StackOverflow Dev Survey results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset examines all aspects of the developer experience from career satisfaction and job search to education and opinions on open data source software.\n",
    "\n",
    "You can get more information on it here: https://insights.stackoverflow.com/survey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Look at the data\n",
    "\n",
    "### Importing libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>Professional</th>\n",
       "      <th>ProgramHobby</th>\n",
       "      <th>Country</th>\n",
       "      <th>University</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>MajorUndergrad</th>\n",
       "      <th>HomeRemote</th>\n",
       "      <th>CompanySize</th>\n",
       "      <th>...</th>\n",
       "      <th>StackOverflowMakeMoney</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HighestEducationParents</th>\n",
       "      <th>Race</th>\n",
       "      <th>SurveyLong</th>\n",
       "      <th>QuestionsInteresting</th>\n",
       "      <th>QuestionsConfusing</th>\n",
       "      <th>InterestedAnswers</th>\n",
       "      <th>Salary</th>\n",
       "      <th>ExpectedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Student</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Not employed, and not looking for work</td>\n",
       "      <td>Secondary school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Student</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Employed part-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>More than half, but not all, the time</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A master's degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Professional developer</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor's degree</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A professional degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>113750.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Professional non-developer who sometimes write...</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Doctoral degree</td>\n",
       "      <td>A non-computer-focused engineering discipline</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A doctoral degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Professional developer</td>\n",
       "      <td>Yes, I program as a hobby</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Master's degree</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>Never</td>\n",
       "      <td>10 to 19 employees</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 154 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent                                       Professional  \\\n",
       "0           1                                            Student   \n",
       "1           2                                            Student   \n",
       "2           3                             Professional developer   \n",
       "3           4  Professional non-developer who sometimes write...   \n",
       "4           5                             Professional developer   \n",
       "\n",
       "                ProgramHobby         Country      University  \\\n",
       "0                  Yes, both   United States              No   \n",
       "1                  Yes, both  United Kingdom  Yes, full-time   \n",
       "2                  Yes, both  United Kingdom              No   \n",
       "3                  Yes, both   United States              No   \n",
       "4  Yes, I program as a hobby     Switzerland              No   \n",
       "\n",
       "                         EmploymentStatus  \\\n",
       "0  Not employed, and not looking for work   \n",
       "1                      Employed part-time   \n",
       "2                      Employed full-time   \n",
       "3                      Employed full-time   \n",
       "4                      Employed full-time   \n",
       "\n",
       "                                     FormalEducation  \\\n",
       "0                                   Secondary school   \n",
       "1  Some college/university study without earning ...   \n",
       "2                                  Bachelor's degree   \n",
       "3                                    Doctoral degree   \n",
       "4                                    Master's degree   \n",
       "\n",
       "                                  MajorUndergrad  \\\n",
       "0                                            NaN   \n",
       "1       Computer science or software engineering   \n",
       "2       Computer science or software engineering   \n",
       "3  A non-computer-focused engineering discipline   \n",
       "4       Computer science or software engineering   \n",
       "\n",
       "                                          HomeRemote  \\\n",
       "0                                                NaN   \n",
       "1              More than half, but not all, the time   \n",
       "2  Less than half the time, but at least one day ...   \n",
       "3  Less than half the time, but at least one day ...   \n",
       "4                                              Never   \n",
       "\n",
       "                CompanySize  ... StackOverflowMakeMoney Gender  \\\n",
       "0                       NaN  ...      Strongly disagree   Male   \n",
       "1        20 to 99 employees  ...      Strongly disagree   Male   \n",
       "2  10,000 or more employees  ...               Disagree   Male   \n",
       "3  10,000 or more employees  ...               Disagree   Male   \n",
       "4        10 to 19 employees  ...                    NaN    NaN   \n",
       "\n",
       "  HighestEducationParents                          Race         SurveyLong  \\\n",
       "0             High school  White or of European descent  Strongly disagree   \n",
       "1       A master's degree  White or of European descent     Somewhat agree   \n",
       "2   A professional degree  White or of European descent     Somewhat agree   \n",
       "3       A doctoral degree  White or of European descent              Agree   \n",
       "4                     NaN                           NaN                NaN   \n",
       "\n",
       "  QuestionsInteresting QuestionsConfusing InterestedAnswers    Salary  \\\n",
       "0       Strongly agree           Disagree    Strongly agree       NaN   \n",
       "1       Somewhat agree           Disagree    Strongly agree       NaN   \n",
       "2                Agree           Disagree             Agree  113750.0   \n",
       "3                Agree     Somewhat agree    Strongly agree       NaN   \n",
       "4                  NaN                NaN               NaN       NaN   \n",
       "\n",
       "   ExpectedSalary  \n",
       "0             NaN  \n",
       "1         37500.0  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "\n",
       "[5 rows x 154 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import ALookAtTheData as t\n",
    "from IPython import display\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(r\"C:\\Users\\sousa\\Desktop\\github\\SODeveloperSurvey\\Data\\survey-results-public.csv\", encoding = 'utf-8')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job there are 51392 rows in the dataset!\n",
      "Nice job there are 154 columns in the dataset!\n"
     ]
    }
   ],
   "source": [
    "#checking rows and columns\n",
    "n_rows = df.shape[0]\n",
    "n_cols = df.shape[1]\n",
    "\n",
    "t.check_rows_cols(n_rows, n_cols) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#info on the t.check_rows_cols function\n",
    "t.check_rows_cols?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'FormalEducation', 'EmploymentStatus', 'Professional', 'Respondent', 'Country', 'ProgramHobby', 'University'}\n",
      "Nice job that looks right!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://bit.ly/2K9X0gD\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cheking for missing values, get set of column names with no missing values\n",
    "no_nulls = set(df.columns[~df.isnull().any()])\n",
    "print(no_nulls)\n",
    "display.HTML(t.no_null_cols(no_nulls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job that looks right!\n"
     ]
    }
   ],
   "source": [
    "#cheking wich columns have the most missing values, getting a set of column names with more than 75% of the data missing\n",
    "most_missing_cols = set(df.columns[df.isnull().mean() > 0.75])\n",
    "\n",
    "t.most_missing_cols(most_missing_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Professional Statuses Counts')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#getting a series of the different professional statuses in the dataset.\n",
    "\n",
    "#getting total counts for each status\n",
    "status_vals = df.Professional.value_counts()\n",
    "\n",
    "#create bar chart for the counts\n",
    "(status_vals/df.shape[0]).plot(kind = 'bar')\n",
    "plt.title('Professional Statuses Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Formal Education Status')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check different Formal education statuses in a series \n",
    "#getting the counts\n",
    "ed_statuses = df.FormalEducation.value_counts()\n",
    "\n",
    "#plotting\n",
    "(ed_statuses/df.shape[0]).plot(kind=\"bar\")\n",
    "plt.title(\"Formal Education Status\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Country')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uvwj4lkuf0BLggGpUztbAtsAPmml6RESMYsquG9srJR0KnAMsAE60fbWkY4CltpcAnwZOlrSMciR/QPXaqyV9EbgGWAn8ne17G/43zKjLp4dx24zdtbhtxk7c9mN3LW6bsRuJO+XF2IiI6LbMjI2I6Lkk+oiInut0ope0jqSN57odERHzWef66CV9DjgEuBe4lDKU84O23zenDVuDquTD84BFDF38tv3BuWpTzA/V38bmjP27+PnctWjuSHqc7R/NdTv6qouLg29n+zZJLwXOAv6JkvBrJ3pJC6t42wEbDrbbfmaNsGcCdwFXAX+q1cBxJL0fOMn21U3GHYr/EMb+HhpJQpKeBmxr+6Tqd75RVSJjpvGuYoKJeAO2n1Aj9oaUWk5/ydjfxatnGrOK+3+Bo4BfservwsCM2zobJO0KXG77D5JeBjwR+FfbN9YMfXw1fPszwOds/65mOx802X7bt9aMvwHwQlY/gDumRswXTLbf9ldmGruLiX49SesB+wEfs32PpKZOS04FvkA5Aj+EMjdgRc2YW9RJNFO4FjihmqR2EvB527+vG1TSPsAHgIcBvwa2An5MSXZ1Yx8FLKaUujgJWA84Bdi1RtjnVz//rvp5cvXzpcAdNeIOYl0LPAc4por545oxAd4EPMb2bxqINUaVMN4LPIQyO12AbTfRzflvwPaStgfeTBla/e/A0+sEtf00SdsCrwaWSvoB5SDm3BmGvJTJZ+c/coZxB74K/L56n7trxhrYu/r5EGAX4FvV492BbwMzTvTY7tQNeCPwP5SjeVGS0Hcain1p9fPKoW3n14z5XmCPln8njwHeA9wIfA7YvWa8K4AHAz+sHu8OnNBQWy+v/t9+OLTtyoZif2+UbdOMOfgdXFn9XI8yIbBuW88D1m3p72EZ8NiWYl9W/TwSOHh4W0PxF1COlP+H8oV6LfCCNv4tNdv5oxZj/yfw50OP/xz4Sp2YnTuit/0R4CNDm26UtHtD4e+pfv5C0vMo5Rq2qBnzIuAMSetU8Zs8uhr08/5FdbuFkqQPl/R62wfMMOw9tn9TXexex/Z5kt7bRHuBP9r24CxM0p81FBfgzyQ9zfZ3q9i7AHXjD/4mfifpccAvKafrdV0PfFvS1xg6InQz125+ZbuJs46J3C7pLcDLgN2qv7/16gaV9ATgVZSz6XOBvW1fJulhwIVM82hW0l/YvlbSEyfab/uymk3+vqTH276qZpyJLLL9i6HHvwIeXSdg5xK9pM2BfwEeZnuvqub9UymnkHW9S9ImwP8HfBTYGPj7mjE/QGnfVa6+npsi6YPAPsA3gX+xPSgv8V5J06kAOt7vJG0EXACcKunXlJnNTfiipE8AD5T0Wsqp+icbin0wcGL1fwjwuyp+HSdI2hT4Z0pJj40oR7N1/by6rV/dmrRU0heA/2Dsl8jMT/1XeQnwt5Sj+V9K2pIGro8BHwM+BbzV9p2DjbZvlvT2GcQ7nFIR9wMT7DNQ57obwNOAV0r6GeV3PDiAa6Kb9tuSzgE+T2nrAZQzwBnr4qibsyl9u2+zvX3VP/1D24+f46ZNqPoP28t2oxdiq9ivptT7X60fWtImnmF/fXWUfRflj/ellJFNp7qh/mRJfw3sUcU/xzPvh11T/I0pf9u1r1e0TdIDKAnifxuMedIEm+2aF5BjFUlbTbTd9S9KD+K/APir6uEFts+oFa+Dif4S2ztJ+qHtHattl9veoUbMN9s+VtJHmbiM8htrxP4M5cLP2TR/io6kh1OuUwxf+b+gidhtqIrb/cL2XdXj+wGb276hgdiNjYSQ9DLbp0g6fKL9df//qm6gk4HB6JBbgFe4pRFUdUm6nclHNs2oK3KSEVONHCFXa2O8z/bxQ9v+0/bzJ3nZqLEbHT3Wps513QB/kPRgqj8OSU+hXP2uY9CfubRmnIn8rLo1foou6T2U07prKPMKoPxeZpTo2/owj/MlyoiCgXurbTs1ELvJkRCDvv0H1IyzJicAh9s+D0DSMyhdWLtM9qLJtHnAYvsB1XscQ7lOcTKrzvjq/I5qJ9wp3APsLunJwOtdVsl7eN2gLY0eG8RufNRUF4/on0jpP38c8CNgIbC/7SvmtGFTaOkU/TrgCbabGt41iDvhh9n2sQ3EXu3sS9IVtrdvIPaPbD+ubpzZMNG/ue7vQdLets+UdNBE+21/dqLt03yPi20/eapt84Wky2w/UdKbKWd7LwbOsD3hRdppxL0c2JEy4mjQs3BlE330KlWA927ygnoXj+ivpozZfQwlCV1HzVIOks5k8iPZfWrEHnOKLqnJU/TrKUcSjSZ64DnjPrj/JulioHaiB1ZI2selvDWS9qW5JdgaGwkh6SOT7a9zdFy5XtI/s2rM/8soZ34zZvvM6mfthD6Je1UmK55G+cwcyKqzyRmrzsw/CjyWcua7APhDA2eRAqjOdC6llFufdDLViNocPdb4qKkuJvoLq2/j+xKlpMsoM/Rm6v3VzxcAD6WcgkH5I76hRlxo4RR9yB3A5ZK+ydj+/7pJqJUPc+UQykiej1E+hDcBr2godpMjIS5tqE1r8mrgHZRhg6J0t72qicBqZ4b3wN8C/1rdDHyv2lbXxyjdkF+idIm8AnhUA3HvGyFl+5uS9gBe2UDcNkePNT5qqjNdN5IeSulbO4XyhzWY8bYxcLztv2jgPS6wvdtU26YZs/FT9KE4rZyiS1pE+SDvyqoP82FNXDAdeo+NKH9/tzcYs9WREF0h6euUGd7/wNAMb9v/NKcNm4SqtVGHuz8kfd927QOianjstoz90qs9YKGt0WNtjJrqUqI/iPJNvJixF01vBz7TxBhhST8Gnmf7+urx1sBZth9bI+YZwGWMPUVfbHu/uu3toiZHxkzyHo3V6Gn66FjSh20ftqbuwjrdhEPvcantJ41LmufbrlWmoIqzEHgtq///1a39cwHwbMpY+l8CvwBeWfeASNJrKOUmtqDMyn4KpVegibObzuhM1011lPpZSS+0fXpLb/P3lMkK11ePFwGvrxmzzVP0bYH/x+pJqFYdD7VUyKvSRo0QgLZq9DRd/2jwhf/+SZ9VTxszvAe+CnwH+AbNdecBvJxyre1QyufwEZSu1LreRBnRdZHt3SX9BeXzOCOzMTKtlc+f50HdiOneKB+6N1P6344Ejmww9gbA9tVtg7n+t07R1u8CzwKupCS1o4F3NBD3S8A7gZ9SEtvXKRUKm2hzmzVCGq/RQwv1j6oYbxpl2wxjP58yye1xlBmVlwL7NBT78pb+71r5fQCXDNo9+Dw38W+gFLh7A2Vo6cbA/wHe3NDvovHPX2e6bgYkHQ/cn/Ih/hTwIuAHtg9uKP4urH5a+u8ziNPaSJ6h9xicol/lamawpO/Y/qupXjtF3B/a3nFw6q9SLfQcN3C6K+kE4KNuoUbIUD/vFcCOtv8k6Qe2d64R8yLbT1GZ4fwRytHxl21vU7Otl3ncED8NTQKcryS9C/i+7bMajtvK76PqOn0VcBil7MFvgfVsP7dm3NaGmbbx+etM182QXap//JW23yHpA9Qp3zlE0snANpRv/+EJSNNO9LQ7kmfgLpViaT+RdCil4t9DGojbViEvaLdGSBs1ehqtfyTpQMpggq0lLRna9QCgqRITj6RcTH8qpdb9hcDfu7r2VNObgLdKupsGivS1/fuw/TfV3aMlnUc50/mvunFpd2Ra85+/Nk7D2rwBF1c/L6L0xW4A/KSh2D+mukDdYHsvGGXbDGPvRCmytQVldt5XgKc0EPc1wKaU+QrXU/q7D2mozVtNdGso9p9Rxl+vSznlfSPw4BrxFlASZJN/D1sBz6Ak36cP3Z5IQ2WLq8/Gy6vfw7qUAQAXN/nv6MLvg9Ln30pXISXxfpUyB2QFZSjkooZiN/7562LXzT9TjqyeBRxH+Tb9lO1/biD2l4A3emyJ0LoxGx/J0wdNjoxpk6TzbDdVBntWrKFb4SLbT2kofivDFdsg6VTgLfP172u2dDHRb+Bqyn81VG9D4C43UAagOrXbAfgBYycq1JkZuydl0tSYkTy2z5l5S++LvRh4G6sXNatbCKq1IZBrGhlje8YjYyYYCSFWrS5k16kRIr2bcrr/BeAPg+2uWc+8xZmggxpIv2NVt8JLKGe+x0G9ZfSaHq44ySiWRtZtkPQtypnvDxj7/1frGpmkR1NW29rc9uNU6unvY/tdNWJOWEBvwDUK6XUx0U900Wa1bTOMPeE4Y9vn14y7AWVhEIBrm/hSquJeB/wj49ajdc0JQpL+i1VDIO/rd7Q9UW3v6ca+gnJR7BsuF5x2Bw60/bq6sdtQffmP55kmtqG4S5lgJqjtt9WJW8UelFIYfLiHl9Ozawy/Vak2ORiuuMNguKLtl8w0Zpta/EyfT/nsfcKrat3UqrWkUihtjWzPeFhoZy7GatXM2PtJ2pGxM2Pv38R71P3Pn4hWX/B3G0m/pyxE8uua4Ve4qhnTsC1s79lCXGh39SpU1jIdruN9Zc2QB3vcRczqYmdttpdJWmD7XuAkSd+vE0/STsBNtreuHh9EOTO7ATi6zpH8kLts3yVpcHZ9raTHNBC3FcOfaUmbAb9xM0e397f9A2nMkrS1Lvy7DC5ZQOk+/lCt1o3TmURPWZz5lZRTxg+wKtHfDry1TuCWTx8Ppox++FYV7xmUi2WPlnSM7ZMnee1UjpL0KcoKU02uJNTmMmmtrV4l6U2UWZuDf/+pkk6w/dEaYb/M6nWUvgQ8qUZMgDskrU+pVXQsZSZo3cJYn6DMLkXSbpTJdP+X0h15AmUocl3LJT2QcvHxXEm/pQw5nVeqrrH3ALdSxqSfDGwGrCPpFbbrjry5RdI2rCqX/iLK/2Ettu+tujcbTfRd7Lppc2Zs41TG07/G9q+qx5tT+vZeQznirHOqdwqlS+hqVnXd2PWno19DKSjV+BBIlSp/d1JGRDS6epWkK4Gn2v7D0HtdOJN2V10Sf0mp2PmPQ7s2Bv6xzjWFKv5WlLVA16cM19wE+LjtZTVi3ldDSdJxlDO+o6vHtRbnWcP7PZ1quKJLnfd5o+oaeyulfSdQVnm7qPp//bzrj89/ZBV3F8rY/J8BL63bbVrFbvy6UGeO6CXtTZmdeHr1+EjKaemNlBl083JlF8qQq18NPf418Gjbt0q6Z00vGtH2bmcJxb1aiEl1WvpV28+mfDE1XU5XjB3LfC9j+6en4zGUGaYPBPYe2n475axhxqrfw7ttv4yyZOOM+17HWSBpXdsrKaPShq971PqsS5qotO/gjG8jypHzfLKu7a8DVGfOFwFUXU21g1fdec+uDibWcYPF+VhV2XZ48IOpsc5tZxI98G7KFX4kPZ8yNvhASvH/4yldO/PRdyT9J+V0H8qX0wXVH8jvasa+SNJ2tq+pGWeMwVHJ+CGQDcS9V9IdqrGe7RROAi5WmQ0JsB8zXDTe9leBr0p6qu0Lm2pgFfteSQslrd/wkfDngfNV1jy4k1KTBkmPov4qbJeyaiTTwH0jmyjLZc4nw2s03zluX+1uDJUlCi+i/I4voKzy1og2hvN2putm3GnpicB1tt9bPW5k1E0bVA4fXkgp+StKfZrTm7ggVI3R34aGu1jaGAI5FPuLlC/scxl7Wlq3hv4g/hMps29F6Rr7Yc14xwLvoiSL/6LUQDrM9imTvnDquJ+g9P0vYezvoe5atE8B/hz4+lAX1qMp65nWHRIq4BFdGJMu6V7K71XA/ShrN1A93tD2ejXjbwA8mXLhf1dKF+oVXjUTt07szYF/AR5mey9J21G6JGd00ALdOqJXdRHvDspp6ceH9jV21Nm0KqF/ubo1ra2RMe+kJOMxQyAbiv216tYYlWp/h1CuK1xF6etu5AIvsIftN0v6G2A5sD+lUFitRE+5gHkz5VpFY+vSDrooxm3774ZiuzpbqnshunW2F7T8FvdSShXcSzl7+BXloKgJn6GcnQ6G2v43pb9+rUj0H6ZM0LiNcnS5FKAaatnYTNamqYWFfqu46wBfq3MxdxKND4GUtKXtn7udZe4+S/nQfYdyfeGxlCJWTRgc+T2XchHv1ob6eN8B5YLx4Mi7Iy6StJPtS+a6IXPsNspBxQeBTzYxmGDIZra/KOktALZXVmcoM9aZRG/7RJUKgg+hlDmIwJsAAAzkSURBVKMd+CUN1XdvybE0vNAvgEtlxisGCbTJ2LQzBPI/qIYpSjrd9gtrxhu2nVdV7/w0ZRZkU86UdC2l6+YNKgtv3FU3qKSnUo7QNgK2rMb/v972G+rGbtnuwCGSbmBV10gjI7I65kBKF+EbgNdUcyAusP3NBmL/QdKDWTV08ynUvMbSmT76rpL0Pdu7thS70end1UW7zSlnTsNDILeinD3MeB1VDZWcVcPleMdfo2n6mo1KbZfbqouo9wc2tv3LmjEvpoxrX+KGZlbOBmW5xjGq4Zp7Uc4gH2L7fg3EfBKlJPbjgB8BC4H9bV8x6Qsn0Zkj+g5rfKHfIU0Nyxv4MPDWoa6EP1FW9VpMWdRk7zW9cARew/0mbC/ptuq+KLOnb6OZWjevGLo/vGsmpavHsH3TuJhNrtjUCts3SnoasK3tk6oznI3mul2zTdLplIloyyhdhi+noTNJ25dWcxQeQ/kbvs52raHYSfTt25hyAXmPoW2mgRr6ts+vjrC2tf2N6mizzkWoRZ6gZIDtpSoLhtex/VDyvd+4xFwrGbd84W2nofsbUgYCXEb9RH+TyiI3Vpkh+0ZKmex5TaUey2JKEjqJcg3jFMrIk95TVWaCMuv2Msow7xdSupR/RDPdej8F3mf7+KFt/2n7+TOO2ZWumzVM2LiPm6nj0SmSXkuZFPMg29uorCF7vO1nzTDeMtuPmu6+tYnKIiQnz7R7bCjOZpTFQZ5N+bL7OmXiX5MX9Ron6XLK3JXLhrqc7luEvO8kXQY8u7oovxulQuigzMRjbdcuM1FdE7qCcoD4ett/rNvd2aUj+uEJG1tSph2LMnPx58DWc9e01Ul6s+1jJX2UCboqGho3/nfAzsDFVcyfVJOcZuoSSa+1/cnhjZIOpvz+o3z4Hl03iO1bKNc/uuaP1TDLwYXCuvV5umbB0EHlSyhrEp8OnF59CTbhDtsvkfRmyoTLF1Ozu7Mzid6rKvIdT7mAdVb1eC+qQk7zzOA0fGmL73F39W0PgKR1qfcHcRhwhsoSaYPEvphSj6X2RJAu0ti1fxdQhm5+sUa8Cb/4B5qaONaiL1aTvR5YnVG+GvjkFK/pk9bKTAwRQHWgeClwDjBpj8ZUOpPoh+xk+5DBA9tnS3rnXDZoDX4OMNG4cUn/p6H3OF/SWyl93n9NGep15kyDudTk2aWaIDUY/fE129+q39TOev/Q/ZWUD2GdyWODL/5dge0oE2GgTMSa92dNtt9f/a3dRumnP9L2uXPcrNnUZpmJgSMHd2x/U9JzKEtjzlhn+ugHqrH036FcADLlYshutudVrRtJ11OGRF06bvs7KOPqm1goZR1KGeTBhd5zbH+qbtwYS9IOlAWsX0wpN3G67Y/VjHkeZdbtPdXj9ShlCzq1bOHaSC2WmahifXP8dbaJtk1HF4/oDwSOAs6gJPoLaG56fpP2B74k6aW2L1TpX/k3Sv/uM+oElrQvZXGQ44BPVqfQC4EnSfqd7TbKLaxVqg/uAZS/rd9QjrzVYCJ+GKX0waC/d6Nq27zW1kzvLmmrzIRKKY/7A5tVczeGF1eq9bfRuSP6AUkb2f7fuW7HZFTWkTyDctF0UNr2QNesWCjpe8ABtm+qHl9OKWG6EXBSnW/+KCT9iXLmeLCrGvGSrneNZfjGxX8VZW7CYKnCp1NWgWqjRERjJC2jhZnewWDhnMMoSX14MZfbKGUWZnwWuU7Nts06SbuoLIxxTfV4e0kfn+Jls64aDrqc0rd2CqUWy+uBjaYaKjqC9QdJvvJd27dWpRDWtlEQbXkhpbzGeZI+KelZMOPa9quxfRKl+uEZ1e2p8z3JV36VJN8O2/9aDTr5B9tbD922r91V2LUj+q5MHVdZoHn84sz31e+uc2Q4xXj3n9reZqaxY6xq+OB+lC6cZ1IKqJ3halGLGnFFGV75SNvHSNoSeKjtJuv0NEar1j5+OvBQ2pnpvVaT9Ezb39Lq60wD9X7HXeyj78TU8cFw0JZcvIbx7q+n2YJea73qYtuplOJuD6JcezmCMsGpjo9TSkw8k7KS0O3A6YydiTufDJe/aGWmd/B0ytrSE5UaqfU77uIR/ZcppUE/RqmZ/kZgse0D5rRhs6iaFDU4ohpc5X8SsAGwn8cuXRjzkKrCaxpb7O2+xXXmK0m72v7eVNtifuniEf0hlKnjD6f0gX+dMn58rWH715Tx7s+kLGANGe/eNfeorB07mGG6kLHL381XH6UqNz3FtpghjVum0A0sFdrFRP8Y22OmjkvaFVjrjiiqxJ7k3k0foVyEfYikd1OuO719bpu0Zir183cBFko6fGjXxtQrpBer245VyxS+X6UUcq1lCruY6DtxRJEibDEZ26dW09sHo3n2m+ejWdanDN9dl7FLH95G+ZKK5jS+TGFn+uiHjigOAz40tGtj4G/mW9/m0KibCYuwtXyxNuYpSS8YjJ6QtKnt3851m6ZD0lYuNem7tgRiZ0i6g1XLFH6jiYqmXRpHP/6IYnCbl0cU1fjXR1IKEu1tezPbDwaeT0YorM2Gu2eaWHZutj2smsfyY5i/81g67kDKjP83AKdJekc1j2PGOnNEPzA4opjrdoxK0qW2nzRu21Lbi+eqTTF31OKSirOhK/NY+kANLlPYmT56SR+2fRjwsUEt7GGuuRBEi26R9HbGFmGb14tLRKvuJ2lHytn0htX9+yaFNFEUq21dmMfSZWphmcLOJHrg5Orn+yd91vzTlSJsMTt+Qel7hVJi4YND+0yZQDWfdXIJxC5Qi8sUdq7rpqu6UIQtYirq6BKIXaAWlynsXKKvxswfDWxFOSOpXTumTdXRz6cotaq3lLQ9ZR3ItWqSV0RMbnhmtKTjgBW2j64eX257h5nG7lLXzcCngb+nrMbThb7BDwHPAZYA2L6i+raO6AxJR06y27bn4ypvXdPaMoVdTPS/t332XDdiOnLxKnpgojHzf0ZZ4ezBQBJ9fa0tU9jFRH+epPdRxqIPl0mdr6MVcvEqJiTp4azqggTA9gVz16I1s/2BwX1JDwDeBLyK0o/8gTW9LkZn+92SvsmqZQoH/errUPrqZ6yLffTnTbDZtuflaIU1XLx6Y0ogrN0kvRd4CWUBncEZnufxMOFBWY/DKXX0Pwv8a9dm9q6tOpfouyZlXWMikq4DnmD77imfPA9UZ9EvAE4AjssIsm7pTKIfVzEPypjjWyjL6P1sDpo0kkHd8am2xdpF0tnA/l1JmNUauncDK1m1chqshYuDd1GX+ugfMMG2RcDbJB1t+7RZbs+kUtY1pnAHcHnVJzt8remNc9ekNbPdpbpYMU5nEr3td0y0veo3/AblotB8krKuMZkl1S2idZ3pupnMfC4O1bUibBHRP505ol+Tajm9eXflv8NF2GIWSNoW+H+U1YQ2HGyfrzO8o9s6k+glXcXYi0AADwJuBl4x+y2aUleLsMXsOIlS7O5DwO6UMema9BURM9SZrhtJW43bZOA3WeUmumiwToGkq2w/vtr2Hdt/Nddti/7pzBF9V/u5u1aELWbNXZLWAX4i6VDgfyjlaCMa15kj+q6SdC0TFGFLWde1W1V7/MeUNYTfCWwCHGv7ojltWPRSEn3LJF1s+8lz3Y6IWHsl0bdM0nsoE6S6UoQtWjQYjSXpTFYfXJDRWNGKJPqWda0IW7RL0pNsXyrp6RPtt33+bLcp+i+JPmKOSdoUeITtK+e6LdFPSfQt6WoRtpgdkr4N7EMZiXU5sAI43/b4v5uI2lKoqD0PGHfbGFgMnC3pgLlsWMwLm9i+jVL69yTbT6KsWRDRuM6Mo++aDhZhi9m1rqQ/B14MvG2uGxP9liP6WVatLJWp7nEMcA6wzPYlkh4J/GSO2xQ9lT76WVYVYXt7Rt1ExGxJ101LOliELWaRpGOBdwF3Av8FbA8cZvuUOW1Y9FKO6FuSImwxGUmX295B0t8A+1HKZJxne/s5blr0UI7oW9LVImwxa9arfj4X+LztW6Vcuol2JNFHzI0zq4J3dwJvkLQQuGuO2xQ9la6biDlSzYi9zfa9ku4PbGz7l3PdruifHNFHzAFJrxi6P7zr32e/NdF3SfQRc2OnofsbAs8CLiOJPlqQrpuIeUDSJsDJKVMcbcjM2Ij54Q5g27luRPRTum4i5sC4hUfWAbYDvjR3LYo+S9dNxBwYt/DISuBG28vnqj3Rb0n0EfOApAXAAbZPneu2RP+kjz5iFknaWNJbJH1M0h4qDgWup5QsjmhcjugjZpGkrwK/BS6kDKncFFgfeJPty+eybdFfSfQRs0jSVbYfX91fQFleckvbt89ty6LP0nUTMbvuGdyxfS/wsyT5aFuO6CNmkaR7gUGpagH3o4yhF2DbG89V26K/kugjInouXTcRET2XRB8R0XNJ9BERPZdEHxHRc0n0ERE99/8DQGSVTzBPk2UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#exploring the different countries of the participants\n",
    "count_vals = df.Country.value_counts()\n",
    "\n",
    "#Since there are too many focusing on the top 15\n",
    "(count_vals[:15]/df.shape[0]).plot(kind=\"bar\")\n",
    "plt.title(\"Country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Column</th>\n",
       "      <th>Question</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Respondent</td>\n",
       "      <td>Respondent ID number</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Which of the following best describes you?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ProgramHobby</td>\n",
       "      <td>Do you program as a hobby or contribute to ope...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Country</td>\n",
       "      <td>In which country do you currently live?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>University</td>\n",
       "      <td>Are you currently enrolled in a formal, degree...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Column                                           Question\n",
       "0    Respondent                               Respondent ID number\n",
       "1  Professional         Which of the following best describes you?\n",
       "2  ProgramHobby  Do you program as a hobby or contribute to ope...\n",
       "3       Country            In which country do you currently live?\n",
       "4    University  Are you currently enrolled in a formal, degree..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the second dataset\n",
    "df2 = pd.read_csv(r\"C:\\Users\\sousa\\Desktop\\github\\SODeveloperSurvey\\Data\\survey-results-schema.csv\")\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get descriptions for each column\n",
    "def get_description(column_name, df = df2):\n",
    "    '''\n",
    "    INPUT\n",
    "    df - df I want to get the column descriptions for\n",
    "    column_name - column I want the description for\n",
    "    \n",
    "    OUTPUT\n",
    "    desc - the description for the column\n",
    "    '''\n",
    "    desc = list(df[df['Column'] == column_name]['Question'])[0]\n",
    "    return desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Which of the following best describes you?'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_description(df.columns[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Let's pretend you have a distant cousin. They are 24 years old, have a college degree in a field not related to computer programming, and have been working a non-coding job for the last two years. They want your advice on how to switch to a career as a software developer. Which of the following options would you most strongly recommend to your cousin?\\nLet's pretend you have a distant cousin named Robert. He is 24 years old, has a college degree in a field not related to computer programming, and has been working a non-coding job for the last two years. He wants your advice on how to switch to a career as a software developer. Which of the following options would you most strongly recommend to Robert?\\nLet's pretend you have a distant cousin named Alice. She is 24 years old, has a college degree in a field not related to computer programming, and has been working a non-coding job for the last two years. She wants your advice on how to switch to a career as a software developer. Which of the following options would you most strongly recommend to Alice?\"]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Career switches.How to get into the field? check CousinEducation\n",
    "list(df2[df2.Column == 'CousinEducation']['Question'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>index</th>\n",
       "      <th>CousinEducation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Take online courses; Buy books and work throug...</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Take online courses</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>None of these</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Take online courses; Part-time/evening courses...</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Take online courses; Bootcamp; Part-time/eveni...</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               index  CousinEducation\n",
       "0  Take online courses; Buy books and work throug...              711\n",
       "1                                Take online courses              551\n",
       "2                                      None of these              523\n",
       "3  Take online courses; Part-time/evening courses...              479\n",
       "4  Take online courses; Bootcamp; Part-time/eveni...              465"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the avice given to the participants\n",
