" : \"interferes_when_treated\",\n",
    "                     \"If you have a mental health issue, do you feel that it interferes with your work when NOT being treated effectively?\" : \"interferes_when_not_treated\",\n",
    "                     \"What is your age?\" : \"age\",\n",
    "                     \"What is your gender?\" : \"gender\",\n",
    "                     \"What country do you work in?\" : \"working_country\",\n",
    "                     \"Do you work remotely?\" : \"work_remotely\"}, inplace=False)\n",
    "\n",
    "# delete unimportant columns\n",
    "data = data.drop(data.columns.difference([\"company_size\", \"is_tech\", \"mental_health_care\", \n",
    "                                        \"formal_discussion\", \"resources_provided\", \"anonymity_protected\", \n",
    "                                        \"ease_of_leave\", \"mental_discussion_negative\", \"physical_discussion_negative\",\n",
    "                                        \"coworker_safe_space\", \"boss_safe_space\", \"mental_as_serious_as_physical\",\n",
    "                                        \"observed_negative_consequences\", \"discuss_mental_health_interview\",\n",
    "                                        \"interview_why\", \"mental_health_hurt_career\", \"negative_coworker_perception\",\n",
    "                                        \"family_openess\", \"bad_environment\", \"family_history\", \"mental_disorder_past\", \n",
    "                                        \"mental_disorder\", \"condition\", \"sought_treatment\", \"interferes_when_treated\",\n",
    "                                        \"interferes_when_not_treated\", \"age\", \"gender\", \"working_country\", \n",
    "                                        \"work_remotely\"]), 1)\n",
    "\n",
    "# only look at tech companies and valid ages\n",
    "data = data[data.is_tech == 1]\n",
    "data = data[data['age'] > 18]\n",
    "data = data[data['age'] < 70]\n",
    "data['age_range'] = pd.cut(data['age'], [18, 25, 32, 39, 46, 53, 60, 67])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now look at the dataset and what the data set looks like, whether variables are categorical, boolean or numerical. Most of this dataset seems to be categorical and boolean which would make predictive variables harder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "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>company_size</th>\n",
       "      <th>is_tech</th>\n",
       "      <th>mental_health_care</th>\n",
       "      <th>formal_discussion</th>\n",
       "      <th>resources_provided</th>\n",
       "      <th>anonymity_protected</th>\n",
       "      <th>ease_of_leave</th>\n",
       "      <th>mental_discussion_negative</th>\n",
       "      <th>physical_discussion_negative</th>\n",
       "      <th>coworker_safe_space</th>\n",
       "      <th>...</th>\n",
       "      <th>mental_disorder</th>\n",
       "      <th>condition</th>\n",
       "      <th>sought_treatment</th>\n",
       "      <th>interferes_when_treated</th>\n",
       "      <th>interferes_when_not_treated</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>working_country</th>\n",
       "      <th>work_remotely</th>\n",
       "      <th>age_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26-100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Not eligible for coverage / N/A</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>I don't know</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Anxiety Disorder (Generalized, Social, Phobia,...</td>\n",
       "      <td>0</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>39</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>(32, 39]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6-25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Anxiety Disorder (Generalized, Social, Phobia,...</td>\n",
       "      <td>1</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>29</td>\n",
       "      <td>male</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Never</td>\n",
       "      <td>(25, 32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6-25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>I don't know</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Always</td>\n",
       "      <td>(32, 39]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  company_size  is_tech               mental_health_care formal_discussion  \\\n",
       "0       26-100      1.0  Not eligible for coverage / N/A                No   \n",
       "1         6-25      1.0                               No               Yes   \n",
       "2         6-25      1.0                               No                No   \n",
       "\n",
       "  resources_provided anonymity_protected               ease_of_leave  \\\n",
       "0                 No        I don't know                   Very easy   \n",
       "1                Yes                 Yes               Somewhat easy   \n",
       "2                 No        I don't know  Neither easy nor difficult   \n",
       "\n",
       "  mental_discussion_negative physical_discussion_negative coworker_safe_space  \\\n",
       "0                         No                           No               Maybe   \n",
       "1                         No                           No               Maybe   \n",
       "2                      Maybe                           No               Maybe   \n",
       "\n",
       "     ...    mental_disorder  \\\n",
       "0    ...                Yes   \n",
       "1    ...                Yes   \n",
       "2    ...                 No   \n",
       "\n",
       "                                           condition sought_treatment  \\\n",
       "0  Anxiety Disorder (Generalized, Social, Phobia,...                0   \n",
       "1  Anxiety Disorder (Generalized, Social, Phobia,...                1   \n",
       "2                                                NaN                1   \n",
       "\n",
       "  interferes_when_treated interferes_when_not_treated age gender  \\\n",
       "0    Not applicable to me        Not applicable to me  39   Male   \n",
       "1                  Rarely                   Sometimes  29   male   \n",
       "2    Not applicable to me        Not applicable to me  38  Male    \n",
       "\n",
       "            working_country work_remotely age_range  \n",
       "0            United Kingdom     Sometimes  (32, 39]  \n",
       "1  United States of America         Never  (25, 32]  \n",
       "2            United Kingdom        Always  (32, 39]  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26-100            245\n",
       "100-500           206\n",
       "6-25              180\n",
       "More than 1000    133\n",
       "500-1000           62\n",
       "1-5                52\n",
       "Name: company_size, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['company_size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mood Disorder (Depression, Bipolar Disorder, etc)                                                                                                                                                                                             118\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)                                                                                                                                          93\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)                                                                                                                                                                                            36\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Attention Deficit Hyperactivity Disorder                                                                                                                                                     21\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Attention Deficit Hyperactivity Disorder                                                                                                 17\n",
       "Attention Deficit Hyperactivity Disorder                                                                                                                                                                                                       13\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Attention Deficit Hyperactivity Disorder                                                                                                                                                   12\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Post-traumatic Stress Disorder                                                                                                           11\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Obsessive-Compulsive Disorder                                                                                                            10\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Stress Response Syndromes                                                                                                                                                                     5\n",
       "Stress Response Syndromes                                                                                                                                                                                                                       5\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Personality Disorder (Borderline, Antisocial, Paranoid, etc)                                                                              3\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Obsessive-Compulsive Disorder                                                                                                                                                               3\n",
       "Psychotic Disorder (Schizophrenia, Schizoaffective, etc)                                                                                                                                                                                        3\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Post-traumatic Stress Disorder                                                                                                                                                              3\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Obsessive-Compulsive Disorder|Post-traumatic Stress Disorder                                                                              2\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Stress Response Syndromes                                                                                                                                                                   2\n",
       "Post-traumatic Stress Disorder                                                                                                                                                                                                                  2\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Stress Response Syndromes                                                                                                                 2\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Anxiety Disorder (Generalized, Social, Phobia, etc)                                                                                                                                           2\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Personality Disorder (Borderline, Antisocial, Paranoid, etc)|Stress Response Syndromes                                                    2\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Attention Deficit Hyperactivity Disorder|Post-traumatic Stress Disorder                                                                                                                       2\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Eating Disorder (Anorexia, Bulimia, etc)                                                                                                  2\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Personality Disorder (Borderline, Antisocial, Paranoid, etc)                                                                                                                                  2\n",
       "Substance Use Disorder                                                                                                                                                                                                                          2\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Post-traumatic Stress Disorder|Dissociative Disorder                                                                                      1\n",
       "Autism - while not a \"mental illness\", still greatly affects how I handle anxiety                                                                                                                                                               1\n",
       "Attention Deficit Hyperactivity Disorder|PDD-NOS (see above)                                                                                                                                                                                    1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Personality Disorder (Borderline, Antisocial, Paranoid, etc)|Post-traumatic Stress Disorder                                               1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Dissociative Disorder                                                                                                                     1\n",
       "                                                                                                                                                                                                                                             ... \n",
       "Substance Use Disorder|Addictive Disorder                                                                                                                                                                                                       1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Attention Deficit Hyperactivity Disorder|Post-traumatic Stress Disorder                                                                                                                     1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Personality Disorder (Borderline, Antisocial, Paranoid, etc)                                                                                                                                1\n",
       "Eating Disorder (Anorexia, Bulimia, etc)|Attention Deficit Hyperactivity Disorder|Post-traumatic Stress Disorder|Substance Use Disorder|Addictive Disorder                                                                                      1\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Eating Disorder (Anorexia, Bulimia, etc)|Attention Deficit Hyperactivity Disorder|Personality Disorder (Borderline, Antisocial, Paranoid, etc)                                                1\n",
       "Post-traumatic Stress Disorder|Dissociative Disorder                                                                                                                                                                                            1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Eating Disorder (Anorexia, Bulimia, etc)|Personality Disorder (Borderline, Antisocial, Paranoid, etc)|Post-traumatic Stress Disorder      1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Attention Deficit Hyperactivity Disorder|Personality Disorder (Borderline, Antisocial, Paranoid, etc)                                     1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Substance Use Disorder                                                                                                                                                                      1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Attention Deficit Hyperactivity Disorder|Post-traumatic Stress Disorder                                                                   1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Eating Disorder (Anorexia, Bulimia, etc)|posttraumatic stress disourder                                                                                                                     1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Attention Deficit Hyperactivity Disorder|Obsessive-Compulsive Disorder                                                                    1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Substance Use Disorder|Addictive Disorder                                                                                                 1\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Attention Deficit Disorder                                                                                                                                                                    1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Eating Disorder (Anorexia, Bulimia, etc)                                                                                                                                                    1\n",
       "Attention Deficit Hyperactivity Disorder|Obsessive-Compulsive Disorder|Stress Response Syndromes                                                                                                                                                1\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Eating Disorder (Anorexia, Bulimia, etc)|Attention Deficit Hyperactivity Disorder|Personality Disorder (Borderline, Antisocial, Paranoid, etc)|Dissociative Disorder                          1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Obsessive-Compulsive Disorder|Post-traumatic Stress Disorder|Stress Response Syndromes                                                    1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Attention Deficit Hyperactivity Disorder|Obsessive-Compulsive Disorder|Post-traumatic Stress Disorder                                     1\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Eating Disorder (Anorexia, Bulimia, etc)|Obsessive-Compulsive Disorder                                                                                                                        1\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Eating Disorder (Anorexia, Bulimia, etc)                                                                                                                                                      1\n",
       "Schizotypal Personality Disorder                                                                                                                                                                                                                1\n",
       "Asperger Syndrome                                                                                                                                                                                                                               1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Personality Disorder (Borderline, Antisocial, Paranoid, etc)|Obsessive-Compulsive Disorder                                                1\n",
       "autism spectrum disorder                                                                                                                                                                                                                        1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Attention Deficit Hyperactivity Disorder|Post-traumatic Stress Disorder|Substance Use Disorder                                            1\n",
       "Seasonal Affective Disorder                                                                                                                                                                                                                     1\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Attention Deficit Hyperactivity Disorder|Obsessive-Compulsive Disorder                                                                                                                        1\n",
       "Mood Disorder (Depression, Bipolar Disorder, etc)|Addictive Disorder                                                                                                                                                                            1\n",
       "Anxiety Disorder (Generalized, Social, Phobia, etc)|Mood Disorder (Depression, Bipolar Disorder, etc)|Substance Use Disorder                                                                                                                    1\n",
