data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"salary_data_cleaned.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>38</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>720</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>34</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>875</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>48</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>850</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>38</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>765</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>29</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>1145</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...      38   \n",
       "1  What You Will Do:\\n\\nI. General Summary\\n\\nThe...      34   \n",
       "2  KnowBe4, Inc. is a high growth information sec...      48   \n",
       "3  *Organization and Job ID**\\nJob ID: 310709\\n\\n...      38   \n",
       "4  Data Scientist\\nAffinity Solutions / Marketing...      29   \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        720                      Tecolote Research\\n        NM          0   \n",
       "1        875  University of Maryland Medical System\\n        MD          0   \n",
       "2        850                                KnowBe4\\n        FL          1   \n",
       "3        765                                   PNNL\\n        WA          1   \n",
       "4       1145                     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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def title_simplifier(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'\n",
    "    \n",
    "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'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"job_simp\"] = df[\"Job Title\"].apply(title_simplifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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_simp, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job_simp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "na        520\n",
       "senior    220\n",
       "jr          2\n",
       "Name: seniority, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seniority'] = df['Job Title'].apply(seniority)\n",
    "df.seniority.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "WA     21\n",
       "NC     21\n",
       "NJ     17\n",
       "FL     16\n",
       "OH     14\n",
       "TN     13\n",
       "DC     11\n",
       "CO     11\n",
       "WI     10\n",
       "IN     10\n",
       "UT     10\n",
       "AZ      9\n",
       "MO      9\n",
       "AL      8\n",
       "MI      6\n",
       "GA      6\n",
       "DE      6\n",
       "KY      6\n",
       "CT      5\n",
       "IA      5\n",
       "LA      4\n",
       "OR      4\n",
       "NE      4\n",
       "NM      3\n",
       "KS      3\n",
       "ID      2\n",
       "MN      2\n",
       "SC      1\n",
       "RI      1\n",
       "Name: job_state, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fix Los Angeles\n",
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2548\n",
       "1      4857\n",
       "2      3462\n",
       "3      3883\n",
       "4      2743\n",
       "       ... \n",
       "737    6162\n",
       "738    6142\n",
       "739    3082\n",
       "740    1642\n",
       "741    3711\n",
       "Name: desc_len, Length: 742, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Job description Lenght\n",
    "\n",
    "df[\"desc_len\"] = df[\"Job Description\"].apply(lambda x: len(x))\n",