    "study = df['CousinEducation'].value_counts().reset_index()\n",
    "study.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>method</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Take online courses; Buy books and work throug...</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Take online courses</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>None of these</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Take online courses; Part-time/evening courses...</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Take online courses; Bootcamp; Part-time/eveni...</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              method  count\n",
       "0  Take online courses; Buy books and work throug...    711\n",
       "1                                Take online courses    551\n",
       "2                                      None of these    523\n",
       "3  Take online courses; Part-time/evening courses...    479\n",
       "4  Take online courses; Bootcamp; Part-time/eveni...    465"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#based on this information I will change the column names to something more significant\n",
    "study.rename(columns = {'index' : 'method', 'CousinEducation' : 'count'}, inplace = True)\n",
    "study.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Take online courses; Buy books and work through the exercises',\n",
       "       'Take online courses', 'None of these',\n",
       "       'Take online courses; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Take online courses; Bootcamp; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Other',\n",
       "       'Return to college; Take online courses; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Take online courses; Contribute to open source; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Take online courses; Contribute to open source; Buy books and work through the exercises',\n",
       "       'Take online courses; Contribute to open source; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Take online courses; Part-time/evening courses; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Buy books and work through the exercises',\n",
       "       'Take online courses; Part-time/evening courses',\n",
       "       'Return to college',\n",
       "       'Get a job as a QA tester; Take online courses; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Take online courses; Contribute to open source; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Return to college; Take online courses; Buy books and work through the exercises',\n",
       "       'Take online courses; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Take online courses; Bootcamp; Buy books and work through the exercises',\n",
       "       'Take online courses; Bootcamp; Part-time/evening courses',\n",
       "       'Take online courses; Part-time/evening courses; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Buy books and work through the exercises',\n",
       "       'Take online courses; Contribute to open source',\n",
       "       'Take online courses; Bootcamp; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Return to college; Take online courses; Bootcamp; Part-time/evening courses',\n",
       "       'Take online courses; Bootcamp',\n",
       "       'Return to college; Take online courses',\n",
       "       'Return to college; Take online courses; Contribute to open source; Buy books and work through the exercises',\n",
       "       'Bootcamp',\n",
       "       'Take online courses; Contribute to open source; Bootcamp; Buy books and work through the exercises',\n",
       "       'Get a job as a QA tester; Take online courses; Buy books and work through the exercises',\n",
       "       'Return to college; Take online courses; Part-time/evening courses',\n",
       "       'Return to college; Take online courses; Bootcamp; Buy books and work through the exercises',\n",
       "       'Contribute to open source',\n",
       "       'Take online courses; Contribute to open source; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Get a job as a QA tester; Take online courses; Contribute to open source; Buy books and work through the exercises',\n",
       "       'Take online courses; Bootcamp; Part-time/evening courses; Conferences/meet-ups',\n",
       "       \"Return to college; Master's degree; Take online courses; Buy books and work through the exercises\",\n",
       "       'Take online courses; Contribute to open source; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Take online courses; Buy books and work through the exercises; Other',\n",
       "       'Take online courses; Participate in online coding competitions; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       \"Master's degree; Take online courses; Part-time/evening courses; Buy books and work through the exercises\",\n",
       "       'Take online courses; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Take online courses; Contribute to open source; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Return to college; Buy books and work through the exercises',\n",
       "       \"Master's degree\",\n",
       "       'Take online courses; Contribute to open source; Part-time/evening courses; Conferences/meet-ups',\n",
       "       'Take online courses; Contribute to open source; Bootcamp; Part-time/evening courses',\n",
       "       'Take online courses; Participate in hackathons; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Take online courses; Contribute to open source; Part-time/evening courses',\n",
       "       'Part-time/evening courses',\n",
       "       'Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Take online courses; Conferences/meet-ups',\n",
       "       'Take online courses; Contribute to open source; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Take online courses; Part-time/evening courses; Conferences/meet-ups',\n",
       "       'Take online courses; Contribute to open source; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Take online courses; Participate in hackathons; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Take online courses; Other',\n",
       "       'Take online courses; Contribute to open source; Bootcamp; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester',\n",
       "       'Take online courses; Contribute to open source; Conferences/meet-ups',\n",
       "       'Take online courses; Bootcamp; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Get a job as a QA tester; Take online courses',\n",
       "       'Buy books and work through the exercises; Other',\n",
       "       \"Return to college; Master's degree\",\n",
       "       'Contribute to open source; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Take online courses; Contribute to open source; Buy books and work through the exercises; Other',\n",
       "       'Get a job as a QA tester; Take online courses; Part-time/evening courses',\n",
       "       'Get a job as a QA tester; Take online courses; Bootcamp; Part-time/evening courses',\n",
       "       'Get a job as a QA tester; Take online courses; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'None of these; Other',\n",
       "       'Return to college; Take online courses; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       \"Master's degree; Take online courses; Contribute to open source; Buy books and work through the exercises\",\n",
       "       'Take online courses; Contribute to open source; Participate in online coding competitions',\n",
       "       \"Master's degree; Take online courses; Buy books and work through the exercises\",\n",
       "       'Get a job as a QA tester; Take online courses; Bootcamp; Buy books and work through the exercises',\n",
       "       'Take online courses; Part-time/evening courses; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Participate in hackathons; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Return to college; Get a job as a QA tester; Take online courses; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Buy books and work through the exercises; Other',\n",
       "       \"Return to college; Master's degree; Take online courses; Part-time/evening courses\",\n",
       "       'Get a job as a QA tester; Buy books and work through the exercises',\n",
       "       'Return to college; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Take online courses; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Conferences/meet-ups',\n",
       "       'Take online courses; Bootcamp; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Get a job as a QA tester; Take online courses; Contribute to open source',\n",
       "       'Take online courses; Contribute to open source; Bootcamp',\n",
       "       'Return to college; Bootcamp; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Take online courses; Part-time/evening courses; Buy books and work through the exercises; Other',\n",
       "       'Take online courses; Part-time/evening courses; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Bootcamp; Buy books and work through the exercises',\n",
       "       'Return to college; Take online courses; Contribute to open source; Part-time/evening courses',\n",
       "       'Return to college; Get a job as a QA tester; Take online courses; Part-time/evening courses',\n",
       "       \"Master's degree; Take online courses; Bootcamp; Buy books and work through the exercises\",\n",
       "       'Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Return to college; Contribute to open source; Buy books and work through the exercises',\n",
       "       \"Return to college; Master's degree; Take online courses\",\n",
       "       'Contribute to open source; Participate in hackathons; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Contribute to open source; Other',\n",
       "       \"Master's degree; Take online courses\",\n",
       "       'Take online courses; Bootcamp; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Contribute to open source; Participate in hackathons; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Get a job as a QA tester; Take online courses; Contribute to open source; Part-time/evening courses',\n",
       "       'Take online courses; Participate in online coding competitions',\n",
       "       'Return to college; Take online courses; Bootcamp',\n",
       "       'Take online courses; Participate in hackathons; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Take online courses; Part-time/evening courses; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Contribute to open source; Bootcamp; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Take online courses; Part-time/evening courses; Conferences/meet-ups',\n",
       "       'Return to college; Take online courses; Contribute to open source',\n",
       "       'Bootcamp; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Return to college; Part-time/evening courses',\n",
       "       'Get a job as a QA tester; Take online courses; Contribute to open source; Conferences/meet-ups',\n",
       "       'Return to college; Take online courses; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Take online courses; Contribute to open source; Bootcamp; Participate in online coding competitions',\n",
       "       'Return to college; Contribute to open source',\n",
       "       'Contribute to open source; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Take online courses; Buy books and work through the exercises; Conferences/meet-ups; Other',\n",
       "       'Contribute to open source; Part-time/evening courses; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Return to college; Take online courses; Part-time/evening courses; Conferences/meet-ups',\n",
       "       \"Master's degree; Take online courses; Part-time/evening courses\",\n",
       "       \"Master's degree; Take online courses; Bootcamp; Part-time/evening courses\",\n",
       "       'Take online courses; Bootcamp; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Take online courses; Contribute to open source; Part-time/evening courses; Participate in hackathons',\n",
       "       'Take online courses; Bootcamp; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Take online courses; Bootcamp; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Contribute to open source; Buy books and work through the exercises',\n",
       "       'Take online courses; Bootcamp; Participate in hackathons; Participate in online coding competitions',\n",
       "       \"Return to college; Master's degree; Take online courses; Bootcamp\",\n",
       "       'Participate in online coding competitions',\n",
       "       'Return to college; Take online courses; Contribute to open source; Bootcamp',\n",
       "       'Take online courses; Part-time/evening courses; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Take online courses; Contribute to open source; Participate in hackathons',\n",
       "       \"Return to college; Master's degree; Contribute to open source; Buy books and work through the exercises\",\n",
       "       \"Return to college; Master's degree; Buy books and work through the exercises\",\n",
       "       \"Master's degree; Take online courses; Contribute to open source; Conferences/meet-ups\",\n",
       "       'Get a job as a QA tester; Take online courses; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Take online courses; Contribute to open source; Other',\n",
       "       \"Master's degree; Take online courses; Participate in online coding competitions; Buy books and work through the exercises\",\n",
       "       'Take online courses; Contribute to open source; Bootcamp; Participate in hackathons',\n",
       "       'Take online courses; Part-time/evening courses; Other',\n",
       "       'Return to college; Contribute to open source; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Return to college; Get a job as a QA tester; Take online courses',\n",
       "       'Contribute to open source; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Take online courses; Bootcamp; Part-time/evening courses; Participate in hackathons',\n",
       "       'Return to college; Bootcamp; Part-time/evening courses',\n",
       "       'Take online courses; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Get a job as a QA tester; Take online courses; Bootcamp',\n",
       "       'Return to college; Contribute to open source; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Contribute to open source',\n",
       "       'Return to college; Bootcamp',\n",
       "       'Return to college; Take online courses; Buy books and work through the exercises; Other',\n",
       "       'Return to college; Get a job as a QA tester; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Participate in online coding competitions; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       \"Master's degree; Contribute to open source\",\n",
       "       'Contribute to open source; Bootcamp; Buy books and work through the exercises',\n",
       "       'Return to college; Get a job as a QA tester; Take online courses; Bootcamp',\n",
       "       'Return to college; Get a job as a QA tester',\n",
       "       'Bootcamp; Part-time/evening courses', 'Participate in hackathons',\n",
       "       'Get a job as a QA tester; Take online courses; Contribute to open source; Bootcamp',\n",
       "       \"Return to college; Master's degree; Part-time/evening courses; Buy books and work through the exercises\",\n",
       "       \"Master's degree; Take online courses; Buy books and work through the exercises; Conferences/meet-ups\",\n",
       "       \"Return to college; Master's degree; Bootcamp; Part-time/evening courses\",\n",
       "       'Take online courses; Bootcamp; Buy books and work through the exercises; Other',\n",
       "       'Contribute to open source; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Contribute to open source; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       \"Return to college; Master's degree; Take online courses; Contribute to open source\",\n",
       "       \"Master's degree; Take online courses; Bootcamp\",\n",
       "       \"Master's degree; Get a job as a QA tester; Take online courses; Buy books and work through the exercises\",\n",
       "       'Contribute to open source; Bootcamp; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Take online courses; Contribute to open source; Conferences/meet-ups; Other',\n",
       "       'Get a job as a QA tester; Take online courses; Bootcamp; Conferences/meet-ups',\n",
       "       'Contribute to open source; Bootcamp; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Return to college; Take online courses; Contribute to open source; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Part-time/evening courses',\n",
       "       'Return to college; Other',\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       \"Master's degree; Buy books and work through the exercises\",\n",
       "       'Return to college; Take online courses; Contribute to open source; Participate in online coding competitions',\n",
       "       'Contribute to open source; Participate in online coding competitions',\n",
       "       'Get a job as a QA tester; Take online courses; Conferences/meet-ups',\n",
       "       'Contribute to open source; Buy books and work through the exercises; Conferences/meet-ups; Other',\n",
       "       'Contribute to open source; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Take online courses; Bootcamp; Participate in online coding competitions',\n",
       "       'Take online courses; Participate in hackathons',\n",
       "       'Bootcamp; Part-time/evening courses; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       \"Return to college; Master's degree; Contribute to open source\",\n",
       "       'Get a job as a QA tester; Contribute to open source; Bootcamp; Buy books and work through the exercises',\n",
       "       \"Master's degree; Take online courses; Contribute to open source; Participate in online coding competitions\",\n",
       "       'Get a job as a QA tester; Take online courses; Contribute to open source; Participate in online coding competitions',\n",
       "       'Get a job as a QA tester; Take online courses; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Return to college; Take online courses; Conferences/meet-ups',\n",
       "       'Part-time/evening courses; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Return to college; Buy books and work through the exercises; Other',\n",
       "       'Take online courses; Conferences/meet-ups; Other',\n",
       "       'Return to college; Take online courses; Participate in hackathons; Buy books and work through the exercises',\n",
       "       \"Master's degree; Take online courses; Part-time/evening courses; Conferences/meet-ups\",\n",
       "       'Contribute to open source; Part-time/evening courses; Conferences/meet-ups',\n",
       "       \"Master's degree; Take online courses; Contribute to open source; Part-time/evening courses\",\n",
       "       'Take online courses; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Contribute to open source; Bootcamp',\n",
       "       'Get a job as a QA tester; Bootcamp; Buy books and work through the exercises',\n",
       "       \"Master's degree; Take online courses; Contribute to open source; Bootcamp\",\n",
       "       'Get a job as a QA tester; Bootcamp',\n",
       "       'Get a job as a QA tester; Contribute to open source; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Take online courses; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Part-time/evening courses; Participate in online coding competitions; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Take online courses; Bootcamp; Participate in hackathons',\n",
       "       'Return to college; Contribute to open source; Bootcamp; Buy books and work through the exercises',\n",
       "       'Return to college; Take online courses; Contribute to open source; Participate in hackathons',\n",
       "       \"Return to college; Master's degree; Bootcamp; Buy books and work through the exercises\",\n",
       "       \"Master's degree; Contribute to open source; Buy books and work through the exercises\",\n",
       "       \"Master's degree; Part-time/evening courses; Buy books and work through the exercises\",\n",
       "       'Return to college; Take online courses; Bootcamp; Conferences/meet-ups',\n",
       "       'Take online courses; Part-time/evening courses; Conferences/meet-ups; Other',\n",
       "       'Contribute to open source; Participate in hackathons; Conferences/meet-ups; Other',\n",
       "       'Return to college; Get a job as a QA tester; Bootcamp; Part-time/evening courses',\n",
       "       'Contribute to open source; Part-time/evening courses; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Return to college; Get a job as a QA tester; Take online courses; Contribute to open source',\n",
       "       'Return to college; Bootcamp; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Take online courses; Bootcamp; Part-time/evening courses; Other',\n",
       "       'Contribute to open source; Bootcamp; Part-time/evening courses; Conferences/meet-ups',\n",
       "       'Take online courses; Contribute to open source; Participate in hackathons; Other',\n",
       "       \"Master's degree; Take online courses; Contribute to open source\",\n",
       "       'Contribute to open source; Bootcamp; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Return to college; Contribute to open source; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Part-time/evening courses; Conferences/meet-ups',\n",
       "       'Take online courses; Contribute to open source; Part-time/evening courses; Other',\n",
       "       \"Return to college; Master's degree; Get a job as a QA tester; Take online courses\",\n",
       "       'Get a job as a QA tester; Bootcamp; Part-time/evening courses; Buy books and work through the exercises',\n",
       "       'Return to college; Bootcamp; Buy books and work through the exercises',\n",
       "       \"Master's degree; Contribute to open source; Part-time/evening courses; Buy books and work through the exercises\",\n",
       "       'Return to college; Contribute to open source; Conferences/meet-ups',\n",
       "       'Return to college; Get a job as a QA tester; Contribute to open source',\n",
       "       'Part-time/evening courses; Buy books and work through the exercises; Other',\n",
       "       \"Return to college; Master's degree; Part-time/evening courses\",\n",
       "       'Return to college; Get a job as a QA tester; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Return to college; Get a job as a QA tester; Buy books and work through the exercises',\n",
       "       \"Master's degree; Take online courses; Part-time/evening courses; Participate in online coding competitions\",\n",
       "       'Return to college; Take online courses; Part-time/evening courses; Other',\n",
       "       'Take online courses; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Bootcamp; Participate in hackathons; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Take online courses; Participate in online coding competitions',\n",
       "       \"Return to college; Master's degree; Bootcamp\",\n",
       "       'Get a job as a QA tester; Take online courses; Buy books and work through the exercises; Other',\n",
       "       'Return to college; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Return to college; Take online courses; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Get a job as a QA tester; Other',\n",
       "       'Bootcamp; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Return to college; Bootcamp; Part-time/evening courses; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Contribute to open source; Conferences/meet-ups',\n",
       "       'Contribute to open source; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Contribute to open source; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Return to college; Take online courses; Part-time/evening courses; Participate in hackathons',\n",
       "       'Contribute to open source; Bootcamp; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Participate in hackathons',\n",
       "       'Contribute to open source; Bootcamp; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Return to college; Get a job as a QA tester; Contribute to open source; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Part-time/evening courses; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Part-time/evening courses',\n",
