project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning\n",
    "\n",
    "* The data is scraped from the website of Glass door using the scraper.py code.\n",
    "* Job Description is breaken into languages required for the job.[Python, R, Java, HTML, Excel,Spark, AWS]\n",
    "* Salary is breaken into hourly salary or employer provided.\n",
    "* Company name simplified\n",
    "* Age of company is simplified. If not available replace with the age calculated by DOB\n",
    "* New CSV file created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Job Title</th>\n",
       "      <th>Salary Estimate</th>\n",
       "      <th>Job Description</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Size</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Type of ownership</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Competitors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$53K-$91K (Glassdoor est.)</td>\n",
       "      <td>Data Scientist\\nLocation: Albuquerque, NM\\nEdu...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Tecolote Research\\n3.8</td>\n",
       "      <td>Albuquerque, NM</td>\n",
       "      <td>Goleta, CA</td>\n",
       "      <td>501 to 1000 employees</td>\n",
       "      <td>1973</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>Aerospace &amp; Defense</td>\n",
       "      <td>Aerospace &amp; Defense</td>\n",
       "      <td>$50 to $100 million (USD)</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Healthcare Data Scientist</td>\n",
       "      <td>$63K-$112K (Glassdoor est.)</td>\n",
       "      <td>What You Will Do:\\n\\nI. General Summary\\n\\nThe...</td>\n",
       "      <td>3.4</td>\n",
       "      <td>University of Maryland Medical System\\n3.4</td>\n",
       "      <td>Linthicum, MD</td>\n",
       "      <td>Baltimore, MD</td>\n",
       "      <td>10000+ employees</td>\n",
       "      <td>1984</td>\n",
       "      <td>Other Organization</td>\n",
       "      <td>Health Care Services &amp; Hospitals</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>$2 to $5 billion (USD)</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$80K-$90K (Glassdoor est.)</td>\n",
       "      <td>KnowBe4, Inc. is a high growth information sec...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>KnowBe4\\n4.8</td>\n",
       "      <td>Clearwater, FL</td>\n",
       "      <td>Clearwater, FL</td>\n",
       "      <td>501 to 1000 employees</td>\n",
       "      <td>2010</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>Security Services</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>$100 to $500 million (USD)</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$56K-$97K (Glassdoor est.)</td>\n",
       "      <td>*Organization and Job ID**\\nJob ID: 310709\\n\\n...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>PNNL\\n3.8</td>\n",
       "      <td>Richland, WA</td>\n",
       "      <td>Richland, WA</td>\n",
       "      <td>1001 to 5000 employees</td>\n",
       "      <td>1965</td>\n",
       "      <td>Government</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Oil, Gas, Energy &amp; Utilities</td>\n",
       "      <td>$500 million to $1 billion (USD)</td>\n",
       "      <td>Oak Ridge National Laboratory, National Renewa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$86K-$143K (Glassdoor est.)</td>\n",
       "      <td>Data Scientist\\nAffinity Solutions / Marketing...</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Affinity Solutions\\n2.9</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>51 to 200 employees</td>\n",
       "      <td>1998</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>Advertising &amp; Marketing</td>\n",
       "      <td>Business Services</td>\n",
       "      <td>Unknown / Non-Applicable</td>\n",
       "      <td>Commerce Signals, Cardlytics, Yodlee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                  Job Title              Salary Estimate  \\\n",
       "0           0             Data Scientist   $53K-$91K (Glassdoor est.)   \n",
       "1           1  Healthcare Data Scientist  $63K-$112K (Glassdoor est.)   \n",
       "2           2             Data Scientist   $80K-$90K (Glassdoor est.)   \n",
       "3           3             Data Scientist   $56K-$97K (Glassdoor est.)   \n",
       "4           4             Data Scientist  $86K-$143K (Glassdoor est.)   \n",
       "\n",
       "                                     Job Description  Rating  \\\n",
       "0  Data Scientist\\nLocation: Albuquerque, NM\\nEdu...     3.8   \n",
       "1  What You Will Do:\\n\\nI. General Summary\\n\\nThe...     3.4   \n",
       "2  KnowBe4, Inc. is a high growth information sec...     4.8   \n",
       "3  *Organization and Job ID**\\nJob ID: 310709\\n\\n...     3.8   \n",
       "4  Data Scientist\\nAffinity Solutions / Marketing...     2.9   \n",
       "\n",
       "                                 Company Name         Location  \\\n",
       "0                      Tecolote Research\\n3.8  Albuquerque, NM   \n",
       "1  University of Maryland Medical System\\n3.4    Linthicum, MD   \n",
       "2                                KnowBe4\\n4.8   Clearwater, FL   \n",
       "3                                   PNNL\\n3.8     Richland, WA   \n",
       "4                     Affinity Solutions\\n2.9     New York, NY   \n",
       "\n",
       "     Headquarters                    Size  Founded   Type of ownership  \\\n",
       "0      Goleta, CA   501 to 1000 employees     1973   Company - Private   \n",
       "1   Baltimore, MD        10000+ employees     1984  Other Organization   \n",
       "2  Clearwater, FL   501 to 1000 employees     2010   Company - Private   \n",
       "3    Richland, WA  1001 to 5000 employees     1965          Government   \n",
       "4    New York, NY     51 to 200 employees     1998   Company - Private   \n",
       "\n",
       "                           Industry                        Sector  \\\n",
       "0               Aerospace & Defense           Aerospace & Defense   \n",
       "1  Health Care Services & Hospitals                   Health Care   \n",
       "2                 Security Services             Business Services   \n",
       "3                            Energy  Oil, Gas, Energy & Utilities   \n",
       "4           Advertising & Marketing             Business Services   \n",
       "\n",
       "                            Revenue  \\\n",
       "0         $50 to $100 million (USD)   \n",
       "1            $2 to $5 billion (USD)   \n",
       "2        $100 to $500 million (USD)   \n",
       "3  $500 million to $1 billion (USD)   \n",
       "4          Unknown / Non-Applicable   \n",
       "\n",
       "                                         Competitors  \n",
       "0                                                 -1  \n",
       "1                                                 -1  \n",
       "2                                                 -1  \n",
       "3  Oak Ridge National Laboratory, National Renewa...  \n",
       "4               Commerce Signals, Cardlytics, Yodlee  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(956, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['hourly'] = df['Salary Estimate'].apply(lambda x: 1 if 'per hour' in x.lower() else 0)\n",
    "df['employer_provided'] = df['Salary Estimate'].apply(lambda x: 1 if 'employer provided salary:' in x.lower() else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Salary Estimate'] != '-1']\n",
    "salary = df['Salary Estimate'].apply(lambda x: x.split('(')[0])\n",
    "minus_Kd = salary.apply(lambda x: x.replace('K','').replace('$',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_hr = minus_Kd.apply(lambda x: x.lower().replace('per hour','').replace('employer provided salary:',''))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['min_salary'] = min_hr.apply(lambda x: int(x.split('-')[0]))\n",
    "df['max_salary'] = min_hr.apply(lambda x: int(x.split('-')[1]))\n",
    "df['avg_salary'] = (df.min_salary+df.max_salary)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['company_txt'] = df.apply(lambda x: x['Company Name'] if x['Rating'] <0 else x['Company Name'][:-3], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " CA             151\n",
       " MA             103\n",
       " NY              72\n",
       " VA              41\n",
       " IL              40\n",
       " MD              35\n",
       " PA              33\n",
       " TX              28\n",
       " NC              21\n",
       " WA              21\n",
       " NJ              17\n",
       " FL              16\n",
       " OH              14\n",
       " TN              13\n",
       " CO              11\n",
       " DC              11\n",
       " IN              10\n",
       " UT              10\n",
       " WI              10\n",
       " AZ               9\n",
       " MO               9\n",
       " AL               8\n",
       " MI               6\n",
       " DE               6\n",
       " KY               6\n",
       " GA               6\n",
       " IA               5\n",
       " CT               5\n",
       " NE               4\n",
       " LA               4\n",
       " OR               4\n",
       " KS               3\n",
       " NM               3\n",
       " ID               2\n",
       " MN               2\n",
       " RI               1\n",
       " Los Angeles      1\n",
       " SC               1\n",
       "Name: job_state, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['job_state'] = df['Location'].apply(lambda x: x.split(',')[1])\n",
    "df.job_state.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['same_state'] = df.apply(lambda x: 1 if x.Location == x.Headquarters else 0, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age'] = df.Founded.apply(lambda x: x if x <1 else 2020 - x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['python_yn'] = df['Job Description'].apply(lambda x: 1 if 'python' in x.lower() else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    740\n",
       "1      2\n",
       "Name: R_yn, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['R_yn'] = df['Job Description'].apply(lambda x: 1 if 'r studio' in x.lower() or 'r-studio' in x.lower() else 0)\n",
    "df.R_yn.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    575\n",
       "1    167\n",
       "Name: spark, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['spark'] = df['Job Description'].apply(lambda x: 1 if 'spark' in x.lower() else 0)\n",
    "df.spark.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    566\n",
       "1    176\n",
       "Name: aws, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['aws'] = df['Job Description'].apply(lambda x: 1 if 'aws' in x.lower() else 0)\n",
    "df.aws.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    388\n",
       "0    354\n",
       "Name: excel, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['excel'] = df['Job Description'].apply(lambda x: 1 if 'excel' in x.lower() else 0)\n",
    "df.excel.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Job Title', 'Salary Estimate', 'Job Description',\n",
       "       'Rating', 'Company Name', 'Location', 'Headquarters', 'Size', 'Founded',\n",
       "       'Type of ownership', 'Industry', 'Sector', 'Revenue', 'Competitors',\n",
       "       'hourly', 'employer_provided', 'min_salary', 'max_salary', 'avg_salary',\n",