    "df[\"desc_len\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      3\n",
       "4      3\n",
       "      ..\n",
       "737    3\n",
       "738    3\n",
       "739    0\n",
       "740    0\n",
       "741    0\n",
       "Name: num_comp, Length: 742, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Niumber of competitors\n",
    "df[\"num_comp\"] = df[\"Competitors\"].apply(lambda x: len(x.split(\",\")) if x != \"-1\" else 0)\n",
    "df[\"num_comp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hourly wage to annual \n",
    "\n",
    "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.hourly ==1][[\"hourly\", \"min_salary\", \"max_salary\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                          Tecolote Research\\n\n",
       "1      University of Maryland Medical System\\n\n",
       "2                                    KnowBe4\\n\n",
       "3                                       PNNL\\n\n",
       "4                         Affinity Solutions\\n\n",
       "                        ...                   \n",
       "737                                      GSK\\n\n",
       "738                               Eventbrite\\n\n",
       "739           Software Engineering Institute\\n\n",
       "740                             Numeric, LLC\\n\n",
       "741             Riverside Research Institute\\n\n",
       "Name: company_txt, Length: 742, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.company_txt\n",
    "#we should remove the new line character"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"company_txt\"] = df.company_txt.apply(lambda x: x.replace(\"\\n\", \"\"))\n",
    "df.company_txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>36.188679</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>1006.260108</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>3877.239892</td>\n",
       "      <td>1.053908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.012102</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>388.559482</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>1525.078365</td>\n",
       "      <td>1.384239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-10.000000</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>135.000000</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>33.000000</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>735.000000</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>37.000000</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>975.000000</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>3739.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.000000</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>1225.000000</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>4746.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</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>2540.000000</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>10059.000000</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    36.188679  1837.154987    0.032345           0.022911   74.719677   \n",
       "std      8.012102   497.183763    0.177034           0.149721   30.980593   \n",
       "min    -10.000000    -1.000000    0.000000           0.000000   15.000000   \n",
       "25%     33.000000  1939.000000    0.000000           0.000000   52.000000   \n",
       "50%     37.000000  1988.000000    0.000000           0.000000   69.500000   \n",
       "75%     40.000000  2007.000000    0.000000           0.000000   91.000000   \n",