       "       'Participate in hackathons; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Get a job as a QA tester; Bootcamp; Part-time/evening courses; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Take online courses; Participate in hackathons; Participate in online coding competitions',\n",
       "       \"Master's degree; Get a job as a QA tester; Take online courses; Part-time/evening courses\",\n",
       "       \"Return to college; Master's degree; Get a job as a QA tester\",\n",
       "       \"Master's degree; Contribute to open source; Buy books and work through the exercises; Conferences/meet-ups\",\n",
       "       'Contribute to open source; Conferences/meet-ups; Other',\n",
       "       'Get a job as a QA tester; Take online courses; Bootcamp; Participate in online coding competitions',\n",
       "       'Return to college; Contribute to open source; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Return to college; Get a job as a QA tester; Part-time/evening courses',\n",
       "       'Get a job as a QA tester; Bootcamp; Part-time/evening courses',\n",
       "       'Return to college; Contribute to open source; Part-time/evening courses',\n",
       "       'Take online courses; Bootcamp; Conferences/meet-ups; Other',\n",
       "       'Get a job as a QA tester; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Take online courses; Contribute to open source; Participate in online coding competitions; Other',\n",
       "       'Get a job as a QA tester; Take online courses; Contribute to open source; Participate in hackathons',\n",
       "       \"Master's degree; Get a job as a QA tester\",\n",
       "       'Return to college; Contribute to open source; Buy books and work through the exercises; Other',\n",
       "       'Part-time/evening courses; Buy books and work through the exercises; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Part-time/evening courses; Buy books and work through the exercises; Conferences/meet-ups\",\n",
       "       'Contribute to open source; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Get a job as a QA tester; Take online courses; Part-time/evening courses; Participate in hackathons',\n",
       "       'Bootcamp; Participate in hackathons; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Take online courses; Part-time/evening courses; Participate in hackathons',\n",
       "       \"Master's degree; Get a job as a QA tester; Take online courses; Contribute to open source\",\n",
       "       'Get a job as a QA tester; Contribute to open source; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       \"Master's degree; Contribute to open source; Bootcamp\",\n",
       "       \"Master's degree; Bootcamp; Part-time/evening courses; Buy books and work through the exercises\",\n",
       "       \"Master's degree; Get a job as a QA tester; Take online courses\",\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       \"Master's degree; Contribute to open source; Participate in online coding competitions; Buy books and work through the exercises\",\n",
       "       'Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Take online courses; Conferences/meet-ups\",\n",
       "       'Get a job as a QA tester; Take online courses; Contribute to open source; Other',\n",
       "       'Bootcamp; Part-time/evening courses; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Participate in online coding competitions; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Participate in hackathons; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Part-time/evening courses; Participate in hackathons; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Bootcamp; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Return to college; Take online courses; Participate in online coding competitions',\n",
       "       'Part-time/evening courses; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Bootcamp; Conferences/meet-ups',\n",
       "       'Return to college; Get a job as a QA tester; Bootcamp; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Part-time/evening courses; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Return to college; Part-time/evening courses; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Return to college; Take online courses; Other',\n",
       "       'Contribute to open source; Bootcamp; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Take online courses; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Return to college; Master's degree; Get a job as a QA tester; Part-time/evening courses\",\n",
       "       'Return to college; Get a job as a QA tester; Take online courses; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Contribute to open source; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Contribute to open source; Bootcamp; Conferences/meet-ups',\n",
       "       'Take online courses; Bootcamp; Other',\n",
       "       'Get a job as a QA tester; Take online courses; Part-time/evening courses; Other',\n",
       "       \"Master's degree; Take online courses; Participate in online coding competitions\",\n",
       "       \"Master's degree; Part-time/evening courses\",\n",
       "       'Return to college; Contribute to open source; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Return to college; Take online courses; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Take online courses; Participate in online coding competitions; Other',\n",
       "       \"Return to college; Master's degree; Take online courses; Conferences/meet-ups\",\n",
       "       'Contribute to open source; Part-time/evening courses; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Contribute to open source; Part-time/evening courses',\n",
       "       'Take online courses; Participate in hackathons; Buy books and work through the exercises; Other',\n",
       "       'Participate in hackathons; Participate in online coding competitions; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Contribute to open source; Buy books and work through the exercises; Other',\n",
       "       \"Return to college; Master's degree; Get a job as a QA tester; Buy books and work through the exercises\",\n",
       "       'Part-time/evening courses; Other',\n",
       "       'Take online courses; Participate in online coding competitions; Buy books and work through the exercises; Other',\n",
       "       'Get a job as a QA tester; Take online courses; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Return to college; Take online courses; Bootcamp; Other',\n",
       "       'Bootcamp; Participate in online coding competitions; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       \"Return to college; Master's degree; Get a job as a QA tester; Contribute to open source\",\n",
       "       \"Return to college; Master's degree; Contribute to open source; Part-time/evening courses\",\n",
       "       'Get a job as a QA tester; Take online courses; Participate in hackathons; Conferences/meet-ups',\n",
       "       \"Master's degree; Take online courses; Participate in hackathons; Buy books and work through the exercises\",\n",
       "       'Get a job as a QA tester; Conferences/meet-ups',\n",
       "       'Bootcamp; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       \"Return to college; Master's degree; Buy books and work through the exercises; Conferences/meet-ups\",\n",
       "       'Contribute to open source; Participate in hackathons; Participate in online coding competitions; Other',\n",
       "       'Buy books and work through the exercises; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Take online courses; Participate in hackathons; Conferences/meet-ups\",\n",
       "       'Get a job as a QA tester; Contribute to open source; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Return to college; Take online courses; Bootcamp; Participate in online coding competitions',\n",
       "       'Get a job as a QA tester; Contribute to open source; Part-time/evening courses; Conferences/meet-ups',\n",
       "       'Part-time/evening courses; Participate in online coding competitions',\n",
       "       \"Return to college; Master's degree; Contribute to open source; Bootcamp\",\n",
       "       \"Return to college; Master's degree; Take online courses; Participate in online coding competitions\",\n",
       "       \"Master's degree; Take online courses; Participate in hackathons; Participate in online coding competitions\",\n",
       "       'Get a job as a QA tester; Contribute to open source; Bootcamp; Part-time/evening courses',\n",
       "       'Return to college; Get a job as a QA tester; Take online courses; Participate in online coding competitions',\n",
       "       \"Master's degree; Take online courses; Contribute to open source; Participate in hackathons\",\n",
       "       'Return to college; Contribute to open source; Other',\n",
       "       'Take online courses; Contribute to open source; Bootcamp; Other',\n",
       "       'Return to college; Conferences/meet-ups',\n",
       "       \"Master's degree; Take online courses; Bootcamp; Conferences/meet-ups\",\n",
       "       'Bootcamp; Part-time/evening courses; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Contribute to open source; Participate in hackathons; Other',\n",
       "       'Return to college; Take online courses; Contribute to open source; Other',\n",
       "       'Return to college; Contribute to open source; Bootcamp; Part-time/evening courses',\n",
       "       'Return to college; Part-time/evening courses; Buy books and work through the exercises; Other',\n",
       "       \"Master's degree; Buy books and work through the exercises; Conferences/meet-ups\",\n",
       "       'Contribute to open source; Part-time/evening courses; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Master's degree; Get a job as a QA tester; Take online courses; Bootcamp\",\n",
       "       'Get a job as a QA tester; Participate in online coding competitions; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Contribute to open source; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Return to college; Take online courses; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Master's degree; Bootcamp; Buy books and work through the exercises\",\n",
       "       'Get a job as a QA tester; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       \"Return to college; Master's degree; Bootcamp; Conferences/meet-ups\",\n",
       "       'Return to college; Contribute to open source; Bootcamp',\n",
       "       'Contribute to open source; Part-time/evening courses; Buy books and work through the exercises; Other',\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Return to college; Take online courses; Bootcamp; Participate in hackathons',\n",
       "       'Get a job as a QA tester; Contribute to open source; Participate in hackathons; Participate in online coding competitions',\n",
       "       \"Master's degree; Bootcamp\",\n",
       "       'Return to college; Part-time/evening courses; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       \"Return to college; Master's degree; Participate in hackathons; Buy books and work through the exercises\",\n",
       "       \"Master's degree; Take online courses; Conferences/meet-ups\",\n",
       "       'Contribute to open source; Bootcamp; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Bootcamp; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Bootcamp; Participate in hackathons; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Master's degree; Contribute to open source; Bootcamp; Part-time/evening courses\",\n",
       "       'Contribute to open source; Bootcamp; Part-time/evening courses',\n",
       "       'Bootcamp; Part-time/evening courses; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Bootcamp; Part-time/evening courses; Participate in hackathons',\n",
       "       \"Return to college; Master's degree; Other\",\n",
       "       'Bootcamp; Part-time/evening courses; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Return to college; Contribute to open source; Participate in online coding competitions',\n",
       "       \"Master's degree; Bootcamp; Part-time/evening courses\",\n",
       "       \"Master's degree; Contribute to open source; Bootcamp; Buy books and work through the exercises\",\n",
       "       \"Master's degree; Contribute to open source; Conferences/meet-ups\",\n",
       "       'Part-time/evening courses; Participate in hackathons; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Contribute to open source; Participate in online coding competitions; Buy books and work through the exercises; Other',\n",
       "       'Bootcamp; Part-time/evening courses; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Bootcamp; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       \"Master's degree; Contribute to open source; Participate in hackathons; Conferences/meet-ups\",\n",
       "       'Return to college; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Get a job as a QA tester; Take online courses; Bootcamp; Participate in hackathons',\n",
       "       'Get a job as a QA tester; Buy books and work through the exercises; Other',\n",
       "       'Return to college; Contribute to open source; Bootcamp; Conferences/meet-ups',\n",
       "       'Return to college; Get a job as a QA tester; Contribute to open source; Bootcamp',\n",
       "       'Return to college; Contribute to open source; Participate in hackathons',\n",
       "       \"Master's degree; Part-time/evening courses; Participate in online coding competitions; Buy books and work through the exercises\",\n",
       "       \"Master's degree; Get a job as a QA tester; Buy books and work through the exercises\",\n",
       "       \"Master's degree; Get a job as a QA tester; Part-time/evening courses; Buy books and work through the exercises\",\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Bootcamp; Buy books and work through the exercises; Other',\n",
       "       \"Master's degree; Take online courses; Part-time/evening courses; Participate in hackathons\",\n",
       "       'Take online courses; Participate in hackathons; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Contribute to open source; Buy books and work through the exercises; Other\",\n",
       "       'Contribute to open source; Bootcamp; Participate in hackathons',\n",
       "       'Get a job as a QA tester; Take online courses; Conferences/meet-ups; Other',\n",
       "       'Participate in hackathons; Participate in online coding competitions',\n",
       "       'Get a job as a QA tester; Contribute to open source; Other',\n",
       "       'Return to college; Get a job as a QA tester; Contribute to open source; Part-time/evening courses',\n",
       "       'Return to college; Get a job as a QA tester; Contribute to open source; Conferences/meet-ups',\n",
       "       'Bootcamp; Other',\n",
       "       'Contribute to open source; Participate in hackathons; Buy books and work through the exercises; Other',\n",
       "       \"Master's degree; Contribute to open source; Participate in hackathons; Participate in online coding competitions\",\n",
       "       'Contribute to open source; Bootcamp; Part-time/evening courses; Participate in hackathons',\n",
       "       'Get a job as a QA tester; Contribute to open source; Bootcamp; Participate in hackathons',\n",
       "       \"Master's degree; Get a job as a QA tester; Part-time/evening courses\",\n",
       "       'Get a job as a QA tester; Contribute to open source; Conferences/meet-ups; Other',\n",
       "       'Return to college; Take online courses; Participate in hackathons',\n",
       "       'Take online courses; Participate in hackathons; Other',\n",
       "       'Get a job as a QA tester; Participate in hackathons; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       \"Master's degree; Conferences/meet-ups\",\n",
       "       'Bootcamp; Part-time/evening courses; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Return to college; Contribute to open source; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Master's degree; Take online courses; Participate in online coding competitions; Conferences/meet-ups\",\n",
       "       \"Return to college; Master's degree; Contribute to open source; Participate in hackathons\",\n",
       "       'Return to college; Contribute to open source; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Take online courses; Other',\n",
       "       \"Master's degree; Contribute to open source; Bootcamp; Conferences/meet-ups\",\n",
       "       'Get a job as a QA tester; Contribute to open source; Participate in hackathons',\n",
       "       \"Master's degree; Contribute to open source; Part-time/evening courses; Participate in online coding competitions\",\n",
       "       \"Return to college; Master's degree; Take online courses; Participate in hackathons\",\n",
       "       'Get a job as a QA tester; Participate in hackathons',\n",
       "       \"Return to college; Master's degree; Contribute to open source; Conferences/meet-ups\",\n",
       "       'Return to college; Get a job as a QA tester; Bootcamp',\n",
       "       'Return to college; Part-time/evening courses; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Return to college; Take online courses; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Return to college; Take online courses; Conferences/meet-ups; Other',\n",
       "       'Contribute to open source; Part-time/evening courses; Participate in hackathons',\n",
       "       'Return to college; Buy books and work through the exercises; Conferences/meet-ups; Other',\n",
       "       'Participate in hackathons; Conferences/meet-ups',\n",
       "       'Contribute to open source; Participate in online coding competitions; Other',\n",
       "       \"Return to college; Master's degree; Get a job as a QA tester; Bootcamp\",\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Buy books and work through the exercises; Other',\n",
       "       'Participate in hackathons; Buy books and work through the exercises',\n",
       "       \"Master's degree; Take online courses; Bootcamp; Participate in online coding competitions\",\n",
       "       \"Return to college; Master's degree; Participate in online coding competitions; Buy books and work through the exercises\",\n",
       "       'Return to college; Bootcamp; Part-time/evening courses; Participate in online coding competitions',\n",
       "       \"Master's degree; Buy books and work through the exercises; Other\",\n",
       "       'Participate in online coding competitions; Other',\n",
       "       'Get a job as a QA tester; Contribute to open source; Part-time/evening courses; Participate in hackathons',\n",
       "       'Get a job as a QA tester; Bootcamp; Participate in hackathons; Conferences/meet-ups',\n",
       "       \"Master's degree; Contribute to open source; Bootcamp; Participate in online coding competitions\",\n",
       "       'Return to college; Get a job as a QA tester; Contribute to open source; Participate in hackathons',\n",
       "       'Contribute to open source; Participate in online coding competitions; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Other\",\n",
       "       'Get a job as a QA tester; Contribute to open source; Bootcamp',\n",
       "       'Contribute to open source; Bootcamp; Participate in hackathons; Other',\n",
       "       'Return to college; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Return to college; Contribute to open source; Part-time/evening courses; Conferences/meet-ups',\n",
       "       \"Master's degree; Get a job as a QA tester; Buy books and work through the exercises; Conferences/meet-ups\",\n",
       "       'Bootcamp; Buy books and work through the exercises; Conferences/meet-ups; Other',\n",
       "       'Return to college; Participate in online coding competitions; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       'Participate in online coding competitions; Buy books and work through the exercises; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Contribute to open source\",\n",
       "       \"Return to college; Master's degree; Participate in hackathons; Conferences/meet-ups\",\n",
       "       'Bootcamp; Participate in online coding competitions',\n",
       "       'Return to college; Contribute to open source; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Bootcamp; Participate in hackathons; Participate in online coding competitions',\n",
       "       \"Master's degree; Get a job as a QA tester; Take online courses; Participate in online coding competitions\",\n",
       "       \"Master's degree; Get a job as a QA tester; Contribute to open source; Buy books and work through the exercises\",\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Conferences/meet-ups; Other',\n",
       "       'Get a job as a QA tester; Bootcamp; Part-time/evening courses; Other',\n",
       "       \"Master's degree; Take online courses; Buy books and work through the exercises; Other\",\n",
       "       'Return to college; Participate in hackathons; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       \"Master's degree; Get a job as a QA tester; Contribute to open source; Part-time/evening courses\",\n",
       "       'Get a job as a QA tester; Contribute to open source; Participate in online coding competitions',\n",
       "       \"Master's degree; Participate in online coding competitions; Buy books and work through the exercises\",\n",
       "       'Get a job as a QA tester; Bootcamp; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Contribute to open source; Bootcamp; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Take online courses; Part-time/evening courses; Other\",\n",
       "       'Return to college; Part-time/evening courses; Other',\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Participate in hackathons; Buy books and work through the exercises',\n",
       "       \"Master's degree; Contribute to open source; Participate in online coding competitions; Conferences/meet-ups\",\n",
       "       \"Master's degree; Get a job as a QA tester; Contribute to open source; Conferences/meet-ups\",\n",
       "       \"Master's degree; Get a job as a QA tester; Bootcamp\",\n",
       "       'Get a job as a QA tester; Participate in online coding competitions',\n",
       "       'Return to college; Contribute to open source; Bootcamp; Participate in online coding competitions',\n",
       "       'Contribute to open source; Bootcamp; Buy books and work through the exercises; Other',\n",
       "       \"Master's degree; Participate in online coding competitions; Buy books and work through the exercises; Conferences/meet-ups\",\n",
       "       \"Master's degree; Contribute to open source; Participate in hackathons; Buy books and work through the exercises\",\n",
       "       'Return to college; Bootcamp; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Return to college; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Participate in hackathons; Other',\n",
       "       'Get a job as a QA tester; Bootcamp; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Contribute to open source; Participate in hackathons; Other',\n",
       "       \"Master's degree; Participate in online coding competitions\",\n",
       "       'Return to college; Get a job as a QA tester; Take online courses; Participate in hackathons',\n",
       "       'Get a job as a QA tester; Take online courses; Participate in online coding competitions; Other',\n",
       "       'Bootcamp; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Return to college; Participate in hackathons; Buy books and work through the exercises; Conferences/meet-ups',\n",
       "       \"Master's degree; Get a job as a QA tester; Bootcamp; Part-time/evening courses\",\n",
       "       'Return to college; Get a job as a QA tester; Participate in online coding competitions',\n",
       "       'Get a job as a QA tester; Contribute to open source; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Bootcamp; Part-time/evening courses; Participate in hackathons',\n",
       "       'Return to college; Participate in online coding competitions',\n",
       "       'Return to college; Contribute to open source; Conferences/meet-ups; Other',\n",
       "       'Get a job as a QA tester; Participate in hackathons; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Bootcamp; Participate in online coding competitions',\n",