       "Name: condition, Length: 81, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes                                404\n",
       "I don't know                       240\n",
       "No                                 169\n",
       "Not eligible for coverage / N/A     65\n",
       "Name: mental_health_care, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['mental_health_care'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change the company size into fewer categorical options.\n",
    "We change the options for company size into 'Small, Medium and Large'. 'Small' companies are companies with sizes from 1-5 and 6-25. 'Medium' companies are companies with sizes from 26-100 and 100-500. 'Large' companies are companies with sizes from 500-1000 and More than 1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename company size to small, medium and large.\n",
    "for comp in data['company_size']:\n",
    "    if comp == \"1-5\":\n",
    "        a = data.loc[data['company_size'] == comp, ['company_size']]\n",
    "        b = data.loc[data['company_size'] == comp, ['mental_disorder']]\n",
    "        a['company_size'] = 'Small'\n",
    "        \n",
    "    elif comp == \"6-25\":\n",
    "        c = data.loc[data['company_size'] == comp, ['company_size']]\n",
    "        d = data.loc[data['company_size'] == comp, ['mental_disorder']]\n",
    "        c['company_size'] = 'Small'\n",
    "        \n",
    "    elif comp == \"26-100\":\n",
    "        e = data.loc[data['company_size'] == comp, ['company_size']]\n",
    "        f = data.loc[data['company_size'] == comp, ['mental_disorder']]\n",
    "        e['company_size'] = 'Medium'\n",
    "        \n",
    "    elif comp == \"100-500\":\n",
    "        g = data.loc[data['company_size'] == comp, ['company_size']]\n",
    "        h = data.loc[data['company_size'] == comp, ['mental_disorder']]\n",
    "        g['company_size'] = 'Medium'\n",
    "        \n",
    "    elif comp == \"500-1000\":\n",
    "        i = data.loc[data['company_size'] == comp, ['company_size']]\n",
    "        j = data.loc[data['company_size'] == comp, ['mental_disorder']]\n",
    "        i['company_size'] = 'Large'\n",
    "        \n",
    "    elif comp == \"More than 1000\":\n",
    "        k = data.loc[data['company_size'] == comp, ['company_size']]\n",
    "        l = data.loc[data['company_size'] == comp, ['mental_disorder']]\n",
    "        k['company_size'] = 'Large'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creates a single dataframe with companies seperated to small, medium and large then move the dataframe into the original data frame and we check to see if it was added propery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "one = a.join(b)\n",
    "six = c.join(d)\n",
    "comp_sze = one.append(six)\n",
    "twtysix = e.join(f)\n",
    "comp_sze = comp_sze.append(twtysix)\n",
    "onehun = g.join(h)\n",
    "comp_sze = comp_sze.append(onehun)\n",
    "fvehun = i.join(j)\n",
    "comp_sze = comp_sze.append(fvehun)\n",
    "thou = k.join(l)\n",
    "comp_sze = comp_sze.append(thou)\n",
    "data['comp_sze'] = comp_sze['company_size']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>company_size</th>\n",
       "      <th>is_tech</th>\n",
       "      <th>mental_health_care</th>\n",
       "      <th>formal_discussion</th>\n",
       "      <th>resources_provided</th>\n",
       "      <th>anonymity_protected</th>\n",
       "      <th>ease_of_leave</th>\n",
       "      <th>mental_discussion_negative</th>\n",
       "      <th>physical_discussion_negative</th>\n",
       "      <th>coworker_safe_space</th>\n",
       "      <th>...</th>\n",
       "      <th>condition</th>\n",
       "      <th>sought_treatment</th>\n",
       "      <th>interferes_when_treated</th>\n",
       "      <th>interferes_when_not_treated</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>working_country</th>\n",
       "      <th>work_remotely</th>\n",
       "      <th>age_range</th>\n",
       "      <th>comp_sze</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26-100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Not eligible for coverage / N/A</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>I don't know</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>...</td>\n",
       "      <td>Anxiety Disorder (Generalized, Social, Phobia,...</td>\n",
       "      <td>0</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>39</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>(32, 39]</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6-25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>...</td>\n",
       "      <td>Anxiety Disorder (Generalized, Social, Phobia,...</td>\n",
       "      <td>1</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>29</td>\n",
       "      <td>male</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Never</td>\n",
       "      <td>(25, 32]</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6-25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>I don't know</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Always</td>\n",
       "      <td>(32, 39]</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  company_size  is_tech               mental_health_care formal_discussion  \\\n",
       "0       26-100      1.0  Not eligible for coverage / N/A                No   \n",
       "1         6-25      1.0                               No               Yes   \n",
       "2         6-25      1.0                               No                No   \n",
       "\n",
       "  resources_provided anonymity_protected               ease_of_leave  \\\n",
       "0                 No        I don't know                   Very easy   \n",
       "1                Yes                 Yes               Somewhat easy   \n",
       "2                 No        I don't know  Neither easy nor difficult   \n",
       "\n",
       "  mental_discussion_negative physical_discussion_negative coworker_safe_space  \\\n",
       "0                         No                           No               Maybe   \n",
       "1                         No                           No               Maybe   \n",
       "2                      Maybe                           No               Maybe   \n",
       "\n",
       "    ...                                             condition  \\\n",
       "0   ...     Anxiety Disorder (Generalized, Social, Phobia,...   \n",
       "1   ...     Anxiety Disorder (Generalized, Social, Phobia,...   \n",
       "2   ...                                                   NaN   \n",
       "\n",
       "  sought_treatment interferes_when_treated interferes_when_not_treated age  \\\n",
       "0                0    Not applicable to me        Not applicable to me  39   \n",
       "1                1                  Rarely                   Sometimes  29   \n",
       "2                1    Not applicable to me        Not applicable to me  38   \n",
       "\n",
       "  gender           working_country work_remotely age_range comp_sze  \n",
       "0   Male            United Kingdom     Sometimes  (32, 39]   Medium  \n",
       "1   male  United States of America         Never  (25, 32]    Small  \n",
       "2  Male             United Kingdom        Always  (32, 39]    Small  \n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creates dummy variables to create three seperate dataframes by company size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "just_dummies = pd.get_dummies(data['comp_sze'])\n",
    "data['Small'] = just_dummies['Small']\n",
    "data['Medium'] = just_dummies['Medium']\n",
    "data['Large'] = just_dummies['Large']\n",
    "s_data = data[data['Small'] == 1]\n",
    "m_data = data[data['Medium'] == 1]\n",
    "l_data = data[data['Large'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Company Size vs Diagnosed Mental Disorder\n",
    "People more likely to say they have been diagnosed with a mental health disorder if they work at larger companies. There is a trend shown below, mental disorder increase as company size increase. This could have many reasons such as:\n",
    "- larger companies don't give the time/resources to help individuals\n",
    "- don't improve the workplace for people who have mental health issues\n",
    "- working in a system that large may create mental illness from low sense of purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x = 'comp_sze', hue = 'mental_disorder', data = data, order = ['Small', 'Medium', 'Large'])\n",
    "plt.title('Company Size vs Diagnosed Mental Disorder')\n",
    "plt.xlabel('Company Size')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Company Size vs Age Range\n",
    "The graph below shows that as that the average age increase as the company size increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x = 'comp_sze', hue = 'age_range', data = data, order = ['Small', 'Medium', 'Large'])\n",
    "plt.title('Company Size vs Age')\n",
    "plt.xlabel('Company Size')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small company mean age is: 31.724137931034484\n",
      "Medium company mean age is: 32.69179600886918\n",
      "Large company mean age is: 34.11794871794872\n"
     ]
    }
   ],
   "source": [
    "print('Small company mean age is:', stats.mean(s_data['age']))\n",
    "print('Medium company mean age is:', stats.mean(m_data['age']))\n",
    "print('Large company mean age is:', stats.mean(l_data['age']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Range vs Mental Disorder (all company sizes)\n",
    "There is a clear increase in the rate of mental disorder as employees age and a confounding variable may be due purely to people becoming more susceptible to mental illness as they age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x = 'age_range', hue = 'mental_disorder', data = data)\n",
    "plt.title('Age Range vs Mental Disorder')\n",
    "plt.xlabel('Company Size')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Range vs Mental Disorder (small, medium and large company size)\n",
    "\n",
    "The trend persist through company size there is a consistent steady increase the older people are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x = 'age_range', hue = 'mental_disorder', data = s_data)\n",
    "plt.title('Age Range vs Mental Disorder (Small)')\n",
    "plt.xlabel('Age Range')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x = 'age_range', hue = 'mental_disorder', data = m_data)\n",
    "plt.title('Age Range vs Mental Disorder (Medium)')\n",
    "plt.xlabel('Age Range')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x = 'age_range', hue = 'mental_disorder', data = l_data, hue_order = ['Yes', 'No'])\n",
    "plt.title('Age Range vs Mental Disorder (Large)')\n",
    "plt.xlabel('Age Range')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mental Disorder vs Observed Negative Consequences\n",
    "People believe that their mental health with affect their career more likely if they have a mental illness. This may due to knowing the severity of mental disorders and self bias they have against themselves for being mentally ill."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x = 'mental_disorder', hue = 'observed_negative_consequences',\n",
    "                   data = data, hue_order = ['Yes', 'No'], order = ['Yes', 'No'])\n",
    "plt.title('Mental Disorder vs Observed Negative Consequences')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observed Consequences vs Mental Disorder vs Believing Mental Health Will Hurt Career\n",
    "We will be seeing if observing negative consequences in the workplace effects the chance that the employee will believe their mental health with hurt their career. \n",
    "We lined this up with whether they have a diagnosed mental disorder. We saw a large positive correlation with having mental disorder and believing it will hurt their career stated previously. We also compared the company size between each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observed Negative Consequences At Work\n",
    "Graphs below will be the Mental Disorder vs Belief Mental Health Hurt Career for the employee that observed negative consequences at work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# it is more likely that I think mental health will hurt my career\n",
    "\n",
    "col = [\"Yes\" if \"Yes\" in each else \"No\" for each in data['mental_health_hurt_career']]\n",
    "temp = data\n",
    "temp['mental_health_hurt_career'] = col\n",
    "\n",
    "temp = temp[temp['observed_negative_consequences'] == \"Yes\"]\n",
    "\n",
    "ax = sns.countplot(x = 'mental_disorder', hue = 'mental_health_hurt_career',\n",
    "                   data = temp, order = ['Yes', 'No']) \n",
    "plt.title('(OBS CONSEQUENCES: YES) Mental Disorder vs Mental Health Will Hurt Career')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For small companies with observed negative consequences, there is not much of a difference between believing that mental health will hurt their career. There is a large majority that mental health issues will hurt their career"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = [\"Yes\" if \"Yes\" in each else \"No\" for each in s_data['mental_health_hurt_career']]\n",
    "temp = s_data\n",
    "temp['mental_health_hurt_career'] = col\n",
    "\n",
    "temp = temp[temp['observed_negative_consequences'] == \"Yes\"]\n",
    "\n",
    "ax = sns.countplot(x = 'mental_disorder', hue = 'mental_health_hurt_career',\n",
    "                   data = temp, hue_order = ['Yes', 'No'], order = ['Yes', 'No']) \n",
    "plt.title('(OBS CONSEQUENCES: YES) Mental Disorder vs Mental Health Will Hurt Career (sm)')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a large difference between the believe that mental health will hurt their career between the diagnosed and the mental disorder free. The belief that having mental health issues is extremely high compared for the diagnosed if there are observed negative consequences in the workplace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAEWCAYAAAA3sa4JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XmcFNW5//HPl0WJgBsSoyJCjBpZBBVxv6KgoNfd4BqFaEJy3eI1MXpDVNSrV6PGSEjkEiUuMa6IwV9cIhpivOKCShS3uASVSBRxQRQj4PP7o85gTdM90wXMNAzf9+s1r+k6darOU9XV1U+fOtWtiMDMzMysiFa1DsDMzMxWPU4gzMzMrDAnEGZmZlaYEwgzMzMrzAmEmZmZFeYEwszMzApzAmFmZmaFVZVASPofSac1dTBmDZH0M0nfq3UcRUm6VtJ/L8fyXSXNl9R6RcZVoa2ZkgY1dTurOknDJT1co7YHSJrVwPxukkJSmyZq/x5Jw9Ljevshtfu1pmh3dSPpJkkHN1NbS563IufZRhMISZ2B44D/zZWtK+kqSf+U9ImkZyV9q2S5mZIWpBPf+5L+IGnT3PwukiZIelfSh2kdwxuIY21JP5f0RlrnK2l6g1yd4Wk9n6TYrpK0bm7+qLSjhubK2qSybo3FlXthzi/5OyK3vl0kPSjpo7T8JElfL4lxqRNP/sSd3nA+K2njryUx/KFk+d9KGlXt/ip5fur+xqR5a0i6XNKsVP53SVdUem5ybUrSQ5LOKSkfJulVSWs1tG2p7gmSXkz77+103HRMsy8FRkpao0L7dfvmqZLyDVKbMxvbhmpoBb7JpuNhcW5f/F3SbyRtWVcnIt6IiA4RsXhFtLmqSfv7s/xrPZVPz792l7ON5UryStZV9g18RbZRst5lPh4l7SxpnnLJqaRfVygbCxAR+0bEdcvQ1hRJ3y4pazAZqmKdVSVLkraUdFvuvP6MpNPVDEn5spC0DdAH+H0Nmm/wPJtXTQ/EcODuiFgA2ZsLMBnYDNgZWAc4A7hY0uklyx4QER2AjYC3gV/k5t0AvJnW04ksSXm7XACpzQeAnsAQYG1gF2Au0D/V+QFwSYplHWCntO77S3bEe8D5DRw41cS1bjqh1/3dkmLYGfgj2ZO+MdAdeAb4v2U4yf20pI0+JfN3krRruQWr2V/JASVtnJzK/wvol+p2BPYEnm4s4Mi+1vQE4HRJPVMsnYHLgG9HxCcNbZukPYCLgKMioiOwNXBrbv2zgReBAxsJpb2kXrnpo4G/NxZ/DU1Nr5N1gEHAAuDJkm1YoRo74dZ6fWX8HTgq115v4EtN3ObqYBrQGtguV7Y78FZJ2b8BDzVjXI2q9piTtDnwGNl5vXdErAMMJTvHdWxo2QrrW2FJRwPb8F3gxqjBV0UXOM9WlUDsC/w5N30s0BUYGhF/j4iFEXEvcCrZG/PaZQL6FLgd6JEr3gG4NiI+johFEfF0RNxTIYbjUpuHRMTzEfF5RLwTERdExN2pzfOAUyLi3hTTTOBwskTgm7l13Qt8VlKWVySuUj8Fro+IKyPio4h4LyJ+AjwOnFvlOqr1U6DSp5kG91cV694BmBgRb0VmZkRcX01QEfEycCFwjaRWwGhgQkT8qcp2p0bE02ld70XEdRHxUa7OFODfG1nPDcCw3PRxQL34JW2srKdpTvrUf2pu3ihJt0q6PvWEPCepX5p3A9m+vSv1GPwold+mrNfrw9QL07OK7a0nIhZHxKsRcSLZa25UWne9T1mp1+K1FNvfJR2TyltJ+omk1yW9k+Jfp2QdJ0h6A3gwlR+b6s+VNLJkH7WSdJay3qO5aZ+s39D6SpZ/QdL+uek26RPgdpLaKes1myvpA0lPSNqwgd1zA9nzWGcYSz+na0q6TFmv29uSxkr6Upo3QFmP2g/Svpmt1GsqaQRwDPCj9Jzelcrrtv0jSc9LOqTBJ7AgSTtJeiRt/18lDcjN+1bafx+l5/q7FdZR9nhMjkn74t3S57ZORCwEHiVLEJD0ZWAN4JaSsi1JCYTK9CSsKCrpTUmvxd+mx+WOubqk5oO0/TuXWe15wCMRcXp6cyQiXoqIoyPig7Tuiq9fZT1HV0m6W9LHwJ4NHWtpmf2V9ZB9kJ7jbUq28UxJzwAfq3wSUe99N73m/0/SFWmdrynr7R4u6c10TA/L1W8svjPSa+AtSceXaX8KjZ9nq0ogegMv5ab3Bu6JiI9L6k0A2pH1StQjaS3gCLIDtc6jwC8lHSmpayMxDALujYj5Febvktq+I1+Y6t+TYl5SDJwNnCupbZl1FYlribSNuwC3lZl9K7BPteuq0i+BLVW+67Kx/dWYR8l6EU6U1FuS8jMl/UrSrxpY/meAyJLGXcl6harxGDBY0nmSdpW0Zpk6L5B17TXkt8CRklpL2prsU8ZjufhbAXcBfwU2AQYCp0kanFvHgcDNwLrAJGAMQEQcC7zBF703P0317wG2AL4MPAXcWOU2V3IH2SfBeiS1J0vK9k29NLsA09Ps4elvT+CrQIe6uHP2IOvZGSypB3AV2YeCjcl63Lrk6p4KHJyW2Rh4n+y4K7u+MttwE7leg1Tn3Yh4iiwBWAfYNLX7PbKel0oeBdaWtLWyT4BHkD3PeZeQvdH1Bb5G9tzmL6d9JbW5CVlP2S8lrRcR48ier7qesQNS/VfJnoN1yN6EfitpowZirJqkTYA/kH0IWB/4ITBBWY8dwDvA/mS9h98CrpC0Xel6GjgeAXYDtiI7vs9Jr4VyHiIlC+n/w+kvX/b3iFjmSw0rWP6Yq4uxrld4apn6g8jORQ1p7PV7NNkHo45k+6bisZaep/FkvQidyC7/Tyo5nx1F9ga9bkQsyjeUXuPdqf++C7AjWY92J+B3ZOenHVL73wTGSOqQ6jYU3xCy423vtM3l3kOqOc9CRDT4BywEvp6bngxcXKHuP4Fj0uOZwHzgA2ARWZdY71zd9YCLgeeAxWQnwR0qrPf+Sm2m+d8E/llh3sXA/enxKOC36fFjwH8AbciSim6NxQV0S3U/KPnbmuzEG/l9lYthCPBZejwceLhMnZnAoPT4WuDTkjauK4mhDXAi8Ggq/y0wqpr9Veb5qfv7TprXGjgJ+D/gX+m5G9bYsVKy/p4pzoNKyituW5q/L9mb+wcpvp8BrXPz9wZeq9Bmft9MJjvBXAyMJHuRzEz1dgTeKFn2v4Df5I6Tybl5PYAF5Z6rCnGsm+JYJ7fN/12hbqXjYQiwsMx2tU/75jDgSyXLPACcmJveiuz12ya3jq/m5p8D3Jybbk/WO1d3HL4ADMzN36ih9ZXZhq8BHwFrpekbgXPS4+OBR4BtqjiWZqbn7yfA/6R9cz+51y5ZwvoxsHluuZ3J3vgABpAlKG1y898BdmrsOcrVn046nis9b42cJz6rawM4E7ihZLn7qPA6A+4Evp/bllmVjsdc+11yZY8DR1ZY9wCyy5sCrgS+Q5Z8vp0r+02u/hSyS5JL7YfU7tcqtDMF+KRkn8xvZFtG8cU5e6ljLlfWplybqc5CYEhjx1kjr9/rc/MbO9auAi4oWedLwB65bTy+gfY3Se23y5UNB17OTfdOdTbMlc0lSxgai288ufcHskSj3vNGA+fZ/F81PRDvU/860btkJ5J6UjfMBml+nYMjYl1gTeBk4M+SvgIQEe9HxFkR0RPYkOzFeWfpp91kbrk2S2LaoEJX0EYlMdX5CdkbS7t8YZVxbRAR6+b+XiDbT59XiHMjYE56vAgo1/PRluxAr3NZSRvDyizza2BDSQeUlDe2v+ocXNLGr2FJV/ovI2JXshfThcD4Bj7BLCUinksPnyszu+K2RcQ9kX0CXB84iOyFk+8u7Uh24mnM9WnZo1j6k+pmwMapK/ADSR8APyZ7vuv8M/f4E6BdheOL1NNxsbLu7nlkJwjIXg/LahOy8Tr1RNbzdwTZJ/bZygaZ1g3S3Rh4PVf9dbI32fx2vZl7vHF+Oq17bm7+ZsDE3D56gSyprrS+0lhfScsckHroDiT75ATZJYn7gJtTN+pPK/QI5t1A9klwOCWXL4DOwFpkY0fq4r03ldeZG/U/7X1C9kZZlqTjct3QHwC9KPac1jtP8MW2Q7Zvh5Ycg7uRXreS9pX0qKT30rz9CrYNSx/Dlbb10TSvF9kn+r9E1nv5Zq5sRY1/OLVkn+zf6BJLq3jMVdDg+bDK12++zcaOtc2AH5Q8t5uSvd6q2Ya681vp+Iz8WLwFABFRWtahivjqve6pf86oU9V5tpoE4hmyDKXOZGDf1M2SdxjZp9VHS8rr3pDuIDv57FZm/rtkA+02JnvjKDWZrMu1tM06U1Pbh+YLU/19yT6ZlbZ5P/AK2af4sqqIK1/34xTH0DKzD+eL61lvAF3zCUk6uX6Z8k9kQ20uJOtavYAs66zT2P4q0saCiPglWYLUo7H6K0pk4zYeILvOmR9MuDXZpYfGTCDrInwtIkr365tk2Xg+iekYEftVG17J9NFkyc4gsu7ubqm8XDJcrUOAv5RtPOK+iNib7KT4IlkiCVlP0Wa5ql3JEtb8SSYf+2yyE1sWbHYcdsrNf5PsUkl+P7WLiH9UWF85dZcxDgKeT0kFkY1TOi8iepBdhtmf+mMcym3362SDKfej5HIl2YeEBUDPXKzrRDY4tRr1tkPSZmT79WSgU3qzm8HyPad5b5L1QOT3bfuIuDh1dU8gO/dsmNq+u4G2G3sOGhTZGLUnyJ6DjSLixTTrL6lsG5pvAOXHZG9+db5Spk5UeFzJZLL3p0qqef3m22nsWHsTuLDkuV0rIm6qJu70XvIq9d93i2gsvnqve7LzRKmqzrPVJBB3k11zqnMDMAu4Tdmglrbp2vFosi70D0tXoMxBZJcHXkhll0jqpWxgVUeyywmvRMTc0uX54s6ICZK+rmxwVydJP5a0X2rzPOAXkoakmLqRjUeYlZYvZySQH3RUNK5SZwHDJJ0qqaOk9ZTdtvVvZF2vkF06+RQ4S9lAsvZk3ezTKJhAJDeQ9fAMKSmruL8aW6Gk05QNOvtS2g/DyDLSRu/EWB6SDlI29mS9dMz0Jzv28knpHmTXKxuUXoR7Ub/3os7jwDxlA5m+lD6B9JK0Q5Whvk02xqBOR7IEdi7Zye+iKtdTT4qju6RfkHUrn1emzoaSDkzHzb/IuoDrbu+8CfjPtI4OKY5bSj51590O7C9pN2V37pxP/XPCWODC9GaKpM7pdVzEzWTjf/6D3CdwSXsqG1/TGphH1vtWzW2qJwB7RckYrIj4nOwN/wplg/6QtInqj2tpSOlz2p7sJD8nretb1E9kl9dvyXpmBqfnvV16zXUhG8S4Zmp7kaR9aXgMVWnsy+Ih4DSyy0p1Hk5l/4yIV5dz/dWaTjZ+qa2ygcvfaKT+HLKe34a2/1xgF0mXKvWAS/qaskG861Lw9VvFsfZr4HuSdkznsfaS/l1f3I5ejdL33apVEd+twHBJPdKHhnPLrKaq82w1CcT1wH5KIzgj4l9kmdqbZG+G88iuU4+MiEtLlr1L0vxU50Ky63t1XdprARPJukleI/vkVPa2kVybL5Jd+5xH9iawQYqByAYP/Zgsa5/HF7ftDEzLl1vv/6X15FUTV92I37q/09P6Hia77n4oWZb3Htlgsb0i4tnctvw76TpmamNj4PCIyGelPyppo9xlGCL7boBzyfWQVLO/krtK2piYyhcAl5N1gb5LNh7isIh4DUDZiN6x5eKpUqVte5/s+uvLKebfApdGxI2p3Y3IekHurKaRiJhW7sSX9tkBZNcL/5628WqyTx/V+B/gJ8q6B39I9hp5HfgH8DxleuEasXPudTKFbODcDnXHTIlWwA/IehveI3uh1/WijSdLHh9K2/UpcEqlRtNr8SSyN/bZZPs/P1DuSrIBpH+U9FHarh2LbFhko96nkvUy3JKb9RWyBGYe2YeKP7P0paZy63s1IqZVmH0mWa/io8q6oieTjQOpxjVAj/Sc3hkRz5O9BqaSvUH3JhsTtEJExJtkn3p/TPYm+CbZYONWkd11dCrZif59sk/IkxpYXenxuCz+TNYLmv+OmodTWXPevnk2sDnZdp9H/cs+S4ns1vALyW6V/0DSTmXqvEo2BqAb8JykD8l6eKaRjdFZltdvxWMtHZ/fIRvA/H6qN7yKdeaNI7uLZll7vBqK7x7g52S9u69QcgdVkfOs6r9nVagkXQS8ExE/L7YNqzdJfcienKMj4r5ax7Oqk3Q58GpENHQHiJnZKk/S74BbI6KqD0wrsN2qz7NVJRC27CTtTvap7ecNdCWbmZmtUpxAmJmZWWH+NU4zMzMrrKm/v96qsMEGG0S3bt1qHYaZ2SrlySeffDciOjde05qCE4iVQLdu3Zg2rdLAcjMzK0fSstz6biuIL2GYmZlZYU4gzMzMrDAnEGZmZlaYx0CYWbNZuHAhs2bN4tNPP611KLYKadeuHV26dKFt28Z+b82akxMIM2s2s2bNomPHjnTr1o1l/5ZeW51EBHPnzmXWrFl079691uFYji9hmFmz+fTTT+nUqZOTB6uaJDp16uReq5WQEwgza1ZOHqwoHzMrJycQZmZmVpgTCDMzMyvMgyhbgO3PuL7WIaw0nrz0uFqHYCuJiy66iB//+MeN1qv7JtgNNthgqXkzZ85k//33Z8aMGcsdz7XXXsu0adMYM2YMd955J1tuuSU9evQAYMCAAVx22WX069ev0HqW1ZQpU1hjjTXYZZddlnkdZu6BMLMW6aKLLqp1CBXdeeedPP/88zVpe9GiRUyZMoVHHnmkydpYvHjxci2/aNGiFRSJNSUnEGZWUzNnzuTrX/863/72t+nVqxfHHHMMkydPZtddd2WLLbbg8ccf5+OPP+b4449nhx12YNttt+X3v/89kH0aP/TQQxkyZAhbbLEFP/rRjwA466yzWLBgAX379uWYY44B4