       "       'company_txt', 'job_state', 'same_state', 'age', 'python_yn', 'R_yn',\n",
       "       'spark', 'aws', 'excel'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_out = df.drop(['Unnamed: 0'], axis =1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_out.to_csv('data_cleaned.csv',index = False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "\n",
    "* Job titles are simplified into distinct Categories\n",
    "* Job Description is omitted\n",
    "* Job states are converted from long text to short forms\n",
    "* Missing Competitiors are replaced with suitable value\n",
    "* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data_cleaned.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Job Title</th>\n",
       "      <th>Salary Estimate</th>\n",
       "      <th>Job Description</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Headquarters</th>\n",
       "      <th>Size</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Type of ownership</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>company_txt</th>\n",
       "      <th>job_state</th>\n",
       "      <th>same_state</th>\n",
       "      <th>age</th>\n",
       "      <th>python_yn</th>\n",
       "      <th>R_yn</th>\n",
       "      <th>spark</th>\n",
       "      <th>aws</th>\n",
       "      <th>excel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$53K-$91K (Glassdoor est.)</td>\n",
       "      <td>Data Scientist\\nLocation: Albuquerque, NM\\nEdu...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Tecolote Research\\n3.8</td>\n",
       "      <td>Albuquerque, NM</td>\n",
       "      <td>Goleta, CA</td>\n",
       "      <td>501 to 1000 employees</td>\n",
       "      <td>1973</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Tecolote Research\\n</td>\n",
       "      <td>NM</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Healthcare Data Scientist</td>\n",
       "      <td>$63K-$112K (Glassdoor est.)</td>\n",
       "      <td>What You Will Do:\\n\\nI. General Summary\\n\\nThe...</td>\n",
       "      <td>3.4</td>\n",
       "      <td>University of Maryland Medical System\\n3.4</td>\n",
       "      <td>Linthicum, MD</td>\n",
       "      <td>Baltimore, MD</td>\n",
       "      <td>10000+ employees</td>\n",
       "      <td>1984</td>\n",
       "      <td>Other Organization</td>\n",
       "      <td>...</td>\n",
       "      <td>87.5</td>\n",
       "      <td>University of Maryland Medical System\\n</td>\n",
       "      <td>MD</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$80K-$90K (Glassdoor est.)</td>\n",
       "      <td>KnowBe4, Inc. is a high growth information sec...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>KnowBe4\\n4.8</td>\n",
       "      <td>Clearwater, FL</td>\n",
       "      <td>Clearwater, FL</td>\n",
       "      <td>501 to 1000 employees</td>\n",
       "      <td>2010</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>KnowBe4\\n</td>\n",
       "      <td>FL</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$56K-$97K (Glassdoor est.)</td>\n",
       "      <td>*Organization and Job ID**\\nJob ID: 310709\\n\\n...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>PNNL\\n3.8</td>\n",
       "      <td>Richland, WA</td>\n",
       "      <td>Richland, WA</td>\n",
       "      <td>1001 to 5000 employees</td>\n",
       "      <td>1965</td>\n",
       "      <td>Government</td>\n",
       "      <td>...</td>\n",
       "      <td>76.5</td>\n",
       "      <td>PNNL\\n</td>\n",
       "      <td>WA</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>$86K-$143K (Glassdoor est.)</td>\n",
       "      <td>Data Scientist\\nAffinity Solutions / Marketing...</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Affinity Solutions\\n2.9</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>51 to 200 employees</td>\n",
       "      <td>1998</td>\n",
       "      <td>Company - Private</td>\n",
       "      <td>...</td>\n",
       "      <td>114.5</td>\n",
       "      <td>Affinity Solutions\\n</td>\n",
       "      <td>NY</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Job Title              Salary Estimate  \\\n",
       "0             Data Scientist   $53K-$91K (Glassdoor est.)   \n",
       "1  Healthcare Data Scientist  $63K-$112K (Glassdoor est.)   \n",
       "2             Data Scientist   $80K-$90K (Glassdoor est.)   \n",
       "3             Data Scientist   $56K-$97K (Glassdoor est.)   \n",
       "4             Data Scientist  $86K-$143K (Glassdoor est.)   \n",
       "\n",
       "                                     Job Description  Rating  \\\n",
       "0  Data Scientist\\nLocation: Albuquerque, NM\\nEdu...     3.8   \n",
       "1  What You Will Do:\\n\\nI. General Summary\\n\\nThe...     3.4   \n",
       "2  KnowBe4, Inc. is a high growth information sec...     4.8   \n",
       "3  *Organization and Job ID**\\nJob ID: 310709\\n\\n...     3.8   \n",
       "4  Data Scientist\\nAffinity Solutions / Marketing...     2.9   \n",
       "\n",
       "                                 Company Name         Location  \\\n",
       "0                      Tecolote Research\\n3.8  Albuquerque, NM   \n",
       "1  University of Maryland Medical System\\n3.4    Linthicum, MD   \n",
       "2                                KnowBe4\\n4.8   Clearwater, FL   \n",
       "3                                   PNNL\\n3.8     Richland, WA   \n",
       "4                     Affinity Solutions\\n2.9     New York, NY   \n",
       "\n",
       "     Headquarters                    Size  Founded   Type of ownership  ...  \\\n",
       "0      Goleta, CA   501 to 1000 employees     1973   Company - Private  ...   \n",
       "1   Baltimore, MD        10000+ employees     1984  Other Organization  ...   \n",
       "2  Clearwater, FL   501 to 1000 employees     2010   Company - Private  ...   \n",
       "3    Richland, WA  1001 to 5000 employees     1965          Government  ...   \n",
       "4    New York, NY     51 to 200 employees     1998   Company - Private  ...   \n",
       "\n",
       "  avg_salary                              company_txt job_state same_state  \\\n",
       "0       72.0                      Tecolote Research\\n        NM          0   \n",
       "1       87.5  University of Maryland Medical System\\n        MD          0   \n",
       "2       85.0                                KnowBe4\\n        FL          1   \n",
       "3       76.5                                   PNNL\\n        WA          1   \n",
       "4      114.5                     Affinity Solutions\\n        NY          1   \n",
       "\n",
       "   age  python_yn  R_yn  spark  aws excel  \n",
       "0   47          1     0      0    0     1  \n",
       "1   36          1     0      0    0     0  \n",
       "2   10          1     0      1    0     1  \n",
       "3   55          1     0      0    0     0  \n",
       "4   22          1     0      0    0     1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Job Title', 'Salary Estimate', 'Job Description', 'Rating',\n",
       "       'Company Name', 'Location', 'Headquarters', 'Size', 'Founded',\n",
       "       'Type of ownership', 'Industry', 'Sector', 'Revenue', 'Competitors',\n",
       "       'hourly', 'employer_provided', 'min_salary', 'max_salary', 'avg_salary',\n",
       "       'company_txt', 'job_state', 'same_state', 'age', 'python_yn', 'R_yn',\n",
       "       'spark', 'aws', 'excel'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def job_simple(title):\n",
    "    if 'data scientist' in title.lower():\n",
    "        return 'data scientist'\n",
    "    elif 'data engineer' in title.lower():\n",
    "        return 'data engineer'\n",
    "    elif 'analyst' in title.lower():\n",
    "        return 'analyst'\n",
    "    elif 'machine learning' in title.lower():\n",
    "        return 'mle'\n",
    "    elif 'manager' in title.lower():\n",
    "        return 'manager'\n",
    "    elif 'director' in title.lower():\n",
    "        return 'director'\n",
    "    else:\n",
    "        return 'na'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def seniority(title):\n",
    "    if 'sr' in title.lower() or 'senior' in title.lower() or 'sr' in title.lower() or 'lead' in title.lower() or 'principal' in title.lower():\n",
    "        return 'senior'\n",
    "    elif 'jr' in title.lower() or 'jr.' in title.lower():\n",
    "        return 'jr'\n",
    "    else:\n",
    "        return 'na'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['job'] = df['Job Title'].apply(job_simple)\n",
    "df['seniority'] = df['Job Title'].apply(seniority)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data scientist    279\n",
       "na                184\n",
       "data engineer     119\n",
       "analyst           102\n",
       "mle                22\n",
       "manager            22\n",
       "director           14\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "na        520\n",
       "senior    220\n",
       "jr          2\n",
       "Name: seniority, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.seniority.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    152\n",
       "MA    103\n",
       "NY     72\n",
       "VA     41\n",
       "IL     40\n",
       "MD     35\n",
       "PA     33\n",
       "TX     28\n",
       "NC     21\n",
       "WA     21\n",
       "NJ     17\n",
       "FL     16\n",
       "OH     14\n",
       "TN     13\n",
       "DC     11\n",
       "CO     11\n",
       "WI     10\n",
       "UT     10\n",
       "IN     10\n",
       "MO      9\n",
       "AZ      9\n",
       "AL      8\n",
       "KY      6\n",
       "MI      6\n",
       "DE      6\n",
       "GA      6\n",
       "IA      5\n",
       "CT      5\n",
       "LA      4\n",
       "NE      4\n",
       "OR      4\n",
       "KS      3\n",
       "NM      3\n",
       "ID      2\n",
       "MN      2\n",
       "RI      1\n",
       "SC      1\n",
       "Name: job_state, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['job_state']= df.job_state.apply(lambda x: x.strip() if x.strip().lower() != 'los angeles' else 'CA')\n",
    "df.job_state.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2536\n",
       "1      4783\n",
       "2      3461\n",
       "3      3883\n",
       "4      2728\n",
       "       ... \n",
       "737    6162\n",
       "738    6130\n",
       "739    3078\n",
       "740    1642\n",
       "741    3673\n",
       "Name: desc_len, Length: 742, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['desc_len'] = df['Job Description'].apply(lambda x: len(x))\n",
    "df['desc_len']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num_comp'] = df['Competitors'].apply(lambda x: len(x.split(',')) if x != '-1' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                     -1\n",
       "1                                                     -1\n",
       "2                                                     -1\n",
       "3      Oak Ridge National Laboratory, National Renewa...\n",
       "4                   Commerce Signals, Cardlytics, Yodlee\n",
       "                             ...                        \n",
       "737                           Pfizer, AstraZeneca, Merck\n",
       "738                      See Tickets, TicketWeb, Vendini\n",
       "739                                                   -1\n",