       "       \"Return to college; Master's degree; Buy books and work through the exercises; Other\",\n",
       "       'Bootcamp; Part-time/evening courses; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Contribute to open source; Participate in hackathons\",\n",
       "       'Part-time/evening courses; Participate in hackathons',\n",
       "       'Return to college; Contribute to open source; Part-time/evening courses; Participate in hackathons',\n",
       "       'Part-time/evening courses; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Participate in online coding competitions',\n",
       "       'Take online courses; Participate in online coding competitions; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Participate in hackathons; Participate in online coding competitions; Buy books and work through the exercises\",\n",
       "       'Bootcamp; Participate in hackathons',\n",
       "       'Return to college; Bootcamp; Buy books and work through the exercises; Other',\n",
       "       'Return to college; Participate in hackathons',\n",
       "       'Return to college; Bootcamp; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Buy books and work through the exercises; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Part-time/evening courses; Participate in hackathons; Conferences/meet-ups\",\n",
       "       'Return to college; Part-time/evening courses; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Part-time/evening courses; Participate in online coding competitions; Buy books and work through the exercises; Other',\n",
       "       \"Master's degree; Participate in hackathons; Buy books and work through the exercises; Conferences/meet-ups\",\n",
       "       'Bootcamp; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Participate in online coding competitions; Buy books and work through the exercises; Conferences/meet-ups; Other',\n",
       "       'Participate in hackathons; Participate in online coding competitions; Other',\n",
       "       'Return to college; Part-time/evening courses; Conferences/meet-ups',\n",
       "       \"Master's degree; Contribute to open source; Part-time/evening courses\",\n",
       "       \"Master's degree; Participate in hackathons; Conferences/meet-ups\",\n",
       "       'Part-time/evening courses; Participate in hackathons; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Contribute to open source; Bootcamp; Other',\n",
       "       'Get a job as a QA tester; Contribute to open source; Bootcamp; Participate in online coding competitions',\n",
       "       \"Return to college; Master's degree; Contribute to open source; Other\",\n",
       "       'Return to college; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Master's degree; Take online courses; Participate in hackathons\",\n",
       "       \"Master's degree; Contribute to open source; Bootcamp; Participate in hackathons\",\n",
       "       'Get a job as a QA tester; Take online courses; Participate in hackathons',\n",
       "       'Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Bootcamp; Part-time/evening courses; Buy books and work through the exercises; Other',\n",
       "       'Return to college; Bootcamp; Participate in hackathons; Conferences/meet-ups',\n",
       "       \"Return to college; Master's degree; Take online courses; Other\",\n",
       "       'Return to college; Contribute to open source; Bootcamp; Participate in hackathons',\n",
       "       'Return to college; Bootcamp; Part-time/evening courses; Participate in hackathons',\n",
       "       \"Return to college; Master's degree; Participate in online coding competitions\",\n",
       "       'Return to college; Contribute to open source; Part-time/evening courses; Other',\n",
       "       'Return to college; Get a job as a QA tester; Part-time/evening courses; Participate in hackathons',\n",
       "       'Return to college; Bootcamp; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Take online courses; Participate in hackathons; Participate in online coding competitions; Other',\n",
       "       'Participate in hackathons; Participate in online coding competitions; Buy books and work through the exercises; Other',\n",
       "       'Return to college; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Bootcamp; Buy books and work through the exercises; Conferences/meet-ups\",\n",
       "       \"Master's degree; Bootcamp; Part-time/evening courses; Participate in hackathons\",\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Conferences/meet-ups',\n",
       "       \"Return to college; Master's degree; Bootcamp; Participate in hackathons\",\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Participate in hackathons; Participate in online coding competitions',\n",
       "       \"Master's degree; Bootcamp; Part-time/evening courses; Conferences/meet-ups\",\n",
       "       \"Master's degree; Bootcamp; Part-time/evening courses; Participate in online coding competitions\",\n",
       "       'Part-time/evening courses; Participate in hackathons; Conferences/meet-ups',\n",
       "       \"Master's degree; Bootcamp; Participate in hackathons\",\n",
       "       \"Return to college; Master's degree; Contribute to open source; Participate in online coding competitions\",\n",
       "       \"Return to college; Master's degree; Conferences/meet-ups\",\n",
       "       'Bootcamp; Part-time/evening courses; Other',\n",
       "       \"Return to college; Master's degree; Participate in hackathons; Participate in online coding competitions\",\n",
       "       'Return to college; Get a job as a QA tester; Contribute to open source; Other',\n",
       "       \"Master's degree; Take online courses; Contribute to open source; Other\",\n",
       "       'Return to college; Get a job as a QA tester; Take online courses; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Contribute to open source; Participate in online coding competitions\",\n",
       "       'Take online courses; Bootcamp; Participate in hackathons; Other',\n",
       "       'Participate in hackathons; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Return to college; Participate in hackathons; Conferences/meet-ups',\n",
       "       \"Master's degree; Get a job as a QA tester; Bootcamp; Buy books and work through the exercises\",\n",
       "       'Bootcamp; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Master's degree; Part-time/evening courses; Participate in hackathons; Participate in online coding competitions\",\n",
       "       \"Master's degree; Take online courses; Bootcamp; Participate in hackathons\",\n",
       "       'Contribute to open source; Part-time/evening courses; Conferences/meet-ups; Other',\n",
       "       'Part-time/evening courses; Participate in hackathons; Conferences/meet-ups; Other',\n",
       "       'Return to college; Bootcamp; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Part-time/evening courses; Participate in hackathons; Participate in online coding competitions',\n",
       "       \"Master's degree; Get a job as a QA tester; Contribute to open source; Bootcamp\",\n",
       "       'Get a job as a QA tester; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Return to college; Contribute to open source; Participate in hackathons; Other',\n",
       "       'Return to college; Get a job as a QA tester; Other',\n",
       "       'Return to college; Get a job as a QA tester; Bootcamp; Conferences/meet-ups',\n",
       "       'Return to college; Get a job as a QA tester; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Participate in hackathons; Conferences/meet-ups',\n",
       "       'Get a job as a QA tester; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Master's degree; Contribute to open source; Part-time/evening courses; Conferences/meet-ups\",\n",
       "       'Contribute to open source; Part-time/evening courses; Participate in online coding competitions; Other',\n",
       "       'Return to college; Get a job as a QA tester; Part-time/evening courses; Conferences/meet-ups',\n",
       "       'Return to college; Bootcamp; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Contribute to open source; Bootcamp; Part-time/evening courses; Other',\n",
       "       'Participate in hackathons; Conferences/meet-ups; Other',\n",
       "       'Participate in hackathons; Buy books and work through the exercises; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Bootcamp; Buy books and work through the exercises; Other\",\n",
       "       \"Master's degree; Contribute to open source; Conferences/meet-ups; Other\",\n",
       "       'Take online courses; Part-time/evening courses; Participate in online coding competitions; Other',\n",
       "       'Contribute to open source; Part-time/evening courses; Other',\n",
       "       'Get a job as a QA tester; Bootcamp; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Get a job as a QA tester; Bootcamp; Part-time/evening courses; Participate in online coding competitions',\n",
       "       \"Master's degree; Bootcamp; Participate in online coding competitions; Buy books and work through the exercises\",\n",
       "       'Take online courses; Bootcamp; Participate in online coding competitions; Other',\n",
       "       'Get a job as a QA tester; Participate in online coding competitions; Other',\n",
       "       'Get a job as a QA tester; Participate in hackathons; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       'Part-time/evening courses; Participate in online coding competitions; Other',\n",
       "       'Get a job as a QA tester; Bootcamp; Participate in hackathons',\n",
       "       'Return to college; Part-time/evening courses; Participate in hackathons',\n",
       "       'Return to college; Bootcamp; Participate in hackathons',\n",
       "       'Get a job as a QA tester; Participate in hackathons; Buy books and work through the exercises; Other',\n",
       "       \"Return to college; Master's degree; Conferences/meet-ups; Other\",\n",
       "       \"Master's degree; Contribute to open source; Other\",\n",
       "       \"Master's degree; Bootcamp; Participate in hackathons; Buy books and work through the exercises\",\n",
       "       'Contribute to open source; Part-time/evening courses; Participate in hackathons; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Participate in hackathons; Buy books and work through the exercises\",\n",
       "       \"Return to college; Master's degree; Participate in online coding competitions; Conferences/meet-ups\",\n",
       "       'Get a job as a QA tester; Bootcamp; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       'Return to college; Get a job as a QA tester; Conferences/meet-ups',\n",
       "       'Part-time/evening courses; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Bootcamp; Participate in hackathons; Participate in online coding competitions; Other',\n",
       "       \"Master's degree; Contribute to open source; Participate in online coding competitions\",\n",
       "       \"Master's degree; Get a job as a QA tester; Take online courses; Participate in hackathons\",\n",
       "       \"Master's degree; Part-time/evening courses; Other\",\n",
       "       'Return to college; Get a job as a QA tester; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Master's degree; Take online courses; Conferences/meet-ups; Other\",\n",
       "       \"Return to college; Master's degree; Participate in online coding competitions; Other\",\n",
       "       'Bootcamp; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Part-time/evening courses; Participate in online coding competitions; Conferences/meet-ups\",\n",
       "       \"Return to college; Master's degree; Bootcamp; Other\",\n",
       "       'Get a job as a QA tester; Bootcamp; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Return to college; Bootcamp; Other',\n",
       "       'Return to college; Get a job as a QA tester; Participate in hackathons',\n",
       "       'Return to college; Bootcamp; Part-time/evening courses; Other',\n",
       "       'Return to college; Bootcamp; Participate in online coding competitions',\n",
       "       \"Master's degree; Part-time/evening courses; Participate in online coding competitions\",\n",
       "       \"Master's degree; Participate in online coding competitions; Buy books and work through the exercises; Other\",\n",
       "       'Return to college; Participate in online coding competitions; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Participate in hackathons\",\n",
       "       'Return to college; Get a job as a QA tester; Participate in hackathons; Buy books and work through the exercises',\n",
       "       'Contribute to open source; Bootcamp; Participate in online coding competitions; Other',\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Other',\n",
       "       'Return to college; Get a job as a QA tester; Part-time/evening courses; Other',\n",
       "       'Part-time/evening courses; Participate in hackathons; Other',\n",
       "       'Return to college; Get a job as a QA tester; Part-time/evening courses; Participate in online coding competitions',\n",
       "       \"Master's degree; Take online courses; Participate in hackathons; Other\",\n",
       "       'Return to college; Part-time/evening courses; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Master's degree; Participate in hackathons; Buy books and work through the exercises; Other\",\n",
       "       \"Master's degree; Part-time/evening courses; Participate in hackathons\",\n",
       "       \"Master's degree; Participate in hackathons; Participate in online coding competitions; Other\",\n",
       "       'Get a job as a QA tester; Bootcamp; Buy books and work through the exercises; Other',\n",
       "       \"Return to college; Master's degree; Get a job as a QA tester; Participate in hackathons\",\n",
       "       'Get a job as a QA tester; Participate in hackathons; Conferences/meet-ups; Other',\n",
       "       'Return to college; Bootcamp; Participate in hackathons; Other',\n",
       "       'Get a job as a QA tester; Contribute to open source; Part-time/evening courses; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Participate in hackathons; Conferences/meet-ups\",\n",
       "       \"Master's degree; Participate in hackathons\",\n",
       "       'Part-time/evening courses; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Bootcamp; Part-time/evening courses; Other\",\n",
       "       'Return to college; Get a job as a QA tester; Participate in hackathons; Other',\n",
       "       \"Master's degree; Take online courses; Other\",\n",
       "       'Bootcamp; Participate in online coding competitions; Buy books and work through the exercises; Other',\n",
       "       'Return to college; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Return to college; Participate in hackathons; Participate in online coding competitions; Other',\n",
       "       'Get a job as a QA tester; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Contribute to open source; Other\",\n",
       "       'Get a job as a QA tester; Contribute to open source; Participate in online coding competitions; Other',\n",
       "       'Bootcamp; Participate in online coding competitions; Other',\n",
       "       \"Return to college; Master's degree; Part-time/evening courses; Conferences/meet-ups\",\n",
       "       'Get a job as a QA tester; Bootcamp; Other',\n",
       "       'Return to college; Participate in hackathons; Buy books and work through the exercises; Other',\n",
       "       \"Master's degree; Contribute to open source; Part-time/evening courses; Other\",\n",
       "       \"Master's degree; Contribute to open source; Part-time/evening courses; Participate in hackathons\",\n",
       "       'Return to college; Take online courses; Participate in hackathons; Other',\n",
       "       'Take online courses; Part-time/evening courses; Participate in hackathons; Other',\n",
       "       'Return to college; Participate in hackathons; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Part-time/evening courses; Conferences/meet-ups\",\n",
       "       \"Master's degree; Get a job as a QA tester; Participate in online coding competitions; Conferences/meet-ups\",\n",
       "       \"Master's degree; Get a job as a QA tester; Bootcamp; Participate in hackathons\",\n",
       "       \"Master's degree; Get a job as a QA tester; Part-time/evening courses; Participate in hackathons\",\n",
       "       'Part-time/evening courses; Participate in hackathons; Participate in online coding competitions; Other',\n",
       "       'Part-time/evening courses; Participate in online coding competitions; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Bootcamp; Participate in online coding competitions\",\n",
       "       'Bootcamp; Part-time/evening courses; Participate in hackathons; Other',\n",
       "       \"Master's degree; Take online courses; Bootcamp; Other\",\n",
       "       \"Return to college; Master's degree; Part-time/evening courses; Other\",\n",
       "       'Get a job as a QA tester; Contribute to open source; Bootcamp; Other',\n",
       "       \"Master's degree; Bootcamp; Conferences/meet-ups\",\n",
       "       \"Master's degree; Bootcamp; Other\",\n",
       "       \"Master's degree; Get a job as a QA tester; Participate in hackathons; Participate in online coding competitions\",\n",
       "       'Return to college; Get a job as a QA tester; Buy books and work through the exercises; Other',\n",
       "       \"Master's degree; Part-time/evening courses; Participate in hackathons; Buy books and work through the exercises\",\n",
       "       \"Return to college; Master's degree; Get a job as a QA tester; Conferences/meet-ups\",\n",
       "       'Return to college; Get a job as a QA tester; Participate in online coding competitions; Buy books and work through the exercises',\n",
       "       \"Master's degree; Bootcamp; Participate in hackathons; Conferences/meet-ups\",\n",
       "       'Return to college; Get a job as a QA tester; Contribute to open source; Participate in online coding competitions',\n",
       "       \"Return to college; Master's degree; Part-time/evening courses; Participate in online coding competitions\",\n",
       "       'Get a job as a QA tester; Participate in hackathons; Participate in online coding competitions',\n",
       "       'Return to college; Participate in online coding competitions; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Bootcamp; Participate in online coding competitions\",\n",
       "       'Get a job as a QA tester; Part-time/evening courses; Participate in online coding competitions; Other',\n",
       "       'Return to college; Contribute to open source; Participate in online coding competitions; Other',\n",
       "       'Get a job as a QA tester; Participate in online coding competitions; Buy books and work through the exercises; Other',\n",
       "       \"Master's degree; Contribute to open source; Participate in hackathons; Other\",\n",
       "       'Get a job as a QA tester; Take online courses; Participate in hackathons; Other',\n",
       "       \"Master's degree; Participate in hackathons; Participate in online coding competitions; Conferences/meet-ups\",\n",
       "       \"Master's degree; Bootcamp; Participate in hackathons; Participate in online coding competitions\",\n",
       "       \"Master's degree; Participate in hackathons; Participate in online coding competitions\",\n",
       "       \"Return to college; Master's degree; Bootcamp; Participate in online coding competitions\",\n",
       "       'Return to college; Participate in hackathons; Other',\n",
       "       'Participate in hackathons; Buy books and work through the exercises; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Bootcamp; Conferences/meet-ups\",\n",
       "       'Return to college; Take online courses; Participate in online coding competitions; Other',\n",
       "       'Return to college; Participate in hackathons; Participate in online coding competitions; Conferences/meet-ups',\n",
       "       \"Master's degree; Get a job as a QA tester; Conferences/meet-ups\",\n",
       "       'Get a job as a QA tester; Participate in online coding competitions; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Part-time/evening courses; Buy books and work through the exercises; Other\",\n",
       "       'Bootcamp; Participate in online coding competitions; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Get a job as a QA tester; Participate in online coding competitions; Buy books and work through the exercises\",\n",
       "       \"Return to college; Master's degree; Get a job as a QA tester; Other\",\n",
       "       'Participate in hackathons; Participate in online coding competitions; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Bootcamp; Participate in online coding competitions; Conferences/meet-ups\",\n",
       "       'Return to college; Part-time/evening courses; Conferences/meet-ups; Other',\n",
       "       \"Master's degree; Part-time/evening courses; Conferences/meet-ups\",\n",
       "       \"Return to college; Master's degree; Part-time/evening courses; Participate in hackathons\"],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "study.method.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a list of the individual methods suggested\n",
    "values_in_method = ['Take online courses', 'Buy books and work through the exercises','None of these', \n",
    "                    'Get a job as a QA tester', 'Return to college', 'Participate in online coding competitions',\n",
    "                   'Part-time/evening courses', 'Contribute to open source', 'Bootcamp', 'Conferences/meet-ups',\n",
    "                   'Other', 'Participate in hackathons', 'Master\\'s degree' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_and_plot(df, title = 'Education Method counts', plot = True):\n",
    "    '''\n",
    "    INPUT \n",
    "        df - a dataframe holding the CousinEducation column\n",
    "        title - string the title of your plot\n",
    "        axis - axis object\n",
    "        plot - bool providing whether or not you want a plot back\n",
    "        \n",
    "    OUTPUT\n",
    "        study_df - a dataframe with the count of how many individuals\n",
    "        Displays a plot of pretty things related to the CousinEducation column.\n",
    "    '''\n",
    "    study = df['CousinEducation'].value_counts().reset_index()\n",
    "    study.rename(columns={'index': 'method', 'CousinEducation': 'count'}, inplace=True)\n",
    "    study_df = t.total_count(study, 'method', 'count', values_in_method)\n",
    "\n",
    "    study_df.set_index('method', inplace=True)\n",
    "    if plot:\n",
    "        (study_df/study_df.sum()).plot(kind='bar', legend=None);\n",
    "        plt.title(title);\n",
    "        plt.show()\n",
    "    props_study_df = study_df/study_df.sum()\n",
