Part_I_exploration_template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I - (Dataset Exploration for a Salary Survey)\n",
    "\n",
    "## by Theodore Kabangu Nkashama\n",
    "\n",
    "## Introduction\n",
    "\n",
    "> In this project we will explore a dataset related to a salary survey. It contains salary amounts in US dollar for 27147 \n",
    "> persons from different origins, ethnicities, age ranges, industries and so forth who filled the survey form for the purpose.\n",
    "\n",
    "\n",
    "## Preliminary Wrangling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collecting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading data\n",
    "df_salary = pd.read_csv('salary_survey_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27147, 23)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataset description\n",
    "\n",
    "Our dataset has 27147 rows and 23 columns that are described as follows:\n",
    "1. **Unnamed: 0** which is an id column\n",
    "2. **Timestamp** this is the date and time the person information were saved\n",
    "3. **Age** this is the person's age range\n",
    "4. **Industrie** this is specifies in which Indrustry the responding person works in\n",
    "5. **Occupation** this is the job position of tha person in their work industry\n",
    "6. **Salaire** this the salary amount in local currency for the responding person\n",
    "7. **Monnaie_salaire** this specifies the local currency of the above-mentioned salary\n",
    "8. **Pays** this is the home country of the person\n",
    "9. **Ville** this is the home city of the person\n",
    "10. **Annee_exp_totale** this is the range of the person's total number of years of experience in working\n",
    "11. **Annee_exp_present** this is the range of the person's number of years of experience in the actual position\n",
    "12. **Niveau_edu** this is the highest study levelof the person\n",
    "13. **Sexe** this is the sex of the person\n",
    "14. **Race** this shows all ethnicities the rresponding person belongs to\n",
    "\n",
    "> Race and ethnicity columns\n",
    "15. **Anotheroptionnotlistedhereorprefernottoanswer** \n",
    "16. **AsianorAsianAmerican**\n",
    "17. **BlackorAfricanAmerican**\n",
    "18. **Hispanic_Latino_or_Spanish origin**\n",
    "19. **MiddleEasternorNorthernAfrican**\n",
    "20. **NativeAmericanorAlaskaNative**\n",
    "21. **White**\n",
    "\n",
    "> These columns contain 1 (Yes) if the person belongs to that ethnicity, and 0 (No) if not\n",
    "\n",
    "22. **coef_monnaie** this gives the coefficient by which local salary shall be multiplied in order to convert it into US dollar\n",
    "23. **Salaire_USD** this is the salary amount in US dollar corresponding to local salary of the person."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Age</th>\n",
       "      <th>Indrustrie</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Salaire</th>\n",
       "      <th>Monnaie_salaire</th>\n",
       "      <th>Pays</th>\n",
       "      <th>Ville</th>\n",
       "      <th>Annee_exp_totale</th>\n",
       "      <th>...</th>\n",
       "      <th>Race</th>\n",
       "      <th>Anotheroptionnotlistedhereorprefernottoanswer</th>\n",
       "      <th>AsianorAsianAmerican</th>\n",
       "      <th>BlackorAfricanAmerican</th>\n",
       "      <th>Hispanic_Latino_or_Spanish origin</th>\n",
       "      <th>MiddleEasternorNorthernAfrican</th>\n",
       "      <th>NativeAmericanorAlaskaNative</th>\n",
       "      <th>White</th>\n",
       "      <th>coef_monnaie</th>\n",
       "      <th>Salaire_USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-04-27 11:02:10</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Education (Higher Education)</td>\n",
       "      <td>Research and Instruction Librarian</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>usa</td>\n",
       "      <td>Boston</td>\n",
       "      <td>5-7 years</td>\n",
       "      <td>...</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-04-27 11:02:22</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Computing or Tech</td>\n",
       "      <td>Change &amp; Internal Communications Manager</td>\n",
       "      <td>54600.0</td>\n",
       "      <td>GBP</td>\n",
       "      <td>united kingdom</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>8 - 10 years</td>\n",
       "      <td>...</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.15</td>\n",
       "      <td>62790.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-04-27 11:02:38</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Accounting, Banking &amp; Finance</td>\n",
       "      <td>Marketing Specialist</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>us</td>\n",
       "      <td>Chattanooga</td>\n",
       "      <td>2 - 4 years</td>\n",
       "      <td>...</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>34000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2021-04-27 11:02:41</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Nonprofits</td>\n",
       "      <td>Program Manager</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>usa</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>8 - 10 years</td>\n",
       "      <td>...</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>62000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2021-04-27 11:02:42</td>\n",
       "      <td>25-34</td>\n",
       "      <td>Accounting, Banking &amp; Finance</td>\n",
       "      <td>Accounting Manager</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>us</td>\n",
       "      <td>Greenville</td>\n",
       "      <td>8 - 10 years</td>\n",
       "      <td>...</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0            Timestamp    Age                     Indrustrie  \\\n",
       "0           0  2021-04-27 11:02:10  25-34   Education (Higher Education)   \n",
       "1           1  2021-04-27 11:02:22  25-34              Computing or Tech   \n",
       "2           2  2021-04-27 11:02:38  25-34  Accounting, Banking & Finance   \n",
       "3           3  2021-04-27 11:02:41  25-34                     Nonprofits   \n",
       "4           4  2021-04-27 11:02:42  25-34  Accounting, Banking & Finance   \n",
       "\n",
       "                                 Occupation  Salaire Monnaie_salaire  \\\n",
       "0        Research and Instruction Librarian  55000.0             USD   \n",
       "1  Change & Internal Communications Manager  54600.0             GBP   \n",
       "2                      Marketing Specialist  34000.0             USD   \n",
       "3                           Program Manager  62000.0             USD   \n",
       "4                        Accounting Manager  60000.0             USD   \n",
       "\n",
       "             Pays        Ville Annee_exp_totale     ...        Race  \\\n",
       "0             usa       Boston        5-7 years     ...       White   \n",
       "1  united kingdom    Cambridge     8 - 10 years     ...       White   \n",
       "2              us  Chattanooga      2 - 4 years     ...       White   \n",
       "3             usa    Milwaukee     8 - 10 years     ...       White   \n",
       "4              us   Greenville     8 - 10 years     ...       White   \n",
       "\n",
       "  Anotheroptionnotlistedhereorprefernottoanswer AsianorAsianAmerican  \\\n",
       "0                                             0                    0   \n",
       "1                                             0                    0   \n",
       "2                                             0                    0   \n",
       "3                                             0                    0   \n",
       "4                                             0                    0   \n",
       "\n",
       "  BlackorAfricanAmerican  Hispanic_Latino_or_Spanish origin  \\\n",
       "0                      0                                  0   \n",
       "1                      0                                  0   \n",
       "2                      0                                  0   \n",
       "3                      0                                  0   \n",
       "4                      0                                  0   \n",
       "\n",
       "   MiddleEasternorNorthernAfrican  NativeAmericanorAlaskaNative  White  \\\n",
       "0                               0                             0      1   \n",
       "1                               0                             0      1   \n",
       "2                               0                             0      1   \n",
       "3                               0                             0      1   \n",
       "4                               0                             0      1   \n",
       "\n",
       "   coef_monnaie  Salaire_USD  \n",
       "0          1.00      55000.0  \n",
       "1          1.15      62790.0  \n",
       "2          1.00      34000.0  \n",
       "3          1.00      62000.0  \n",
       "4          1.00      60000.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27147 entries, 0 to 27146\n",
      "Data columns (total 23 columns):\n",
      "Unnamed: 0                                       27147 non-null int64\n",
      "Timestamp                                        27147 non-null object\n",
      "Age                                              27147 non-null object\n",
      "Indrustrie                                       27147 non-null object\n",
      "Occupation                                       27147 non-null object\n",
      "Salaire                                          27147 non-null float64\n",
      "Monnaie_salaire                                  27147 non-null object\n",
      "Pays                                             27147 non-null object\n",
      "Ville                                            27147 non-null object\n",
      "Annee_exp_totale                                 27147 non-null object\n",
      "Annee_exp_present                                27147 non-null object\n",
      "Niveau_edu                                       27147 non-null object\n",
      "Sexe                                             27147 non-null object\n",
      "Race                                             27147 non-null object\n",
      "Anotheroptionnotlistedhereorprefernottoanswer    27147 non-null int64\n",
      "AsianorAsianAmerican                             27147 non-null int64\n",
      "BlackorAfricanAmerican                           27147 non-null int64\n",
      "Hispanic_Latino_or_Spanish origin                27147 non-null int64\n",
      "MiddleEasternorNorthernAfrican                   27147 non-null int64\n",
      "NativeAmericanorAlaskaNative                     27147 non-null int64\n",
      "White                                            27147 non-null int64\n",
      "coef_monnaie                                     27147 non-null float64\n",
      "Salaire_USD                                      27147 non-null float64\n",
      "dtypes: float64(3), int64(8), object(12)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# dataset description\n",
    "df_salary.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's make all columns names be in lower case\n",
    "df_salary.rename(str.lower, axis= 'columns', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White                                            22723\n",
       "AsianorAsianAmerican                              1315\n",
       "BlackorAfricanAmerican                             645\n",
       "Anotheroptionnotlistedhereorprefernottoanswer      583\n",
       "Hispanic,Latino,orSpanishorigin                    568\n",
       "Hispanic,Latino,orSpanishorigin,White              374\n",
       "AsianorAsianAmerican,White                         336\n",
       "BlackorAfricanAmerican,White                       121\n",