       "740                                                   -1\n",
       "741                                                   -1\n",
       "Name: Competitors, Length: 742, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Competitors']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['min_salary'] = df.apply(lambda x: x.min_salary*2 if x.hourly ==1 else x.min_salary, axis =1)\n",
    "df['max_salary'] = df.apply(lambda x: x.max_salary*2 if x.hourly ==1 else x.max_salary, axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hourly</th>\n",
       "      <th>min_salary</th>\n",
       "      <th>max_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     hourly  min_salary  max_salary\n",
       "165       1          34          48\n",
       "177       1          42          68\n",
       "197       1          36          50\n",
       "202       1          42          68\n",
       "210       1          30          50\n",
       "246       1          34          48\n",
       "271       1          42          68\n",
       "337       1          36          50\n",
       "345       1          48          78\n",
       "357       1          42          68\n",
       "363       1          50          56\n",
       "408       1          42          58\n",
       "409       1          20          34\n",
       "492       1          36          50\n",
       "509       1          48          78\n",
       "533       1          42          68\n",
       "542       1          50          56\n",
       "617       1          42          58\n",
       "618       1          20          34\n",
       "645       1          54          94\n",
       "666       1          36          50\n",
       "682       1          48          78\n",
       "711       1          42          68\n",
       "724       1          50          56"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.hourly ==1][['hourly','min_salary','max_salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['company_txt'] = df.company_txt.apply(lambda x: x.replace('\\n', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                          Tecolote Research\n",
       "1      University of Maryland Medical System\n",
       "2                                    KnowBe4\n",
       "3                                       PNNL\n",
       "4                         Affinity Solutions\n",
       "                       ...                  \n",
       "737                                      GSK\n",
       "738                               Eventbrite\n",
       "739           Software Engineering Institute\n",
       "740                             Numeric, LLC\n",
       "741             Riverside Research Institute\n",
       "Name: company_txt, Length: 742, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['company_txt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Rating</th>\n",
       "      <th>Founded</th>\n",
       "      <th>hourly</th>\n",
       "      <th>employer_provided</th>\n",
       "      <th>min_salary</th>\n",
       "      <th>max_salary</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>same_state</th>\n",
       "      <th>age</th>\n",
       "      <th>python_yn</th>\n",
       "      <th>R_yn</th>\n",
       "      <th>spark</th>\n",
       "      <th>aws</th>\n",
       "      <th>excel</th>\n",
       "      <th>desc_len</th>\n",
       "      <th>num_comp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>742.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.618868</td>\n",
       "      <td>1837.154987</td>\n",
       "      <td>0.032345</td>\n",
       "      <td>0.022911</td>\n",
       "      <td>74.719677</td>\n",
       "      <td>128.149596</td>\n",
       "      <td>100.626011</td>\n",
       "      <td>0.557951</td>\n",
       "      <td>46.591644</td>\n",
       "      <td>0.528302</td>\n",
       "      <td>0.002695</td>\n",
       "      <td>0.225067</td>\n",
       "      <td>0.237197</td>\n",
       "      <td>0.522911</td>\n",
       "      <td>3869.545822</td>\n",
       "      <td>1.053908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.801210</td>\n",
       "      <td>497.183763</td>\n",
       "      <td>0.177034</td>\n",
       "      <td>0.149721</td>\n",
       "      <td>30.980593</td>\n",
       "      <td>45.220324</td>\n",
       "      <td>38.855948</td>\n",
       "      <td>0.496965</td>\n",
       "      <td>53.778815</td>\n",
       "      <td>0.499535</td>\n",
       "      <td>0.051882</td>\n",
       "      <td>0.417908</td>\n",
       "      <td>0.425651</td>\n",
       "      <td>0.499812</td>\n",
       "      <td>1521.495868</td>\n",
       "      <td>1.384239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.300000</td>\n",
       "      <td>1939.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2801.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.700000</td>\n",
       "      <td>1988.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>97.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3731.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>122.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4740.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10051.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Rating      Founded      hourly  employer_provided  min_salary  \\\n",
       "count  742.000000   742.000000  742.000000         742.000000  742.000000   \n",
       "mean     3.618868  1837.154987    0.032345           0.022911   74.719677   \n",
       "std      0.801210   497.183763    0.177034           0.149721   30.980593   \n",
       "min     -1.000000    -1.000000    0.000000           0.000000   15.000000   \n",
       "25%      3.300000  1939.000000    0.000000           0.000000   52.000000   \n",
       "50%      3.700000  1988.000000    0.000000           0.000000   69.500000   \n",
       "75%      4.000000  2007.000000    0.000000           0.000000   91.000000   \n",
       "max      5.000000  2019.000000    1.000000           1.000000  202.000000   \n",
       "\n",
       "       max_salary  avg_salary  same_state         age   python_yn        R_yn  \\\n",
       "count  742.000000  742.000000  742.000000  742.000000  742.000000  742.000000   \n",
       "mean   128.149596  100.626011    0.557951   46.591644    0.528302    0.002695   \n",
       "std     45.220324   38.855948    0.496965   53.778815    0.499535    0.051882   \n",
       "min     16.000000   13.500000    0.000000   -1.000000    0.000000    0.000000   \n",
       "25%     96.000000   73.500000    0.000000   11.000000    0.000000    0.000000   \n",
       "50%    124.000000   97.500000    1.000000   24.000000    1.000000    0.000000   \n",
       "75%    155.000000  122.500000    1.000000   59.000000    1.000000    0.000000   \n",
       "max    306.000000  254.000000    1.000000  276.000000    1.000000    1.000000   \n",
       "\n",
       "            spark         aws       excel      desc_len    num_comp  \n",
       "count  742.000000  742.000000  742.000000    742.000000  742.000000  \n",
       "mean     0.225067    0.237197    0.522911   3869.545822    1.053908  \n",
       "std      0.417908    0.425651    0.499812   1521.495868    1.384239  \n",
       "min      0.000000    0.000000    0.000000    407.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000   2801.000000    0.000000  \n",
       "50%      0.000000    0.000000    1.000000   3731.000000    0.000000  \n",
       "75%      0.000000    0.000000    1.000000   4740.000000    3.000000  \n",
       "max      1.000000    1.000000    1.000000  10051.000000    4.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Job Title', 'Salary Estimate', 'Job Description', 'Rating',\n",
       "       'Company Name', 'Location', 'Headquarters', 'Size', 'Founded',\n",
       "       'Type of ownership', 'Industry', 'Sector', 'Revenue', 'Competitors',\n",
       "       'hourly', 'employer_provided', 'min_salary', 'max_salary', 'avg_salary',\n",
       "       'company_txt', 'job_state', 'same_state', 'age', 'python_yn', 'R_yn',\n",
       "       'spark', 'aws', 'excel', 'job', 'seniority', 'desc_len', 'num_comp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots\n",
    "\n",
    "* Histograms for various fields that might contribute to the salary predictions.\n",
    "* Correlation between these fields plotted using diverging palette\n",
    "* Bar plots of different location and company textas and Headquarters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb817431b90>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Rating.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb816c19f90>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.avg_salary.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb816b13d50>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.age.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb816a9a510>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.desc_len.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>avg_salary</th>\n",
       "      <th>Rating</th>\n",
       "      <th>desc_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019655</td>\n",
       "      <td>0.021655</td>\n",
       "      <td>0.163911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_salary</th>\n",
       "      <td>0.019655</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013492</td>\n",
       "      <td>0.078808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rating</th>\n",
       "      <td>0.021655</td>\n",
       "      <td>0.013492</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>desc_len</th>\n",
       "      <td>0.163911</td>\n",
       "      <td>0.078808</td>\n",
       "      <td>-0.012281</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age  avg_salary    Rating  desc_len\n",
       "age         1.000000    0.019655  0.021655  0.163911\n",
       "avg_salary  0.019655    1.000000  0.013492  0.078808\n",
       "Rating      0.021655    0.013492  1.000000 -0.012281\n",