    "    return props_study_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IgmAA2lEAs4Axle11gdntCpC0CnAZcLTtGxvtth924hngDJKpKQiCIOgQ7SiAqcB4SeMkLQvsDUxup/N8/E+As21f3LRv7fxXwG7AnUO58CAIgmB4DKoAbM8DDgOuBO4GLrI9Q9IkSbsASNpc0ixgD+D7kmbk0/cE3gAc1MLd81xJdwB3AGsBJxS9syAIgmBA2vECwvblwOVNbcdU3k8lmYaaz/sR8KN++nzzkK40CIIgKEpEAgdBEPQooQCCIAh6lFAAQRAEPUoogCAIgh4lFEAQBEGPEgogCIKgRwkFEARB0KOEAgiCIOhRQgEEQRD0KKEAgiAIepRQAEEQBD1KKIAgCIIeJRRAEARBjxIKIAiCoEcJBRAEQdCjhAIIgiDoUdoqCCNpR+CbwCjgh7ZPatr/BuAbwMbA3rYvqew7EDg6b55g+6zc/lrgTGAFUrGZI2wPqdh8tzL2qMuGfM4DJ+1Uw5UEQdDNDKoAJI0CTgG2JxWInyppsu27Kof9BTgI+GTTuWsAxwITAQM353MfB74LHALcSFIAOwJXDPeGgvYZqqIJJRME3UU7JqAtgJm277P9LHABsGv1ANsP2L4deL7p3LcBV9t+LD/0rwZ2zAXhV7F9Qx71n00qDB8EQRB0iHYUwDrAQ5XtWbmtHfo7d538ftA+JR0iaZqkaXPmzGlTbBAEQTAY7SgAtWhr11bf37lt92n7VNsTbU8cPXp0m2KDIAiCwWhHAcwCxlS21wVmt9l/f+fOyu8Xpc8gCIKgAO0ogKnAeEnjJC0L7A1MbrP/K4EdJK0uaXVgB+BK2w8DcyVtJUnAAcDPFuH6gyAIgkVkUAVgex5wGOlhfjdwke0ZkiZJ2gVA0uaSZgF7AN+XNCOf+xhwPEmJTAUm5TaADwM/BGYC9xIeQEEQBB2lrTgA25eTXDWrbcdU3k+lr0mnetzpwOkt2qcBrx7KxQZBEATliEjgIAiCHiUUQBAEQY8SCiAIgqBHCQUQBEHQo4QCCIIg6FFCAQRBEPQooQCCIAh6lFAAQRAEPUoogCAIgh4lFEAQBEGPEgogCIKgRwkFEARB0KOEAgiCIOhRQgEEQRD0KKEAgiAIepS2FICkHSXdI2mmpKNa7F9O0oV5/02Sxub2/SRNr7yel7Rp3jcl99nY98KSNxYEQRAMzKAKQNIo4BTg7cAEYB9JE5oOOxh43PbLga8DJwPYPtf2prY3BfYHHrA9vXLefo39tv9R4H6CIAiCNmlnBrAFMNP2fbafBS4Adm06ZlfgrPz+EuAtudZvlX2A84dzsUEQBEE52lEA6wAPVbZn5baWx+Qawk8AazYdsxcLK4Azsvnn8y0URhAEQVAj7SiAVg9mD+UYSVsCT9m+s7J/P9sbAa/Pr/1bCpcOkTRN0rQ5c+a0cblBEARBO7SjAGYBYyrb6wKz+ztG0tLAqsBjlf170zT6t/3X/HcucB7J1LQQtk+1PdH2xNGjR7dxuUEQBEE7LN3GMVOB8ZLGAX8lPcz3bTpmMnAgcAOwO/Ab2waQtBSwB/CGxsFZSaxm+xFJywA7A78a5r0ESyBjj7psyOc8cNJOS6ycIOgmBlUAtudJOgy4EhgFnG57hqRJwDTbk4HTgHMkzSSN/PeudPEGYJbt+yptywFX5of/KNLD/wdF7igIgiBoi3ZmANi+HLi8qe2Yyvv/kEb5rc6dAmzV1PZv4LVDvNYgCIKgIBEJHARB0KO0NQMIgiARaw1BNxEzgCAIgh4lFEAQBEGPEgogCIKgRwkFEARB0KOEAgiCIOhRQgEEQRD0KKEAgiAIepRQAEEQBD1KKIAgCIIeJRRAEARBjxIKIAiCoEcJBRAEQdCjhAIIgiDoUUIBBEEQ9ChtKQBJO0q6R9JMSUe12L+cpAvz/pskjc3tYyU9LWl6fn2vcs5rJd2Rz/mWpFaF5YMgCIKaGFQBSBoFnAK8HZgA7CNpQtNhBwOP23458HXg5Mq+e21vml8fqrR/FzgEGJ9fOy76bQRBEARDpZ0ZwBbATNv32X4WuADYtemYXYGz8vtLgLcMNKKXtDawiu0bcvH4s4Hdhnz1QRAEwSLTjgJYB3iosj0rt7U8xvY84AlgzbxvnKRbJV0r6fWV42cN0icAkg6RNE3StDlz5rRxuUEQBEE7tFMSstVI3m0e8zCwnu1HJb0W+KmkV7XZZ2q0TwVOBZg4cWLLY4Kg2xhq6ckoOxksCu3MAGYBYyrb6wKz+ztG0tLAqsBjtp+x/SiA7ZuBe4EN8vHrDtJnEARBUCPtKICpwHhJ4yQtC+wNTG46ZjJwYH6/O/Ab25Y0Oi8iI+llpMXe+2w/DMyVtFVeKzgA+FmB+wmCIAjaZFATkO15kg4DrgRGAafbniFpEjDN9mTgNOAcSTOBx0hKAuANwCRJ84DngA/Zfizv+zBwJrACcEV+BUHQIYZqZoIwNXUb7awBYPty4PKmtmMq7/8D7NHivEuBS/vpcxrw6qFcbBAEQVCOiAQOgiDoUUIBBEEQ9CihAIIgCHqUUABBEAQ9SiiAIAiCHiUUQBAEQY/SlhtoEATBohLxBksuMQMIgiDoUWIGEARBVxAzjaETM4AgCIIeJRRAEARBjxIKIAiCoEeJNYAgCIIh0E3FemIGEARB0KOEAgiCIOhRQgEEQRD0KG0pAEk7SrpH0kxJR7XYv5ykC/P+mySNze3bS7pZ0h3575sr50zJfU7PrxeWuqkgCIJgcAZdBM41fU8BticVc58qabLtuyqHHQw8bvvlkvYGTgb2Ah4B3ml7tqRXk8pKrlM5b79cGSwIgiDoMO3MALYAZtq+z/azwAXArk3H7Aqcld9fArxFkmzfant2bp8BLC9puRIXHgRBEAyPdhTAOsBDle1Z9B3F9znG9jzgCWDNpmPeA9xq+5lK2xnZ/PN5SWolXNIhkqZJmjZnzpw2LjcIgiBoh3YUQKsHs4dyjKRXkcxC/6+yfz/bGwGvz6/9Wwm3fartibYnjh49uo3LDYIgCNqhHQUwCxhT2V4XmN3fMZKWBlYFHsvb6wI/AQ6wfW/jBNt/zX/nAueRTE1BEARBh2hHAUwFxksaJ2lZYG9gctMxk4ED8/vdgd/YtqTVgMuAz9r+feNgSUtLWiu/XwbYGbhzeLcSBEEQDIVBFUC26R9G8uC5G7jI9gxJkyTtkg87DVhT0kzgSKDhKnoY8HLg803unssBV0q6HZgO/BX4QckbC4IgCAamrVxAti8HLm9qO6by/j/AHi3OOwE4oZ9uX9v+ZQZBEASliUjgIAiCHiUUQBAEQY8SCiAIgqBHCQUQBEHQo4QCCIIg6FFCAQRBEPQooQCCIAh6lFAAQRAEPUoogCAIgh4lFEAQBEGP0lYqiCAIgqBzjD3qsiGf88BJOw35nJgBBEEQ9CihAIIgCHqUUABBEAQ9SiiAIAiCHiUUQBAEQY/SlgKQtKOkeyTNlHRUi/3LSbow779J0tjKvs/m9nskva3dPoMgCIJ6GVQBSBoFnAK8HZgA7CNpQtNhBwOP23458HXg5HzuBFIN4VcBOwLfkTSqzT6DIAiCGmlnBrAFMNP2fbafBS4Adm06ZlfgrPz+EuAtkpTbL7D9jO37gZm5v3b6DIIgCGpEtgc+QNod2NH2B/L2/sCWtg+rHHNnPmZW3r4X2BI4DrjR9o9y+2nAFfm0Afus9H0IcEje3BC4Z4j3uBbwyBDPGSqdkBFyllwZIWfJlRFyEi+1Pbq5sZ1IYLVoa9Ya/R3TX3urmUdLTWT7VODUgS5wICRNsz1xUc9fUmSEnCVXRshZcmWEnIFpxwQ0CxhT2V4XmN3fMZKWBlYFHhvg3Hb6DIIgCGqkHQUwFRgvaZykZUmLupObjpkMHJjf7w78xsm2NBnYO3sJjQPGA39os88gCIKgRgY1AdmeJ+kw4EpgFHC67RmSJgHTbE8GTgPOkTSTNPLfO587Q9JFwF3APOBQ288BtOqz/O0BwzAfLWEyQs6SKyPkLLkyQs4ADLoIHARBEHQnEQkcBEHQo4QCCIIg6FFCAQTBIiJpBUkbLu7rGC6SlpK09eK+jtJIWmlxX0NJ6rifrlMAkk6UtIqkpSVdKenvkvYtLGObxpch6b2SvibppSVl5L6/nO9lGUm/lvSIpPfWIEf5Po7J2+tJ2qK0nG5C0juB6cAv8/amkop7sknaQ9LK+f3Rkn4s6TUlZdh+Hvjfkn22QtL6kpbL77eTdLik1WqQs7Wku4C78/Ymkr5TWEbHlGad99N1CgB4u+0ngZ2Bf5DyEH2msIzvAk9J2gT4NPAgcHZhGQA7VO5lFrAB8Kka5HwHeB2wT96eS8rVVBxJy0s6Mj/ILpX0cUnLF5bRCQV9HCmlyT8BbE8HxhaWAfB523MlbQu8jZRy5bs1yLlK0ntyCpe6uBR4TtLLSZ6D44DzapDzddJn9SiA7duAN5QU0CmlmantfrpRATRcW98BnG/7EfqJMh4G83Kcw67AN21/E1i5sAyAZfLfxr08VoMMSGk4DgX+A2D7cWDZmmSdTVLK3wb+D3glcE5hGZ1Q0PNsP1G4z1Y8l//uBHzX9s+o57s5ErgYeFbSk5LmSnqysIznbc8D3gV8w/bHgbULywDA9kNNTc+1PHB4dEJpAvXdTzcWhb8i5yZ6DjhU0lrAM4VlzJX0WWB/4PU5u+kyg5yzKPxc0h+Bp4GPSBpNfkgX5r/5HgyQ5TxfgxyADW1vUtm+RtJthWXMs21JDQV9mqQDBz1raNyZTYujJI0HDgeuLywD4K+Svg+8FTg5m1CKD9xs1zGAaea/kvYhBY2+M7fV8bt5KJtnnANNDyebTwpzJLASaVbzNCn1jW2vUlhOffdju+tewAuBpfP7FwDrFO7/xaQv//V5ez3ggJruZXVgVH6/EvDiGmTsR4rEngV8kZRwb4+a7udMYKvK9pbAdwrLuBb4LPDn/F2NAu4oLGPF/FlNBabl98vX8HmtCLwbGJ+31yaZBkvLEfBekskJUqqWLQrLmAB8C9gnb48DjqrhXtYCzgX+TjID/whYs7ScTr3qvJ+uCwSTtAJwBCn73YezvXG87SsGOXWocl6a+/2VpBVJD+m5hWWsSFI069k+JI80N7T9i5JysqxXAG8hPQh+bbuOEROS7iZldf1LblqPNJp5njR62riAjBcD+wJTbf9O0nrAdrbrWKepnTw7exGVGbvtv/R/xiLJ+C7pO3iz7VdKWh24yvbmJeV0E9n0sx8wzvbxksYAa9v+w2K+tLbpRgVwPnAHsK/tV+eH6O9tb1ZQxgdJKarXsL1+fjB/z/ZbSsnIci4EbibNLl6dldsNtjctLGcrYEZDgWWvkwm2byopJ/c94GKs7QcLyXkxaZHWJEXwtxL9Vvr/OQuvLT1Bmg1833YRU52kjwLHkkZ/DbNcEUXZJOcW26+RdGvjtyLpNvc11w1XxjakxfOXkpRZw2TyslIyspwNSOtAL8q/m42BXWyfUFhOR5SmpC8DJ5BMwb8ENgE+5pxmfzh04yLweNtfAv4LYPspWqelHg6HAtsAT2YZfyaZnUqzvu0vs+BeGnbG0nwX+Fdl+9/U42nSeMA/ScoYu2bjZfvBgg//D5CSDr6blJzwRknvL9F3hftIn9kP8utJ0kN6g7xdiiNIs75X2d4ov4o+/DOdWAc6DfgasC2wOTAx/y3ND0gmwMbv5nZyfrLCdMp5ojZvwG5cBH42uxU2/pHHAc8WlvGM7Wcbi/9KKbDrmEo9m0f9jXtZn/IL2pBmgvOv3/bz+Z7KC5KOBw4C7mXBZ2bgzQXFfArYzPajWeaapAXa0wvK2Mx21RXv55J+a/sNkkomNnyINLOom28BPwFeKOmLJMX5+cIynihtiu2HFW3/ock5Z14NcjrlPLGQN2Apx6NuVACTSNOkdSWdBbyRVLO4JNdK+h9gBUnbAx8Bfl5YBqSp/y+BMZLOJc06DqpBzn2SDmfBqP8jpBFuHexJmtmUVspVZpFiGRrMJT1ISzJa0noNW3xeZ1gr7yt5b/cBUyRdRkX52/5aQRnYPlfSzSxYB9qthnWgayR9Bfgxfe/llsJyHsmDpcaDeXfg4cIyoLXSPLoGObV5A3bdGgDM18Rbk/6Rr7f9j8L9L0VSKjtkGVcCP3QNH6IelXEAACAASURBVGYevW6V5dzoFNdQWsYLSf/Mbyb9aH5NsjEW/dyyrEuBD9fRd0XG2cBGwM9I97MryST0Jyjz8JT0DuB7pJmMSB4tHwGmAB+0/Y3hyshyjm3VbvsLJfqvyDnH9v6DtQ1TxjUtmm275OwPSS8jpUzeGngcuB/Yr5SJsUlWp5wnVgeetP1cDnJcucS6VtcpgLygebvtp7LP8WbAt71wIEUpeWsA62Y7Y+m+twGm2/63UgqI15D82ov9I+cp7OG2v16qz0HkTSQ9mO+k7yhwl4IyWj40K7KKPDyzT/4rSD/+P5Za+O1H1sqkh+W/Bj140fq/xfZrKtsN19kJdcirizw42932RflBuVRp77wmeZ3w0KrNG7AbFcDtpFXyjUj+smeRPADeWFDGFGAX0pc+HZgDXGv7yFIyspzGvWxMimQ9HXh3yXvJcqbY3q5knwPImgF8n+SpNd9eavvaTsgvReVH+VLbH6zLRVfSq0mR0mvkpkdIXmFF1hmUAhr/B1gBeKrRTDJjnWr7syXkZFmrksyajbWTa4FJLhxR3ViLKdlnP3KqHlrPscCrqbSHVm3egN2oABrubJ8HHrb9w+bRTQEZt9reLHubjLF9rKTba3TNOwb4q1NEa9F7yXK+SPLKuZDkAQTUYptF0rWlFVgLGdfQYlG+pKmhgy661wOfs31N3t4O+JLtoonIJJ1Y8mHfj4xLSTO/s3LT/sAmtt9dWM7nSfby5v/noqlUlCogbtlwNqgL5SLwdbjoduMi8L8lfYoU1bhdnhKWDjdfWtLapAXNzxXuu0oj5cR7gTeovpQTjYfJpEpbac+cBjdLOpEUeVzXQuAnK++XB95DeS+Q9W3vlc2M2H5apVwz+rJS4+Gf5UxRPWmOfyFppTrNjaTP7D2V7S9Iml6w/wYNl99DK20GisYb0DkPrdq8AbtRAexFemB+yPbD2TujqMcE6UF5JXCd7al50enPhWVAupd9gYNt/y3fy1dKC7H9ptJ9DkAjIG+r6iVQUNnYvrmp6feSSpuYOuWie18e0TYS5r2XtKhZmu8Cm2hBAr3TSGbHkrO1pyVta/s6mL/G9XTB/gGwPa50n1UkNUy9HfHQokZvwK4yAeUR8uW237a4r2W45Hu50vZbOyDrmFbttie1al/SyQvzDZYCXgt8y3ax4i3Z/fdoUn6bq8g/SttTSsnIclYHvkAKnhLwW+C4HHRUUk7t5sasXM4mmRsFPEb6zIomA5TUyqT0BGlRe9jeZ4M4GbiO301d3oBdNQPILlLPSlolR84VRdKnbX9Z0rdpbWM+vJSsfC9PSVq19CJZC/5deb88KeKwFnc2AEk7kVJCz68DUPhHczPp+xHJ9HM/hWNBbF8t6RYW/CiPqMNFNz/oi/1fDUDtGW7zg34TSavk7eK/0czBpPoWDdPZdsCNwAaSJtkeVvrxhheZpD1sX1zdJ2mP4fQ9AMuTXFqXBiZIwvZvh9tpVymAzL+A2yRdRd8FoBIeOo2H4rQCfbXDf4A7JF1N33sp+kCw3aewhaSvkmz0xZH0PVKGyzcBPyQFzxRNnlWnCUALV+NqBBitpxQYVmQtQ9I3bH9MrXMOFXWbzTTMje+vy9yY3WbfQyqcs3RjyaSGEfPzwCtt/z3LfRHJxLUlaQZVqv7EZ0k1FAZrGxaSTiZ9PzOo5IMi3cuw6EYF8Kv8Ko7tn+e/Zw12bCEuy69OsyLlF8wabG174+w19QVJ/0uKDK0FSafaPqRglwNVgSq5ltF4SH21UH8Dkh/6lwLjc9MjpCjXkvyMZIq5mXrWSxqMbTz8M/8ANnBKofDf4XYu6e2ktAzrSPpWZdcq1JNyYjeSi3Hxz6zrFIDt0+qWkUfke9j+Z95eHbig9NpDpxSNpDtYMMocBYymr0dQSRqLfk9JegmpzF2di3YTS3bWqQXzykL2pk4V5+Yj6QiSD30xVMlwC6wPrEOKdC6Z4XZd2zsW7K8/fifpFywYie8O/DZ7T/2zQP+zSVaAPUjR5SbFAfwd+HiB/pu5j2SOCwUwGJL+TOsp8wYFxYxuPPxz348rpVMoiqT7aX0vpUfnO1fezwP+7lS6rw5+oVQI/CvALaT7+2FNsiCN/orRzwLjfGyXns0cCHyzqe2gFm3D5VBS+uybIGW4reF/+npJG9m+o3C/zRxKygTbWDg/C7jUyeOlhAK/i1QHYFmSy6lIBXTOAIoFAlbWGp8Cpkv6NX29jYZtCu46BUD60hssT9LSqxaW8Zz6JgJ7KfVkA62OXhv3skY/xy4yth/MHhqvz02/BYqntsiyjs9vL82jtOVriASdvzjXGHG2WrBbRN45wD5TyJyV4wv2BcZJqq7HrEwuDl6Y2jLcVmaYSwPvk3Qf6UFWS+SsbUuaRso+2ijY9AL6JggcDl/O/b3UC2porEIy132VlMK7BI21xptZeE2uzHfTTW6g/SHpOtvbDn5k2/3tSEo21ZiGvwE4xPaVpWQMILvoveQ+jwA+yIKH17tIaQC+XVJOlnUocG6T+Wwf298pKGMh98XSLo11kwcV44ATgaMqu+aScl0VnaEpFR35J3AA8FFSYru7bA870FEdKgJUkVdrwaZsZdjATQ/P7Dn1R9vjW5+5yPKOaGUGbG5bpL67TQEoVf9psBRpFH2E7Y0K9S9gXZItu+ECeEMdLoBNHieNe/lwiRDwJjm3A6+z/e+8vRLpnooXHpE03U3pElQJcR9m343FuT1JaQAarEKqcLbFcGVUZHUkr02nUIsMt7ZLFrbpSMbR3Od0sjnLC1In3FHwGfCn/kzKA+0bhrxWA5oiv5luNAGdUnk/D3iA5EJVhDy9/Knt11LQ3tcPVY+Txr3sWYMckRaxGjQSW9XBUpLmF6DJo6ZSVZQai3O7kKbNDeZSfnHudFJem8b3sT/JBlwkr01jpidpLn2n+w2zySol5FT4aB5Rzn/olxplVnhVdSN/968t2H+Dugs23SXpADfVmFZKofHHUkI6YQbsuhlAJ5B0CnCm7amL+1pKoBTafiDJ7U+k/PlnulBO+yZZXyXVhP0e6Uf5IeAh258oKGMZ0uBmPdv3lOq3SUarmcxCbSOFOkeZ6mDG0SyvNnNW7n8dkrn0aRYEHW5Our932f5rITm1mwG7TgEo5U3/PH2n5ie4YE5wSXcBG5JG5P+mpsWsTpoZsrlpfroB27eWlpHlLEWyz741y7oK+IHtYqX0JL2TtBi3rO1xkjYlfW4law7cAHzKffPafNX260rJyP3WajapjDK3BX5X2bUy8JwLpiJRBzKOZjkdKdgk6c2kWY2AGbZ/XbL/TtCNCuBikm9uNeXsK23vXlBGy0WtGhazOpU+d31glu1nlNINbwycXXV1LSirtgWtSn83kwKyplRswEXTdWelchYLPMwep568Ns2FWpYmjf6KFGpZDIvNq5OCzappQIYd0RosGt2oADoyNZe0LTDe9hlKJShfYLtolsYO3st00gLzWFLWwZ+TIg/fUVJOllWbqaHS3022t1Tf/OnF6zXkfmvJa9PCbNJYk6nFbNIJlOpnHEFyophOcqK4wYXqNKhvQONC1PH9j3SWWtwXUAP/kTR/Gq5UIrJoqT6lbICfIeX9gBSl96OSMjJPZ0XTkFtL+lzg+TzSezcpB/zHgbVLCpC0j1Jem3GSJldeUyjv136npH2BUZLGKwXUXF9SgKQvSVrN9pO2n5S0uqQTSvVv+0TbKwNfsb2K7ZXza82R+PDPHEGylT/oFFG9GamaXil2JsVp/DK/9suvy4FLCsrpCEqBX41cQPVgu6tepEIWM4CZ+XUHsFlhGdNJI7JbK22313AvmwK3kdYaHgBuJZmASsu5CdiHZG4al9vuLCzjpaSsjDeQcsw3Xq8Bli4sa0Xgi8DU/DqBFHBWUsatLdpuqeG7WYpUA+DzeXsMsEVpOZ14AVPz3+nAco33Ncj5fTttS/qLFHH8RlISys3yb2X+q4SMrnMDdcrG+CqlnPByPeXanrVtSQ1XxjoqNGF7Op1Jn/s+kjfOF23fL2kchWc0TusjDwKvU8rOuHnedbcL25ltPwV8TtKXnGMbamCUpOWcE3QpFYdZrgY5p5AyQL4ZOJ6U7fYUFnx+tSBpDLC37ZIZQWcppQH5KXC1pMdJrrulWUl9C89sDdTyG62ZY0jrMuuycFGrMokHF7eWq0FrHg+sVtleHfhCYRmfJBU2v48UQXsDcHgN9/KlFvdywuL+jId5T3uQFMFZpOIg9wO7F5axNWn09Je8vQnwncIyPg1cR/I2eX9+/+kaPq9b8t/qbPO2mr6btYAPk1KB3Evyaqrr/+CNpHiNZWro+7UsmDnfT5pxFBkxL44XefZXx6sbF4EXWlBstfBYQM729I2avLpk/1lGR+6lk0i6DdjeuTJTXkD/lQtGN0u6iZQBcrIXLALfafvVpWTkPnek4s7qGlKB5HvZmmQ+eU3+vK5q/r8YRv8rk1J/7AtsQIoF2cv2uiX6b5J1sJuy9Uo6yfZR/Z0zTHmrkKwAIzI6u4qkXVjgDj7FdpEg1K4zAZGm5svafhZA0vKUizQl9zkhP/CvrrRt58LlAOmcmaGTLOW+ZfkepQZnBNsPqW+N9uf6O3YYMhqLjXXyLdJD+UWSvkhSbEcX7P8fpII8R5NqXFvSuwr2X2V3Sf+xfS6ApO9Q4/+z6zOZdhRJJ5JSW5ybm46QtI0LOAN0owK4gGRfPJ1kJzuYBR9cKS6SdDYppfHypOyAE0ll6EryI+DXks4g3cv7WRATMFL5paQrgfPz9l4kL42SPJTtvpa0LKmkYm0lLuvE9rk5rqGRyGw32yXv5X+AvUkVs86TdOEgxw+HdwOTJT0PvB14zPZHapTXLexEqgvxPICks0gOIcNWAF1nAgKQtDN9p+ZFq2rlRd+TSbbGlUkK5mQXjGatyOqEmWED4FMkT535gwIX8s9uIa+aq/23totWnpK0FilffjXa+AjX4xBQO5UobZO8WYqUnWyS8TKSJ9jepECtY4Gf2P5Tgb6rKcxXJi0C/560yIntx4Yro5tRSta4XeNzyp/nFBeIa+hKBVA3eVT5RWB7Ul7wo21fsHivatHJdvnvkfKazDeVeEFVqtLyXkSa0hr4Q5NJaMSQ/w8amR/vsT3scoMtZBxDWji/lKTMdgMutl0s5qCFzI1IawJ72l6/QH+Nwkaq/G1gFy5wpFSY/Ze250o6muQ2eUIdirMTKKXrOIlU5F6ktYDPlnjmhAJYBPID82ckj6M1SR5B/3XBdBOdRNLNTtlNOyFrT5LpbArpn/n1pJw6xQJ1shvrR8nFxxvtLpsLaDuSOe4BmF8R6kAXTmsg6W5SHMt/8vYKJM+gVxaWMx5o9HmL7Vkl++8kjajvHER5Iikv1P/Y3nIxX9oiI2ltkuuvSGmu/1ak31AAQ0fSRNvTmtr2t31Of+csyUg6jrQY+BP6lpwrPjXvkBfQbcBppCDA+WY528Xq6Ga7/L7O2UazGe380opU0hWkgjmNAjqrAT+yvfPAZ7bd/2rAmSRX2VtJD5iNSQ4OHwbelhe7S8iqvRhQ7vdW25vlxdM7bJ/XyqMu6FIFkKfm69meWaOMai6gtYCVXTgXUKfIU/Rmik/Ns6w+hTmUMjfe5kLFOnKfN9U92lOL3EKt2grI+Slp5Hc1yXyyPSnm4B8w/Lqwks4h+fxPqiwyiuQVtCUpJ1SRCleqsRhQU5+/AP5KWgN6LSl9yh9KDjK6ha5TAJJ2IkXNVVMBH2u7mGubUi6giaQfxwaSXkKyy25TSkaW0yq51ROkoicnjMRFTUlfIY0wq15At9v+TEEZ+5IWMq+i74ymmA244mXWmPXtR0pp8b5SMrKcAwfab3tYXmGS/tzfA17SP4BtbP95ODIq/d1OSmVSLQZ0u+1XDXzmkOWsCOxIGv3/OZtPNrJ9VUk53UA3KoCGy9w1rqEcXO5vOik3xy2uMdukUmGL54DzctPe+e+TwLa2BypQPhQ5KwJHkmZNh2R78Ialgk1ayKvbC+hEUurse1lgAnJJryZJywGHUrkP4JRG/MlIQdJM2y/vZ1+/ymERZX2FtC5TWzGgbkV1ZR/2EhDqXPIF3Jj/1paojTSdhAVh+iuVlpH77TepFWl0U0rOhaTUBnfm7RWoIUlXC7lrkQchhfv9I2kGWOe1H9FOWwE5O5Ns84+RFP9c4MmC/Z9FcsdUU/vRpJoQJe9lKdK6wiUkr6b/B4wq2P/9pPQsN9X53Xf6RXLJ/Tnwp7z9klbPhkV5dWM66Luzp8lSksZJ+gZwY2EZF0n6PrCapA8Cv6JSS7UgL5A035YtaQuS2ymkGsGlWN/2l4H/Ath+GsrWBJa0laQpkn4saTNJd5Kyj/49xzqU5DZgtcJ9NtPKNHNQDXK+kWWt6QVpoUvWA/4osBEwU9Klki6RdC8pE+1hBeVg+3nb37W9u+332P6+7WIR2rbHAeuT3Ga7iXeR8ib9G8D2bFI8xbDpxkjgw0gjmudJXi1XkqIdi2H7q0q5gJ4klYY8xjXkAgI+AJwu6QWkB/KTwAdyINqJBeU8m90LG7bZ9anYzgvxf6TvYVXgN8Dbbd8o6RWk9YCSKRVeBPxR0lT6rgEM2w1UHSjU3cRDpJlZLbZap3QJe+TvfALp/+wztu8tLSubFk/McqoVwYo5G9i2pJ9QT7H5xUVt2Ye7bg2gG1GqDSzXUKIx9789aco/gbRwug2pvOGUgjLme4BIutsVP/bSniCS3tiq3QXcQNX5Eoqbk+JNrqWvMmtOD7zEI+k6kjnj66TCLe8j/V8fW1jOKcCZtqeW7HdxIemTJKeG7Un/d+8HzrP97WH33W0KQNLLSQuaY+kbBLTD4rqmRSUvNL6Hhe9lUg2y1iSV6BNpHeWRwv3Pz2KqhevcFslwqpRj6JfAFbb/ONz+lgQkXUWqAdAc0/CFxXZRi0gj4LDqlCHpd7ZfX1jOXaSZ+QMks4lIk4MRWxJSNWUf7kYT0CWkIKAfUUMGyA7zM5Lb582UN8k080YW5JtZhmQ+K8kmkp4k/QOvkN+Tt5fv/7QhcSDJ/e+4HJh1E0kh/Nr2vwrJ6DRrjMTBSz/8J8d9/FnSYSRf/RfWIOftNfS5WHFT9uFSdOMMoCP58rPNfD3nSNCaZBTPYd+PnO8AL6evb/69tg+tW3Zd5AfNlqSHwVtIwUBX5cXuEYOkk4DfuEM+7JJeSF/7/F8K9r05KSvraiSz1qrAl22XdtKoz21yMZDdpk8mKUuxYEYzbGeAblQAxwIPs3Bag2K5wSW9k5RfpBpsNqnEImOTnFOBb9u+o2S/LeTMAF7dWGjMD887XDhAZ3GSo7Xf5pyLfqQgaS7JzfhZspcWhX78TXJ2Af6X5GL4D1Jm2LtH4v9ApwI1O4WkmcA7XTYNONCdJqAP5L+fr7QZWK+gjONI2SynQKrdK2lswf4bbAscpJSq4Rnqs2XeQ/p8HszbY4DbC8voGDmA7gTSqP+XpDw3H7NdrM6xpG1I/weNFNqN76Zo+gzbRdz92uB40hrQr5zy6LyJlB66GOpc2vF3kQM1c/+zlSqfjVT+XsfDH7pQAdge0wEx82w/IRV1lW9Fp2yZa5LiJ/6QtzcHbmi4OZae2XSAHWx/Wqmy1SySX/g1lC10fxrwcZpSaNeBaioH2MR/bT8qaSlJS9m+RtLJhWVcTIoC/gH1fma1uU0uJqYpFer5KX2tGj8ebsddowAkvdH2tfnHshC2J7dqX0TuVMo3Myr7Nh8OXF+qc0mrZJPV3FJ9DsIxHZLTKZbJf99BytD5WA3K+gnbV5TutJm8BrA5fcsBbuvydXT/meNNfgucq5QHqKhLK2ng9N3CfbaiOVDz/cAPOyC3LlYBniJ5ATUwMGwF0DVrAJJOsH20UnbDZmz7gIKyVgQ+R8UtCzjeOWd7gf5/YXtn9S2k0aC4mSHLfBHpQQMjuEgLzH9o7kYyAW1BWnT8hQtmCM0yRpF+hLUknMtybqdvOcBRpDQnpfNOrUT6vJYiJbZblZS6edjBbVpQEexwOpd2vBa3yW6jaxRAsOioA0VaOo1SrvknbT+XH24ru1ARjdz/NS2aXdqerRrLAXaKfgYyDYoPaCSd7Kbssq3aRgqS1gW+TQrQNCkd+BEuULSnaxSApAHzotv+VkFZGwCfZOEAreI1dCWtw8KLZqWrTtVepKWTqObsptlLanfbF5XobxBZtZUD7FZauYKrhmy9nULS1aSMwA3rxnuB/WxvP9y+u2YNABjdQVmNxawfUuNiVl6E2wu4qyLHJDttSZZqMvk8CiM6UeAZpMXZrfP2LNJ3VkQB2H4+BzLVrgBsny9pCgvKAX6m5Eymm5D0YeAjwMvyzKnByqQi9COV0bbPqGyfKeljJTrumhlAJ1GHauhKugfY2HatUcDqQJGWTiJpmu2J1RxDkm4rOaOR9HmSzfxCcpZGKG/Pzp5Mv7H9RN5ejWQS+mlJOU0yVwfG2B5RrsBKObNWp0WepjrWGTqFpF+RynY2fp/7AO+z/ZZh991tCiAH/Lyfhc0zhxTou6OLWUr1YPfoRBoD1VykpZNIup4U/ft7269RynR5vu0tCsroSBlNda6M4hRSyuGlgenAHOBa20eWlNMJ8vc9y/YzkrYjDW7Odk3JFOtG0nqkbLqvI1kArietATw44Int9N2FCuD3pPz/ffyzbV9YoO9OL2ZdSgpi+jV9Fc2w6sB2O+pAdtNO0cp2rcIV7nKfjULqHyCN/o+tw27eoTWt6aRI4LEkD73JpDWgd5SU0w100xpAg5VcU4k5p4ITSFq+2eVTUqmEZlUm51cwBGxfLekWFmQ3PcLls5u2dCu2fXZJOaQgoK8Bp5AGHx8lDW5Ks7RS7dw9SS7OxengmtbztuflWe03bH9b0q2FZdSOpIHic2z7+OHK6EYFcIWkHVxv8qzrgeaEc63ahoXts9SBpHPdRsVuflneXk3SboXt5ptX3i9PMjndApRWAB8lpTVpzGCvIs1uSjOJNFq+zvZUSS8DihSDr7AbaSRed2bb/2bvqQNIdQdgQXDgSOLfLdpWAg4mRe8PWwF0ownocVIQy1OkBFqNHC1rDHhie32/GFiHlFJgXxaYglYBvmf7FcOV0SSvI0nnsqxlgQ3y5j22/zvQ8UsynbKbN/W/KnBOHd9Nt9CpNS1JE0gF52/IXlTjgL1sn1Sn3DpRymV0BOnhfxHwvyWCNbtxBrBWjX2/jVT3dV1S5sSGAniSwmUnM8excNK5caWF5IWys0gFNASMkXRgadtsB2nlwlr3//pTpKpNI5JswjwYeBV900G/v6CYp4Dpkmpd07J9F8lRo7F9PymWYsSRHU+OJEVnnwW8xvbjpfrvOgWQIz/fQd/kWUXqzdo+CzhL0ntsX1qiz0FolXSujinb/5ISqN0D8wPdzmfk1lWt3W4u6ecs+C6WIi04X1xSRoc5B/gjaZAzifTAKZ2BsiNrWupA7eFOkN2z3w2cCmxUx8ypG01AXyR5fZyXm/YGrrddh920ViSdRvIAOopUGvJwYBnbHyosp5WnyUiOnFyJZDd/K2lGcxVwgu1WNtVFlVGtOzwPeLBEaP7iouIFdLvtjSUtQ8qhUzy6vW7UodrDdSPpedJMaR59B35REKY/cgTgZrafy9tLA7eMxIeZ+iadg7RId0KzB1IBOaeT/sEaoeb7AUvbfl9JOd1Ep/LNSGqVwuQJYJrtnxWU8wfbW0j6LSma9m+kpIDDHjVLusj2npLuoMUMtgZX047UHu4Gus4ElFkFaNjJRnIhiA1tf46a3PIqfBg4lDTDEMkt75SaZdZGh3I1bQ80P+zf3qJtuCwPvIIF5qX3ADOAgyW9yXaRlADAqTkC+GiSmeYF9C2qNByOyH93LtTfYHSq9vCIpxtnAO8luUf9mvQw2w44xoVLAUramoUfMEVdAJUyTq5N+vFfYHtGyf4rco6w/c3B2kYKSsntvsfCwYDDXgeo5psB7q3sWpkUefze4cpokvcb0vrMvLy9NMmktT2pbOeEkvK6AXWw9vBIp+sUAMyPNtySpAButP3Xwv2fA6xPCpmfH9BSR4Rudj3dkxRAswpwoe0TCstolT2xVrfJOqkzV1On883kfFBbeEEuoFWBm2y/YiR/R8GSQVcqgLqRdDcwwR388CRtBHya5M+8bKE+9yHFM2wL/K6ya2XgOdtvLSGn00g6js7katoWGG/7jJyDauXsclhSxsEks8wUmJ8O+kskL63jbH+qpLyRjHIJ0/6IGI2FCQWwCEi6GDjc9sM1y3klaeS/B/AIcAFwaYkAkNz/S4FxtBjNkrKBli4J2BE6kahN0rGkfDMb2t5A0kuAi21vU0pGRdbapHgQkRZmZ5eW0SnqjGyXNAd4iKQcb6IpZ5fta0vLHOmEAhgCFd/vlYFNgT/Qd4RZdIQh6UbSP/PFI/lH343khGObkTzMGimna3Gd7VACteVJaxvbsqDq1HdLepzVHdmuVC5ze1K65I2By0hZYGtZO+sGutILSNJWwAa2z5a0JilB3F8KdP3VAn20je2tGiOmTsod6ajmimCZZ21bkrPMlQr2PZ9KArUZwPO5uY4EameTZn7fztv7kNyC9ygo4zgWjmwfW6rz7Pr9S+CXkpYj3cMUSZNsf3vgs3uTrlMAko4mBYKtT/qnXp4UFLbtcPvu9BSyOmICas0F1GXUWhEsc5Gk7wOrSfogqQbFDwv236BTCdQ2dN+COddkb6qStIpsL0p+8O9EeviPBb4F/Lg2gSOcrlMAwO7kqTmA7b9KGnbEXBVJc1k4oOUJYBrwCdv3FRJ1HDWOmKrUaZtdDKxve6+8yI3tp1X4qWP7q0p1B54ENiS5Gl9dUkbmPlImy7oVwK2Stmq4SkrakvJlFO+UtC8wKs/KDidl0S2CpLOAVwNXAF+wfWepvruVblQAzzRNzVesQcbXgNmkmYVI6SZeDNwDnE6KPShBV0VsCQAAGldJREFU7SMm6MqZxrNZoTX+B9anhgdofuBfnWWMkrRf6XgTak6gVonOXQY4QFLDVLoeKW9/ST5KCmp8hvTbuZICKY0r7E9KobwBcHjld1MsdUK30Y0K4MeSTgFWlfQ+UobDMwY5Z6jsaHvLyvapkm60PUlSyaygtY6YKhxHh2YaHeJYki14jKRzyRXBSnScZ5OHktKCTyYpgEOBT5HiQkorgLoTqHUqOhdgp+bIdkl7UCiJnu1WWWCDAehKLyBJbyflzxEpodUVhfu/gZRo6pLctDtwZF60XSgX/TDkdCoX0E22t1TfIuojMhlcNvWsSxo5NyqC3ehCFcEk/YyUZuQGUhGY1UkzpyNsTy8hY3EhaROgkS/nd7aLrgH0E3C4UFvQObpOAahFNTBJH7T9g4IyXgZ8kwVFmm8EPk7KOfJa29cNs//PAr+03ZEydupQ1tFOUXMkcDXB2ChSfMZ6tucWltPpBGpHAB9kwYLpu4BTS3jP5AHZO0gR7dXa3KuQAiq3GK6MYNHoRgVwI/CZhseOpE8Ab7O9w8BnLjlI2hvYkVQQ/jbSotZVLlgIokleq5nG8R3wPKmFbAI80/bUGvruM2KtawQraW3bD+dgvYWw/WBhebcDr3NOmZ3dWm8ooWjyzGJTUp2Bap3bucA1df1fB4PTjQpgNCkA5GOk4habAHvafragjDNoPSorWT2pIWszkjLYARgF/Io0O/hDQRl72L54sLaRgqS7SJ45D5AWBRuLgCUeZs+xoFargBVI5qYRvdCYZxqbN8yLOTBsamO2U0jGMh7BpUa7ka5TADA/gdrVpEW5A20/P8gpQ+3/PZXN5UnT5dmlPDMGkLsKKdLxbbYPKdhvV9lmOzVqrpN+XI3nU1rRSDoSOJCUPwlS/MGZtr9RUEZXVOrqJrrGC0ipGHz1B7McMAZ4VJJdoCh8AzeVg5R0PmlkXpRsmvkEMCY/8F9EcnMt8vCv2GbXUd/CI6uQqhCNSGw/qL6J2kaT8tuPGGyvDCBpEqk4yzmkWcZ+1FDjwvbXJE0hBUwKeF8Na1BnsKBS15vIlboKywiGQNfMAPKCXL/kMPG6ZG8IXGb75YX7vZAU0XqA7Vdn3/YbCnoZdaVtVh1M1FY3DQ+twdqG0f8qtp9UKj7ejIEnS/12FJW6lji6ZgZQ/SdVypm+PpVpJmUjDhvTc+W/f6N8JSioOaI1u/ndJulcj9DMn/3wLvpGg8+WNFIrwz0naT9SJliTUhyUHMycR4oFuJmF684CvEDSD2yXiG+JSl1LGF2jABoo5U8/khSocwewOclNc7tSMhrT8w7QkYhW0g+y1aL2SLXNdiRRW4fYl+Ry3KjOdl1uK4LtnfPfca3255n1nUAJBfAxYEWSm/HxwJtJ6w7BYqJrTEANsjfDFmRTiaRXAUfb3qewnF1IxTkAprhspsmGjO1JxUAmkMoAbgMcZHtKYTlrVjaXJ2WAXMP2Mf2cskQj6ZPAeNKC+YmkRG3nlfBpD4ZPdmZw6diJYOh0owKYantzpXztW9h+VoVL50k6iTSzaIT97wNMs/3ZUjIqstakhojWNuReZ3vYGVQ7iaTlGrELWXlWo8HrSNRWO5LWJaVo3oYFefqPsD1rsV7YIiBpImkhuDGDfgJ4vwvUag4Wja4xAUlaOtuxH5a0GvBz4EpJjwF/LyzuHfD/27v3KLvK+ozj34ebQSRcvBYppirqihVDBMFLkYCyKgqCVVAJtYqK9RbrUmt1uRZirVdarSheuCgXWaiASmlViKjcESMSrkVFFARRRKFikISnf7z7kJPJhDB79j57zp7ns9aszNlnZr+/JDPnPe/t92PBYHupShbCHwGNdwCUqawNKf9Xu0nCdqPpbSUNb/fcgLKAOo5z5hcCCyWdYPtgqkRtY+44yjz9IC//4ura8zqLqL5jgTfYPheg2ql1HKV4S3SgNx0ApTrXQq/OYPleSXsCW1AOhjVtS2BQY3aLFu6PpGMpvxwTi4E0nd/8iKHPV1IOUB3QcBujsImkVwLPlPTiiU823XGOyMNtDycz/IKkt7bVmKRHsOYe/SYKKQ3cOXjxr+59XrWhIjrSpw5grd0xtpe21NYHKfnTz6na3Y123v3vant+C/ddg+1FbbcxIq+n7JPfEthnwnNtdJyj8FtJiymlQaFMN97WdCPVmtYRwDbArZQSlFcDT26wmUtUiuicTPn/OJBSsWshgO1lDbYVD0Bv1gAk3UjJ0z8p2+t8bortDLJNrqSsAwi42PYtTdx/QlvHAEfYbjov+8R2Hko5oDNcD/Zw242/0IyCpENsH9N1HE2QtB1wJKsTD15AWQNoOhfQjym7cs62vaOkRcDLGz5xfs79PG3bezTVVjwwfeoAbgaOYh0nC22/r8G2Wss2OaGd3ShrGbdQtn82ltNmQjtnUWrMnlhdOgjY3fZzm2xnlCQ9k1IScLiQ+vGdBTTDSbrU9k5VR7Cj7XslXeJk6uy1Pk0B3Wz78BG1dZGknd1CtskJjqVUOVrO6jWANmxte7gy079K2q/F9lol6QTKQcDLWH1oypQa0WOl2mCwxPbvq8dbUUaFTSce/L2kh1DeCJwk6VYaSgciabHtE6t8Q2tpanQeU9enDmCUOUUWAYdKuoGGs01O8AvbbVaDGjhHJQX1l6vHL6GdhfNR2YmSZ74Pw9sdBi/+ALZvrzLENu1FwJ8odS0OomxsaOoN1eAg3jjuLOu1Pk0BbW37d+v/ykbaGlWO9k9TFjTPYM16sI0sZk5IabEZq98tbwj83xinNv4K8BbbN3cdy3RVUzK7D/IyVTl7vucG0zTH7NWbEcCoXvyrtm6ojsg/knb/DTelvPAPF7NpbDfLCFNajNrDgKskXcKaHec4Frk/ArhA0lcp//cHAB/oNqR6RjidFQ9Qb0YAoyTpzZRdM79maH9+C1NArZL0JNvXTDgIdp9x3ZYn6TmTXXdVJW7cSJpP2aEjYGnbu8LaMtmJ/KZP6cfUpAOoQdJPgF3a2iYp6Z22PyLpk0xeeayRwjOSPmf7devYnjfW2/IkPZKyTRfgEtu3dhnPTCdpie1PrO/aNNvIdNYM05spoBH7JSWPSVuurv68tMU2qF78N6Akyzu/zbZGSdIBwEeB71LeNX9S0jtsf7XTwGa2V7I64+jAP0xybTqGp7OgpLcYy+msvsgIYAqGtrE9mVJz9kzWnGNudDubRlSrV9KFtp/R5D27VL3TfN7gXb9KRbCzbT+128hmnqrWxCsohwDPHXpqc2BV02dB+jKd1RcZAUzNYNH0F9XHJtUH3E/91mn4F2Dii/1k16br2yp1jk/rydbJDSZM+dxGSXI3lqpdZ9vbPruqD7FRg6mULwBupiycD+eEuhO4vIkGtGbVsVsoye0Gz41s916sLSOAGtp+Z67VtXoPAE4ZemouZX97o6czq+2gm1EO/qxg9bmGcd0G+lFKEr1B/pwDgcttt1G1rVWSXgu8jnJY73EqhdU/Y3vPFtpqpaOR9F+2XyjpetauOmaPb+GhsZcOoAZJy2wvXN+1ady/l7V62ybp8cAjbZ9fZQMdFDi/HTjJ9k87DbCGQV0LSr6pHatr99XUbbCdkXU0MXNkCmgKht6ZP1rSfw49NZeGjs3DGrV6v2T7nqrthW1ty5S0dOIv+mTXxsDHqUoXVoflToP7CpF8nLUzhI6Du12KGgGl7gXtTDe+kaqjAbB9XZUaujE9+jnrjXQAU/Mrys6cfSlFtAfupByhb9Tgxb9yNNDICGNA0hxKjdaHVYdyBuk05lLSAo+bebbXmre2famkeaMPpxHfk/RuYFOVKmdvoJwMb1prHU0Pf856Ix3AFEz2znyE2sh1dCilUPc2lA5t0MYdwKdaaK9tc+7nuU1HFkWz3gUcQkkIeCjw35Q3A01rs6Pp289Zb2QNYExI2s/211q695vdg4Lpkk4GvmP78xOuHwLsZfvAbiKb+arzIIcwVEcZOLqpXWFV6pR3T8g6Gx1LBzCDjXLOtA/586vTv6cDf2b1FN1OlK26+7uFoj1tk/Qs4DBKha6NGOOdM307b9IHmQKqQdI82z+fcK2x+gCjnjPtS/5827+m1ANeBPx1dflM29/pMKzpOoayvvRDVv/fNEbSi4BtbX+qenwx8PDq6X9u+NBh386bjL2MAGqQtAzYx/ZN1ePnAEc2tTVP0hJWz5nexJpzpp+3fWQT7Qy1dzX9yZ/fK5Iutr1Li/c/H3iZ7V9Wjy8D9qScCzmuydFm386b9EFGAPUcCnxN0j6UnTn/Rtke2gjbn5B0JKObM70CeBTlRGjMLOdUB9tOY820I01tCd5k8OJfOa9KcnibpM3W9U119Dj9+NjKCKAmSc8APkt5J/MC279poY2RzJlW2UAXAH3In98rbWdqlfQT249fx3M/tf24JtoZuudWwPYM7diy/f0m24gHLiOAKZB0BmvujX4wJSvoMZLaeMEc1ZzpYS3eO6bB9qKWm7hY0msn2Tl1KOUNQWMkvQZYAmxLWW/aFbiQkhwuOpARwBSsq9DIgBsuODLKOdPkz59ZNKJC6tVp369RRn6DaaWnAQ8C9qsW1hshaTnlZ+wi2wskPQl4X7bndicjgCmw/b1qP/O3mk6Tu472RjJnmvz5M9JICqlXHf0zJe1BSXMO7e2cWmF7hSQkPcilGt0TW2gnHqCMAGqQ9A3gYNttFoUZtNX6nGny58coSDodeBVlh9selCR9G9tubANFTE1GAPWsAJZLOgv44+CiGyrVODDCOdNe5c/vgwnJBtfS9M/aKNjev/r0sGpxewvgmx2GNOulA6jnzOqjbUtYPWe6aDBn2kI735T0LdbMn/8/LbQTD9wP1/8l46E62Ph64PGUnEbHNL1eFvVkCqgmSZsAT6geXttGcjhJP7C9c3U4Zxfbd0u6zPaCFtoazp//fdunN91GzE6STgHuoZScfD5wg+0l3UYVkBFALZJ2B74I/JzygvmXkl7Zwn7mGyVtSdmlcZak2ykpqRsxXEBlQv783SQ9bhwLqPSNpCcAb2ftPE3jtHVy/uCUvKRjaHh7adSXDqCeIyjZJa+F+35JT6Zsn2vMCOZM7yugMsFdjG8Blb75CvAZSgroxnMBjch9o2PbKwc1B6J76QDq2Xjw4g9g+38lbdzUzUc4Z9rHAip9s9L2UV0HMU1PlXRH9bkoNQfuILmAOpcOoJ5Lq6HsCdXjg2h20e6LrDlnOp+yINy0PhZQ6ZszJL2BkuZ6OE3H77oLaWpsb9h1DDG5LALXIOlBlBqq9y2aAp+2fff9fuMDv//yoTnTjSgncxstB1ndOwVUZjhJ109yeSzrAcTMkw6ghurU5EW272rp/suGX/AnPm6wnd4VUImIBy4dQA2SjqccyrqNMk1zLiWN7u0N3X8Vqw+YiTIdcxctzZlOKKBy5ZgXUOmVam3pH4HdqkvfBT7bxrbjmH3SAUyDpG2Al1C26W1jO2sq0ShJRwMbU9aFAA4GVtl+TXdRRV+kA6hB0mLgb4CnAL8FzgPOtX1hp4FF70j68cScTJNdi6gj71jr+TjwU8r+7HMm1geOaNCq4UN5kh7L+J4HiBkmI4CaJD2ZMi/7bEq2zmttH9xtVNE3kvYEjgN+RlkDegzwKtuTVQqLmJKMAGqQNBfYjvLLOI9yQvfeLmOKfrK9VNL2wBMpHcA1TW03jsgIoAZJl1Pm/c+jJE67seOQIiKmLB1ARMQslaIfERGzVNYAImY4SY+mrDcNp4NuOvV4zELpACJmMEkfplRou4rV2z9NyT8VMS1ZA5gCSZ+k/PJNahzrtMbMJulaYIfs/Ik2ZA1gai6lJE2bAywErqs+FpDDOdGOn1FSQUQ0LiOAGqrqXHsNEnJVCbu+bXtRt5FF30g6FXgqsJQ16wFktBnTljWAerYBNgcGRTkeUl2LaNo3qo+IxqUDqOdDwI+qkQDAc4DDugsn+sr2F9f/VRH1ZAqoJkmPAnapHl6c4inRJElftn2ApOVMsvHA9g4dhBU9kw6gpuzNjjZJ+gvbN0t6zGTP275h1DFF/6QDqGFob/aVrE4CZ9v7dhdVRMTUpAOoIXuzI6IPcg6gnuzNjoixl11A9dwFXCYpe7OjdZI2BbazfW3XsUS/pAOoJ3uzYyQk7QN8DNgE+CtJC4DDs94UTcgaQMQMJumHwB7Ad23vWF27PNtAowkZAdRQlej7IDCfkhcIANuP7Syo6KuVtv8gqes4ooeyCFzPccBRwEpgEXA8cEKnEUVfXSHpFcCGkravMtJe0HVQ0Q/pAOrZ1PZSyhTaDbYPowzTI5r2ZuDJlM0GXwL+ACzpNKLojUwB1bNC0gbAdZLeBNwEPKLjmKKfXmD7PcB7BhckvRT4SnchRV9kEbgGSTsDVwNbAu8H5gIftX1Rp4FF70haZnvh+q5F1JEOIGIGkvR8YG/gAOCUoafmAvNtP72TwKJXMgUUMTP9ilKBbl9KFbqBO4F/6iSi6J2MACJmMEkbDyrPRTQtI4CImW2epJw5iVZkG2gNkj4iaa6kjSUtlfRbSYu7jit6KWdOojXpAOrZy/YdwAuBG4EnAO/oNqToqZw5idZkCqieQSrovYGTbf8uR/WjJTlzEq3JCKCeMyRdA+wELJX0cGBFxzFFP70VeDDwFuBpwMHAKzuNKHoju4BqkrQVcIftVZI2Ax5i+9ddxxX9JGkupezonV3HEv2REUANko61fbvtVYNLlDwtEY2StJOk5cDlwHJJP5b0tK7jin5IB1DPTZKOgvtGAt8GTuw2pOipY4E32J5nex7wRsrOoIhpyxRQTZI+DGxBmZf9kO1TOw4pekjS+baftb5rEXWkA5gCSS8efgi8F7gE+CaA7dO6iCv6S9J/UBaBTwYMHAjcDpwKYHtZd9HFuEsHMAWS7m/obduvHlkwMStIOud+nrbtnAmI2tIBRETMUjkIVoOkbYFPAs+iDMvPA5bYvrHTwKI3JC22faKkt032vO1/H3VM0T/ZBVTPccA3gG2ARwNnkJ0Z0azNqj83X8dHxLRlCqgGSZfZXrC+axERM1lGAPX8VtJiSRtWH4uB27oOKvpH0hclbTn0eCtJx3YZU/RHOoB6Xk0p1XdL9fGS6lpE03aw/fvBA9u3Azt2GE/0SBaBa7D9C0qpvoi2bSBpq+qFH0lbk9/baEh+kGrILqAYoSOACyR9tXr8UuADHcYTPZJF4BoknUVJ/jaozLQYOMj287qLKvpK0nxKERgBS21f1XFI0RPpAGrILqBom6S5tu+opnzWYvt3o44p+idTQPUMagCfXD1+OdkFFM36EqXk6A8p04wDqh6nKHxMW0YANUjaDjgSeEZ16XzKGsAN3UUVETE16QAiZjBJS23vub5rEXXkHEANkh4r6QxJv5F0q6SvS8qQPBojaU41//+w6vDX1tXHPEoKkohpyxpAPV8CPgXsXz1+GWU9YJfOIoq+OZRSEH4byjqAqut3UH72IqYtU0A1SLrY9i4Trl1ke9euYor+kbQh8G7b7+86luinTAFNwWAYDpwj6V2S5kl6jKR3Amd2HV/0i+1VwN5dxxH9lRHAFEi6nrIFT5M8bdtZB4hGSXofcDlwmvPLGg1LBxAxg0m6k1IbYCWwguocgO25nQYWvZAOICJilsouoIgZTtJWwPbAnME129/vLqLoi3QAETOYpNcAS4BtgcuAXYELKcnhIqYlu4BqkHSqpBdIyr9ftG0JsDNwg+1FlGIwv+k2pOiLvIDVcxTwCuA6SR+S9KSuA4reWmF7BYCkB9m+BnhixzFFT6QDqMH22bYPAhYCPwfOknSBpFdJ2rjb6KJnbqxqAn+N8nP2deBXHccUPZFdQDVJeiilEMzBlF/Ik4BnA0+xvXuHoUVPSXoOsAXwTdt/7jqeGH/pAGqQdBrwJEpFsC/YvnnouUtt79RZcNELkuYArwceDywHjrG9stuoom/SAdQgaQ/b3+k6jugvSacA9wDnAs+nLAIv6Taq6Jt0ADVI+vvJrts+ftSxRD9JWm77KdXnGwGX2F7YcVjRMzkHUM/OQ5/PAfYElgHpAKIp9ww+sb1Smiz9VMT0ZATQAElbACfY3rfrWKIfJK0C/jh4CGwK3EVyAUWDMgJoxl2Uo/oRjbC9YdcxRP+lA6hB0hmUtNBQzlLMB77cXUQREVOXKaAaqv3YAyspOzRu7CqeiIg60gFMk6SHAbelWEdEjJukgpgCSbtK+q6k0yTtKOkK4Arg15L+tuv4IiKmIiOAKZB0KfBuynH8zwHPt31RlQzuZNs7dhpgRMQUZAQwNRvZ/rbtrwC32L4IoMrQGBExVtIBTM29Q5//acJzGUpFxFjJFNAUDB3OGT6YQ/V4ju2kgo6IsZEOICJilsoUUETELJUOICJilkoHEBExS6UDiKhJ0gJJew89PkzS26dxv2l9f8RUpQOIqG8BsPd6vypihkoHELOapHmSrpF0tKQrJJ0k6bmSzpd0naSnS9pM0rGSfiDpR5JeJGkT4HDgQEmXSTqwuuX8Kl3IzyS9Zaidt1X3v0LSW4euv0fStZLOBp442r99zHbZBhqzmqR5wE+AHYErgR8APwYOAfYFXgVcBVxl+0RJWwKXVF//UmAn22+q7nUYsBewCNgcuBZ4FLAD8AVgV8qZkYuBxZQ3YF8AdqGkZl8GfMb2x9r8O0cMpB5ABFxvezmApCuBpbYtaTkwD9gW2Hdofn4OsN067nWm7buBuyXdCjwSeDZwuu0/Vm2cBvwNpQM43fZd1fVvtPK3i1iHdAARcPfQ5/cOPb6X8juyCvg729cOf5OkXdZzr1XV999fQd8MwaMzWQOIWL9vAW9WVZld0iDr652UqZ71+T6wn6QHS9oM2B84t7q+v6RNJW0O7NN86BHrlg4gYv3eD2wMXF7VgHh/df0cyqLv8CLwWmwvo8z1X0KZ/z/a9o+q66cAlwGnUjqFiJHJInBExCyVEUBExCyVDiAiYpZKBxARMUulA4iImKXSAUREzFLpACIiZql0ABERs9T/Ax/+UVg8km1bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "props_df = clean_and_plot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for the existence of bias towards their own degrees\n",
    "def higher_ed(formal_ed_str):\n",
    "    '''\n",
    "    INPUT\n",
    "        formal_ed_str - a string of one of the values from the Formal Education column\n",
    "    \n",