       "MiddleEasternorNorthernAfrican,White                79\n",
       "NativeAmericanorAlaskaNative,White                  65\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary.race.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Issue\n",
    "\n",
    "**race** column should be dropped for there are columns related to ethnicity in the same dataset instead of having both **race** and ethnicity values columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make them easy to address, we'll replace each of them by a descriptive string\n",
    "\n",
    "list_ = ['unnamed: 0', 'timestamp', 'age', 'industrie', 'occupation', 'salaire',\n",
    "       'monnaie_salaire', 'pays', 'ville', 'annee_exp_totale',\n",
    "       'annee_exp_present', 'niveau_edu', 'sexe', 'race',\n",
    "         'other_no_say', 'asian_a_american',\n",
    "       'black_af_american', 'hispanic_latino_spanish',\n",
    "       'mid_east_north_african', 'nat_american_alaska_native',\n",
    "       'white', 'coef_monnaie', 'salaire_usd']\n",
    "\n",
    "df_salary.rename(columns =  lambda x: str(list_[list(df_salary.columns).index(x)]) if (x not in list_) else x, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's drop unuseful columns\n",
    "df_salary.drop(['unnamed: 0', 'timestamp', 'race', 'salaire', 'monnaie_salaire', 'coef_monnaie'], axis = 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27147 entries, 0 to 27146\n",
      "Data columns (total 17 columns):\n",
      "age                           27147 non-null object\n",
      "industrie                     27147 non-null object\n",
      "occupation                    27147 non-null object\n",
      "pays                          27147 non-null object\n",
      "ville                         27147 non-null object\n",
      "annee_exp_totale              27147 non-null object\n",
      "annee_exp_present             27147 non-null object\n",
      "niveau_edu                    27147 non-null object\n",
      "sexe                          27147 non-null object\n",
      "other_no_say                  27147 non-null int64\n",
      "asian_a_american              27147 non-null int64\n",
      "black_af_american             27147 non-null int64\n",
      "hispanic_latino_spanish       27147 non-null int64\n",
      "mid_east_north_african        27147 non-null int64\n",
      "nat_american_alaska_native    27147 non-null int64\n",
      "white                         27147 non-null int64\n",
      "salaire_usd                   27147 non-null float64\n",
      "dtypes: float64(1), int64(7), object(9)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_salary.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11 - 20 years       9345\n",
       "8 - 10 years        5225\n",
       "5-7 years           4719\n",
       "21 - 30 years       3540\n",
       "2 - 4 years         2875\n",
       "31 - 40 years        840\n",
       "1 year or less       483\n",
       "41 years or more     120\n",
       "Name: annee_exp_totale, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary.annee_exp_totale.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11 - 20 years       6352\n",
       "5-7 years           6323\n",
       "2 - 4 years         6022\n",
       "8 - 10 years        4821\n",
       "21 - 30 years       1828\n",
       "1 year or less      1396\n",
       "31 - 40 years        367\n",
       "41 years or more      38\n",
       "Name: annee_exp_present, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary.annee_exp_present.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                             object\n",
       "industrie                       object\n",
       "occupation                      object\n",
       "pays                            object\n",
       "ville                           object\n",
       "annee_exp_totale              category\n",
       "annee_exp_present             category\n",
       "niveau_edu                      object\n",
       "sexe                            object\n",
       "other_no_say                     int64\n",
       "asian_a_american                 int64\n",
       "black_af_american                int64\n",
       "hispanic_latino_spanish          int64\n",
       "mid_east_north_african           int64\n",
       "nat_american_alaska_native       int64\n",
       "white                            int64\n",
       "salaire_usd                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert annee_exp_totale and annee_exp_present into ordered categorical types\n",
    "exp_order = ['1 year or less',\n",
    "             '2 - 4 years',\n",
    "             '5-7 years',\n",
    "             '8 - 10 years',\n",
    "             '11 - 20 years',\n",
    "             '21 - 30 years',\n",
    "             '31 - 40 years',\n",
    "             '41 years or more']\n",
    "ordered_var = pd.api.types.CategoricalDtype(ordered = True, categories = exp_order)\n",
    "df_salary.annee_exp_totale = df_salary.annee_exp_totale.astype(ordered_var)\n",
    "df_salary.annee_exp_present = df_salary.annee_exp_present.astype(ordered_var)\n",
    "df_salary.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27054, 17)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting persons with salary >= 2000 USD\n",
    "df_salary = df_salary[df_salary.salaire_usd >= 2000]\n",
    "df_salary.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "> After a bit of cleaning, our dataset contains 27054 persons and 17 features retained for exploration. We have got rid of \n",
    "> unuseful columns and all persons whose annual salary is lesser than 2000 USD in order to approach a realistic situation.\n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "> Through this exploration we are interested in figuring out what features impact the **salary** of a person in our dataset. \n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "> We expect that **industry**, **job position** and **years of experience** will strongly determine salary level. \n",
    "> Moreover, we think **education level** may have at leat a minor effect when compared to that of these three main features.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n",
    "\n",
    "> In this section, investigate distributions of individual variables. If\n",
    "you see unusual points or outliers, take a deeper look to clean things up\n",
    "and prepare yourself to look at relationships between variables.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1: What is the distribution of our feature of interest (salary)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102000000.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the max salary\n",
    "df_salary['salaire_usd'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2350.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the min salary\n",
    "df_salary['salaire_usd'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3af3746898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The standard-scaled plot is computationally expensive and trickier.\n",
    "# Because of high values therein let's directly reduce values using log10\n",
    "binsize = 0.05\n",
    "bins = 10 ** np.arange(3, np.log10(df_salary['salaire_usd'].max())+binsize, binsize)\n",
    "plt.figure(figsize=[10, 5])\n",
    "plt.hist(data = df_salary, x = 'salaire_usd', bins = bins)\n",
    "plt.xscale('log')\n",
    "plt.xticks([1e4, 5e4, 1e5, 25e4, 5e5, 1e6], ['10k', '50k', '100k', '250k', '500k', '1M'])\n",
    "plt.xlabel('Annual Salary ($)')\n",
    "plt.title('Annual salary distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14037, 17)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistics to confirm the visual\n",
    "df_salary[(df_salary.salaire_usd >= 50000) & (df_salary.salaire_usd <= 100000)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27054, 17)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "From this visual we notice the salary has almost a normal distribution with the peak between 50000 and 100000. In fact, statistics prove that more than 50% of persons actually in our dataset are paid between 50000 and 100000 USD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 : How are indutries distributed in the data ?\n",
    "\n",
    "Let's now take industry feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1185"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see how many industries are here\n",
    "len(df_salary.industrie.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Computing or Tech                       4510\n",
       "Education (Higher Education)            2375\n",
       "Nonprofits                              2362\n",
       "Government and Public Administration    1846\n",
       "Health care                             1835\n",
       "Accounting, Banking & Finance           1742\n",
       "Engineering or Manufacturing            1640\n",
       "Marketing, Advertising & PR             1090\n",
       "Law                                     1063\n",
       "Business or Consulting                   820\n",
       "Name: industrie, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since a plot for all these 1185 values is hardly ever readable, let's take the first 10\n",
    "df_salary.industrie.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iIjpKgpuIiIjoKAluIiIioqNkQXFEG9zz6FOMOuj0djcj5gNTs7A8YsBl5CYiIiI6SoKbiIiI6CgJbiIiIqKjJLiJiIiIjjJowY2kFSX9StKDkqZKuknSjoNVX7tIGi9plUEq+1RJuzRJf0jSNEm3S9q0hTYe1+TY8+X3KpIm9bF9i0h6UtL3esgzVtIlfSz3ZElr95JnX0lNV2KWejfrS73lvJGSPlLb/7ikQ/paTkREtM+gBDeSBFwIXGt7DdujgN2AVQejvlJnu+78Gg8MSnDTi4NsjwQOAX42t4XZ/rPtNwVSvdgOuA/YtXzmA8L2Z23f3UueE233dLvRWKDb4KaXv5WRwOvBje2LbB/RU1siImL+MlgjN1sDr9o+sSvB9iO2fwIgaYikX0qaIekOSVuV9FskrdN1jqTJkkZJWkrSKZJuK/l3KMfHS5oo6WLg8vKv9cmSJkm6V9KZXV+6kh6W9N0ygjRF0kaSLpP0R0n71uo8qNQzXdLhJW24pHsk/VzSXZIul7REGVUZDZxZRlGWqHeCpM+Vsu6UdJ6kJUv6qZKOlXRjGdnapaRL0nGS7pb0G+BfWujra4F31/prdNleXtLDtXzvlHSppPskHdpYSLnGmWV7YUlHl89nuqT9mtS9O/Bj4P+AD9TK+nDp/+uBnWrph0k6rfTfw5J2kvT9Us+lkhbt5jqel/Sd0oc3S1qxVtaBZXv/0mfTJZ0taTiwL/Dl8rlsUfr8h5KuBo6UtEnp/zvK7/dJWgz4FjCunDdOtVEvSe+SdGWp50pJq/X0eUZERHsMVnCzDnB7D8e/CGB7PaovyNMkDQHOBnYFkLQysIrtqcDXgatsbwxsBRwlaalS1qbA3ra3LvsbAgcAawNrAJvX6v2T7U2B64BTgV2ovpS/VercDngPsAnVv+BHSdqynPse4Ke21wH+DuxsexIwBdjD9kjbLzVc5/m2N7a9AXAP8JnasZWBMcBHga6RgR2B9wHrAZ+jychDg48BM1rItwmwR7muT3YFD01MAFYHNrS9PnBmY4YSyG0DXAKcRfU5Uj7Hn5d2bQGs1HDqmsD2wA7A/wJXl7+Dl0p6o6WAm0sfXkvVL40OqbV1X9sPAycCx5TP5bqS773Atra/CtwLbGl7Q+CbwHdtv1q2zynnndNQz3HA6bU+ObZ2rLvPcw6SJpTAesqsF5/rLktERAyAebKgWNJPy7+8bytJY4AzAGzfCzxC9cVzLvDJkmdXYGLZ3g44RNI0YDIwBFitHLvC9tO16m61/ajt14BpwPDasYvK7xnALbafs/034GVJy5Z6tgPuoArORlAFNQAP2Z5Wtqc2lNvMupKukzSDKrBYp3bsQtuvlemXFUvalsBZtmfb/jNwVQ9lH1X6YwJzBk3NXGH7qRKAnU/1GTSzLXCi7VkADf3b5aNUgcmLwHnAjpIWpuqzh2w/YNtUAUzd72z/k+ozWBi4tKTPoPs+fZUqgILm/T6davTs34FZPVzXRNuzy/YywMQyWnUMc342zWwK/Kpsn8Gcfdjd5zkH2yfZHm179CJLDm2huoiI6I/BWqdyF7Bz147tL0panmqUA6Db9Rm2H5P0lKT1gXHA52v5d7Z9Xz2/pPcDLzQU80ptezZzXmPXsdca8r1W8gn4nu051rCUaY7GcueYgmriVOATtu+UNJ5qHUh37az3h1soF6o1N40LgGfxRsA6pOFYY7k91aMW2rE7sHlt6msY1ajak72c+wqA7dck/bMEQPDGZ9Conqfx8+yyPVVg+HHgG6pNbTao/618myo427F8vpN7aHMz9ets9nlGRMQ8NlgjN1cBQyR9oZa2ZG37WqqRDCS9l2oUpitwORs4GFjGdtd0y2XAftLr62c2HKR2XwZ8WtLSpZ53SOpt3ctzQLN/hg8FHi9rSfZoof5rgd3KmpeVqYKFvngYGFW2G9d9/Kuk5cp00ieAG3oo53JgX5WFt5KWqx+U9DaqUYvVbA+3PZxqqnF3qume1SWtWbLv3sdr6BNJCwHvtH011d/NssDS9Py5QDVy81jZHl9L7+m8G6kWxkP1eV7fv1ZHRMRgGpTgpvxL+xPAB1XdsnwrcBrw/0qW44GFy3TNOcB4213/8p1E9QVybq3IbwOLAtPLNMK3B6ndl1NNO9xU2jaJnr8goRqdOVHdLCgGvgHcAlxB9aXfmwuAB6imaE4Armm99QAcDXxB0o3A8g3HrqeaSpkGnGd7SuPJNSdTLRKeLulO4FMNx3eiWgNVH634NdXIiammyn5TFhQ/0sdr6KuFgf8tn9cdVOts/g5cTDVVNk3SFt2c933ge5JuKGV0uRpYu2tBccM5+wP7SJoO7Al8aaAvJiIi5p7eGPGPiHllqZVW94g9D293M2I+kBdnRrRO0lTbPd0QA+QJxREREdFhEtxERERER0lwExERER2lXa8siFigrbXqMKZkrUVExKDIyE1ERER0lAQ3ERER0VES3ERERERHSXATERERHaWlBcWSlgS+SvW4/c9Jeg/wPtuX9HJqRHTjnkefYtRBp7e7GdFmeYBfxOBodeTml1QvBty07D8K/M+gtCgiIiJiLrQa3Kxp+/vAPwFsv0TefBwRERHzoVaDm1fLSyENUN74/ErPp0RERETMe60+xO9Q4FLgnZLOBDYHxg9WoyIiIiL6q6WRG9tXADtRBTRnAaNtTx68ZkWnk/R8w/54Scf1s6yxki6pbW9WO3aqpF3mrrUREfFW0mNwI2lE+b0R8C7gceDPwGolLWJ+MxbYrLdMA0VSXmESETGf6e1/zF8BJgA/6OaYga0HvEWxwJO0AnAisFpJOsD2DZI2AX4ELAG8BOxj+77aecOBfYHZkv4d2K8c2lLSV4CVgINtT+qmzr2AA6n+rqfb3lPSx4D/BhYDngL2sP1XSYcBqwDDgScl7QkcQRVYLQ781PbPBqY3IiKir3oMbmxPkLQQ8N+2b5hHbYoFwxKSptX2lwMuKts/Bo6xfb2k1YDLgLWAe4Etbc+StC3wXWDnrgJsPyzpROB520cDSPoMsDIwBhhR6pgjuJG0DvB1YHPbT0parhy6HviAbUv6LHAw1fOeAEYBY2y/JGkC8KztjSUtDtwg6XLbDzXUM4HqHwssNnRYvzotIiJ61+uQuu3XJB3NG8+4iRgIL9ke2bUjaTwwuuxuC6wtvf60gbdJGgosA5xWHiJpYNEW67rQ9mvA3ZJW7Ob41sAk208C2H66pK8KnCNpZarRm3qwclF5JALAdsD6tbU9ywDvaciP7ZOAkwCWWml1t9j2iIjoo1bXC1wuaWfgfNv5n3IMtoWATWvBAwCSfgJcbXvHMgU1ucXy6o8t6O75TKI85qDBT4Af2r5I0ljgsNqxFxrO38/2ZS22JyIiBlGrz7n5CjAReEXSPyQ9J+kfg9iuWLBdDvxn146krhGeZYDHyvb4Juc+BwztY31XArtKGlbq65qWqte3dw/nXwZ8QdKi5fz3Slqqj22IiIgB0uqt4ENtL2R7MdtvK/tvG+zGxQJrf2C0pOmS7qZaJAzwfeB7km4AFm5y7sXAjpKmSdqilcps3wV8B7hG0p3AD8uhw4CJkq4DnuyhiJOBu4HbJc0Efkbro6IRETHA1Mosk6QrbW/TW1pEtGaplVb3iD0Pb3czos3y4syIvpE01fbo3vL1+K9LSUOAJYHlJb2dN9YrvI3qVtiIiIiI+UpvQ+efBw6gCmSm8kZw8w/gp4PYroiIiIh+6e05Nz8GfixpP9s/mUdtioiIiOi3Vhc9/kXSUNvPSfpvYCPgf2zfPohti+hYa606jClZbxERMShavRX8GyWwGQN8CDgNOGHwmhURERHRP60GN7PL7+2BE2z/muqJrRERERHzlVaDm8ck/QzYFfhteX9Oq+dGREREzDOtPudmSeDDwAzbD5R37axn+/LBbmBEJ8pzbhYceZZNxMAZkOfc1CwPTCkFr1bS7u1n2yIiIiIGTavBzW+oXiwoYAiwOnAfsM4gtSsiIiKiX1oKbmyvV9+XtBHVA/4iIiIi5iv9WhRcnm+z8QC3JSIiImKutTRyI+krtd2FqB7i97dBadE8IGlH4HxgLdttXTskaSzwqu0by/6+wIu2Tx/gek4FPgg8SzW1eJbtfq1olTQeGG37PxvSB6ztkkT1du0xVI8imGD7piZ5JwMrAy+VpP+xPUnSjbY3m9u2RETEW0ura26G1rZnUa3BOW/gmzPP7A5cD+wGHNbepjAWeB64EcD2iYNY10HlS38IcLek020/NFCFD3DbxwDvoVrXtQRz/g12Zw/bUxrak8AmImIB1NK0lO3Daz/fsX2m7ZcHu3GDQdLSwObAZ6iCm/qxgyXNkHSnpCNK2rsl/b6k3S5pTVWOkjSz5B9X8o6VdEmtvOPKKAeSHpZ0eCljhqQRkoYD+wJfljRN0haSDpN0YDlnsqQjJd0q6X5JW5T0JSWdK2m6pHMk3SKp11vjaoaU3y+U8r4p6bZyPSeVUZOm9Tf02faSbpK0/AC3/VVgRWBR2y/a/msfrq+rbc+X32NLeyZJulfSmbVr7NO1S1pY0tHlM5wuab+SPkrSNZKmSrqsPC4hIiLaoMeRG0kXU90l1S3bHx/wFg2+TwCX2r5f0tOSNrJ9u6R/K8feb/tFScuV/GcCR9i+oIx4LATsBIwENqC6Tf42Sde2UPeTtjeS9B/AgbY/K+lE4HnbRwNI2qbhnEVsbyLpI8ChwLbAfwDP2F5f0rrAtBav/ShV7wZ7N3Cs7SdK+nG2v1XqPwP4KHBxD/VT8u4IfAX4iO1nSlwwUG3/K/A24FRJe7j3BzKdKalrWmob2081HN+QahToz8ANVAHu9f249glUdwtuaHuWpOUkLQr8BNjB9t9KsPsd4NO9tDkiIgZBbyM3RwM/AB6iWs/w8/LzPDBzcJs2aHYHzi7bZ5d9qL64fmn7RQDbT0saCrzD9gUl7eVyfAzVmpXZZUThGlpbYH1++T0VGN5ie7s7Z0zXNdieCUxvsayDbI8EVgK2kdQ1bbNVGUGZAWzNnLf4N2vzVsD/A7a3/cwgtH0SsA3wInAMgKTjJW3fJP8etkeWn8bABuBW24/afo0qoOpqT1+vfVvgRNuzyjU8DbwPWBe4QtI04L+BVRsbIGmCpCmSpsx68bkmlxEREXOrx5Eb29cASPq27S1rhy5ucaRiviJpGNUX2LqSDCwMWNLBVM/waRwdeNNQRC/ps5gzYBzScPyV8ns2ra936u6cZvW3xPbzqhbhjpF0O3A81QLhP0k6jDnb3azNDwJrAO+lPOBxoNou6V+A5W3fJ+nzwHmSDgVGAwf1dn4vbXm9PWUkrq/X3uzv5C7bm/bUANsnASdB9YTi/lxERET0rtVbwVeQtEbXjqTVgRUGp0mDahfgdNvvsj3c9jupRqXGAJcDn1b1qgkkLWf7H8Cjkj5R0hYvx68FxpX1FysAWwK3Ao8Aa5d8y1CNPPTmOXpfLNvoeqr3fCFpbeD15xBJOl3SJj2dLGkR4P3AH3njy/xJVeuRdmmxDY9QTc+dLqkvD3Ns2vaav1WHtZXt2VRTQV8Cbrf9Qh/q6k1/rv1yYN/Sh5Tpy/uo/hvZtKQt2sc+iYiIAdRqcPNlYHJZZDkZuBo4YNBaNXh2By5oSDsP+JTtS4GLgCllauHAcnxPYH9J06nuaFqplDEduBO4CjjY9l9s/wk4txw7E7ijhTZdDOyosqC4xes4nurLdDrV1NB0qlu8AdYHHm9y3lHl2qYDM4Dzbf+daqpxBnAhcFuLbcD2fcAewERJaw5A27vKNbAz8J3S3guB/wQ+IKnV4KuV9vfn2k8G/g+YLulOqr+dV6kCoyNL2jQgd2pFRLRJSy/OhGrUAhhRdu+1/UpP+WPwSFqY6i6il0tQcSXV9NAQ4Be2P9nWBvagWdtLgLDAyIszFxx5cWbEwNEAvzgTYBTVospFgA0kMdAPmouWLQlcXe7SEfCFEhy8Csy3gU3RrO0REREDotUnFJ8BrEk13D67JBtIcNMGtp+jWlz7lvNWbntERLw1tDpyMxpYu4VnjURERES0VasLimdSLaSNiIiImK+1OnKzPNW7iG6l9ryQt+gTiiPabq1VhzElC00jIgZFq8HNYYPZiIg9facNAAAYNUlEQVSIiIiB0lJw0/Wk4oiIiIj5XW8vzrze9hhJzzHnI+dF9ay1tw1q6yIiIiL6qLd3S40pv/v6eoCI6ME9jz7FqIPyJIVOkQf1RcxfWr1bKiIiIuItIcFNREREdJQENxEREdFREtxERERER0lwUyNptqRptZ9D5qKsGweybU3q+PjctHEA6h8vyZK2qaXtWNJ2GYT6PinpHklX9+Pc8ZJW6cd5oyUd29fzIiKiffryVvAFwUu2Rw5EQbY3G4hympG0iO2LgIsGs55u6pzVkDwD2B24suzvBtw5SE34DPAftvsc3ADjqV4j8udWTyjXOwWY0o/6IiKiTTJy0wJJD0s6XNLtkmZIGlHSV5B0RUn/maRHJC1fjj1ffo+VNFnSJEn3SjpTksqxUZKukTRV0mWSVi7pa0q6tKRfV6vvVEk/LCMXR5bRiONqx46VdKOkB7tGTiQtJOl4SXdJukTSb7sbVZE0UtLNkqZLukDS20v6ZEnflXQN8KVuuuc6YBNJi0paGng31dvju8r9pqTbJM2UdFLt2idLOlLSrZLul7RFSX/9msr+JaUPvwmMAU6UdJSk4aVvbi8/m9XOObh8TndKOqJc72jgzDIit0T5TLs+q9GSJpftw0o7LwdOL3VfUjt2Smn7g5L2r9X5jfL5XiHpLEkHtvK3FRERAy/BzZyW0JzTUuNqx560vRFwAtD1xXUocFVJvwBYrUm5GwIHAGsDawCbS1oU+Amwi+1RwCnAd0r+k4D9SvqBwPG1st4LbGv7q93UszJVAPBR4IiSthMwHFgP+CywaZM2ng78P9vrU43GHFo7tqztD9r+QTfnGfg98CFgB948knSc7Y1trwssUdrWZRHbm1D1zaH0wPa3qEZQ9rB9EPAE8K+l78cBxwJI+jfgE8D7bW8AfN/2pNq5I22/1FNdwChgB9uf6ubYiHKtmwCHlqBuNLAz1ee8E1UgFRERbZJpqTn1NC11fvk9leoLDKpAYkcA25dKeqbJubfafhRA0jSqYOPvwLrAFWUwY2Hg8TL6sRkwsaQDLF4ra6Lt2U3qudD2a1QvOV2x1saJJf0v3a1XkbQMVQDT9ZqN04CJtSznNKmvy9nA/sAywFeBr9WObSXpYGBJYDngLuDicqzep8N7qaPRosBxkkYCs6mCPoBtgV/afhHA9tN9LBfgoh4CoN/YfgV4RdITwIpUffzrrnMkXdzdiZImABMAFhs6rB/NioiIViS4aV3X29Bn80a/qUneZufWzxdwl+05RlIkvQ34ew9B1gst1qOG33OjpzqxfaukdamCw/u7gjJJQ6hGnUbb/pOkw4Ah3bS33qezmHNEsZ6/7svAX4ENSv6XS7qY81UhzdTraayj1T6uf5a9sn0S1agcS620eittjIiIfsi01Ny5HtgVQNJ2wNv7cO59wAqSNi3nLyppHdv/AB6S9MmSLkkbzGUbdy5rb1YExjZmsP0s8EzXuhdgT6CvL0v9L+YcsYE3goYny4hUK3dQPQyMLO19J9X0T3eWAR4vI1J7Uo18AVwOfFrSkgCSlivpzwH114g8TDX9BNWU0ty4HviYpCHlOrefy/IiImIuZORmTkuUaaMul9ru6Vbrw4Gzytqca4DHqb5Ee2X71bLQ9dgyLbQI8COqaZs9gBMk/TfV9MvZ9P8OpPOAbajuFLofuAV4tpt8e1Mt1l0SeBDYpy+V2P5dN2l/l/RzqjU8DwO3tVDUDcBD5ZyZwO1N8h0PnFeCwKspoy1lenAkMEXSq8BvqYKuU6mu7yWqdUeHA7+Q9DWqPuk327dJuojqM3qEan1Pd30cERHzgOyMjveXpMWB2bZnlRGYEwbqVvKBJGlp289LGgbcCmxu+y/tblcnqfXxksC1wATbzQIzllppdY/Y8/B518AYVHlxZsS8IWmq7V5v2sjIzdxZDThX0kLAq8Dn2tyeZi6RtCywGPDtBDaD4iRJa1NNxZ3WU2ATERGDK8HNXLD9ANXtv/M122Pb3YZO1+S28YiIaIMsKI6IiIiOkpGbiDZYa9VhTMk6jYiIQZGRm4iIiOgoCW4iIiKioyS4iYiIiI6S4CYiIiI6ShYUR7TBPY8+xaiDTm93M95S8qC8iGhVRm4iIiKioyS4iYiIiI6S4CYiIiI6SoKbiIiI6CgJbvpIkiWdUdtfRNLfJF3Sx3LG9uUcSSMlfaS2/3FJh/Slzr6QdKeks3o4PlzSzAGqa7ikT9X2R0s6tof8q0iaNBB1l/I+JuluSTMlfaeHfOPLZz2t5P9cN+n3SvryQLUtIiL6LsFN370ArCtpibL/r8BjfSlAUn/uUhsJvB7c2L7I9hH9KKdXktai+tvYUtJSg1FHra5FgOHA68GN7Sm29292ju0/295lAJvxI2B72+sCJ/eS9xzbI4GxwHclrdiQvjnwdUnvHMD2RUREHyS46Z/fAduX7d2B10c4JG0i6UZJd5Tf7yvp4yVNlHQxcHm9MEkbl/xrSFpK0imSbitpO0haDPgWMK6MDowr5R1Xzj9V0rGlvgcl7VLSF5J0vKS7JF0i6bddx3rxKeCM0s6P19o5qozo3AR8sZZ+i6R1avuTS943XUuTvjgC2KJc25fro1qSPljSp5UyhtZHjUpZ50u6VNIDkr5fa8dnJN1f2vPzrv7qxqvAqgC2H2qhf7D9BPBH4F0N6U8BfwBWbqWciIgYeAlu+udsYDdJQ4D1gVtqx+4FtrS9IfBN4Lu1Y5sCe9veuitB0mbAicAOth8Evg5cZXtjYCvgKGDRUtY5tkfaPqebNq0MjAE+ShUsAOxENSqyHvDZUn8rxgHnUAVtu9fSfwnsb7uxnLOBXcv1rAysYntqd9dSGwmq98UhwHXl2o5pKPtA4ItlVGQL4KVu2juytHk9qgDwnZJWAb4BfIBqdG1EdxcqaSHgHuAUSav31CkN560BrEEVyNTTVwOGANO7OWeCpCmSpsx68blWq4qIiD5KcNMPtqdTBQ27A79tOLwMMLGMLBwDrFM7doXtp2v7awEnAR+z/X8lbTvgEEnTgMlUX5SrtdCsC22/ZvtuoGuqZAwwsaT/Bbi6t0IkbQz8zfYjwJXARpLeLmkZYFnb15SsZ9ROOxf4ZNneFZjYwrU09kUzNwA/lLR/qX9WN3mutP2s7ZeBu6lGUzYBrrH9tO1/1trUaD/gLuALwMWSViijb83yjyvXcxbw+do1jJN0F/Ag8OPSljnYPsn2aNujF1lyaAuXHhER/ZHgpv8uAo6mNiVVfBu4uqzf+BjVF3qXFxryPg68DGxYSxOwcxnFGGl7Ndv3tNCeVxrKqP/ui92BEZIeppp2eRuwcynL3Z1g+zHgKUnrU42gnF2rv9m1NPZFt8q6os8CSwA3S+puBKZ+7bOpnrzd6rV/iCo4+j3V1N9vgL2oRq660zV69n7bFzSkr0M1uvQDSSu1WH9ERAywBDf9dwrwLdszGtKX4Y0FxuN7KePvVGt3vitpbEm7DNhPkgAkdQU+zwF9/ef+9cDOZe3NilSLYCnlfk/SjvXMZYrmk8D6tofbHg7sAOxu++/As5LGlOx7NNR1NnAwsEytT5pdS6Om1yZpTdszbB8JTKHJ9FI3bgU+WEadFqEK0LpzB/DvkhayfS7wANWao9+0WM8cbN9ENar1pf6cHxERcy/BTT/ZftT2j7s59H3ge5JuABZuoZy/Uo3w/FTS+6lGfhYFppeprW+XrFcDa3ctKG6xmecBjwIzgZ9RrQ16thxbD/hLQ/4tgcfKSEyXa0u9KwP7lHbexJvXvkwCdqOaourS7FoaTQdmlcXKjbdRH6DqFu07S52/6+mCu5Rr+C7VNf+earrq2W6yfodqlGempKnAX6n66lcl2OuPI4F9JGXuKSKiDWR3O9MQHULS0raflzSMajRjc9t/kXSZ7Q+1u32DqXbtiwAXAKc0TCW1zVIrre4Rex7e7ma8peTFmREhaart0b3ly1vBO98lkpYFFgO+XRYW0+mBTXGYpG2p1j1dDlzY5vZERMQ8kOCmw9ke2+42tIvtA9vdhoiImPey5iYiIiI6SkZuItpgrVWHMSVrSCIiBkVGbiIiIqKjJLiJiIiIjpLgJiIiIjpK1txEtME9jz7FqINOb3cz3hLyfJuI6KuM3ERERERHSXATERERHSXBTURERHSUBDcRERHRURLcREREREdJcBNRI+n5drchIiLmToKbiIiI6CgJbiJ6Ieljkm6RdIek30tasaTPkLSsKk9J2quknyFp2/a2OiJiwZXgJqJ31wMfsL0hcDZwcEm/AdgcWAd4ENiipH8AuLmxEEkTJE2RNGXWi88NfqsjIhZQeUJxRO9WBc6RtDKwGPBQSb8O2BJ4BDgBmCDpHcDTtt+0dsf2ScBJAEuttLrnRcMjIhZEGbmJ6N1PgONsrwd8HhhS0q+lGq3ZApgM/A3YhSroiYiINklwE9G7ZYDHyvbeXYm2/wQsD7zH9oNU01cHkuAmIqKtEtxEzGlJSY/Wfr4CHAZMlHQd8GRD/luA+8v2dcA7qIKciIhok6y5iaix3Szg/3WT/HvWtm8k/2CIiGi7/I84IiIiOkqCm4iIiOgoCW4iIiKio2TNTUQbrLXqMKYctVe7mxER0ZEychMREREdJcFNREREdJQENxEREdFRsuYmog3uefQpRh10erubMeimZl1RRLRBRm4iIiKioyS4iYiIiI6S4CYiIiI6SoKbiIiI6CgJbuZTkmZLmibpTkm3S9qsn+XsK+ktsapT0iaSrpV0n6R7JZ0sacl5UO94SceV7U9IWrt27FuSth3sNkRExMDJ3VLzr5dsjwSQ9CHge8AH+1qI7RMHumEDQdIitmfV9lcEJgK72b5JkoCdgaHAi/OwaZ8ALgHuBrD9zXlYd0REDICM3Lw1vA14BkDSWEmXdB2QdJyk8WX7CEl3S5ou6eiSdpikA8v2ZElHSrpV0v2StijpC0s6StJt5dzPl/SVy0jKNEkzJW1R8p5a9mdI+nJjYyW9S9KVpawrJa1W0k+V9ENJVwNHNpz2ReA02zcBuDLJ9l8lLSfpwlLezZLWr13bKeW6HpS0f0lfStJvyqjXTEnjSvrDkpYv26MlTW5o92bAx4GjyjWvWdq8S+38w8tI2gxJI0r6CpKuKOk/k/RIVz0RETHvZeRm/rWEpGnAEGBlYOueMktaDtgRGGHbkpZtknUR25tI+ghwKLAt8BngWdsbS1ocuEHS5cBOwGW2vyNpYWBJYCTwDtvrlnq7q+c44HTbp0n6NHAs1YgIwHuBbW3PbjhnXeC0Jm0+HLjD9ickbQ2cXtoBMALYimqE5z5JJwAfBv5se/vSxmWalDsH2zdKugi4xPakcm5jtidtbyTpP4ADgc9S9eNVtr8n6cPAhFbqi4iIwZGRm/nXS7ZH2h5B9WV9urr5pq35B/AycLKknWg+lXN++T0VGF62twP2KsHULcAw4D3AbcA+kg4D1rP9HPAgsIakn5Qv8n90U8emwK/K9hnAmNqxid0ENr0ZU8rB9lXAsFrA8hvbr9h+EngCWBGYAWxbRqm2sP1sH+vrSXf9NwY4u7TvUsooWyNJEyRNkTRl1ovPDWCTIiKiLsHNW0CZqlkeWAGYxZyf25CSZxawCXAe1SjJpU2Ke6X8ns0bI3cC9ivB1Ejbq9u+3Pa1wJbAY8AZkvay/QywATCZairp5FYuobb9QpM8dwGjmhzrLqjrKvOVWtpsqpGp+0tZM4DvSepaN1PvuyG9NbqJZv3XK9sn2R5te/QiSw7tZ/UREdGbBDdvAWVtx8LAU8AjwNqSFi+jF9uUPEsDy9j+LXAAb0zbtOIy4AuSFi1lvbesW3kX8ITtnwO/ADYqa0kWsn0e8A1go27KuxHYrWzvAVzfQhuOA/aW9P7adf+7pJWAa0s5SBpLNTXU3YhR13mrAC/a/l/g6FobH+aNAGrnJqc/RzXF1RfXA7uWurcD3t7H8yMiYgBlzc38q2vNDVQjA3uX6Zw/SToXmA48ANxR8gwFfi1pSMn/poW+PTiZaorl9jL19Teq0Z+xwEGS/gk8D+wFvAP4paSuwPi/uilvf+AUSQeVsvbprQFl4fBuwNGS/gV4jSqoOR84rNQ5nWq6be9eiluPalHwa8A/gS+U9MOBX0j6GtX0W3fOBn5eFifv0lu7a+WeVRYuXwM8ThUkRUREG8h277kioqmyCHu27VmSNgVO6LqNv5mlVlrdI/Y8fN40sI3y4syIGEiSptoe3Vu+jNxEzL3VgHPLaNarwOfa3J6IiAVagpuIuWT7AWDDdrcjIiIqWVAcERERHSXBTURERHSUTEtFtMFaqw5jShbbRkQMiozcREREREfJreARbSDpOeC+drdjPrU88GS7GzEfSr80l75prtP65l22V+gtU6alItrjvlae1bAgkjQlffNm6Zfm0jfNLah9k2mpiIiI6CgJbiIiIqKjJLiJaI+T2t2A+Vj6pnvpl+bSN80tkH2TBcURERHRUTJyExERER0lwU3EPCbpw5Luk/QHSYe0uz2DTdIpkp6QNLOWtpykKyQ9UH6/vaRL0rGlb6ZL2qh2zt4l/wOS9m7HtQw0Se+UdLWkeyTdJelLJX2B7h9JQyTdKunO0i+Hl/TVJd1SrvEcSYuV9MXL/h/K8eG1sv6rpN8n6UPtuaKBJ2lhSXdIuqTsp29qEtxEzEOSFgZ+CvwbsDawu6S129uqQXcq8OGGtEOAK22/B7iy7EPVL+8pPxOAE6D6sgcOBd4PbAIc2vWF/xY3C/iq7bWADwBfLH8PC3r/vAJsbXsDYCTwYUkfAI4Ejin98gzwmZL/M8Aztt8NHFPyUfpyN2Adqr/B48t/g53gS8A9tf30TU2Cm4h5axPgD7YftP0qcDawQ5vbNKhsXws83ZC8A3Ba2T4N+EQt/XRXbgaWlbQy8CHgCttP234GuII3B0xvObYft3172X6O6svqHSzg/VOu7/myu2j5MbA1MKmkN/ZLV39NAraRpJJ+tu1XbD8E/IHqv8G3NEmrAtsDJ5d9kb6ZQ4KbiHnrHcCfavuPlrQFzYq2H4fqCx74l5LerH86vt/KdMGGwC2kf7qmXaYBT1AFa38E/m57VslSv8bXr78cfxYYRgf2S/Ej4GDgtbI/jPTNHBLcRMxb6iYttyy+oVn/dHS/SVoaOA84wPY/esraTVpH9o/t2bZHAqtSjSis1V228nuB6RdJHwWesD21ntxN1gWub+oS3ETMW48C76ztrwr8uU1taae/lukUyu8nSnqz/unYfpO0KFVgc6bt80ty+qew/XdgMtWapGUldb02qH6Nr19/Ob4M1VRoJ/bL5sDHJT1MNa29NdVITvqmJsFNxLx1G/CecmfDYlQL+i5qc5va4SKg646evYFf19L3KncFfQB4tkzLXAZsJ+ntZaHsdiXtLa2sffgFcI/tH9YOLdD9I2kFScuW7SWAbanWI10N7FKyNfZLV3/tAlzl6iFuFwG7lTuGVqdaiH3rvLmKwWH7v2yvans41f8/rrK9B+mbOdnOT37yMw9/gI8A91OtIfh6u9szD673LOBx4J9U/1r8DNWc/5XAA+X3ciWvqO4m+yMwAxhdK+fTVIse/wDs0+7rGqC+GUM1FTAdmFZ+PrKg9w+wPnBH6ZeZwDdL+hpUX8B/ACYCi5f0IWX/D+X4GrWyvl766z7g39p9bQPcT2OBS9I3b/7JE4ojIiKio2RaKiIiIjpKgpuIiIjoKAluIiIioqMkuImIiIiOkuAmIiIiOkqCm4iI6BdJB0hast3tiGiUW8EjIqJfylNyR9t+st1tiajLyE1ERAeTtJek6ZLulHSGpHdJurKkXSlptZLvVEm71M57vvweK2mypEmS7pV0ZnlC8v7AKsDVkq5uz9VFdG+R3rNERMRbkaR1qJ5Cu7ntJyUtB5wGnG77NEmfBo4FPtFLURsC61C9e+iGUt6xkr4CbJWRm5jfZOQmIqJzbQ1M6go+bD8NbAr8qhw/g+oVEL251fajtl+jekXE8EFoa8SASXATEdG5RPXuqp50HZ9F+U4oL/RcrJbnldr2bDLqH/O5BDcREZ3rSmBXScMAyrTUjVRvkwbYA7i+bD8MjCrbOwCLtlD+c8DQgWpsxEBJ9B0R0aFs3yXpO8A1kmZTvWl7f+AUSQcBfwP2Kdl/Dvxa0q1UQdELLVRxEvA7SY/b3mrgryCif3IreERERHSUTEtFRERER0lwExERER0lwU1ERER0lAQ3ERER0VES3ERERERHSXATERERHSXBTURERHSUBDcRERHRUf4/OefOKmu6B1MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3af38defd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the top 10 industries\n",