       "desc_len    0.163911    0.078808 -0.012281  1.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['age','avg_salary','Rating','desc_len']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8168ba990>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gaYakGZ2dna3+lc2sKh0qflVgoLv27weujoiXASRdB4wA9gSu1IrfFmulP+8ALpZ0BfCLVLY/8KOIWAoQEc+18L17ANsCd6TvWBO4K3e+69ozyRL8m0REJ1nCB4im7CJqNlSt7hPyo9txB/BCROz0pooRJ0r6O+AgYJaknQAVXKMvAm6MiKN7OP9q+nMZvi9stnqo0YT8gU7ptwKHSVpb0ijgEOBlYIGkIwGU2TG93yIi7omIM4BngY2BqcCJkoanOuu28L13A++TtGX6zDqSthrgv5uZ1cgrI9YqfPVF0oFpHOdhSacXnF9L0uXp/D2SxvR1zQFNpBFxH3A5MAu4CrgtnToWOEHS/cBc4NBUflYaAHqALAnfD1wIPA7MTvWPaeF7nwGOBy6TNJsssXpQycxWImkYcD7wIbLbgUdL2rZbtROA5yNiS+B7wLf7uu6Ad3Mj4hvANwpOHVhQt+h+5VKygaJTC851//y+ufc3s2JwK19nTO79DGDf7nXMbMjYHXg4Ih4FkPQzsobdvFydQ4Ez0/ufA+dJUkT0eMuxPndrzcwGQH4GTnpNyJ3eEHgid7wwlVFUJw16LwLe0dt3NmLgRdLVZFOi8v41IqZUEY+Z1Ve3GTjdFY1QdW9ptlJnJY1IpBFxWNUxmNlqYSHZoHaXjYCneqizMA16jwZ6nYbprr2ZDSXTgbGSNpO0JtkDRNd1q3Md8PH0/qPAzb3dH4WGtEjNzAZCRCyVNBGYAgwD/isi5kr6GjAjIq4D/hP4qaSHyVqiR/V1XSdSM2ukUaNGrdKM/Ii4Hri+W9kZufdLgCP7c0137c3MSnIiNTMryYnUzKwkJ1Izs5KcSM3MSnIiNTMryYnUzKwkJ1Izs5KcSM3MSlIfj5AOVf5HMWuv+uwTMgD8iGgPnr/0yqpDaMnbjzmSxVNvrjqMloz64H4AvPb4woojac2am2zEosk3VB1GS0Yfkq2bPn+vAyqOpDVjb1+9VsB0197MrCQnUjOzkpxIzcxKciI1MyvJidTMrCQnUjOzkpxIzcxKciI1MyvJidTMrCQnUjOzkpxIzcxKciI1MyvJidTMrCQnUjOzkpxIzcxKciI1MyvJidTMrCQnUjOzkpxIzcxK6jORShoj6feSfixprqSpktaWNE3SbqnOepIeS++Pl3SNpMmSFkiaKOlUSb+TdLekdXv5ri0l3STpfkn3SdpCmbMkPSBpjqTxqe6+km6RdIWkP0j6d0nHSro31dsi1btY0o8k3ZbqHTwg/3JmZkmrLdKxwPkR8bfAC8ARfdTfDjgG2B34BvByROwM3AX8Uy+fuyR9z47AnsAfgcOBnYAdgf2BsyT9Taq/I3AKsD1wHLBVROwOXAicnLvuGGAf4CDgR5JGdP9iSRMkzZA0o7Ozs4+/npnZCq3uIrogImal9zPJElNvfhsRi4HFkhYBk1P5HGCHog9IGgVsGBFXA0TEklS+F3BZRCwD/izpFuA9wIvA9Ij4Y6r3CDA19z3jcpe/IiLeAOZLehTYBpiVO09EdAJdGTSasouomVWv1Rbpq7n3y8gS8NLc57u38PL138gdv0HPybunfa572/+61e/pvk+99603swFTZrDpMWDX9P6jZQOJiBeBhZI+AiBpLUnrALcC4yUNk7Q+sDdwbz8vf6SkjnTfdHPgobLxmpl1KZNIzwY+LelOYL0Biuc4YJKk2cCdwLuBq4HZwP3AzcAXIuJP/bzuQ8AtwK+BE7tuG5iZDYQ+75FGxGNkg0ddx2fnTufvd34pnb8YuDhXf0zu/UrnCr5rPrBfwanT0itfdxowLXe8b0/ngDsi4l96+l4zszI8j9TMrKRWR+0HlKTzgfd1Kz43Ii4a6O+KiOMH+ppmZnmVJNKIOKmK7zUzawd37c3MSnIiNTMryYnUzKwkJ1Izs5KcSM3MSnIiNTMryYnUzKwkJ1Izs5KcSM3MSnIiNTMryYnUzKwkJ1Izs5IU4V03Cvgfxay9ettCqHHcIi2mdrwkfapd1x7KsTYt3ibF2sZ4VytOpINrQtUB9EOTYoVmxdukWKF58Q46J1Izs5KcSM3MSnIiHVydVQfQD02KFZoVb5NihebFO+g8am9mVpJbpGZmJTmRmpmV5ERqZlaSE+kgkPSWqmNohaSDJfn/hAEgaU1JO0jaXtKaVcdTZx5saiNJewIXAiMjYhNJOwKfiojPVBxaIUn/A7wXuAq4KCJ+X3FIvZL0/YLiRcCMiLh2sOPpjaStgNOATYHhXeURsV9lQfVC0kHAj4BHyJ5E2ozs/+6vKw2sppxI20jSPcBHgesiYudU9kBEbFdtZD2T9FbgaOATZGsOXARcFhGLKw2sgKROYBvgylR0BDAX2Bh4NCI+W1Vs3Um6nywxzQSWdZVHxMzKguqFpAeBgyPi4XS8BfCriNim2sjqaXjfVayMiHhCWunR4mU91a2DiHhR0lXA2sBngcOA0yR9PyJ+UG10b7IlsF9ELAWQ9ENgKvABYE6VgRVYGhE/rDqIfni6K4kmjwJPVxVM3TmRttcTqXsf6R7TJKC23WVJHyZriW4B/BTYPSKelrQOWdx1S6QbAm8h686T3m8QEcskvVpdWIUmS/oMcDWwPLaIeK66kHo1V9L1wBVkPZMjgemSDgeIiF9UGVzdOJG214nAuWQ/8AvJWksnVRpR744AvhcRt+YLI+JlSZ+sKKbefAeYJWka2X28vYFvpsG9m6oMrMDH05+n5coC2LyCWFoxAvgzsE86fgZYFziELG4n0hzfIzUAJA0DpkTE/lXH0h+S/gbYnSyR3hsRT1Uckg1BbpG2UZNGlVN3+GVJoyNiUd+fqI0OstbScGBLSVt2b1HXQbo9ciqwSURMkDQW2DoifllxaIUkbQacDIxh5VkGH64qpjpzIm2vERSPKp8gaVydRpWTJcAcSTcCf+0qjIhJ1YXUM0nfBsaT/Zu+kYoDqF0iJZv9MBPYMx0vJPt/UctEClwD/CcwmRX/ttYDJ9L2atKoMsCv0qspPkLWqqvbwFKRLSJivKSjASLiFXWbzlEzSyKiqEdlBZxI26tJo8pExE+qjqGfHgXWIDcKXmOvSVqbtB9YmpdZ57jPlfQVsl/8+VkG91UXUn05kbZXk0aVSfftvgVsS3ZbAoCIqOvI8stk/76/YeUf9jreivgKcAOwsaRLgPcBx1caUe+2B44D9mPl2ya1fBKrah61bzNJG5D9h3yQrEW6sI6DIQCSbif7gf8e2TSXT5D9H/lKpYH1QNLHi8rr2rKW9A5gD7JfqndHxLMVh9Sj9GTTDhHxWtWxNIETaRtJ+mfgFGAjYBbZD9FdNX6+emZE7CppTkRsn8pui4j3Vx1bU0napbfzde0qS7ocODki/DRTC9y1b69TgPeQtT7GSdoG+GrFMfVmSVr9ab6kicCTwDsrjulNJF0RER+TNId0zzEvInaoIKyenNPLuTp3ld8FPChpOivfNvH0pwJOpO21JCKWSELSWhHxoKStqw6qF58F1iF7lPXrZD/khd3nip2S/jy40ihaEBHjWqkn6QMRcWO74+mHWt7OqSt37dtI0tVk9xk/S5aUngfWiIh/qDSw1YSkb0fEv/ZV1gSS7ouIXm8DDDZJ7yLrUUH21Ji7+T1wIh0kkvYBRgM31O0GvqTJFHSRu9S1O1eUfCTNrlnXviWSfte11GIdSPoYcBYwjWxw7P3AaRHx8yrjqit37QdJRNxSdQy9OLvqAPpD0qeBzwCbS5qdOzUKuKOaqEqrW4vmi8B7ulqhktYnm7LnRFrAidTqnuSLXAr8mmzO6+m58sU1XpauaTq6deX/grcm6pETqS3XlAn5aVGVRWQr+SPpnWTxjpQ0MiIerzK+VfRY1QF0c4OkKcBl6Xg82S8vK+DfMJZ3EfBDYCkwDvhvsgWea0nSIZLmAwuAW8iSUS1/2CWdJOltueO3p4WeAYiIw6uJrFhEnAZcAOwA7Ah0RsQXqo2qvjzYZMs1bUJ+2gdpP+CmiNhZ0jjg6IiYUHFobyJpVkTs1K2sVgNMeWkZvT9GxJJ0vDbwroh4rNLAasotUstbaUK+pMOo4YT8nNcj4i9Ah6SOiPgtsFNfH6pIR361p7SQdp23OL6SlZfPW8aK5SCtG98jtbymTMjv8oKkkWTrj14i6Wmy2xJ1NAW4QtKPyEboTyRbxKSuhuen6UXEa97bvmfu2luh1DIdGREvVh1LT9IqWq+Q9ayOJZune0lqpdZK+vecAOxPNi9zKnBhRNRyV9m0uPcPIuK6dHwoMCki/r7ayOrJidSWk3QpWUtpGdlq7qOB70bEWZUG1qLUXT4qIi6pOpbeSFoX2CgiZvdZuSJpvdRLgA1S0ULguIh4pLqo6sv3SC1v29QC/QhwPbAJ2RKAtSLprZL+r6TzJH1QmYlkCz1/rOr4ikialuJel2wlsIskfbfquHoSEY9ExB5kU+H+NiL2zCfRnpYwHKqcSC1vDUlrkCXSayPider3xA1kU7K2Jtuu5Z/JuslHAodGxKFVBtaL0emX1OHARRGxK1k3v9Yi4qWIWFxw6pSCsiHLg02WdwHZXMz7gVslbQrU8R7p5rnpWRcCz5Ltzln0A18Xw9PW0R8je/yy6eq839Sgc4vUlouI70fEhhHxD5HdPH+cbGI+UKvu3Otdb9JgzYKaJ1GAr5GN3D8SEdMlbQ7MrzimMurYU6mMB5usZXVZ6k3SMlZsFy1gbbL9mwRERLy1qtiGijo/TFAFt0itP2rRnYuIYRHx1vQaFRHDc+9rmUQlbSXpN5IeSMc7SPpS1XGV0NRVttrCLVJrWV1apE0k6RbgNOCCrpacpAciYrtqIyuW1gX4J2AMubGUmu7QWjkPNll/1KJF2lDrRMS9uadEob5PYUE2/e1uspkRb/RRd8hzIrX+cHdu1T2bJrkHgKSPAn+sNqRejYiIU6sOoinctbflJBX94CwCZkbErMGOZ3WSRuk7gT3J9u5aABwbEf9baWA9kPQvwEvAL1l5F1EvnF3AidSWS4+I7gZMTkUHAdOBbYArI+I7VcXWVAW/nNYmG+T9K0BE1PLpJkknAd8AXmDFVKeo2yLfdeGuveW9A9glIl4CkPQVsj169iZ79t6JtP9GpT+3JtuR81qye83Hka1aVVenAltGxLNVB9IETqSWtwmQ3+H0dWDTiHhF0qs9fMZ6ERFfBZA0leyX1OJ0fCb1Xt9zLtncXGuBE6nlXQrcLenadHwIcFlarm5edWGtFrr/knqNbGpRXS0DZkn6LSvfI/X0pwK+R2orkbQrsBdZ9/P2iJhRcUirBUlfJHvO/mqye46HAZdHxLcqDawHPT0OHBE/GexYmsCJ1JaTdC7ZD/edVceyOpK0C9C1/9WtEfG7KuOxgeNEasulVsh4YCuyltPlbpEOTZIWULAwiUftizmR2pukxYePAI4iW55ubMUh2SCT9I7c4Qiy9V7XjYgzKgqp1rxoiRXZkmzu6BjgwWpDsSpExF9yrycj4j/INkO0Ah61t+UkfZtsBfdHgMuBr0fEC9VGZVVI93O7dJA9qDGqh+pDnhOp5S0ge4Rxc2AtYAdJRESdJ45be5zDinukS8l2TjiysmhqzonU8pYBNwMbkW3QtgdwF+7SDUUfIrtPPoYVeeIospX+rRvfI7W8SWSPMf5vRIwDdgaeqTYkq8g1ZA9kvE62eMlLrNiVwLpxi9TylkTEEklIWisiHpS0ddVBWSU2iogDqw6iKZxILW9hWhn9GuBGSc8DT1Uck1XjTknbR8ScqgNpAs8jtUKS9gFGAzdExGt91bfVi6R5ZNPgFpA9a9+1seAOlQZWU06kZvYmkjYtKq/rQtRVcyI1MyvJo/ZmZiU5kZqZleREamZWkhOpmVlJTqRmZiX9f5xJbzdZnbXFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(df[['age','avg_salary','Rating','desc_len','num_comp']].corr(),vmax=.3, center=0, cmap=cmap,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat = df[['Location', 'Headquarters', 'Size','Type of ownership', 'Industry', 'Sector', 'Revenue', 'company_txt', 'job_state','same_state', 'python_yn', 'R_yn',\n",
    "       'spark', 'aws', 'excel', 'job', 'seniority']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Location: total = 20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Headquarters: total = 20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for company_txt: total = 20\n"
     ]
    },
    {
     "data": {
      "image/png": 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ko8ygVv6bA7sBL5d0bfl57YBsSZIkGTkGkupp+3JAgzh3kiRJMgOyfZIkSZL+k84/SZJkBEnnnyRJMoKk80+SJBlB0vknSZKMIOn8kyRJRpB0/kmSJCNIOv8kSZIRJJ1/kiTJCJLOP0mSZARJ558kSTKCpPNPkiQZQdL5J0mSjCDp/JMkSUaQdP5JkiQjSDr/JEmSESSdf5IkyQiSzj9JkmQESeefJEkygqTzT5IkGUHS+SdJkowgA3P+kl4j6deSfivpo4OyI0mSZBQZiPOXtCjwDWAr4NnAzpKePQhbkiRJRpFBrfyfD/zW9u9tPwx8F3j9gGxJkiQZOWS7/yeV3gi8xvbby/PdgBfY/s+m9+wD7FOergf8epppVwbu6sCsTscvTHPMBBtmyhwzwYaZMsdMsGGmzDETbKgyx5q2Z7V6YbEOT9wuanFs3FXI9pHAkZUnlObantO2QR2OX5jmmAk2zJQ5ZoINM2WOmWDDTJljJtjQ6RyDCvvcCqzR9Hx14G8DsiVJkmTkGJTz/yWwrqS1JS0BvBk4e0C2JEmSjBwDCfvYflTSfwLnA4sCR9u+qcNpK4eIejR+YZpjJtgwU+aYCTbMlDlmgg0zZY6ZYENHcwxkwzdJkiQZLFnhmyRJMoKk80+SJBlB0vknyQggaUVJGwzajmTmMLTOX9JnJS0naTFJ50u6XdJb+mzDLEkfk3SkpKMbPzXnWErSgZK+VZ6vK2mbfs8xybzv63SObiCp54kJknYtxYYTj+/d789VKyQtImm5mmMuKd+RlYDrgGMkfanmHItKeqqk2Y2fmuOXlrRIefwMSa+TtHjNOb4r6dWSWtUH1ZnndZK+UH627WSuNs+/qqSjJJ1bnj9b0l4159hc0tLl8a6SviRpzbbsGdYNX0nX2t5I0nbAG4D3AxfZ3rDC2MOYUFTWjO33VrThCuCnwDzgsabxZ1QZX+Y4pYzf3fb6kpYErrS9UT/nmGTeP9uu/GWXtDXwHOCJjWO2P11x7OW2tyiPj7e9W9NrV9vepMIc+0/1uu1JHZ+ka4CX2L5vwvFlgUtsP2+68zeNWRQ43/Yrqo6ZZJ6TgHcSn615wPLAl2wfWnH8NbY3lvR2YA3bB0m63nalOwBJ+wIHAbcDj5fDrjq+zDEPeDGwInAVMBd4wPYuNeZ4DbAnsAlwCnCs7d9WHV/m+CwhK3NiObQzMNf2ARXGftj25yfzGzX8xbnAMcDHbW9YFjXX2H5ujf/H9cCGwAbA8cBRwA62/6PqHA0GVeHbDRq2vxY42fZdkqpeyeZ2yYalbH+kwznWsb2TpJ0BbD/YxgqnG3O0ovIcko4AlgJeBnwbeCPwixrnWrrp8XPatGPZGuebyKITHT+A7fvqrlRtPybpAUnL276nA5uebfteSbsAPwI+QlwEKjl/YDFJTwHeBHy8jfPvB6xn+x9tjG0g2w+UFe5hxYleU2cC2+cB50laEdgFuFjSH4BvEd/9RytMszWwke3HASQdB1wDTOv8gZvLv536jZVtnyrpAJif8v7YdIMm8KhtS3o98FXbR0l6azvGDLPzP1fSjcSq6D2SVgYeqjLQ9nFdsuGHkl5r+0cdzPFwWakbQNI6VPx/dHmOVtS5LXyR7Q3KyvJTkr4InNmlc1Wyw/anapxvIotLWtr2/c0Hy8p/iTbm+zdwg6QLgflzVl0lNtm0OLAd8HXbj9RY4AB8mqil+ZntX0p6GvCbGuP/AnRy8QKQpM0Ip90IcdT2O8XxvwXYDbgeOAnYAngrUPUOawXgn+Xx8lXPbfsH5W5ufdsfqmz0gtwv6UmMfU9fSP3f733l4rEr8JJiV63FSYOhdf62PyTpUOCf5Qr6b2CHOnNImkWspp7N+FDFyytOsR/wMUkPAY8QK1TbrhObPRg4D1hD0onA5sQtbh0OajHHHlUGSrqP1s5VwJI1bHiw/PuApKcC/wDWrjF+BUnbE/tQK0hq/C1FjS8qgKQnEo5mYgjqbVMMOwo4XdK7bP+xzLMWIT1+VJ3zF84pP53wTeCPRLz+shLbvbfqYNunAac1Pf89ESKdkqbw2e+BSySdQ9NiYqrwWQv2I1bXZ9m+qVyALq4xHkmnAs8lHP4bbN9aXjqxxl3EZ4FrJF1MfKZeQrVVPzD/bq5y6G8S9ieUDNaR9DNgFnGHXIediIvgXrZvK3swVe8ExzF0MX9Jr5vqdduVZSIkXUDEED9IxFbfCtzZhVBOLcpq4IXEh/Iq27WV/roxRydIOhA4DNiScJgGvm37wIrjj5nqdduVL4iSTgNuIb4knyZWnTfb3m+ace8kHMIyhP33A/9j+/Cq554w35LAbNvTKdLWmXOximEOJD0DOBxYtewFbQC8zvZnphl30BQvu+o+Tplrx3IRmvLYNHO8CrjQHTqrEgLblPiO/Nz2bTXHfxFYl7igNt/NTXuHq9j0fiERCl2v2PBr24/UsaGbDKPzP36Kl2179xpzzbP9vOZNMEmX1tk8Kbej6zJ+hXlZjfEX2d5yumPTzLE98JNGfFnSCsBLbX+v6hzdRNITgCd2GO/u5PyNjc7rSyhqcWIDttIdnaRliO/GfeX5G+ps4pcx2wJfAJawvbakjYBP255y8VLG7mr7hMk2sKuuvCVdCnwI+KbtjcuxG22vX3F8Nxz3Apv1rY5NM8dihLz7S8qhS4FvVbkISnqm7VsktTyf7atr2NFqgeJp7iibx19pe7Oq55tkjh2AzwGrEBeQdqINwBCGfZqzQLpA46r7d0Wmyt8IhdFKKLIo9itjriWu7FcC0zqZEppYCli5XEAam5rLAU+takPhINtnNZ7Yvrus3qZ1/k1hn+ZNVROfjSVsV/qMSHoPcKLtu20/pEg/fbft/636nyjxyxUbdy0K0b89gPfbflbVeRj7u94taX3gNmCtqoNt/78Jh74M1HL+RDjv+cAlZc5rJVUNgzU2v1ttYNdZrS1l+xcT9v4r3TUUDqApbDTFsQWQtBWRjLGapK81vbRcTRsg7iSXBhpp1LsCGzPW72Mq9i/v+2KL10yF7+r8N9e4+5yECyS9ATizg7uYzwPb2r552ndOw9A5/2YkvZoF47r/XWOKz0haHvgAEbJYjkgZrcp+xG3kVbZfJumZQNVNx3cA7yMcffPq417iw16HVvUalf62tsc5mLLB+e5i31ktB7Vmb9vz7bb9L0l7A5Wcv6Q3EzHu+yX9hnCexxMKsJXTAgtHlgvqJ4gY6zJApfDTZOa1MeZR2/dMcLxVN66/WR7+2PbPxhkibV7DhrvK5n9jg/GNwN+nG9Qlx/03IjvmdUSGUoP7qPcdA3ihx6dwXyDpuioDbe9T/n1ZzXMugKTPA58h9rfOI1Iu32f7hIpT7E9cxBp7lO2s2m/vhuOHOPNQ/hBO5SSiN8AhwK8IddB+2vDL8u+1wBMaj2vOsW8X7Dga+BKwDvA0YqV6bM05ViAc7u+JD/iTao6/nhJGLM8XBW6qMf5G4Onl8SbEBuP2bf4+1q5yrMZ8f25jzFHEnsP1RFjwMOCImnNcXeXYFOOfBvwYeAD4K3A5sFaFcRsSd1x/IvbBGj87EHdmdf4Pi7f7e2+a45pmu4m7uGtqjF+TSLOEuDv/ILBdG3ZcW/7dHjgOWAm4rtP/X00bvkrsU+5c/h47EHn+teca5pX/Fo547nW2DyxX5bpx2WNoXbRRKYYH3Fri698DLpT0L+o3pblH0gL7FLa/U2OOfYmV7SnEauIC4D1VBipSZD9AZBEcDWzs9mL15wOnKvL9TWygn1dj/MMuhTu2r5b0BzeFsmpyBnEBaeZ0YNJsDUk3MHnW05PbsGFfIrf+IWKRcj5xUZ2Wkhr5ImDWhLj/csRFtRKO7J5XKCpCF3GLOoZJxl0HXCfpBFfcXJ6C50s6mHDAizG22n1ajTk+TGQ7/V8Z/3TG0kanRNIniQuXJX2XSAu9BNha0ktt16lib6RUNmqL/qma5TSd7hESn4EHgFc1HTP10qqB4Q77NFIL/y3pyURq4Vo15/hh0+MnElf0ys7b9vbl4cElhWx56jk8iLBRsw1bEmGgys7fkZv+0ZrnbfAn4E6i8vABYK/mD7Srp/V9hAgVvYuxC9C3a9ixygRHt0zz8yp2lLDbc4Dlm1JFIb4wT2w9aj4dy2FM4HnAJ23PL64qm45VNhiXIEJVizE+7n8vFVIDJ9swbvxdp/tdNl8IWzk316jwJe6A3s+EKvg62L5Q0nrAs4jP1q9sPzjNsAZvLuOWAv4MPNlRdLYYccdehx9IuoXwPe9WpIr/u+rgTvYIG7jzfYf5DLPzP7esur9A/CIfo4bDhAVlGCSdTNwmV0JRpHGT7ftsX1ri5RsDP69hw74T5lyeiHVXpqT0fZC4+M3/m7padsuhjK14266Qtf24pGOJrKN2Uhu/NeH8E59XYT3Cia8ANGu33AfsPdVA23+a7DVFTnadWDvESv+Xkt5k+/Zy7NsseEfSypZLJV0OPNftFa5NtWFchW5eCO+xfW47AyX9R/ldTMyQWk1R7PZPQsbk8RbDG/zb9sNEIeTvbD8A86trH65jj+2PSvoccK9LFTfw+hpTdLJHCICk1YkQ4ubE9/ZyYD+P1T5UZmidv+2Dy8PTJP0QWNL2P6cYUoV1gTrCVYcz/st8f4tjdXmg2FGH04AjCOdSa3XV9HvsiPIFPZRYtdZKbSz8w/bXO7HB9vfLZ+EjrrfxPx21xMwKvyZ+H5dI2sv2FdTYOC7OZaU2zovtb5bMqXttf7mN8ZNeCNvgYkUx5pmMLxSrcgf0SiKtc8dJXn8SEQZ79RRzNAoGBSynNooHVbR9ytNXuKS62r5f0seBj1WZh7gQ/VsSkp7gSEFdr+LYBscQYcTG72TXcuyVNecZvjz/BoqCnO/avrs8XxHY0XbltmZasLr1NuCAiXcEU4y/1hPE01RDOKu8/wdNNixCVBuf5hqFZir1ClXfP2HsJ6d42bYPqWoDcft6icdyyuuIiNXK/Z5mrovdheyOpvlqCdyVMVfb3kTSusRezNHA2+r8H9VBUVEZ39HvodzZHkaETZYgHO39rpGdUsKhE3HFu9Iq8x9re48pXu+4eLD5sznxc1rncyvpLKJ6/33Ed+VfxIb4a6uML3O08jkLHKvC0K78gXfaPqLxxJFa+C5q9LT0hDTHNvi9pPcSq32IFMnf15zjC02PHwX+1MYt3A8kvZtIzWxeXVW5E7q/xbGliQ21JxGZVFVoldo4KK6Q9HXC6TY7zUlXmxP2CMa9RD2Zi+Zx2P6NpBcTq7O6evorEXtZzY6yzuZe7d/DBL5OxMxPA+YAuxObrZXpxkW4LOwOJLR8GqGOz9j+11SOv5y/GzFyTfK41fOpbOnGHuFdknYFTi7PdyY+I7UZ5pX/DW6SQlWUT1/vitWLZUxH1bWSVgG+Rnw5DVxE5P3eUcOGz01c5bc6Ns0cf2hxuG5GRSPHfz/C8Z8KfLHq/0XSUcT//6OEfsx7iVXNOyuOf5QIeS3wEjVzodtZbXZjhVjBrtm2/9zpPDXO19GqW9Jc23M0vgL+CtsvqjC2K1XKZa7zCTnoRj79W4DNbb9q8lHdo4sr//l7hOX5soRya+U9QoWWz9eBzQifcwUR868dqhtm5/8l4ClErNtElsntrpC6pbHq2ouBlzK+uvZc16sm7YhWH566oaMu2LASUYCyC5G//FXb/6o5x1JEauOriN/n+cAhtitlQ6hIMtQyfAaiLmm/l7naEajrGpIuI1Ijv02ERP8O7OFqPTPeUfYeWuoE1dnIbhXW7CTUWReF7PL9jN0FNhYpImRMKqlqKkToNnFxumXBOrdb4c66DHPY50NEmOX9jKUWfnPKEWN0VF3bjS94CVG9G3iaokFDg2WBn7UeNeV867OgOum02U9lM24HIlz2XC8obVCJkkXxcdrTje8qJWPqIMZrwXza/dEa+lX5txs9I44nBOpeTZNAXdXBXfg97EbsQ/0n8T1bgwqqoDBWpVzHyU/BpZLeaPt0mB+iayuDqB1sV66tmAa5abXtyJCrKp/StUVFszF1xyw0SNrX9mFtjNvWofHdsomCK/QLKF/MFQmp2eYc/fsqxuqb5zqIuIN5NtH0YyvgcttVcsIfJ/YJHmX8h6pWuD2ZCjsAACAASURBVEWdpZsi6WPuUoaOpDOIiuHG32E3YEPbtSS/2zz38bZ3k7Sf7a92OFenAnUd/x7UoTJpN+5eFMWTyzOm2bQ4Yzr4tj1tVpSkHYHzHM15PkFk5H2mxv5Hx0g6kygwa94jfJnt7SqM7djnLDDnsDl/SSfb3rncQrW6AtbJpmipAFpxxbwoIffbVnMHScs5ujS1/ODWuQAoinI2JEreN5S0KiGn3Lc+pQqtlSNYsKXlvEkHjR/fldaaZa6uZUTURdKviIvv2YwPKQK1/66/sP38En55NxF6+UXVvZxOfw/qQJm0aY625LUnzDHlytv2tOnNTRfQLYgF1xeAj9l+QVU7OqVLe4QdK602GMawT8PZ1m2C0Iq2q2vdeXOHk4himnm0VtWss1n7YLmFfFTR5PuOquPLyuydRBbH9YQ+Ujsl/Y+6Td37QrdaawI8KGkL25cDKMTQqlaEjkPSHODvtv9accgRRAbH01iwmrfu37UhUHcg7QnUdfp7OJgFlUnXqjEeQq9pR0mvt32coi/x+XUmKN+15Qntqua7hytqTNO4QGwNHO6oCTm4jh2dUpz8mzucpm2l1YkMnfP3WBrkXrbHFVdI+m+qF1x0o7r2Gkln00Yetu1tyr91ul1NxlxFtfO3iIvJ/6N6/9zjiNvpnxKaJc8hMn7q0km6aVu3rVPwLuC48vcUUQnaVp9TQqNnA0n/Z3un6d5s+2vA1yQdbvtdbZ6zwTFlVXsp9S4aDd4JfKf8HiDyyuv8HrqRvtuRvDaAov/v/sBqwA2UKlnizqoqf5X0TWID+3OKnhOt1HB7hkIOYm8WDI1OGwJTdyWyY85hC/s0mCRL5roqmQhTzLk4kS5aKdtHHTZ3KHN0mm4qYHXbfynP1wKWs339VOOaxs9PmS2bT79oJ/tAHaablovopNQJNTTNuVwZW7n14RRzLesKwmhdvJNC0p+Ju4hTCNmMWl9WSWvb/kPz76FxrOL4jtJ3yxxvJ4T2NiBqHZYBDvSYbHWVOW4g7kCutL2RpOcAn7C9c405lgJeA9zgqL14CpHgcEHVOTpF0hXEImtiaHTaolJJGwIbEaGz5sLM+4CLXTM7D4bQ+Ut6B/HlegZRQt9gWWCe7cq3VWpdXXuq7XZF0iqjLqabqrMK37bzlruJpDuJhuEnE9pIE2Pll9aY60lElktzUdCnbU9bDKMOu6JJOoWxO6mtiKK9du6kGput2xKhgucBPyCq2i+vOL7VAqnyZ0Udpu92C0m/tL2ppGuB59t+WG2kBhcH+uLy9KcO9dK+0Y19J9Vo4zntXEPo/FckKk9bZclU3jgpczW3a6xdXas2e6SWsfsxlm76V8ac3b1Ei7rKOjeSvkHo9/+y6pimsY0cZhifx1w322cp4tZ8tu19FLIG69n+4TRDG+MXJfRJdiZWiecQsrk31fn/lLkuBC5jrChoF8KBv6LC2FabpJUdTbfupFrMuyKh5b6Lp0k91Ji66ecZ2yODWFh8yPZzOrVnOspm8fUuxUcKGZE3ECqy+1W5+2g4unJXuDshPb4FEcZb2vZratizHxFyaYRktweOdBvZfu0i6TPAFbZ/1MEcf6B1okvtsODQOf8Gklq2OrRdV0+/Exs66pFa3t9WuumEOX5F3An9ibFiFLu/hWKnELezu5cL4ZKU2/Q25noCcRE4lFix1/r9tFrdqlSrVhi7QIGdJlSTTzO+q3dSZYGyE3EX8UvglOnCBJJeD2xHdNFqDqfdR9w5TLlROuGOeAGqhOAUtSsvdMgnb0M0G9qZUL3d0fZUYmyNOVrduWxJpH2eY/uh1iMntWczh/w5ih4HV/bjO6LxrVKXJvbEHqHmAqvM9aSmp08kBN5Wsj2VRldLhm7Dt4mLGPuFPpEoQPkdIes7JVpQ0E1Nc9X5Y3TaIxXgtkY8We3nH29V85y9YB3bO0naGcD2g6q5U1ic/taEk1iLSIur3aSCUJJ8MyFRAZEZdk7FsXMV1ePfID4T+zK+DeF0bCipsccgYMnyvJ0v+h8IufJTiRV7Kx2mBbD9feD7kjazfWUN2xs09KZEJBG8vY057CKfTBQRHuVI+51XEgOqsMDnx/ZFbdjSmKs5JfSxVvP3AneuIdY818TQ5VcU8t+j4/wnxsQlPZ9QzKvCRUR3pjOJlVC7eitt9UidwIG2T1PkH7+a+OIdDtTJP54Jt28Pl9V+43exDk1ZP9Mh6ThgfaJy81O2b+zAlncQIagTij2LEr2B92d6B9x2VzToajUoREFWJ5vV10h6DzULrJr3VyT9vzr7LU1I0jJECHFLxvdynq6xToOJncwm2llZH4jYbP65QlkT4s7oqBrjO6bT5I7y/uY7oUUIwb22Li5D6/wnUlbglfLMbW+nSH/bAfhW2Xw9hbgQ1KmufQ8hi/BMSX8F/kDoa9ehG/nH5zD+LmhtYjO857HdJg4iMlPWkHQi0WxijxrjdyNCVs8A3tt001B7xdzJSsuddUXrNsuVi2K7jTs6kocotLuw+Apx13IvUdQ1F0DSxlRfIC1KZAd1vEK3/aUSpt28zLen7Ws6nbcKxb8sDaxc9m6akztahq+n4ItNjx8F/gi8qS27hjjm31zxuQiRDfFk27WaGijElXYidMv/u+ZqojFHrR6pE8b+kNjwfQXxf3iQ2CTsJGV1E+Adtt/R7hxtnvdJRGs6Ed2K7urn+ZvsEOHo1rZ9iKQ1gKfYnrT2QdJXbL9vsnh3lTh3tykb1ycxVnuyK7HhW+kzrjblITS+6nxiNlrl2g1JqwGrEE3OHy/HnkKki057t93tzLOSVLAq43Pse66yOiG5o3lPsnZyR1ftGmLn36wz37gCntYUZ5xu/IuI2PKLiRXVKbZ/WtOGFYgshLUY/4GqI0XQk/zjbn9xpjjPZ4Evt8q0Uk1p6i7adDjwOPBy288qq60LbG86xZjn2Z43IQNsPm2GPjpiksyjOvIMbclDNGWUtFpxu53Mknaok2VVYa59ibvT2xmL9/c7KaLt5I6pwl9QOwQWcw6r82+gaIdWObZcxvwRuBv4LvATJmzSVt1sVRRtXEVUHc7vI+qa1aqdrkgmfDAad0ErVcmo6BRJ9xC/y3fbPmfCa4OqGWh00ZrvPFShALD8HY6zXTd01xMk/Rg4lvGNO/asGiPWWIHVc8s8tQusBomklWqGYaea67fAC1psmPYcTd4oCKimCKBJpLGb5qitnjq0Mf+ywXsUkfY1W1HA8XZPkGyYhD8SK5tXM1bA0sCM75w0FU+0PeUVeTomrEgaFxBTr+tTc4z7UeCHxJe+H/yeiNefqChB/6DHioAG1dbrkeLIG5vPs2i6OE+GQ0NmlqQlHE2/B83biMYdX2ascUel6vESzrzXUfl5Ge3JQwyUbjn+wl8YUwLtN1MJLFbqzNaOc5+OoV35S7qKiNV/z23m2HfBhvcTOjo/pA09mzJHV1ck5Uu/TIdZInXO11hlP5EoKno58Bbb13fztr2mTbsQn41NCO2iNxJyANOKXyn0XzYh8uOb9Zpq31YPGkmX2X7J9O/sqQ1fIDSKahfrddmOo4g08HMY/10dqr+rpNWJ/cl2kwDmM7Qrf2KD9U8TUsmnlXZtRq01vg+pkQXwMFGI9HHGNgnrKjd2vCJRKCW+k/j/zwOWl/Ql24d2Mm8dymr/vWX1/wONF5/qK7ZPVDSU35K4+9jOdtUsl7+Vn0VoM4WuUxTVsJNh21X7Kl8o6YMs2MO3myvq6biFUCddjEi3PNn9aaozkT+XnyXKz0CQtDULpt5+usYUxxBJADuW57uWY7USXWC4V/5nAJ8jJHQ3JfKzN7e945QDx8/Rkca3pN8Rq/a2s1q6sSJpbAKWFe/zgI8QOkf9qF5cYHVfwixHA6/tct57FXtq93KeZJ6lXbGoqttI+kCLw0sTTVGeZHuZivO0JbanSXpMNE1Q++IhaT2iDmdnolPdt2y36jHcUxR9c+02O9Z1eO4jCD2vlxGtMd9IbMDvVWOOrvWqGOaV/7uICtDZRLz8x+VYHTrNsb+J1k3H69CNFcniJY1vO+Drth+R1K+r+gLxTNt3AtsqGlb3FUdfg+vUZrN0SZsRe0nLMLaX9A7bVatSO8b2/Fzu4qz2Ixzndxmf5z3dPO3KhbfqMTF/WmruH5T9l2eWn7uA64D9FX1+O9W3r2rD+kTK7Erl+V2EFEk/w1EvKovN621/StIXqV/BfpekXRmfBNBWyHhonb+70xihU43vx4BrJV3M+FV75VTPLm3kfJPYxL4OuEzSmkQOcc9pjjWqRR9hIhuq3zwFuEnSLxgf7qiSq/8VIhHg7DLmOkl9j5uX1ff+RL3CcUTj71qyvWVB8C7GevheQuhQPTLpILrWY6Jhw5cIjaGLiDqaRq3F5yS11RqyTY4E9m/cbUh6KSFd8aI+2tBopPOAQpvsH0RBZh2akwAg7qIqS8g3M7TOv4RLPmD77vJ8ReDztveuMc2biBz7L9i+u+TY12nL+L3y00ylFbe6IJ7V9N6vEXdBDf4k6WVVx3cDTdJHmApd0XpARxdU23/pZC+pUyQdSlSfH0nUfLQbojic6HfbkFbYrRyrrNVTvlfrMj5GfVkNG24kNttb3SE/v8Y8nbJ0c5jJ9iWK4sx+8kNFbdChRJc3E+GfypS72a4UHA5zzL9VrLlWXrlCf+ZW2w+VlcAGwHcaF5Q2bFoDeHOVjVZNUkzUwPX0659AyOWuxfhagTobSR2hGdBHuBtIOp1QoPw6Ua38XmBOv8ITxYbHiTvJR2khQOjqMtsL1DZUqXdoeu/biZDT6oRUwwsJJcyqqdCNeTq9gHSMQtPnasZXS89xhebpPbLnCUSqeKXNb0l7A5c4CkFFhCYbEtl7uI1G9EO78gcWkbS8x5purEiscupwBjBH0tOJX+bZxE76a6tOIGllYud9Z6LN3FlTjwjqOPcKfJ/IGJpHDTG1LtN2H+FuU/YaDgOeReyjLArcX9FpvpPQzV8NuJWawm7dwHa32gs+Jmkd278DkPQ06t3F7EdpmWj7ZYo+AbXuqia7gFC9lqZbvI2wvRFjv4zqQpBdQVHN/wGi58XekmZLerGr9bzYjyjUg/A1GxLfr42Jz+uLWw+bnGF2/l8BrlToyJuI/3++5hyPO5pF7AB8xfZhkqZN8yybcNsDbyGEyM4CnmZ79Zrn7xaru0Zjix7RSR/hbvN14vNwGqF6uDux8pyWkrm1S+9M6ysfIuStf0/cNaxJPYf3b9v/ltSopL+lZO3UoeMLSKeUDeeP1dmL6xHHEN+NzcrzW4nPaBXn/2jTXs02RITiH8CPJdX1e8AQO3/bx0i6mkibErCT7RtqTvOIQn9+d8ayVqrcPdxBOLZPAJfbtqL936C4QtJz2/j/d4VyG/rZEi47QtJ51Ogj3Ats/1bSoo4G6McopDimRdLaRNrwWowPofVd2K1TbF+k0lGN+I7c4npSKLeWC/r3iJqBfzFemKwK3biAdISjcrutNqddppOeF4+XPcl/EfUr/9X02pLtGDO0zh/mZ2L8hRJLlPRU1+vktSdxm/9fjkbXazPW+m8qPkasLA8HTip3H4NkC2CPktf9EH0WrSoXv+8RNQbY/mM/zjsFD0hagsjE+jwhIVx1c+97RAjwB1SQhJjJKKqu381YL+OfSjrCFXvw2m4saA4uGW3LE7LddejGBaQbXKNoB3ka4zPA2mkW1C6d9Lz4JDCXCGGe3UhRLXuHv2/HmGHe8N2aSHdancgdXg34je1n9tGGpxHxtzcTYYWDgLNs/1+NOVpl/dxD/KG/WeWLWlI7F8Clf2o/UAd9hHtgy5pE7ccSwPsJp/W/tn9bYezPXbHIb6Yj6VSidWNjQbMzsKLrFUJ2TQa5OKrliar6vmonSTqmxWF7msY2XbbhlUS04NnEXtLmxGbtJRXHLwYs25zyWzKW1E5G2DA7/2uJkuYLHJrlrwTeYPudNeZYl6jsHZeb7naaIUvPJb5cO9lep8a4rwKzGCva2ImQ3l2SCJ3sNsXYrlditotmRh/htgq7JszxFuJCfgHjazdqZ1MMmi5k+7QUHazyN50pn01Jq3sS3RtJ29r+QT/saDrnjOh5AcPt/OfaniPpOmCjEnr4he3KucOK3pcHEXcQ2xJhINmeUj61m6iF+FbjmKSbbE/ajUszRHe92DIT7j7mp/pKOsP2G9qY47NEPvzvGO/w+p2d0jGSjgWOsH1Vef4C4K2uWK2sDkQHJ3w2ZxOxagErAH92FwvJprHj18CrJ4YiJe1J1B9UXqh1YMOU6eeDWlgMc8z/nnLLcznwHUl3UD9Gu2TZFFNxUgdL+ilxQegXs5pXrJJmAyuX16a8Ne7XF6gin5l4lyLpeMKR9ovmi2C7F77ticytmSDp3BaKmgsTyQu7S/pzeb4m8KsaU7UtOtj4bCr0bM62/aPyfCuior5fvJ/Ya3it7d8UGw4gMvWmrLXpIs2SHM8jQrqNz2odCfmuMszOfzvitvx9RLbO8kytm92KfyuEwH4j6T+JdoqrdNXK6fkAcLlCJE5Eufe7y4WtVlOYATPuDqXEivudYeFJHtfhOmJ1ukBnsiFimy7N83vgEkmdyCBv2hyKtX2uxnfh6ym2fyTpIeBcSdsR1c2bAi9xTbmMDmyYX22vKE5t29lLWtwT5DkkrdxO+Ghonb9Lv1xF4cS0Ou2T8D5CZe+9wCHEFfit7UykKDJbo256Y/lwrkuIXjXS8RqbvF9px5Z+UlZRHwOWlNTQExJx13Jkn83ZsNigFva4YpHXqsAtkn7JeIc3NKmeE0NtklZhvN5SVbohOniXQi79BOKCvCttCpG1S7m734PQNroC2LJqxlMvzGlnkEKu5XjgCYpapH2aQlkXEHL09eYc4pj/2wmH/RgR7ml8wWf30YZLCJ2NxYjqxTuBS12zu5ein/BajM+oGIQmTttI+qztAwZtR6doBvXw7RRJryNCDk8l7mTWBG6eah9pknnalkEuG78HMSYudynw6T5u+N7H2N7DE4BHGN/Dt5JURhftaau1aVmM7GH7JklvJBJVdrN9ldpsmjS0K39Cs35Dt2gcPh0l33dSaqzylrd9b7kQHWP7IEm1Vv4lLr4OcfFolN6bCoJoMyWjovBDFQ18heTsJsBX+7nh2w2G0clPwSFEZsmPS0bcy4iMtEqoCzLI5TO4Xxn/FNt/r2F/x9geSEOeZiQdxtiKf3VNaHTkapXHSzR+77ZPl3QzcKakj9Lm3cQwO//f075s8WbEZtbJwM9pnS1ThcUUVXdvIrp5tcMc4Nlu7xasq7rrHXI4EXbZEPgwUSj1Hfq3qdYVmlaKEKGOxamuCzTTeMT2PyQtImkR2xdL+lyN8d2WQT6HNsITCwFzmx7Pa3OORyQ92fZtAOUOYEtCGqKtjKVhdv4fBX6m6OXbHJutEnJ5MlEjsDOx638O0V6ubmOHTwPnExIPvyxFX7+pOceNxZ7aK6IZlu3zaEm3fT2x4j9KUlv7J4Nk4kqxbBL2U3q4m9wtaRlCxOzEkhH3aI3x3ZZBbneRNdTY7kbixkeJ/ajbmua9tVyQ2xIeHOaY/8+JVfsNNKV42j6q5jxPIC4ChxKxyMO6aWeF818MbERoBbW9wagBy+ZKupQo/X8boTB4JyHv3Lcir14h6Srbfe9K1inFUT9INCjahciIO6FqOFBdlkGW9G7b/zv9O5PJKAkuTy9Pf+16Wk3j5xpi53+l7c2mf+ek459AtG/cmdhsPRs42vZfK4xtjuEtQMUYXmOujjcY1SXd9U6Q9GTiLuoXti9XdL86ph9FNN1EofDaYBEiLPcfnXzWZhKSfmZ784rvXZFQ4NyCWLVfBhxcNUVSXeqnnESKJ7FA3R34A/HZXAU4zPb/SNrY9rSKxM0Mc9jnIklvIwS4mlfM0+4DSDoOWB84F/iU7RtrnrsRw9uckIZoCLvtSM2YXpc2GAcum2v7Nkk/Ad4i6QTiAzrjU1Vb0Fwr8ijRHvP1gzGlJ1TOhitOvm0ZZHfYTzkZxxeJtPQ1m9LclwO+IOlwoiNhrTDwMK/8/9LicKVUT0WnpIayXyedki4GXtUouihX5wuaizqmGHu57S0mbDDWtqHM9Uvbmyr0jl7g6Ex2re2Nqs7RLpKeQQjbNRpJnwJ80HZLuYdksEj683TfEUlfsf0+TdJqtE5IsiwINiXCmnX7KS9UlO/K4cCqtteXtAHwOtufqTD2t8C6ExNDSjHlXcBWLjIeVRnKlX+5ndyx7n+2gbvXKempwLJAI4a6TDlWxYYtyr/dSEUbpGzuLcBPgW1dVDMlvb9P5+4akj45xcu23beq1E6ZELoa9xLVtN8bMf4vdMGcvt6BznC+RTTY+SaA7eslnQRM6/yJxlOtLsSPSbqzHV84lM6/3E5+hYhtD5L/IXTCGxkR/wEcXHVwt2Ki7o7ueru8gVj5X6xo4vJdhjOr4/4Wx5YG9gKeROTMDwtTyZxM2zXK9rzyb8chSduXKkT/1rX947JhuWin8w4pS9n+hcb3b6maffUrSbt7QvFnqam5uR1jhjnscwgw1/b3B2zHk4GG/vvPG3m4NcafCBzQSUxUIQa3AP2Ms5bMku2I8M/LCV2is2xf0C8bukWpaN2PcPynAl9sp5hw2NGYQFwzjV4Tn3EFtU9F4/F9gJVsr6OQMjnC9pZdN3iGI+lc4D+B02xvUip197K9VYWxqxH9hx9krL5nU+JObvsqiSoLzDnEzv9fxAr3IeIX0oiVT1n12qVzd02itRsx0aYvqYhUz7WJNLBaZfzdolQe70j0NhgaKeRi9/5EWuRxRL1CX8S/ZiKKLmiPASeVQ28mPmP3AFvYnlZIsexDPZ9YGG1cjt1g+7m9sXrmUuqAjiSK5P5FJEXs4hpV8JJeTogoCrjJ9kVt2zPEzr/lraOjZ2uvz33xFC+7jsPrRqpnizk3Ad5h+x3tzjFqSDoU2IH4cn7DbejYLGy0SgttHKvqwFU6o6nozyi6UV29MNR/tEu5S16kkbUzKIYy5g/zNzqWJ0qbmxULKzXq7vDc02bz1Jir6zFR21dL2rQ7Fo4MHyDuIj8BfLwpLjsQAbAZwjKSXmD75wCSnk8kNUD1WPWlkhqqr68kegr3tXvWTEHRxesgSk9lRTOpT1cJn/XEniFe+e9F3KKvRlT5NvLcX9pHGxYH3sWYYuElRN/dRyYdtOAcHcdEJTVLWixC6Kc8yfarq86RLHyUhcQHgNm29y6frfVsT7vpW8ZvChzNmMO/j9DDvwnY2vapFeZYhNg7eRVxIT3f9rdq/2cWAiRdSBTKNXoq7wK81HY/m9uM2TPEzv8GIpZ4pe2NJD2HaMtWWbWwCzZ8mxD+amh37AY8ZvvtNeboOCYqqbnzWKMw6QwPTrM8mQFIOoXYHNy95JUvSfm+1JxnecJX3N2GDfvZ/up0x0YBSfNsP2/Csbm25wzCnqEN+wD/tv2gJCQt4VC5e2afbdjU45th/0TRU7gOD9l+uBFmKDHRulfkX9ke19BG0o603+QmWThYx/ZOknYGKN+Xymm4klYF/ht4qu2tJD0b2Mz19LPeCkx09Hu0ODYKXCzpzUQGGcAbCVHJgdCtYqdB8PdS2PQD4HxJZwC399mGxyTN164pu/l1N5wnxkRPo35MtFUTlaFvrJJ0zMNltW+A8lmtIwR2LKFa2yhc/D+i+920SNq5VAivLenspp+L6XMnr0Ej6T5FV7l3EJlTDxN/h+8SPYYHY9ewhn2aUehaLw+c4w5U7to87zFEbwERnZL2dJMMboU5FoiJAt9uVc3XYuxWwGuJfgKnNL20HNEjYFiliJMuUBYTnyD0py4gtKj2sH1JxfEN2ZBrmkKSlWRDShLD2kTHqY82vXQfUdhYR1o66QHDHPah3MLOYqzC7Un0T9ag0Rt0XWA9mN9/t9bFp1QrnwBcZvvXNU34G1Fw8zrGC8rdxwBXFMnMwPaFkq4mKuEF7Od6jb7vLxkqjTuHFxI5/lXO/SfgT5Ium5i2rGgo85EadiwUFH+1C7C27UMkrQE8xfYvBmLPsK78Jb2baKbyD8b0/G372X22o6P+u4o+q4cSbdrWlrQRkf5Vp8hrsVxJJRORtD3wE9v3lOcrENkl36s4fhPgMEIB90ZiobWj7cr7WmrRs1bS9aOY569Q33wceLntZykksy+wPZC07GF2/r8lNp/uHKANLfvvup6e/zxCDuGSplvrWl8OSX+gtfpiP9s4JjOMViEa1Wz2XRIQGne2v66axizpXURO/zrAb5teWhb4me1dq9qwsNC4EE4Io103IWmkbwxz2OdWxtQ0B0Un/XcbPGr7nhpJGJPZ0eCJhLRCz2UukhlPq4SOWt/5ckd5E8QegqQP235lhaEnEf0yFoj5u2InsYWQRxTKBI0w2iyauhD2m6Fz/pIaq+rfEqmVP2R8M5ev9dGctvvvNs8h6S3AomX/4L3UrFJuUSH4lVI9OJVMcbLwM1fSl4BvEA5nXyo0G1LoxxxBZPl8j0j3/A6x+v+vKicuoaZ7CKE/JK1CLEyWkbSMR7O5y9eAs4BVJf0Xker5iUEZM3TOn4g7QjjcvxOZLYNiZUJqtZP+u/sCHy/jTyayfWrJB2u80Fyj9WA3+gQkw82+wIFEJpiIjJ8qzb6/SFSdXwlsBVwFHNhOYZakbYEvEReSO4iMuJsJcbKRwvaJJcy7JfH32M52W3LM3WAoY/4K9cXZwO9doW1jD+3ouihbm3Y0p5Y2Kny/0Eb2UJIssEkr6XdusxdzKXp8OfDjIuz2MmBn2/t0ydyhQtIWhI7XMSXss4ztPwzClqFb+Uvag8iO+SMwW9JeVbVKesBzgRPdgeyvorXbB1kwY6iyMmg3heaShYcOPlsraHw3MDU/t31mDTMesf0PSYtIWsT2xSXVc+QoMixziA30YwhpmBOI+ou+M3TOn2iDtr7t2yU9nWg5Nyjn/2TglyWXH/V8UwAAGjtJREFU+mhCtKrurdRpRHz129SsDtZ4QbcFsP2lmrYkCxftfrYuZXw3sObnJpqKVOVuScsQgmYnSrqD6oqgCxvbAxsDVwPY/puicdBAGLqwT4tb0gXEkvpsj4jq3D2Jq/qpwFG2f1dxfNv2KxrRX0tkVTwE49sn2s7+qSPMoL8bxYaliWZLixAFTssDJ4xixo+kX9h+flPK59KE0N5Aah6GceW/eslgaLBG83PbU66Gu41tS7oNuI1Y0awInC7pQtsfrjDFD0rB2lmM3zSu8uXYhOiutDWRxXEycFGHqafJwkMnn62uYLvRne5xivqtpJ8xoFDHgDlV0jeJsNrewNuIpu4DYRhX/ntN9XpNxcFObXkvoVp4F3Fr/T3bjxS9nt9U2SQrBVoTcd0CrVJpvDPwCuAjts+uMz5Z+OjWZ6vbSPqL7TUGacOgKHpLzb0NLhyULUO38u+nc6/AysAOntCDs+j1bFNlAttrd2pEyRrYmNiAvpVIqUtGnE4+W2UB80LbveiMN1wrzi5SnP2FklZmwOqmQ7fyn2m0m7ol6eW2fzIhq2I+VTIqJO0J7EQUz5wOnGo7HX8CzO/ktT/RyWsf1e/kdaXtzdo8d8vPNbHiPcL2rEleX+gognj/QygSHEIkqaxM7IPsbvu8gdiVzr99mlO3bD9D0lOB0zyh6fUkYz9l+yBJx7R42bbfVmGOx4kWlo1qyXF/zJrFZslChjrs5CXpU8D1wJl195Em+VzPx/aedeYbZiTNBT5GbHYfCWxl+ypF86mT62gtddWudP7to2jBuDFwdbuibB2ev2WRWYN+F5slMwuVFoHtColJug9YmkgTfRBGupl92zQL7Em62fazml6rJbTXTYYu5t9A0hOIdnDPIcIeAPS5cvDhku3TEGpauu4Ek+Tq3wPMs33tVGPTuSfT0FEnL9spEdIdmsXbHpzw2sBW38PcxvE7ROXiNsDPCenYfjcsn5i69WMi66cOc4B3AquVn32AlwLfklQlVTRJJuMg4DwiHfpE4CKg8mdKwa6SDizP15CU3eHqs6Gke8ud1AblceP5cwdl1NCGfRq3S40wi6TFidSpyrIIXbKjo9QtSecDb7D9/8rzZYjN2+2J1X9fm9MkCxeKTlyNTl5XuUYnL3XQfETSjrZPk7T2oLRrkqkZ5pV/o6nE3ZKeRahYrtlvI2xfaPtDtj9ISEzvUnOK2URD5waPAGvafpCKt+iS1q95zmQhpmwkNtRe1yTUb/9GaGFtMtXYCbzA9nsod9RFw2qJimMPKP+eUeN8SR8Z2pg/cFRZiRxEyCAvVR73HEnLEdK4qwFnAxeW5x8i5BZOrDHdScBVkr5fnm8LnFz2D35VcY4jJC0BHAucZPvuGudPFj72J8KHX2zxmgmVzSp00nzkHwq12bUlLVBwmJlog2dowz6DpDjqfxF651sSkg5LEA2yp9yknWS+OUS5u4DLbc9tY451iXLxHYFfAMcMsnowGX7KXexOhIzIcUTzkQNtn1ph7BJl3PHA2ye+nskKg2fonL/GOnm1xH3o5CXpBtvPLY8XJeQdZtu+r835FgVWZbzsbu1OR2We7YiOQfcSF5OP1ZTgTRYSJL2HkBy/uzxfkdDS/98aczyTseYjF7lm8xFJs2zfqVCvdGNvKxk8wxj2aVQGrgs8H/hBeb4NIT3bD+Y3sbb9mKQ/dOD49yXCVbcT+dQibrPrNHDfgFAV3ZoIQW1r++pSdHYl9SR4k4WHvW1/o/HE9r9KVlol5y/peNu7Abe0OFaVVSVdQPSUlqQ7gbfavrHGHEkPGLqVf4OSJbOjSyevEoc/xfZWfTj3Y0BDrVDAksADtFEEI+m3xMZa2zofki4j1AFPLxvFza/tZvv4dudOhhdJ1wMbNqpzy53h9bYrtVDUgvLpiwI31MlAk3QF8HHbF5fnLwX+2/aLqv9Pkl4wzNk+azI+r/8hoGORtCrYXtT2cuVnWduLNT2uW/34F6KoqxPOtH18s+OXtF+xNR3/6HI+UYuypaIp+8lE3v+USDpgkpz0O4DvTzN8Iks3HD+A7UuIquFkwAzzyv+TRC78GUSYZAdCUrlW8/NBI+kooq3bOYzXXK/chWviCq0cG1jZeDIzUChz7kPIfDcauH/bdqWuXpI+a/uA6d855RxnEZ2rGouQXYE5trfrZN6kc4bW+QNI2hR4SXl6me1fDtKedijicAvgCl24JO0MvAV4MdEmr8GywGO2X9EVI5OhpmTePAf4ax3VV0mbA9favl/SrkT2zlc9QcJ8mjlWBD4FbFEOXQZ8yh30vU66w7A7/xcCz7D9nVLJuHQ7WTIzDUmL2Z62z6mkNYlQ12eBjza9dB8R2x3VXqkjjaQjgMNs3yRpeWLT/zFi0/WDtk+uOM/1wIZE8sHxwFFE/4opBQWT4WBoY/6SPkFkyXyiHHoiUTA1FEi6vOnxxLj8L6rMUVZgPwXut31p08/V6fhHmhfbvqk83hP4v5Ka/DxqaPsAj5bN4tcTK/6vEneVyULA0Dp/ouDktZSsG9t/BYZJarZ502uiPIOoSInfPlBWeEkC4+VCXgl8D8D2bTXnuU/SAcBuwDkl22fx7piYDJphzPNv8NAEOeWlBm1QTTzJ41bPp+PfwA2SLmQsBRXbUxbEJQstdyvaiP6VqBzfCyKcSKQlV2UnYk/pbbZvkzQbOLSOIZI2t/2z6Y4l/WeYnf+Zkr4BLK9oZ7gXMGX3oBnGCpK2J+6+VtBY2zsRHX/qcE75SRKAdxBV3k8G3te04t+SGp+T4vDPIAoqISrZz6ppy2HERvF0x5I+M+wbvlsxXk753AGbVBl1uc2domnHbNu/7siwJCmUauB9gJVsr1P0o46wvWWFsZsBLwLeB3y56aXlgO1dsZtY0juGduUv6VXF2Z/bdGxv298aoFmVqevcp0LStsAXCHG5tSVtBHw6lRNHG0mrE6vsLQg1zssJ8cFbK07xHkJC5ecAtn8jaZWKY5cAliF8TPMm8b3Efl0yYIbW+QOflvRQQx1Q0geAVxMyB6PGwcSX9BIA29dK6ku1czKjOYbIgNuxPN+1HHtlxfEP2X5YivyDsmdQKVRQvpeXSjq2URdQis6WaUiyJINlmLN9tgUOlfQiSZ8iCp22GbBNg+JR2xMlIoY3npd0i1m2j7H9aPk5ljFhxCpcKuljwJKKjnWnMSakWJXPSlquqT/FryV9qOYcSQ8YWudv+07gdcA3iUKnHWw/PPWohZYbJb0FWFTSupIOA64YtFHJwLlL0YN30fKzK1BHQPCjwJ3ADcQm8o8Yq6upyrPLSn+7Mn42kTqaDJih2/CV9C/Gr2qfQEgsm1DUXGkghrVJSVH9ALFZu3fZVFvP9g9rzvFxmja/gUNs97uhfTKDKKmZXwc2I74fVxAx/8ryDF2w4SZgIyL89HXbl0q6Ljd8B88wOv9Fp3q9qmjVTEHSKcA8YHfb65esnSttb9TGXMsRF8C2egskSTNF2+dgQkF3McYky59WY473Ah8BriP6TcwGTrD94q4bnNRi6Jx/M6WqdR1C2gEA20MV7pA01/acZhXOuiujInB3NGNZFfcQhTnzum9xMtOR9GHbny/hvwW+4FWL/yTdAryfWJzMX1R10nuizFtJuyrpLUOb7SNpL6JR9WpETHJT4CrgpQM0qx0eLqv9RqXyOjRJO1fkKODdtn9a5tiCyOqo3A0sWahotFqs3Qt6Ave0WzsjaVfbJ0jaf5K3VJYsT3rD0Dp/onhkDhEiebGk51B/M2omcBDRYGMNSf+/vTsPlrQq7zj+/c0QUDaFCIIaGRhZwxYUF0KxlwETtARkEdkEEitWoiSSmBK0JGIqspiURUGQFAkyFRHZBJVFHBgpkiDgsARSJTCQUCHAZBBQFoeZX/44bzM9d3ruMnP7Pbe7f5+qqen37ft2PzN173NPP+85z5lHWY5/4hRf48VO4gewfUez+UaMINvXN3//85pcL6mz+na+pHMo24B27zVx7yReptO7Ko3gZqiBLftI+qntPSUtBN7bzEceqA1MVCZQv4OyBeT7KTXVf7O9eJLXd35IjwPWp+zUZEpPludsf2Hag44ZT9JbKAu0nqOUA8+hTIV+FPhz249McP38cZ627QMmGcds4E9tf33CL47WDVzy79QLJX0POJ4yU2ZvYAmln//BVQOcIkn32H73Gl47LT+kMVxUNky/mzLqPpBSArye8gvgWNv7tRjLfNv7t/V+MXmDmPx7bVl4IKUZ2vdtT7VeXlXTnO6fBnEXspiZOhMGmk+WT9h+Z9dzCyc7k0zSV4Gv2f5Fc7wJ5ZPDpMurks6m/GxewcodZydTOoo+GsTkP1ClnYlIegjYDniC8sPRmU434c3aiW6qeQr7AMfw6B4gjR0s9Ro8jfM6q/ysTeX65ut7fTrNp9IZYBBv+G42zgyCQUx4h6zFtePdVBus3+oxnbZpyqLqekxzPJWeT7Mlrdf5NN3MSltvKoGk5DNzDWLyn03pFjjp3a5msq6mV5vTtV5hktf+Q/P3Kpu9S/rstAQYg+gjXY/PHfPc2OPxXA7c2rQfN/BJYEoziCStBxwOzKEr39g+ayqvE9NvEMs+U/rYOdNJ+jBwHvA24BnKasqHbf/2Wr7uf3XXeiPWhKSDgYOaw1ts3zTF62+kLDocu1DsvGkLMtbIII78h2LE3+WvKdM8f2T7dyTtDxwzDa87bP9PUcfPKPv2unk8Ve8YtBl4o2IQu3pOuIvQgFnaLJefJWmW7fmURlhra7A+0sWMI+lI4C7K5itHAv8uaaobsdwpaZdpDy7W2sCN/G0vqR3DNPuFpA2BBcA8Sc8Ak+p70qzi7ZXkxdQ26o7o5QvAnrafAZC0GfAj4LsTXSjpAcr35jrASZIeo6wSnvRstuivgav5D5tmk4uXKZ/CjqXMiZ63ts2zYnRJup5xPvlNdntPSQ/Y3qXreBZwX/e5ca7darzn22wrHb0N3Mh/CB0F/MT2z5niTIqI1ejM6DkM2IIyawfKvaTHp/A6N0q6idI2BMr36mQbvW0OvGVsYziV/ab/h7KuJSrKyL8ySWdR2lPMoSzJ/wnll8HCmnHF4JO0wPY+E52b4DUOo3x/Clhg+5pJXncbcKLtx8ecfxdwcRZ51ZfkP0M0C2hOBT4HvN32uJvWRExE0sPA79t+rDneGviB7R3X8PVmA0fbnjeJr31gdeWh7OQ1M6TsU5mkMyhtnDekTKX7HGX0H7G2TgNua262Qvl0+UcTXdTsCPdpyl4Z3wNuaY5PBxYCEyZ/xp9wsME4z0VLMvKvTNK9lNk93wdup7R0zt67MS2aFbY7NIf/OZnGh5Kuo7SD/lfK1OpNgHUp+/9Oqhwp6SLKZvFnuCvJSPoysKXtP5zSPySmXZL/DCBpI0pddW/KfOqnbe9dN6oYdJLWp+x2t5XtUyVtC2xv+4YJrnu9ZNOUehYD75zK3tDNLLZLgPdSPi0A7Ea5r3WK7V9O+R8U0ypln8ok7Uzps74vZWey/yZln5gel1LaKnygOX4SuBIYN/kDSzsPbC+TtGgqib+57lfAMZK2ATqtSv6jc/8h6svIvzJJnXLPHcBPbS+d4JKISZF0t+33dLdmnszNVknLWNF7v7Ng8CVWLNDauJ9xRzsy8q/vFtt/131C0mds/32tgGJo/LqZRWYASXPp2ot3dTLTbDQMYm+fYXN8j3Mnth1EDKUvATcCvyVpHnAr8Bd1Q4qZImWfSiQdA3ycUu9f0PXURsAy2wf1vDBiCiT9JqVrrCgzyRa3/P5zgSdtvyppP2BX4LLO1pBRT5J/JZK2B7YE/gb4fNdTLwL3255Uc7eI1ZH0UeDHtp9vjt8M7Gf72hZjWEiZyDAHuImybmB72x9qK4boLcm/ks6mNJIut/2J2vHE8Om1WXvbe2B3fZ+fDrxi+xvDtg/3oMoN33rWlXQC8IGmf8pKbF9dIaYYLr3u6bX9M7+0KXGeABzanPuNlmOIHpL86/kUpYXzm1nxQ9FhIMk/1tbdks4HLqB8T/0JZd5/m06ifK+fbXtR01/o8gmuiRak7FOZpJNt/2PtOGL4NKtsz6TswSvgZuArzQKsGHFJ/pVJWpcyMuq02b0duCiLvWIYSFpEj41lbG9TIZzokuRfmaRLKDXQzkYux1Gmep5SL6oYBpK2o3SJnUNXibfNXvrNVNOONwAfAza1/cW2Yojekvwr67XcPv3OYzpIug+4iFLnX9Y5b7vtuv9KJN2RxoX15YZvfcskzbX9KEDTCGvZBNdETMZrti+sGYCkPboOZ1Hm/G9UKZzokuRf3+nA/GbDDQFbUWZIRKyt6yX9MXANXT19bC9pMYbzuh6/RtlD+MgW3z9WI2WfGaDZcGN7SvKf1IYbERNpbraO5dxsDUhjt2ok7SlpC4Am2e8OnAWcI2nTqsHFULC9dY8/rSZ+SZ+RtLGKSyTdK+mDbcYQvWXkX0mzfeNBtpdI2gf4NmURzu7AjraPqBpgDIVms6CdKDNtALB9WYvvf5/t3ST9HmUf4DOBS23vMcGl0Wep+dczu6v2ehRwse2rgKuaZlgRa0XSl4D9KMn/B8AhlE2DWkv+lFImwIcoSf8+SRrvgmhHyj71zJbU+eV7IPDjrufySzmmwxGU763/tX0SZQ/d9VqO4R5JN1OS/03NftXLW44hekiSqedfgNslLQZeptm3V9K7gOdrBhZD42XbyyW9Jmlj4Bmg7Zu9J1NKmY/ZfqlZ9JXZbDNAkn8lts+WdCulp//NXnHzZRal9h+xtu5uevh/k7LQ65fAXS3HYErZ6Q8oExo2oOv+Q9STG74RI0DSHGBj2/e3/L4XUso8B9jeUdImlMHOnm3GEatKzT9iSEm6TtLHJW1g+/G2E3/jfbY/DbwCYPs5YN0KccQYSf4Rw+t8YG/gIUlXSjpCUtsll6WSZtN09pS0GbnhOyOk7BMx5JrkewBwKnCw7Y1bfO9jKVOZ96B0rj0COMP2lW3FEL0l+UcMMUlvpOwU10nAN9hudUKBpB0oU04F3Gr74TbfP3pL8o8YUpKuAN4H3Ah8B7jNdisllwlalLya3cTqS/KPGFKSDgZusd16i/CuHbw6q3k7iUasmGL+edvz2o4tiiT/iCEj6bDxnrd9dVuxrE5z4/d22zvVjmVUZZFXxPA5tPl7c2AvVrQO2R+4Daie/G0/K+kva8cxyjLyjxhSkm4ATrX9VHO8JXCB7XE/GcRoyDz/iOE1p5P4G08D27XxxpK2buN9Ys0l+UcMr9sk3STpREknUNo6z2/pvb8L0PSvihkoZZ+IISbpo8A+zeEC29e09L4/A64FTgG+PvZ52+e3EUesXkb+EUPM9jW2T7N9GvCspAtaeuujKf181gE26vEnKsvIP2KISdodOIaywncRcLXtb7T4/ofY/mFb7xeTl6meEUNG0naUkfcxwP8BV1AGevtXCOdOSeezovR0O3CW7WxYVFlG/hFDRtJyys5wJ9t+pDn3mO22d/FC0lXAg5SmbgDHAbtluml9GflHDJ/DKSP/+ZJuBL7NijYLbZtr+/Cu4y9LWlgpluiSG74RQ6a5yXsUsANlRe9pwFslXSjpgy2H87KkvTsHkn6Xsmd1VJayT8QIaLpsfgw4yvYBLb7vbsBlwJuaU88BJ1TaVSy6JPlHRN9J2hjA9gu1Y4kiyT8iYgSl5h8RMYKS/CMiRlCmekZEX0naC5hDV76xfVm1gAJI8o+IPpL0LWAusBDobCdpygygqCg3fCOibyQ9DOzkJJoZJzX/iOinB4EtagcRq0rZJyKmnaTrKeWdjYCHJN0FvNp53vaHa8UWRZJ/RPTDubUDiPGl5h8RfdPs5fuU7Vea4zcCb7X9eNXAIjX/iOirK4HlXcfLmnNRWZJ/RPTTOrZ/3TloHq9bMZ5oJPlHRD89K+n1m7uSPgIsrhhPNFLzj4i+kTQXmAe8rTn1JHCc7UfrRRWQ2T4R0SeSZgHvtv1+SRtSBpsv1o4rioz8I6JvJC2wvc/EXxltS/KPiL6RdCZl28YrgF91ztteUi2oAJL8I6KPJC3qcdq2t2k9mFhJkn9ExAjKDd+I6BtJx/c6n37+9SX5R0Q/7dn1+A3AgcC9pJ9/dSn7RERrJL0J+Fa6etaXFb4R0aaXgG1rBxEp+0REH3X19Ycy2NwJ+E69iKIjZZ+I6BtJ+3YdvgY8YfvJWvHECkn+EREjKDX/iOgbSYdJ+rmk5yW9IOlFSS/Ujisy8o+IPpL0CHCo7YdrxxIry8g/Ivrp6ST+mSkj/4iYdpIOax7uC2wBXAu82nne9tU14ooVkvwjYtpJunScp237k60FEz0l+UdEjKDU/CNi2kn6mqRP9Th/mqS/rRFTrCwj/4iYdpIeAna2vXzM+VnA/bZ3rhNZdGTkHxH94LGJvzm5HFCFeGKMJP+I6IeXJK3SwK0593KFeGKMNHaLiH74IvBDSV8B7mnOvQf4K+Cz1aKK16XmHxF9IWln4HSgU99/EDjX9gP1ooqOJP+IiBGUmn9ExAhK8o+IGEFJ/hERIyjJPyL6RtJ2km6V9GBzvKukM2rHFUn+EdFf36RM71wKYPt+4OiqEQWQ5B8R/bW+7bvGnHutSiSxkiT/iOinxZLmAgaQdATwVN2QAjLPPyL6SNI2wMXAXsBzwCLgE7YfrxlXJPlHRAskbQDMsv1i7ViiSPKPiL6RtB5wODCHrl5its+qFVMUaewWEf10HfA8pbnbqxN8bbQoI/+I6BtJD2bjlpkps30iop/ulLRL7SBiVRn5R8S0a1b0LqeUlrcFHqOUfUTZ5WvXiuEFqflHRH+8Hdi9dhCxekn+EdEPi2w/UTuIWL0k/4joh80l/dnqnrR9fpvBxKqS/COiH2YDG1Jq/DED5YZvREw7Sffa3qN2HLF6meoZEf2QEf8Ml5F/REw7SZvaXlI7jli9JP+IiBGUsk9ExAhK8o+IGEFJ/hERIyjJPyJiBP0/W18qK4N+cVQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in df_cat[['Location','Headquarters','company_txt']].columns:\n",
    "    cat_num = df_cat[i].value_counts()[:20]\n",
    "    print(\"graph for %s: total = %d\" % (i, len(cat_num)))\n",
    "    chart = sns.barplot(x=cat_num.index, y=cat_num)\n",
    "    chart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Job Title', 'Salary Estimate', 'Job Description', 'Rating',\n",
       "       'Company Name', 'Location', 'Headquarters', 'Size', 'Founded',\n",
       "       'Type of ownership', 'Industry', 'Sector', 'Revenue', 'Competitors',\n",
       "       'hourly', 'employer_provided', 'min_salary', 'max_salary', 'avg_salary',\n",
       "       'company_txt', 'job_state', 'same_state', 'age', 'python_yn', 'R_yn',\n",
       "       'spark', 'aws', 'excel', 'job', 'seniority', 'desc_len', 'num_comp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>analyst</th>\n",
       "      <td>65.857843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data engineer</th>\n",
       "      <td>105.403361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>117.564516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>168.607143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manager</th>\n",
       "      <td>84.022727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mle</th>\n",
       "      <td>126.431818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>84.853261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                avg_salary\n",
       "job                       \n",
       "analyst          65.857843\n",
       "data engineer   105.403361\n",
       "data scientist  117.564516\n",
       "director        168.607143\n",
       "manager          84.022727\n",
       "mle             126.431818\n",
       "na               84.853261"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index = 'job', values = 'avg_salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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></th>\n",
       "      <th>avg_salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <th>seniority</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">analyst</th>\n",
       "      <th>jr</th>\n",
       "      <td>56.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>61.155405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>79.092593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">data engineer</th>\n",
       "      <th>na</th>\n",
       "      <td>96.701220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>124.689189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">data scientist</th>\n",
       "      <th>jr</th>\n",
       "      <td>106.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>107.043011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>138.956522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <th>na</th>\n",
       "      <td>168.607143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manager</th>\n",
       "      <th>na</th>\n",
       "      <td>84.022727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">mle</th>\n",
       "      <th>na</th>\n",
       "      <td>119.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>142.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">na</th>\n",
       "      <th>na</th>\n",
       "      <td>73.988189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>109.061404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          avg_salary\n",
       "job            seniority            \n",
       "analyst        jr          56.500000\n",
       "               na          61.155405\n",
       "               senior      79.092593\n",
       "data engineer  na          96.701220\n",
       "               senior     124.689189\n",
       "data scientist jr         106.500000\n",
       "               na         107.043011\n",
       "               senior     138.956522\n",
       "director       na         168.607143\n",
       "manager        na          84.022727\n",
       "mle            na         119.133333\n",
       "               senior     142.071429\n",
       "na             na          73.988189\n",
       "               senior     109.061404"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index = ['job','seniority'], values = 'avg_salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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></th>\n",
       "      <th>avg_salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_state</th>\n",
       "      <th>job</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">WI</th>\n",
       "      <th>na</th>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>113.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analyst</th>\n",
       "      <td>58.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">WA</th>\n",
       "      <th>na</th>\n",
       "      <td>97.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>99.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AZ</th>\n",
       "      <th>analyst</th>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>124.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">AL</th>\n",
       "      <th>na</th>\n",
       "      <td>43.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data engineer</th>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analyst</th>\n",
       "      <td>62.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          avg_salary\n",
       "job_state job                       \n",
       "WI        na               54.000000\n",
       "          data scientist  113.500000\n",
       "          analyst          58.833333\n",
       "WA        na               97.500000\n",
       "          data scientist   99.764706\n",
       "...                              ...\n",
       "AZ        analyst          55.000000\n",
       "          na              124.500000\n",
       "AL        na               43.750000\n",
       "          data engineer    65.000000\n",
       "          analyst          62.200000\n",
       "\n",
       "[114 rows x 1 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index = ['job_state','job'], values = 'avg_salary').sort_values('job_state', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_salary</th>\n",
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       "      <th rowspan=\"3\" valign=\"top\">TX</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">NY</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">MN</th>\n",
       "      <th>data engineer</th>\n",
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       "      <th>analyst</th>\n",
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       "      <th>manager</th>\n",
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       "      <th>data scientist</th>\n",
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       "      <th rowspan=\"4\" valign=\"top\">MD</th>\n",
       "      <th>na</th>\n",
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       "      <th>na</th>\n",
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       "      <th>data scientist</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">KY</th>\n",
       "      <th>na</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>KS</th>\n",
       "      <th>mle</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">IN</th>\n",
       "      <th>na</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>2</td>\n",
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       "      <th>data engineer</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">IL</th>\n",
       "      <th>analyst</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mle</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data engineer</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
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       "      <td>2</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">IA</th>\n",
       "      <th>analyst</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>data engineer</th>\n",
       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">DC</th>\n",
       "      <th>data scientist</th>\n",
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       "      <th>data engineer</th>\n",
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       "      <th>na</th>\n",
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       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">CO</th>\n",
       "      <th>mle</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>data engineer</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>na</th>\n",
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       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>3</td>\n",
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       "      <th rowspan=\"6\" valign=\"top\">CA</th>\n",
       "      <th>na</th>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>mle</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manager</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data engineer</th>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
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       "      <td>18</td>\n",
       "    </tr>\n",
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       "      <th rowspan=\"4\" valign=\"top\">AZ</th>\n",
       "      <th>data scientist</th>\n",
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       "      <td>1</td>\n",
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       "      <th>na</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">AL</th>\n",
       "      <th>na</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data engineer</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analyst</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          avg_salary\n",
       "job_state job                       \n",
       "WI        na                       4\n",
       "          data scientist           3\n",
       "          analyst                  3\n",
       "WA        na                       1\n",
       "          data scientist          17\n",
       "          analyst                  3\n",
       "VA        na                       5\n",
       "          mle                      2\n",
       "          data scientist          18\n",
       "          data engineer           10\n",
       "          analyst                  6\n",
       "UT        data engineer            3\n",
       "          analyst                  4\n",
       "          data scientist           3\n",
       "TX        na                       7\n",
       "          data scientist          13\n",
       "          data engineer            8\n",
       "TN        data scientist           1\n",
       "          data engineer           10\n",
       "          analyst                  2\n",
       "SC        na                       1\n",
       "RI        analyst                  1\n",
       "PA        manager                  3\n",
       "          data engineer            1\n",
       "          data scientist           6\n",
       "          analyst                  8\n",
       "          mle                      7\n",
       "          na                       8\n",
       "OR        data scientist           3\n",
       "          data engineer            1\n",
       "OH        na                       3\n",
       "          data scientist           7\n",
       "          analyst                  4\n",
       "NY        na                      12\n",
       "          mle                      2\n",
       "          data scientist          40\n",
       "          data engineer            4\n",
       "          analyst                 14\n",
       "NM        data scientist           3\n",
       "NJ        data scientist           4\n",
       "          analyst                  6\n",
       "          data engineer            2\n",
       "          manager                  2\n",
       "          director                 2\n",
       "          na                       1\n",
       "NE        na                       4\n",
       "NC        na                      12\n",
       "          data scientist           3\n",
       "          data engineer            6\n",
       "MO        na                       3\n",
       "          manager                  1\n",
       "          data scientist           3\n",
       "          analyst                  2\n",
       "MN        data engineer            1\n",
       "          analyst                  1\n",
       "MI        manager                  2\n",
       "          data scientist           4\n",
       "MD        na                      16\n",
       "          data scientist          13\n",
       "          data engineer            3\n",
       "          analyst                  3\n",
       "MA        na                      46\n",
       "          manager                  5\n",
       "          director                 6\n",
       "          data scientist          30\n",
       "          data engineer           12\n",
       "          analyst                  4\n",
       "LA        analyst                  1\n",
       "          data engineer            2\n",
       "          na                       1\n",
       "KY        na                       4\n",
       "          data scientist           2\n",
       "KS        mle                      3\n",
       "IN        na                       4\n",
       "          data scientist           2\n",
       "          data engineer            4\n",
       "IL        analyst                  3\n",
       "          na                       6\n",
       "          mle                      2\n",
       "          director                 6\n",
       "          data scientist          15\n",
       "          data engineer            8\n",
       "ID        analyst                  2\n",
       "IA        analyst                  2\n",
       "          data engineer            3\n",
       "GA        data scientist           3\n",
       "          na                       3\n",
       "FL        data scientist           7\n",
       "          data engineer            3\n",
       "          analyst                  6\n",
       "DE        na                       6\n",
       "DC        data scientist           5\n",
       "          data engineer            4\n",