    "    OUTPUT\n",
    "        return 1 if the string is  in (\"Master's degree\", \"Doctoral\", \"Professional degree\")\n",
    "        return 0 otherwise\n",
    "    \n",
    "    '''\n",
    "    if formal_ed_str in (\"Master's degree\", \"Doctoral\", \"Professional degree\"):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "Name: FormalEducation, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"FormalEducation\"].apply(higher_ed)[:5] #Test your function to assure it provides 1 and 0 values for the df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job!  That's right.  The percentage of individuals in these three groups is 0.23069738480697385.\n"
     ]
    }
   ],
   "source": [
    "df['HigherEd'] = df[\"FormalEducation\"].apply(higher_ed)\n",
    "higher_ed_perc = df['HigherEd'].mean()\n",
    "t.higher_ed_test(higher_ed_perc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4     1\n",
      "6     1\n",
      "7     1\n",
      "9     1\n",
      "14    1\n",
      "Name: HigherEd, dtype: int64\n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "5    0\n",
      "Name: HigherEd, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#check proportion of people that completed Higher ed and their responses\n",
    "ed_1 = df[df['HigherEd'] == 1] # Subset df to only those with HigherEd of 1\n",
    "ed_0 = df[df['HigherEd'] == 0] # Subset df to only those with HigherEd of 0\n",
    "\n",
    "\n",
    "print(ed_1['HigherEd'][:5]) #Assure it looks like what you would expect\n",
    "print(ed_0['HigherEd'][:5]) #Assure it looks like what you would expect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row0\" class=\"row_heading level0 row0\" >Take online courses</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row0_col0\" class=\"data row0 col0\" >0.207892</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row0_col1\" class=\"data row0 col1\" >0.2099</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row0_col2\" class=\"data row0 col2\" >-0.00200857</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row1\" class=\"row_heading level0 row1\" >Buy books and work through the exercises</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row1_col0\" class=\"data row1 col0\" >0.162073</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row1_col1\" class=\"data row1 col1\" >0.161205</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row1_col2\" class=\"data row1 col2\" >0.00086766</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row2\" class=\"row_heading level0 row2\" >Part-time/evening courses</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row2_col0\" class=\"data row2 col0\" >0.103298</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row2_col1\" class=\"data row2 col1\" >0.103248</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row2_col2\" class=\"data row2 col2\" >4.98833e-05</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row3\" class=\"row_heading level0 row3\" >Contribute to open source</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row3_col0\" class=\"data row3 col0\" >0.097821</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row3_col1\" class=\"data row3 col1\" >0.10323</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row3_col2\" class=\"data row3 col2\" >-0.00540923</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row4\" class=\"row_heading level0 row4\" >Bootcamp</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row4_col0\" class=\"data row4 col0\" >0.0746172</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row4_col1\" class=\"data row4 col1\" >0.071824</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row4_col2\" class=\"data row4 col2\" >0.0027932</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row5\" class=\"row_heading level0 row5\" >Return to college</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row5_col0\" class=\"data row5 col0\" >0.0687279</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row5_col1\" class=\"data row5 col1\" >0.0689754</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row5_col2\" class=\"data row5 col2\" >-0.000247487</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row6\" class=\"row_heading level0 row6\" >Conferences/meet-ups</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row6_col0\" class=\"data row6 col0\" >0.0658422</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row6_col1\" class=\"data row6 col1\" >0.0739201</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row6_col2\" class=\"data row6 col2\" >-0.00807796</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row7\" class=\"row_heading level0 row7\" >Master's degree</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row7_col0\" class=\"data row7 col0\" >0.0589517</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row7_col1\" class=\"data row7 col1\" >0.0293459</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row7_col2\" class=\"data row7 col2\" >0.0296058</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row8\" class=\"row_heading level0 row8\" >Get a job as a QA tester</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row8_col0\" class=\"data row8 col0\" >0.0484688</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row8_col1\" class=\"data row8 col1\" >0.0457388</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row8_col2\" class=\"data row8 col2\" >0.00273003</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row9\" class=\"row_heading level0 row9\" >Participate in online coding competitions</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row9_col0\" class=\"data row9 col0\" >0.0453475</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row9_col1\" class=\"data row9 col1\" >0.0508806</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row9_col2\" class=\"data row9 col2\" >-0.00553309</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row10\" class=\"row_heading level0 row10\" >Participate in hackathons</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row10_col0\" class=\"data row10 col0\" >0.0316254</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row10_col1\" class=\"data row10 col1\" >0.0395937</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row10_col2\" class=\"data row10 col2\" >-0.00796823</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row11\" class=\"row_heading level0 row11\" >Other</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row11_col0\" class=\"data row11 col0\" >0.0269729</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row11_col1\" class=\"data row11 col1\" >0.0338607</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row11_col2\" class=\"data row11 col2\" >-0.00688774</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_023bd158_3b09_11ea_ac89_181dea531514level0_row12\" class=\"row_heading level0 row12\" >None of these</th>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row12_col0\" class=\"data row12 col0\" >0.00836278</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row12_col1\" class=\"data row12 col1\" >0.00827705</td>\n",
       "                        <td id=\"T_023bd158_3b09_11ea_ac89_181dea531514row12_col2\" class=\"data row12 col2\" >8.57315e-05</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x16002be4188>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check your subset is correct - you should get a plot that was created using pandas styling\n",
    "#which you can learn more about here: https://pandas.pydata.org/pandas-docs/stable/style.html\n",
    "\n",
    "ed_1_perc = clean_and_plot(ed_1, 'Higher Formal Education', plot=False)\n",
    "ed_0_perc = clean_and_plot(ed_0, 'Max of Bachelors Higher Ed', plot=False)\n",
    "\n",
    "comp_df = pd.merge(ed_1_perc, ed_0_perc, left_index=True, right_index=True)\n",
    "comp_df.columns = ['ed_1_perc', 'ed_0_perc']\n",
    "comp_df['Diff_HigherEd_Vals'] = comp_df['ed_1_perc'] - comp_df['ed_0_perc']\n",
    "comp_df.style.bar(subset=['Diff_HigherEd_Vals'], align='mid', color=['#d65f5f', '#5fba7d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking how often each of these individual values appear with the help of a function\n",
    "def occurence_counts(df, col1, col2, occurence):\n",
    "    '''\n",
    "    INPUT: \n",
    "    df - pandas df I want to do the search in\n",
    "    col1 - column I want to look through\n",
    "    col2 - column I want to count values from\n",
    "    occurence - list of strings to search for in the df rows\n",
    "    \n",
    "    output - novel df with the individual occurence counts\n",
    "    '''\n",
    "    from collections import defaultdict\n",
    "    new_df = defaultdict(int)\n",
    "    for val in occurence:\n",
    "        for idx in range(df.shape[0]):\n",
    "            if val in df[col1][idx]:\n",
    "                new_df[val] += int(df[col2][idx])\n",
    "    new_df = pd.DataFrame(pd.Series(new_df)).reset_index()\n",
    "    new_df.columns = [col1, col2]\n",
    "    new_df.sort_values('count', ascending = False, inplace = True)\n",
    "    \n",
    "    return new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>method</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Take online courses</td>\n",
       "      <td>15246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Buy books and work through the exercises</td>\n",
       "      <td>11750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Part-time/evening courses</td>\n",
       "      <td>7517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Contribute to open source</td>\n",
       "      <td>7423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Bootcamp</td>\n",
       "      <td>5276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Conferences/meet-ups</td>\n",
       "      <td>5244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Return to college</td>\n",
       "      <td>5017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Participate in online coding competitions</td>\n",
       "      <td>3610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Get a job as a QA tester</td>\n",
       "      <td>3376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Participate in hackathons</td>\n",
       "      <td>2747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Master's degree</td>\n",
       "      <td>2639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Other</td>\n",
       "      <td>2348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>None of these</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       method  count\n",
       "0                         Take online courses  15246\n",
       "1    Buy books and work through the exercises  11750\n",
       "6                   Part-time/evening courses   7517\n",
       "7                   Contribute to open source   7423\n",
       "8                                    Bootcamp   5276\n",
       "9                        Conferences/meet-ups   5244\n",
       "4                           Return to college   5017\n",
       "5   Participate in online coding competitions   3610\n",
       "3                    Get a job as a QA tester   3376\n",
       "11                  Participate in hackathons   2747\n",
       "12                            Master's degree   2639\n",
       "10                                      Other   2348\n",
       "2                               None of these    604"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "study_df = occurence_counts(study, 'method', 'count', values_in_method)\n",
    "study_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>method</th>\n",
       "      <th>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Take online courses</td>\n",
       "      <td>15246</td>\n",
       "      <td>20.943171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Buy books and work through the exercises</td>\n",
       "      <td>11750</td>\n",
       "      <td>16.140775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Part-time/evening courses</td>\n",
       "      <td>7517</td>\n",
       "      <td>10.325975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Contribute to open source</td>\n",
       "      <td>7423</td>\n",
       "      <td>10.196849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Bootcamp</td>\n",
       "      <td>5276</td>\n",
       "      <td>7.247551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Conferences/meet-ups</td>\n",
       "      <td>5244</td>\n",
       "      <td>7.203594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Return to college</td>\n",
       "      <td>5017</td>\n",
       "      <td>6.891768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Participate in online coding competitions</td>\n",
       "      <td>3610</td>\n",
       "      <td>4.958996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Get a job as a QA tester</td>\n",
       "      <td>3376</td>\n",
       "      <td>4.637554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Participate in hackathons</td>\n",
       "      <td>2747</td>\n",
       "      <td>3.773507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Master's degree</td>\n",
       "      <td>2639</td>\n",
       "      <td>3.625149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Other</td>\n",
       "      <td>2348</td>\n",
       "      <td>3.225408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>None of these</td>\n",
       "      <td>604</td>\n",
       "      <td>0.829705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       method  count          %\n",
       "0                         Take online courses  15246  20.943171\n",
       "1    Buy books and work through the exercises  11750  16.140775\n",
       "6                   Part-time/evening courses   7517  10.325975\n",
       "7                   Contribute to open source   7423  10.196849\n",
       "8                                    Bootcamp   5276   7.247551\n",
       "9                        Conferences/meet-ups   5244   7.203594\n",
       "4                           Return to college   5017   6.891768\n",
       "5   Participate in online coding competitions   3610   4.958996\n",
       "3                    Get a job as a QA tester   3376   4.637554\n",
       "11                  Participate in hackathons   2747   3.773507\n",
       "12                            Master's degree   2639   3.625149\n",
       "10                                      Other   2348   3.225408\n",
       "2                               None of these    604   0.829705"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a new column with the percentages for each of the methods\n",
    "study_df['%'] = study_df['count']/np.sum(study_df['count'])*100\n",
    "study_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#What if we take into account the input of those that are the highest earners? Or the greatest job satisfaction?\n",
    "def relevant_method(df, col_name, col_mean, occurences):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df - df I want to do the search in\n",
    "    col_name - col name I want to look through\n",
    "    col_count - col I want to count values from\n",
    "    col_mean - col I want the mean from\n",
    "    occurences - list of strings I want to search for in each row of df[col]\n",
    "    \n",
    "    OUTPUT:\n",
    "    df_all - holds sum, square, total, mean, variations and standard dev\n",
    "    '''\n",
    "    from collections import defaultdict\n",
    "    new_df = defaultdict(int)\n",
    "    squares_df = defaultdict(int)\n",
    "    denoms = dict()\n",
    "    for val in occurences:\n",
    "        denoms[val] = 0\n",
    "        for idx in range(df.shape[0]):\n",
    "            if df[col_name][idx] and df[col_mean][idx] > 0:\n",
    "                new_df[val] += df[col_mean][idx]\n",
    "                squares_df[val] += df[col_mean][idx]**2\n",
    "                denoms[val] += 1\n",
    "                \n",
    "    #turn into dataframe\n",
    "    new_df = pd.DataFrame(pd.Series(new_df)).reset_index()\n",
    "    squares_df = pd.DataFrame(pd.Series(squares_df)).reset_index()\n",
    "    denoms = pd.DataFrame(pd.Series(denoms)).reset_index()\n",
    "    \n",
    "    #change the column names\n",
    "    new_df.columns = [col_name, 'col_sum']\n",
    "    squares_df.columns = [col_name, 'col_squares']\n",
    "    denoms.columns = [col_name, 'col_total']\n",
    "    \n",
    "    #merge the 2 dataframes\n",
    "    df_means = pd.merge(new_df, denoms)\n",
    "    df_all = pd.merge(df_means, squares_df)\n",
    "    \n",
    "    #additional columns to help with the analysis\n",
    "    df_all['mean_col'] = df_means['col_sum']/df_means['col_total']\n",
    "    df_all['var_col'] = df_all['col_squares']/df_all['col_total'] - df_all['mean_col']**2\n",
    "    df_all['std_col'] = np.sqrt(df_all['var_col'])\n",
    "    df_all['lower_95'] = df_all['mean_col'] - 1.96*df_all['std_col']/np.sqrt(df_all['col_total'])\n",
    "    df_all['upper_95'] = df_all['mean_col'] + 1.96*df_all['std_col']/np.sqrt(df_all['col_total'])\n",
    "    \n",
    "    return df_all\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all = relevant_method(df, 'CousinEducation', 'Salary', values_in_method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>CousinEducation</th>\n",
       "      <th>col_sum</th>\n",
       "      <th>col_total</th>\n",
       "      <th>col_squares</th>\n",
       "      <th>mean_col</th>\n",
       "      <th>var_col</th>\n",
       "      <th>std_col</th>\n",
       "      <th>lower_95</th>\n",
       "      <th>upper_95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Take online courses</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Buy books and work through the exercises</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>None of these</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Get a job as a QA tester</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Return to college</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Participate in online coding competitions</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Part-time/evening courses</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Contribute to open source</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Bootcamp</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Conferences/meet-ups</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Other</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Participate in hackathons</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Master's degree</td>\n",
       "      <td>7.257437e+08</td>\n",
       "      <td>12885</td>\n",
       "      <td>6.135964e+13</td>\n",
       "      <td>56324.696464</td>\n",
       "      <td>1.589627e+09</td>\n",
       "      <td>39870.129079</td>\n",
       "      <td>55636.263747</td>\n",
       "      <td>57013.129181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              CousinEducation       col_sum  col_total  \\\n",
       "0                         Take online courses  7.257437e+08      12885   \n",
       "1    Buy books and work through the exercises  7.257437e+08      12885   \n",
       "2                               None of these  7.257437e+08      12885   \n",
       "3                    Get a job as a QA tester  7.257437e+08      12885   \n",
       "4                           Return to college  7.257437e+08      12885   \n",
       "5   Participate in online coding competitions  7.257437e+08      12885   \n",
       "6                   Part-time/evening courses  7.257437e+08      12885   \n",
       "7                   Contribute to open source  7.257437e+08      12885   \n",
       "8                                    Bootcamp  7.257437e+08      12885   \n",
       "9                        Conferences/meet-ups  7.257437e+08      12885   \n",
       "10                                      Other  7.257437e+08      12885   \n",
       "11                  Participate in hackathons  7.257437e+08      12885   \n",
       "12                            Master's degree  7.257437e+08      12885   \n",
       "\n",
       "     col_squares      mean_col       var_col       std_col      lower_95  \\\n",
       "0   6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "1   6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "2   6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "3   6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "4   6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "5   6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "6   6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "7   6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "8   6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "9   6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "10  6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "11  6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "12  6.135964e+13  56324.696464  1.589627e+09  39870.129079  55636.263747   \n",
       "\n",
       "        upper_95  \n",
       "0   57013.129181  \n",
       "1   57013.129181  \n",
       "2   57013.129181  \n",
       "3   57013.129181  \n",
       "4   57013.129181  \n",
       "5   57013.129181  \n",
       "6   57013.129181  \n",
       "7   57013.129181  \n",
       "8   57013.129181  \n",
       "9   57013.129181  \n",
       "10  57013.129181  \n",
       "11  57013.129181  \n",
       "12  57013.129181  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now lets sort the results based on the mean\n",
    "df_all.sort_values('mean_col', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2602, 155)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what does the data suggest about bootcamp completions in job placement and salary?\n",
    "#filter the dataframe to only the bootcamp grads\n",
    "bootcamp_df = df[df['TimeAfterBootcamp'].isnull() == False]\n",
    "not_bootcamp_df = df[df['TimeAfterBootcamp'].isnull() == True]\n",
    "bootcamp_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male                                                       0.863071\n",
       "Female                                                     0.112494\n",
       "Other                                                      0.006455\n",
       "Gender non-conforming                                      0.005533\n",
       "Male; Other                                                0.002766\n",
       "Male; Gender non-conforming                                0.001383\n",
       "Female; Gender non-conforming                              0.001383\n",
       "Male; Transgender                                          0.000922\n",
       "Transgender                                                0.000922\n",
       "Transgender; Gender non-conforming                         0.000922\n",
       "Male; Female                                               0.000922\n",
       "Female; Transgender; Gender non-conforming                 0.000922\n",
       "Male; Female; Transgender; Gender non-conforming; Other    0.000922\n",
       "Male; Gender non-conforming; Other                         0.000461\n",
       "Male; Female; Transgender; Gender non-conforming           0.000461\n",
       "Female; Gender non-conforming; Other                       0.000461\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#bootcamps are often a way to fill gaps in representation in the tech community\n",
    "#lets check this by gender\n",
    "bootcamp_df['Gender'].value_counts()/(bootcamp_df.shape[0] - sum(bootcamp_df['Gender'].isnull()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wow, almost 90% are male in people that complete bootcamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male                                                       0.903857\n",
       "Female                                                     0.071659\n",
       "Other                                                      0.006418\n",
       "Male; Other                                                0.005019\n",
       "Gender non-conforming                                      0.004501\n",
       "Male; Gender non-conforming                                0.001886\n",
       "Female; Transgender                                        0.001703\n",
       "Transgender                                                0.001612\n",
       "Female; Gender non-conforming                              0.000791\n",
       "Male; Female                                               0.000395\n",
       "Male; Female; Transgender; Gender non-conforming; Other    0.000395\n",
       "Transgender; Gender non-conforming                         0.000395\n",
       "Male; Transgender                                          0.000274\n",