OCDD2b77benZ8+ejBs3rur4Fi9ezHe+8x169uzJPvvsw4IFCwB49dVXGTJkCNtvvz277747L774IgB33XUXO+64I9tuuy2DBg3i7bffrre+Rx55hEmTJnHGGWfQt29fXn31VQBuu+02+vfvz5Zbbslf/vKXBmN66623ltpmgA4dOix5fPvttzN8+HAAhg8fzumnn86ee+7JEUccwdixY7niiivo27dvxbbefvttDjnkEPr06UOfPn2WJByV9mOHDh0455xz2HHHHZk6dSpPPvkke+yxB9tvvz2DBw9m9uzZDe63fIxnnnlmw0+KrRR8CcPMau6VV17htttuY9y4ceywww787ne/4+GHH2bSpElcdNFF9OjRg7322ovx48fzwQcf0L9/fwYNGgTA9OnTefrpp1lzzTXZaqutOOWUU7j44osZM2YM06dPX9LG+PHjWX/99VmwYAE77LADhx12GJ06dWo0tpdffpmbbrqJX//61xx++OFMmDCBb37zm4wYMYKxY8eyxRZb8Nhjj3HiiSfy4IMPsttuu/Hoo48iiauvvpqf/vSnXH755UvWt8suu3DggQey//77841vfGNJ+aJFi3j88ce5++67Oe+885g8eXLFmMpt86abbtrgdvztb39j8uTJtG7dmlGjRtGhQwd++MMfVqx/6qmnssceezBx4kQWL17M/PnzG9yPH3/8Mb169eL8889n4cKF7LHHHvz+97+nc+fO3HLLLYwcOZLx48dX3G+lMdrKzwmEmdVc9+7d6d27NwA9e/Zk4MCBSKJ3797MnDmTWbNmMWnSJC677DIg+z6JN954A4CBAweyzjrrANCjRw9ef/31sm+mo0ePZuLEiQC8+eabvPzyy1UlEN27d6dv374AbL/99sycOZP58+fzyCOPMHTo0CX1/vWvfwHZl2UdccQRzJ49m88++6zqLz869NBD67XRkGq3OW/o0KGF3pgffPBBrr8+G1/VunXrJe1V2o+tW7fmsMMOA+Cll15ixowZ7L333kDWi7PRRhs1uN+WJUarLScQZlZza6655pLHrVq1WjLdqlUrFi1aROvWrZkwYQJbbbVVveUee+yxesu2bt267PXzKVOmMHnyZKZOncpaa63FgAEDqv5iotL1L1iwgM8//5x11123Xg9HnVNOOYXTTz+dAw88kClTpjBq1KhC7VTahoZiqquf/76E0u1r3759VXE0pKH92K5duyVv/hFBz549mTp1ar3l582bV3G/ragYrfl4DEQJSZtK+pOkFyQ9J+n7qXx9SfdLejn9X6/C8sNSnZclDWve6M1apsGDB/OLX/yCiADg6aefbnSZtm3bsnDhQgA+/PBD1ltvPdZaay1efPFFHn300eWKZ+2116Z79+7cdtttQPaG+de//nVJW5tssgkA1113XdnlO3bsyEcffbRcMZSz4YYb8sILL/D5558v6SVY1vYHDhzIVVddBWQ9CPPmzat6P2611VbMmTNnSQKxcOFCnnvuuQb3m616nEAsbRHwg4jYGtgJOElSD+As4IGI2AJ4IE3XI2l94FxgR6A/cG6lRMPMqnf22WezcOFCttlmG3r16sXZZ5/d6DIjRoxgm2224ZhjjmHIkCEsWrSIbbbZhrPPPpuddtppuWO68cYbueaaa+jTpw89e/ZcMrBz1KhRDB06lN13373sraEARx55JJdeeinbbrvtkkGUK8LFF1/M/vvvz1577cVGG21Usd4BBxzAxIkTGxxEeeWVV/KnP/2J3r17s/322/Pcc89VvR/XWGMNbr/9ds4880z69OlD3759lwzCrLTfbNWjuozeypP0e2BM+hsQEbMlbQRMiYitSuoelep8N03/b6p3U0Nt9OvXL6ZNm7bMMfp7IL7g74FYub3wwgtsvfXWtQ7DVkHljh1JT0ZE41+eYU3CPRANkNQN2BZ4DNgwImYDpP9fLrPIJsCbuelZqczMzKxF8SDKCiR1ACYAp0XEvCp/zKVcpbJdPJJGACMAunbtuqxhmtkKMHfuXAYOHLhU+QMPPFDVnRpN4b777lvq+xC6d+/e4NiGZXHhhRcuGZNQZ+jQoYwcOXKFtmMtjxOIMiS1JUseboyIO1Lx25I2yl3CeKfMorOAAbnpLsCUcm1ExDhgHGSXMFZQ6Ga2DDp16lTxzoBaGTx4MIMHD27ydkaOHOlkwZaJL2GUUNbVcA3wQkT8LDdrElB3V8UwoNzIn/uAfSStlwZP7pPKzMzMWhQnEEvbFTgW2EvS9PS3H3AxsLekl4G90zSS+km6GiAi3gMuAJ5If+enMjMzsxbFlzBKRMTDlB/LALDURdKImAZ8Ozc9HhjfNNGZmZmtHJxAmJkVsKJvm27o1uOIYPfdd2fkyJHsu+++ANx6662MHz+ee++9d4XGYVaUEwgzs5WUJMaOHcvQoUPZc889Wbx4MSNHjnTyYCsFj4EwM1uJ9erViwMOOIBLLrmE8847j+OOO47NN9+c6667jv79+9O3b19OPPFEPv/8cxYtWsSxxx5L79696dWrF6NHj651+NaCuQfCzGwld+6557LddtuxxhprMG3aNGbMmMHEiRN55JFHaNOmDSNGjODmm29m880359133+XZZ58F4IMPPqhx5NaSOYEwM1vJtW/fniOOOIIOHTqw5pprMnnyZJ544gn69cu+xXnBggVsuummDB48mJdeeonvf//77Lfffuyzzz41jtxaMicQZmargFatWtGqVXbVOSI4/vjjueCCC5aq98wzz3DPPfcwevRoJkyYwLhx45o7VFtNeAyEmdkqZtCgQdx66628++67QPZV3G+88QZz5swhIhg6dCjnnXceTz31VI0jtZbMPRBmZgWsDL/42rt3b84991wGDRrE559/Ttu2bRk7diytW7fmhBNOICKQxCWXXFLrUK0FcwJhZrYKGDVqVL3po48+mqOPPnqpek8//XQzRWSrO1/CMDMzs8KcQJiZmVlhTiDMzMysMCcQZmZmVpgTCDMzMyvMCYSZmZkV5ts4zcwKeOP83it0fV3PebbROpI4/fTTufzyywG47LLLmD9//lK3dpo1J/dAlJA0XtI7kmbkym6RND39zZQ0vcKyMyU9m+pNa76ozawlW3PNNbnjjjuWfPOk2crACcTSrgWG5Asi4oiI6BsRfYEJwB0NLL9nqtuvCWM0s9VI3S9uXnHFFUvNe/311xk4cCDbbLMNAwcO5I033qhBhLY6cgJRIiIeAt4rN0+SgMOBm5o1KDNb7Z100knceOONfPjhh/XKTz75ZI477jieeeYZjjnmGE499dQaRWirGycQxewOvB0RL1eYH8AfJT0paURDK5I0QtI0SdPmzJmzwgM1s5Zl7bXX5rjjjmP06NH1yqdOnbrkK62PPfZYHn744VqEZ6shJxDFHEXDvQ+7RsR2wL7ASZL+rVLFiBgXEf0iol/nzp1XdJxm1gKddtppXHPNNXz88ccV62QdpWZNzwlElSS1AQ4FbqlUJyLeSv/fASYC/ZsnOjNbHay//vocfvjhXHPNNUvKdtllF26++WYAbrzxRnbbbbdahWerGd/GWb1BwIsRMavcTEntgVYR8VF6vA9wfnMGaGZNr5rbLpvSD37wA8aMGbNkevTo0Rx//PFceumldO7cmd/85jc1jM5WJ04gSki6CRgAbCBpFnBuRFwDHEnJ5QtJGwNXR8R+wIbAxNR92Ab4XUTc25yxm1nLNH/+/CWPN9xwQz755JMl0926dePBBx+sRVi2mnMCUSIijqpQPrxM2VvAfunxa0CfJg3OzMxsJeExEGZmZlaYEwgza1YRUesQbBXjY2bl5ATCzJpNu3btmDt3rt8QrGoRwdy5c2nXrl2tQ7ESHgNhZs2mS5cuzJo1C395mhXRrl07unTpUuswrIQTCDNrNm3btqV79+61DsPMVgBfwjAzM7PCnECYmZlZYU4gzMzMrDAnEGZmZlaYEwgzMzMrzAmEmZmZFeYEwszMzApzAmFmZmaFOYEwMzOzwpxAmJmZWWFOIMzMzKwwJxBlSBov6R1JM3JloyT9Q9L09LdfhWWHSHpJ0iuSzmq+qM3MzJqPE4jyrgWGlCm/IiL6pr+7S2dKag38EtgX6AEcJalHk0ZqZmZWA04gyoiIh4D3lmHR/sArEfFaRHwG3AwctEKDMzMzWwk4gSjmZEnPpEsc65UT3+qkAAARgElEQVSZvwnwZm56VipbiqQRkqZJmjZnzpymiNXMzKzJOIGo3lXA5kBfYDZweZk6KlMW5VYWEeMiol9E9OvcufOKi9LMzKwZOIGoUkS8HRGLI+Jz4NdklytKzQI2zU13Ad5qjvjMzMyakxOIKknaKDd5CDCjTLUngC0kdZe0BnAkMKk54jMzM2tObWodwMpI0k3AAGADSbOAc4EBkvqSXZKYCXw31d0YuDoi9ouIRZJOBu4DWgPjI+K5GmyCmZlZk3ICUUZEHFWm+JoKdd8C9stN3w0sdYunmZlZS+JLGGZmZlaYEwgzMzMrzAmEmZmZFeYEwszMzApzAmFmZmaFOYEwMzOzwpxAmJmZWWFOIMzMzKwwJxBmZmZWmBMIMzMzK8wJhJmZmRXmBMLMzMwKcwJhZmZmhTmBMDMzs8KcQJiZmVlhTiBKSBov6R1JM3Jll0p6UdIzkiZKWrfCsjMlPStpuqRpzRe1mZlZ83ICsbRrgSElZfcDvSJiG+BvwH81sPyeEdE3Ivo1UXxmZmY15wSiREQ8BLxXUvbHiFiUJh8FujR7YGZmZisRJxDFHQ/cU2FeAH+U9KSkEc0Yk5mZWbNqU+sAViWSRgKLgBsrVNk1It6S9GXgfkkvph6NcusaAYwA6Nq1a5PEa2Zm1lTcA1ElScOA/YFjIiLK1YmIt9L/d4CJQP9K64uIcRHRLyL6de7cuSlCNjMzazItOoGQtGs1ZVWsZwhwJnBgRHxSoU57SR3rHgP7ADPK1TUzM1vVtegEAvhFlWVLSLoJmApsJWmWpBOAMUBHsssS0yWNTXU3lnR3WnRD4GFJfwUeB/4QEfeuqA0xMzNbmbTIMRCSdgZ2ATpLOj03a22gdUPLRsRRZYqvqVD3LWC/9Pg1oM8yBWxmZraKaZEJBLAG0IFs+zrmyucB36hJRGZmZi1Ii0wgIuLPwJ8lXRsRr9c6HjMzs5amRSYQOWtKGgd0I7etEbFXzSIyMzNrAVp6AnEbMBa4Glhc41jMzMxajJaeQCyKiKtqHYSZmVlL09Jv47xL0omSNpK0ft1frYMyMzNb1bX0Hohh6f8ZubIAvlqDWMzMzFqMFp1ARET3WsdgZmbWErXoBELSceXKI+L65o7FzMysJWnRCQSwQ+5xO2Ag8BTgBMLMzGw5tOgEIiJOyU9LWge4oUbhmJmZtRgt/S6MUp8AW9Q6CDMzs1Vdi+6BkHQX2V0XkP2I1tbArbWLyMzMrGVo0QkEcFnu8SLg9YiYVatgzMzMWooWfQkj/ajWi2S/yLke8FltIzIzM2sZWnQCIelw4HFgKHA48Jgk/5y3mZnZcmrRCQQwEtghIoZFxHFAf+DsxhaSNF7SO5Jm5MrWl3S/pJfT//UqLDss1XlZ0rBydczMzFZ1LT2BaBUR7+Sm51LdNl8LDCkpOwt4ICK2AB5I0/Wk39k4F9iRLFk5t1KiYWZmtipr6QnEvZLukzRc0nDgD8DdjS0UEQ8B75UUHwRclx5fBxxcZtHBwP0R8V5EvA/cz9KJiJmZ2SqvRd6FIelrwIYRcYakQ4HdAAFTgRuXcbUbRsRsgIiYLenLZepsAryZm56VysrFOAIYAdC1a9dlDMnMzKw2WmoPxM+BjwAi4o6IOD0i/pOs9+HnTdiuypRFmTIiYlxE9IuIfp07d27CkMzMzFa8lppAdIuIZ0oLI2Ia0G0Z1/m2pI0A0v93ytSZBWyam+4CvLWM7ZmZma20WmoC0a6BeV9axnVOAuruqhgG/L5MnfuAfSStlwZP7pPKzMzMWpSWmkA8Iek7pYWSTgCebGxhSTeRjZfYStKstNzFwN6SXgb2TtNI6ifpaoCIeA+4AHgi/Z2fyszMzFqUFjmIEjgNmCjpGL5IGPoBawCHNLZwRBxVYdbAMnWnAd/OTY8HxhcN2MzMbFXSIhOIiHgb2EXSnkCvVPyHiHiwhmGZmZm1GC0ygagTEX8C/lTrOMzMzFqaljoGwszMzJqQEwgzMzMrzAmEmZmZFeYEwszMzApzAmFmZmaFOYEwMzOzwpxAmJmZWWFOIMzMzKwwJxBmZmZWmBMIMzMzK8wJhJmZmRXmBMLMzMwKcwJhZmZmhTmBMDMzs8KcQBQgaStJ03N/8ySdVlJngKQPc3XOqVW8ZmZmTaVNrQNYlUTES0BfAEmtgX8AE8tU/UtE7N+csZmZmTUn90Asu4HAqxHxeq0DMTMza25OIJbdkcBNFebtLOmvku6R1LNcBUkjJE2TNG3OnDlNF6WZmVkTcAKxDCStARwI3FZm9lPAZhHRB/gFcGe5dUTEuIjoFxH9Onfu3HTBmpmZNQEnEMtmX+CpiHi7dEZEzIuI+enx3UBbSRs0d4BmZmZNyQnEsjmKCpcvJH1FktLj/mT7eG4zxmZmZtbkfBdGQZLWAvYGvpsr+x5ARIwFvgH8h6RFwALgyIiIWsRqZmbWVJxAFBQRnwCdSsrG5h6PAcY0d1xmZmbNyZcwzMzMrDAnEGZmZlaYEwgzMzMrzAmEmZmZFeYEwszMzApzAmFmZmaFOYEwMzOzwpxAmJmZWWFOIMzMzKwwJxBmZmZWmBMIMzMzK8wJhJmZmRXmBMLMzMwKcwJhZmZmhTmBMDMzs8KcQBQkaaakZyVNlzStzHxJGi3pFUnPSNquFnGamZk1pTa1DmAVtWdEvFth3r7AFulvR+Cq9N/MzKzFcA/EincQcH1kHgXWlbRRrYMyMzNbkdwDUVwAf5QUwP9GxLiS+ZsAb+amZ6Wy2flKkkYAIwC6du3adNGuZt44v3etQ1hpdD3n2VqHAMD2Z1xf6xBWGhM7XlrrEFYaK8vxacvOPRDF7RoR25FdqjhJ0r+VzFeZZWKpgohxEdEvIvp17ty5KeI0MzNrMk4gCoqIt9L/d4CJQP+SKrOATXPTXYC3mic6MzOz5uEEogBJ7SV1rHsM7APMKKk2CTgu3Y2xE/BhRMzGzMysBfEYiGI2BCZKgmzf/S4i7pX0PYCIGAvcDewHvAJ8AnyrRrGamZk1GScQBUTEa0CfMuVjc48DOKk54zIzM2tuvoRhZmZmhTmBMDMzs8KcQJiZmVlhTiDMzMysMCcQZmZmVpgTCDMzMyvMCYSZmZkV5gTCzMzMCnMCYWZmZoU5gTAzM7PCnECYmZlZYU4gzMzMrDAnEGZmZlaYEwgzMzMrzAmEmZmZFeYEwszMzApzAlElSZtK+pOkFyQ9J+n7ZeoMkPShpOnp75xaxGpmZtbU2tQ6gFXIIuAHEfGUpI7Ak5Luj4jnS+r9JSL2r0F8ZmZmzcY9EFWKiNkR8VR6/BHwArBJbaMyMzOrDScQy0BSN2Bb4LEys3eW9FdJ90jq2cA6RkiaJmnanDlzmihSMzOzpuEEoiBJHYAJwGkRMa9k9lPAZhHRB/gFcGel9UTEuIjoFxH9Onfu3HQBm5mZNQEnEAVIakuWPNwYEXeUzo+IeRExPz2+G2graYNmDtPMzKzJOYGokiQB1wAvRMTPKtT5SqqHpP5k+3du80VpZmbWPHwXRvV2BY4FnpU0PZX9GOgKEBFjgW8A/yFpEbAAODIiohbBmpmZNSUnEFWKiIcBNVJnDDCmeSIyMzOrHV/CMDMzs8KcQJiZmVlhTiDMzMysMCcQZmZmVpgTCDMzMyvMCYSZmZkV5gTCzMzMCnMCYWZmZoU5gTAzM7PCnECYmZlZYU4gzMzMrDAnEGZmZlaYEwgzMzMrzAmEmZmZFeYEwszMzApzAmFmZmaFOYEoSNIQSS9JekXSWWXmrynpljT/MUndmj9KMzOzpuUEogBJrYFfAvsCPYCjJPUoqXYC8H5EfA24ArikeaM0MzNrek4giukPvBIRr0XEZ8DNwEEldQ4CrkuPbwcGSlIzxmhmZtbk2tQ6gFXMJsCbuelZwI6V6kTEIkkfAp2Ad/OVJI0ARqTJ+ZJeapKIVzObwQaU7OvV1rnOW1c2Pj5zVszxudmKWIktGycQxZQ74mMZ6hAR44BxKyIo+4KkaRHRr9ZxmJXj49NaEl/CKGYWsGluugvwVqU6ktoA6wDvNUt0ZmZmzcQJRDFPAFtI6i5pDeBIYFJJnUnAsPT4G8CDEbFUD4SZmdmqzJcwCkhjGk4G7gNaA+Mj4jlJ5wPTImIScA1wg6RXyHoejqxdxKslXxaylZmPT2sx5A/HZmZmVpQvYZiZmVlhTiDMzMysMCcQtkpR5mFJ++bKDpd0by3jMsuTFJIuz03/UNKoGoZktsI5gbBVSrqj5XvAzyS1k9QeuBA4qbaRmdXzL+BQSRvUOhCzpuIEwlY5ETEDuAs4EzgXuD4iXpU0TNLjkqZL+pWkVpLaSLpB0rOSZkg6tbbR22piEdkdF/9ZOkPSZpIekPRM+t+1+cMzW36+jdNWVecBTwGfAf0k9QIOAXZJt9uOI7uF9lVgg4joDSBp3VoFbKudXwLPSPppSfkYsqT3OknHA6OBg5s9OrPl5ATCVkkR8bGkW4D5EfEvSYOAHYBp6bfLvkT2myT3AVtJuhK4G/hjrWK21UtEzJN0PXAqsCA3a2fg0PT4BqA0wTBbJTiBsFXZ5+kPst8gGR8RZ5dWkrQN2U+wnwocxhc/YmbW1H5O1lP2mwbq+Mt4bJXkMRDWUkwGDq8btCapk6SukjqTfWHabWTjJbarZZC2eomI94BbgRNyxY/wxTfUHgM83Nxxma0I7oGwFiEinpV0HjBZUitgIdndGouBa5Rd1wiygZdmzely4OTc9KnAeElnAHOAb9UkKrPl5K+yNjMzs8J8CcPMzMwKcwJhZmZmhTmBMDMzs8KcQJiZmVlhTiDMzMysMCcQZjWWfrnxhtx0G0lzJP2/5Vjnj6usN7PcDz6l8mfT3/OS/lvSmmnexpJuX9bYGohluKQxK3q9ZtY0nECY1d7HQC9JX0rTewP/WM51VpVANGLP9Bsi/YGvkv04FBHxVkR8Y3lWnH6WfbnOP5L8PTZmNeQEwmzlcA/w7+nxUcBNdTMktZc0XtITkp6WdFAqHy7pDkn3Snq57kebJF0MfCn9KumNqexOSU9Kek5Soa/yjoj5ZF/KdbCk9SV1kzQjrbdn7hdQn5G0RSo/Pf366QxJp6WybpJekPQrsq933lTStyT9TdKfgV1z29xZ0oS0zU9I2jWVj5I0TtIfgeuL7mQzW3GcQJitHG4GjpTUDtgGeCw3byTwYETsAOwJXCqpfZrXFzgC6A0cIWnTiDgLWBARfSPimFTv+IjYHugHnCqpU5HgImIe8Hdgi5JZ3wOujIi+ad2zJG1P9u2KOwI7Ad+RtG2qvxXZL1FuS/ZLqueRJQ57Az1y670SuCJt82HA1bl52wMHRcTRRbbBzFYsdwGarQQi4hlJ3ch6H+4umb0PcKCkH6bpdkDX9PiBiPgQQNLzwGZkv0Ja6lRJh6THm5IlAnMLhqkyZVOBkZK6AHdExMuSdgMmRsTHKa47gN2BScDrEfFoWnZHYEpEzEn1bgG2TPMGAT3SL6sCrC2pY3o8KSLyv25pZjXgBMJs5TEJuAwYAOR7CAQcFhEv5StL2hH4V65oMWVe05IGkL0h7xwRn0iaQpaEVC29eXcD/gasU1ceEb+T9BjZ5Zf7JH2b8olGnY9Lpit9l36rFG+9RCElFKXrMLMa8CUMs5XHeOD8iHi2pPw+4JT0g2DkLgc0ZKGktunxOsD7KXn4OtllhapJ6gD8CrgzIt4vmfdV4LWIGE2WAG0DPEQ2XmKtdKnlEOAvZVb9GDAg/XJqW2Bobt4fyf0AlaS+RWI2s6bnBMJsJRERsyLiyjKzLgDaAs+kwYsXVLG6can+jcC9QBtJz6RlH21wyS/8KbX3OPAG8N0ydY4AZkiaDnydbHzDU8C1abnHgKsj4unSBSNiNjCK7DLIZLKBlXVOBfqlgZnPk421MLOViH+N08zMzApzD4SZmZkV5gTCzMzMCnMCYWZmZoU5gTAzM7PCnECYmZlZYU4gzMzMrDAnEGZmZlbY/wdRBGJPwVyHhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = [\"Yes\" if \"Yes\" in each else \"No\" for each in m_data['mental_health_hurt_career']]\n",
    "temp = m_data\n",
    "temp['mental_health_hurt_career'] = col\n",
    "\n",
    "temp = temp[temp['observed_negative_consequences'] == \"Yes\"]\n",
    "\n",
    "ax = sns.countplot(x = 'mental_disorder', hue = 'mental_health_hurt_career',\n",
    "                   data = temp, order = ['Yes', 'No'])\n",
    "plt.title('(OBS CONSEQUENCES: YES) Mental Disorder vs Mental Health Will Hurt Career (med)')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is interesting change between medium and large companies. Large companies  has a much higher ratios for believieng mental health will hurt their career for those with no mental disorder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = [\"Yes\" if \"Yes\" in each else \"No\" for each in l_data['mental_health_hurt_career']]\n",
    "temp = l_data\n",
    "temp['mental_health_hurt_career'] = col\n",
    "\n",
    "temp = temp[temp['observed_negative_consequences'] == \"Yes\"]\n",
    "\n",
    "ax = sns.countplot(x = 'mental_disorder', hue = 'mental_health_hurt_career',\n",
    "                   data = temp, order = ['Yes', 'No'])\n",
    "plt.title('(OBS CONSEQUENCES: YES) Mental Disorder vs Mental Health Will Hurt Career (lg)')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No Observed Negative Consequences At Work\n",
    "Graph below show Mental Disorder vs Belief Mental Health Will Hurt Career for those that did not see observe negative consequences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all company sizes, those without mental disorder believe that have one will hurt their career much less likely then those with mental disorders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# it is more likely that I think mental health will hurt my career\n",
    "\n",
    "col = [\"Yes\" if \"Yes\" in each else \"No\" for each in data['mental_health_hurt_career']]\n",
    "temp = data\n",
    "temp['mental_health_hurt_career'] = col\n",
    "\n",
    "temp = temp[temp['observed_negative_consequences'] == \"No\"]\n",
    "\n",
    "ax = sns.countplot(x = 'mental_disorder', hue = 'mental_health_hurt_career',\n",
    "                   hue_order = ['Yes', 'No'], data = temp) \n",
    "plt.title('(OBS CONSEQUENCES: NO) Mental Disorder vs Mental Health Will Hurt Career')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For small companies, the belief that mental health will hurt their career is much higher in ratio for those with mental illnesses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = [\"Yes\" if \"Yes\" in each else \"No\" for each in s_data['mental_health_hurt_career']]\n",
    "temp = s_data\n",
    "temp['mental_health_hurt_career'] = col\n",
    "\n",
    "temp = temp[temp['observed_negative_consequences'] == \"No\"]\n",
    "\n",
    "ax = sns.countplot(x = 'mental_disorder', hue = 'mental_health_hurt_career',\n",
    "                   data = temp, hue_order = ['Yes', 'No'], order = ['Yes', 'No']) \n",
    "plt.title('(OBS CONSEQUENCES: NO) Mental Disorder vs Mental Health Will Hurt Career (sm)')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With or without a mental disorder, it is about even with the belief that mental health will hurt their career."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = [\"Yes\" if \"Yes\" in each else \"No\" for each in m_data['mental_health_hurt_career']]\n",
    "temp = m_data\n",
    "temp['mental_health_hurt_career'] = col\n",
    "\n",
    "temp = temp[temp['observed_negative_consequences'] == \"No\"]\n",
    "\n",
    "ax = sns.countplot(x = 'mental_disorder', hue = 'mental_health_hurt_career',\n",
    "                   data = temp, hue_order = ['Yes', 'No'], order = ['Yes', 'No']) \n",
    "plt.title('(OBS CONSEQUENCES: NO) Mental Disorder vs Mental Health Will Hurt Career (med)')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For large companies, having mental disorders will make the employee believe that having a believe if having a mental disorder will hurt your career."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = [\"Yes\" if \"Yes\" in each else \"No\" for each in l_data['mental_health_hurt_career']]\n",
    "temp = l_data\n",
    "temp['mental_health_hurt_career'] = col\n",
    "\n",
    "temp = temp[temp['observed_negative_consequences'] == \"No\"]\n",
    "\n",
    "ax = sns.countplot(x = 'mental_disorder', hue = 'mental_health_hurt_career',\n",
    "                   data = temp, hue_order = ['Yes', 'No'], order = ['Yes', 'No']) \n",
    "plt.title('(OBS CONSEQUENCES: NO) Mental Disorder vs Mental Health Will Hurt Career (lg)')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall and throughout company size and seeing negative consequences or not, having a mental disorder will make one believe that it will affect their career more than those without one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mental Discussion Negative vs Mental Disorder\n",
    "This graph shows if those with and without mental illness and believe that there will be a negative effect speaking to their boss about mental illness. More believe that there won't be a negative effect. Those with mental disorder did believe in the negative effect more often than those without."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mental_discussion_negative_boss = pd.DataFrame(data['mental_discussion_negative'])\n",
    "mental_discussion_negative_boss['mental_disorder'] = data['mental_disorder']\n",
    "\n",
    "ax = sns.countplot(x = 'mental_discussion_negative', hue = 'mental_disorder',\n",
    "                   data = mental_discussion_negative_boss, order = ['Yes', 'Maybe', 'No']) \n",
    "plt.title('Mental Discussion Negative vs Mental Disorder')\n",
    "plt.xlabel('Mental Discussion Negative')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert the booleans to number.\n",
    "We turned 'Yes' into 1.0, 'No' to -1.0 and neutral answers like 'I don't know, N/A, Maybe' into 0.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>is_tech</th>\n",
       "      <th>mental_health_care</th>\n",
       "      <th>formal_discussion</th>\n",
       "      <th>resources_provided</th>\n",
       "      <th>mental_discussion_negative</th>\n",
       "      <th>coworker_safe_space</th>\n",
       "      <th>mental_disorder_past</th>\n",
       "      <th>mental_disorder</th>\n",
       "      <th>sought_treatment</th>\n",
       "      <th>age</th>\n",
       "      <th>Small</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>878.0</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "      <td>878.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.267654</td>\n",
       "      <td>-0.556948</td>\n",
       "      <td>-0.257403</td>\n",
       "      <td>-0.226651</td>\n",
       "      <td>-0.062642</td>\n",
       "      <td>0.185649</td>\n",
       "      <td>-0.018223</td>\n",
       "      <td>0.572893</td>\n",
       "      <td>32.752847</td>\n",
       "      <td>0.264237</td>\n",
       "      <td>0.513667</td>\n",
       "      <td>0.222096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.762656</td>\n",
       "      <td>0.774930</td>\n",
       "      <td>0.794671</td>\n",
       "      <td>0.730424</td>\n",
       "      <td>0.748663</td>\n",
       "      <td>0.896426</td>\n",
       "      <td>1.000404</td>\n",
       "      <td>0.494940</td>\n",
       "      <td>7.114904</td>\n",
       "      <td>0.441177</td>\n",
       "      <td>0.500098</td>\n",
       "      <td>0.415892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       is_tech  mental_health_care  formal_discussion  resources_provided  \\\n",
       "count    878.0          878.000000         878.000000          878.000000   \n",
       "mean       1.0            0.267654          -0.556948           -0.257403   \n",
       "std        0.0            0.762656           0.774930            0.794671   \n",
       "min        1.0           -1.000000          -1.000000           -1.000000   \n",
       "25%        1.0            0.000000          -1.000000           -1.000000   \n",
       "50%        1.0            0.000000          -1.000000            0.000000   \n",
       "75%        1.0            1.000000           0.000000            0.000000   \n",
       "max        1.0            1.000000           1.000000            1.000000   \n",
       "\n",
       "       mental_discussion_negative  coworker_safe_space  mental_disorder_past  \\\n",
       "count                  878.000000           878.000000            878.000000   \n",
       "mean                    -0.226651            -0.062642              0.185649   \n",
       "std                      0.730424             0.748663              0.896426   \n",
       "min                     -1.000000            -1.000000             -1.000000   \n",
       "25%                     -1.000000            -1.000000             -1.000000   \n",
       "50%                      0.000000             0.000000              1.000000   \n",
       "75%                      0.000000             0.750000              1.000000   \n",
       "max                      1.000000             1.000000              1.000000   \n",
       "\n",
       "       mental_disorder  sought_treatment         age       Small      Medium  \\\n",
       "count       878.000000        878.000000  878.000000  878.000000  878.000000   \n",
       "mean         -0.018223          0.572893   32.752847    0.264237    0.513667   \n",
       "std           1.000404          0.494940    7.114904    0.441177    0.500098   \n",
       "min          -1.000000          0.000000   19.000000    0.000000    0.000000   \n",
       "25%          -1.000000          0.000000   28.000000    0.000000    0.000000   \n",
       "50%          -1.000000          1.000000   32.000000    0.000000    1.000000   \n",
       "75%           1.000000          1.000000   37.000000    1.000000    1.000000   \n",
       "max           1.000000          1.000000   63.000000    1.000000    1.000000   \n",
       "\n",
       "            Large  \n",
       "count  878.000000  \n",
       "mean     0.222096  \n",
       "std      0.415892  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      0.000000  \n",
       "max      1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['mental_disorder'] = data['mental_disorder'].apply(convert)\n",
    "data['mental_health_care'] = data['mental_health_care'].apply(convert)\n",
    "data['formal_discussion'] = data['formal_discussion'].apply(convert)\n",
    "data['mental_discussion_negative'] = data['mental_discussion_negative'].apply(convert)\n",
    "data['mental_disorder_past'] = data['mental_disorder_past'].apply(convert)\n",