       "          analyst                  2\n",
       "CT        na                       3\n",
       "          data scientist           2\n",
       "CO        mle                      1\n",
       "          data engineer            3\n",
       "          analyst                  1\n",
       "          na                       3\n",
       "          data scientist           3\n",
       "CA        na                      27\n",
       "          mle                      5\n",
       "          manager                  9\n",
       "          data scientist          68\n",
       "          data engineer           25\n",
       "          analyst                 18\n",
       "AZ        data scientist           1\n",
       "          data engineer            5\n",
       "          analyst                  1\n",
       "          na                       2\n",
       "AL        na                       2\n",
       "          data engineer            1\n",
       "          analyst                  5"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index = ['job_state','job'], values = 'avg_salary', aggfunc = 'count').sort_values('job_state', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>avg_salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_state</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>142.522059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UT</th>\n",
       "      <td>140.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>127.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <td>117.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <td>117.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>115.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>113.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>113.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>113.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <td>109.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>108.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>108.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>106.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>106.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>105.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>100.730769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>99.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>98.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>97.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>84.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KY</th>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>81.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NM</th>\n",
       "      <td>74.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>69.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           avg_salary\n",
       "job_state            \n",
       "DC         149.000000\n",
       "CA         142.522059\n",
       "UT         140.500000\n",
       "MO         127.666667\n",
       "IL         117.233333\n",
       "NC         117.000000\n",
       "NY         115.250000\n",
       "MA         113.750000\n",
       "WI         113.500000\n",
       "PA         113.333333\n",
       "MD         109.115385\n",
       "CO         108.666667\n",
       "VA         108.416667\n",
       "NJ         106.875000\n",
       "MI         106.625000\n",
       "OH         105.285714\n",
       "TX         100.730769\n",
       "WA          99.764706\n",
       "OR          98.500000\n",
       "FL          97.357143\n",
       "TN          96.000000\n",
       "IN          84.500000\n",
       "KY          84.000000\n",
       "CT          84.000000\n",
       "GA          81.333333\n",
       "NM          74.333333\n",
       "AZ          69.500000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df[df.job == 'data scientist'], index = 'job_state', values = 'avg_salary').sort_values('avg_salary', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivots = df[['Rating', 'Industry', 'Sector', 'Revenue', 'num_comp', 'hourly', 'employer_provided', 'python_yn', 'R_yn', 'spark', 'aws', 'excel', 'Type of ownership','avg_salary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>python_yn</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revenue</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1 to $2 billion (USD)</th>\n",
       "      <td>16.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$1 to $5 million (USD)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$10 to $25 million (USD)</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$10+ billion (USD)</th>\n",
       "      <td>66.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$100 to $500 million (USD)</th>\n",
       "      <td>47.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$2 to $5 billion (USD)</th>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$25 to $50 million (USD)</th>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$5 to $10 billion (USD)</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$5 to $10 million (USD)</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$50 to $100 million (USD)</th>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$500 million to $1 billion (USD)</th>\n",
       "      <td>29.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than $1 million (USD)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown / Non-Applicable</th>\n",
       "      <td>93.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "python_yn                            0      1\n",
       "Revenue                                      \n",
       "$1 to $2 billion (USD)            16.0   44.0\n",
       "$1 to $5 million (USD)             1.0    7.0\n",
       "$10 to $25 million (USD)          16.0   16.0\n",
       "$10+ billion (USD)                66.0   58.0\n",
       "$100 to $500 million (USD)        47.0   44.0\n",
       "$2 to $5 billion (USD)            17.0   22.0\n",
       "$25 to $50 million (USD)          24.0   16.0\n",
       "$5 to $10 billion (USD)            9.0   10.0\n",
       "$5 to $10 million (USD)            9.0    9.0\n",
       "$50 to $100 million (USD)         21.0   25.0\n",
       "$500 million to $1 billion (USD)  29.0   28.0\n",
       "-1                                 1.0    NaN\n",
       "Less than $1 million (USD)         1.0    3.0\n",
       "Unknown / Non-Applicable          93.0  110.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df_pivots, index = 'Revenue', columns = 'python_yn', values = 'avg_salary', aggfunc = 'count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Fitting\n",
    "\n",
    "* data transformed to EDA data csv\n",
    "* Data is split into training and testing data\n",
    "\n",
    "### Models Used \n",
    "* Multiple Linear Regression\n",
    "* Lasso Regression \n",
    "* Random Forest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('eda_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Job Title', 'Salary Estimate', 'Job Description',\n",
       "       'Rating', 'Company Name', 'Location', 'Headquarters', 'Size', 'Founded',\n",
       "       'Type of ownership', 'Industry', 'Sector', 'Revenue', 'Competitors',\n",
       "       'hourly', 'employer_provided', 'min_salary', 'max_salary', 'avg_salary',\n",
       "       'company_txt', 'job_state', 'same_state', 'age', 'python_yn', 'R_yn',\n",
       "       'spark', 'aws', 'excel', 'job_simp', 'seniority', 'desc_len',\n",
       "       'num_comp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_model = df[['avg_salary','Rating','Size','Type of ownership','Industry','Sector','Revenue','num_comp','hourly','employer_provided',\n",
    "             'job_state','same_state','age','python_yn','spark','aws','excel','job_simp','seniority','desc_len']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dum = pd.get_dummies(df_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df_dum.drop('avg_salary', axis =1)\n",
    "y = df_dum.avg_salary.values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-20.766855128917538"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "X_sm = X = sm.add_constant(X)\n",
    "model = sm.OLS(y,X_sm)\n",
    "model.fit().summary()\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Lasso\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train, y_train)\n",
    "\n",
    "np.mean(cross_val_score(lm,X_train,y_train, scoring = 'neg_mean_absolute_error', cv= 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>alpha</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.13</td>\n",
       "      <td>-19.257798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    alpha      error\n",
       "12   0.13 -19.257798"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm_l = Lasso(alpha=.13)\n",
    "lm_l.fit(X_train,y_train)\n",
    "np.mean(cross_val_score(lm_l,X_train,y_train, scoring = 'neg_mean_absolute_error', cv= 3))\n",
    "\n",
    "alpha = []\n",
    "error = []\n",
    "\n",
    "for i in range(1,100):\n",
    "    alpha.append(i/100)\n",
    "    lml = Lasso(alpha=(i/100))\n",
    "    error.append(np.mean(cross_val_score(lml,X_train,y_train, scoring = 'neg_mean_absolute_error', cv= 3)))\n",
    "    \n",
    "plt.plot(alpha,error)\n",
    "\n",
    "err = tuple(zip(alpha,error))\n",
    "df_err = pd.DataFrame(err, columns = ['alpha','error'])\n",
    "df_err[df_err.error == max(df_err.error)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-14.905736296979951"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "np.mean(cross_val_score(rf,X_train,y_train,scoring = 'neg_mean_absolute_error', cv= 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=150, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {'n_estimators':range(10,300,10), 'criterion':('mse','mae'), 'max_features':('auto','sqrt','log2')}\n",
    "\n",
    "gs = GridSearchCV(rf,parameters,scoring='neg_mean_absolute_error',cv=3)\n",
    "gs.fit(X_train,y_train)\n",
    "\n",
    "gs.best_score_\n",
    "gs.best_estimator_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "tpred_lm = lm.predict(X_test)\n",
    "tpred_lml = lm_l.predict(X_test)\n",
    "tpred_rf = gs.best_estimator_.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.866666666666665"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "mean_absolute_error(y_test,tpred_lm)\n",
    "mean_absolute_error(y_test,tpred_lml)\n",
    "mean_absolute_error(y_test,tpred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.139011273555065"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test,(tpred_lm+tpred_rf)/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model file dumps the models implemented and later implemets the hihgest accuracy model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickl = {'model': gs.best_estimator_}\n",
    "pickle.dump( pickl, open( 'model_file' + \".p\", \"wb\" ) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = \"model_file.p\"\n",
    "with open(file_name, 'rb') as pickled:\n",
    "    data = pickle.load(pickled)\n",
    "    model = data['model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.276666666666664"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.array(list(X_test.iloc[1,:])).reshape(1,-1))[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 79,
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   ],
   "source": [
    "list(X_test.iloc[1,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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