       "Male; Female; Transgender; Gender non-conforming           0.000182\n",
       "Female; Transgender; Gender non-conforming                 0.000182\n",
       "Male; Female; Transgender                                  0.000122\n",
       "Male; Transgender; Gender non-conforming                   0.000122\n",
       "Gender non-conforming; Other                               0.000122\n",
       "Male; Female; Other                                        0.000061\n",
       "Male; Gender non-conforming; Other                         0.000061\n",
       "Female; Transgender; Gender non-conforming; Other          0.000030\n",
       "Female; Transgender; Other                                 0.000030\n",
       "Female; Other                                              0.000030\n",
       "Male; Female; Gender non-conforming                        0.000030\n",
       "Male; Transgender; Other                                   0.000030\n",
       "Male; Female; Transgender; Other                           0.000030\n",
       "Transgender; Other                                         0.000030\n",
       "Male; Female; Gender non-conforming; Other                 0.000030\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets check on the non-bootcamps\n",
    "not_bootcamp_df['Gender'].value_counts()/(not_bootcamp_df.shape[0] - sum(not_bootcamp_df['Gender'].isnull()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suprisingly the bootcamps are performing better than the non-bootcampers coming to the scene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bachelor's degree                                                    0.475019\n",
       "Master's degree                                                      0.225211\n",
       "Some college/university study without earning a bachelor's degree    0.162567\n",
       "Secondary school                                                     0.069562\n",
       "Doctoral degree                                                      0.022291\n",
       "Professional degree                                                  0.016141\n",
       "I prefer not to answer                                               0.011914\n",
       "Primary/elementary school                                            0.009224\n",
       "I never completed any formal education                               0.008071\n",
       "Name: FormalEducation, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking a breakdown by formal education\n",
    "bootcamp_df['FormalEducation'].value_counts()/(bootcamp_df.shape[0] - sum(bootcamp_df['FormalEducation'].isnull()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### looks like most of the people going for a bootcamp already had at least a bachelors degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bachelor's degree                                                    0.417565\n",
       "Master's degree                                                      0.216335\n",
       "Some college/university study without earning a bachelor's degree    0.157942\n",
       "Secondary school                                                     0.117381\n",
       "Doctoral degree                                                      0.025620\n",
       "I prefer not to answer                                               0.022095\n",
       "Primary/elementary school                                            0.020967\n",
       "Professional degree                                                  0.013794\n",
       "I never completed any formal education                               0.008301\n",
       "Name: FormalEducation, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_bootcamp_df['FormalEducation'].value_counts()/(not_bootcamp_df.shape[0] - sum(not_bootcamp_df['FormalEducation'].isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "I already had a job as a developer when I started the program    0.458493\n",
       "Immediately upon graduating                                      0.112990\n",
       "I got a job as a developer before completing the program         0.096849\n",
       "One to three months                                              0.087625\n",
       "I haven't gotten a job as a developer yet                        0.081091\n",
       "Less than a month                                                0.059954\n",
       "Four to six months                                               0.040354\n",
       "Longer than a year                                               0.032667\n",
       "Six months to a year                                             0.029977\n",
       "Name: TimeAfterBootcamp, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how long after a bootcamp does an individual find a job?\n",
    "bootcamp_df['TimeAfterBootcamp'].value_counts()/bootcamp_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16002fc3388>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#what is the salary breakdown of the people working in these bootcamps?\n",
    "bootcamp_df['Salary'].hist(bins = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It seems like there is enormous inconsistency in the salary remuneration from the bootcamps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore job satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21435242839352428"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what is the proportion of missing values in job satisfaction column\n",
    "df.JobSatisfaction.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.957078462452943"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean value for Jobsatisfaction\n",
    "df.JobSatisfaction.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmploymentStatus\n",
       "Employed full-time                                      6.926805\n",
       "Employed part-time                                      6.821468\n",
       "I prefer not to say                                          NaN\n",
       "Independent contractor, freelancer, or self-employed    7.252823\n",
       "Not employed, and not looking for work                  7.000000\n",
       "Not employed, but looking for work                           NaN\n",
       "Retired                                                      NaN\n",
       "Name: JobSatisfaction, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using groupby to determine the group with highest job satisfaction\n",
    "df.groupby('EmploymentStatus').mean()['JobSatisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CompanySize\n",
       "5,000 to 9,999 employees    6.748153\n",
       "10,000 or more employees    6.810345\n",
       "I don't know                6.842679\n",
       "500 to 999 employees        6.883661\n",
       "1,000 to 4,999 employees    6.884509\n",
       "100 to 499 employees        6.937089\n",
       "20 to 99 employees          6.949092\n",
       "10 to 19 employees          7.000799\n",
       "Fewer than 10 employees     7.008749\n",
       "I prefer not to answer      7.280835\n",
       "Name: JobSatisfaction, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#company size vs jobsatisfaction\n",
    "df.groupby('CompanySize').mean()['JobSatisfaction'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job! That's what we found as well!\n"
     ]
    }
   ],
   "source": [
    "#using a dictionary to check data exploration solutions\n",
    "a = 6.957\n",
    "b = 0.214\n",
    "c = 'full-time'\n",
    "d = 'contractors'\n",
    "e = 'retired'\n",
    "f = 'yes'\n",
    "g = 'no'\n",
    "h = 'hard to tell'\n",
    "\n",
    "job_sol_1 = {'The proportion of missing values in the Job Satisfaction column': b,\n",
    "             'According to EmploymentStatus, which group has the highest average job satisfaction?': d, \n",
    "             'In general, do smaller companies appear to have employees with higher job satisfaction?': f}\n",
    "             \n",
    "t.jobsat_check1(job_sol_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProgramHobby\n",
       "No                                           6.833825\n",
       "Yes, I program as a hobby                    6.913072\n",
       "Yes, I contribute to open source projects    7.041535\n",
       "Yes, both                                    7.108041\n",
       "Name: JobSatisfaction, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#does programming outside of word lead to higher job satisfaction?\n",
    "df.groupby('ProgramHobby').mean()['JobSatisfaction'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I realize that I am asking the wrong question, I think that people that have higher satisfaction in the work place will be more likely to engage in programming actives outside of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HomeRemote\n",
       "Never                                                      6.632502\n",
       "It's complicated                                           6.840830\n",
       "More than half, but not all, the time                      6.953184\n",
       "About half the time                                        6.973702\n",
       "A few days each month                                      7.077509\n",
       "Less than half the time, but at least one day each week    7.120125\n",
       "All or almost all the time (I'm full-time remote)          7.388430\n",
       "Name: JobSatisfaction, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#does flexibility influence job satisfaction?\n",
    "df.groupby('HomeRemote').mean()['JobSatisfaction'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FormalEducation\n",
       "I never completed any formal education                               6.833333\n",
       "Bachelor's degree                                                    6.866363\n",
       "Master's degree                                                      6.953151\n",
       "Primary/elementary school                                            6.956897\n",
       "I prefer not to answer                                               7.005682\n",
       "Some college/university study without earning a bachelor's degree    7.081334\n",
       "Professional degree                                                  7.087459\n",
       "Secondary school                                                     7.143082\n",
       "Doctoral degree                                                      7.348857\n",
       "Name: JobSatisfaction, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('FormalEducation').mean()['JobSatisfaction'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job! That's what we found as well!\n"
     ]
    }
   ],
   "source": [
    "a = 'yes'\n",
    "b = 'no'\n",
    "c = 'hard to tell'\n",
    "\n",
    "job_sol_2 = {'Do individuals who program outside of work appear to have higher JobSatisfaction?': a,\n",
    "             'Does flexibility to work outside of the office appear to have an influence on JobSatisfaction?': a, \n",
    "             'A friend says a Doctoral degree increases the chance of having job you like, does this seem true?': a}\n",
    "             \n",
    "t.jobsat_check2(job_sol_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling\n",
    "### Predicting Salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Respondent</th>\n",
       "      <th>CareerSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>StackOverflowSatisfaction</th>\n",
       "      <th>Salary</th>\n",
       "      <th>ExpectedSalary</th>\n",
       "      <th>HigherEd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>51392.000000</td>\n",
       "      <td>42695.000000</td>\n",
       "      <td>40376.000000</td>\n",
       "      <td>20600.000000</td>\n",
       "      <td>36592.000000</td>\n",
       "      <td>12891.000000</td>\n",
       "      <td>2566.000000</td>\n",
       "      <td>51392.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>25696.500000</td>\n",
       "      <td>7.300574</td>\n",
       "      <td>6.957078</td>\n",
       "      <td>3.952282</td>\n",
       "      <td>8.429875</td>\n",
       "      <td>56298.480641</td>\n",
       "      <td>33142.220468</td>\n",
       "      <td>0.230697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>14835.736854</td>\n",
       "      <td>1.955444</td>\n",
       "      <td>2.167652</td>\n",
       "      <td>7.564722</td>\n",
       "      <td>1.390635</td>\n",
       "      <td>39880.905277</td>\n",
       "      <td>30162.988829</td>\n",
       "      <td>0.421283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>12848.750000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>26440.371839</td>\n",
       "      <td>4490.492484</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>25696.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>29364.263691</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>38544.250000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>80000.000000</td>\n",
       "      <td>50403.225806</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>51392.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>197000.000000</td>\n",
       "      <td>187500.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Respondent  CareerSatisfaction  JobSatisfaction  HoursPerWeek  \\\n",
       "count  51392.000000        42695.000000     40376.000000  20600.000000   \n",
       "mean   25696.500000            7.300574         6.957078      3.952282   \n",
       "std    14835.736854            1.955444         2.167652      7.564722   \n",
       "min        1.000000            0.000000         0.000000      0.000000   \n",
       "25%    12848.750000            6.000000         6.000000      1.000000   \n",
       "50%    25696.500000            8.000000         7.000000      1.000000   \n",
       "75%    38544.250000            9.000000         8.000000      4.000000   \n",
       "max    51392.000000           10.000000        10.000000     40.000000   \n",
       "\n",
       "       StackOverflowSatisfaction         Salary  ExpectedSalary      HigherEd  \n",
       "count               36592.000000   12891.000000     2566.000000  51392.000000  \n",
       "mean                    8.429875   56298.480641    33142.220468      0.230697  \n",
       "std                     1.390635   39880.905277    30162.988829      0.421283  \n",
       "min                     0.000000       0.000000        0.000000      0.000000  \n",
       "25%                     8.000000   26440.371839     4490.492484      0.000000  \n",
       "50%                     9.000000   50000.000000    29364.263691      0.000000  \n",
       "75%                    10.000000   80000.000000    50403.225806      0.000000  \n",
       "max                    10.000000  197000.000000   187500.000000      1.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job that looks right!\n"
     ]
    }
   ],
   "source": [
    "#use desbribe to answer multiple questions\n",
    "a = 40\n",
    "b = 'HoursPerWeek'\n",
    "c = 'Salary'\n",
    "d = 'Respondent'\n",
    "e = 10\n",
    "f = 'ExpectedSalary'\n",
    "\n",
    "desc_sol = {'A column just listing an index for each row': d,\n",
    "       'The maximum Satisfaction on the scales for the survey': e,\n",
    "       'The column with the most missing values': f,\n",
    "       'The variable with the highest spread of values': c}\n",
    "\n",
    "# Check your solution\n",
    "t.describe_check(desc_sol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000016003A6A2C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000016003ACB408>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000016003AFFCC8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000016003B39E08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000016003B70108>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000016007045D08>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001600BD1CE48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001600BD56F08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001600BD60AC8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15,20))\n",
    "ax = fig.gca()\n",
    "df.hist(ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1600c5e0688>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#I will use a correlation matrix to see how the attributes relate to each opther\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "sns.heatmap(df.corr(), annot = True, fmt=\".2f\", cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job that looks right!\n"
     ]
    }
   ],
   "source": [
    "#using the correlation heatmap to answer questions\n",
    "a = 0.65\n",
    "b = -0.01\n",
    "c = 'ExpectedSalary'\n",
    "d = 'No'\n",
    "e = 'Yes'\n",
    "f = 'CareerSatisfaction'\n",
    "g = -0.15\n",
    "\n",
    "scatter_sol = {'The column with the strongest correlation with Salary': f,\n",
    "       'The data suggests more hours worked relates to higher salary': d,\n",
    "       'Data in the ______ column meant missing data in three other columns': c,\n",
    "       'The strongest negative relationship had what correlation?': g}\n",
    "\n",
    "t.scatter_check(scatter_sol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Salary</th>\n",
       "      <th>CareerSatisfaction</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>StackOverflowSatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>113750.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Salary  CareerSatisfaction  HoursPerWeek  JobSatisfaction  \\\n",
       "0       NaN                 NaN           0.0              NaN   \n",
       "1       NaN                 NaN           NaN              NaN   \n",
       "2  113750.0                 8.0           NaN              9.0   \n",
       "3       NaN                 6.0           5.0              3.0   \n",
       "4       NaN                 6.0           NaN              8.0   \n",
       "\n",
       "   StackOverflowSatisfaction  \n",
       "0                        9.0  \n",
       "1                        8.0  \n",
       "2                        8.0  \n",
       "3                       10.0  \n",
       "4                        NaN  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Subset to only quantitative vars\n",
    "num_vars = df[['Salary', 'CareerSatisfaction', 'HoursPerWeek', 'JobSatisfaction', 'StackOverflowSatisfaction']]\n",
    "\n",
    "\n",
    "num_vars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25083670610211706"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of individuals that reported a salary\n",
    "prop_sals = 1 - df.isnull()['Salary'].mean()# Proportion of individuals in the dataset with salary reported\n",
    "\n",
    "prop_sals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Salary</th>\n",
       "      <th>CareerSatisfaction</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>StackOverflowSatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>113750.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>100000.0</td>\n",
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       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>9.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>82500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>100764.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Salary  CareerSatisfaction  HoursPerWeek  JobSatisfaction  \\\n",
       "2   113750.0                 8.0           NaN              9.0   \n",
       "14  100000.0                 8.0           NaN              8.0   \n",
       "17  130000.0                 9.0           NaN              8.0   \n",
       "18   82500.0                 5.0           NaN              3.0   \n",
       "22  100764.0                 8.0           NaN              9.0   \n",
       "\n",
       "    StackOverflowSatisfaction  \n",
       "2                         8.0  \n",
       "14                        8.0  \n",
       "17                        8.0  \n",
       "18                        NaN  \n",
       "22                        8.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing rows with missing salary values\n",
    "sal_rm = num_vars.dropna(subset=['Salary'], axis=0)# dataframe with rows for nan Salaries removed\n",
    "\n",
    "sal_rm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Salary</th>\n",
       "      <th>CareerSatisfaction</th>\n",
       "      <th>HoursPerWeek</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>StackOverflowSatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>175000.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>14838.709677</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>15674.203822</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>43010.752688</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>65000.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Salary  CareerSatisfaction  HoursPerWeek  JobSatisfaction  \\\n",
       "25  175000.000000                 7.0           0.0              7.0   \n",
       "34   14838.709677                10.0           1.0              8.0   \n",
       "52   15674.203822                 6.0           4.0              5.0   \n",
       "57   43010.752688                10.0           2.0              6.0   \n",
       "70   65000.000000                 8.0           2.0              5.0   \n",
       "\n",
       "    StackOverflowSatisfaction  \n",
       "25                        9.0  \n",
       "34                       10.0  \n",
       "52                        8.0  \n",
       "57                       10.0  \n",
       "70                        7.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing rows with nans in any column of num_vars\n",
    "all_rm = num_vars.dropna(axis=0) # dataframe with rows for nan Salaries removed\n",
    "\n",
    "all_rm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_2 = all_rm[['CareerSatisfaction', 'HoursPerWeek', 'JobSatisfaction', 'StackOverflowSatisfaction']]\n",
    "y_2 = all_rm['Salary']\n",
    "\n",
    "# Split data into training and test data, and fit a linear model\n",
    "X_2_train, X_2_test, y_2_train, y_2_test = train_test_split(X_2, y_2 , test_size=.30, random_state=42)\n",
    "lm_2_model = LinearRegression(normalize=True)\n",
    "\n",
    "# If our model works, it should just fit our model to the data. Otherwise, it will let us know.\n",
    "try:\n",
    "    lm_2_model.fit(X_2_train, y_2_train)\n",
    "except:\n",
    "    print(\"Oh no! It doesn't work!!!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.030994664959115625"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_preds = lm_2_model.predict(X_2_test)# Predictions here\n",
    "r2_test = r2_score(y_2_test, y_test_preds) # Rsquared here\n",
    "\n",
    "# Print r2 to see result\n",
    "r2_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### second approach with fine combed imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The r-squared score for your model was 0.030994664959115625 on 1602 values.'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Only use quant variables and drop any rows with missing values\n",
    "\n",
    "num_vars = df[['Salary', 'CareerSatisfaction', 'HoursPerWeek', 'JobSatisfaction', 'StackOverflowSatisfaction']]\n",
    "df_dropna = num_vars.dropna(axis=0)\n",
    "\n",
    "#Split into explanatory and response variables\n",
    "X = df_dropna[['CareerSatisfaction', 'HoursPerWeek', 'JobSatisfaction', 'StackOverflowSatisfaction']]\n",
    "y = df_dropna['Salary']\n",
    "\n",
    "#Split into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state=42) \n",
    "\n",
    "lm_model = LinearRegression(normalize=True) # Instantiate\n",
    "lm_model.fit(X_train, y_train) #Fit\n",
    "        \n",
    "#Predict and score the model\n",
    "y_test_preds = lm_model.predict(X_test) \n",
    "\"The r-squared score for your model was {} on {} values.\".format(r2_score(y_test, y_test_preds), len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the num_vars dataframe from drop the missing values of the response (Salary) - store this in drop_sal_df, \n",
    "#then impute the values for all the other missing values with the mean of the column - store this in fill_df\n",
    "drop_sal_df = num_vars.dropna(subset=['Salary'], axis=0) #Drop the rows with missing salaries\n",
    "\n",
    "fill_mean = lambda col: col.fillna(col.mean()) # Mean function\n",
    "\n",
    "fill_df = drop_sal_df.apply(fill_mean, axis=0) #Fill all missing values with the mean of the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The r-squared score for your model was 0.04072431792894737 on 3868 values.'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Split into explanatory and response variables\n",
    "X = fill_df[['CareerSatisfaction', 'HoursPerWeek', 'JobSatisfaction', 'StackOverflowSatisfaction']]\n",