    "ind_order = df_salary.industrie.value_counts()[:10].index\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = df_salary, y = 'industrie', color = base_color, order = ind_order);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "From this visual we understand that among the top 10 industries picked up _Computing or Tech_ is the first with more than 4000 persons, _Higher education_ and _Nonprofits_ are respectively the second and the third with more than 2000 persons each, the rest of them have less than 2000 persons and we noted _Business or Consulting_ which is the last with less than 1000 persons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3: How are occupations distributed in data ?\n",
    "\n",
    "Let's take occupation (job position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13932"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see how many occupations are here\n",
    "len(df_salary.occupation.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Software Engineer           278\n",
       "Project Manager             223\n",
       "Senior Software Engineer    193\n",
       "Director                    190\n",
       "Program Manager             148\n",
       "Librarian                   146\n",
       "Teacher                     145\n",
       "Manager                     134\n",
       "Executive Assistant         132\n",
       "Product Manager             121\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since it impossible to plot all these values, let's take the first 10\n",
    "df_salary.occupation.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3af3911ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the top 10 occupations\n",
    "ind_order = df_salary.occupation.value_counts()[:10].index\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = df_salary, y = 'occupation', color = base_color, order = ind_order);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "_Software engineer_ and _Project Manager_ are highly occupied positions respectively the first with 278 persons and the second with 223. All the rest have less than 200 persons and among them _Product Manager_ is the last with 121 persons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4 : How are experiences ranges distributed in data ?\n",
    "\n",
    "Let's take experience columns (annee_exp_totale and annee_exp_present) to see it clearer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11 - 20 years       9323\n",
       "8 - 10 years        5215\n",
       "5-7 years           4708\n",
       "21 - 30 years       3520\n",
       "2 - 4 years         2857\n",
       "31 - 40 years        833\n",
       "1 year or less       478\n",
       "41 years or more     120\n",
       "Name: annee_exp_totale, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary.annee_exp_totale.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46233e7128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the ranges\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = df_salary, y = 'annee_exp_totale', color = base_color);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "Hre we notice that 19246 (more than 70%) persons have their total years of experience between 5 and 20, among these 9323 persons have between 11 and 20 years, 5215 persons have between 8 and 10 years, 4708 have between 5 and 7 years. Among the rest of persons 3520 have between 21 and 30 years, 2857 between 2 and 4 years, 833 between 31 and 40 years, 478 have 1 year or less and finally 120 have 41 or more years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11 - 20 years       6335\n",
       "5-7 years           6312\n",
       "2 - 4 years         5999\n",
       "8 - 10 years        4802\n",
       "21 - 30 years       1818\n",
       "1 year or less      1386\n",
       "31 - 40 years        364\n",
       "41 years or more      38\n",
       "Name: annee_exp_present, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary.annee_exp_present.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3af3746518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the ranges\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = df_salary, y = 'annee_exp_present', color = base_color);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "We notice that more than 17000 persons have years of experience between 5 and 20 in their actual jobs. Among these we find three ranges : _11 - 20 years_ with 6335 persons, _5-7 years_ with 6312, and _8 - 10 years_ with 4802. The rest of persons are shared between other ranges among which _2 - 4 years_ is mentioned with 5999 persons and _41 years or more_ which is the last one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5 : How are study levels distributed in data ?\n",
    "\n",
    "Let's take niveau_edu feature to see that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College degree                        13176\n",
       "Master's degree                        8614\n",
       "Some college                           1992\n",
       "PhD                                    1380\n",
       "Professional degree (MD, JD, etc.)     1285\n",
       "High School                             607\n",
       "Name: niveau_edu, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary.niveau_edu.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46236d8710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the education levels\n",
    "base_color = sb.color_palette()[0]\n",
    "educ_index = df_salary.niveau_edu.value_counts().index\n",
    "sb.countplot(data = df_salary, y = 'niveau_edu', color = base_color, order = educ_index);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "More than 21000 persons have either a college degree or a master's degree. _College degree_ is the highest education level for 13176 persons. 8614 have a _master's degree_, 1992 persons have followed _some college_ education. Only 2600 persons or so have either PhD or a Professional degree. And 607 persons have got High School degree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?\n",
    "\n",
    "> From the visual we notice the salary has almost a normal distribution the peak of which is between 50000 and 100000. \n",
    "> In fact, that is reinforced by statistics proof showing that more than 50% of persons actually in our dataset are paid \n",
    "> between 50000 and 100000 USD, which expalins why the peak is between those values. \n",
    "> Looking at the plot basis, just 28 persons have less than 10000 USD and 57 persons more than 500000 USD.\n",
    "> We have considered salary values lesser than 2000 as outliers and got rid of them. Because of high values found, \n",
    "> we have used log 10 transformation to adapt the plot and make it easily readable.\n",
    "\n",
    "### Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?\n",
    "\n",
    "> Form the above-mentioned important features, we have taken the top 10 industries and occupations with high number of persons.\n",
    "> _Computing or Tech_ industry and related positions stood out to have more persons. We have noticed that the two experience\n",
    "> columns were prone to be ordinal categorical variables as the ranges of values therein are ordered in ascending order. \n",
    "> So we converted them that way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration\n",
    "\n",
    "> In this section, we'll investigate relationships between pairs of variables in our\n",
    "data using the variables that we introduced in some the previous section (univariate exploration)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 : How are salaries distributed through the top 10 industries ?\n",
    "\n",
    "Here we will plot relationship between industry and salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data for top 10 industies\n",
    "industries = df_salary.industrie.value_counts()[:10].index\n",
    "df_top10_ind = df_salary[df_salary.industrie.isin(industries)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f221c8cd390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot for all salaries first\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.violinplot(data = df_top10_ind, y = 'industrie', x = 'salaire_usd', color = base_color, inner = None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "We observe that for all top 10 industries high density is shown before 500000 USD. So we have decided to split the data and get jut salaries from the minimum up to 500000 USD ensuring the plotting order does not change for the top 10 industries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting to get salaries from minimum up to 500000 USD\n",
    "# this helped get rid of more outliers\n",
    "df_top10_ind = df_top10_ind[(df_top10_ind.salaire_usd <= 500000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f221c72ea90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "sb.violinplot(data = df_top10_ind, y = 'industrie', x = 'salaire_usd', color = base_color, inner = None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d886b54e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To make things clearer let's these top 10 industries against salary average\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.barplot(data= df_top10_ind, x= 'salaire_usd', y= 'industrie', color = base_color, errwidth = 0)\n",
    "plt.xlabel('Average salary (USD)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "Now we clearly see that _Computing or Tech_ industry has the best distribution of salaries with an average greater than 120000 USD. The next is _Law_ with about 110000 USD average followed by _Business or Consulting_. The rest of industies have less than 100000 USD average, among them _Higher Education_ is the last with generally low salaries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 : How are salaries distributed through the top 10 occupations ?\n",
    "\n",
    "Here we will plot relationship between occupation and salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data for top 10 occupations\n",
    "occupations = df_salary.occupation.value_counts()[:10].index\n",
    "df_top10_occ = df_salary[df_salary.occupation.isin(occupations)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f221c7dd4a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot for all occupations first\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.violinplot(data = df_top10_occ, y = 'occupation', x = 'salaire_usd', color = base_color, inner = None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting to get salaries from minimum up to 500000 USD\n",
    "# this helped get rid of more outliers\n",
    "df_top10_occ = df_top10_occ[(df_top10_occ.salaire_usd <= 500000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f221f3cc320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "sb.violinplot(data = df_top10_occ, y = 'occupation', x = 'salaire_usd', color = base_color, inner = None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d883b0400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To make things clearer let's these top 10 occupations against salary average\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.barplot(data= df_top10_occ, x= 'salaire_usd', y= 'occupation', color = base_color, errwidth = 0)\n",
    "plt.xlabel('Average salary (USD)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "The position of _Senior Software Engineer_ has generally the highest average salary, more than 150000 USD, followed by _Software engineer_ with about an average of 140000 USD, _Product Manager_ and _Director_ with an average between 120000 and 140000 USD. The rest of occupations have an average of less than 100000 USD, among them _Teacher_ is the last with generally low salaries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3 : How are salaries distributed through the experience ranges ?\n",
    "\n",