       "max     50.000000  2019.000000    1.000000           1.000000  202.000000   \n",
       "\n",
       "       max_salary   avg_salary  same_state         age   python_yn  \\\n",
       "count  742.000000   742.000000  742.000000  742.000000  742.000000   \n",
       "mean   128.149596  1006.260108    0.557951   46.591644    0.528302   \n",
       "std     45.220324   388.559482    0.496965   53.778815    0.499535   \n",
       "min     16.000000   135.000000    0.000000   -1.000000    0.000000   \n",
       "25%     96.000000   735.000000    0.000000   11.000000    0.000000   \n",
       "50%    124.000000   975.000000    1.000000   24.000000    1.000000   \n",
       "75%    155.000000  1225.000000    1.000000   59.000000    1.000000   \n",
       "max    306.000000  2540.000000    1.000000  276.000000    1.000000   \n",
       "\n",
       "             R_yn       spark         aws       excel      desc_len  \\\n",
       "count  742.000000  742.000000  742.000000  742.000000    742.000000   \n",
       "mean     0.002695    0.225067    0.237197    0.522911   3877.239892   \n",
       "std      0.051882    0.417908    0.425651    0.499812   1525.078365   \n",
       "min      0.000000    0.000000    0.000000    0.000000    407.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000   2801.000000   \n",
       "50%      0.000000    0.000000    0.000000    1.000000   3739.000000   \n",
       "75%      0.000000    0.000000    0.000000    1.000000   4746.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000  10059.000000   \n",
       "\n",
       "         num_comp  \n",
       "count  742.000000  \n",
       "mean     1.053908  \n",
       "std      1.384239  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      3.000000  \n",
       "max      4.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.hist(x = df[\"Rating\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import seaborn as sns\n",
    "#sns.histplot(x = df[\"Rating\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kAnnOXdLAW63fYW71fkOv3mfwlbskFchwl6QC9ey0TERcDHwM2AD8Y2be1Kt9SSrDap0eGQQ9eeUeERuAvwfeBJwLbI+Ic3uxL0nSz+rVaZkLgIOZ+Vhm/i8wAVzao31JkuaJzOz+RiP+CLg4M/+0evwO4Dcz87qmZcaB8erha4BHul7I2rcReLLfRfSJvQ+uQe6/273/Smae0WpGr865R4uxn/q/SGbuBnb3aP/rQkRMZuZov+voB3sfzN5hsPtfzd57dVrmEHBW0+MzgSd6tC9J0jy9CvdvAFsi4uyI+HngcuCuHu1LkjRPT07LZOZsRFwHfIm5j0LuycyHerGvdW6QT0vZ++Aa5P5XrfeevKEqSeovv6EqSQUy3CWpQIZ7D0XEdERMRcT+iJisxk6PiHsj4nvV/WlNy18fEQcj4pGIeGP/Kl+5iNgTEcci4kDT2Ip7jYht1X+zgxHxdxHR6mO1a84C/d8QEYer478/It7cNK+Y/iPirIj494h4OCIeiog/q8aLP/6L9N7/Y5+Z3np0A6aBjfPGPgTsrKZ3An9TTZ8LfAs4ETgbeBTY0O8eVtDrhcDrgAOd9Ap8Hfht5r4r8UXgTf3urYP+bwDe22LZovoHNgGvq6ZPAb5b9Vj88V+k974fe1+5r75LgVur6VuBtzSNT2Tmi5n5OHCQucs4rAuZeT/w9LzhFfUaEZuAX8zMr+bcs/0zTeusaQv0v5Ci+s/MI5n5zWr6eeBhYDMDcPwX6X0hq9a74d5bCXw5IvZVl1sAqGXmEZh7YgCvrsY3A//dtO4hFn+SrAcr7XVzNT1/fD27LiIerE7bHD8tUWz/ETEMvBb4GgN2/Of1Dn0+9oZ7b70+M1/H3NUx3xMRFy6y7JKXbCjIQr2W9t/gk8CvAluBI8CuarzI/iNiCLgD+PPMfG6xRVuMrev+W/Te92NvuPdQZj5R3R8D7mTuNMvR6k8wqvtj1eIlXrJhpb0eqqbnj69LmXk0M1/KzJeBT/OT02zF9R8Rr2Au3P45Mz9bDQ/E8W/V+1o49oZ7j0TEyRFxyvFp4A3AAeYuw3BltdiVwOer6buAyyPixIg4G9jC3Bss69mKeq3+dH8+In6r+qTAO5vWWXeOB1vlMuaOPxTWf1XrzcDDmfmRplnFH/+Fel8Tx77f7zaXegPOYe5d8W8BDwHvr8ZfBdwHfK+6P71pnfcz9+75I6zxTwm06Hcvc39+/h9zr0KuaadXYLT6h/Ao8HGqb1Gv9dsC/f8TMAU8WP2j3lRi/8DvMHcK4UFgf3V78yAc/0V67/ux9/IDklQgT8tIUoEMd0kqkOEuSQUy3CWpQIa7JBXIcJekAhnuklSg/wdGh/MeLga0KwAAAABJRU5ErkJggg==\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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": [