    "y = fill_df['Salary']\n",
    "\n",
    "#Split into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state=42) \n",
    "\n",
    "lm_model = LinearRegression(normalize=True) # Instantiate\n",
    "lm_model.fit(X_train, y_train) #Fit\n",
    "        \n",
    "#Predict and score the model\n",
    "y_test_preds = lm_model.predict(X_test) \n",
    "\n",
    "#Rsquared and y_test\n",
    "rsquared_score = r2_score(y_test, y_test_preds)\n",
    "length_y_test = len(y_test)\n",
    "\n",
    "\"The r-squared score for your model was {} on {} values.\".format(rsquared_score, length_y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Third approach with encoding of cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The r-squared score for the model using only quantitative variables was 0.04072431792894737 on 3868 values.'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Only use quant variables and drop any rows with missing values\n",
    "num_vars = df[['Salary', 'CareerSatisfaction', 'HoursPerWeek', 'JobSatisfaction', 'StackOverflowSatisfaction']]\n",
    "\n",
    "#Drop the rows with missing salaries\n",
    "drop_sal_df = num_vars.dropna(subset=['Salary'], axis=0)\n",
    "\n",
    "# Mean function\n",
    "fill_mean = lambda col: col.fillna(col.mean())\n",
    "# Fill the mean\n",
    "fill_df = drop_sal_df.apply(fill_mean, axis=0)\n",
    "\n",
    "#Split into explanatory and response variables\n",
    "X = fill_df[['CareerSatisfaction', 'HoursPerWeek', 'JobSatisfaction', 'StackOverflowSatisfaction']]\n",
    "y = fill_df['Salary']\n",
    "\n",
    "#Split into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state=42) \n",
    "\n",
    "lm_model = LinearRegression(normalize=True) # Instantiate\n",
    "lm_model.fit(X_train, y_train) #Fit\n",
    "        \n",
    "#Predict and score the model\n",
    "y_test_preds = lm_model.predict(X_test) \n",
    "\"The r-squared score for the model using only quantitative variables was {} on {} values.\".format(r2_score(y_test, y_test_preds), len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for cat columns\n",
    "cat_df = df.select_dtypes(include=['object']) # Subset to a dataframe only holding the categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use cat_df and the cells below to fill in the dictionary below the correct value for each statement.\n",
    "np.sum(np.sum(cat_df.isnull())/cat_df.shape[0] == 0)# Cell for your work here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.sum(cat_df.isnull())/cat_df.shape[0] > .5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.sum(cat_df.isnull())/cat_df.shape[0] > .75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Provide the key as an `integer` that answers the question\n",
    "\n",
    "cat_df_dict = {'the number of columns with no missing values': 6, \n",
    "               'the number of columns with more than half of the column missing': 50,\n",
    "               'the number of columns with more than 75% of the column missing': 13\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
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       "      <td>1</td>\n",
       "      <td>a</td>\n",
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       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>b</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col1  col2\n",
       "0    a   1.0\n",
       "1    a   NaN\n",
       "2    b   3.0\n",
       "3    b   NaN\n",
       "4    a   5.0\n",
       "5  NaN   6.0\n",
       "6    b   7.0\n",
       "7  NaN   8.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create dummies\n",
    "dummy_var_df = pd.DataFrame({'col1': ['a', 'a', 'b', 'b', 'a', np.nan, 'b', np.nan],\n",
    "                             'col2': [1, np.nan, 3, np.nan, 5, 6, 7, 8] \n",
    "})\n",
    "                            \n",
    "dummy_var_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  1  0\n",
       "1  1  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  1  0\n",
       "5  0  0\n",
       "6  0  1\n",
       "7  0  0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(dummy_var_df['col1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pull a list of the column names of the categorical variables\n",
    "cat_cols_lst = cat_df.columns\n",
    "\n",
    "def create_dummy_df(df, cat_cols, dummy_na):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df - pandas dataframe with categorical variables you want to dummy\n",
    "    cat_cols - list of strings that are associated with names of the categorical columns\n",
    "    dummy_na - Bool holding whether you want to dummy NA vals of categorical columns or not\n",
    "    \n",
    "    OUTPUT:\n",
    "    df - a new dataframe that has the following characteristics:\n",
    "            1. contains all columns that were not specified as categorical\n",
    "            2. removes all the original columns in cat_cols\n",
    "            3. dummy columns for each of the categorical columns in cat_cols\n",
    "            4. if dummy_na is True - it also contains dummy columns for the NaN values\n",
    "            5. Use a prefix of the column name with an underscore (_) for separating \n",
    "    '''\n",
    "    for col in  cat_cols:\n",
    "        try:\n",
    "            # for each cat add dummy var, drop original column\n",
    "            df = pd.concat([df.drop(col, axis=1), pd.get_dummies(df[col], prefix=col, prefix_sep='_', drop_first=True, dummy_na=dummy_na)], axis=1)\n",
    "        except:\n",
    "            continue\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12891, 21111)\n"
     ]
    }
   ],
   "source": [
    "#Dropping where the salary has missing values\n",
    "df  = df.dropna(subset=['Salary'], axis=0)\n",
    "\n",
    "#Pull a list of the column names of the categorical variables\n",
    "cat_df = df.select_dtypes(include=['object'])\n",
    "cat_cols_lst = cat_df.columns\n",
    "\n",
    "df_new = create_dummy_df(df, cat_cols_lst, dummy_na=False) #Use your newly created function\n",
    "\n",
    "# Show a header of df_new to check\n",
    "print(df_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_fit_linear_mod(df, response_col, cat_cols, dummy_na, test_size=.3, rand_state=42):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df - a dataframe holding all the variables of interest\n",
    "    response_col - a string holding the name of the column \n",
    "    cat_cols - list of strings that are associated with names of the categorical columns\n",
    "    dummy_na - Bool holding whether you want to dummy NA vals of categorical columns or not\n",
    "    test_size - a float between [0,1] about what proportion of data should be in the test dataset\n",
    "    rand_state - an int that is provided as the random state for splitting the data into training and test \n",
    "    \n",
    "    OUTPUT:\n",
    "    test_score - float - r2 score on the test data\n",
    "    train_score - float - r2 score on the test data\n",
    "    lm_model - model object from sklearn\n",
    "    X_train, X_test, y_train, y_test - output from sklearn train test split used for optimal model\n",
    "    \n",
    "    Your function should:\n",
    "    1. Drop the rows with missing response values\n",
    "    2. Drop columns with NaN for all the values\n",
    "    3. Use create_dummy_df to dummy categorical columns\n",
    "    4. Fill the mean of the column for any missing values \n",
    "    5. Split your data into an X matrix and a response vector y\n",
    "    6. Create training and test sets of data\n",
    "    7. Instantiate a LinearRegression model with normalized data\n",
    "    8. Fit your model to the training data\n",
    "    9. Predict the response for the training data and the test data\n",
    "    10. Obtain an rsquared value for both the training and test data\n",
    "    '''\n",
    "    #Drop the rows with missing response values\n",
    "    df  = df.dropna(subset=[response_col], axis=0)\n",
    "\n",
    "    #Drop columns with all NaN values\n",
    "    df = df.dropna(how='all', axis=1)\n",
    "\n",
    "    #Dummy categorical variables\n",
    "    df = create_dummy_df(df, cat_cols, dummy_na)\n",
    "\n",
    "    # Mean function\n",
    "    fill_mean = lambda col: col.fillna(col.mean())\n",
    "    # Fill the mean\n",
    "    df = df.apply(fill_mean, axis=0)\n",
    "\n",
    "    #Split into explanatory and response variables\n",
    "    X = df.drop(response_col, axis=1)\n",
    "    y = df[response_col]\n",
    "\n",
    "    #Split into train and test\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=rand_state)\n",
    "\n",
    "    lm_model = LinearRegression(normalize=True) # Instantiate\n",
    "    lm_model.fit(X_train, y_train) #Fit\n",
    "\n",
    "    #Predict using your model\n",
    "    y_test_preds = lm_model.predict(X_test)\n",
    "    y_train_preds = lm_model.predict(X_train)\n",
    "\n",
    "    #Score using your model\n",
    "    test_score = r2_score(y_test, y_test_preds)\n",
    "    train_score = r2_score(y_train, y_train_preds)\n",
    "\n",
    "    return test_score, train_score, lm_model, X_train, X_test, y_train, y_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_score, train_score, lm_model, X_train, X_test, y_train, y_test = clean_fit_linear_mod(df_new, 'Salary', cat_cols_lst, dummy_na=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rsquared on the training data was 1.0.  The rsquared on the test data was -0.5245827416783675.\n"
     ]
    }
   ],
   "source": [
    "#Print training and testing score\n",
    "print(\"The rsquared on the training data was {}.  The rsquared on the test data was {}.\".format(train_score, test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, that rsquared smells like overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fourth approach "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create the X (explanatory matrix) and y (response vector) to be used in the model\n",
    "def clean_data(df):\n",
    "    '''\n",
    "    INPUT\n",
    "    df - pandas dataframe \n",
    "    \n",
    "    OUTPUT\n",
    "    X - A matrix holding all of the variables you want to consider when predicting the response\n",
    "    y - the corresponding response vector\n",
    "    \n",
    "    This function cleans df using the following steps to produce X and y:\n",
    "    1. Drop all the rows with no salaries\n",
    "    2. Create X as all the columns that are not the Salary column\n",
    "    3. Create y as the Salary column\n",
    "    4. Drop the Salary, Respondent, and the ExpectedSalary columns from X\n",
    "    5. For each numeric variable in X, fill the column with the mean value of the column.\n",
    "    6. Create dummy columns for all the categorical variables in X, drop the original columns\n",
    "    '''\n",
    "    # Drop rows with missing salary values\n",
    "    df = df.dropna(subset=['Salary'], axis=0)\n",
    "    y = df['Salary']\n",
    "    \n",
    "    #Drop respondent and expected salary columns\n",
    "    df = df.drop(['Respondent', 'ExpectedSalary', 'Salary'], axis=1)\n",
    "    \n",
    "    # Fill numeric columns with the mean\n",
    "    num_vars = df.select_dtypes(include=['float', 'int']).columns\n",
    "    for col in num_vars:\n",
    "        df[col].fillna((df[col].mean()), inplace=True)\n",
    "        \n",
    "    # Dummy the categorical variables\n",
    "    cat_vars = df.select_dtypes(include=['object']).copy().columns\n",
    "    for var in  cat_vars:\n",
    "        # for each cat add dummy var, drop original column\n",
    "        df = pd.concat([df.drop(var, axis=1), pd.get_dummies(df[var], prefix=var, prefix_sep='_', drop_first=True)], axis=1)\n",
    "    \n",
    "    X = df\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use the function to create X and y\n",
    "X, y = clean_data(df)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_fit_linear_mod(df, response_col, test_size=.3, rand_state=42):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df - a dataframe holding all the variables of interest\n",
    "    response_col - a string holding the name of the column\n",
    "    test_size - a float between [0,1] about what proportion of data should be in the test dataset\n",
    "    rand_state - an int that is provided as the random state for splitting the data into training and test\n",
    "\n",
    "    OUTPUT:\n",
    "    X - cleaned X matrix (dummy and mean imputation)\n",
    "    y - cleaned response (just dropped na)\n",
    "    test_score - float - r2 score on the test data\n",
    "    train_score - float - r2 score on the test data\n",
    "    lm_model - model object from sklearn\n",
    "    X_train, X_test, y_train, y_test - output from sklearn train test split used for optimal model\n",
    "\n",
    "    This function cleans the data and provides the necessary output for the rest of this notebook.\n",
    "    '''\n",
    "    #Dropping where the salary has missing values\n",
    "    df  = df.dropna(subset=['Salary'], axis=0)\n",
    "\n",
    "    #Drop columns with all NaN values\n",
    "    df = df.dropna(how='all', axis=1)\n",
    "\n",
    "    #Pull a list of the column names of the categorical variables\n",
    "    cat_df = df.select_dtypes(include=['object'])\n",
    "    cat_cols = cat_df.columns\n",
    "\n",
    "    #dummy all the cat_cols\n",
    "    for col in  cat_cols:\n",
    "        df = pd.concat([df.drop(col, axis=1), pd.get_dummies(df[col], prefix=col, prefix_sep='_', drop_first=True, dummy_na=True)], axis=1)\n",
    "\n",
    "\n",
    "    # Mean function\n",
    "    fill_mean = lambda col: col.fillna(col.mean())\n",
    "    # Fill the mean\n",
    "    df = df.apply(fill_mean, axis=0)\n",
    "\n",
    "    #Split into explanatory and response variables\n",
    "    X = df.drop(response_col, axis=1)\n",
    "    y = df[response_col]\n",
    "\n",
    "    #Split into train and test\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=rand_state)\n",
    "\n",
    "    lm_model = LinearRegression(normalize=True) # Instantiate\n",
    "    lm_model.fit(X_train, y_train) #Fit\n",
    "\n",
    "    #Predict using your model\n",
    "    y_test_preds = lm_model.predict(X_test)\n",
    "    y_train_preds = lm_model.predict(X_train)\n",
    "\n",
    "    #Score using your model\n",
    "    test_score = r2_score(y_test, y_test_preds)\n",
    "    train_score = r2_score(y_train, y_train_preds)\n",
    "\n",
    "    return X, y, test_score, train_score, lm_model, X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cutoffs here pertains to the number of missing values allowed in the used columns.\n",
    "#Therefore, lower values for the cutoff provides more predictors in the model.\n",
    "cutoffs = [5000, 3500, 2500, 1000, 100, 50, 30, 25]\n",
    "\n",
    "r2_scores_test, r2_scores_train, lm_model, X_train, X_test, y_train, y_test = find_optimal_lm_mod(X, y, cutoffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_optimal_lm_mod(X, y, cutoffs, test_size = .30, random_state=42, plot=True):\n",
    "    '''\n",
    "    INPUT\n",
    "    X - pandas dataframe, X matrix\n",
    "    y - pandas dataframe, response variable\n",
    "    cutoffs - list of ints, cutoff for number of non-zero values in dummy categorical vars\n",
    "    test_size - float between 0 and 1, default 0.3, determines the proportion of data as test data\n",
    "    random_state - int, default 42, controls random state for train_test_split\n",
    "    plot - boolean, default 0.3, True to plot result\n",
    "\n",
    "    OUTPUT\n",
    "    r2_scores_test - list of floats of r2 scores on the test data\n",
    "    r2_scores_train - list of floats of r2 scores on the train data\n",
    "    lm_model - model object from sklearn\n",
    "    X_train, X_test, y_train, y_test - output from sklearn train test split used for optimal model\n",
    "    '''\n",
    "    r2_scores_test, r2_scores_train, num_feats, results = [], [], [], dict()\n",
    "    for cutoff in cutoffs:\n",
    "\n",
    "        #reduce X matrix\n",
    "        reduce_X = X.iloc[:, np.where((X.sum() > cutoff) == True)[0]]\n",
    "        num_feats.append(reduce_X.shape[1])\n",
    "\n",
    "        #split the data into train and test\n",
    "        X_train, X_test, y_train, y_test = train_test_split(reduce_X, y, test_size = test_size, random_state=random_state)\n",
    "\n",
    "        #fit the model and obtain pred response\n",
    "        lm_model = LinearRegression(normalize=True)\n",
    "        lm_model.fit(X_train, y_train)\n",
    "        y_test_preds = lm_model.predict(X_test)\n",
    "        y_train_preds = lm_model.predict(X_train)\n",
    "\n",
    "        #append the r2 value from the test set\n",
    "        r2_scores_test.append(r2_score(y_test, y_test_preds))\n",
    "        r2_scores_train.append(r2_score(y_train, y_train_preds))\n",
    "        results[str(cutoff)] = r2_score(y_test, y_test_preds)\n",
    "\n",
    "    if plot:\n",
    "        plt.plot(num_feats, r2_scores_test, label=\"Test\", alpha=.5)\n",
    "        plt.plot(num_feats, r2_scores_train, label=\"Train\", alpha=.5)\n",
    "        plt.xlabel('Number of Features')\n",
    "        plt.ylabel('Rsquared')\n",
    "        plt.title('Rsquared by Number of Features')\n",
    "        plt.legend(loc=1)\n",
    "        plt.show()\n",
    "\n",
    "    best_cutoff = max(results, key=results.get)\n",
    "\n",
    "    #reduce X matrix\n",
    "    reduce_X = X.iloc[:, np.where((X.sum() > int(best_cutoff)) == True)[0]]\n",
    "    num_feats.append(reduce_X.shape[1])\n",
    "\n",
    "    #split the data into train and test\n",
    "    X_train, X_test, y_train, y_test = train_test_split(reduce_X, y, test_size = test_size, random_state=random_state)\n",
    "\n",
    "    #fit the model\n",
    "    lm_model = LinearRegression(normalize=True)\n",
    "    lm_model.fit(X_train, y_train)\n",
    "\n",
    "    return r2_scores_test, r2_scores_train, lm_model, X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r2_scores_test, r2_scores_train, lm_model, X_train, X_test, y_train, y_test = find_optimal_lm_mod(X, y, cutoffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1089\n",
      "0.7316720515685996\n",
      "0.7968545319831759\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape[1]) #Number of columns\n",
    "print(r2_scores_test[np.argmax(r2_scores_test)]) # The model we should implement test_r2\n",
    "print(r2_scores_train[np.argmax(r2_scores_test)]) # The model we should implement train_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>est_int</th>\n",
       "      <th>coefs</th>\n",
       "      <th>abs_coefs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>HigherEd</td>\n",
       "      <td>4.224896e+15</td>\n",
       "      <td>4.224896e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>FormalEducation_Professional degree</td>\n",
       "      <td>-4.224896e+15</td>\n",
       "      <td>4.224896e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>FormalEducation_Master's degree</td>\n",
       "      <td>-4.224896e+15</td>\n",
       "      <td>4.224896e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Country_United States</td>\n",
       "      <td>5.673087e+04</td>\n",
       "      <td>5.673087e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Country_Norway</td>\n",
       "      <td>4.054668e+04</td>\n",
       "      <td>4.054668e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Country_Australia</td>\n",
       "      <td>4.010954e+04</td>\n",
       "      <td>4.010954e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>384</td>\n",
       "      <td>Currency_Swiss francs</td>\n",
       "      <td>3.675333e+04</td>\n",
       "      <td>3.675333e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Country_Denmark</td>\n",
       "      <td>3.049288e+04</td>\n",
       "      <td>3.049288e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>YearsCodedJob_20 or more years</td>\n",
       "      <td>2.998107e+04</td>\n",
       "      <td>2.998107e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Country_New Zealand</td>\n",
       "      <td>2.994889e+04</td>\n",
       "      <td>2.994889e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Country_Israel</td>\n",
       "      <td>2.951237e+04</td>\n",
       "      <td>2.951237e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Country_Canada</td>\n",
       "      <td>2.936999e+04</td>\n",
       "      <td>2.936999e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Country_Ireland</td>\n",
       "      <td>2.855122e+04</td>\n",
       "      <td>2.855122e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>Country_Switzerland</td>\n",
       "      <td>2.750020e+04</td>\n",
       "      <td>2.750020e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>379</td>\n",
       "      <td>Currency_Mexican pesos (MXN$)</td>\n",
       "      <td>-2.732533e+04</td>\n",
       "      <td>2.732533e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>YearsCodedJob_12 to 13 years</td>\n",
       "      <td>2.527792e+04</td>\n",
       "      <td>2.527792e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>YearsCodedJob_18 to 19 years</td>\n",
       "      <td>2.512708e+04</td>\n",
       "      <td>2.512708e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>YearsCodedJob_19 to 20 years</td>\n",
       "      <td>2.493766e+04</td>\n",
       "      <td>2.493766e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>YearsCodedJob_15 to 16 years</td>\n",
       "      <td>2.371631e+04</td>\n",
       "      <td>2.371631e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>YearsCodedJob_17 to 18 years</td>\n",
       "      <td>2.353654e+04</td>\n",
       "      <td>2.353654e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 est_int         coefs     abs_coefs\n",
       "4                               HigherEd  4.224896e+15  4.224896e+15\n",
       "52   FormalEducation_Professional degree -4.224896e+15  4.224896e+15\n",
       "50       FormalEducation_Master's degree -4.224896e+15  4.224896e+15\n",
       "43                 Country_United States  5.673087e+04  5.673087e+04\n",
       "29                        Country_Norway  4.054668e+04  4.054668e+04\n",
       "9                      Country_Australia  4.010954e+04  4.010954e+04\n",
       "384                Currency_Swiss francs  3.675333e+04  3.675333e+04\n",
       "16                       Country_Denmark  3.049288e+04  3.049288e+04\n",
       "124       YearsCodedJob_20 or more years  2.998107e+04  2.998107e+04\n",
       "28                   Country_New Zealand  2.994889e+04  2.994889e+04\n",
       "24                        Country_Israel  2.951237e+04  2.951237e+04\n",
       "14                        Country_Canada  2.936999e+04  2.936999e+04\n",
       "23                       Country_Ireland  2.855122e+04  2.855122e+04\n",
       "39                   Country_Switzerland  2.750020e+04  2.750020e+04\n",
       "379        Currency_Mexican pesos (MXN$) -2.732533e+04  2.732533e+04\n",
       "115         YearsCodedJob_12 to 13 years  2.527792e+04  2.527792e+04\n",
       "121         YearsCodedJob_18 to 19 years  2.512708e+04  2.512708e+04\n",
       "122         YearsCodedJob_19 to 20 years  2.493766e+04  2.493766e+04\n",
       "118         YearsCodedJob_15 to 16 years  2.371631e+04  2.371631e+04\n",
       "120         YearsCodedJob_17 to 18 years  2.353654e+04  2.353654e+04"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def coef_weights(coefficients, X_train):\n",
    "    '''\n",
    "    INPUT:\n",
    "    coefficients - the coefficients of the linear model \n",
    "    X_train - the training data, so the column names can be used\n",
    "    OUTPUT:\n",
    "    coefs_df - a dataframe holding the coefficient, estimate, and abs(estimate)\n",
    "    \n",
    "    Provides a dataframe that can be used to understand the most influential coefficients\n",
    "    in a linear model by providing the coefficient estimates along with the name of the \n",
    "    variable attached to the coefficient.\n",
    "    '''\n",
    "    coefs_df = pd.DataFrame()\n",
    "    coefs_df['est_int'] = X_train.columns\n",
    "    coefs_df['coefs'] = lm_model.coef_\n",
    "    coefs_df['abs_coefs'] = np.abs(lm_model.coef_)\n",
    "    coefs_df = coefs_df.sort_values('abs_coefs', ascending=False)\n",
    "    return coefs_df\n",
    "\n",
    "#Use the function\n",
    "coef_df = coef_weights(lm_model.coef_, X_train)\n",
    "\n",
    "#A quick look at the top results\n",
    "coef_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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