    "Here we will plot relationship between annee_exp_totale and salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d87daecc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get salaries <= 500000 and plot\n",
    "df_salary_500 = df_salary[df_salary.salaire_usd <= 500000]\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.violinplot(data = df_salary_500, y = 'annee_exp_totale', x = 'salaire_usd', color = base_color, inner = None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will plot relationship between annee_exp_present and salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d88116cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get salaries <= 500000 and plot\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.violinplot(data = df_salary_500, y = 'annee_exp_present', x = 'salaire_usd', color = base_color, inner = None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d86f425f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot these against average salary\n",
    "\n",
    "# For annee_exp_totale\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.barplot(data= df_salary, x= 'salaire_usd', y= 'annee_exp_totale', color = base_color, errwidth = 0)\n",
    "plt.xlabel('Average salary (USD)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26997, 17)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_salary_500.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d86f14b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# annee_exp_present\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.barplot(data= df_salary, x= 'salaire_usd', y= 'annee_exp_present', color = base_color, errwidth = 0)\n",
    "plt.xlabel('Average salary (USD)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "The Best distribution belongs to _11-20 years_, _21-30 years_ and _31-40 years_ ranges. _41 years or more_ and _8-10 years_ ranges have the next best distribution. The rest of ranges have low density.\n",
    "\n",
    "Considering average salary, persons with total years between 31 and 40 have the highest average salary followed by those having from 8 to 10 years and those having from 21 to 30 years. On the other side, the average salary of persons starting their job or with 1 year of present experience is the highest.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4 : How are salaries distributed through study levels ?\n",
    "\n",
    "Here we will plot relationship between niveau_edu and salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d86fdb7f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get salaries <= 500000 and plot\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.violinplot(data = df_salary_500, y = 'niveau_edu', x = 'salaire_usd', color = base_color, inner = None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d8728ae80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# niveau_edu\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.barplot(data= df_salary, x= 'salaire_usd', y= 'niveau_edu', color = base_color, errwidth = 0)\n",
    "plt.xlabel('Average salary (USD)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "The _Professional degree_ has the best distribution, followed by PhD. The rest of levels seem to have the lowest salaries, with _Some college_ and _High School_ levels having peaker distributions. _High school_ is level has generally the lowest salaries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ralationship between industry and study level features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Computing or Tech                       4510\n",
       "Education (Higher Education)            2375\n",
       "Nonprofits                              2362\n",
       "Government and Public Administration    1846\n",
       "Health care                             1835\n",
       "Accounting, Banking & Finance           1742\n",
       "Engineering or Manufacturing            1640\n",
       "Marketing, Advertising & PR             1090\n",
       "Law                                     1063\n",
       "Business or Consulting                   820\n",
       "Name: industrie, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top10_ind.industrie.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d877de860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10, 10))\n",
    "sb.countplot(data = df_top10_ind, y = 'industrie', hue = 'niveau_edu');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between occupation and study level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Software Engineer           278\n",
       "Project Manager             223\n",
       "Senior Software Engineer    193\n",
       "Director                    190\n",
       "Program Manager             148\n",
       "Librarian                   146\n",
       "Teacher                     145\n",
       "Manager                     134\n",
       "Executive Assistant         132\n",
       "Product Manager             121\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top10_occ.occupation.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d862c3b70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10, 10))\n",
    "sb.countplot(data = df_top10_occ, y = 'occupation', hue = 'niveau_edu');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "Under univariate exploration section, we stated that _Computing or Tech_ industry count is the highest among top 10 industries picked in the dataset, also _master_ and _College_ degrees were the most found among persons. From this plot we understand that those industries with higher counts generally have higher numbers of _master_ and _College_ degrees, which explain a relationship between industry and study level. The same observation is valid for the top 10 occupations picked too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?\n",
    "\n",
    "> Now we clearly see that _Computing or Tech_ industry has the best distribution of salaries with an average greater \n",
    "> than 120000 USD. The next is _Law_ with about 110000 USD average followed by _Business or Consulting_. \n",
    "> The rest of industies have less than 100000 USD average, among them _Higher Education_ is the last with generally \n",
    "> low salaries.\n",
    "> \n",
    "> The position of _Director_ has the best density followed by _Software engineer_, _Program Manager_, _Manager_, _Product\n",
    "> Manager_ and _Senior Software Engineer_ have the next best density. _Teacher_, _Librarian_, _Executive Assistant_ have \n",
    "> low density. The position of _Senior Software Engineer_ has generally the highest average salary, more than 150000 USD, \n",
    "> followed by _Software engineer_ with about an average of 140000 USD, _Product Manager_ and _Director_ with an average \n",
    "> between 120000 and 140000 USD. The rest of occupations have an average of less than 100000 USD, among them _Teacher_ is \n",
    "> the last with generally low salaries.\n",
    "> \n",
    "> For experience ranges of years, the Best distribution belongs to _11-20 years_, _21-30 years_ and _31-40 years_ ranges. \n",
    "> _41 years or more_ and _8-10 years_ ranges have the next best distribution. The rest of ranges have low density. Considering\n",
    "> average salary, persons with total years between 31 and 40 have the highest average salary followed by those having from 8 to\n",
    "> 10 years and those having from 21 to 30 years. On the other side, the average salary of persons starting their job or with 1\n",
    "> year of present experience is the highest.\n",
    "> \n",
    "> The _Professional degree_ has the best distribution, followed by PhD. The rest of levels seem to have the lowest salaries,\n",
    "> with _Some college_ and _High School_ levels having peaker distributions. _High school_ is level has generally the lowest \n",
    "> salaries.\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    ">Under univariate exploration section, we stated that _Computing or Tech_ industry count is the highest among top 10 industries\n",
    "> picked in the dataset, also _master_ and _College_ degrees were the most found among persons. From these plots we understand \n",
    "> that those industries with higher counts generally have higher numbers of _master_ and _College_ degrees, which explain a \n",
    "> relationship between industry and study level. The same observation is valid for the top 10 occupations picked too.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration\n",
    "\n",
    "> Create plots of three or more variables to investigate your data even\n",
    "further. Make sure that your investigations are justified, and follow from\n",
    "your work in the previous sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For bivariate exploration we plotted industry against average salary. Here, we'll add study level feature to turn that plot into multivariate plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJElSZ+sNOwBpNtp6uwWuIS5JkmYUeywkSZIkdWZiIUmSJKkzEwtJkiRJnZlYSJIkSerMxEKSJElSZyYWkiRJkjozsZAkSZLUmYmFJEmSpM5MLCRJkiR15pu3pSH47q13sMtxpw87DEnT3NJFRww7BEn6DXssJEmSJHVmYiFJkiSpMxMLSZIkSZ2ZWEiSJEnqzMRCkiRJUmcmFpIkSZI6M7GQJEmS1JnvsZCGYMGc2zh33qJhhyHNCtsev2LYIUjSrGCPhSRJkqTOTCwkSZIkdWZiIUmSJKkzEwtJkiRJnZlYSJIkSerMxEKSJElSZy43Kw3BjXPmcNimGw87DGl2OHGPYUegaeLSYy4ddgjStGaPhSRJkqTOTCwkSZIkdWZiIUmSJKmzWZlYJDk4SSXZYR2IZd8kz+77fHSSI6bgPKcluSnJsiTXJ3l7h7aOTHLSGOVrLPb0nJLkuiQrkuw+Qd2LktzQrm1ZkkNa+WVrIhZJkiRNbrZO3j4MuAR4GXDCcENhX+Bu4DKAqjp5Cs91XFUtTrIBcF2S06vqpjXV+BqOfU/gScBTgQ2BeZPUP7yqrhwVz7PHqyxJkqQ1a9b1WCR5NLAH8Cp6iUX/vre0X8evSfLeVvbEJF9tZVclWdB+TV+U5NpW/9BWd98kn+9r76QkR7btm5O8o7WxIskOSeYDRwNvbr+075XkhCTHtmMuSvK+JJcn+V6SvVr5o5J8OsnyJGcl+XaShatwGzZof/+ytXd8kiva9ZySJBOdf9Q9e36SbybZYg3Hfj+wJbB+Vf2qqv57Fa5vJLa729/7tngWt96aM/uucZWuPcmcJO9v3+HyJMe08l2SXJxkaZIvJ9l6VeOVJEmazmZjj8WLgC9V1feS3Jlk56q6Kslz277fr6pfJdms1T8TeG9Vndt+6X8E8GJgR+CZwBbAFUmWDHDu26tq5ySvBY6tqlcnORm4u6reD5DkgFHHrFdVuyV5HvB24EDgtcDPquoZSZ4GLBvw2hcl+WvgicCHquonrfykqnpnO/8ngBcAn5vg/LS6BwN/ATyvqn7WnsnXVOz/DWwMnJbk8KqqSa7tzCT3tO0DquqOUft3otf78WPgUnrJ5SWrce1HAdsBO1XVA0k2S7I+cCLwwqr6aUs03wX8aX8ASY5qx7Pl3Lm8++zZ+N9PM8U+Sy4edgiSpHXMrOuxoDcM6lNt+1PtM/QeGv+1qn4FUFV3JpkHbFNV57aye9v+PYFPVtWD7Zf0i4FdBzj3Oe3vpcD8AeMd65g9R66hqq4Flg/Y1nFVtSOwFXBAHp7bsV/rOVgB7E/vAXyymPcD/j/g+VX1symIfTFwAPAr4IMAST6c5Pnj1D+8qnZsf0YnFQCXV9WtVfUQvWRmJJ5VvfYDgZOr6oF2DXcC2wNPA76SZBnw18DjRwdQVadU1cKqWrjJ+uuPcxmSJEnT06z6yTTJ5vQeHp+WpIA5QCV5CxBg9K/iv/UT/CTlD7BysrbBqP33tb8fZPB7P9Yx451/IFV1d5KLgD2TXAV8GFhYVT9McgIrxz1ezDcCTwCeDKw0t6Fr7El+B9iiqm5I8ufAZ9KbbL4QOG6y4yeJ5TfxtB6oVb328f6dfKeqxp1gLkmSNNPNth6LQ4DTq+r3qmp+Vf0ucBO9X9EvAP40yaMAkmxWVb8Abk3yolY2t+1fAhzaxts/FtgbuBy4BXhKq7cJvV/cJ3MXk09MHu0S4KUtpqcATx/ZkeT0JLtNdHCS9YDfB/6Thx+kb09v/skhA8ZwC70hYacneepklQeJvc9Pe7uzX1U9SG/40BuBq6rql6twrsmszrVfABzd7iFtyNwNwGPTVq5Ksv4q3hNJkqRpb7YlFocB544q+wzw8qr6EnA+cGUbznJs2/8nwBuSLKe3ctNWrY3lwDXA14G3VNVtVfVD4NNt35nA1QPE9Dng4LTJ2wNex4fpPcgupzccaTnw87bvGcB/jXPconZty4EVwDlV9T/AR9vn84ArBoyBqroBOBw4O8mCNRD7SLsFvAR4V4v3POD1wLPSlpJdE1bz2k8FfgAsT3INvX8799NLSt7XypYBrkglSZJmlUw+J1brmiRz6K2WdG97oP8avSFJGwD/UlV/PNQAJzBe7O3hfNbYft68OmWnnYcdhrTanLwtSbNHkqVVNekKpLNqjsUM8ijgwrYaUYDXtAfz+4F1NqloxotdkiRJ05iJxTRUVXfRm8g87Uzn2NekezbaihW7Hjt5RWmKvP4DBw07BEnSDDPb5lhIkiRJmgImFpIkSZI6M7GQJEmS1JmJhSRJkqTOTCwkSZIkdWZiIUmSJKkzEwtJkiRJnfnmbWkIttl803rtcw8YdhiStE542xmLhx2CpAkM+uZteywkSZIkdWZiIUmSJKkzEwtJkiRJnZlYSJIkSerMxEKSJElSZyYWkiRJkjpbb9gBSLPR1tstcHlFSZI0o9hjIUmSJKkzEwtJkiRJnZlYSJIkSerMxEKSJElSZyYWkiRJkjozsZAkSZLUmYmFJEmSpM5MLCRJkiR1ZmIhSZIkqTMTC0mSJEmdrTfsAKTZ6Lu33sEux50+7DCkdcrSRUcMOwRJUgf2WEiSJEnqzMRCkiRJUmcmFpIkSZI6M7GQJEmS1JmJhSRJkqTOTCwkSZIkdeZys9IQLJhzG+fOWzTsMKTOtj1+xbBDkCStI+yxkCRJktSZiYUkSZKkzkwsJEmSJHVmYiFJkiSpMxMLSZIkSZ2ZWEiSJEnqzOVmpSG4cc4cDtt042GHIXV34h7DjqCTS4+5dNghSNKMYY+FJEmSpM5MLCRJkiR1ZmIhSZIkqTMTC61TklSSD/R9PjbJCUOMZ68k30myLMk2SRa38h2TPG9YcUmSJK1rTCy0rrkPeHGSLdbWCZPMmWD34cD7q2rHqvpRVR3SyncETCwkSZIaEwutax4ATgHePHpHkt9L8rUky9vf27by05J8KMllSW5Mckgr3zfJkiTnJrkuyclJHtH23Z3knUm+Deye5IAkVydZkeRjSeYmeTXwUuD4JGcmmZ/k2iSPBN4JHNp6Mg5Nsk/bXtbambeW7pckSdI6wcRC66J/Ag5Pssmo8pOA06vqGcCZwIf69m0N7Am8AHhvX/luwF8CTwcWAC9u5RsB11bV7wNXAqcBh1bV0+ktw/yaqjoVOB84rqoOH2mwqu4HjgfOaj0ZZwHHAq+rqh2BvYB7ut0CSZKk6cX3WGidU1W/SHI68AZWfkDfnYcTg08Af9e377yqegi4LsmWfeWXV9WNAEk+SS/5WAw8CHym1dkeuKmqvtc+fxx4HfAPqxD2pcDfJzkTOKeqbh1dIclRwFEAW86dy7vP9r+fpp99llw87BAkSesoeyy0rvoH4FX0ehbGU33b9/VtZ5w6/Z/vraoHx6i/WqrqvcCrgQ2BbyXZYYw6p1TVwqpauMn663c9pSRJ0jrFxELrpKq6E/g0veRixGXAy9r24cAlAzS1W5Lt2tyKQ8c55npgfpInts9/Akz2s+xdwG/mUSRZUFUrqup99IZW/VZiIUmSNJOZWGhd9gGgf3WoNwCvTLKc3sP/Gwdo45v05lxcC9wEnDu6QlXdC7wSODvJCuAh4ORJ2r0QeMrI5G3gTW1i9zX0hm/9+wCxSZIkzRipGj1SRJoZkuwLHFtVLxh2LKNtP29enbLTzsMOQ1plzrGQpNknydKqWjhZPXssJEmSJHXmsjSasarqIuCiIYchSZI0K5hYSENwz0ZbsWLXY4cdhtaA13/goGGHIEnSOsGhUJIkSZI6M7GQJEmS1JmJhSRJkqTOTCwkSZIkdWZiIUmSJKkzEwtJkiRJnfnmbWkIttl803rtcw8YdhiSZpC3nbF42CFImqF887YkSZKktcbEQpIkSVJnJhaSJEmSOjOxkCRJktSZiYUkSZKkzkwsJEmSJHW23rADkGajrbdb4NKQkiRpRrHHQpIkSVJnJhaSJEmSOjOxkCRJktSZiYUkSZKkzkwsJEmSJHVmYiFJkiSpMxMLSZIkSZ1N+h6LJAuBvYDHAfcA1wJfrao7pzg2SZIkSdPEuD0WSY5MchXwf4ENgRuAnwB7Al9J8vEk266dMCVJkiStyybqsdgI2KOq7hlrZ5IdgScBP5iKwCRJkiRNH6mqYccgzTobbbVd7fAn7xh2GJLWsqWLjhh2CJK0ypIsraqFk9WbcPJ2kv2SnJPkO+3P4iT7rrEoJUmSJM0IE82xeD7wMeBzwMuBw4EvAh9L8ry1E54kSZKk6WCiORbHAS+qqmv6ypYluRI4kV6SIUmSJEkTDoXaalRSAUBVLQe2nLqQJEmSJE03EyUWv1zNfZIkSZJmmYmGQi1Icv4Y5QGeMEXxSJIkSZqGJkosXjjBvvev6UCk2WTBnNs4d96iYYchDc22x68YdgiSpDVs3MSiqi7u/5xkfeBpwI+q6idTHZgkSZKk6WOi5WZPTvLUtr0JcA1wOnB1ksPWUnySJEmSpoGJJm/vVVXfaduvBL5XVU8HdgHeMuWRSZIkSZo2Jkos7u/b/gPgPICqum1KI5IkSZI07UyUWPxPkhck2QnYA/gSQJL1gA3XRnCSJEmSpoeJVoX6c+BDwFbAm/p6Kg4AvjDVgUmSJEmaPiZaFep7wB+OUf5l4MtTGZQkSZKk6WXcxCLJiUD1FRVwO3BhVV0y1YFJM9mNc+Zw2KYbDzsMaXhO3GPYEWgWuPSYS4cdgjSrTDQU6soxyjYDFiU5q6r+YYpikiRJkjTNTDQU6uNjlSc5GbgMMLGQJEmSBEy8KtSYquqeqQhEs0uSu0d9PjLJSavZ1r5JPt+3/ey+faclOaRbtJIkSZrMKiUWSdZL8krg1imKR+pqX+DZk1VaU9ryy5IkSbPeuIlFkruS/KL/D/Aj4Ln0lqKVpkSSxyb5TJIr2p89WvluSS5LcnX7e/tRx80HjgbenGRZkr3arr1b/RvH671IckSS5UmuSfKJVnZQkm+38301yZat/IQkpyS5ADg9yZwki1qsy5P4/0OSJM06E82xmLc2A9Gss2GSZX2fNwPOb9v/CHywqi5Jsi295Y3/F3A9sHdVPZDkQODdwEtGGqiqm9scoLur6v0ASV4FbA3sCezQzrG4P5AkTwXeBuxRVbcn2aztugR4VlVVklcDbwH+su3bBdizqu5JchTw86raNclc4NIkF1TVTd1vkyRJ0vQw0XKz86vq5gn2B9imqhwWpdVxT1XtOPIhyZHAwvbxQOApvX9iAGycZB6wCfDxJE+it/zx+gOe67yqegi4bqTXYZT9gcVVdTtAVd3Zyh8PnJVka+CRQH+icH7ffKPnAM/o6w3ZBHjSqPq0BOQogC3nzuXdZzuKSmvXPksuHnYIkqQZbKInm0VJHgF8FlgK/BTYAHgisB+9N3C/HedbaM17BLD76IUC2rtVLqyqg9uwp4sGbO++/mbG2B9WfmfLiBOBv6+q85PsC5zQt++Xo44/pr08clxVdQpwCsD28+aNdT5JkqRpa9w5FlX1x8DfANsD/wR8g16S8WrgBmD/qvrK2ghSs84FwOtHPiQZ6dnYhN48H4Ajxzn2LmBVh/F9DXhpks3b+UaGQvWf7xUTHP9l4DVJ1m/HPznJRqsYgyRJ0rQ24ViMqrqO3thzaW16A/BPSZbT+ze6hN6k7L+jNxTqL4Cvj3Ps54DFSV4IHDPIyarqO0neBVyc5EHganqJywnA2Ul+BHwL2G6cJk4F5gNXtSGCPwVeNMi5JUmSZopUOSJDWtu2nzevTtlp52GHoVnGORaSpNWRZGlVLZys3iq/IE+SJEmSRjOxkCRJktTZpOtdJvkM8DHg39uSnZI6umejrVix67HDDkPT3Os/cNCwQ5Ak6TcG6bH4CPBy4PtJ3ptkhymOSZIkSdI0M2liUVVfrarDgZ2Bm4GvJLksyStHlteUJEmSNLsNNMeire9/JL13WFwN/CO9RMP3WEiSJEkaaI7FOcAOwCeAg6rqv9qus5JcOZXBSZIkSZoeJkwskjwCWFZVLx5r/yDr2UqSJEma+SYcCtVWgXruWopFkiRJ0jQ16Zu3k7wDWA6cU76mW1ojttl803rtcw8YdhiSpFngbWcsHnYImuYGffP2pHMsgL8ANgIeSHIvEKBjtqJlAAAgAElEQVSqauOOMUqSJEmaISZNLKpq3toIRJIkSdL0NUiPBUk2BZ4EbDBSVlVLpiooSZIkSdPLIMvNvhp4I/B4YBnwLOCbwP5TG5okSZKk6WKQF+S9EdgVuKWq9gN2An46pVFJkiRJmlYGSSzurap7AZLMrarrge2nNixJkiRJ08kgcyxuTfIY4DzgK0l+Bvx4asOSJEmSNJ1M+h6LlSon+wCbAF+qqvunLCpphlu4cGFdeeWVww5DkiRpUp3fY5FkszGKV7S/Hw3cuZqxSZIkSZphJhoKtRQoei/EG62AJ0xJRJIkSZKmnXETi6rabm0GIkmSJGn68gV5kiRJkjrzBXmSJEmSOvMFeZIkSZI68wV5kiRJkjrzBXmSJEmSOps0saiqg9vmCUkupL0gb0qjkiRJkjStTDoUKsmCJHNHPgLzgUdNZVCSJEmSppdU1cQVkmXAQnoJxZeB84Htq+p5Ux6dNENttNV2tcOfvGPYYUiaRZYuOmLYIUiappIsraqFk9UbZPL2Q1X1AHAw8A9V9WZg664BSpIkSZo5Bkksfp3kMOAVwOdb2fpTF5IkSZKk6WaQxOKVwO7Au6rqpiTbAWdMbViSJEmSppNBVoW6DnhD3+ebgPdOZVCSJEmSppdBeiwkSZIkaUImFpIkSZI6G+TN2wAk2aiqfjmVwUizxYI5t3HuvEXDDkPSGrLt8SuGHYIkDd0gL8h7dpLrgO+2z89M8uEpj0ySJEnStDHIUKgPAv8buAOgqq4B9p7KoCRJkiRNLwPNsaiqH44qenAKYpEkSZI0TQ0yx+KHSZ4NVJJH0lt69rtTG5YkSZKk6WSQHoujgdcB2wC3Aju2z5IkSZIEDPaCvNuBw9dCLJIkSZKmqUkTiyQfGqP458CVVfXZNR+SNPPdOGcOh2268bDDkLSmnLjHsCOYcS495tJhhyBpFQ0yFGoDesOfvt/+PAPYDHhVkn+YwtgkSZIkTRODTN5+IrB/VT0AkOQjwAXAHwC+EUiSJEnSQD0W2wAb9X3eCHhcVT0I3DclUUmSJEmaVgZJLP4OWJbkX5OcBlwNvD/JRsBXpzK42SzJg0mWJbkmyVVtyd/VaefoJEes6fimQpLdkixJckOS65OcmuRRa+G8RyY5qW2/KMlT+va9M8mBUx2DJEnSdDfIqlD/kuSLwG5AgL+qqh+33cdNZXCz3D1VtSNAkv8NvAfYZ1UbqaqT13Rga0KS9UaG17XPWwJnAy+rqm8mCfASYB7wq7UY2ouAzwPXAVTV8Wvx3JIkSdPWQG/eBu4F/gu4E3hikr2nLiSNYWPgZwBJ9k3y+ZEdSU5KcmTbfm+S65IsT/L+VnZCkmPb9kVJ3pfk8iTfS7JXK5+TZFGSK9qxf97Kt249CMuSXJtkr1b3tPZ5RZI3jw42ye8l+Vpr62tJtm3lpyX5+yQXAu8bddjrgI9X1TcBqmdxVf13ks2SnNfa+1aSZ/Rd28fadd2Y5A2tfKMkX2i9PdcmObSV35xki7a9MMlFo+J+NvBHwKJ2zQtazIf0Hf+O1oO0IskOrfyxSb7Syv85yS0j55EkSZotBllu9tXAG4HHA8uAZwHfBPaf2tBmvQ2TLKO3KtfWTHK/k2wGHAzsUFWV5DHjVF2vqnZL8jzg7cCBwKuAn1fVrknmApcmuQB4MfDlqnpXkjnAo+itELZNVT2tnXes85wEnF5VH0/yp8CH6PUEADwZOLDN0en3NODj48T8DuDqqnpRkv2B01scADsA+9Hr2bihLS7wh8CPq+r5LcZNxml3JVV1WZLzgc9X1eJ27Ohqt1fVzkleCxwLvJreffx6Vb0nyR8CR43VfpKjRvZtOXcu7z57kLUTJK0p+yy5eNghSNKMNkiPxRuBXYFbqmo/YCfgp1MalaANhaqqHeg9KJ+eMZ5y+/yCXs/SqUlezPjDh85pfy8F5rft5wBHtETm28DmwJOAK4BXJjkBeHpV3QXcCDwhyYntIfoXY5xjd+Df2vYngD379p09RlIxmT1bO1TV14HN+5KFL1TVfe1Fjj8BtqS3WtmBrXdmr6r6+SqebyJj3b89gU+1+L5E610arapOqaqFVbVwk/XXX4MhSZIkDd8gicW9VXUvQJK5VXU9sP3UhqV+bXjQFsBjgQdY+XvboNV5gN48mM/Q6x340jjNjazk9SAP91gFOKYlMjtW1XZVdUFVLQH2Bn4EfCLJEVX1M+CZwEX0hi+dOsgl9G3/cpw63wF2GWffWAnVSJv9K5M9SK9H5nutrRXAe5KMzJPov3cbTBb0OMa7f5IkSbPaIInFrW24y3nAV5J8FvjxJMdoDWpj+ecAdwC3AE9JMrf9an9Aq/NoYJOq+iLwJh4eKjSILwOvSbJ+a+vJbZ7C7wE/qaqPAv8C7NzmDjyiqj4D/A2w8xjtXQa8rG0fDlwyQAwnAa9I8vt91/1/kmwFLGntkGRfesORxuopGTnuccCvquoM4P19Md7Mw8nLS8Y5/C56w6pWxSXAS9u5nwNsuorHS5IkTXuDrAp1cNs8oU263YTxfw3XmjMyxwJ6v4i/og0h+mGSTwPL6b0J/epWZx7w2SQbtPq/Nal6AqfSG9ZzVRtu9VN6vR77Ascl+TVwN3AEvfea/GuSkaT0/47R3huAjyU5rrX1yskCaJO0X0ZvKePfAR6il1CcA5zQzrmc3hCvV0zS3NPpTcB+CPg18JpW/g7gX5L8Fb0hX2P5FPDRNhH8kMni7mv3k22S+MX0Fjq4a8BjJUmSZoRU1fg7ew+Py0cm6kr6bW3C+4NV9UCS3YGPjCwVPJ7t582rU3Yaq7NH0lRx8rYkrZ4kS6tq4WT1JuyxqKqH2pKd21bVD9ZceNKMsi3w6ZaI3w/82ZDjkSRJWusGWe9ya+A7SS6nb+JtVf3RlEUlTSNV9X16q6VJkiTNWoMkFu+Y8iikWeaejbZixa7HDjsMzWCv/8BBww5BkjTLDDJ5++K2OtCTquqrSR5Fb4UiSZIkSQIGWG42yZ8Bi4F/bkXb0Ft6VpIkSZKAwd5j8TpgD9obltt48t+ZyqAkSZIkTS+DJBb3VdX9Ix+SrMfKb1KWJEmSNMsNklhc3F4otmGSPwDOBj43tWFJkiRJmk4GSSzeSu/tySuAPwe+CPz1VAYlSZIkaXqZ8M3bAEkOBr5YVfetnZCkmW+bzTet1z73gGGHIUlaB73tjMXDDkFayaBv3h6kx+KPgO8l+USS57c5FpIkSZL0G5MmFlX1SuCJ9OZWvBz4zySnTnVgkiRJkqaPgXofqurXSf6d3mpQGwIvBF49lYFJkiRJmj4GeUHeHyY5DfgP4BDgVGDrKY5LkiRJ0jQySI/FkcCngD93ArckSZKksUyaWFTVy/o/J9kDeHlVvW7KopIkSZI0rQw0xyLJjvQmbr8UuAk4ZyqDkma6rbdb4HKCkiRpRhk3sUjyZOBlwGHAHcBZ9N57sd9aik2SJEnSNDFRj8X1wDeAg6rqPwCSvHmtRCVJkiRpWploVaiXALcBFyb5aJIDgKydsCRJkiRNJ+MmFlV1blUdCuwAXAS8GdgyyUeSPGctxSdJkiRpGhjkzdu/rKozq+oFwOOBZcBbpzwySZIkSdPGpIlFv6q6s6r+uar2n6qAJEmSJE0/q5RYSJIkSdJYTCwkSZIkdWZiIUmSJKkzEwtJkiRJnU30gjxJU+S7t97BLsedPuwwpFlp6aIjhh2CJM1I9lhIkiRJ6szEQpIkSVJnJhaSJEmSOjOxkCRJktSZiYUkSZKkzkwsJEmSJHXmcrPSECyYcxvnzls07DCkKbft8SuGHYIkaS2xx0KSJElSZyYWkiRJkjozsZAkSZLUmYmFJEmSpM5MLCRJkiR1ZmIhSZIkqTOXm5WG4MY5czhs042HHYY09U7cY9gRaAKXHnPpsEOQNIPYYyFJkiSpMxMLSZIkSZ2ZWEiSJEnqbMoSiyRbJvm3JDcmWZrkm0kOnqrzDUuSI5M8boraPi3JIeOU35RkWZKrkuw+QIwnjbPv7vb345IsXsX41ktye5L3TFBn3ySfX8V2T03ylEnqHJ3kiEnO++xVOW87bsckz+v7/EdJ3rqq7UiSJM02U5JYJAlwHrCkqp5QVbsALwMePxXna+cc1kT0I4EpSSwmcVxV7Qi8Ffjnro1V1Y+r6reSmEk8B7gBeGn7zteIqnp1VV03SZ2Tq+r0CarsC4yZWEzyb2VH4DeJRVWdX1XvnSgWSZIkTV2Pxf7A/VV18khBVd1SVScCJNkgyb8mWZHk6iT7tfJvJ3nqyDFJLkqyS5KNknwsyRWt/gvb/iOTnJ3kc8AF7Vfqi5IsTnJ9kjNHHniT3Jzk3a3n5MokOyf5cpL/THJ03zmPa+dZnuQdrWx+ku8m+WiS7yS5IMmGrTdhIXBm6z3YsP8mJPmz1tY1ST6T5FGt/LQkH0pyWevROaSVJ8lJSa5L8gXgdwa410uAJ/bdr4Vte4skN/fV+90kX0pyQ5K3j26kXeO1bXtOkve372d5kmPGOfdhwD8CPwCe1dfWH7b7fwnw4r7yE5J8vN2/m5O8OMnftfN8Kcn6Y1zH3Une1e7ht5Js2dfWsW37De2eLU/yqSTzgaOBN7fvZa92z/8+yYXA+5Ls1u7/1e3v7ZM8EngncGg77tD09fYk+b0kX2vn+VqSbSf6PiVJkmaTqUosngpcNcH+1wFU1dPpPZx+PMkGwKeAlwIk2Rp4XFUtBd4GfL2qdgX2AxYl2ai1tTvwiqrav33eCXgT8BTgCUD/Woc/rKrdgW8ApwGH0Hsgfmc753OAJwG70fvlepcke7djnwT8U1U9Ffgf4CVVtRi4Eji8qnasqntGXec5VbVrVT0T+C7wqr59WwN7Ai8ARn4RPxjYHng68GeM84v7KAcBKwaotxtweLuuPx55cB/HUcB2wE5V9QzgzNEVWhJ1APB54JP0vkfa9/jRFtdewFajDl0APB94IXAGcGH7d3BPKx9tI+Bb7R4uoXdfRntrX6xHV9XNwMnAB9v38o1W78nAgVX1l8D1wN5VtRNwPPDuqrq/bZ/Vjjtr1HlOAk7vuycf6ts31vcpSZI0a6yV4UNJ/oneQ9f9LTnYEzgRoKquT3ILvYe+TwNfAd5OL8E4uzXxHOCPRn6hBjYAtm3bX6mqO/tOd3lV3drOuwyYD1zS9p3f/l4BPLqq7gLuSnJvkse08zwHuLrVezS9hOIHwE1VtayVL23tTuZpSf4f8JjW1pf79p1XVQ8B1438Cg/sDXyyqh4Efpzk6xO0vSjJXwM/ZeWEZTxfqao7AJKcQ+87uHKcugcCJ1fVAwCj7u+IF9BLCn6V5DPA3yR5M7ADvXv1/XauM+glKiP+vap+nWQFMAf4Uitfwdj39H56yQv07vsfjFFnOb1eo/PoDcEbz9nt3gJsQi+hfRJQwPoTHDdidx7ugfkE8Hd9+8b6PleS5Cjavdhy7lzefbavkdHq22fJxcMOQZKklUzVk813gJeMfKiq1yXZgocfZMccj19VP0pyR5JnAIcCf95X/yVVdUN//SS/D/xyVDP39W0/yMrXOLLvoVH1Hmr1Arynqlaas9CG1oxud6VhT+M4DXhRVV2T5Eh64/7HirP/ftQA7UJvjsXoydYP8HAv1Aaj9o1ud6LzZIA4DgP26BtutTm93qTbJzn2PoCqeijJr6tqpO7IdzBaf53R3+eI59NLyv6IXoLz1DHqwMr/Vv6WXmJ0cPt+L5og5vH0X+d43+fDlatOAU4B2H7evEG/Z0mSpGlhqoZCfR3YIMlr+soe1be9hN6wHJI8mV7vw0jS8CngLcAmVTUyxOfLwDHJb+ZL7DRFcX8Z+NMkj27n2SbJZPMc7gLmjbNvHvBfbe7A4QOcfwnwsjbHYWt6D+qr4mZgl7Y9epz/HyTZrA1hehEw0etWLwCOTpvknGSz/p1JNqbX47FtVc2vqvn0hrcdRm+I0XZJFrTqh63iNaySJI8AfreqLqT372akd2ii7wV6PRY/attH9pVPdNxl9BYhgN73eck49SRJkmadKUks2i/MLwL2SW9Z1MuBjwP/X6vyYWBOGw5zFnBkVY384ruY3sPbp/ua/Ft6Q1WWtwnGfztFcV8A/BvwzRbbYiZ+OIVer8TJGWPyNvA3wLfpDe+6foAQzgW+T29Y0EeAVR3r8H7gNUkuA7YYte8SesN3lgGfqarxhkEBnEpv+NfyJNcALx+1/8X05rz0/0r/WXo9BkVvuM8X2uTtW1bxGlbVHOCM9n1dTW9exf8AnwMOHpm8PcZxfwe8J8mlrY0RFwJPGZm8PeqYNwCvTLIc+BPgjWv6YiRJkqarPDzKRNLasv28eXXKTjsPOwxNY86xkCStLUmWVtVEC/8AvnlbkiRJ0hpgYiFJkiSpM9e7lIbgno22YsWux05eURrHir/83Bpt7/UfOGiNtidJmn3ssZAkSZLUmYmFJEmSpM5MLCRJkiR1ZmIhSZIkqTMTC0mSJEmdmVhIkiRJ6sw3b0tDsM3mm9Zrn3vAsMOQtI542xmLhx2CJI3LN29LkiRJWmtMLCRJkiR1ZmIhSZIkqTMTC0mSJEmdmVhIkiRJ6szEQpIkSVJn6w07AGk22nq7BS4vKUmSZhR7LCRJkiR1ZmIhSZIkqTMTC0mSJEmdmVhIkiRJ6szEQpIkSVJnJhaSJEmSOjOxkCRJktSZiYUkSZKkzkwsJEmSJHVmYiFJkiSps/WGHYA0G3331jvY5bjThx2GpNW0dNERww5BktY59lhIkiRJ6szEQpIkSVJnJhaSJEmSOjOxkCRJktSZiYUkSZKkzkwsJEmSJHXmcrPSECyYcxvnzls07DCkCW17/IphhyBJmkbssZAkSZLUmYmFJEmSpM5MLCRJkiR1ZmIhSZIkqTMTC0mSJEmdmVhIkiRJ6szEQpIkSVJnvsdCGoIb58zhsE03HnYY0sRO3GPYEUiz3qXHXDrsEKSB2WMhSZIkqTMTC0mSJEmdmVhIfZLcPewYJEmSpiMTC0mSJEmdmVhIk0hyUJJvJ7k6yVeTbNnKVyR5THruSHJEK/9EkgOHG7UkSdLaZWIhTe4S4FlVtRPwKeAtrfxSYA/gqcCNwF6t/FnAt9Z2kJIkScPkcrPS5B4PnJVka+CRwE2t/BvA3sAtwEeAo5JsA9xZVb81VyPJUcBRAFvOncu7z/a/n9YN+yy5eNghSJJmAHss9P+3d+/hUVVn38e/NwEBAQMiKkKVqCCHnAiE85maKCKKSgGtgCiggFKeFzzUipRHbT3UtoKCUBS1CBSQakU0IAQERUIgkHAMaqooIuADEk6SZL1/zM44CUkIhGSA/D7XlSsza6+99r1XJsncs9baW05uIjDJORcBDAOqeOUr8I1SdAQSgT3AHfgSjhM456Y651o651qGVqpU6kGLiIiIlCUlFiInFwp86z0emFvonPsGuARo6Jz7Et+UqTEUkliIiIiInM+UWIjkdaGZ7Qz4+h9gPDDXzD4B9uar/zmw3Xv8CVAPX4IhIiIiUq5okrdIAOdcYcn2u4XUvzvg8acoWRcREZFySm+CRERERESkxJRYiIiIiIhIiWkqlEgQHKl2OamxY4IdhpyDRv7l5mCHICIiUiCNWIiIiIiISIkpsRARERERkRJTYiEiIiIiIiWmxEJEREREREpMiYWIiIiIiJSYEgsRERERESkxc84FOwaRcqde7Vpu+I3dgx2GiIicxR7/57xghyACgJklO+danqyeRixERERERKTElFiIiIiIiEiJKbEQEREREZESU2IhIiIiIiIlpsRCRERERERKTImFiIiIiIiUmBILEREREREpMd3HQiQIWrZs6dauXRvsMEREyoXjx4+zc+dOjh49GuxQRM5qVapUoX79+lSqVClPeXHvY1Gx1CITEREROQvs3LmTGjVq0KBBA8ws2OGInJWcc+zbt4+dO3cSFhZ2Wm1oKpSIiIic144ePUrt2rWVVIgUwcyoXbt2iUb2lFiIiIjIeU9JhcjJlfT3RImFiIiIiIiUmBILERERESnUuHHjWLJkSbDDOKlBgwYxb968YIdRrmnxtoiIiIgUasKECcEOQc4RGrEQERERETIyMmjSpAlDhgyhWbNmxMXFceTIEf9IwKJFi/jNb37jr5+YmMjNN98MQEJCAm3btiUmJoY+ffqQmZkJ+JKS2NhYwsPDGTp0KLm3OejSpQu5l13fu3cvDRo0KDSu7Oxsxo4dS2xsLJGRkbz66quA7ypGI0eOpGnTptx000388MMP/n0aNGjA3r17AVi7di1dunQ5Y/0khVNiISIiIiIApKenM2LECDZt2kTNmjWZP3++f9v111/P6tWrOXToEABz5syhb9++7N27l6eeeoolS5awbt06WrZsyYsvvgjAyJEjSUpKIi0tjSNHjvD++++fckzTp08nNDSUpKQkkpKSmDZtGl999RULFixg27ZtpKamMm3aND799NMz0wly2pRYiIiIiAgAYWFhREdHA9CiRQsyMjL82ypWrMgNN9zAf/7zH7Kysli4cCG33HILq1evZvPmzbRv357o6GjeeOMN/vvf/wKwbNkyWrduTUREBEuXLmXTpk2nHFNCQgJvvvkm0dHRtG7dmn379pGens6KFSvo378/ISEhXHHFFXTr1u2M9IGcPq2xEBEREREAKleu7H8cEhLCkSNH8mzv27cvL7/8MhdffDGxsbHUqFED5xzXX389s2bNylP36NGjDB8+nLVr1/KrX/2K8ePH+++RULFiRXJycvz1iuKcY+LEicTHx+cp/+CDDwq9POqptC9njhILkSDYsnMfLca+GewwROQ8kPz8gGCHIOVIly5duPfee5k2bRp9+/YFoE2bNowYMYIdO3Zw7bXXcvjwYXbu3Mmll14KwCWXXEJmZibz5s3jjjvuAHxrIJKTk2nVqtVJr+QUHx/P5MmT6datG