    "#we might have to normalize the age, if we want to use it in a regression modell, because its not normally distributed\n",
    "\n",
    "df.age.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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. desc_len.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column = [\"age\", \"avg_salary\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ep4GfAG1O/UTmhzPz8xHRB9wJ/EZ1jj7gU5n5+Zn7p0mnuFyjOSki5gErOPUrZgDfBn49M98I/CnwF5n502r7c5l5bWZ+bpJTLQbeDLwD2FT1vQsYAF4P/AGnflRH6gln8pprFkXENzgVwruBHVX/JcBoRCwFEpg/xfP9Y2aeBL4VEa2q783A31f9P4yIsbqKl6bLmbzmmqOZeS3wGuBlwK1V/0eAscy8BvhNYOEUz3d8wna86FXqOUNec1JmHgL+EPhQRMzn1Ez+v6vd75tw6DPAxdM8/VeA346Ivmp2P9hdtdL5M+Q1Z2XmHuCbwI3AXwIbI+JfgXkTDhsDrqpuu/zdKZ76C8B+4DHgE8BXgUO1FS5Ng7dQSg2IiJdn5uGI+Hnga8CyzPxhr+vS3OMHr1Izvlg9aPUy4CMGvHrFmbwkFcw1eUkqmCEvSQUz5CWpYIa8JBXMkJekgv0/GioqvDsWIkUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column = [\"Rating\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>0.163542</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>0.078875</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>-0.012215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>desc_len</th>\n",
       "      <td>0.163542</td>\n",
       "      <td>0.078875</td>\n",
       "      <td>-0.012215</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  0.163542\n",
       "avg_salary  0.019655    1.000000  0.013492  0.078875\n",
       "Rating      0.021655    0.013492  1.000000 -0.012215\n",
       "desc_len    0.163542    0.078875 -0.012215  1.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"age\", \"avg_salary\", \"Rating\", \"desc_len\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\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",
    "\n",
    "sns.heatmap(df[[\"age\", \"avg_salary\", \"Rating\", \"desc_len\", \"num_comp\"]].corr(), vmax = .3, cmap = cmap, center = 0,\n",
    "           linewidths = .8, cbar_kws = {\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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_simp', 'seniority', 'desc_len',\n",
       "       'num_comp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#look at the categorical variables\n",
    "df_cat = df[['Location', 'Headquarters', 'Size','Type of ownership', 'Industry', 'Sector', 'Revenue', 'company_txt',\n",
    "             'job_state','same_state', 'python_yn', 'R_yn',\n",
    "       'spark', 'aws', 'excel', 'job_simp', 'seniority']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph for Location: total = 200\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 = 198\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 Size: total = 9\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 Type of ownership: total = 11\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 Industry: total = 60\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 Sector: total = 25\n"
     ]
    },
    {
     "data": {
      "image/png": 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GkswVRrJSkLQUIRAOt/1TANs32b7f9gPAd4DNWrW1fZDtTW1vOn/+/JkbdJIkyWLAjK8UJAk4BPi97S827F+z2BsAtgUun+mxJcNnr2NeXPnYL7/qhCGOJEmSVoxCffQM4PXAZZIWln0fBnaQtDGhProOeNsIxpYkSbJYMwrvo7MBtXjrVzM9liRJkmQyGdGcJEmS1EmhkCRJktRJoZAkSZLUSaGQJEmS1EmhkCRJktRJoZAkSZLUGVmaiySpykt+vnPlY3/9ikOGOJIkGX9SKCRjy1bHfqrScb/a9qNDHkmSzB1SfZQkSZLUSaGQJEmS1EmhkCRJktRJm0KSNPDSn36t8rG/fOW7hjiSJBkNKRSSZJpsfcz3Kh/7i1e9aWjjSJJBkEIhSUbE1kcfWem4X7x6uyGPJEkmSKGQJHOIVxxdPcP8z1+9Vf31tsecXanNsa/aov56u2OurNzXka96fOVjk9lNGpqTJEmSOikUkiRJkjqpPkqSZGDse+wN1Y/d9mH114cf889KbXZ81fyex5T0Rq4UkiRJkjq5UkiSZE5yyo+qrS4Anv+6iRXGxQffXKnNk9760J7