    "data['coworker_safe_space'] = data['coworker_safe_space'].apply(convert)\n",
    "data['resources_provided'] = data['resources_provided'].apply(convert)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "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>is_tech</th>\n",
       "      <th>mental_health_care</th>\n",
       "      <th>formal_discussion</th>\n",
       "      <th>resources_provided</th>\n",
       "      <th>mental_discussion_negative</th>\n",
       "      <th>coworker_safe_space</th>\n",
       "      <th>mental_disorder_past</th>\n",
       "      <th>mental_disorder</th>\n",
       "      <th>sought_treatment</th>\n",
       "      <th>age</th>\n",
       "      <th>Small</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>232.0</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.103448</td>\n",
       "      <td>-0.672414</td>\n",
       "      <td>-0.564655</td>\n",
       "      <td>-0.271552</td>\n",
       "      <td>-0.043103</td>\n",
       "      <td>0.129310</td>\n",
       "      <td>-0.129310</td>\n",
       "      <td>0.530172</td>\n",
       "      <td>31.724138</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.771565</td>\n",
       "      <td>0.705892</td>\n",
       "      <td>0.687017</td>\n",
       "      <td>0.766993</td>\n",
       "      <td>0.782849</td>\n",
       "      <td>0.907212</td>\n",
       "      <td>0.993748</td>\n",
       "      <td>0.500168</td>\n",
       "      <td>7.917963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       is_tech  mental_health_care  formal_discussion  resources_provided  \\\n",
       "count    232.0          232.000000         232.000000          232.000000   \n",
       "mean       1.0           -0.103448          -0.672414           -0.564655   \n",
       "std        0.0            0.771565           0.705892            0.687017   \n",
       "min        1.0           -1.000000          -1.000000           -1.000000   \n",
       "25%        1.0           -1.000000          -1.000000           -1.000000   \n",
       "50%        1.0            0.000000          -1.000000           -1.000000   \n",
       "75%        1.0            0.250000          -1.000000            0.000000   \n",
       "max        1.0            1.000000           1.000000            1.000000   \n",
       "\n",
       "       mental_discussion_negative  coworker_safe_space  mental_disorder_past  \\\n",
       "count                  232.000000           232.000000            232.000000   \n",
       "mean                    -0.271552            -0.043103              0.129310   \n",
       "std                      0.766993             0.782849              0.907212   \n",
       "min                     -1.000000            -1.000000             -1.000000   \n",
       "25%                     -1.000000            -1.000000             -1.000000   \n",
       "50%                      0.000000             0.000000              0.000000   \n",
       "75%                      0.000000             1.000000              1.000000   \n",
       "max                      1.000000             1.000000              1.000000   \n",
       "\n",
       "       mental_disorder  sought_treatment         age  Small  Medium  Large  \n",
       "count       232.000000        232.000000  232.000000  232.0   232.0  232.0  \n",
       "mean         -0.129310          0.530172   31.724138    1.0     0.0    0.0  \n",
       "std           0.993748          0.500168    7.917963    0.0     0.0    0.0  \n",
       "min          -1.000000          0.000000   19.000000    1.0     0.0    0.0  \n",
       "25%          -1.000000          0.000000   26.000000    1.0     0.0    0.0  \n",
       "50%          -1.000000          1.000000   30.000000    1.0     0.0    0.0  \n",
       "75%           1.000000          1.000000   36.000000    1.0     0.0    0.0  \n",
       "max           1.000000          1.000000   61.000000    1.0     0.0    0.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_data['mental_disorder'] = s_data['mental_disorder'].apply(convert)\n",
    "s_data['mental_health_care'] = s_data['mental_health_care'].apply(convert)\n",
    "s_data['formal_discussion'] = s_data['formal_discussion'].apply(convert)\n",
    "s_data['mental_discussion_negative'] = s_data['mental_discussion_negative'].apply(convert)\n",
    "s_data['mental_disorder_past'] = s_data['mental_disorder_past'].apply(convert)\n",
    "s_data['coworker_safe_space'] = s_data['coworker_safe_space'].apply(convert)\n",
    "s_data['resources_provided'] = s_data['resources_provided'].apply(convert)\n",
    "s_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "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>is_tech</th>\n",
       "      <th>mental_health_care</th>\n",
       "      <th>formal_discussion</th>\n",
       "      <th>resources_provided</th>\n",
       "      <th>mental_discussion_negative</th>\n",
       "      <th>coworker_safe_space</th>\n",
       "      <th>mental_disorder_past</th>\n",
       "      <th>mental_disorder</th>\n",
       "      <th>sought_treatment</th>\n",
       "      <th>age</th>\n",
       "      <th>Small</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>451.0</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>451.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.356984</td>\n",
       "      <td>-0.589800</td>\n",
       "      <td>-0.266075</td>\n",
       "      <td>-0.248337</td>\n",
       "      <td>-0.055432</td>\n",
       "      <td>0.190687</td>\n",
       "      <td>-0.015521</td>\n",
       "      <td>0.569845</td>\n",
       "      <td>32.691796</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.720379</td>\n",
       "      <td>0.755885</td>\n",
       "      <td>0.780414</td>\n",
       "      <td>0.718310</td>\n",
       "      <td>0.720516</td>\n",
       "      <td>0.893931</td>\n",
       "      <td>1.000990</td>\n",
       "      <td>0.495647</td>\n",
       "      <td>6.305934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       is_tech  mental_health_care  formal_discussion  resources_provided  \\\n",
       "count    451.0          451.000000         451.000000          451.000000   \n",
       "mean       1.0            0.356984          -0.589800           -0.266075   \n",
       "std        0.0            0.720379           0.755885            0.780414   \n",
       "min        1.0           -1.000000          -1.000000           -1.000000   \n",
       "25%        1.0            0.000000          -1.000000           -1.000000   \n",
       "50%        1.0            1.000000          -1.000000            0.000000   \n",
       "75%        1.0            1.000000          -1.000000            0.000000   \n",
       "max        1.0            1.000000           1.000000            1.000000   \n",
       "\n",
       "       mental_discussion_negative  coworker_safe_space  mental_disorder_past  \\\n",
       "count                  451.000000           451.000000            451.000000   \n",
       "mean                    -0.248337            -0.055432              0.190687   \n",
       "std                      0.718310             0.720516              0.893931   \n",
       "min                     -1.000000            -1.000000             -1.000000   \n",
       "25%                     -1.000000            -1.000000             -1.000000   \n",
       "50%                      0.000000             0.000000              1.000000   \n",
       "75%                      0.000000             0.000000              1.000000   \n",
       "max                      1.000000             1.000000              1.000000   \n",
       "\n",
       "       mental_disorder  sought_treatment         age  Small  Medium  Large  \n",
       "count       451.000000        451.000000  451.000000  451.0   451.0  451.0  \n",
       "mean         -0.015521          0.569845   32.691796    0.0     1.0    0.0  \n",
       "std           1.000990          0.495647    6.305934    0.0     0.0    0.0  \n",
       "min          -1.000000          0.000000   21.000000    0.0     1.0    0.0  \n",
       "25%          -1.000000          0.000000   28.000000    0.0     1.0    0.0  \n",
       "50%          -1.000000          1.000000   32.000000    0.0     1.0    0.0  \n",
       "75%           1.000000          1.000000   36.000000    0.0     1.0    0.0  \n",
       "max           1.000000          1.000000   58.000000    0.0     1.0    0.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_data['mental_disorder'] = m_data['mental_disorder'].apply(convert)\n",
    "m_data['mental_health_care'] = m_data['mental_health_care'].apply(convert)\n",
    "m_data['formal_discussion'] = m_data['formal_discussion'].apply(convert)\n",
    "m_data['mental_discussion_negative'] = m_data['mental_discussion_negative'].apply(convert)\n",
    "m_data['mental_disorder_past'] = m_data['mental_disorder_past'].apply(convert)\n",
    "m_data['coworker_safe_space'] = m_data['coworker_safe_space'].apply(convert)\n",
    "m_data['resources_provided'] = m_data['resources_provided'].apply(convert)\n",
    "m_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Cang\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "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>is_tech</th>\n",
       "      <th>mental_health_care</th>\n",
       "      <th>formal_discussion</th>\n",
       "      <th>resources_provided</th>\n",
       "      <th>mental_discussion_negative</th>\n",
       "      <th>coworker_safe_space</th>\n",
       "      <th>mental_disorder_past</th>\n",
       "      <th>mental_disorder</th>\n",
       "      <th>sought_treatment</th>\n",
       "      <th>age</th>\n",
       "      <th>Small</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195.0</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.502564</td>\n",
       "      <td>-0.343590</td>\n",
       "      <td>0.128205</td>\n",
       "      <td>-0.123077</td>\n",
       "      <td>-0.102564</td>\n",
       "      <td>0.241026</td>\n",
       "      <td>0.107692</td>\n",
       "      <td>0.630769</td>\n",
       "      <td>34.117949</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.691438</td>\n",
       "      <td>0.855484</td>\n",
       "      <td>0.785811</td>\n",
       "      <td>0.707275</td>\n",
       "      <td>0.773094</td>\n",
       "      <td>0.890013</td>\n",
       "      <td>0.996743</td>\n",
       "      <td>0.483839</td>\n",
       "      <td>7.668481</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       is_tech  mental_health_care  formal_discussion  resources_provided  \\\n",
       "count    195.0          195.000000         195.000000          195.000000   \n",
       "mean       1.0            0.502564          -0.343590            0.128205   \n",
       "std        0.0            0.691438           0.855484            0.785811   \n",
       "min        1.0           -1.000000          -1.000000           -1.000000   \n",
       "25%        1.0            0.000000          -1.000000           -0.500000   \n",
       "50%        1.0            1.000000          -1.000000            0.000000   \n",
       "75%        1.0            1.000000           0.500000            1.000000   \n",
       "max        1.0            1.000000           1.000000            1.000000   \n",
       "\n",
       "       mental_discussion_negative  coworker_safe_space  mental_disorder_past  \\\n",
       "count                  195.000000           195.000000            195.000000   \n",
       "mean                    -0.123077            -0.102564              0.241026   \n",
       "std                      0.707275             0.773094              0.890013   \n",
       "min                     -1.000000            -1.000000             -1.000000   \n",
       "25%                     -1.000000            -1.000000             -1.000000   \n",
       "50%                      0.000000             0.000000              1.000000   \n",
       "75%                      0.000000             0.500000              1.000000   \n",
       "max                      1.000000             1.000000              1.000000   \n",
       "\n",
       "       mental_disorder  sought_treatment         age  Small  Medium  Large  \n",
       "count       195.000000        195.000000  195.000000  195.0   195.0  195.0  \n",
       "mean          0.107692          0.630769   34.117949    0.0     0.0    1.0  \n",
       "std           0.996743          0.483839    7.668481    0.0     0.0    0.0  \n",
       "min          -1.000000          0.000000   20.000000    0.0     0.0    1.0  \n",
       "25%          -1.000000          0.000000   29.000000    0.0     0.0    1.0  \n",
       "50%           1.000000          1.000000   32.000000    0.0     0.0    1.0  \n",
       "75%           1.000000          1.000000   39.000000    0.0     0.0    1.0  \n",
       "max           1.000000          1.000000   63.000000    0.0     0.0    1.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_data['mental_disorder'] = l_data['mental_disorder'].apply(convert)\n",
    "l_data['mental_health_care'] = l_data['mental_health_care'].apply(convert)\n",
    "l_data['formal_discussion'] = l_data['formal_discussion'].apply(convert)\n",
    "l_data['mental_discussion_negative'] = l_data['mental_discussion_negative'].apply(convert)\n",
    "l_data['mental_disorder_past'] = l_data['mental_disorder_past'].apply(convert)\n",
    "l_data['coworker_safe_space'] = l_data['coworker_safe_space'].apply(convert)\n",
    "l_data['resources_provided'] = l_data['resources_provided'].apply(convert)\n",
    "l_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Company Size vs Believing Mental Health Issues Will Hurt Career\n",
    "\n",
    "The trend is not extremely clear but the large company size has the highest ratios of Yes. We hypothesize that because the company is large, people in the company believe for themselves to be easily replacable and not important that is also the reason that causes higher mental disorder rates in large companies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x = 'comp_sze', hue = 'mental_health_hurt_career', data = data,\n",
    "                order = ['Small', 'Medium', 'Large'], hue_order = ['Yes', 'No'])\n",
    "plt.title('Company Size vs Believing Mental Health Issues Will Hurt Career')\n",
    "plt.xlabel('Company Size')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mental diagnoses\n",
    "Since the mental diagnosis was the multiple choice and surveytakers were allowed to choose multiple answers, we recorded each instance of each mental illness. Mood disorders, such as depression, and anxiety disorder for all company sizes which made sense from the fact the the two previously mentioned are the most prevalent illnesses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mood</th>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anxiety</th>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADD</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTSD</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCD</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stress Response</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personality</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Substance Abuse</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eating</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addiction</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychotic</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seasonal Affective</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Count\n",
       "Mood                  322\n",
       "Anxiety               225\n",
       "ADD                    80\n",
       "PTSD                   39\n",
       "OCD                    28\n",
       "Stress Response        19\n",
       "Personality            19\n",
       "Substance Abuse        13\n",
       "Eating                 12\n",
       "Addiction               9\n",
       "Psychotic               4\n",
       "Seasonal Affective      1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mood = 0\n",
    "anx = 0\n",
    "att = 0\n",
    "stress = 0\n",
    "ptsd = 0\n",
    "psych = 0\n",
    "ocd = 0\n",
    "pers = 0\n",
    "eat = 0\n",
    "sub = 0\n",
    "addict = 0\n",
    "seas = 0\n",
    "for cond in data['condition']:\n",
    "    if type(cond) is str:\n",
    "        if 'Mood' in cond:\n",
    "            mood = mood + 1\n",
    "        if 'Anxiety' in cond:\n",
    "                anx = anx + 1\n",
    "        if 'Attention' in cond:\n",
    "            att = att + 1\n",
    "        if 'Stress Response' in cond:\n",
    "            stress = stress + 1\n",
    "        if 'Post-traumatic' in cond:\n",
    "            ptsd = ptsd + 1\n",
    "        if 'Psychotic' in cond:\n",
    "            psych = psych + 1\n",
    "        if 'Obsessive' in cond:\n",
    "            ocd = ocd + 1\n",
    "        if 'Personality' in cond:\n",
    "            pers = pers + 1\n",
    "        if 'Eating' in cond:\n",
    "            eat = eat + 1\n",
    "        if 'Substance Use' in cond:\n",
    "            sub = sub + 1\n",
    "        if 'Addictive' in cond:\n",
    "            addict = addict + 1\n",
    "        if 'Seasonal' in cond:\n",
    "            seas = seas + 1\n",
    "            \n",
    "condition = pd.DataFrame([mood,anx,att,ptsd,ocd,stress,pers,sub,eat,addict,psych,seas], \n",
    "['Mood', 'Anxiety', 'ADD', 'PTSD', 'OCD', 'Stress Response', 'Personality', 'Substance Abuse', 'Eating', 'Addiction', 'Psychotic', 'Seasonal Affective'], columns = ['Count'])\n",
    "condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition['Count'].plot(kind='bar');\n",
    "plt.title('Mental Disorder Counts (all)')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mood</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anxiety</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADD</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTSD</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCD</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stress Response</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personality</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Substance Abuse</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eating</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addiction</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychotic</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seasonal Affective</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Count\n",
       "Mood                   74\n",
       "Anxiety                50\n",
       "ADD                    11\n",
       "PTSD                    7\n",
       "OCD                     7\n",
       "Stress Response         7\n",
       "Personality            10\n",
       "Substance Abuse         2\n",
       "Eating                  3\n",
       "Addiction               4\n",
       "Psychotic               1\n",
       "Seasonal Affective      0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mood = 0\n",
    "anx = 0\n",
    "att = 0\n",
    "stress = 0\n",
    "ptsd = 0\n",
    "psych = 0\n",
    "ocd = 0\n",
    "pers = 0\n",
    "eat = 0\n",
    "sub = 0\n",
    "addict = 0\n",
    "seas = 0\n",
    "for cond in s_data['condition']:\n",
    "    if type(cond) is str:\n",
    "        if 'Mood' in cond:\n",
    "            mood = mood + 1\n",
    "        if 'Anxiety' in cond:\n",
    "                anx = anx + 1\n",
    "        if 'Attention' in cond:\n",
    "            att = att + 1\n",
    "        if 'Stress Response' in cond:\n",
    "            stress = stress + 1\n",
    "        if 'Post-traumatic' in cond:\n",
    "            ptsd = ptsd + 1\n",
    "        if 'Psychotic' in cond:\n",
    "            psych = psych + 1\n",
    "        if 'Obsessive' in cond:\n",
    "            ocd = ocd + 1\n",
    "        if 'Personality' in cond:\n",
    "            pers = pers + 1\n",
    "        if 'Eating' in cond:\n",
    "            eat = eat + 1\n",
    "        if 'Substance Use' in cond:\n",
    "            sub = sub + 1\n",
    "        if 'Addictive' in cond:\n",
    "            addict = addict + 1\n",
    "        if 'Seasonal' in cond:\n",
    "            seas = seas + 1\n",
    "            \n",
    "condition = pd.DataFrame([mood,anx,att,ptsd,ocd,stress,pers,sub,eat,addict,psych,seas], \n",
    "['Mood', 'Anxiety', 'ADD', 'PTSD', 'OCD', 'Stress Response', 'Personality', 'Substance Abuse', 'Eating', 'Addiction', 'Psychotic', 'Seasonal Affective'], columns = ['Count'])\n",
    "condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition['Count'].plot(kind='bar');\n",