5UqVWL79u3Uq1ePTp068eqrrzJgwAB++OEHli1bxp133pmn/RtvvDHPdC4pXZoKJSIiIiLFEhISQs+ePVm0aBE9e/YEoE6dOsyYMYP+/fsTGRlJmzZt2Lp1KzVr1mTIkCFERERw6623Ehsb629nzJgxTJ48mXbt2vkXWRfmvvvuo2nTpsTExBAeHs6wYcPIysqid+/eNGzYkIiICB544AE6d+7s3+fJJ59k1KhRdOzYkZCQkNLpDDmB5a7OF5GyU+3yMNf47j8GOwwROQ9oxOLktmzZQpMmTYIdhsg5oaDfFzNLds61PNm+GrEQEREREZES0xoLEREREQm6jz76iEceeSRPWVhYGAsWLAhSRHKqlFiIiIiISNDFx8efcOUnObdoKpSIiIiIiJSYRixEguCakO9ZUOP5YIchIsVw5bjUYIcgInJO0IiFiIiIiIiUmBILERERkVJmZtx9993+51lZWdSpU8d/L4hTsX//fl555ZXTiqNBgwantV9GRgbh4eGnta+UH5oKJSIiIuVKi7FvntH2inMvkWrVqpGWlsaRI0eoWrUqixcvpl69eqd1vNzEYvjw4cXexznH2XLvsuzsbN207jylEQsRERGRMnDjjTeycOFCAGbNmkX//v3929asWUO7du1o3rw57dq1Y9u2bQBs2rSJVq1aER0dTWRkJOnp6Tz66KN88cUXREdHM3bsWACef/55YmNjiYyM5MknnwR8owxNmjRh+PDhxMTE8M0331CnTh0ADh06xE033URUVBTh4eHMmTPnhHiTk5OJioqibdu2vPzyy/7y7Oxsxo4d6z/eq6++CkBOTg7Dhw+nWbNm9OzZkx49ejBv3jzAN1IyYcIEOnTowNy5c/niiy+44YYbaNGiBR07dmTr1q0A7Nmzh9tvv53Y2FhiY2NZtWrVGf0ZSOnSiIWIiIhIGejXrx8TJkygZ8+ebNy4kcGDB/PJJ58A0LhxY1asWEHFihVZsmQJv//975k/fz5Tpkxh1KhR3HXXXfz8889kZ2fz5z//mbS0NFJSUgBISEggPT2dNWvW4JyjV69erFixgiuvvJJt27bx+uuv+6dOJSUlAfDhhx9yxRVX+BOdAwcOnBDvPffcw8SJE+ncubM/gQGYPn06oaGhJCUlcezYMdq3b09cXBzJyclkZGSQmprKDz/8QJMmTRg8eLB/vypVqrBy5UoAunfvzpQpU2jYsCGff/45w4cPZ+nSpYwaNYrRo0fToUMHvv76a+Lj49myZUsp/DSkNCixEBERESkDkZGRZGRkMGvWLHr06JFn24EDBxg4cCDp6emYGcePHwegbdu2PP300+zcuZPbbruNhg0bntBuQkICCQkJNG/eHIDMzEzS09O58sorueqqq2jTps0J+0RERDBmzBgeeeQRevbsSceOHU+IZ//+/XTu3BmAu+++m0WLFvmPt3HjRv9oxIEDB0hPT2flypX06dOHChUqcPnll9O1a9c8bfbt29cf36effkqfPn38244dOwbAkiVL2Lx5s7/8p59+4uDBg9SoUeNk3StnASUWIkHwZUgI/WtdFOwwRKQ4JrYPdgTnvFUPajpLrl69ejFmzBgSExPZt2+fv/yJJ56ga9euLFiwgIyMDLp06QLAnXfeSevWrVm4cCHx8fH84x//4Oqrr87TpnOOxx57jGHDhuUpz8jIoFq1agXG0ahRI5KTk/nggw947LHHiIuLY9y4cXnaNLMC93XOMXHixBNuZpc7+lGY3FhycnKoWbOmf8QlUE5ODp999hlVq1Ytsi05O2mNhYiIiEgZGTx4MOPGjSMiIiJP+YEDB/yLuWfMmOEv//LLL7n66qt56KGH6NWrFxs3bqRGjRocPHjQXyc+Pp7XXnuNzMxMAL799lt++OGHIuP47rvvuPDCC/ntb3/LmDFjWLduXZ7tNWvWJDQ01D91aebMmXmON3nyZP+oyvbt2zl06BAdOnRg/vz55OTksHv3bhITEws89kUXXURYWBhz584FfInKhgM5LswAACAASURBVA0bAIiLi2PSpEn+ugUlH3L2UmIhIiIiUkbq16/PqFGjTih/+OGHeeyxx2jfvj3Z2dn+8jlz5hAeHk50dDRbt25lwIAB1K5dm/bt2xMeHs7YsWOJi4vjzjvvpG3btkRERHDHHXfkSTwKkpqa6l8U/vTTT/OHP/zhhDqvv/46I0aMoG3btnlGEO677z6aNm1KTEwM4eHhDBs2jKysLG6//Xbq16/vL2vdujWhoaEFHn/mzJlMnz6dqKgomjVrxrvvvgvASy+9xNq1a4mMjKRp06ZMmTKlWP0qZwc7Wy49JlKeVL+yuosaGxXsMEREykSwp0Jt2bKFJk2aBDWG8iIzM5Pq1auzb98+WrVqxapVq7j88suDHZacgoJ+X8ws2TnX8mT7asQigJllm1lKwNejJWjr0zMZWyHH6FWSGM/A8QeZmTOz7gFlvb2yO0rheH3MbIuZLTuNfQeZ2RWnsV9LM3vpVPcTEREpj3r27El0dDQdO3bkiSeeUFJRzmjxdl5HnHPRZ6Ih51y7M9FOYcysonPuPeC90jxOAcfMylecCvQHPvae9wM2lFII9wLDnXOnnFgAg4A04Lvi7uCd71pg7WkcT0REpNwpbF2FlA8asSgGM8swsz+a2TozSzWzxl55HTNb7JW/amb/NbNLvG2Z3vcuZpZoZvPMbKuZzTTvMgtm1sLMlptZspl9ZGZ1vfJrzOxDr/yTgOPNMLMXvU/sn/U+hZ8UsO0lM/vUzL7MHTEwswpm9oqZbTKz983sg4JGE8ws2sxWm9lGM1tgZrW88kQze8bMlgMnTgqFT4BWZlbJzKoD1wIpAe2OM7MkM0szs6kB555oZs+a2Roz225mHb1y/zl5z9/3+nAc0AGYYmbPm1kDr2/WeV/tAvZ52Ps5bTCzP3vn2xKY6Y1EVfV+prk/q5Zmlug9Hu/FmQC86R37/YBtr3mxf2lmDwUc8wnv57vYzGaZ2ZjivLZEREREzhcascirqpkFXn7gT8653FtR7nXOxZjZcGAMcB/wJLDUOfcnM7sBGFpIu82BZvg+LV8FtDezz4GJwC3OuT1m1hd4GhgMTAXud86lm1lr4BWgm9dWI+DXzrlsMxuU7zh18b35boxvJGMecBvQAIgALgW2AK8VEOObwIPOueVmNsE7t99522o65zoXcm4OWALEA6HeccMCtk9yzk0AMLO3gJ7Af7xtFZ1zrcysh3e8XxdyDJxzE8ysGzDGObfWzC4ErnfOHTWzhsAsoKWZ3QjcCrR2zh02s4udcz+a2cjcfb1YCjsUQAugg3PuiJl1ybetMdAVqAFsM7PJQBRwO76fc0VgHZCcv1EzG4r3GrmscmWematfPyk/Oq9YHuwQRESklOmdTV5FTYV6x/uejO/NOvjexPcGcM59aGb/V8i+a5xzOwG8xKUBsB8IBxZ7b3JDgF3ep/7tgLkBb34rB7Q11zmXTcH+7ZzLATab2WUBMc71yr+3AtYnmFkovuQh9z//G8DcgCpz8u+Tz2zgIXyJxf8Dfh+wrauZPQxcCFwMbOKXxCKwTxuc5Bj5VQImmVk0kI0v4QJfcvK6c+4wgHPux1NsF+A959yRQrYtdM4dA46Z2Q/AZfj6+N3cfczsPwXt6Jybii9p5LoaNXTVBBERETmvKLEovmPe92x+6bciP/YuYN/A/Q3Y5JxrG1jRzC4C9heR4Bwq5nEs3/eSKOqYOOfWmFk4vsRse25CZGZV8I22tHTOfWNm44EqBcQb2KdZ5J2iF1g/0GhgN77RggrAUa/c8I2inEzgcfIfo7h9HPizFBERESnXtMaiZFYCvwEwszig1insuw2oY2Ztvf0rmVkz59xPwFdm1scrNzMryXVJVwK3e2stLgO65K/gnDsA/F/uOgfgbuBU5y08Rt6RCvjlDftebySmOFeKygCivXh/BbQqpF4osMsbibkb34gPQAIw2JsqhZld7JUfxDd9KfA4LbzHtxcjrqKsBG42syreed5UwvZEROQ88/3339OvXz+uueYamjZtSo8ePdi+fXuh9TMyMggPDwd8C6J79uxZVqHmObbIqdCIRV7511h86Jwr6nKufwRmeesjlgO78L2BPSnn3M/eouKXvKlIFYG/4ZsqdBcw2cz+gG/Kz2xO/0pL84Hu+K6ItB34HDhQQL2B+BZGXwh8CdxzKgdxzi0qoGy/mU3Dd+WoDCCpGE2tAr7y9knDt16hIK8A870EbBneKIM3JS0aWGtmPwMf4Et4ZuA7vyNAW3w/u+lm9nt8fXLanHNJZvYevp/Rf/FdRaqgPhYRkbPA1xMiTl7pFFw5LrXI7c45evfuzcCBA5k9ezbgu6P07t27adSoUZH7nuuys7MJCQk5eUU5L+gGeSVgZpWBbOdcljfyMPlMXa72TDKz6s65TDOrDawB2jvnvg92XOeTgD6+EFgBDHXOFZYUcV2NGm5q85iyC1AkyLR4W4Ip/w2/yjqxWLp0KePHj2fFihUnbHPO8fDDD7No0SLMjD/84Q/07duXjIwMevbsSVpaGomJibzwwgu8//77HDp0iAcffJDU1FSysrIYP348t9xyC4cPH2bQoEFs3bqVJk2akJGRwcsvv0zLli1JSEjgySef5NixY1xzzTW8/vrrVK9ePU8cycnJDB48mAsvvJAOHTqwaNEi0tLSyM7O5tFHHyUxMZFjx44xYsQIhg0bRk5ODiNHjmT58uWEhYWRk5PD4MGDueOOO2jQoAGDBw8mISGBkSNHEhsby4gRI9izZw8XXngh06ZNo3HjxuzZs4f777+fr7/+GoC//e1vtG/f/sz9YOS0lOQGeRqxKJkrgX+ZWQXgZ2BIkOMpzPtmVhO4APhfJRWlYqqZNcU3/euNopIKEREpX9LS0mjRokWB29555x1SUlLYsGEDe/fuJTY2lk6dOhXa1tNPP023bt147bXX2L9/P61ateLXv/41kydPplatWmzcuJG0tDSio32fc+7du5ennnqKJUuWUK1aNZ599llefPFFxo0bl6fde+65h4kTJ9K5c2fGjh3rL58+fTqhoaEkJSVx7Ngx2rdvT1xcHMnJyWRkZJCamsoPP/xAkyZNGDx4sH+/KlWqsHLlSgC6d+/OlClTaNiwIZ9//jnDhw9n6dKljBo1itGjR9OhQwe+/vpr4uPj2bJly2n3swSfEosScM6l47vE6FnNOdcl2DGc75xzdwY7BhEROfesXLmS/v37ExISwmWXXUbnzp1JSkoiMjKywPoJCQm89957vPDCCwAcPXqUr7/+mpUrVzJqlO92U+Hh4f79V69ezebNm/0jAT///DNt2+a5bgwHDhxg//79dO7su7L83XffzaJFi/zH27hxI/PmzfPXTU9PZ+XKlfTp04cKFSpw+eWX07Vr1zxt9u3bF4DMzEw+/fRT+vTp49927JjvOihLlixh8+bN/vKffvqJgwcPUqNGDeTcpMRCJAiOVLuc1FjdQ0/OPyP/cnOwQxA56zRr1sz/xjy/U52S7pxj/vz5XHfddcVqxznH9ddfz6xZs4pss7D7OznnmDhxIvHx8XnKFy5cWGSc1apVAyAnJ4eaNWuSkpJyQp2cnBw+++wzqlatWmRbcu7QVaFERERESlG3bt04duwY06ZN85clJSWxfPlyOnXqxJw5c8jOzmbPnj2sWLGCVq0KuyAixMfHM3HiRH8isX79egA6dOjAv/71LwA2b95Maqpv3UebNm1YtWoVO3bsAODw4cMnXI2qZs2ahIaG+qcuzZw5M8/xJk+ezPHjxwHYvn07hw4dokOHDsyfP5+cnBx2795NYmJigfFedNFFhIWFMXeu7/ZYzjk2bPBdjyYuLo5Jkyb56xaUfMi5RYmFiIiISCkyMxYsWMDixYu55ppraNasGePHj+eKK66gd+/eREZGEhUVRbdu3Xjuuee4/PLLC23riSee4Pjx40RGRhIeHs4TTzwBwPDhw9mzZw+RkZE8++yzREZGEhoaSp06dZgxYwb9+/cnMjKSNm3asHXr1hPaff311xkxYgRt27bNM4Jw33330bRpU2JiYggPD2fYsGFkZWVx++23U79+fX9Z69atCQ0NLTDmmTNnMn36dKKiomjWrBnvvvsuAC+99BJr164lMjKSpk2bMmXKlJJ0s5wFdFUokSC48vKG7uG7Xgx2GCJnnKZCydmooKvcnG+ys7M5fvw4VapU4YsvvqB79+5s376dCy64oNSOmZmZSfXq1dm3bx+tWrVi1apVRSZFcm7QVaFEREREyrHDhw/TtWtXjh8/jnOOyZMnl2pSAdCzZ0/279/Pzz//zBNPPKGkQpRYiIiIiJzratSowdq1a8v0mIWtq5DyS2ssRERERESkxDRiIRIE2cf3cmD3G8EOQ+SMe/q3el2Xpcf/WfAlTEVEgkEjFiIiIiIiUmJKLEREREREpMSUWIiIiIiUspCQEKKjowkPD6dPnz4cPnyYjIwMwsPDC6w/aNAgwsLCiIqKolGjRgwYMIBvv/22jKMWOTVaYyEiIiLlSvuJ7c9oe6seXHXSOlWrVvXfWfquu+5iypQp3HbbbUXu8/zzz3PHHXfgnONvf/sbXbt2JS0trdQvIytyujRiISIiIlKGOnbsyI4dOwDfje2GDBlCs2bNiIuL48iRIyfUNzNGjx7N5ZdfzqJFi8o6XJFiU2IhIiIiUkaysrJYtGgRERERAKSnpzNixAg2bdpEzZo1mT9/fqH7xsTEsHXr1rIKVeSUaSqUSBDUDbtGl4kUESlHjhw5QnR0NOAbsbj33nv57rvvCAsL85e3aNGCjIyMQttwzpVFqCKnTYmFiIiISCkLXGMRqHLlyv7HISEhBU6FyrV+/Xq6d+9eKvGJnAlKLERERETOYs45Jk6cyK5du7jhhhuCHY5IobTGQkREROQsNHbsWP/lZpOSkli2bJmuCCVnNdN8PZGy17JlS7d27dpghyEiUi5s2bKFJk2aBDsMkXNCQb8vZpbsnGt5sn01YiEiIiIiIiWmxEJEREREREpMiYWIiIiIiJSYEgsRERERESkxJRYiIiIiIlJiSixERERERKTEdIM8kSDYsnMfLca+GewwROQMSX5+QLBDkLPc008/zdtvv01ISAgVKlTg1VdfpXXr1sEO6wQzZsxg7dq1TJo0ifHjx1O9enXGjBkT7LDkHKHEQkRERMqV5Z06n9H2Oq9YXuT2zz77jPfff59169ZRuXJl9u7dy88//3xGYxA5G2gqlIiIiEgp2rVrF5dccgmVK1cG4JJLLuGKK64A4OOPP6Z58+ZEREQwePBgjh07BkCDBg34/e9/T9u2bWnZsiXr1q0jPj6ea665hilTpvjbfv7554mNjSUyMpInn3yywON/+OGHxMTEEBUVRffu3QH48ccfufXWW4mMjKRNmzZs3LixyHP44osvuOGGG2jRogUdO3Zk69at/vI2bdoQGxvLuHHjqF69+inFJucXJRYiIiIipSguLo5vvvmGRo0aMXz4cJYv941wHD16lEGDBjFnzhxSU1PJyspi8uTJ/v1+9atf8dlnn9GxY0cGDRrEvHnzWL16NePGjQMgISGB9PR01qxZQ0pKCsnJyaxYsSLPsffs2cOQIUOYP38+GzZsYO7cuQA8+eSTNG/enI0bN/LMM88wYEDR0/mGDh3KxIkTSU5O5oUXXmD48OEAjBo1ilGjRpGUlORPloobm5x/lFiIiIiIlKLq1auTnJzM1KlTqVOnDn379mXGjBls27aNsLAwGjVqBMDAgQPzvPnu1asXABEREbRu3ZoaNWpQp04dqlSpwv79+0lISCAhIYHmzZsTExPD1q1bSU9Pz3Ps1atX06lTJ8LCwgC4+OKLAVi5ciV33303AN26dWPfvn0cOHCgwPgzMzP59NNP6dOnD9HR0QwbNoxdu3YBvmleffr0AeDOO+/071Oc2OT8ozUWIiIiIqUsJCSELl260KVLFyIiInjjjTeIjo4ucp/cqVMVKlTwP859npWVhXOOxx57jGHDhhXahnMOMyuwPL+C6gHk5ORQs2ZNUlJSiow3f/sni03OPxqxEBERESlF27Zty/NpfUpKCldddRWNGzcmIyODHTt2APDWW2/RuXPxF5bHx8fz2muvkZmZCcC3337LDz/8kKdO27ZtWb58OV999RXgW1sB0KlTJ2bOnAlAYmIil1xyCRdddFGBx7nooosICwvzT6NyzrFhwwYA2rRpw/z58wGYPXv2KcUm5x+NWIgEwTUh37OgxvPBDkOk3LpyXGqwQ5ByJDMzkwcffJD9+/dTsWJFrr32WqZOnUqVKlV4/fXX6dOnD1lZWcTGxnL//fcXu924uDi2bNlC27ZtAd+Uq3/+859ceuml/jp16tRh6tSp3HbbbeTk5HDppZeyePFixo8fzz333ENkZCQXXnghb7zxRpHHmjlzJg888ABPPfUUx48fp1+/fkRFRfG3v/2N3/72t/zlL3/hpptuIjQ0tNixyfnHChoKE5HSFVmvqnt/2LXBDkOk3FJiUb5s2bKFJk2aBDuM89Lhw4epWrUqZsbs2bOZNWsW7777brDDkhIo6PfFzJKdcy1Ptq9GLERERETktCQnJzNy5Eicc9SsWZPXXnst2CFJECmxEBEREZHT0rFjR/96CxEt3hYRERERkRJTYiEiIiIiIiWmxEJEREREREpMayxEguDLkBD61yr4euEiUgYmtg92BEG36sFVwQ5BRM4zGrEQERERKWXVq1fP83zGjBmMHDkSgClTpvDmm28WuX9g/aK8//77NG/enKioKJo2bcqrr75aaN2MjAzCw8OLEf3JDRo0iHnz5p2RtuTcpRELERERKVcm/b//nNH2Rv7l5hLtfyo3xSvK8ePHGTp0KGvWrKF+/focO3aMjIyMM9K2SHFoxEJEREQkiMaPH88LL7wAQFJSEpGRkbRt25axY8fmGVH47rvvuOGGG2jYsCEPP/zwCe0cPHiQrKwsateuDUDlypW57rrrANi9eze9e/cmKiqKqKgoPv30UwCys7MZMmQIzZo1Iy4ujiNHjgCQkpJCmzZtiIyMpHfv3vzf//1fkeUioMTilJmZM7O3Ap5XNLM9Zvb+KbbT5VT2MbNoM+sR8LyXmT16Ksc8FWa2wcxmFbG9gZmlnaFjNTCzOwOetzSzl4qof4WZnbHxVjO72cw2m1mamT1dRL1B3s86xas/pIDyrWY2+kzFJiIi54cjR44QHR3t/xo3blyB9e655x6mTJnCZ599RkhISJ5tKSkpzJkzh9TUVObMmcM333yTZ/vFF19Mr169uOqqq+jfvz8zZ84kJycHgIceeojOnTuzYcMG1q1bR7NmzQBIT09nxIgRbNq0iZo1azJ//nwABgwYwLPPPsvGjRuJiIjgj3/8Y5HlIqDE4nQcAsLNrKr3/Hrg21NpwMxOZwpaNOBPLJxz7znn/nwa7ZyUmTXB99roZGbVSuMYAceqCDQA/ImFc26tc+6hwvZxzn3nnLvjDIbxN+Am51w48I+T1J3jnIsGugDPmNll+crbA4+b2a/OYHwiInKOq1q1KikpKf6vCRMmnFBn//79HDx4kHbt2gFw55135tnevXt3QkNDqVKlCk2bNuW///3vCW384x//4OOPP6ZVq1a88MILDB48GIClS5fywAMPABASEkJoaCgAYWFhREdHA9CiRQsyMjI4cOAA+/fvp3PnzgAMHDiQFStWFFoukkuJxelZBNzkPe4P+D/ZN7NWZvapma33vl/nlQ8ys7lm9h8gIbAxM4v16l9tZtXM7DUzS/LKbjGzC4AJQF/vU/G+XnuTvP1nmNlL3vG+NLM7vPIKZvaKmW0ys/fN7IPcbSdxJ/CWF2evgDhbeCMZnwEjAso/N7NmAc8TvbonnEshffFnoKN3bqMDR3PMrLNXnuK1USNwtMRr6x0z+9DM0s3suYA47jWz7V4803L7qwA/A/UBnHNfFaN/cM79AHwBXJWvfB+wA6hbnHZERERyOeeK3F65cmX/45CQELKysgqsFxERwejRo1m8eLF/BKKkbYoUhxKL0zMb6GdmVYBI4POAbVuBTs655sA44JmAbW2Bgc65brkFZtYOmALc4pz7EngcWOqciwW6As8Dlby25jjnop1zcwqIqS7QAeiJ7406wG34RgMigPu84xdHX2AOvoSpf0D568BDzrn87cwGfuOdT13gCudcckHnEjACEtgXjwKfeOf213xtjwFGeKMBHYEjBcQb7cUcgS/5+pWZXQE8AbTBN6rUuKATNbMKwBbgNTMLK6pT8u13NXA1viQisPxKoAqwsbhtiYiIANSqVYsaNWqwevVqAGbPnn1K+2dmZpKYmOh/npKSwlVX+T7/6t69O5MnTwZ86yp++umnQtsJDQ2lVq1afPLJJwC89dZbdO7cudBykVy6KtRpcM5tNLMG+N50f5Bvcyjwhpk1BBy+pCDXYufcjwHPmwBTgTjn3HdeWRzQy8zGeM+rAFcWI6x/O+dygM0B03M6AHO98u/NbNnJGjGzWGCPc+6/ZrYT3xvuWkAOUNM5t9yr+hZwo/f4X8Bi4El8CcbcYpxL/r4ozCrgRTObCbzjnNtpZvnrfOycO+DFvxnfKMIlwPLcY5jZXKBRAe0/CGwCXgH+Y2ZdgTBgrHOuTwH1+5pZB+AYMMw596MXT19v3+uAIc65o/l3NLOhwFCAyypX5pm5+vWT8qvziuUnryRSDk2fPp0hQ4ZQrVo1unTp4p+yVBzOOZ577jmGDRtG1apVqVatGjNmzADg73//O0OHDmX69OmEhIQwefJk6tYtfHD9jTfe4P777+fw4cNcffXVvP7660WWi4ASi5J4D3gB31z72gHl/wssc8719pKPxIBth/K1sQvfm+3mQG5iYcDtzrltgRXNrPVJ4jkWWD3f91PRH2hsZhne84uA24F5+BKlEzjnvjWzfWYWiW/kYFjA8Qs7l/x9USDn3J/NbCG+9SWrzezXQP437YHnno3vdV3cc48HnnPOJZrZBGAhsAbfiE1B5jjnCrqQ+Bzn3EgzawssNLNFzrnv853LVHyJJNfVqFH0eLeIiJSakl4e9nRkZmbmeT5o0CAGDRoE+K4KlatZs2Zs3Ogb9P7zn/9My5YtT6gPvvtV5FejRg0++CD/550+l112Ge++++4J5Wlpv1yHZcyYMf7H0dHR/pGTQIWV5yYwUr5pKtTpew2Y4JxLzVceyi+LuQedpI39+NZqPGNmXbyyj4AHzfsY3Myae+UHgRqnGONK4HZvrcVl+JIgvHb/ZGa9Ayt704L6AJHOuQbOuQbALUB/59x+4ID3aT3AXfmONRt4GAgN6JPCziW/Qs/NzK5xzqU6554F1lLIlKYCrAE6m1ktb4H47YXUWw/81swqOOf+BaTjW2OysJjHycM59xm+0ZxRp7O/iIiUbwsXLiQ6Oprw8HA++eQT/vCHPwQ7JJFiU2JxmpxzO51zfy9g03PAn8xsFRBSwPb87ewGbgZe9j7J/19806c2eguU/9erugxomrt4u5hhzgd2AmnAq/jWghzwtkUA3+er3wn41jkXeJWrFd5x6wL3eHF+xolrHeYB/fBNi8pV2LnktxHI8haG579U6+/MdxnYDd4xFxV1wrm8c3gG3zkvATbzy7kHehrf6EaamSUDu/H11dteonU6ngXuMbNTTQRFRKSc69u3LykpKaSlpbFw4ULq1KkT7JBEis1OdgUCObeZWXXnXKaZ1cb3KX5759z3ZvaRcy4+2PGVpoBzrwgsAF5zzi0Idlzgmwo1tXlMsMMQCRqtsZCytGXLFpo0aRLsMETOCQX9vphZsnOu5cn21RqL89/7ZlYTuAD439x5/+d7UuEZ763JqILvsrb/DnI8IiIiIuctJRbnOedcl2DHECzOuTEnryUiIiIiZ4ISC5EgOFLtclJjlfdI+ZX6//4T7BCCLhhXJhIRKU1avC0iIiJSykJCQvxXe+rTpw+HDx8+pf3nzp1LkyZN6Nq16xmJZ9y4cSxZsuSMtJUrMTGRnj17nrRely5dWLt27Rk99ulyztGtWzf/DQPNjLvvvtu/PSsrizp16vjPa8aMGdSpU4fmzZvTsGFD4uPj+fTTT096nBkzZjBypO9q9ePHj6devXpER0fTsGFDbrvtNjZv3nza55CYmFisGAL169eP9PT00z5mYTRiISIiIuXK07+944y29/g/5520TtWqVUlJSQHgrrvuYsqUKfzP//yPf7tzDuccFSoU/Jnv9OnTeeWVV85YYjFhwoQz0k4wZWVlUbFiyd7KfvDBB0RFRXHRRRcBUK1aNdLS0jhy5AhVq1Zl8eLF1KtXL88+ffv2ZdKkSQAsW7aM2267jWXLlp3SBQJGjx7tv2/InDlz6NatG6mpqad1FbDExESqV69Ou3btir3PAw88wHPPPce0adNO+XhF0YiFiIiISBnq2LEjO3bsICMjgyZNmjB8+HBiYmL45ptvmDVrFhEREYSHh/PII48AviRg5cqV3H///YwdO5bs7GzGjh1LbGwskZGRvPrqqwDs2rWLTp065bkPRnZ2NoMGDSI8PJyIiAj++te/Ar4b7s2b50uIPv74Y5o3b05ERASDBw/m2DHffWcbNGjAk08+SUxMDBEREWzduhWANWvW0K5dO5o3b067du3Ytm1b/lPM48iRI/Tr14/IyEj69u3LkSO/XLE+ISGBtm3bEhMTQ58+ffw3Evzggw9o3LgxHTp04KGHHvKPGIwfP56hQ4cSFxfHgAEDCu0LgOeff95f/uSTTxYY28yZM7nlllvylN14440sXOi7ndWsWbPo379/oefWtWtXhg4dytSpU4vsg6L07duXuLg43n777SLr7dmzh9tvv53Y2FhiY2NZtWoVGRkZTJkyhb/+9a9ER0fzySefsHv3bnr37k1UVBRRUVEFjmZ07NiRJUuWkJWVddpxF0SJhYiIiEgZycrKYtGidCPJGwAAEVtJREFURURERACwbds2BgwYwPr166lUqRKPPPIIS5cuJSUlhaSkJP79738zbtw4WrZsycyZM3n++eeZPn06oaGhJCUlkZSUxLRp0/jqq694++23iY+PJyUlhQ0bNhAdHU1KSgrffvstaWlppKamcs899+SJ5+jRowwaNIg5c+aQmppKVlYWkydP9m+/5JJLWLduHQ888AAvvPACAI0bN2bFihWsX7+eCRMm8Pvf/77Ic548eTIXXnghGzdu5PHHHyc5ORmAvXv38tRTT7FkyRLWrVtHy5YtefHFFzl69CjDhg1j0aJFrFy5kj179uRpLzk5mXfffZe333670L5ISEggPT2dNWvWkJKSQnJyMitWrDghtlWrVtGiRYs8Zf369WP27NkcPXqUjRs30rp16yLPLyYmxp90na7itDFq1ChGjx5NUlIS8+fP57777qNBgwbcf//9jB49mpSUFDp27MhDDz1E586d2bBhA+vWraNZs2YntFWhQgWuvfZaNmzYUKK489NUKBEREZFSduTIEaKjowHfp8X33nsv3333HVdddRVt2rQBICkpiS5duvinw9x1112sWLGCW2+9NU9bCQkJbNy40T/icODAAdLT04mNjWXw4MEcP36cW2+9lejoaK6++mq+/PJLHnzwQW666Sbi4uLytLVt2zbCwsJo1KgRAAMHDuTll1/md7/7HQC33XYbAC1atOCdd97xH2/gwIGkp6djZhw/frzIc1+xYgUPPfQQAJGRkURGRgKwevVqNm/eTPv27QH4+eefadu2LVu3buXqq68mLCwMgP79++cZEejVqxdVq1Ytsi8SEhJISEigefPmAGRmZpKenk6nTp3yxPbjjz9So0be+9lGRkaSkZHBrFmz6NGjR5HnBr5pbCVVnDaWLFmSZy3GTz/9xMGDB0+ot3TpUt58803At7YnNDS0wPYuvfRSvvvuuxMSq5JQYiEiIiJSygLXWASqVq2a/3Fx36A655g4cSLx8SfekmrFihUsXLiQu+++m7FjxzJgwAA2bNjARx99xMsvv8y//vUvXnvttWIfs3LlyoDvDWrutJknnniCrl27smDBAjIyMujSpctJYzazAs/j+uuvZ9asWXnK169fX2Rb+fusoL746KOPeOyxxxg2bFiRbVWsWJGcnJwT1rb06tWLMWPGkJiYyL59+4psY/369SW+AeP69etp2bLo+8/l5OTw2Wef+ZOqkjp69OgZayuXEguRIMg+vpcDu98IdhhSjhVnsamIlK3WrVszatQo9u7dS61atZg1axYPPvjgCfXi4+OZPHky3bp1o1KlSmzfvp169eqxd+9e6tWrx5AhQzh06BDr1q2jR48eXHDBBdx+++1cc801DBo0KE9bjRs3JiMjgx07dnDttdfy1ltv0blz5yLjPHDggH9B84wZM056Xp06dWLmzJl07dqVtLQ0Nm7cCECbNm0YMWKE/9iHDx9m586dNG7cmC+//JKMjAwaNGjAnDlzCm27sL6Ij4/niSee4K677qJ69ep8++23VKpUiUsvvTTP/tdddx1ffvkl1157bZ7ywYMHExoaSkREBImJiYUef/ny5UydOpVly5YB+Bd1514Bqjjmz59PQkICf/nLXwB47LHHaNWqFb17985TLy4ujkmTJjF27FgAUlJSiI6OpkaNGv6rWgF0796dyZMn87vf/Y7s7GwOHTrkX5weaPv27QVOkyoJrbEQEREROQvUrVuXP/3pT3Tt2pWoqChiYmJOWFgMcN9999G0aVNiYmIIDw9n2LBhZGVlkZiYSHR0NM2bN2f+/PmMGjWKb7/9li5duhAdHc2gQYP405/+lKetKlWq8Prrr9OnTx8iIiKoUKEC999/f5FxPvzwwzz22GO0b9+e7Ozsk57XAw88QGZmJpGRkTz33HO0atUKgDp16jBjxgz69+9PZGQkbdq0YevWrVStWpVXXnmFG264gQ4dOnDZZZcVOp2nsL6Ii4vjzjvvpG3btkRERHDHHXcUOG3opptuKjBxqF+/PqNGjSrwmHPmzCE6OppGjRrxzDPPMH/+fP+IxdatW6ldu/YJ+2RlZflHfwD/YuuGDRvyz3/+k6VLl/qnwKWmpnL55Zef0MZLL73E2rVriYyMpGnTpkyZMgWAm2++mQULFvgXb//9739n2bJlRERE0KJFCzZt2gRAjx49+O677wDYvXs3VatWpW7dugWe4+myMzEvTEROTb3atdzwG7sHOwwpxzRiIeXJli1bSjxVRcpWZmYm1atXxznHiBEjaNiwIaNHjz7jx9m1axcDBgxg8eLFZ6S9nj178s4773DBBRfkKR89ejQNGzZk+PDhJ20jPj6ejz766IzEU5i//vWvXHTRRdx7770nbCvo98XMkp1zRc/VQiMWIiIiInKWmTZtGtHR0TRr1owDBw6cdK3E6apbty5DhgzJM5WoJN5///0Tkoobb7yRjRs3ctdddxWrjdJOKgBq1qzJwIEDz3i7GrEQCQKNWEiwacRCyhONWIgUn0YsREREREQkqJRYiIiIyHlPMzRETq6kvye63KxIENQNu0ZTUUREykiVKlXYt28ftWvXLvB+CiLiSyr27dtHlSpVTrsNJRYiIiJyXqtfvz47d+5kz549wQ5F5KxWpUoV6tevf9r7K7EQERGR81qlSpUICwsLdhgi5z2tsRARERERkRJTYiEiIiIiIiWmxEJEREREREpMN8gTCQIzOwhsC3YcZ4lLgL3BDuIsov74hfriF+qLX6gv8lJ//EJ98Ysz3RdXOefqnKySFm+LBMe24tzBsjwws7Xqi1+oP36hvviF+uIX6ou81B+/UF/8Ilh9oalQIiIiIiJSYkosRERERESkxJRYiATH1GAHcBZRX+Sl/viF+uIX6otfqC/yUn/8Qn3xi6D0hRZvi4iIiIhIiWnEQkRERERESkyJhUgZM7MbzGybme0ws0eDHc+ZYGa/MrNlZrbFzDaZ2SivfLyZfWtmKd5Xj4B9HvP6YJuZxQeUF9g/ZhZmZp+bWbqZzTGzC8r2LE+NmWWYWap33mu9sovNbLF3DovNrJZXbmb2knfOG80sJqCdgV79dDMbGFDewmt/h7evlf1ZnpyZXRfw808xs5/M7Hfl5bVhZq+Z2Q9mlhZQVuqvg8KOEUyF9MXzZrbVO98FZlbTK29gZkcCXh9TAvY5pXMuql+DqZD+KPXfCzOr7D3f4W1vUDZnXLhC+mJOQD9kmFmKV35evzas8P+n58bfDeecvvSlrzL6AkKAL4CrgQuADUDTYMd1Bs6rLhDjPa4BbAeaAuOBMQXUb+qde2UgzOuTkKL6B/gX0M97PAV4INjnfZI+yQAuyVf2HPCo9/hR4FnvcQ9gEWBAG+Bzr/xi4Evvey3vcS1v2xqgrbfPIuDGYJ9zMfokBPgeuKq8vDaATkAMkFaWr4PCjnEW9kUcUNF7/GxAXzQIrJevnVM658L6NdhfhfRHqf9eAMOBKd7jfsCcs7Ev8m3/CzCuPLw2KPz/6Tnxd0MjFiJlqxWwwzn3pXPuZ2A2cEuQYyox59wu59w67/FBYAtQr4hdbgFmO+eOOee+Anbg65sC+8f7NKUbMM/b/w3g1tI5m1J1C77YIe853AK86XxWAzXNrC4QDyx2zv3onPs/YDFwg7ftIufcZ873H+BNzo3+6A584Zz7bxF1zqvXhnNuBfBjvuKyeB0UdoygKagvnHMJzrks7+lqoH5RbZzmORfWr0FVyGujMGfy9yKwn+YB3XM/sQ6WovrCi+03wKyi2jhfXhtF/D89J/5uKLEQKVv1gG8Cnu+k6Dfg5xxvWL058LlXNNIbnn0tYFi1sH4orLw2sD/gDci50G8OSDCzZDMb6pVd5pzbBb5/HsClXvmp9kc973H+8rNdP/K+OSivr42yeB0Udoyz2WB8n57mCjOz9Wa23Mw6emWnc87n2t/d0v698O/jbT/g1T9bdQR2O+fSA8rKxWsj3//Tc+LvhhILkbJV0KdC582l2cysOjAf+J1z7idgMnANEA3swjecDYX3w6mWn83aO+digBuBEWbWqYi6531/ePO7ewFzvaLy/NooTLk9dzN7HMgCZnpFu4ArnXPNgf8B3jazizi9cz6X+qksfi/Opf4A6E/eDyTKxWujgP+nhVYtoCxofzeUWIiUrZ3ArwKe1we+C1IsZ5SZVcL3R3Cmc+4dAOfcbudctnMuB5iGb9geCu+Hwsr34hverZiv/KzlnPvO+/4DsADfue/OHWb3vv/gVT/V/thJ3ikjZ31/4Euw1jnndkP5fm1QNq+Dwo5x1vEWlfYE7vKmZuBN+dnnPU7Gt46gEad3zufM390y+r3w7+NtD6X4U7LKlBffbcCc3LLy8Noo6P8p58jfDSUWImUrCWhovqt1XIBvash7QY6pxLw5sNOBLc65FwPKA+eq9gZyr/jxHtDPfFcnCQMa4ltMVmD/eG82lgF3ePsPBN4tzXMqCTOrZmY1ch/jW6Cahu+8c6/MEXgO7wEDvKt7tAEOeMPQHwFxZlbLmxIRB3zkbTtoZm28vh/AWdwfnjyfOpbX14anLF4HhR3jrGJmNwCPAL2cc4cDyuuYWYj3+Gp8r4MvT/OcC+vXs04Z/V4E9tMdwNLchO4s9Gtgq3POP3XnfH9tFPb/lHPl74YL8up3femrvH3hu4LDdnyfsjwe7HjO0Dl1wDeUuhFI8b56AG8BqV75e0DdgH0e9/pgGwFXNCqsf/Bd9WQNvkWLc4HKwT7vIvrjanxXZ9kAbMo9D3zzmD8G0r3vF3vlBrzsnXMq0DKgrcHeOe8A7gkob4nvTccXwCS8G56ejV/AhcA+IDSgrFy8NvAlU7uA4/g+Kby3LF4HhR3jLOyLHfjmgef+3ci9WtHt3u/OBmAdcPPpnnNR/XoW9kep/14AVbznO7ztV5+NfeGVzwDuz1f3vH5tUPj/03Pi74buvC0iIiIiIiWmqVAiIiIiIlJiSixERERERKTElFiIiIiIiEiJKbEQEREREZESU2IhIiIiIiIlpsRCRERERERKTImFiIjIWcDMZpjZHSepM8HMfl1WMRUSwyAzmxTMGETk7FTx5FVERETkbOCcG1dQuZmFOOeyyzoeEZFAGrEQEREpJWZWzcwWmtkGM0szs75mNs7MkrznU83MCtivwDqBoxpmluHVWwn0MbNrzOxDM0s2s0/MrHERceUZHTGzTO97XTNbYWYp3rE7euX3mNl2M1sOtD+zvSQi5wslFiIiIqXnBuA751yUcy4c+BCY5JyL9Z5XBXoWsF9x6gAcdc51cM7NBqYCDzrnWgBjgFdOI947gY+cc9FAFJBiZnWBP+JLKK4Hmp5GuyJSDiixEBERKT2pwK/N7Fkz6+icOwB0NbPPzSwV6AY0K2C/4tQBmANgZtWBdsBcM0sBXgXqnka8ScA9ZjYeiHDOHQRaA4nOuT3OuZ9zjykikp/WWIiIiJQS59x2M2sB9AD+ZGYJwAigpXPuG+8NfJXAfcysCr7RhkLrBDjkfa8A7PdGGoojy9sHb5rVBV68K8ysE3AT8JaZPQ/8BLhitisi5ZhGLEREREqJmV0BHHbO/RN4AYjxNu31RhkKugpUlWLUycM59xPwlZn18Y5rZhZVxC4ZQAvv8S1AJW+/q4AfnHPTgOlevJ8DXcystplVAvqcLB4RKZ80YiEiIlJ6IoDnzSwHOA48ANyKb4pUBr6pR3k45/ab2bSi6hTiLmCymf0BX6IwG9hQSN1pwLtmtgb4mF9GProAY83sOJAJDHDO7fJGTT4DdgHrgJBixiQi5Yg5p9FNEREREREpGU2FEhERERGREtNUKBERkfOUmT3OiWsi5jrnng5GPCJyftNUKBERERERKTFNhRIRERERkRJTYiEiIiIiIiWmxEJEREREREpMiYWIiIiIiJSYEgsRERERESmx/w+VyMHwrBInOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d87dd6e10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10, 10))\n",
    "sb.barplot(data= df_top10_ind, x = 'salaire_usd', y= 'industrie', hue= 'niveau_edu', errwidth = 0);\n",
    "plt.ylabel('Average salary (USD)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "Previously we noticed that _Computing or Tech_ industry has the highest count among the top 10 industries. We also remarked that _Professional degree_ has the highest average salary under bivariate exploration. Here we can see that for the first industry _Computing or Tech_ the average salary for Professional degree is the highest too, but that relationship is not the same for the rest of industries. For instance, the second industry is _Higher education_ but the average salary of _Professional degree_ in that industry is not the second highest average for this study level. This means that the relationship between industry and study level in view of average salary is not monotonic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?\n",
    "\n",
    "> During univariate exploration we noticed that _Computing or Tech_ industry has the highest count among the top 10 industries. \n",
    "> We also remarked that _Professional degree_ has the highest average salary under bivariate exploration. We also showed a \n",
    "> relationship between industry and study level previously. Combining industry, study level and average salary we can see that\n",
    "> for the first industry _Computing or Tech_ the average salary for Professional degree is the highest.\n",
    "\n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "\n",
    "> By examining the relationship between industry, study level and average salary we notice that relationship is not the same\n",
    "> for the rest of industries. \n",
    "> For instance, the second industry is _Higher education_ but the average salary of _Professional degree_ in that industry is \n",
    "> not the second highest average for this study level through all 10 industries. In fact, the second highest average salary for\n",
    "> Professional degree belong to Health care industry which is neither the second industry with the highest count nor the \n",
    "> industry with the second highest average salary. This means that the relationship between industry and study level in view of\n",
    "> average salary is not monotonic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "During the data exploration process, we covered univariate, bivariate and multivariatte explorations respectively to understand our dataset through one, two and three variables.\n",
    "\n",
    "Under univariate exploration, we visualized our feature of interest using a histogram to see salary distribution. Looking at it we understood salary is almost normally distributed. Consequently we visualized other retained features : industry, job position and experience features using barplots since they are all categorical variables (two nominal and two ordinal). \n",
    "From plots under univariate exploration we noticed that: \n",
    "\n",
    "1. _Computer or Tech_ industry is the first of top 10 industries picked and _Business or Consulting_ is the last one.\n",
    "2. _Software Engineer_ is the first of top 10 occupations picked and _Porduct Manager_ the last one\n",
    "3. The group of persons with total years of experience _between 11 and 20_ has the highest count and that of persons having 41 years or more has the lowest count. This is the same observation even for years of experience in current jobs.\n",
    "4. _College degree_ has the highest count and _High school_ has the lowest one.\n",
    "\n",
    "From plots under bivariate exploration we noticed that:\n",
    "\n",
    "1. _Computer or Tech_ has the best distribution of salary with the highest average salary and _Higher education_ the lowest salaries.\n",
    "2. _Senior Software Engineer_ has the highest average salary among the top 10 occupations and _Teacher_ occupation has the lowest one.\n",
    "3. Persons with total years of experience between 31 and 40 have the highest average salary. On the other side, the average salary of persons starting their job or with 1 year of present experience is the highest.\n",
    "4. Average salary for _Professional degree_ is the highest while that of _High school_ is the lowest.\n",
    "5. There seems to be a relationship between industry and study level according to which as the industry count grows greater generally the number of persons for each study levels grows almost accordingly. This observation is the same even for occupations\n",
    "\n",
    "From plots under multivariate exploration, we understand that even though there is a relationship between industry and study level as stated before, that relationship changes its behaviour when plotted against average salary. This led to conclude that it not a monotonic relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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