    "plt.title('Mental Disorder Counts (sm)')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mood</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anxiety</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADD</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTSD</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCD</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stress Response</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personality</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Substance Abuse</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eating</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addiction</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychotic</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seasonal Affective</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Count\n",
       "Mood                  163\n",
       "Anxiety               121\n",
       "ADD                    46\n",
       "PTSD                   20\n",
       "OCD                    16\n",
       "Stress Response         9\n",
       "Personality             6\n",
       "Substance Abuse         7\n",
       "Eating                  4\n",
       "Addiction               3\n",
       "Psychotic               2\n",
       "Seasonal Affective      0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mood = 0\n",
    "anx = 0\n",
    "att = 0\n",
    "stress = 0\n",
    "ptsd = 0\n",
    "psych = 0\n",
    "ocd = 0\n",
    "pers = 0\n",
    "eat = 0\n",
    "sub = 0\n",
    "addict = 0\n",
    "seas = 0\n",
    "for cond in m_data['condition']:\n",
    "    if type(cond) is str:\n",
    "        if 'Mood' in cond:\n",
    "            mood = mood + 1\n",
    "        if 'Anxiety' in cond:\n",
    "                anx = anx + 1\n",
    "        if 'Attention' in cond:\n",
    "            att = att + 1\n",
    "        if 'Stress Response' in cond:\n",
    "            stress = stress + 1\n",
    "        if 'Post-traumatic' in cond:\n",
    "            ptsd = ptsd + 1\n",
    "        if 'Psychotic' in cond:\n",
    "            psych = psych + 1\n",
    "        if 'Obsessive' in cond:\n",
    "            ocd = ocd + 1\n",
    "        if 'Personality' in cond:\n",
    "            pers = pers + 1\n",
    "        if 'Eating' in cond:\n",
    "            eat = eat + 1\n",
    "        if 'Substance Use' in cond:\n",
    "            sub = sub + 1\n",
    "        if 'Addictive' in cond:\n",
    "            addict = addict + 1\n",
    "        if 'Seasonal' in cond:\n",
    "            seas = seas + 1\n",
    "            \n",
    "condition = pd.DataFrame([mood,anx,att,ptsd,ocd,stress,pers,sub,eat,addict,psych,seas], \n",
    "['Mood', 'Anxiety', 'ADD', 'PTSD', 'OCD', 'Stress Response', 'Personality', 'Substance Abuse', 'Eating', 'Addiction', 'Psychotic', 'Seasonal Affective'], columns = ['Count'])\n",
    "condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition['Count'].plot(kind='bar');\n",
    "plt.title('Mental Disorder Counts (med)')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mood</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anxiety</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADD</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTSD</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCD</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stress Response</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personality</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Substance Abuse</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eating</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addiction</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Psychotic</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seasonal Affective</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Count\n",
       "Mood                   85\n",
       "Anxiety                54\n",
       "ADD                    23\n",
       "PTSD                   12\n",
       "OCD                     5\n",
       "Stress Response         3\n",
       "Personality             3\n",
       "Substance Abuse         4\n",
       "Eating                  5\n",
       "Addiction               2\n",
       "Psychotic               1\n",
       "Seasonal Affective      1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mood = 0\n",
    "anx = 0\n",
    "att = 0\n",
    "stress = 0\n",
    "ptsd = 0\n",
    "psych = 0\n",
    "ocd = 0\n",
    "pers = 0\n",
    "eat = 0\n",
    "sub = 0\n",
    "addict = 0\n",
    "seas = 0\n",
    "for cond in l_data['condition']:\n",
    "    if type(cond) is str:\n",
    "        if 'Mood' in cond:\n",
    "            mood = mood + 1\n",
    "        if 'Anxiety' in cond:\n",
    "                anx = anx + 1\n",
    "        if 'Attention' in cond:\n",
    "            att = att + 1\n",
    "        if 'Stress Response' in cond:\n",
    "            stress = stress + 1\n",
    "        if 'Post-traumatic' in cond:\n",
    "            ptsd = ptsd + 1\n",
    "        if 'Psychotic' in cond:\n",
    "            psych = psych + 1\n",
    "        if 'Obsessive' in cond:\n",
    "            ocd = ocd + 1\n",
    "        if 'Personality' in cond:\n",
    "            pers = pers + 1\n",
    "        if 'Eating' in cond:\n",
    "            eat = eat + 1\n",
    "        if 'Substance Use' in cond:\n",
    "            sub = sub + 1\n",
    "        if 'Addictive' in cond:\n",
    "            addict = addict + 1\n",
    "        if 'Seasonal' in cond:\n",
    "            seas = seas + 1\n",
    "            \n",
    "condition = pd.DataFrame([mood,anx,att,ptsd,ocd,stress,pers,sub,eat,addict,psych,seas], \n",
    "['Mood', 'Anxiety', 'ADD', 'PTSD', 'OCD', 'Stress Response', 'Personality', 'Substance Abuse', 'Eating', 'Addiction', 'Psychotic', 'Seasonal Affective'], columns = ['Count'])\n",
    "condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition['Count'].plot(kind='bar');\n",
    "plt.title('Mental Disorder Counts (lg)')\n",
    "plt.xlabel('Mental Disorder')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression: Age Vs Mental Healthcare\n",
    "There is a clear and strong postive correlation from the graphs below. The most outstanding graph was the graph of the large company size data with strongest and clearest trend of the three company sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Healthcare')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "x = np.array(data['age'])\n",
    "X = []\n",
    "for i, each in enumerate(x):\n",
    "    X.append([i, each])\n",
    "X = np.array(X)\n",
    "\n",
    "y = np.array(data['mental_health_care'])\n",
    "Y = []\n",
    "for i, each in enumerate(y):\n",
    "    Y.append([i, each])\n",
    "Y = np.array(Y)\n",
    "\n",
    "reg = LinearRegression().fit(X, Y)\n",
    "plt.scatter(x, reg.predict(X)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Health Care')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Healthcare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Healthcare')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "xs = np.array(s_data['age'])\n",
    "Xs = []\n",
    "for i, each in enumerate(xs):\n",
    "    Xs.append([i, each])\n",
    "Xs = np.array(Xs)\n",
    "\n",
    "ys = np.array(s_data['mental_health_care'])\n",
    "Ys = []\n",
    "for i, each in enumerate(ys):\n",
    "    Ys.append([i, each])\n",
    "Ys = np.array(Ys)\n",
    "\n",
    "regs = LinearRegression().fit(Xs, Ys)\n",
    "plt.scatter(xs, regs.predict(Xs)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Health Care (sm)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Healthcare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Healthcare')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztvXmYXVWZ7//5pkggwUiIRBsqCQGMUSYJFJO0rSIIiEAp+EgQG+7VRn4XWhRNA920hMEfCAreVhywW8VmCCCYTgNNWhuhLyiQxCTGALmEORWQMAQEAiThvX/sfYpdp/bZa1XtM1XV+3me81Sdtaf37HP2etd6pyUzw3Ecx3GKGNVqARzHcZz2x5WF4ziOE8SVheM4jhPElYXjOI4TxJWF4ziOE8SVheM4jhPElUWKpA9KWtlqOYYDkqZKellSR6tlceKR9Jikg1p07TskfaFg+88kXdBMmcoi6VpJ3U26lkl6d/r/pZJOrvc1RpyyqPVAmNn/MbMZrZCpGklzJG1IO9x1kn4raf9WyxWLmT1hZm8zs03NuJ4SHpF0fwOvsUDSeTntR0l6WtJmAzjXienDfWlVe3fa/rM6yDstPVe0XIHz9eus632NzHlPlHRXyXOMSZ+jhyS9kj73P5E0rT5SBq+/O/B+4N+acb0qLgH+QdKYep50xCmLdqPgQbvOzN4GbAP8BrihydcfSvwV8E5gR0l7N+gaPwM+J0lV7Z8DrjazjQM838PAZ6ru/18D/3fwIjoZfgEcCRwHbEXScS8GPjrQEw3yGfkiye+i6VnPZvYU8CDJ568brixSJH1Y0urM+8ckfU3SHyS9KOk6SVtktn9C0tLMyH/3zLYzJT0s6c+S7pf0ycy2EyXdLekySc8Dc4rkSjuhq4FOSZMir7+npCXp9W9IZb8g+zklnSHpaeCnEec7Q1JPer6Vkj6atu8jaZGklyT9qTJSrh5xStpO0nxJz0taJelvMueeI+l6ST9Pz79CUlf0F5dwAskI7tb0/14k7SDpv9Nz/1rS5ZKuymzfL/286yQtk/ThGteYB0wEPpg5dmvgE8DP0/cfT7/vP6f362sFMj8NLAcOSY+dCHwAmF8lf035lJhuzk9/T3+W9J+Stkk3/3f6d52SGer+knaSdLuk5yQ9K+lqSRMKZBwQkjaX9C1JT6S/hx9KGptu21rSzZLWSnoh/X9yzjneB/wQ2D+Ve11m89aSbkk/672Sdqohx0HAwcBRZrbQzDaa2YtmdrmZ/Uu6z/+Q9EB6rkckfTFz/ICfkRwOA+7MnDP73K9Lr/mBtP1JSc9IOiGzf817mW6fLekpSWsk/c+c698BHF4g38AxsxH1Ah4DDspp/zCwumq/+4DtSDqJB4CT0217As8A+wIdJB3UY8Dm6fZPp8eNAj4DvAJsm247EdgI/C2wGTA2R5Y5wFXp/2OAi4Bngc1C10/3fxw4DRgNfAp4A7gg8zk3At9M9x8bON8M4Elgu/T4acBO6f+/Az6X/v82YL/MPpaR907g+8AWwB7AWuCjmc/6GvDx9NoXAvdk7sX3ge8XfJ/jgJfS449O79OYzPbfAd9K78tfpvtW7m0n8Fx67CiSDuY5YFKNa/0Y+OfM+y8CSzPvnwI+mP6/NbBnjfOcCNxFMuq9Lm37X8CPgAuAn8XIR9IhPAy8J/0e7wAuyvsO0rZ3p+fYHJhEolC+E3o20m0/I/0NZdqqv+fvkCi7icB44N+BC9Nt70i/n3HpthuAeZlz3QF8IXt/cq7/PLAPyXNzNTC3hqwXAXcG+oHDgZ0AAR8CXq18XwzwGck595bpfZmUaTsxPef/SI+/AHgCuDy9xseAPwNvi7iXhwJ/AnZNr3VNer13Z673KeD3de0763myofCq9UCQryyOz7y/GPhh+v8PgPOrjl8JfKjGNZeSjHIqP5onAjLOIeng1wGbSDqID2e217w+iUmmB1Bm2130VRZvAFtEnu/d6UNyEDC6ap//Bs4Ftqlqn5b+eDcDpqSfYXxm+4W81SHOAX6d2bYzsH4A3+fxJMpns/ShWwd8Mt02NX1Ax2X2v4q3lMUZwL9WnW8BcEKNa/0l8CKpggfuBr6S2f4EiQJ5e0DmE9PvZCzJQ78VcA9wAH2VRaF8JB3s2Zlt/wu4rfo7KJCjG1gSejbSbT8jUerrMq+XMt+zSAZFO2WO2R94tMb59gBeyLy/g7CyyCrqjwMP1jj3j6mhSAruxTzgtME8Iznn6kzvS/b4E4GHMu93S/d5V6btufS+FN5L4Cekg4L0/XvorywOBh4ZyD0IvdwMVczTmf9fJRk9A2wPfDWdTq5Lp8pTSGYTSPrrzHR1HckIYJvMuZ6MuPb1ZjYBeBfwR2CvzLai628H9Fj6i6lxvbVm9lrM+cxsFfBlkk79GUlzJW2XHvd5kh/qg5IWSvpEzufYDnjezP6caXuc5IGqUH2ft1C8nfgEknu10cxeB27iLVNU5dqvZvbP3ovtgU9Xfe6/BLbNu5CZ3UWimI6StCOwN8morsLRJJ3Y45LuVCAowczWA7cAZ5Mo3LurdomRr9ZvtB+S3pl+fz2SXiJRnNvU2j+Hb5nZhMoLyJphJpHMGhZnZL0tbUfSOEk/kvR4eu3/BiZoYBFzsZ/1OWp8hxUkHSbpHiWm0XUk31v2XkQ/Izmnr5jOxle1/ynz/3oAM6tuexuBe5leM/s7fjxHhvEZOerCcHButoIngW+Y2TeqN0janmRk81Hgd2a2SdJSktFCBas+rhZm9mxqT10o6RpLnFdF1/8QiX9DGYUxhcRcUev6Nc+XynANcI2kt5OYSr5JYn56CJglaRTJtPcXkt5RdfgaYKKk8RmFMZVk9lOK1OZ9ILCPpKPT5nEkymYbErPQREnjMgpjSuYUT5KM3P+GeH5O4oieAfxn9mE3s4UkimQ0cCpwfdX1ap3vdpIZWjWDka9XnJy2C9P23c3sOSVhnd8bxLnzeJaks9vFzPK+26+S3LN9zexpSXsAS+j7XFSIfj5q8GvgNEmTzWx19UZJmwM3knyP/2ZmGyTNo/gZLXxGspjZK5Iq5sG1g5A/dC+fou/vamrOPu8Dlg3i2jUZqTOL0ZK2yLwGqjR/DJwsaV8lbCnpcEnjecteuRYSRxrJzGLQmNmDJOaHv4u4/u9IzD6nStpM0lEkdt5BfR5JMyQdmD5gr5H8iDeln+14SZPM7E3eGsX0CZc1syeB3wIXpvd6d5IZydVl7knK50iih2aQTN/3IHlAVwOzzOxxYBEwR0ko5f7AEZnjrwKOkHSIpI5Uvg8rx/Ga4eckJrm/Aa6sNKbn/6ykrcxsA4mJJiZ0+E4Sk8F3c7YNRr4Ka4E3gR0zbeOBl0mc3p3A7IjzRJH+Bn4MXCbpnQCSOiUdkrn2+vTaE4FzCk73J2CyBhn6aWa/Bn4F/FLSXulzMF7SyUqcwWNITJZrgY2SDiPxGRRR9MzlcSuJGXcw8ofu5fXAiZJ2ljSO/Hv5IeA/BnP9WoxUZXEryQ+38pozkIPNbBFJZ/E94AVgFYlNEjO7H/g2Saf9JxLbZLV5YTBcApwk6Z2B679BMsr/PEkHfjxwM/D6YD4PyUNVcbA/TRKi+vfptkOBFZJeBv43cGzV1L3CLBIb+hrgl8A5ZvarmA+tJArkhzU2n0Di/H46+yKJpqmYoj5LYu99jsQfcB3pvUgV2VHp51lLMnqcTcFzYWaPkSi/LamKXCJRXo+lZpaTSe59IZbwX2b2fM62AcuXOfZV4BvA3akpYz+S2cueJH6XW0hMdvXkDJLfzj3pPfg1iSKHxGE7luR3dA+JWaUWtwMrgKclPTtIWY4hec6vI/m8fwS6SPxjfwa+RNLpvkASaFD9XfYh8IzkcQXwWalfqHUsNe+lmf0Hyf28Pd3n9uyBkrYl8f3NG+S1c1Ff07YzHJF0L4lz/qetlqXVSLqOxDFaNLJ1nNJIuobEn1bXTjviut8GHjaz79f1vK4shh+p32IlySjusyQj7R1Tf8eIQkmS3vPAoySmhnnA/ma2pKWCOc4Qwx3cw5MZJFPst5E4to8ZiYoi5S9IzC3vIPFl/H+uKBxn4PjMwnEcxwkyUh3cjuM4zgAYNmaobbbZxqZNm9ZqMRzHcYYUixcvftbMJoX2GzbKYtq0aSxatKjVYjiO4wwpJOVlgPfDzVCO4zhOEFcWjuM4ThBXFo7jOE4QVxaO4zhOEFcWjuM4ThBXFo7jOE4QVxaO4zhOEFcWjuM4TpCGKgtJh0paKWmVpDML9jtGkknqqmqfKullSV9rpJyO4zhOMQ1TFkrW1r0cOIxkIY5ZknbO2W88yUIk9+ac5jLqvNqT4ziOM3AaObPYB1hlZo+kq7fNJVn1q5rzgYtJluzsJV0f+BGSFbMcx3GcFtJIZdFJsgxkhdVpWy+SZgJTzOzmqvYtSZYVzFvEPrvfSZIWSVq0du1g1kV3HMdxYmiksshbe7Z38QxJo0jMTF/N2e9c4DIze7noAmZ2hZl1mVnXpEnBoomO4zjOIGlk1dnVwJTM+8nAmsz78cCuwB3pmuZ/AcyXdCSwL3CMpIuBCcCbkl4zs+81UF7HcRynBo1UFguB6ZJ2AHqAY4HjKhvN7EVgm8p7SXcAXzOzRcAHM+1zgJddUTiO47SOhpmhzGwjcCqwAHgAuN7MVkg6L509OI7jOEOEYbMGd1dXl/niR47jOAND0mIz6wrt5xncjuM4ThBXFo7jOE4QVxaO4zhOEFcWjuM4ThBXFo7jOE4QVxaO4zhOkEYm5TmO47Qt85b0cMmClaxZt57tJoxl9iEz6J7ZGT5whOLKwnGcEce8JT2cddNy1m/YBEDPuvWcddNyAFcYNXAzlOM4I45LFqzsVRQV1m/YxCULVrZIovbHlYXjOCOONevWD6jdcWXhOM4IZLsJYwfU7riycBxnBDL7kBmMHd3Rp23s6A5mHzKjRRK1P+7gdhxnxFFxYns0VDyuLBzHGZF0z+x05TAA3AzlOI7jBHFl4TiO4wRxZeE4juMEaaiykHSopJWSVkk6s2C/YySZpK70/cGSFktanv49sJFyOo7jOMU0zMEtqQO4HDgYWA0slDTfzO6v2m888CXg3kzzs8ARZrZG0q4k63i7J8pxHKdFNHJmsQ+wysweMbM3gLnAUTn7nQ9cDLxWaTCzJWa2Jn27AthC0uYNlNVxHMcpoJHKohN4MvN+NVWzA0kzgSlmdnPBeY4GlpjZ69UbJJ0kaZGkRWvXrq2HzI7jOE4OjVQWymmz3o3SKOAy4Ks1TyDtAnwT+GLedjO7wsy6zKxr0qRJJcV1HMdxatFIZbEamJJ5PxlYk3k/HtgVuEPSY8B+wPyMk3sy8Evgr83s4QbK6TiO4wRopLJYCEyXtIOkMcCxwPzKRjN70cy2MbNpZjYNuAc40swWSZoA3AKcZWZ3N1BGx3EcJ4KGKQsz2wicShLJ9ABwvZmtkHSepCMDh58KvBv4R0lL09c7GyWr4ziOU4zMLLzXEKCrq8sWLVrUajEcx3GGFJIWm1lXaD/P4HYcx3GCuLJwHMdxgniJcsdxBsS8JT2+DkSb0MzvwpWF4zjRzFvSw1k3LWf9hk0A9Kxbz1k3LQdwhdFkmv1duBnKcUYY85b0cMBFt7PDmbdwwEW3M29JT/SxlyxY2ds5VVi/YROXLFhZbzGdAM3+Lnxm4TgjiLKj0TXr1g+ovVG4Kaz534XPLBxnBFF2NLrdhLEDam8EFYXXs249xlsKbyAzpOFAs78LVxaOM4IoOxqdfcgMRo/qW/Zt9Cgx+5AZpWWLxU1hCbMPmcHY0R192saO7mjYd+HKwnFGEHUZjVaXCM0rGdpA2sUU1mq6Z3Zy4ad2o3PCWAR0ThjLhZ/azaOhHMcpz+xDZjD7hmVsePOtyg0DmRlcsmAlGzb1rfqwYZNxyYKVTfMZbDdhLD05iqGZprB2oXtmZ9Puu88sHGekUWJm0A6j+mabX5wEVxaOM4IomhnE0A4O7mabX5wEN0M5zgiiHg7ubOgttGZU30zzi5PgMwvHGUGUnRn4qH7k4jMLx2kjGp1sVo+ZQWhU7wlzwxNXFo7TJsxb0tMnUqln3Xpm37AMqF+tn8p5GtWZe+2o4YsvfuQ4dSQ0qi7avse5/8m69Rv6nXPC2NEsPedjTfsMZTjgottzw1o7J4zl7jMPbIFEg2ekzJDaYvEjSYdKWilplaQzC/Y7RpJJ6sq0nZUet1LSIY2U03HqQagMRWh7nqIoam9H2iG0th54SZH+NExZSOoALgcOA3YGZknaOWe/8cCXgHszbTsDxwK7AIcC30/P5zhtS6gMxUgoU9Gs0NoylXNjGAnf1UBp5MxiH2CVmT1iZm8Ac4GjcvY7H7gYeC3TdhQw18xeN7NHgVXp+RynbQmNqkPbR9VIjqvV3o40o3ZUM0b9w2WGVE+ilIWk7SUdlP4/Np0NhOgEnsy8X522Zc87E5hiZjcP9Nj0+JMkLZK0aO3atREiOU7jCI2qQ9vfrOE+rNXetjS4dlQzRv3tkHzYbgSVhaS/AX4B/ChtmgzMizh33k+k92cvaRRwGfDVgR7b22B2hZl1mVnXpEmTIkRynMYRKkMR2j5h7Ojc82bb62F+aaQJp2yGeAzNGPV7SZH+xMwsTgEOAF4CMLOHgHdGHLcamJJ5PxlYk3k/HtgVuEPSY8B+wPzUyR061nHaju6ZnRy9VycdSsY6HRJH7/VWTkIooU01RuCV9nqYXxptwmlGR96MUb8nH/YnJs/idTN7Q+kvVtJm5Izyc1gITJe0A9BD4rA+rrLRzF4Etqm8l3QH8DUzWyRpPXCNpEuB7YDpwH1Rn8hxWsS8JT1cd9+TbErD0TeZcd19T9K1/cQ+CqNWh7Pu1RrRUGl7kfklthOLOUeZkNFmVIRtVskRLynSl5iZxZ2S/h4YK+lg4Abg30MHmdlG4FRgAfAAcL2ZrZB0nqQjA8euAK4H7gduA04xs01FxzhOq5kzf0Wf0t8AG9405sxf0fu+yAQUGjHXY9QeOkfZmUczzDc+6m8NMTOLM4HPA8uBLwK3Av8cc3IzuzXdP9v29Rr7frjq/TeAb8Rcx3HqwdnzlnPtvcnMoENi1r5TuKB7t+jjQ3kSoezm0Ii5HqP20DnKzjwanSFewUf9zadQWaS5DVea2fHAj5sjkuM0hqJO7ux5y7nqnid6991k1vt+IAqjiFBHHOpo62F+CZ0jduZRVM7DO/LhSaGyMLNNkiZJGpPmSjjOkCTUyV1775O5x11775PRymLrcaN5IcfvsPW4JJopxoxU1NHGjtrLjPzrMfNwhicxZqjHgLslzQdeqTSa2aWNEspxBkNRJxnq5DbVqJFWqz2Pc47Yhdm/WNYndHR0hzjniF2A+piRYiq+lhn5l515OMOXGAf3GuDmdN/xmZfjtA2Viq1Zx+zsG5b1OmZDnVxHjbjVWu15dM/s5JJj3t/H8XrJMe/vY0ZqtPO3bMJayHnsyWojl+DMwszObYYgjlOGokik7pmdwVH9rH2n9PFZVJi175R+bUXUw4xUhnqM/MvMPJzhS0wG9yRJl0i6VdLtlVczhHOcWEKRSB95b36Gf6X9gu7dOGCniX22HbDTxLo5t5tFo0f+ocTDetHoQoHOwIkxQ10NPAjsAJxL4sNY2ECZHKfu/ObB/NphlfZ5S3r4/RMv9tn2+yderGsnVa8M7KJONMbUVaYjnrekhxsX9/RJPLxxcU/b3Sen/sQoi3eY2b8AG8zsTjP7nySlORynbRhd45dcaQ+ZZ5pRnK7sNWI60ZDPoWxHPBTuk9MYYqKhKvP7pyQdTuLwntw4kRxn4GyqEbRUaQ/5LPK2FbUPhrL+hNiw1SKfQ9nQ12ZEQ3nEVXsSM7O4QNJWJNVhv0aSvf2VhkrljDjK2qhD5b1DPotmUNaf0IxyHyGaEQ3lEVftSVBZmNnNZvaimf3RzD5iZnuZ2fxmCOeMDOpho64V4FppD/ksYuUMKbSifcouDFSPTrTsOZoR/uvlwduToBlK0pXAaWa2Ln2/NfDt1HfhOFEU1V2KNY0UJd2N7hBv5NiiRncknXPZEXVMslvMPmUWBooNWy26T2VDX+sV/tsO9aWcgRHjs9i9oigAzOyFdIU7x4kiVHcpxl8Q6ojzFAXQ2142ezpGoYX2KVoYKKYjjOlEQ/epHh1x2dpPXl9qaBLjsxiVziYAkDSROCXjOACFdZdiKRshE/JZhDK4YxRa2TW4Y+ie2cndZx7Ioxcdzt1nHtivQx0KkURDQUanPzHK4tvAbyWdL+l84LfAxY0VyxlO1KPuUtlopZDPolamdqU9phxI2TW460Gj16tohoxOexLj4P45cAzwJ+AZ4FNm9q+NFswZPoQ62piOuGztplAHdUH3bhy/39Q+Mh2/39Rev0qMwgs5sJvhuA0ppHYY1Xu009Ak1pz0IPBCZX9JU82sfyEdZ9hSZmGgUN2l/Xbcmrsffr7f9v127LV+lp6dbDV2dG5JkK3Gju79/4Lu3Wp+pg4p91r9lFWBA7sZjtuhUDXW60sNTWKiof4WOIdkZrGJ5OdvwO4Rxx4K/G+gA/hnM7uoavvJwCnpeV8GTjKz+yWNJsnn2DOV8edmduEAPpdTR8ouDFTZp5ayeey5/I4q2z6hRmc/IdPZF1FrAhJbVDZGWcU4sBvtuC27XkUz8GinoUnMzOI0YIaZPTeQE6er7F0OHAysBhZKmm9m92d2u8bMfpjufyRwKXAo8GlgczPbTdI44H5J15rZYwORwakPV+fMCirtsbOLolF7zGi3bGeftyhRUXs1MTOLdhi1w9CoGuvRTkOPGAf3k8CLwb36sw+wysweSVfZmwscld3BzF7KvN2SZMZC+ndLSZsBY4E3gOy+ThOpZeiJd08XE2PDXlejU6+0V1ajq6bSXtbnETOzGAq2+FDtKMepRc2ZhaTT038fAe6QdAvwemV7xEp5nSSKpsJqYN+c65wCnA6MAQ5Mm39BolieAsYBXzGzfkZtSScBJwFMnTo1II7TSIqSrEJ85L2Tcn0a2XDXkPkktEpdWZ9HzMxi9iEzmH3Dsj7ragwkQxvK3cdY6pEnEcr1cBPT8KPIDFVZDe+J9DUmfUHcoDJvyNbvODO7HLhc0nHA2cAJJLOSTcB2wNbA/5H0azN7pOrYK4ArALq6uuo10B12hB7eMs7ryvmDmcsFxJTiCCmUkB28s4ay6Ywc9UcrmxIZ2mXvYzMIyRj7GVyhDD1qKovKCnmSPm1mN2S3Sfp0xLlXA9ng9ckkFWtrMRf4Qfr/ccBtZrYBeEbS3UAXySzHGQChh7es8xrKVzKNyaG4edlTufvcvOypXjkbaauPmVnEOLjLrBPeDsRkqYc+w1BQik5/YnwWZ0W2VbMQmC5pB0ljgGOBPgUIJU3PvD0ceCj9/wngQCVsSbJ+xoMR13SqCMXVx2RXh4r0lU2Yi/EnhFbCCxFjqy8qAhgzswjdh1BCXLs4yIuoR5Z6O+R6OAOnyGdxGPBxoFPSP2U2vR3YGDqxmW2UdCqwgCR09idmtkLSecCitHLtqZIOIlkz4wUSExQkUVQ/Bf5I0if91Mz+MOBP5wQf3phOcNyYDl55Y1O/fcaNSRLMonMQalCPDO8YimYeodFujBkrdB9Co+52CGsNEcpXifkMQ0EpOv0pmlmsARYDr6V/K6/5wCExJzezW83sPWa2k5l9I237eqXEuZmdZma7mNkeafnzFWn7y2b26XTbzmZ2yeA/4shmqxp5CJX2mFH9qzmKIttetrOv5TfIto+rsRRetr3Mmhih0W5M9nXoPoQ6yaFQmjsUwhzzGYZC1JjTn5rKwsyWmdnPgJ3M7MrM6yYze6F5IjpleGNjfkdfaQ/VRILwwx3T2RdRj04ypuZRkTIJdeQxZqzQfQjdx+6ZnRy9V2efkiNH79UZbSprBqEQ5pj7NBSUotOfIjPUctLoJeUMJ8wsmMHttJ5XN7xZ2N61/USuvueJPmFqStsrhJzDMaGvZdcvCH2OkIknZGaKMZ+EQk5D9ym0fd6SHm5c3NM7E9lkxo2Le+jafuKAIo0aST3uk2dwD02KQmc/0TQpnJYxZ/6KfvHMlrZnO/NFjz/fJ7w2O+INRSo1Y/2C0MwgpEzqkdkcuk+hTrIekUaNpl4Z4J7BPfQoCp19vJmCOK0hJsooNOINnaMendzW40bnluaoZGiHRrwxZqaKrIMd7YbuU+U6tc7ZjPUwyuKzgpFLTCHB/YDvAu8jScrrAF4xs7c3WDYnkkYnOJXt7OvRyYUytEMj3nqYT0KUvU8hGdslWip0nzzhbngSk2fxPWAWSQ7EWOALJMrDqRNlnJYhx24o2ilUUwnK51HUI/qle2Ynn9l7Sh/n72f2ntJnxF7kHG6GU7WsUgzJOBQcw+2wuJLTGGKUBWa2Cugws01m9lPgI40Va+RQNoonFPIZinY6fPdtc7fXah8M9Yp2yjPxVO5FaHszCuiVVYohGZtVBLCRIcjtRKsjy4YaMSXKX00zsJdKupikuN+WjRVr5FA2iidmBTiovZZETF2mstTDzl0P52+jnar1cpKHIoka+RnKRly1g18lhnaILBtqxCiLz5HMQE4FvkJS7+noRgo1kigbxRNjxy5aS6KsiQniFiZqdLRTO3RSoWgoaH97fqP9Lu1CO0SWDTVi1uB+nCT0flszO9fMTk/NUk4dCJkuYrJ+i9Z9DlF2nQeIW5io7JQ/lIke2t4M5i3p4br7nuxjCrvuvif7mMrawZ5fJjkxxFDwq0B7DC6GGkFlIekIYClwW/p+D0nzi49yYgk9XDGdYHWZiYHUVIop1TH9nflWx0p7aBW6eUt6OP26pX06ydOvWzqgTjKkkJqhsELMmb+iz1oWABveNObMXwE0z55f9DnnLelh9g3L+nwXs29Y1rtPo/0u7YKXHBk4MWaoOSTrS9wBYGZLJU1rmETDjJDZIWTPD3WCc+avoKp/4k3rm1RXJENMEcBX36iRPV2jvZqzbvoD1Xu+mbbHdiIhhRQqQxFjoy5rIgrlmzRjNBv6nEUKrXtmZ1Q2foihkHDXLsvLDiVilMVGM3sxr+SHU0ysE63o4Qp1gqEOKiRDzMyibCe3vkapjmx7qKMOKbX6MaTWAAAcwUlEQVSQrbxsIEE9aIY9P/Q5Q7+XZgQ8tAOeXDhwYkJn/5iuYtchabqk7wK/bbBcw4JYs0OR2aDsdDkkQ4zPIiRDWb9HjC0/pNRCvpsygQRZOYvMWKGclaGQ6zGSbPndMzu5+8wDefSiw7n7zANdUQSIURZ/C+xCsv72tcBLwJcbKdRwISbSKNRRhjqYUAcVkiFmZhGSIaZybRExHXWUQipY0rRsIEGMQjvniF0Y3VGlsDJZ5u2Q6xH6vcQMTjw/YWQSEw31qpn9g5ntbWZd6f+vNUO4oUDRgxPTwYU6ylAHE+qgQoytsU5Etj2UHd21/UQ6qkb1HaPUp3JtETGj2ZBSK1rSFMIKL9RJxii07pmdXHLM+/t8V5cc8/5+JsdGjmZDnzP0e6nlm6i0t0tEl9N8ikqU/zv0K0jai5kd2RCJhhDN8gcU+TS6Z3Zyw6InuPvh53vb9pm2dXQn9PrGfH9Ctj1UIO+SBSvZVOU03fTmW2tPh1aZi7Hli/wfY6XbiykUWJQDEXJ4Nss8U9bJHrLFh7aHfBaenzByKZpZfAv4NnApSRHBb1e9gkg6VNJKSasknZmz/WRJyyUtlXSXpJ0z23aX9DtJK9J9thjIB2sGodFmzKJAZX0SZ89b3kdRANz98POcPW951PHVkVR57aHPGepIQ6PVGFt+rVFLpT10H2PKgRTNnmLNM2VG3c0atRfNboZC8qPTGopWyrszfd0BvJx5f6eZ3Rk6saQOkrW0DwN2BmZllUHKNWa2m5ntAVxMopiQtBlwFXCyme0CfJhkne62oh7LZIY60hDX3vtkYfuWYzpyt9dqzyP0OUMdadF6F1AfW37oXocUXkiZxHxPZfMo6pGHEcqjCBH6Lj0/YeQSVUiQAnNUAfsAq8zsETN7A5gLHNXnpGYvZd5umbnOx4A/mNmydL/nzCx/fdAWErNMZqgTLBuqGDJ1fXLP/A63VnseocTAae/Ivw+V9pg1M8oSmhmUjYaK+Z7aIRIplBgYIqR0yw5unKFLkc8i653skLQ1mfgSM3u+/1F96ASyw97VwL451zkFOJ1krYwD0+b3ACZpATAJmGtmF+ccexJwEsDUqVMD4tSfmASmUIJSTMTU2fOW1ywEGMo/CHVyIX8ChBMD73kkf0n2Wu3VVEbDlU6uMhqGt2zs40aPyl1adVzqiK9VaqPiVwn5RULfQ0xHHuN7KfJJbFWjxtZASpaUVcxlfRrO8KVoZrEYWJT+fTvw+/T/SnuIvC6mX69mZpeb2U7AGcDZafNmwF8Cn03/flLSR3OOvSKN0OqaNKn5I5t6PDi1MhEq7WfPW85V9zzRpxO86p4nen0SobDVUCcYU1sqlBgY48gvImY0vPnofLNZpT10jtCIORS5FmN+CV0j5JOIKVnSDMr4NJzhS5HPYgcz2zH9W/3aMeLcq0kq1FaYDKwp2H8u0J059k4ze9bMXgVuBfaMuGbdaWTRNQg7bkM+ibJhq0BhfgKEO8qQwgsRMxoum8keMgmGFF6M+SV0jZCpK/QZY4hZzKoM7rMYucSU+xgsC4HpknYAeoBjgeOyO0iabmYPpW8PJ1mND2AB8HeSxgFvAB8CLmuEkEVmgVBobDPKN8TkFxSFrYYoyk+IDSsdN6aDV97o71IaNwAneoh63Osik2DIHBc7iyyzxnY9PmNo+dmyeE2lkUusg3vAmNlGkjUwFgAPANeb2QpJ50mq5GicmobGLiXxW5yQHvsCSWTUQpKKt783s1vqLWPILBAaCdajfEPZkWAznKoh53Geoihqr2ZcjcTAbHtoZF/2PpYtFxLDFjU+Z6W9Hr+nmMTAMgyVqrJO/WnkzAIzu5XEhJRt+3rm/9MKjr2KJHy2YYQSjGISvSrnGWwS1TlH7MKXr1ua2x5DaDS62SixMSeZYrNRcQX4IJyUF1O5tiyhkf05R+zC6dcv7ZMfMkrx9xEIlgspO+oPJUDG/J5ikvYaXfV1KFSVdepPbDRUPyKiodqeepgFyj44ix7Pv42LHn+e7pmdwVXoZh8yo08kEfQdEX/r0+/nK9ct7eMbUdoOSXhr3mfMhsOGlGpZB3delFN1ez1G9kUdbcgcF2t+KbpGTAJk0e/JlwJ1WklsNFT1KyYaqu0JOeuaUSU05MCOiZApWvyoe2YnH9ipr97/wE4TezuX6uzvCtn2UEddj9X2QoRyPYrW9YBwslrMLDJkfqlHBnZRQEWzFk9ynDwaGQ3V9oSUQTPss6FReShCJtRJli0HAmGlGvoMoSzyGqb8Pu1vbMz3f1TaQ9FQodDamCifUBHAsp15SNl42KrTSqIc3JK2lrSPpL+qvBotWDOIUQaNrhIaGpWHRtShTjI0c4khpFQn1JCx0v5qDUd3pb2GFapPe4ypqojQfarHLLJsZx5SNh626rSSoINb0heA00jyJJYC+wG/461s6yFNWZ9D2Sqhs/adkpsFXkmqe+X1/E6uVns1Zf0JUH7p12aEGG89bnTu0qux0VD1CFYIfc5QFnpMrTEPW3VaRUw01GnA3sA9ZvYRSe8Fzm2sWEODejgcK2U7apXziBl1F1GvSKUyS7/Wo5MLKYOdtx2f63/ZedvxUcdDeOBQVHYFwuVfNh/dkassKlnooXIfvhSo00pilMVrZvaaJCRtbmYPSvKhDPWr7X9B9259Op16Epq51INmdHKh0NhQfaqY0NqiWWKl7EqFStkVeEvhh8J7Q0o1JpjBw1adVhHjs1gtaQIwD/iVpH+juGzHiKEdHI6hZLSu7Sfmpg8MqBxIgBhT2aLHn+fpF1/DgKdffK1myHAReWVNKpQ1t4WcyzG+n1AdrpDPoR7lPhynUcQsq/pJM1tnZnOAfwT+hapS4yOVdlivOLRM5pz5K/rVnzLeipaqRy2hkKksVAwxhtCyqSFCUWMh53KMMgoFK5Rd2tVxWklQWUj618r/6cJH84GfNFSqNqKosy9bZbQehMo7hKKAamU4V7eXUXr1iMiKKeVeROg+1COXJKRQQtF3zcjrcZzBEuOz6NNrpCvg7dUYcdqLkAM7ZIuvh08jZr2JMnbsUAY5lHfk1yMiK0RZR34okinG9xPKtofweurgDmynPSkq93EW8PfAWEmVFe1EUgX2iibI1nJiOvsyVUZjiFlgqYhQuObVOeeutFcct6H7sGWNqrMDWbq1LCGFNHpUvrmskvgXitgKRa1BOHEwBndgO+1KTWVhZhcCF0q60MzOaqJMbUNMZ18UQRObX1B0jrILLIXCNUPraUDYBBRKugsRE9YamjmEZmCbanzQSnv3zE4WPf58H2WQrawL4ai1somDjtPOxDi4z5LUKekDwy2DO0TI4RjyScTYoOtR4qHIn1CPCJuyWeadNe5jpf3w3bfN3Z5tD60IGLrXoSJ+tZZlrXdAguMMVWIc3BcBd5MseTo7fX2twXK1BaE1FEIRNDHlRMqWeAgpm1BHHkPIxBPKD8hWsM1SaY+ZPV3QvRvH7ze1z5oax+83tXekH7rXodX8YpZ2DdHoVeocp5XEOLg/Ccwws9cbLUy7EerEYhcOKrJBly3xEPIn1GNd57GjR7E+x5QyNjX4h2YvoYS5WN9OyAxUdK9Dq/nFLO0aoi5rajhOmxKTlPcIMCKHRjHrXeQxkLj40DlCI+aQjHm+gGx7zGg4tGhP2aq0zcgvKOtXiaUocdBxhjIxyuJVYKmkH0n6p8or5uSSDpW0UtIqSWfmbD9Z0nJJSyXdJWnnqu1TJb0sqSVmr2asdxFazhOKK9+GZAyZX0JJfRC294fMdSGfR+j4etAMhVQ2cdBx2pkYZTEfOB/4LX0XQCokzce4HDgM2BmYVa0MgGvMbDcz2wO4mGTd7SyXAf8RIWNDaNZ6F3m28nrJGIp2qseazSFzXcg5XTbiK4Z6KPZQYmI7lH9xnEYR9FmY2ZWSxgJTzWwgQ6R9gFVm9giApLkkZULuz5z7pcz+W5Lp2yR1k5jAXhnANetKTJJUyCcRKmF+1k1/yD3urJv+ENVh1yORq2xsf6iTvKB7Nx5d+3KfqrAH7DSx1/9QNjs7hrL3KSYxsRml2B2nVcSsZ3EE8C1gDLCDpD2A88zsyMChnUC2nsNqYN+c858CnJ6e/8C0bUvgDOBgCiKvJJ0EnAQwderU0EcZFGU60pgOJs9xXNQ+UGJyGMquybFFDQf4FqkDfN6SHn7/xIt9tv3+iReZt6SH7pmd0dnXZeUs813GJGj6ehPOcCbGDDWHZJawDsDMlgI7RByXZ6ju1yOY2eVmthOJcjg7bT4XuMzMXi66gJldYWZdZtY1aVL97Nv1ohlrJodCZ0O1n+pRvyqk8OpRpK/RdbZCjv7YyLdGL8PrOK0iJnR2o5m9qL6jvBij+moga6yeTHFp87nAD9L/9wWOkXQxMAF4U9JrZva9iOu2Dc0wr4RGvKHaTzEj5lDJkBCh+xAzs4iRs8zM4/Ddt80tq1JJDIw1MXm5Dme4EvO0/1HScUCHpOmSvkvi7A6xEJguaQdJY4BjSZzlvUiannl7OPAQgJl90Mymmdk04DvA/z/UFAXEVSqtVT8p217kWA2NeEMVX2NGzJuPzpexVns1ofsQM7MIyRkz8yi6jyEnu1eEdUY6Mcrib0kqz74OXAu8BHw5dJCZbQROBRYADwDXm9kKSedJqvg7TpW0QtJSEr/FCYP4DG1LTCcYiv8vm6FdjxyHsiVDQjKEyoFA+HOGTF1ly6q4ickZ6cREQ70K/EP6GhBmditwa1Xb1zP/nxZxjjkDvW4zKTJ9xJQXD5k3ymZoh0w8MU7ZslE+ofsQU1k39DlDpq7QfYz5jG5ickYyNWcWkuYXvZopZCspMl3Uo5BgaJ/QiDc06t9vx61zt1faY0bMIRlDM4PQ8TF5FqFM9FDyYUxZFTczOU5timYW+5OEvl4L3Evt53HYEgp9DY1WY/M0ivYJjXhD2x97Lr+TzLaHRswhGUOzk9DxMYEAoRlSKPkwdJ984SHHKaZIWfwFSZ7DLOA44BbgWjOLL8M5xAkpg2Zk7IY64tD2esnYyBXeYqKhyq62F2NuczOT49SmaPGjTcBtwG2SNidRGndIOs/MvtssAVtJaMQbGq3GJOXFLN1atChPaHuMLT4m5DS0T1FHO29JD7NvWNZbxqRn3Xpm37Cs97gYRRDye4SSD33m4DjlKIyGkrS5pE8BVwGnAP8E3NQMwdqBUMhnyM4dk5QXE8Vz4+KePovy3Li4p9cvElq0JyRjpSPP+l1m37Csn28mZp9avp3QWhExIcahzxFTELGoIKPjOMUUObivJMmn2BM418z2NrPzzWzELB0WGvGWLR8es09ImYQ64pCMMYv+hPYJOfpDa0XEzCxCn6MeBREdx6lNkc/icyRF/N4DfCmTwS3AzOztDZat5cSEvhaZX7YaOzq3o8zmDITMRMFoqIhFe4pkjDk+tE9MdnURE2rcpwlVuRUxjnhXDo7TGGrOLMxslJmNT19vz7zGjwRFAeXDKWNWqQtdox7LojaakEKrtf5Ppb0eq/k5jtNY4or7jFDKZu3GZD4H144OdKRl132uVd8p2x7q7ENZ4KHFk8pmiDuO03hiCgmOaMqYNuqxvkEoGS1UAK8ehDr7UFhqKDTW14FwnPbHZxYNJMaMFYo0CmUml11lLq+abHV7KEM7NDsKObA9e9px2h+fWTSQmNj+okij7pmdwczkZiUGZvMkIH+d8FozsFCggOdAOE7748qiwYTMWDHRSEXUK+kuRPXsoPp90TU8e9pxhj5uhmpzQg7sbGXWLJX2eqwwN2f+in5+izeN6DwLL+/tOEMfVxYtJhSNFMpMvnnZU7nHV9pDSX0x0VRl8iwqePa04wxtXFk0mKIyGBBeha57Zief2XtKb+RQh8Rn9p7S29mGOvKQT6NW1NRAoqma4TdxHKe1uLJoIDEmoFCOQag2VIhQDkTZaKqYaziOM/RpqLKQdKiklZJWSTozZ/vJkpZLWirpLkk7p+0HS1qcblss6cBGylmGoplDjHmm7HKhIWYfMoPRVVl12UimmLUkYq7hoa+OM7xpWDSUpA7gcpI1MVYDCyXNN7P7M7tdY2Y/TPc/ErgUOBR4FjjCzNZI2pVkHe+2M3KHyovHmGdCGdqhc4RKcycnqz5533/zwnMHUmnDQ18dZ/jTyNDZfYBVZvYIgKS5wFFAr7Iws5cy+29J2m+Z2ZJM+wpgC0mbm9nrDZR3wNRjXedQhnboHOccsQuzf7GMDZsyORAZB/glC1b22QawYZP1yhjK44A4heShr44zvGmkGaqTZFnWCqvJmR1IOkXSw8DFwJdyznM0sCRPUUg6SdIiSYvWro23sdeLeqzrXHbNjFBp7nqYmWLWinAcZ3jTyJlFXi/YbyBrZpcDl0s6DjgbOKH3BNIuwDeBj+VdwMyuAK4A6Orqiltfs47UY13nmDUzilbCq+wz2FF97Kwh9DkcxxneNFJZrAamZN5PBtYU7D8X+EHljaTJwC+BvzazhxsiYUnqkZkcKoVRayW8ru0n1qWzDpmxYj+H4zjDm0aaoRYC0yXtIGkMcCwwP7uDpOmZt4cDD6XtE4BbgLPM7O4GyliK7pmdHL1XZ58ciOpRf4hQBnbMSnZFhMxcvsKc4zgxNGxmYWYbJZ1KEsnUAfzEzFZIOg9YZGbzgVMlHQRsAF7gLRPUqcC7gX+U9I9p28fM7JlGyTsYauVADGTUH8pzKFs7ata+U3JLmM/a961Jn88aHMcJ0dBCgmZ2K3BrVdvXM/+fVuO4C4ALGilbLEUF8souJwqNz36+oHs3gD4+j1n7TultdxzHicGrzhZQjzyKEM1Y+OeC7t1cOTiOUwov91FAKHs6psxFqDaUZz87jjMUcGVRQNk8ipjaUKHy3TFrZDuO4zQaN0MVUDaPItanUeRg3nx0R+7Sp7Wq1TqO4zQCVxYFlM2jiM2eLnKih6rSOo7jNANXFgWUzVzukHIztLO5DyEnejMc4I7jOCFcWQQok4MQKuUBYVNVzOzGcRyn0biyaCATxo7OTZ6bkFnDIuRE97pMjuO0A64sAhT5E0K8sXFTsD3GzOQZ1o7jtJoRH39ZlAcRE/paRF4UU3W751k4jjMUGNHKIqQMyi5pGkMoz8JxHKcdGNFmqJBzuWw5j6glT3Ezk+M47c+InlmElEFMOY8ifIU5x3GGCyNaWYSUQWitiRC+VoTjOMOFEW2GCuUwhNaaiMFNTI7jDAdGtLII5TA0eq0Jx3GcocKIVhZQPPL3UhuO4zgJDfVZSDpU0kpJqySdmbP9ZEnLJS2VdJeknTPbzkqPWynpkEbKWQvPgXAcx0lo2MxCUgdwOXAwsBpYKGm+md2f2e0aM/thuv+RwKXAoanSOBbYBdgO+LWk95hZfkp0g/BSG47jOAmNNEPtA6wys0cAJM0FjgJ6lYWZvZTZf0ugUmHvKGCumb0OPCppVXq+3zVQ3lzcQe04jtNYZdEJPJl5vxrYt3onSacApwNjgAMzx95TdWy/HlvSScBJAFOnTq2L0I7jOE5/GumzUE5bv5rdZna5me0EnAGcPcBjrzCzLjPrmjQpLvfBcRzHGTiNVBargSmZ95OBNQX7zwW6B3ms4ziO00AaqSwWAtMl7SBpDInDen52B0nTM28PBx5K/58PHCtpc0k7ANOB+xooq+M4jlNAw3wWZrZR0qnAAqAD+ImZrZB0HrDIzOYDp0o6CNgAvACckB67QtL1JM7wjcApzY6EchzHcd5CVmPpz6FGV1eXLVq0qNViOI7jDCkkLTazrtB+I7qQoOM4jhOHKwvHcRwniCsLx3EcJ4grC8dxHCeIKwvHcRwniCsLx3EcJ4grC8dxHCeIKwvHcRwniCsLx3EcJ4grC8dxHCeIKwvHcRwniCsLx3EcJ4grC8dxHCeIKwvHcRwniCsLx3EcJ4grC8dxHCeIKwvHcRwnyLBZKU/SWuDxGpu3AZ5tojiDZSjI6TLWB5exPriM5dnezCaFdho2yqIISYtilg1sNUNBTpexPriM9cFlbB5uhnIcx3GCuLJwHMdxgowUZXFFqwWIZCjI6TLWB5exPriMTWJE+Cwcx3GccoyUmYXjOI5TAlcWjuM4TpBhpywkTZH0G0kPSFoh6bS0faKkX0l6KP27dRvKOEdSj6Sl6evjLZRxC0n3SVqWynhu2r6DpHvT+3idpDFtKOPPJD2auY97tErGjKwdkpZIujl93zb3sUDGdryPj0lansqzKG1rm2e7QMa2ebYHy7BTFsBG4Ktm9j5gP+AUSTsDZwL/ZWbTgf9K37ebjACXmdke6evW1onI68CBZvZ+YA/gUEn7Ad9MZZwOvAB8vg1lBJiduY9LWydiL6cBD2Tet9N9rFAtI7TffQT4SCpPJXehnZ7tCtUyQvs824Ni2CkLM3vKzH6f/v9nkh9/J3AUcGW625VAd2skLJSxbbCEl9O3o9OXAQcCv0jbW30fa8nYVkiaDBwO/HP6XrTRfYT+Mg4x2ubZHs4MO2WRRdI0YCZwL/AuM3sKks4aeGfrJHuLKhkBTpX0B0k/aYPpdIekpcAzwK+Ah4F1ZrYx3WU1LVZy1TKaWeU+fiO9j5dJ2ryFIgJ8B/g74M30/Ttos/tIfxkrtNN9hGQw8J+SFks6KW1rt2c7T0Zoo2d7MAxbZSHpbcCNwJfN7KVWy5NHjow/AHYiMak8BXy7heJhZpvMbA9gMrAP8L683ZorVdXFq2SUtCtwFvBeYG9gInBGq+ST9AngGTNbnG3O2bVl97GGjNBG9zHDAWa2J3AYifn2r1otUA55MrbVsz0YhqWykDSapBO+2sxuSpv/JGnbdPu2JCPRlpEno5n9Ke383gR+TNJBtxwzWwfcQeJfmSBps3TTZGBNq+TKkpHx0NTMZ2b2OvBTWnsfDwCOlPQYMJfE/PQd2us+9pNR0lVtdh8BMLM16d9ngF+SyNRWz3aejO36bA+EYacsUnvwvwAPmNmlmU3zgRPS/08A/q3ZslWoJWPlB5/ySeCPzZYtI8skSRPS/8cCB5H4Vn4DHJPu1ur7mCfjg5mOQyT265bdRzM7y8wmm9k04FjgdjP7LG10H2vIeHw73cdUji0lja/8D3wslamdnu1cGdvp2R4sm4V3GXIcAHwOWJ7asgH+HrgIuF7S54EngE+3SD6oLeOsNDzRgMeAL7ZGPAC2Ba6U1EEyqLjezG6WdD8wV9IFwBISpdduMt4uaRKJuWcpcHILZazFGbTPfazF1W12H98F/DLRXWwGXGNmt0laSPs827Vk/Nc2erYHhZf7cBzHcYIMOzOU4ziOU39cWTiO4zhBXFk4juM4QVxZOI7jOEFcWTiO4zhBXFk4Th2Q9ElJJum9rZbFcRqBKwvHqQ+zgLtIktocZ9jhysJxSpLW+DqApMz4sWnbKEnfV7LOxs2SbpV0TLptL0l3poXmFlRl9zpOW+LKwnHK0w3cZmb/F3he0p7Ap4BpwG7AF4D9obcm2HeBY8xsL+AnwDdaIbTjDIThWO7DcZrNLJLigJAU4ptFsrbGDWnhuKcl/SbdPgPYFfhVWhKig6QKqeO0Na4sHKcEkt5BUkl2V0lG0vkbSbXR3EOAFWa2f5NEdJy64GYoxynHMcDPzWx7M5tmZlOAR4FngaNT38W7gA+n+68EJknqNUtJ2qUVgjvOQHBl4TjlmEX/WcSNwHYkK+D9EfgRyUqIL5rZGyQK5puSlpFUc/1A88R1nMHhVWcdp0FIepuZvZyaqu4jWUHt6VbL5TiDwX0WjtM4bk4XZxoDnO+KwhnK+MzCcRzHCeI+C8dxHCeIKwvHcRwniCsLx3EcJ4grC8dxHCeIKwvHcRwnyP8DlnYymaBse+0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "xm = np.array(m_data['age'])\n",
    "Xm = []\n",
    "for i, each in enumerate(xm):\n",
    "    Xm.append([i, each])\n",
    "Xm = np.array(Xm)\n",
    "\n",
    "ym = np.array(m_data['mental_health_care'])\n",
    "Ym = []\n",
    "for i, each in enumerate(ym):\n",
    "    Ym.append([i, each])\n",
    "Ym = np.array(Ym)\n",
    "\n",
    "regm = LinearRegression().fit(Xm, Ym)\n",
    "plt.scatter(xm, regm.predict(Xm)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Health Care (med)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Healthcare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Healthcare')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "xl = np.array(l_data['age'])\n",
    "Xl = []\n",
    "for i, each in enumerate(xl):\n",
    "    Xl.append([i, each])\n",
    "Xl = np.array(Xl)\n",
    "\n",
    "yl = np.array(l_data['mental_health_care'])\n",
    "Yl = []\n",
    "for i, each in enumerate(yl):\n",
    "    Yl.append([i, each])\n",
    "Yl = np.array(Yl)\n",
    "\n",
    "regl = LinearRegression().fit(Xl, Yl)\n",
    "plt.scatter(xl, regl.predict(Xl)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Health Care (lg)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Healthcare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression: Age Vs Mental Discussion Negative\n",
    "There is a unclear and week postive correlation from the graphs below. The most outstanding graph was the graph of the large company size data with strongest and clearest trend of the three company sizes. The belief that self mental disorder discussion with the boss does not increase much with age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Health Discussion Negative Effect')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = np.array(data['mental_discussion_negative'])\n",
    "Y2 = []\n",
    "for i, each in enumerate(y2):\n",
    "    Y2.append([i, each])\n",
    "Y2 = np.array(Y2)\n",
    "\n",
    "reg2 = LinearRegression().fit(X, Y2)\n",
    "\n",
    "plt.scatter(x, reg2.predict(X)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Health Discussion Negative Effect (all)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Health Discussion Negative Effect')\n",
    "# the younger you are, the MORE likely you are to say that having a discussion with your boss about\n",
    "# mental health issues will NOT have negative consequences\n",
    "# if you are older, you are MORE likely to say that having such a discussion with your boss \n",
    "# will result in negative consequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Health Discussion Negative Effect')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAccAAAEWCAYAAAATnlw4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztvXn8HFWV//3+EBIImyEQIARCAB1AZP/KIi4QUBzZIosjAoIDw6A/t5kxEn7yPIYRHqKouI066MgiiGwaQRwjuyMKTiJhU1CUJSRhJ+xiCOf5o24nnU7X0lVdXdXd5/169au7bi331K3qe+4999xzZWY4juM4jrOC1aoWwHEcx3HqhitHx3Ecx2nBlaPjOI7jtODK0XEcx3FacOXoOI7jOC24cnQcx3GcFnIrR0lvk3RfN4UZViRNlvSCpFFVy+JkR9KDkvavKO+bJJ2YsP98SWd0Oc97JO3TzWsWYZjqoFA/bNXjPDeW9EtJz0v6kiLOk/SMpN/2IP9/lvSVLl9zDUn3Stoo7dhU5RhXAZjZ/5jZNnmF7CaSZkpaGl6gJZJ+LWmvquXKipk9bGbrmNmyXuQXXvK/SPp9iXnMkfTvbdIPlfSopNU7uNbxkkzSl1vSp4X087sg75RwrcxypVxvFeXU7Tyarnu8pF8VOL8h1wvh85ikn0p6Z/NxZra9md1UWOAuUVYd1FQe17SkXyRpZrfza5P/Kg2fUD/8pcv57CPptabn3vg06s6TgCeB9czs34C3Au8ENjOz3Qvkm/q+ShoDnAacnTefdpjZK8D3gFPSju07s2pCxXKpma0DbAjcCFze4/z7ibcDGwFbSXpzSXmcDxwrSS3pxwIXm9mrHV7vz8A/tJT/B4E/5hfRaWFc+A/tBFwL/FjS8dWKVCl7Stq7aiFKZlFQvM2f34R9WwC/txWRYrYAHjSzF3sg16HAvWa2sIRr/wA4TtIaSQcVMavuI+mRpu0HJX1K0p2SnpV0qaQ1m/YfJGl+U89ux6Z9MyT9OXTffy/pvU37jpd0i6RzJD0NzEySK1S6FwOTJE3ImP+ukm4P+V8eZD+j+T4lnSLpUeC8DNc7RdLCcL37JO0X0neXNFfSc6F1/uWQvlKPQtKmkq6S9LSk+yX9U9O1Z0q6TNKF4fr3SBrJ/OAijgN+Avws/F6OpC21wpRynaT/kHRR0/49w/0ukXSH4s1ss4HxwNuazl0fOAi4MGy/Jzzv50N5fSpB5keBu4ADwrnjgbcAV7XIHytfaJF/LrxPz0v6haQNw+5fhu8ljdazpK0l3SDpKUlPSrpY0rgEGTtCkYnni5IeDu/DtyWNDfvWV9R7e0KRGeunkjZrc43tgG8DewW5lzTtXl/SNeFeb5O0dRa5zOxRM/sq0X/t85JWC3kttyLFvcth31ubnsECBQWrlh6RmnoQijhH0uOK6o87Jb0p7Gv7nmjVOmi7kMeS8L84pGnf+eFd7qQ8vgDEmqaVv06JfbaSziT6z3wjPM9vhHST9Prwfj+qpiEYSe+VdGf4vZpW1KdPKaorxqfcZ7t7O5+obvh0kOOfge+y4j07PUMZbC7pR+E+n5L0jZT3tZm/B25uutaainruT4W8/lfSxmHfTZLOCPm/IOlqSRuE/+tz4dgpjWuZ2SPAM8CeiYVgZokf4EFg/zbp+wCPtBz3W2BTokrxD8DJYd+uwOPAHsCoUOgPAmuE/UeG81YD/gF4EZgY9h0PvAp8DFgdGNtGlpnAReH3GGAWkTlg9bT8w/EPAZ8ARgOHAX8Dzmi6z1eBz4fjx6ZcbxtgAbBpOH8KsHX4/Rvg2PB7HWDPpmOsSd6bgW8CawI7A08A+zXd61+B94S8zwJubSqLbwLfTHieawHPhfMPD+U0pmn/b4AvhnJ5azi2UbaTgKfCuasRmVieAibE5PUd4LtN2/8MzG/aXgy8LfxeH9g15jrHA78CPkBkIQD4CPCfRJXX+VnkA24i6oH+XXiONwGz2j2DkPb6cI01gAlECvQraf+NsO98wjvUlNb6nL9CpNzHA+sCVwNnhX0bhOezVth3OTC76Vo3ASc2l0+b/J8Gdif631wM/DBG1lXuPaRvFdK3a71f4t/lycDzwFFE/6cNgJ1bZW6Vm6jRMw8YBwjYjhV1QNv3hKY6KOR1P/B/id7dqUGObQqUxzrAwqZ7vgiY2YU6JfOzbUoz4PXh95+BdzbtuxyYEX5/ErgV2CzI8p/AJTH3ubz8srzDtLxnKWUwCrgDOAdYm6gue2vc+9om7/8FjmypO64OZTYK2I3I3Nsor/uBrYHXAb8nsijtH571hcB5Lde/Cvh4ogxJO5MqgNaCDccd07T9BeDb4fe3gM+1nH8f8I6YPOcDhzYV5MMpMs4ML98SYBlRhbhP0/7Y/IlMjAsBNe37FSsrx78Ba2a83uvDC7M/MLrlmF8CpwMbxlVOwObhHtZt2n8WKxTATOC6pn1vBF5Oe45Nxx9DpGxXDy/xEuC9Yd9koobAWk3HX8QK5XgK8P2W680BjovJ663As4QGDXAL8C9N+x8meunXS5H5+PBMxgKPEf0BbgX2ZmXlmCgf0Z/otKZ9HwF+3voMEuSYBtye9t8I+84nasQsafo81/ScRdQI3LrpnL2AB2KutzPwTNP2TaQrx+aGyXuIzFSdKMc1Q/rerfdL/Lt8KvDjmHyWy9wqN5Ey+yNRa361lvPaviesrBzfRmRdWK1p/yWsUGa5yiO8I7c2/Rca18tdp3TybJvSmpXjGcD3wu91w3u0Rdj+A6EhHbYnAktbn21T+b3Gyu/oEmDtpjJLUo5JZbAXoZ6J+z+n/Of/BLy7afsfgV8DO8a8V59p2v4S8N9N2wfT1CgPaRcD/2+SDN0ec3y06fdLRC0viGzV/xa6w0tCV3pzot4ikj7Y1DVfAryJaOywwYIMeV9mZuOAjYG7iVoWDZLy3xRYaKHEYvJ7wsz+muV6ZnY/UettJvC4pB9K2jScdwJRr+Xe0NU/qM19bAo8bWbPN6U9RNQratBazmsq+1jocURl9apFg9M/YoVptZH3S03HN5fFFsCRLff9VqI/4CqY2a+I/iCHKvK0ezORvb/B4USV1EOSblaKE5WZvQxcQzRQv6GZ3dJySBb54t7RVZC0UXh+CyU9R1Q5bhh3fBu+aGbjGh9gx6Z9E4hawfOaZP15SEfSWpL+U9JDIe9fAuPUmUdz5nuNofHOPd1mX9y7vDlRz6YjzOwG4BvAfwCPSTpX0nphd5b3ZFNggZm91pSW9r/JUh7fATaWdHBLeu46pQvP9gfAYYrGzA4DfmdmDzXJ9eMmmf5A1NjeOOZai5rf0fDJOqaYVAabAw9Z574FDZ4hUvwNvk/U0P2hpEWSviBpdNP+x5p+v9xmu/VZr0vUEIilVw45C4AzWx7AWmZ2iaQtiF7AjwIbhErkbqKWdQNrc822mNmTRK3MmZIalWJs/kQmm0nSSo4jm7deNuv9BBl+YGZvJXp5jMgki5n9ycyOInKG+TxwhaS1W669CBgvqfnFmEzUEi1EGNeYChwTxi0eBY4A3qNo7G1xyHutptOay2IBUc+s+b7XNrNZCdleSOQ4cyzwCzNb/tKa2f+a2aFE5TEbuCzDbVwI/BvRn6WVPPItF6dN2lkhfUczW4+o193qYJSXJ4n+tNs3yfo6ixxiILrHbYA9Qt5vD+nt8s/8/+iQ9xJZQVaZLpHwLi8gMm+140WiBkGDTVqu+TUz2w3YnkjxTg/pWd6TRcDmCuOjgcL/GzNbStRD/hwrl32ROiXt2SY+TzP7PZHi/3uioYbmBucC4O9b5FrTynFsSSqDBcDkmEZ7lvf1TqJ3IDrBbKmZnW5mbyTyNTiIqF7Jy3ZEZt9YsirH0WFAtPHp1GPzO8DJkvZQxNqSDgwKYG2iwnoCQNKHiHqOuTGze4laGZ/OkP9viFpWH5W0uqRDicYlct2PpG0kTQ2tur8SVYDLwr0dI2lCaN02Wi0rTd8wswVE5oOzQlnvSNRKv7hImQSOJTJdbUNkytmZ6AV8BDgqtD7nEjUsxoQWenOL+SLgYEkHSBoV5NtHbRxFmriQyMT8T8AFjcRw/aMlvS5UQM/RUhYx3Ew0Dvj1NvvyyNfgCSITU/NcsnWBF4icdCYRKutuEN6B7wDnKMy5kjRJ0gFNeb8c8h4PfDbhco8Bmylyfy+MovltHw15ntrSG2scE/cuXwzsL+l94f+0gaSdw/75RD2etSS9nui9blzvzeH/NJpIif4VWNbBe3JbOO/TkkYrcsQ6GPhh8RLh+0RDEO9uSitSp6Q928dY+T1sxw+AjxMp1mbP/G8DZ4ZOB5ImhPzLIKkMfkvUSJgV0tfUCs/fLO/rz4jMs4T72FfSDop6188RmYpzTX0L/+XxREMzsWRVjj8jepiNz8xOhDGzuUSV4zeIusv3E9mdG62gLxG9UI8BOxCNTRXlbOAkSRul5P83ItPECUR/8mOAnwKv5Lkfoj9RwyHoUaLW7v8N+94N3CPpBeCrwPtbzLUNjiIa91gE/Bj4rJldm+WmFXk8fjtm93FEzjqPNn+I/lAN0+rRROMFTxGNbVxKKIuguA8N9/MEUetwOgnvkZk9SKTs16bFs5RIWT+oyLR0MlHZJ2IR15vZKqa+PPI1nfsScCZwiyIT0Z5EPYZdicZNryEyQXeTU4jenVtDGVxH1HCByFlnLNF7dCuRyTWOG4B7gEclPVlAniWSXiTyCn4PkUPE92KObfsum9nD4dx/IzLHzieaGgKRc8bfiP7nF7Byg289osr2GaJe0VNEjmGQ4T0J/+NDiHpTTxI5pn0wNJQLYdH8488SVaiNtCJ1Stqz/SpwhCJP1q/FiHUJ0ZjhDcFa1nzuVcAvJD0frr9Hwu1tqlXnOR6ecPxyUspgGVHj5PVEY8aPEDlbQrb39WpgW60YktoEuIJIMf6BqJF8Ucy5aXwAuCAMK8Wilc3iDoCk24icic6rWpaqkXQpkeNCUs/FcZwEvE7pHEknAW80s0928ZprEJlT325mjyce68oRJL2DaFzlSaKe07eBrcxscaWCVYCioABPAw8A7yIa49nLzG6vVDDH6SO8Tul/BiHaSzfYhmiQfx0iT7sjhvgl3oTIfLgBkSnkw64YHadjvE7pc7zn6DiO4zgt9F1sVcdxHMcpm6E0q2644YY2ZcqUqsVwHMfpG+bNm/ekmU1IP3IwGErlOGXKFObOnVu1GI7jOH2DpIfSjxoc3KzqOI7jOC24cnQcx3GcFlw5Oo7jOE4Lrhwdx3EcpwVXjo7jOI7TQmblKOkTWdIcx3Ecp9/ppOd4XJu047skh+M4juPUhtR5jpKOIlriY0tJzUsOrUu0rIzjOI7jDBRZggD8mmjRyg2J1l1s8DzRas2O4ziOM1CkKsewOvxDko4GFjUW55U0FtgMeLBUCR3HcRynx3Qy5ngZ8FrT9jLg8u6K4ziO4zjV04lyXN3M/tbYCL/HdF8kx3Ecx6mWTgKPPyHpEDO7CkDSoUSrXDuO4zgZmX37Qs6ecx+LlrzMpuPGMv2AbZi2y6SqxXJa6EQ5ngxcLOk/ACNaJf6DpUjlOI4zgMy+fSGn/uguXl66DICFS17m1B/dBeAKsmZkNqua2Z/NbE9gO2B7M3uLmd1fnmiO4ziDxdlz7luuGBu8vHQZZ8+5ryKJnDg6iZCzsaT/Ai43s+clvVHSCSXK5jiOM1AsWvJyR+lOdXTikHM+MAfYNGz/EfhktwVyHMcZVDYdN7ajdKc6OlGOG5rZ8ukcZvYq0XSO3EgaL+laSX8K3+vHHPdzSUsk/bQlfUtJt4XzL5Xk3rNObZl9+0L2nnUDW864hr1n3cDs2xfW8ppOeUw/YBvGjh61UtrY0aOYfsA2FUnkxNGJcnxR0gZEzjhI2hN4tmD+M4DrzewNwPVhux1nA8e2Sf88cE44/xnAzbxOLWk4Yixc8jLGCkeMIsqsjGs65TJtl0mcddgOTBo3FgGTxo3lrMN2cGecGiIzy3agtCvwdeBNwN3ABOAIM8sdQk7SfcA+ZrZY0kTgJjNr24SStA/wKTM7KGwLeALYxMxelbQXMNPMDkjLd2RkxObOnZtXbGcA6LU7/d6zbmBhm3GlSePGcsuMqbW5puPEIWmemY1ULUevyBJ4/Egzu5yoZ/YOYBtAwH1mtrRg/hub2WKAoCA36uDcDYAlwbwL0dSS2NpN0knASQCTJ0/OKa4zCFThTl+GI4Y7dzhOeWQxq54avq80s1fN7B4zuzurYpR0naS723wOzS92dOk2abHdYDM718xGzGxkwoQJBbN2+pmy3OmTxv/KcMRw5w7HKY8sQQCelnQjqy5ZBYCZHZJ0spntH7dP0mOSJjaZVR/PIE+DJ4FxklYPvcfNgEUdnO8MKWX0uNJ6o9MP2Gal/VDcEaOMazqOE5FFOb4H2BX4PisvWdUNriJaRHlW+P5J1hPNzILSPgL4YafnO8PLpuPGth2rK9LjSuqNTttl0nJzbTfHOcu4puM4EakOOZK+b2bHSvq0mX2hq5lH3q+XAZOBh4EjzexpSSPAyWZ2Yjjuf4BtgXWIFlg+wczmSNqKSDGOB24HjjGzV9LydYec4Wb27QuZfsUdLF224t0fPUqcfcROuRXLljOuaWvTF/DArAPzCeo4NcIdclZlN0lbAEdL+g4tY31m9nTezM3sKWC/NulzgRObtt8Wc/5fgN3z5u8MMa2aLJvTdqyXaxm9UcdxqiOLcvw28HNgK2AeKytHC+nOEDAoqwmcPec+lr62sjZc+potN4HGkTSuWNfxv0F5Zo7Ta1KVo5l9DfiapG+Z2Yd7IJNTIXGV6SCtJpDXISdpXLExr7BOiqjfnpkrcqdOZJnnONXMbjCzD0va0sweaNp3mJn9qFwRnW6TRwGmOZzkzbMK8ppA05Rqs+NNHSj6zPKS51n3myJ3Bp8s8xy/2PT7ypZ9p3VRFqcHJIUcS6pM0xRD0hy/uoU5yxvfst/mFVYRJCDvs/alnJy6kUU5KuZ3u22nR+QNOJ1XAY5ba3TbfePWGp1aIdat4ssb37Jo0OheBwmvQpnnfdYe7cepG1kccizmd7ttpwcUMUElVUJJ5sYXX3m1zVlglm6+q2PFl8cEWmReYRVmwyqchPI+a/f2depGFuW4VYiMo6bfhO0tS5PMiSVNGSWN+SRVQkmV6b9cOr+tLM++vJRnX24fSbBRIQ5SxZd3XLGK8b8qggTkfdZ19fZ1hpcsyrE5BuoXW/a1bjsdksd5Ial1XiSMWVJlevac+xIrvaR9aRVfXmedOjn5pFFV77nXTkJ5lZxH+3HqRpapHDf3QpBhJK+pLal1niWM2dyHnuaS2xawzIxREofvtqICjatM0yq9pH1JFV/eMug378ZB6j0nUUTJ1c3b1xlusvQcnZLIax7NY/5s9iq9ct5CloWwgcvMuHLeQka2GJ9YMWWp9JL2xVV8ec2NVU1TyMswmQ1dyTmDgCvHCiliHoV85s8iSiWp0stbIbaTtTk9roFQRyefJDOvmw0dp7/oWDlKWtvMXixDmGGjqHk0zvzZLqh2o4eSZb5iLyvwUdLyXmxrelIDIc1M2ev7yNKY8R6V4/QPWeY5AiDpLZJ+D/whbO8k6ZulSTYEJM2bK9QzSphwkzT3rYrJ+u0UYyM9qYGQVHZV3Efd5nI6jlOMzMoROAc4gGjJKMzsDuDtZQg1LCRNRs87gTspqDYkK+QqKvhJMfczadzYxAZCUtlVcR91NPM6jpOfjsyqZrZAWikozrK4Y51s5PUOjSNL/E9oP/aV5syTRF4z5r7bTuCiWx9um37jvU8kmk7jyq4KRTUs3qiOMyx0ohwXSHoLYJLGAB8nmFid7pPXgSNLJR2nVPJW8EWmVVxz5+LY9M8evH2uBkIVimqYvFEdZxjoRDmeDHwVmAQ8AvwC+D9FMpc0HrgUmAI8CLzPzJ5pc9zPgT2BX5nZQU3p5wPvAJ4NScebWfvuT01J83Ds1IGjSCWd99wiHrDPvNQ+us4zLy3N3UDIch/ddthxb1THGSw6UY4ys6O7nP8M4HozmyVpRtg+pc1xZwNrAf/cZt90M7uiy3L1hDImshedhJ3n3DLNmGXEQC0rgEAdvVH7KYqQ49SJTpTjryU9QNTTu9LMlnQh/0OBfcLvC4CbaKMczex6Sfu0pvc7ZU1kL1JJ5zm3iBlz3NjRLGkTm3Xc2PargGQl6T76LYBAXvotipDj1InM3qpm9gai9Ru3B34n6aeSjimY/8ZmtjhcfzGwUY5rnCnpTknnSFoj7iBJJ0maK2nuE088kVferjIoHo5ZlnKKW65p5iHbM3q1lVc+G72amHnI9qXJOyjlnoZPL3Gc/HTqrfpb4LeS/j/gy0S9vYuSzpF0HbBJm12f6STvGE4FHgXGAOcS9Tr/vd2BZnZuOIaRkZFaLLU1KB6O3TBj9sPKEWVShvlzWBoBjlMGmZWjpPWA9wLvB7YGfgzsnnaeme2fcM3HJE00s8WSJgKPZ5UnXLvh6viKpPOAT3VyftUMkodjETNmXjNwXoVSt3Ivy/xZx0aA4/QLnfQc7wBmA/9uZr/pUv5XAccBs8L3Tzo5uUmxCpgG3N0luXpC0cVz6+RokSRPGT2YIgqlbp6lZY2B1q0R4Dj9RCfKcSuzmFhf+ZkFXCbpBOBh4EgASSPAyWZ2Ytj+H2BbYB1JjwAnmNkc4GJJE4gWXp5PNN2k5xRRVHl6TXVztEiTp4weTFGF0uveahJlmT/r1ggoQt0ag87gk6ocJX3FzD4JXCVpFeVoZofkzdzMngL2a5M+FzixafttMedPzZt3t6hCUeVd6qos0uQpowdTxXhaP5o/6zi9pFOKlrsrVicPWXqO3w/fXyxTkH6limkBRZe6ykve5aOm7ZK8wHIeqhhPK/qs86zP6RQr97pZWZz+IXUqh5nNCz93NrObmz/AzuWKV3+qiuMZl57FfT9uWkUSSStdpAVJj1tgucgqGVmmj3SbIs86qfySgqg7xcrdp7M4eelkVY7j2qQd3yU5+pa8q2c0yKOoiix1lXc5p6RKZt9tJ7Q9p5FeRgVVhUIp8qzTymDaLpO4ZcZUHph1ILfMmOqKsYki5e7TWZy8pCpHSUdJuhrYUtJVTZ8bCctXDTNFejB5FVWRpa7yKqqkSubGe9sHVWikl+lw0kuFUuRZeyWdnyLlXrTx6gwvWcYcfw0sBjYEvtSU/jxwZxlC9RNFPAKLjKXkXeoqbyWdNMaXds0i44N1cqYo8qx9zmF+ipS7j+c6eUlVjmb2EPAQsFf54vQneT0Cy+hNpFUkeSvppErm7Dn3JV4zbwVVR2eKvM86ad1KJ5285T5I01mc3tJJhJw9ga8D2xGFaxsFvGhm65Uk28BTVm8iqSLJq6jSKpmka+atoPotQHhSLzfN9FynHvKgMQjTWZze00kQgG8QhY67HBgBPgi8vgyhhoUqTD5ltKSzXDNPBdVP43Rpvdyqpt84jpOPTgOP3y9plJktA86T9OuS5BoK+snkk1aBl9E676dxurRebtK99FsP2XGGgU6U40uSxgDzJX2ByEln7XLEGh6SlEoZpra8vZQqKvB+cqZI6+Um3cu/XDq/o2s6jlM+ncxzPJZonPGjwIvA5sDhZQjl5J/mkUYZUznKIm0uY545omWRNmWgyPQbx3F6T+aeY/BaBXgZOL0ccZwGZfXUypjKUSZxPeu6jdNl6eXmnX7jOE7v6cRb9S6gNfD4s8Bc4IwQRNzpEkVDlfVyKkcVlBXnNC9Fxo/7aezZcYaFTsYc/xtYBvwgbL+faKmoZ4HzgYO7KtmQk1eJpfWoyprK0Wu6Eee0273OIk5JPt3AcepFJ8pxbzPbu2n7Lkm3mNneko7ptmDDTl4lltajKtrDqUsFXsTMW5V3qM9ldJz+oRPluI6kPczsNgBJuwPrhH2vdl2yISevEsvSo6qTkoN8SqOImXeQ1oJ0HKccOlGOJwLfk9RQiM8DJ0paGzgrT+aSxgOXAlOAB4H3mdkzLcfsDHwLWI/IrHummV0a9m0J/BAYD/wOONbM/pZHljqSR4n109xAyK80+i3Oqc9ldJz+IvNUDjP7XzPbgWgNx13MbEcz+62ZvWhml+XMfwZwvZm9Abg+bLfyEvBBM9seeDfwFUnjwr7PA+eE858BTsgpRy3p9nJWdaTIclZ5V+Xot7UgHcfpPZmVo6SNJf0X8EMzWyLpjZKKKqNDgQvC7wuAaa0HmNkfzexP4fci4HFggiQBU4Erks7vV9LmOcYpzmm7TOLw3SYxSgJglMThu9XLjNrMsMyfHLfW6I7SHceplk7MqucD5wGfCdt/JDKJ/leB/Dc2s8UAZrZY0kZJB4dxzjHAn4ENgCVm1hjvfASI1QCSTgJOApg8eXIBkXtDWo8qzhQJcOW8hSyzaNbNMjOunLeQkS3G11JBppk4y3Ji6fX8yVdanmVauuM41dJJhJwNg/n0NYCglFL/2ZKuk3R3m8+hnQgqaSLwfeBDZvYa0TSSVlrnYa7YYXaumY2Y2ciECfVZJiiul5LUo0pSnEXMlFUQt2TTvttOKC1KUBJlld9LS1/rKN1xnGrppOf4oqQNCAooLGH1bNpJZrZ/3D5Jj0maGHqNE4lMpu2OWw+4BjjNzG4NyU8C4yStHhT1ZsCiDu6ncpJ6KUUWF+50X5UkLeV0471P9NyJxccGHceBznqO/wpcBWwt6RbgQuBjBfO/Cjgu/D4O+EnrASHY+Y+BC83s8ka6mRlwI3BE0vl1JqmXkuQ0kjR+lSVOZ51ikiYpoyoUVVlxTseNjXlmMemO41RLJ96qvwPeAbwF+GdgezO7s2D+s4B3SvoT8M6wjaQRSd8Nx7wPeDtwvKT54bNz2HcK8K+S7icagywy/tlzkir/JKcRizEem6V7YlZhqkwiSdFX4cSSpfzyNCxmHrI9o1dbeSRg9Gpi5iHbd0dwx3G6SqpZVdLbY3btJQkz+2XezEM81v3apM8lmleJmV0EXBRz/l+A3fPmXzVpzihxTiPPvry07fWefXlp6vy/us23S1L0nZ7TDZLKr4izTt3C7zmOk0yWMcfpbdIM2IlonG9Um/1OBqYfsA3Tr7iDpctW1PajRyl1vl2vYBx7AAAgAElEQVRepQr1G1NLUvSdntNMES/XuPIr2rAYhMhEjjMspCpHM1spoLiktxJN51hMtLajU4TWXlDTdlzlVSR0Wt0i6IxbazTPvLSqshu31mjWGrN6KcHX81K3hkURPJyd4yTTSRCA/STdBHwO+LKZ7WlmV5cm2RBw9pz7WPraytpx6WvG2XPuSxwbTJvEnkTdIugUGT+No6zpGIO0KHG/TflxnF6TZczxQKKe4rPAZ8zsltKlGhLyzmVsmOfytPDTxr6STG1lmOGKjJ/GUVYPr25rWhahXY88Kd1xho0sY45XE0WfeQo4RVrZ487MDilBrqGg23MZs5InOgzER+UpoiCLjJ/mvWZeBsmpZpS0PIpSa7rjONmU476lSzGkJPVEzp5zX61Wjmj8breviHIoozdWZg+vbk41eWmnGJPSHWfYyOKQc3MvBBlUkkyRST2RuQ89zUW3PrzK9eLCrXWDKiLvlNEbG6QeXllMiuldT+rD8VPHKYNOwsc5HZLFIzCuJ5IUVq0s0syRZfVky+iNDUoPrywGafzUccqgk/BxTocU8QisYtpAkndo3bxcnWIU8Xh2nGHAe44lUkTBVTEfMYs50k2V8fTbpHrvXTtOPJmVo6S/I4qWs0XzeWY2tQS5BoIiCq4qs1dShVmkMq2b4ui2PD6p3nEGi056jpcD3wa+Q4Z1HJ1iCm6QnErqpjjKkKduMWsdp5m6NU77gU6U46tm9q3SJBlAiiq4vD21uv0R6qY4ypBnkELLOYNF3Rqn/UInyvFqSR8hWlvxlUaimT3ddakGiF6P69Txj1CF4khqIJQhT91i1jpOg7o1TvuFTrxVjyMac/w1MC985pYhlJNO3LqCdYyZ2euYpGlrVpYhj3vzOnXFrRr56GSx4y3bfLYqUzinPUmVfx3/CL1WHGkNhDLk8akRTl0ZpID5vaQTb9XRwIeBxuLHNwH/aWbpi+vFX3M8cCkwBXgQeJ+ZPdNyzM7At4D1iByBzjSzS8O+84F3EAVFBzjezObnladfSKr862je67VzUVoDoSx5fGqEU0c84EM+Ohlz/BYwGvhm2D42pJ1YIP8ZwPVmNkvSjLB9SssxLwEfNLM/SdoUmCdpjpktCfunm9kVBWToO5Iq/3P+YefUP0Jeh53TZt/FJbctYJkZoySO2mNzzpi2QyaZe6k4sjQQkuSpm0OTU0/65T0ZJM/3XtKJcnyzme3UtH2DpDsK5n8osE/4fQFRb3Ql5Whmf2z6vUjS48AEYAlDSlLln2VJqjwOO6fNvmulWK/LzJZvZ1WQvaJIS3n27QuZfsUdLF0WBeBeuORlpl8RveZemRSnXxRKGnV0fEvCrRqd04lDzjJJWzc2JG1F8fmOG5vZYoDwvVHSwZJ2B8YAf25KPlPSnZLOkbRGwrknSZorae4TT5QXn7QXpI2ZTdtlErfMmMoDsw7klhlTV4lwk8dh55LbFnSUXiVFxv9Ov/qe5YqxwdJlxulX31OStMNDmqNUP1FHxzenu3TSc5wO3CjpL4CIIuV8KO0kSdcBm7TZ9ZkO8kbSROD7wHFm9lpIPhV4lEhhnkvU6/z3dueb2bnhGEZGRvp6XZ4iCxbnddjptyWO8raUn3mp/RB6XLqTnUGaUlBHxzenu2RWjmZ2vaQ3ANsQKcd7zeyVlNMws/3j9kl6TNJEM1sclN/jMcetB1wDnGZmtzZde3H4+Yqk84BPZb2fupDXzJRnweJpu0zK7bDTb4vjDor5rirKKL9BUih1dHxzukuqWVXS1PB9GHAg8Hpga+DAkFaEq4jmTxK+f9Im/zFEgQcuNLPLW/ZNDN8CpgF3F5Snp6SZmeLmMiaRZu6JWw8ybZ3Io/bYvKP0KilivotT9fVsApRDWebPQZpS4PNaB58sPcd3ADcAB7fZZ8CPCuQ/C7hM0gnAw8CRAJJGgJPN7ETgfUTTRzaQdHw4rzFl42JJE4jqrvnAyQVk6TlpiizPgH9a6zxtnci4HsMZ03bggSde4JY/rwiItPfW42vnjAPFzHdxRuJ6Go/L6eGVZf4cpCkF7gE6+KQqRzP7bPhOHV/sFDN7CtivTfpcwhQRM7sIuCjm/L5eESRJkeWtoNLMPUl5JplkAX738LMrnfO7h59l9u0La1chFDHfTYopv0k17N2U5TFZlvlz0BSKe4AONpm9VSV9QtJ6iviupN9JeleZwg06SWamvBVUmrknKc8khdxP3nlFzHf9ZC4r65mUaf5M8qR2nDrRyVSOfzSz54B3EU25+BCRWdTJSVJFnLeCSpvGkJRnkkLuJ2eKIgqun8LAlfVM+qmB4Dhl0clUjoZPwnuA88zsjuAI4ySQNCaUZGaa+9DTK026b5DmONO4btKCxXF5nj3nvkSTbL945zXKrzmaz+G7ZTeB9Yu5rCyPyUEzfzpOHjpRjvMk/QLYEjhV0rrAaynnDDVZxoTiKuI0x5kixOW577YTYhXyyBbj+8aZYvbtC7ly3sLlU0+WmXHlvIWMbDF+oCr4Mh1c+qWB4Dhl0YlZ9QSi2KdvNrOXiOKsdt1JZ5AoMiZUhRkzSSH3k7mxn8ZHi9BPz8Rx+o1Oeo57AfPN7EVJxwC7Al8tR6zBoIiCq2KScZbVLPqh4u2n8dGi9MszcZx+o5Oe47eAlyTtBHwaeAi4sBSpBoR+85oct9bojtLryiBNNnccpxo6UY6vmpkRraTxVTP7KrBuOWL1F3GRbPrNazIuTGo3wqfmifaT9zz3tnQcpyidmFWfl3QqcAzwdkmjiMYdh5osTjd5vf56bTJ79uX2wbXj0rOSd7J63vPc29JxnKJ0ohz/AfgAcIKZPSppMnB2OWL1D2mRbOo4JhQ3vaSscc680X6KhDGrY7k7jtM/dLIqx6PAl5u2H8bHHAs7f/R69Yik3ljSVI4i5C2jYXKscRynXmRWjpKeZ0X85TFEJtUXzOx1ZQhWN8robVWxmnieaQ5F51bmLSNfFqhcfFkvx4kns0OOma1rZuuFz5rA4cB/lCdafUhawqeI80cV8/GqCBGXt4zcsaY8ylqWynEGhU7GHFfCzGZLmtFNYepKkhK7ZcbU5ce0a4Entc6rMBuOW2t021Xtx601mrXGrF5JOLK4MnLHmuLElW1Zy1I5zqDQiVm1eWHj1YAR6rvMXVfJOzk+zWxahdkwabrG9AO2YfoVd7B02YqDRo9Spp5amokubxm5Y01+82dS2fp4ruMk08k8x4ObPgcAzxPNeRx48k4qTzObVmE2XBIzLWN5eqvyzND8mX37QqZfccdKJrrpV9yRyURXx1BveedkliVLXvNnUtl6oITi1Ok9cbpPJ2OOH2r6/JOZnWlmjxfJXNJ4SddK+lP4Xr/NMVtImidpvqR7JJ3ctG83SXdJul/S18paJSSvEsvS4+z1RP9RMUU0SuLsOfex9LWVteHS1yxVUZ1+9T0r9TYBli4zTr/6nlR56taDqdtYXFnxeX08txh1e0+c7tOJWfUC4BNmtiRsrw98ycz+sUD+M4DrzWxWGL+cAZzScsxi4C1m9oqkdYC7JV1lZouIQtqdBNwK/Ax4N/DfBeRpS96xryxm016bDZfF2FWXmeVWVO3GMJPSmynq7dvt8ci6jcWlPZOkMkgq20Ebz+21523d3hOn+3TikLNjQzECmNkzknYpmP+hwD7h9wXATbQoRzP7W9PmGoTerqSJwHpm9puwfSEwjRKUI+RTYmUuKZSX9WMcctYv0SEnibxlVHQaTFxlWreebJKCSyuDOr5/ZVDFlKi6vSdO9+lkzHG1ZrOnpPEU8HYNbGxmiwHC90btDpK0uaQ7gQXA50OvcRLwSNNhj4S0tkg6SdJcSXOfeKL4mohZmLbLJA7fbdJyU2ani+6WwSstrd3m9CpMbWllFDeuU8TcmGQSq9tYXNIzSSuDJLP9IJkFqxi3rtt74nSfTpTbl4BfS7oibB8JnJl2kqTrgE3a7PpM1ozNbAGwo6RNgdlBhnaDZ7HuI2Z2LnAuwMjISE+8bOu46O5LS9uvT/3S0tdyT7kYN3Z0W0efcWPTQ+8mlRFQirdlUmVaVpSgvCQ9k3+5dH7bc5rLIM7iMUhmwSp6ccPSKx9mOgkfd6GkucBUIsV0mJn9PsN5+8ftk/SYpIlmtjiYSRMdfMxskaR7gLcBtwCbNe3eDFiU4VZ6Rj9WQHmmXMw8ZHumX37HSs48o1cTMw/ZPjW/tFZ/krdlXhNwUmWatOBzVcQ9k7LKoN+oYkrUoI3ZOquS2awqaWvgz2b2DeAuYH9J4wrmfxVwXPh9HPCTNvluJmls+L0+sDdwXzDDPi9pz+Cl+sF251dJHSuguN5cWi8vTdGffeROK5nvzj5yp0wVRd6IPUVMwEkmsTo+szjKKoN+oyrP22m7TOKWGVN5YNaB3DJjqivGAaOTMccrgWWSXg98F9gS+EHB/GcB75T0J+CdYRtJI5K+G47ZDrhN0h3AzcAXzeyusO/DQZb7gT9TkjNOXrJUQL2eK3XQThM7Sm+QZVpKUkURd59JZZS0r8g0mKTKtJ+URlll0G9UMSXKGXw6GXN8zcxeDZFyvmpmX5d0e5HMzewpYL826XOBE8Pva4EdY86fC7ypiAxlkjYuUYWXXV6zYVkB1tPKqIxxnTSTWD+NJeWdCjRoZsEypkR5YPbhphPluFTSUUTmy4ND2tAvdpxEWgVUxZhkXrNhEQeEIrFp4/YVbVjEVaaDpjSS8NB88VTRcHXqRSfK8UPAycCZZvaApC2Bi8oRa3BIqoDqFng8iSJKI29s2qR9ZTYsXGk4/ehM53SXTrxVfw98vGn7AcIYoZOPKqLDJAUeTyOv0sirkJPoJ8cZp//w98tJdciRdFn4vkvSna2f8kUcXPI6RRSZwP1sTODxuPRuUEQhx9FPjjNl4sGvy8HfLydLz/ET4fugMgUZRvKaKouYfMqaE5bUky1DIRedhD0IzhY+LlYePsnfSVWOjfBuwLPAG8LvP5rZs6VJNUTkMVUWMfmU8aevYt3KImOgg6JUfFysPIbJMctpT6pylDSGKOzaNOABoug4W0j6MXByS2BwpwcUUTZl/OnTKumyWuF5x0AHRan4uFi5uGPWcJPFrHoa0ZSNzc3seQBJ6wL/Afw/4eP0kKLKptt/+izeqFCfVvigKJUqwqY5zrCQRTkeBuxuZi81EszseUkfIVpH0ZVjj6lK2cSN09Vx3cokBkWp+LiY45RHFuX4WrNibGBmL0jqyeoWzqr0WtkUiXJTN7JELqpLLzeJuvXIHWeQyKIcLQT8brdEVPv1j5yBo2iUmzLIq8SSlEq/OeukNZL6RdE7Tt3IohxfB8yjw/UTnWz0S+VVJMpNGZQVPq5MZ51eP+t+U/SOUydSgwCY2RQz28rMtmzz2aoXQg4q/bQae90mRZe1+ntZzjpVPOuyyshxhoFOlqxyukw/VV77bjuho/SyaedQk5SelbIaAVU860HxynWcKnDlWCH9VHnlXeqqLEapnZU/Pj0rZa1zWMWzrltv33H6CVeOFdJPlVfdFPmymMCscelZKWvh3Cqe9SAtaOw4vaaTJauQNArYuPk8M3s4b+aSxgOXAlOAB4H3mdkzLcdsAfwIGEUUjODrZvbtsO8mYCLQqKHfZWaP55Wn11Q1BSLJMaTIXMZeyjopRp5JQZ4izi9lOBdV8ax9qofj5EeWsaUt6WPAZ4HHWDGFw8xsx9yZS18AnjazWZJmAOub2Sktx4wJcr4iaR3gbuAtZrYoKMdPmdncTvIdGRmxuXM7OiWRIhVx1R6MEFXSZx22A0CufY0pEN2+j7JkrYp+8Ux2nHZImmdmI1XL0Ss6UY73A3uY2VNdy1y6D9jHzBZLmgjcZGaxTWlJGwC3A3vWRTkmVeB1rPj2nnVDYo8rbt8tM6bGVu5llUGSrEnypJ0Hrqgcp1OGTTl2YlZdQLQyRzfZuLHqR1CQG7U7SNLmwDXA64HpZraoafd5kpYBVwJnWIy2l3QScBLA5MmTu3YD/RbEOs/YYdpcxrLKIO/cyrTzfP6f4zhpZFmV41/Dz78AN0m6Bnilsd/Mvpxy/nXAJm12fSarkGa2ANhR0qbAbElXmNljwNFmtjAEQr8SOBa4MOYa5xKtLsLIyEjXghfUzVEljbSxwzzjimWVQd5xzrTz+q1B4zhO78nirbpu+DwMXAuMaUpbJ+1kM9vfzN7U5vMT4LFgTiV8JzrThB7jPcDbwvbC8P088ANg9wz301X6yeMUkj0Y83o3llUGeeVJO6/fGjRlMfv2hew96wa2nHENe8+6oZbBJxynKrIsdnw6gKQjzezy5n2SjiyY/1XAccCs8P2T1gMkbQY8ZWYvhxivewNflrQ6MM7MnpQ0GjgIuK6gPB3Tb0G3s3gwdjoWV+Z6jXnkSTtvUFblKIKblh0nmU4ccn5nZrumpXWUeeRgcxkwmahneqSZPS1phGgh5RMlvRP4ElEcVwHfMLNzJa0N/JJoescoIsX4r2a2rF1ezdTJW3VQ8uwnB5d+c6IqgyxOS47TzLA55KQqR0l/D7wHeB/RnMQG6wFvNLOemzKL0m3lWBa99g5Nk2WQFMpps+/iktsWsMyMURJH7bE5Z0zboWqxesaWM65pu2qAgAdmHdhrcZw+YNiUY5Yxx0VEq3L8NXw3PlcBB5Qn2nCTFKi6ijid/RQHNo3Zty/kynkLl0fTWWbGlfMWDtWYW7+NlTtOr8myKscdZnY+sLWZXdD0+VFrNBuneyQpoyocSgbJiWWQFH1ePLSc4ySTZSrHXYR1G9UmqHORCDlOPEnKqAqHknFrjeaZl5a2TS+TMsYyB0nR58VDyzlOMlmCABxUuhTOKiQpwCo8ZOOGphvpvQgf1y2PyiKNi35yPCrCsNyn48SRZSrHQ70QxFmZJAVYRav/2ZdX7TU20stSYmVN1s/buBik6Q9J9wIMzH06Tl4yh4+TtCfwdWA7okAAo4AXzWy9kmQbCuJa6HUzeyX1tooqsbgyKMv8mbdsBymyTtq466Dcp+PkpZPYqt8A3g9cDowAHySKderkZPbtC5l+xR0sXRbZJhcueZnpV9wBrIgb2q4yKtqDyWMyS+pt/cul89uek0WJJd1LmeOceZalGqSxyiIxdh1nGOhosWMzux8YZWbLzOw8YN9yxBoOTr/6nuWKscHSZcbpV9+TeF4Rb8ukKSKN/e1CiiUtAlxkWkDSvaSNc/aaOk5/SAsBF7c/6V7qeJ+O02s66Tm+FNZWnB/WYVwMrF2OWMNBu15RUnqDIj2YNMWa1CON620VcRDKcy9x459lU7dQgWkWhKT9Sfcy96GnuejWVdcw33fbCWXfkuPUhk56jseG4z8KvAhsDhxehlBOMkVa9knKKG+PNKlXmUY/9WCm7TKJw3ebxKgwpWmUxOG7dW6e7RZpzyttjDTumd147xNt84tLd5xBJHPP0cwekjQWmNgIRu4UY9zY0Sxp0wsaNzZ5TK1IDybJsaZIjzTPGB6k30vdemrtIuuMbDG+EgWZ9rzKWg/TcYaBzD1HSQcD84Gfh+2dJV1VlmDDwMxDtmf0aisHVhi9mph5yPaJ5xXpqcWZxvbddkKhnlre5Y+SemNF7rMM6hZZJ+155X2edeuxO04VdDLmOJNovcSbAMxsvqQpXZdoiCgyXSNvTy3JZJbWi8saCL0T79m03lje+yyDuvWo0p5XXgvD9AO2WcmLGmD0KHloOWeo6EQ5vmpmz7YLIefkp9eVf1IFn6SskxRgkfl//TR3sKoQenGkNa4KzZVt9QiuyEPYcaqiE+V4t6QPAKMkvQH4OPDrcsRyyiItdFqcsi4rEHrdemNJ1G1qCaQ3rvI0vs6ecx9LX2uZYvSa1bLB4jhl0Ym36seA7YFXgEuA54BPliGUUx55V2NIC4TejixjVP00vpUUQm+Q6KcGi+OURWblaGYvmdlnzOzNZjYSfv+1SOaSxku6VtKfwvf6CceuJ2mhpG80pe0m6S5J90v6mtzmm0peJ5ckJVZk+aOqlk7K40DUT4q8CMNyn46TRJYlq64mYcTBzA4pkP8M4HozmyVpRtg+JebYzwE3t6R9CzgJuBX4GfBu4L8LyDMU5DG1JTlpTNtlEnMfeppLblvAMrO28//qFEM2rwNRmUEA6rQKRt2CHThOFWQZc/xi+BbwHeDELuZ/KLBP+H0BkSfsKspR0m7AxkTTSEZC2kRgPTP7Tdi+EJiGK8dUclfEMU4aaR6nacqo105JeZ2AylLkdVvto25B7x2nCrIsWbW8tybphebtLrCxmS0O+SyWtFHrAZJWA75EFKFnv6Zdk4BHmrYfCWltkXQSUS+TyZMnF5e8T8lbESc5aUDyKg5pyqjXvaYqgh0kUUeP3TpNoXGcKujEWxVyOHRLug7YpM2uz2S8xEeAn5nZgpYhxXbji0nm33OBcwFGRkaG1jE9b0VcZBWHpHOr6DUVWew4ibxK3h1gHKd+ZBlzHN+0OSo4zSxXTGb2dNL5ZrZ/wrUfkzQx9BonAo+3OWwv4G2SPgKsA4yR9ALwVWCzpuM2Axal3c+wk7ciTlIoL/3t1cT5f2WuBZmHvMEOkiii5MtS1o7j5CeLt+o8YG74Xg/4XfjdSC/CVcBx4fdxwE9aDzCzo81ssplNAT4FXGhmM4I59nlJewYv1Q+2O79q8oZVK4u4CetpE9mTvErT5v8lnVtFrynJYzdtSa84ioSWq8pj13GceLKMOW5ZYv6zgMsknQA8DBwJIGkEONnM0px/PgycD4wlcsSplTNO3RwtIP9E9iQnjbjFjhvz/5LOPXvOfZX0mvIEO0gaHy06jtnI2x1gHKcedDrm2FXM7ClWdrJppM+ljVesmZ1PpAybj3tTeRIWo46OFkUmsscplCxmwTLWgiyDvOOjRU2j7gDjOPWikwg5TofU0dGijAneRcyCdVt5I6l8kho7bhp1nMHClWOJ1DHSyPQDtmH0qJZlsgquuFBUwU3bZRK3zJjKA7MO5JYZUyvtQeUdH63bQsiO4xSjU2/VVUjzVh0G4sahahtRpYQVFwbFLJh3fLRuCyE7jlOMLGOO84iqz7h5hVt1VaI+I4vTTZ0iqqStuFDWhPzTZt+1Uni5o/bYnDOm7VD4umWQZ3y0juPLjuPkp2pv1b4nrVKsW0SVdj2fRnpZ3rWnzb6Li259ePn2MrPl23VVkO3I47HrE/kdpz/pyFs1BAB4A7BmI83MftltofqJKpxuiuQ5Slpu+mtNL6v3c8ltC2LT+0k5QjGPXcdx+ofMDjmSTgR+CcwBTg/fM8sRq3+owummSJ7tFGMjvSxFn5RnEeoUYMG9VR1nsOjEW/UTwJuBh8xsX2AX4IlSpOojqqgUi+Q5KUaBTho3tjRFPypmmc249CzkjWRTFnWbkuI4TjE6Mav+1cz+KglJa5jZvZKGvllcRXSTInmmedCW4V171B6brzTm2JyeRpyDUB0dYAbFY9dxnM6U4yOSxgGzgWslPYMH+gaqqRTz5plFsXZb0TfGFTv1Vk1yEEpyLHIcxylKZuVoZu8NP2dKuhF4HTWLZeoUpyxFf8a0HTp2vknqHSY5FjmO4xSlE4ec7zd+m9nNZnYV8L1SpHJKo25jdUkkOQiV5eTjOI4DnTnkbN+8IWkUsFt3xXHKpsjSSr0myUEoybHIcRynKKnKUdKpkp4HdpT0XPg8T7Qwce3WT3SSSZuu0S/TI3zqhOM4ZZIlQs5ZwFmSzjKzU3sgk1MiSZPV67b+ZBXOQ47jOACyDsZoJE0CtqBJqfZjhJyRkRGbO3du1WJUQqsChKjHddZhO8QG1p40biy3zJjaSzEdx6kZkuaZ2UjVcvSKzN6qkmYB7wd+DzRqViOKmpOLsOLHpcAU4EHgfWb2TMyx6wF/AH5sZh8NaTcBE4FGjf4uM3s8rzzDQFnxQcsKWO44jlMFncxzfC+wjZm90sX8ZwDXm9ksSTPC9ikxx34OuLlN+tFmNpzdwJx0Oz5o3cyxjuM4RenEW/UvwOgu538ocEH4fQEwrd1BknYDNgZ+0eX8nSbyOrn0kwes4zhOFjrpOb4EzJd0PbC892hmHy+Q/8ZmtjhcZ7GkjVoPkLQa8CXgWGC/Ntc4T9Iy4ErgDIsZRJV0EnASwOTJkwuI3P/EmUDzhqWrYmUSx3GcMulEOV4VPh0h6Tpgkza7PpPxEh8BfmZmC7Rq9JOjzWyhpHWJlOOxwIXtLmJm5wLnQuSQkzHvwtRtLC7NBJonQo4v1+Q4zqDRSfi4CySNBSabWWZ7mZntH7dP0mOSJoZe40SiuZOt7AW8TdJHgHWAMZJeMLMZZrYw5PG8pB8AuxOjHKugjmNxZQTsTgtm7jiO0290Ej7uYGA+8POwvbOkjnuSLVwFHBd+H0eboAJmdrSZTTazKcCngAvNbIak1SVtGGQZDRwE3F1Qnq5Sx7G4MkygvlyT4ziDRidm1ZlEPbObAMxsvqQtC+Y/C7hM0gnAw8CRAJJGgJPN7MSEc9cA5gTFOAq4DvhOQXm6Sh3H4soygfpyTY7jDBKdKMdXzezZlnG/QmN3ZvYUbZxswtSMVRSjmZ0PnB9+v0jNY7vWcSzOTaCO4zjpdDKV425JHwBGSXqDpK8Dvy5JroGgjvE/3QTqOI6TTic9x48ReZi+AlwCzCGamO/EkHdqRC/kqloGx3GcOtNRbNVBYZhjqzqO4+TBY6u2kOaRamaHdE8cx3Ecx6meLGbVvYAFRKbU24BVZuI7juM4ziCRRTluArwTOAr4AHANcImZ3VOmYI7jOI5TFaneqma2zMx+bmbHAXsC9wM3SfpY6dI5juM4TgVk8laVtAZwIFHvcQrwNeBH5YnlOI7jONWRxSHnAuBNwH8Dp5tZrUK0OY7jOE63ydJzPBZ4Efg74ONNEXIEmJmtV5JsjuM4jlMJqcrRzDqJouM4juM4fY8rPsdxHMdpwZWj4ziO47TgytFxHMdxWnDl6L0nX64AAAULSURBVDiO4zgtDGXgcUlPAA+VcOkNgSdLuO4g4WWUjJdPMl4+6ZRVRluY2YQSrltLhlI5loWkucMUtT4PXkbJePkk4+WTjpdRd3CzquM4juO04MrRcRzHcVpw5dhdzq1agD7AyygZL59kvHzS8TLqAj7m6DiO4zgteM/RcRzHcVpw5eg4juM4LbhyzImkzSXdKOkPku6R9ImQPl7StZL+FL7Xr1rWKpC0pqTfSrojlM/pIX1LSbeF8rlU0piqZa0SSaMk3S7pp2Hby6cJSQ9KukvSfElzQ5r/xwKSxkm6QtK9oS7ay8unO7hyzM+rwL+Z2XbAnsD/kfRGYAZwvZm9Abg+bA8jrwBTzWwnYGfg3ZL2BD4PnBPK5xnghAplrAOfAP7QtO3lsyr7mtnOTXP3/D+2gq8CPzezbYGdiN4lL58u4MoxJ2a22Mx+F34/T/RSTgIOBS4Ih10ATKtGwmqxiBfC5ujwMWAqcEVIH9ryAZC0GXAg8N2wLbx8suD/MUDSesDbgf8CMLO/mdkSvHy6givHLiBpCrALcBuwsZkthkiBAhtVJ1m1BJPhfOBx4Frgz8ASM3s1HPIIUYNiWPkK8GngtbC9AV4+rRjwC0nzJJ0U0vw/FrEV8ARwXjDNf1fS2nj5dAVXjgWRtA5wJfBJM3uuannqhJktM7Odgc2A3YHt2h3WW6nqgaSDgMfNbF5zcptDh7J8mtjbzHYF/p5o6OLtVQtUI1YHdgW+ZWa7AC/iJtSu4cqxAJJGEynGi83sRyH5MUkTw/6JRL2moSaYem4iGpsdJ2n1sGszYFFVclXM3sAhkh4EfkhkTv0KXj4rYWaLwvfjwI+JGln+H4t4BHjEzG4L21cQKUsvny7gyjEnYXzov4A/mNmXm3ZdBRwXfh8H/KTXstUBSRMkjQu/xwL7E43L3ggcEQ4b2vIxs1PNbDMzmwK8H7jBzI7Gy2c5ktaWtG7jN/Au4G78PwaAmT0KLJC0TUjaD/g9Xj5dwSPk5ETSW4H/Ae5ixZjR/yUad7wMmAw8DBxpZk9XImSFSNqRyBlgFFEj7DIz+3dJWxH1lMYDtwPHmNkr1UlaPZL2AT5lZgd5+awglMWPw+bqwA/M7ExJG+D/MQAk7Uzk0DUG+AvwIcL/DS+fQrhydBzHcZwW3KzqOI7jOC24cnQcx3GcFlw5Oo7jOE4Lrhwdx3EcpwVXjo7jOI7TgitHx+khkt4rySRtW7UsjuPE48rRcXrLUcCviCb+O45TU1w5Ok6PCHF49yZahur9IW01Sd8Ma17+VNLPJB0R9u0m6eYQdHtOIySY4zjl48rRcXrHNKK19/4IPC1pV+AwYAqwA3AisBcsj9v7deAIM9sN+B5wZhVCO84wsnr6IY7jdImjiIKLQxQi7iiidS4vN7PXgEcl3Rj2bwO8Cbg2CuPLKGBxb8V1nOHFlaPj9IAQD3Qq8CZJRqTsjBWxQ1c5BbjHzPbqkYiO4zThZlXH6Q1HABea2RZmNsXMNgceAJ4EDg9jjxsD+4Tj7wMmSFpuZpW0fRWCO84w4srRcXrDUazaS7wS2JRoXb67gf8kWtXlWTP7G5FC/bykO4D5wFt6J67jDDe+KofjVIykdczshWB6/S2wd1irz3GcivAxR8epnp+GhaHHAJ9zxeg41eM9R8dxHMdpwcccHcdxHKcFV46O4ziO04IrR8dxHMdpwZWj4ziO47TgytFxHMdxWvj/Adu4JmMzBfr1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ys2 = np.array(s_data['mental_discussion_negative'])\n",
    "Ys2 = []\n",
    "for i, each in enumerate(ys2):\n",
    "    Ys2.append([i, each])\n",
    "Ys2 = np.array(Ys2)\n",
    "\n",
    "regs2 = LinearRegression().fit(Xs, Ys2)\n",
    "\n",
    "plt.scatter(xs, regs2.predict(Xs)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Health Discussion Negative Effect (sm)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Health Discussion Negative Effect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Health Discussion Negative Effect')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ym2 = np.array(m_data['mental_discussion_negative'])\n",
    "Ym2 = []\n",
    "for i, each in enumerate(ym2):\n",
    "    Ym2.append([i, each])\n",
    "Ym2 = np.array(Ym2)\n",
    "\n",
    "regm2 = LinearRegression().fit(Xm, Ym2)\n",
    "\n",
    "plt.scatter(xm, regm2.predict(Xm)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Health Discussion Negative Effect (md)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Health Discussion Negative Effect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Health Discussion Negative Effect')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yl2 = np.array(l_data['mental_discussion_negative'])\n",
    "Yl2 = []\n",
    "for i, each in enumerate(yl2):\n",
    "    Yl2.append([i, each])\n",
    "Yl2 = np.array(Yl2)\n",
    "\n",
    "regl2 = LinearRegression().fit(Xl, Yl2)\n",
    "\n",
    "plt.scatter(xl, regl2.predict(Xl)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Health Discussion Negative Effect (lg)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Health Discussion Negative Effect')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression: Age Vs Mental Disorder\n",
    "There is a clear and moderate postive correlation from the graphs below. The most outstanding graph was the graph of the large company size data with strongest and clearest trend of the three company sizes. This follows our theory that people are more susceptible to metal illness with age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Disorder')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEWCAYAAACaBstRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XuYXFWZ7/Hvrzvd0AmXJhIc0iQEkYmCAQItBHEUUAcdECLKYAQHfJxBzxwdHTVjOHLmBAcfojmDOh4dxcsIIshFiSiMEQV0REGCIUSQDBfDpQmChACSQDrJe/7Yu5Lq6r1W7d51734/z9NPulbt2ntVpWu/e6/Lu2RmOOecc3l0tboCzjnnOocHDeecc7l50HDOOZebBw3nnHO5edBwzjmXmwcN55xzuXnQqDNJfyFpTavrMR5IminpT5K6W10Xl5+ktZLeWMPrT5f043rWKXCcWZJM0qQ67vNASStybnuWpF+UPTZJL09/v1DS++tVr3ryoFFQ6IthZv9lZrNbUadKkhZLGk5PvBsk/VLSUa2uV15m9rCZ7WJmW5txPCUelHRPA4+xXNInM8pPlvT4WE5g6UnHJF1YUT4/Lf9mHepb1xOrpG9K2izpufTnt5IukLR7aRsz+7aZ/WU9jtcC/wL83zrsZynwCUm9ddhXXXnQGCciX+orzGwXYE/gJuCqJh+/k7wO2At4maRXN+gY3wTeLUkV5e8Gvm1mW8a4vweA0yo+/78B/rt4FRvuM2a2KzANeA8wD7hF0pRGHbDef59Z+5O0N3AssKzW/ZvZOuBe4KRa91VvHjTqTNIxkh4te7xW0sck3SXpGUlXSNq57PkTJd1ZdidwcNlziyQ9kF6R3SPpbWXPnSXpFkmflbQeWByrV3oy+jYwIGlazuMfJmllevyr0rqfX/4+JX1c0uPAf+TY38clDaX7WyPpDWn5EZJWSHpW0h9KV86VV7mSpku6VtJ6SfdL+ruyfS+WdKWkS9L93y1pMPd/XOJM4PvA9env20naT9LP033/RNIXJV1a9vy89P1ukLRK0jGBYywDpgJ/UfbaPYATgUvSx3+V/n8/l35eH4vU+XFgNXB8+tqpwGuAayvqH6yfpJsl/Uv69/ScpB9L2jN9+ufpvxuU3LEeJWl/STdKekrSHyV9W1J/pI6ZzOwFM7ud5MT4EpIAMqLZRonPSnoi/f7cJelV6XO7p//fT0p6SNK5krrK9jHi+yGpW9L/Tev8IHBCxWe0u6SvS1qXfu7nK20azfl9exPwGzN7oWyfwe9wDjdX1rEtmJn/FPgB1gJvzCg/Bni0YrtfA9NJTha/A96fPncY8ARwJNBNcqJaC+yUPn9q+rou4DTgeWDv9LmzgC3AB4FJQF9GXRYDl6a/9wJLgD8Ck6odP93+IeBDQA9wCrAZOL/sfW4BPp1u31dlf7OBR4Dp6etnAfunv/8KeHf6+y7AvLJtrKy+PwO+BOwMHAo8Cbyh7L2+APxVeuwLgFvLPosvAV+K/H9OBp5NX//29HPqLXv+VyTNDr3Aa9NtS5/tAPBU+toukpPHU8C0wLG+Cnyt7PH7gDvLHq8D/iL9fQ/gsMB+zgJ+AbyL5I4S4O+BrwDnA9/MUz+Sk9MDwJ+n/483A0uy/g/Sspen+9iJ5G7h58Dnqn030ue+Sfo3VFF+Sdl7OAv4Rfr78cAdQD8g4JXs+A5cQhLkd03r+d/Ae0PfD+D9JFfvM0i+izcx8u9rWfrZTSG54/w18L4xfN+WAl+sKKv2Hf5F2bYGvLzs8SkkQajl57sR76nVFejUn9AXg+ygcUbZ488AX05//3fgXypevwZ4feCYdwInp7+fBTxcpY6LSU70G4CtJCeKY8qeDx6fpKlmCFDZc79gZNDYDOycc38vJwkobwR6Krb5OXAesGdF+azSlzr9om8Fdi17/gJ2nBgXAz8pe+5AYNMY/j/PIAlCk0hOhhuAt6XPzUxPGJPLtr+UHUHj48C3Kva3HDgzcKzXAs+QnniAW4B/LHv+YZJAsluVOp+V/p/0AX8AdgduBY5mZNCI1o8kSJxb9tzfAz+q/D+I1GM+sLLadyN97ptkB40lwA3l7yv9/TiSYDAP6Crbvht4ETiwrOx9wM2h7wdwI+kFW/r4L8v+vl6a7q+v7PkFwE1j+L59lTTYRrap/A7HgsabgAfz/g0368ebp5rj8bLfN5JcTQPsC3w0bTLYIGkDyclxOoCkvylr6tkAvIqkb6LkkRzHvtLM+km+FL8FDi97Lnb86cCQpX+9geM9aWW34rH9mdn9wIdJTu5PSPqOpOnp695LcpV7r6TbJZ2Y8T6mA+vN7LmysodIrqJLKj/nnZW/LftMks9qi5m9CHyPHU1UpWNvLNu+/LPYFzi14n2/Ftg760Bm9guSAHWypJcBrwYuK9vk7SR3BQ9J+pmqDF4ws03AdcC5JIH3lopN8tQv9Dc6iqS90v+/IUnPkgTQPUPb5zQArK8sNLMbgf8HfBH4g6SLJO2WHq90N1xS+fdQ+fc6vaKs/LX7ktxRryv7jL5CcscR2l+lp0nuerbL8R2O2ZXk4qWteNBorUeAT5lZf9nPZDO7XNK+JFcuHwBekp74f0tyi16SO0Wxmf2R5EpssZIOu+jxSZpIBqQRHbYzKneb9/2kdbjMzF5L8gU1kqYtzOw+M1tA8gX9NHC1RneKPgZMlVT+pZxJcjdUE0n7kFzRnqFkBNPjwDuAv0rb9telx55c9rLyz+IRkiv58vc9xcyWRA57CUmH9buBH5vZH0pPmNntZnYyyeexDLgyx9u4BPgo8K2M54rUb3t1MsouSMsPNrPdSO7SKjv2c5O0C8kd6H9lVsDs38zscOAgkouLhSTNh8Mkf0sllX8PlXVfx8j/t5llvz9CcqexZ9lntJuZHRTZX6W70vqV3lee73DMK4FVObdtGg8atemRtHPZz1hHaHwVeL+kI9MOvymSTkhPjFNI/kifBJD0HpKrlMLM7F6SZol/ynH8X5E0B31A0iRJJwNHFH0/kmZLOk7STiR9D5vS/SPpDEnTzGwbO66sRgyzNbNHgF8CF6Sf9cEkdyjfruUzSb2bpAlkNklfyaEkX/5HgQVm9hCwgiTg9qZX/m8te/2lwFslHZ92tu6sZKDAPpFjXkJyovw74OJSYbr/0yXtbmbDJH0neYYc/4ykOeMLGc8VqV/Jk8A24GVlZbsCfyLpHB8gOYmPmaSdJB1OEhifJh1MUbHNq9O/px6S/oAXgK2WDMO+EvhU+ve1L/CR9L2GXAn8g6R9lAw+WFR6wpLRSj8G/lXSbpK6lHT4v34Mb+kG4DDtGOhS63f49cB/jmH7pvCgUZvrSU5+pZ/FY3mxma0gOWn8P5Ivzf0k7ZyY2T3Av5KcvP8AzCFp+67VUuBsSXtVOf5mko6495KcyM8AfkhyNTbm90PST1DqiH+c5Cr6f6XPvRm4W9KfgM8D76xo9ipZQNLG/hhwDfB/zOyGPG9a0pclfTnw9JkkneSPl/8AX2ZHE9XpwFEk/ULnA1eQfhZpQDs5fT9Pkly1LiTy/TKztSRBcAoVI51IgtjatOnn/SSffZQlfmpmWU08Y65f2Ws3Ap8iGRK7QdI8kv6nw0j6Za4jacobi3+S9BxJc9QlJB3drzGz5zO23Y3kYuRpkuakp9gxD+KDJIHkQZK+ncuAb0SO+1WSi6ZVwG8y6v03JE1e96THu5pAE2OW9G7xRpLPuqbvcNoacCB1GL5bbxrZZO1cmKTbSDrxR10RTjSSrgDuNbP/0+q6uPYh6UCSO8cjrIaTq6R/BR4wsy/VrXJ14kHDBaW35mtI7g5OJ7nyfll6Kz+hKJnstx74Pcmom2XAUWa2sqUVc67JxsMsXtc4s0nagXchGcf/jokYMFJ/RtKc8RKSvo7/4QHDTUR+p+Gccy437wh3zjmX27hrntpzzz1t1qxZra6Gc851lDvuuOOPZjat2nbjLmjMmjWLFStypbN3zjmXkvRQ9a28eco559wYeNBwzjmXmwcN55xzuXnQcM45l5sHDeecc7l50HDOOZebBw3nnHO5edBwzjmXmwcN55xzuXnQcM45l9u4SyPinHMTzbKVQyxdvobHNmxien8fC4+fzfy5Aw05lgcN55zrYMtWDnHO91azaThZSn5owybO+d5qgIYEDm+ecs65DrZ0+ZrtAaNk0/BWli5f05DjedBwzrkO9tiGTWMqr5UHDeec62DT+/vGVF4rDxrOOdfBFh4/m76e7hFlfT3dLDx+dkOO5x3hzjnXwUqd3T56yjnnXC7z5w40LEhU8uYp55xzubU0aEh6s6Q1ku6XtCjj+fdLWi3pTkm/kHRgK+rpnJuYlq0c4uglN7Lfous4esmNLFs51OoqtVzLgoakbuCLwFuAA4EFGUHhMjObY2aHAp8BLmxyNZ1zE1Rp0tzQhk0YOybNTfTA0co7jSOA+83sQTPbDHwHOLl8AzN7tuzhFMCaWD/n3ATW7ElznaKVHeEDwCNljx8FjqzcSNL/BD4C9ALHZe1I0tnA2QAzZ86se0WdcxPPUGByXKh8omjlnYYyykbdSZjZF81sf+DjwLlZOzKzi8xs0MwGp02bVudqOucmom5lnaLC5RNFK4PGo8CMssf7AI9Ftv8OML+hNXLOudRWy24ND5VPFK0MGrcDB0jaT1Iv8E7g2vINJB1Q9vAE4L4m1s85N4ENBNJwhMonipYFDTPbAnwAWA78DrjSzO6W9ElJJ6WbfUDS3ZLuJOnXOLNF1XXOTTDNTs/RKVo6I9zMrgeuryj757LfP9T0SjnnHM1PzwHNXUypKE8j4pxzAc1Mz9HsxZSK8qDhnGs7nXDFXW+xeSHt9N49aDjn2kqnXHHXW7MXUyrKExY659rKRJ2J3ezFlIryoOGcayudcsVd72SGnTJay5unnHNtZXp/X2aqjna64m5EE1orRmsV4UHDOddWFh4/e8QJGdrvirtRndbNHK1VlAcN51xb6YQr7k5pQmsEDxrOubbT7lfcndCE1ijeEe6cc2PUKZ3WjeB3Gs65jtIOE/86oQmtUTxoOOc6RjtN/Gv3JrRG8eYp51zHmKgT/9qJBw3nXMeYyKOW2oU3TznnGqbe/Q8TedRSu/A7DedcQ5T6H4Y2bMLY0f9QS7qNiTxqqV140HDONUQj+h/mzx3gglPmMNDfh0iWXr3glDkTskO6Vbx5yjnXEI3qf5ioo5bahd9pOOcaolNSfbux8aDhnGsI738Yn7x5yjnXEO02a7odZpKPBx40nHMN0y79D8tWDrHw6lUMbzUgGcm18OpVwPheQrYRPGg452rSiCv4eu/zvB/cvT1glAxvNc77wd0eNMbIg4ZzrrBG5IJqxD6f3jg8pnIX5h3hzrnCGjEXo9o+6702txsbv9NwzlUVai7KSukBBMvziM3vKHoX0t/Xw4ZNo+8q+vt6CtdzomrpnYakN0taI+l+SYsynv+IpHsk3SXpp5L2bUU9nZvIYulAupT9mlB5HrH5HUXvbBafdBA9FZXq6RKLTzqoeEUnqJYFDUndwBeBtwAHAgskHVix2Upg0MwOBq4GPtPcWjo3vhRp2omdqLdZ9mtC5XnE5ncUnWU+f+4AS089ZET6kaWnHuKd4AW08k7jCOB+M3vQzDYD3wFOLt/AzG4ys43pw1uBfZpcR+fGjaIJBGtJB1IkSMXyS/ks89ZrZZ/GAPBI2eNHgSMj278X+M+sJySdDZwNMHPmzHrVz7lxJXbHELvijqUjf/7FLcG+glpGQYXmdyw8fvaIfUK+WebttOJfp2vlnUZWq2fmTa2kM4BBYGnW82Z2kZkNmtngtGnT6lhF58aPancMobuCWHNRrK+gnbLc+op/9dPKO41HgRllj/cBHqvcSNIbgU8ArzezF5tUN+fGnf7JPZnzEvonV78rWPHQei6/7RG2mtEt8fbDR94JZI2s+scr7sysRyuy3PqKf/XTyqBxO3CApP2AIeCdwLvKN5A0F/gK8GYze6L5VXRu/LBA57RZ9Svx794xxNZ0B1vN+O4dQwzuO3X7CTzrJN5Oq+y1U106Xcuap8xsC/ABYDnwO+BKM7tb0iclnZRuthTYBbhK0p2Srm1RdZ3reM9k9D2UymPzLYo27bRTltt2qkuna+nkPjO7Hri+ouyfy35/Y9Mr5dw4FbvafvyZF7bfSZTrlmoa5grNzXIbmoTYbhl3O5ksdM/aoQYHB23FihWtroZzLXXustUj+iAWHDmDwX2nZo48uuCUOXw40P8ASWdzVrAZ6O/jlkXHNaT+RVT2y8CO9+fBoTpJd5jZYLXtPPeUcx0qNNrp3GWrufTWh0f0QVx668OseGh9cOTRQKBtfyC9Iu+Eph0fIdUcnnvKuQ4UG+10+W2PZL7m8tse4fz52VfdC4+fPWK9CYCebo1owgk17bTL4kY+Qqo5/E7DuQ4Uu6rO6psAguXbVT6do+W66CzzRvDZ4s3hQcO5DhS7qi6SRHDp8jUMVySMGt5mLF2+JhoY2qlJqFOa0TqdBw3nOlDsqnqnSdlf61A5xINQLDC0U5NQ0dnibmyifRppJtqLzeyMJtXHOZdDLAdTaCb2C8Pbgv0PseG4scBQbdJcs/s72mVN8vEseqdhZluBaZJ6m1Qf51wOsavq/snZCwtN7u0ONjPFmnZidzWx17VTf4ernzyjp9YCt6SzsZ8vFZrZhY2qlHOuutBV9YsVTUklz28eXV5qZirNtwjdFYTuamIjq45ecmM0q267jLpyY5MnaDyW/nQBuza2Os6NT0VPkFmT9M6fPyf63MbhbWOqW6l5KRSEqg25Db2uEcu2utarGjTM7DwASVPM7Plq2zvnRqp2ggwFlNIkvZLSJL2S2HNj0a3qa7MW6SuI9XcUXdvDtV7VoCHpKODrJIkDZ0o6BHifmf19oyvn3HhQbVhq+aS6oQ2bWHj1KgAuuy07CFx228Moczma8MS+mKrzNyh2p1Sks94n4rW/PENuPwccDzwFYGargNc1slLOjSexZprzfnD3iFnYAMNbjfN+cHd0/e3YBL7+vuyO8ND9RCiFSEnRDm1ftnV8yjVPw8wqL1+ye9qcc6PETpBZiyIBwfKSUJNStxRcTe/0eTOjk99CuawaMYHPJ+J1rjxB4xFJrwFMUq+kj5Gsf+Gcy+HYV2QvQRwqL+nryf569vV0seDIGZnPLThyBvPnDrD01ENGXOEvPfUQzp8/J3jlH7ubKDqBL7ZPn4jXuaqmRpe0J/B54I0kd7g/Bj5kZk81vnpj56nRXbs5esmNwdTiz7+4hQ0ZiyP19/Uwbdde7nti9NiTA/aawg0fOSY6sqqedQSCz8VSo8f22U4p1V0ib2r0PKOn/gicXpdaOTcBxVbF+9xph7LwqlUj8j71dCVNTKE1LkqB5Pz5c4JBYqwd17G7ic+edmiwQzumnVKMuPoJBg1JXyCS59LM/qEhNXKujRUZRdQtBVfFi82BiC2MVK2OY50DERseW3TVO1+Xe3yK3WmU2niOBg4Erkgfnwrc0chKOdeOlq0cCg6PLc2ryGouKpyqvKAicyBiw2Oh2DyNavuM8dni7SsYNMzsYgBJZwHHmtlw+vjLJP0azk0oseGxKx5aH5xsF1suNXZX0NMFWZO7A/3j2xVpFmrEGtpF9+mzxdtbnjQi00nSh6xPH++Sljk3LoWucmPDY2Or5f3rXx8SXBUvdlewJZANJFReEmsWil3BF80QW+99+mzx9pYnaCwBVkq6KX38emBxw2rkXAvFmqBiqjVBba2YqVd63Ii046FmoWNfMS3avFakSagRdwXegd7eoje6kgT8BDgSuCb9OarUdOXceBNrgqpln5Wzu7dZUl5L2vGFV68aMQdi4dWrts+BOGzm7iNed9jM3bnurnXB91Z01ncjJv75bPH2Vm09DQOWmdnjZvb99OfxJtXNuaYrOkO76D5jgWH+3AHefvjA9tnf3RJvPzxp7okFt3OXreaWB9aPeO6WB9ZH61H05N+IuwKfLd7e8jRP3Srp1WZ2e8Nr41yTtMvonFhn8bKVQ3z3jqEdTVxmfPeOIQb3nVqofyWm6Mm/f3JPZl1CC0Hl0YhOeVc/eYLGscD7JD1EsgiTSG5CDq714JLeTDLbvBv4mpktqXj+dSQJEw8G3mlmV9d6TOdi7fD9fT3BGdpZ5Y1U9Op/rEN5J/d0sceUnQrNqQgdqtbRxL5sa/vKk3vqLcD+wHHAW4ET039rkq4//sV0/wcCCyQdWLHZw8BZwGW1Hs+5ktjJ+MRD9s58Tag8j1DW2f6+nsI5n2L7zLM+RrneSd3Mekl2cAiVlzwTCKShctf5qgYNM3sI6CcJFG8F+tOyWh0B3G9mD5rZZuA7wMkVx15rZncBY1uKzLmI2Mn4pnufzHwuVJ5HKOvs4pMOigawWIdwLLiFkhmGPLNpmFsffDrzuVB5eV3GUu46X9WgIelDwLeBvdKfSyV9sA7HHgDKG18fTcuca6jJvd3B8kZ07Iayzs6fOxA9XqxDOBbczp8/hzPmzRzRgX7GvJnBdTOm9/cVnrXundYTT54+jfcCR5aWepX0aeBXwBdqPHbWPXShllBJZwNnA8ycObOWOrlxJJTW4/nN2cvBPL95K3tEOnb/9MJwcIb2GJflHrHf0PFiHcLVVr7LSmZY2ZcDO07wH71yVTA/Vox3Wk88eYKGGLno0lbCi4CNxaNA+X30PsBjRXZkZhcBF0GSGr32qrlOV2197ZAXh7MDyovDW4OBoVrAiHW8V+tIDnUIVxu1FBsdllVemQalJE9Tl3daTyx5gsZ/ALdJuoYkWJxMsmZ4rW4HDpC0HzAEvBN4Vx3261w0rUfMxkAECJWXxDLZxvotinYkx4JbtVnaWSf40l1JvdbncONXnvU0LpR0M/BakqDxHjNbWeuBzWyLpA8Ay0mG3H7DzO6W9ElghZldK+nVJLPQ9wDeKuk8Mzuo1mO78a8RmWVjw3EPmr7rqAl1APNetge/zCiHfKlCQmLBrWjuptj6HM6VVA0akvYH7jaz30g6BvgLSb83sw21HtzMrgeuryj757LfbydptnIuU6gZJnblXzRwnHjI3plNOCcesnewY3rtU/HAcOwrpmXus7QUbJHV+Tx3k2ukPPM0vgtslfRy4GvAfvi8CdcGYvmXYmtox4Q66wRcd9e6zOeuu2td1VFQPd0VQ27TLLexfZb6ZcpnhF9668Ocu2x19D34MFjXSHmCxjYz2wKcAnzezP4RKD7Tybk6ieVfCg07PX/+HCYHFqSY3NPFa/afmvnca/aPp+4Ipc3YXl55c2M7XhvaZ6xfJja5b+Hxs+mumBfS3aXtiQ6PXnIj+y26jqOX3Fg1IaFzlfIEjWFJC4C/AX6YlhVPLONcnVRLLji471T+bPedEfBnu+/M4L7ZAaHcnY88M6byktgoqKXL14xYAxxgeJsVTgey1Sw6YXDFQ+szU7FfteLhQplsnSuXJ2i8BzgK+JSZ/T4d7XRpY6vlXG1iTVexTuTYHI6YUF6qDZuG654OpLS2+GlHzBhxJ3XaETOYP3cgeIdyywPr657G3E08edKI3GNm/2Bml6ePf1+ZWNC5dtOIdTFiYif4eqcDWXDkjGAG3GUrh8bc0e8d5G4sgkFD0pXpv6sl3VX507wquomuSDt8I9bFiIk1JcU6wqulAzm6oo/l6P2ncv78OdFhtWNNWFhLGnM38cSG3H4o/ffEZlTEuSyx5VdFdt6ZUHkjDQSG1ZbyPRVZ7nXZyiF+vXZkwsBfr326agbc0+fNzBzGG0p3UmsaczexBO80zKw0FvAZdiQr3GBmD9Upy61zI2TdUcSamU6fl51nLFReEhs9VVRpXkVWedHlXmPvPfa6UId/aFJ7s9cJcZ0t1jzVK+mbwFqSvE5fBdZK+oak3uZUz00UoY7rWDPT4L5TR/0Bd0HVUVK9k7Kz3IbK84g1M8XeQyzYFH3dWPttxtqc5Sa22KXVuSRDa2eY2VwzOxSYSdKk9b+bUTk3cYSuqmOWLl8zaqGVbWl5TCMWDio6Czs2uS+mSJAKqSW1ipt4YkHjFODvzOy5UkH6+98Db2t0xdzEMtYT3eSerprWtR5LefkxQ+WxfcaG1UYnDEZeV89UIaF1NpzLEgsa28xsY2Whmf2J5vczunHi3GWr2f+c65m16Dr2P+f6qikxYmIn6tAfdheNWdc6ts/YRLyY2OuKBj5fMMnVKhY0TNIekqZW/uDLr7oCYrmUervH1q6+cXhb9EQd+gPdRnwiXrVjhspjTV6xlftidxOx18VSo8fyZ11wypwR+7vglDm+FoYbk9iQ292BO6jjCntuYovlUirSrl705N8I1VKch9axiGXOjb2uyLofFtmfc3kFg4aZzWpiPdw4EkpXXu81LmLzNPp6ujJPoJMD5XnE1tNYePzs4FKqMbEO7aKqzRlxrhbFB6Y7l6G0alyjk+L19/UEb3eN4sNqY007i086iIouBrqUlM+fO1Co6adah3ZoNnyoOa83nWnufReuUTxouLqKpbeop2qdyEWH1RadMFjU5N7sIDa5tzsagEPDkYe3WuEA5lweedYId26UUBNUVrMIECwv6qoVo/sByvVPzh7O2j+5h2c3bQmu6hdbK3vuJ38cnNkNBNOdxE7WGwPZczdu3hoNwLG7rNIxPUi4RggGjXSUVJCZZS987Ma9ZSuH+OhVq7bnTxrasImPXrWqqXXIWo+7XGxkVbW+ldBa2bE5FbGUH7GTd+zkH2u6ii1n61wjxe407iD52w2NnnpZQ2rk2t4nrlmdmYDvE9cUn3NRb7GRVY3oKC6aVbdLjLp7KZXvvfvY1xavtpytc7WKJSzcz8xelv5b+eMBYwIrulBRKB9gDXkCg2Id2kU7ihuR6HCnSdmv3WlSV7SeseVsnWukXH0akvYADgB2LpWZ2c8bVSk3PoVGuhYcARsVa/YpNRdl9clAuL+mFqF9vhB48y8Mb6taz1AzmnONVDVoSPpbkrU19gHuBOYBvwKOa2zVXKs14uRZT5O6xJaMtp1QeblQR3Fs/Y4iE+pK+yyfw1EaBQXFJwU61yp57qs/BLwaeMjMjgXmAsVnHrmOUOrsLh/u+dGrVtV9vkVRgmCaJNpjAAAXEklEQVRgqBYwIDz/oRHLxMZGQcVW9XOuHeVpnnrBzF6QhKSdzOxeSf4XPc61e2d3LXlsYlf+sQ7t2Az03SOzxasOQ67cqSfpcW0sz53Go5L6gWXADZK+DzzW2Gq5VuuEzu6iik5AjPWTHDR918znDpq+a7RTfunyNQxXBOfhbVb3yZDO1UvVOw0zK62dsVjSTSSJDP+zHgeX9Gbg80A38DUzW1Lx/E7AJcDhwFPAaWa2th7Hdok3XXgz9z3x/PbHB+w1hRs+ckzh/UnZ1+Oh8locsNeUEXUvL3/gyeeDQ1mLTkCMDdW99cGnM14Btz74dOG5GM61o6rXf5K+VfrdzH5mZtcC36j1wJK6gS8CbwEOBBZIOrBis/cCT5vZy4HPAp+u9bhuh8qAAXDfE8/zpgtvLrzPzYH0FqHyWtzwkWN46a4jVx5+6a693PCRYzIDBmTPicgr1v9QNBljbK1v59pRnkaDEUl+0pP94XU49hHA/Wb2oJltBr4DnFyxzcnAxenvVwNvkHzKa71kXaXHytvNspVDrK/og1i/cbixnfWB/ofKRIYlofISTy7oOk0waEg6R9JzwMGSnk1/ngOeAL5fh2MPAOULLDyalmVuY2ZbgGeAl2TU9WxJKyStePJJH9g1UcRGOsX6EYqK9T+E1pDqVpKmPUtfT5cnF3QdJ7aexgXABZIuMLNzGnDsPIs75VoAyswuAi4CGBwc9LEnFdp9vkVRRVJ3GMnJelPG3IpQeUms/yH0Rze8DZaeejAfueLOEasJdgEXnHIw4HMxXGep2jxlZudIGpD0GkmvK/3U4diPAuWJcvZh9Kis7dtImkTSCe+JEsegWetbtJtQ4r5uiQtOOXjUH375STyk6Lrc8+cOcOFph464m7jwtEM9ULiOlGdG+BLgncA9QGmcogG1phG5HThA0n7AUHqMd1Vscy1wJskM9HcAN5oVXOZtnAvdTTRrfYt2E+uYnj93gKtWPDwiU+5R+09l/twBPnzFncF9xtblrsbvJtx4kWdy39uA2Wb2Yj0PbGZbJH0AWE4y5PYbZna3pE8CK9JRWl8HviXpfpI7jHfWsw7jRSz1RTsN6ewCshp/QuW1iKUOP3fZ6lGp1W95YD3nLlsdXdI1lDl34/C26OucG0/yjJ56EGjIX76ZXW9mf25m+5vZp9Kyf04DBmb2gpmdamYvN7MjzOzBRtSj08U6hIs2qTTCuwKr34XKaxG707jstuwFnC677WEWn3QQPRVDnnq6VHWlwKKvc67T5AkaG4E7JX1F0r+VfhpdMZdfrEM4thhRs11317oxldcitDbGQH9fdA7H/LkDLD31kBH9D0tPPYT5cweCdw39fT3R1zk3nuRpnro2/XEtVmQUVGwxopjYbOui8ziKLlRURGiRolB5Hicesnfma088ZG/A+y3cxJAnjcjFkvqAmWY2vntP21gsyV5MbGW42OzoP/5p85jKS2JJ/Zp5c1P0rib2Od90b/YcoFC5c+NRnjQibyVZR+NH6eNDJfmdR5MVHQVVNJ1G0buCWJ6lZipa/9jn3E6DCpxrlTx9GotJUn5sADCzO4H9Glgnl6Fokr1O0S65YWKfs+eJci5f0NhiZs9UlPlciQY5d9lq9j/nemYtuo79z7mec5clTSOxyWpF7REYQbXH5J5op28j1PsPqmgakdjn7HminMsXNH4r6V1At6QDJH0B+GWD6zUhnbtsNZfe+vD24aJbzbj01oc5d9nq6BDSA/aakvncAXtNiZ48YyOrOmEIaSywFW0mqzYp0PNEuYkuT9D4IEmm2xeBy4FngQ83slIT1bcDo3q+fevD0SvgWKd17OT5TGAE1TObhgsPIW1EosCQ0qilrPIpvd2Zz4XKS2JDdSEZIXXLouP4/ZITuGXRcR4w3ISTJ/fURjP7hJm92swG099faEblJprYCT52BRzr9N1pUvZ/8U6TuugNPFcqX/HQeh5/5gUMePyZF1jxUDKLOnr3EnkP9RYbIbUxsMJgqLzEm6CciwsOua02QsrMTqp/dVxILC1GbKGfF7dkJ+gIlZeeKzWVlZSaygBes//UUWk4YuV5HF3gtUWz3MaU7hzGY1Zg5+ohNk/jKJK1LC4HbqN9Brh0vCKT9IquDFdUrKkslILknnXPFT/e3x3F6V/91YjAUSSQ1INP0nMuLBY0/gx4E7CAJPvsdcDlZnZ3Myo2Xi1bOcRHr1rF1m07kgt+9KpVLa7VaLFmpkbN7D51cCZrn9q0PZieOjizIUEjtta3cy4u2KdhZlvN7EdmdiYwD7gfuFnSB5tWu3HoE9es3h4wSrZuMz5xTfXZ3eNZKVNv+bofpUy99eb9Fs4VF00jImkn4ASSu41ZwL8B32t8tTpfqAnq+UBHbKi8HfV2i81bR9+LhMrzCGXqDZnc08XGyCp7Md5v4VxxsY7wi4FXAf8JnGdmv21arTpcbH2L8SAUGIoGDBh701bvpO5o0Ki2voX3WzhXTGzI7buBPwc+BPxS0rPpz3OSnm1O9TpTbH2L2HDVorO+GzFbvN1Vy9LbCZMTnetEsT6NLjPbNf3ZrexnVzPbrZmV7DSxjuLTAwsOnT5vJguOnJH5XKi8pNkjqzqBr2/hXGPkWU/D1dH58+cAcPltj7DVjG6JBUfO4Pz5c1i2cojLbn14xNKnXcDgvlOja0DE5nDsuvOkMS9fWk1PF2S1DIXKW8WboJyrvzxpRFyTLF2+ZtRa2dvS8pjYnUYs1UZRCjR7hcq3Pz/G8momYrOcc63mdxpNFptpXTT9eWyhpWt+M5T5mmt+M8SU3u7MUVuh8pKiHeGxuR9jHQ3V0wWnHTEj8w6sWnOec644v9NosstveyRYHrtyntyT/V81uacrutBSbIhvs4f/xlKx906KJxKsNGWnHs6fP4cz5s3c/rl1S5wxb+b2JkDnXP35nUaNzl22elT/RF9PF5syrppD5RDvtN5qRu+kSZlX4tWGnraTWCr2sfavlDL0nj9/jgcJ55rI7zRqEFr/IhQYNg1vKzzkNnRSLdqZXYu+wF1PqLwklop9rHy1POdaw4NGDUJNTSHdUuH0512BaNOl+GJEjfD2w/cZU3lJKNFhqLzEU3441z48aNRgrPMgqm0fa/OP9VscNH3XzOdC5bW66d4nx1ReEmueivHV8pxrH96nkUMoj1S1tSwqDfT38fgzLwTnVBQ9qYYywd7ywPpoOo2iTVtFR3kVbZ7y+RbOtY+W3GlImirpBkn3pf/uEdjuR5I2SPphs+tYEsq+umzlUHBoZ2jN7mNfMS066zvWbxEbPRWz+KSDRvWjKC1vtljzVNF+Eudcc7XqG7kI+KmZHQD8NH2cZSlJDqyWieWRCg353Lg5uyP8pnufZHDfqZnPhcpLQkNSqw1VXfHQ+lH9KJaWN9vzL24Jll9wysGj/hi7gAtOObjh9XLO5SdrQX4iSWuAY8xsnaS9gZvNLLNnU9IxwMfM7MQ8+x4cHLQVK1bUra6zFl0XfG7tkhMyy/dbdF1mh7dIrqqzclPtESivVWziX6ifpBXWLjmh0IqGzrn6kHSHmQ1W265VfRovNbN1AGng2KuWnUk6GzgbYObM7ISAzTQ9sDJcqBySZIZF1wGPiXWgtxvvu3Cu/TWseUrSTyT9NuPn5Hofy8wuMrNBMxucNm1aXfddZDjrsa/IrkOovKRoltt653SqRazvpZ3q6ZwrpmFBw8zeaGavyvj5PvCHtFmK9N8nGlWPWhVZlyE2JDV2Uh3cd2pmu361/o7Y3I9GmBQ4y09SvO8llhbeOdcZWtURfi1wZvr7mcD3W1SPqoqsy/BYoAkqVF4Sy3IbG10Um/jXCFsC0WiLxYfVeq4o5zpfqzrCXwJcCcwEHgZONbP1kgaB95vZ36bb/RfwCmAX4CngvWa2PLbveneEF3H0khsz+y4GIn0akDTThDrQT583MzOj6xmB8pIuGBWIYuW1Cr3Hgf4+bll0XAOO6Jyrh7wd4S250zCzp8zsDWZ2QPrv+rR8RSlgpI//wsymmVmfme1TLWC0wrKVQxy95Eb2W3QdRy+5MZnXcfxserormrS6VTX1RWweQ9FZ2KHA0KgUhwuPn+1pP5wbx3xGeA2WrRzinO+tZtNwkkp8aMMmzvneat5++MDoW4b0cWjdiMk9XdF5DBsCw3GrNXk1W6nZzofOOjc+edCowdLla7YHjJJNw1u3p0ovN7zNWLp8TTCVee+kbjYG+gM2b7Vgs09sGG+r+NBZ58Yvz9FQg9BVfmhOxWMbNhXOv9TsZp9md6475zqDB40ahNZ0CJ1XJ/d2B18zPR2dFdrf/LkDTc32GquLz7dwbuLyoFGD0GS90Hi0jZu3Ru8YXrN/9nyMUvn8uQPcsug4fr/kBG5ZdFzNAaO3O/s039stQst9b7XwvAqfb+Hc+Od9GjWoNnKpkhHvKD7vB3dnvu6edc8B2UvLVpvj0NstNmdEgN5ujUrEWBIqLykdc6x1cc51Pg8aNaj3yKVQwsKnNw5vX1q2pLS0bDVZASNWDvlmkvva3M5NTB40ahDKWBtTWp+jdDVfWp+jmtDSsmNdcjavvp6uzLXOfX0L5yY2PwPkkDWBD6qvqJcltj5HTGz98Ebw9S2cc1n8TqOK0AQ+oNByqbEmqJhmr4vhk/Scc1k8aFQRmsC3dPmamta5yBLLPdUdCA6h8nrwSXrOuUrePFVFLGNtkYARm+MQG8qa0b0AECwvKWWUzSofCMwZCZU755wHjSpik/FCJ+SQyT1d0bUviq6nERPrC/Hkgs65sfKgkQp1dsdOrEXuNPYIZLLdY3JPdD2NomJ3E82eZe6c63zep0G8szvWIbx0+ZoxJQvMSlRY8uLw1oZksl14/OwR7w1G3k14v4Vzbiw8aBDv7C6dVLNOrKETcuW+yoUCx8bhbewRmPdRZD5IiY+Ccs7VkwcNqi/PumzlUOZJN3RC/vAVdxaqR2w9jZhqE/H8bsI5Vy8eNAivSTG9v69q01XWCTkWNPr7ejLnd4TKIUn5ERre2y3xQuDuJVTunHNFeUc48c7uWNNVEYtPOoieikUperrE4pMOir5uwZEzguWxEV7OOVdPHjSIr1VRrekqS39fYK3vvh7mzx3gtCNmbB+u2y1x2hEzqjYfxYbj+tBZ51yzePNUKtTuH2u6Cll80kEsvGoVw2VTtUt3E8tWDnHF7TuWg91qxhW3P8LgvlPp6cqerNfTRXQ47i2LjgO8s9s513geNKqoNmQ11EkO2SfxuZ/8cTBhYWzWd7U7Hu/sds41gweNKmIBIE8neaWiCQuL3PE451y9edDIIRQAqs3vGKvYyKpqdzzOOdcM3hFeg1iTUSgtyeTAIkaTe7qiI6s85Ydzrh205E5D0lTgCmAWsBb4azN7umKbQ4F/B3YDtgKfMrMrmlvTuFCTUf/knmCzVe+k7sxZ4b2TuqvO3vZ+C+dcq8katPJb9KDSZ4D1ZrZE0iJgDzP7eMU2fw6Ymd0naTpwB/BKM9sQ2/fg4KCtWLGirvUNdXZX9mlA0mS006SuzGamgf4+HtuwKbhmxu+XnFDXejvnXF6S7jCzwWrbtap56mTg4vT3i4H5lRuY2X+b2X3p748BTwDTmlbDVCkwDKUn+9Jdw7KVQ8Emo2cCM7tLQSeLd2g75zpBq4LGS81sHUD6716xjSUdAfQCDwSeP1vSCkkrnnzyybpWtMiM8Fhg8Il4zrlO1rCgIeknkn6b8XPyGPezN/At4D1mljmTwcwuMrNBMxucNq2+NyPVOrsXXr1qxF3IwqtXcewrpgUDg3doO+c6WcM6ws3sjaHnJP1B0t5mti4NCk8EttsNuA4418xubVBVo2LzI877wd2ZE/Wuu2sdF5wyxzu0nXPjTqvmaVwLnAksSf/9fuUGknqBa4BLzOyq5lZvh9j8iFA226c3DntgcM6NS63q01gCvEnSfcCb0sdIGpT0tXSbvwZeB5wl6c7059BmV9Sbk5xzboeW3GmY2VPAGzLKVwB/m/5+KXBpk6uWKXTXIAgOn3XOufHIZ4TXIDTDpfkzX5xzrjk8aNRgIDC0NlTunHOdzoNGDXzOhXNuovEstzWolivKOefGGw8aNfKhtc65icSbp5xzzuXmQcM551xuHjScc87l5kHDOedcbh40nHPO5eZBwznnXG4eNJxzzuXmQcM551xuHjScc87l5kHDOedcbjIbX4m8JT0JPNSg3e8J/LFB++5U/pmM5p/JaP6ZjNZun8m+Zjat2kbjLmg0kqQVZjbY6nq0E/9MRvPPZDT/TEbr1M/Em6ecc87l5kHDOedcbh40xuaiVlegDflnMpp/JqP5ZzJaR34m3qfhnHMuN7/TcM45l5sHDeecc7l50MggaYakmyT9TtLdkj6Ulk+VdIOk+9J/92h1XZtF0s6Sfi1pVfqZnJeW7yfptvQzuUJSb6vr2mySuiWtlPTD9LF/JtJaSasl3SlpRVo2Yb8/AJL6JV0t6d703HJUJ34mHjSybQE+amavBOYB/1PSgcAi4KdmdgDw0/TxRPEicJyZHQIcCrxZ0jzg08Bn08/kaeC9Laxjq3wI+F3ZY/9MEsea2aFlcxEm8vcH4PPAj8zsFcAhJH8zHfeZeNDIYGbrzOw36e/PkfznDgAnAxenm10MzG9NDZvPEn9KH/akPwYcB1ydlk+ozwRA0j7ACcDX0sdign8mERP2+yNpN+B1wNcBzGyzmW2gAz8TDxpVSJoFzAVuA15qZusgCSzAXq2rWfOlzTB3Ak8ANwAPABvMbEu6yaMkwXUi+RzwT8C29PFL8M8EkguKH0u6Q9LZadlE/v68DHgS+I+0KfNrkqbQgZ+JB40ISbsA3wU+bGbPtro+rWZmW83sUGAf4AjglVmbNbdWrSPpROAJM7ujvDhj0wnzmZQ52swOA95C0rz7ulZXqMUmAYcB/25mc4Hn6YCmqCweNAIk9ZAEjG+b2ffS4j9I2jt9fm+SK+4JJ72tvpmkv6df0qT0qX2Ax1pVrxY4GjhJ0lrgOyTNUp9jYn8mAJjZY+m/TwDXkFxkTOTvz6PAo2Z2W/r4apIg0nGfiQeNDGm79NeB35nZhWVPXQucmf5+JvD9ZtetVSRNk9Sf/t4HvJGkr+cm4B3pZhPqMzGzc8xsHzObBbwTuNHMTmcCfyYAkqZI2rX0O/CXwG+ZwN8fM3sceETS7LToDcA9dOBn4jPCM0h6LfBfwGp2tFX/L5J+jSuBmcDDwKlmtr4llWwySQeTdNR1k1xsXGlmn5T0MpKr7KnASuAMM3uxdTVtDUnHAB8zsxMn+meSvv9r0oeTgMvM7FOSXsIE/f4ASDqUZMBEL/Ag8B7S7xId9Jl40HDOOZebN08555zLzYOGc8653DxoOOecy82DhnPOudw8aDjnnMvNg4ZzdSTpbZJM0itaXRfnGsGDhnP1tQD4BclkP+fGHQ8aztVJmqvsaJJU6O9My7okfSldg+SHkq6X9I70ucMl/SxN6re8lE7CuXbmQcO5+plPsl7CfwPrJR0GnALMAuYAfwscBdtzm30BeIeZHQ58A/hUKyrt3FhMqr6Jcy6nBSQJCyFJI7KAZN2Rq8xsG/C4pJvS52cDrwJuSFKd0Q2sa251nRs7DxrO1UGaV+k44FWSjCQIGDtyMI16CXC3mR3VpCo6VxfePOVcfbwDuMTM9jWzWWY2A/g98Efg7WnfxkuBY9Lt1wDTJG1vrpJ0UCsq7txYeNBwrj4WMPqu4rvAdJK1FH4LfIUkU/IzZraZJNB8WtIq4E7gNc2rrnPFeJZb5xpM0i5m9qe0CevXJKvaPd7qejlXhPdpONd4P0wXsOoF/sUDhutkfqfhnHMuN+/TcM45l5sHDeecc7l50HDOOZebBw3nnHO5edBwzjmX2/8Hpt2L9+Jyz04AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y3 = np.array(data['mental_disorder'])\n",
    "Y3 = []\n",
    "for i, each in enumerate(y3):\n",
    "    Y3.append([i, each])\n",
    "Y3 = np.array(Y3)\n",
    "\n",
    "reg3 = LinearRegression().fit(X, Y3)\n",
    "\n",
    "plt.scatter(x, reg3.predict(X)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Disorder (all)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Disorder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Disorder')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ys3 = np.array(s_data['mental_disorder'])\n",
    "Ys3 = []\n",
    "for i, each in enumerate(ys3):\n",
    "    Ys3.append([i, each])\n",
    "Ys3 = np.array(Ys3)\n",
    "\n",
    "regs3 = LinearRegression().fit(Xs, Ys3)\n",
    "\n",
    "plt.scatter(xs, regs3.predict(Xs)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Disorder (sm)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Disorder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Disorder')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ym3 = np.array(m_data['mental_disorder'])\n",
    "Ym3 = []\n",
    "for i, each in enumerate(ym3):\n",
    "    Ym3.append([i, each])\n",
    "Ym3 = np.array(Ym3)\n",
    "\n",
    "regm3 = LinearRegression().fit(Xm, Ym3)\n",
    "\n",
    "plt.scatter(xm, regm3.predict(Xm)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Disorder (md)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Disorder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mental Disorder')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yl3 = np.array(l_data['mental_disorder'])\n",
    "Yl3 = []\n",
    "for i, each in enumerate(yl3):\n",
    "    Yl3.append([i, each])\n",
    "Yl3 = np.array(Yl3)\n",
    "\n",
    "regl3 = LinearRegression().fit(Xl, Yl3)\n",
    "\n",
    "plt.scatter(xl, regl3.predict(Xl)[:, 1])\n",
    "plt.title('Linear Regression: Age Vs Mental Disorder (lg)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Mental Disorder')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ethics & Privacy\n",
    "\n",
    "While we attempt to draw conclusions, we will be working with personal information of various employees’ age, gender, family mental health, ect. taken from the 2016 Open Sourcing Mental Illness (OSMI) Mental Health Tech Survey. It is important to note that the data we are using has been released to the public under a Creative Commons Attribution - ShareAlike 4.0 International License. This license allows anyone to redistribute, “transform, and build upon the material for any purpose, even commercially” (as described on the [Creative Commons website](https://creativecommons.org/licenses/by-sa/4.0/)). This license gives us permission to use the data for this project. \n",
    "\n",
    "Prior to completing the survey, OSMI informed the participants that “all responses are anonymous, and (that) no identifying information will be collected or stored.” This is true, by the definition of identifiers provided by the Safe Harbour Method, since none of those variables are present within the dataset to start with. The only possible variable that would violate this method would be age, since some participants reported to be over 90 years old. To fully protect the anonymity of the participants in our analysis, any response whose age was outside the range of (18,65) was removed during the data-cleaning process.Also, as mentioned before, the Creative Commons License allows us to modify the dataset in such a way that is needed to reach a conclusion. \n",
    "\n",
    "All the data was ethically procured, since participants elected to take the survey knowing that it was anonymous and with the knowledge that the information collected could be distributed publicly. While the sample size of those who did complete the survey is sufficient to analyze, it cannot necessarily be extrapolated as a reliable sample of the tech industry as a whole due to the fact that there might be some bias. Because the survey was voluntary, there was no means of randomization or random sampling that would reduce the responses at any extreme. For example, someone with the mental illness may be more prone to find the OSMI survey or more inclined to take it, as they would be more affected in raising awareness for mental illness in their industry. This may lead to bias in the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion & Discussion\n",
    "Based on our analysis, we’ve concluded that company size has a strong correlation with both frequency of mental disorder diagnosis in employees as well as negative beliefs about how mental health is addressed in their workplace. There is a clear correlation between company size and the ratio with and without mental disorders. As the size of the company grows, the employees were more likely to have a mental disorder. In our regression of mental disorder and age, the large size companies had the strongest positive correlation along with all the linear regression graphs. From our bar plot with age range and mental disorder, we say that age may be a confounding variable to the correlation we found. Mental disorder increased through age, regardless of company size. Peaking in growth around the 32-39 age group which is said to be around the middle aged crisis. Another reason for this increase in mental disorder as people age may be due to the fact that there is a strong positive linear relationship between age and having mental health care. It could be that the reason rates in get higher as people age is due to having better coverage and the ability to be diagnosed. The larger amount of mental disorders in larger company may also be due to the fact that the average age of an employee in a company increase with size. Small, medium and large company average ages are 31.7, 32.7 and 34.1 respectively. There is a clear indication that the average age may take partial effect for the negative effect on mental health and increase in mental disorders as company sizes increase. Age has a clear cut effect on mental illness and that has a confounding effect on our claim though there is a seen effect of company size on the mental health of employees.\n",
    "\n",
    "Our other hypothesis effect that larger companies have a more negative environment of mental health due to the fact that purely by size, a single individual does not feel important and replaceable in the system. Their sense of purpose and self worth would be diminished by the stark environment without much meaningful connection to those they spend most of their day with. This was not as clear so we chose not to reject the null hypothesis that larger companies have a more negative environment. The clearer trend we saw from the analysis of negative environment had to do with the perceived effect of having a mental disorder in the workplace would cause. In large companies, those who do not have mental illness will determine their believe of whether mental health issue can hurt their career based on the observed negative consequences. While those with a diagnosed mental disorder will have less of a change in their belief that mental health issues will hurt their career with observing negative consequences at work or not. If there are observed negative consequences at work, those with mental disorders or not have about the same rate with those affected by mental disorder being a little higher of believing that mental health issues will hurt their career at around 65-75% believing yes. On the other hand, in the circumstance of no observed negative consequences, those with mental disorder believed that having mental health issues will negatively affect their career is around 45% while those without mental disorders were 35%. Those with mental disorders still feel the stigma around having one and seem more reluctant to share which can be more detrimental to their mental health in the long run.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}