HNA7MOqEg6cXAgcASwMG2PzPiISVJspjztwP+UfnYR7x3DQD+8cXq3ltrvGf2eG/NKvWRpCWArwMvAR4P7CBp9nxbSZIkY85sWylsBvzJ9rUAkn4MvAKoLnKTJEnmMDcd+NtKx62+5zPqr2/+WvX4lYe+a6uO78+qlQKwFvC3hu3ry74kSZJkBpDtUY+hjqTXAC+y/day/XpgM9u7NxyzK7Br2XwMcHWb060G/KvHIfTTZlz7mu3jm8m+Zvv4ZrKvHN/c6atTm0fabu3fa3vWPICnASc2bO8N7N3nuS6ciTbj2tdsH19+F/ldzMXxzYXvYrapjy4A1pe0rqSlgdcCx414TEmSJIsNs8rQbHuRpHcBJxIuqd+1fcWIh5UkSbLYMKuEAoDtXwHVTentOWiG2oxrX7N9fDPZ12wf30z2leObO331Nb5ZZWhOkiRJRstssykkSZIkI2RshIKkJ4yo33mSVhpF30kyF5C0qqQnjnocSTXGRigA35J0vqR3SFqlSgNJn5O0kqSlJJ0q6V+SdqrQ7kel3QpEtPXVkt4/zfF36m8LSW8ur+dLWndYffWKpA3Kd3d52X6ipI+Oelw1JL1G0oPK649K+qmkTUY9rkZKfE7XfU3vS9JOkj5etteWtFmXNnuW/60kHSLpIkkv7NJmCUkvl7SHpPfUHhU+0xmlrwcDlwCHSvpit3a9IukZkk6W9AdJ10r6s6RrK7RbT9Iy5fWW5fOt0qXN8pI+Juk7ZXt9SVtX6Kvn+4ykd0latdu5y7EP7vSoco5J9OPHOlsfwPrA/sCfgB8BL+hy/MLyvC1wGPBg4JIK/dTa7Qh8EVgKuLRCOwE7AR8v22sTwXmd2uwDHA/8oWw/DPhthb5WBw4Bfl22Hw/s3KXNZcClTY/fAF8CHtKmzZlEepKLG/ZdXmF8zwBOBv4AXAv8Gbi2w/Gv7PTo0O7S8rxF+SyvAM6rML5Tq+yrcJ4VKxxzUZV9Te9/k8gT9vuyvSpwQZc2l5TnFxGu3htV6OdXwE+BT5T/4j7APhU+08Xl+a3AJxp/iwptVy3/qWfVHh2OvYrIlfZQ4CG1R4U+FhKONo8Grin/8V91afMT4AO1/zewHOVe0K2v8lz5PgN8iriPHQm8mGL/bXPsnxuuoeZH22uq3WPWeR9NB9t/LLPUC4GvAE+SJODDtn/aoslS5Xkr4Ajbt8bhXVlK0lLANsDXbP9XUhWL/TeAB4DnAvsBdwDHAE/p0GZb4EnARQC2b6jNfLvwPeBQ4CNl+w/En/qQDm1+DdxPCFSIOBGA28v5XtaizfK2z2/63hZVGN8hwLuBBaXPbrTqu4aJG1craud+KfBN2z+XtG+7E0laFlgeWK3M1GofbCVCIPfKlYTwb9XXS4j/3lqSvtLw1kp0/w6fansTSRcD2P53ie3pRO2zbAUcavsSdf/DP9x2P6qfJSWtCWzHxH+wK5LeCuwJPJy4cW8OnENcM624zfav+xjfAw4X+G2BL9v+au277MB6treXtAOA7bsrfH/Qx33G9kclfQx4IfBm4GuSjgQOsX1N07ED1RyMjVAoOss3Exf/ycDLbF8k6WHEn6rVTeN4SVcBdwPvkDQfuKdCd98GriOWxWdJeiRx4+xGPxfyfbZdEzpFZVWF1WwfKWnv0tciSd1uvs+w/YyG7csk/db2Mzosd/8laT3ixoykVwM3VhhfTxez7TdXPbaJv0v6NvB84LNFZdBJbfo2YC9CACxg4kZ6OzEzn0IHdYqAFTv0dQMxgXl56avGHYTA7MR/FVmFa9/7fGLC0YkFkk4C1gX2LpOLbm1+LemFtk/qclwz+xHxRmfbvkDSo4A/Vmi3JzFJOtf2cyQ9lliltON0SZ8nru97azttX9Sln/+Wm/sbmZhwLNXheID7JC3HxHe+XmOfHejrPlOu+38A/yAmCasCR0s62fYHWrUpE5n1gWUbznNWhTFO6ngsHsBZwBuA5Vq89/oO7VYFliivlwfW6LP/JSsccx4RlHdR2Z5Pg9qlTZv3EULoWmAXQsDtXqGvM4ildK2vzYEzu7S5hBBcte3NmFA5tBwn8CjgFOAu4O/A2cA6Fcb3GeDzRGqTTWqPDsfvVJ7f0+rRod3yhIpp/bK9JvDCCuPr+h03HHsP8Eka1CsNj/8M4r/Tos2OhAroeuDTRA6w7bq0mVe+51XK9kOAJ3Zpsy1wJ3FDu50QWLf3Ot4ePtcF5XkhsEztdYfjT2/xOK1CP48ntAk7lO11gQ91afMCQl36T+BwYmK4ZcXP1dN9BtiDmCicCLwGWKrhN7ymTZu3Eirgf5fv4e4q30XzY7GMU5D0yk7vu7WqqbH96sD/Ag+z/RJFzYen2e6kmkHSjsD2xIV5GPBq4KO2j2pzvIhl9GOJZaSI3FAnd+qntN0E+CrwBOByQgC92valHdo8BfguMbsVcRN4K3AF8FLbR3ZouwIwz/Yd3cZWjj+9xW7bbqkmkPQ229+WtE+r9223nU1K2oIQCoeWWdqKtv9cYYxPB9ahYUVt+/stjvsdIUQWtHjvb7Yf0eb8R9reTtJllNln02fqqLYps+jnEb/VqbZ/3+X4bYmbxG1lexXipvazDm2uJdSkl7mHm0X5nndh6vf3li7tjiVW/HsRKqN/EzfEzvme+6DM+te23S6pZqs2DyEmWCJWM22T1E3nPiPpE0RGh7+0eO9xrX7r8j+qrbI2rq2ybG/faRxTzjMuQqHNhXUbsTz/lO1bGo49tMOpXOGP+2uKvt72RpKWJGbS/1NhnL1eyAtsP7nbedu0XZLIJCvgatv/rdhuZeK/8Z8Kx+5JfBd3AN8hBN6H3Lu6YSgUIbIp8BjbGxR14lGerCZr1e4HwHrEjLWmdrPtPVoc+xjglsYbhKQ1bP9D0uq2b2rTx8McNqJHtnq/1Q2hcXy2X99tX9P7C21v3LTvYttP6tDmROAltrupmZrb/Y4w7E+yGdk+podzPBtYGTjB9n1tjlmZWJE9q+w6E9ivJvg6nPtlwBeApW2vK2nj0u7lHdr0JFT7vc9ImkcY5Xtys5d0ge2nSFpIrPjvbfWbd2NsbAr0YCR1//rpGv3o65G0OXCF7a+X7QdJeqrt8zo0O1fSU2xf0MsAJb0TONwld5TCV3wH29/o0GYZ4FWU2V3NGGZ7vw5dvcX2gZJeRHiAvJkQEh2FwjQu5mWBnYENmaw3bSfI+zXUbwo8vsrsuM1M81eEOqylQCj8ghCin+p0M2/Dho0bxb7QbfLQypbS7R5wI3BGmQg16uy7uZcub/uDXY6pI2kl27drsgvlZeV5ReDWNk2/S6yEtyvbryf+fx1n6cC+hHr0DADbC9Xd1Xsf28fWNmz/p0w6ftbq4H7vM7YfkHSJpLVt/7WHptcXQfUz4GRJ/ybsVj0xTkKhHyMpkl7K1BtMp5sgwJ1lGVkzOG1OrEq68U3iJlA/T4t9zTwHeJukv5TjFUPs6hGyS034QN2ovQvhAdWOnxOfYwHVDGjQn0cL9H8x/4BwQ3wRYczcEei02urXUH85sAbVjOatqPIdLC3pjcDTW6kaWqkXykTkw8Bykm5v6Oc+uue6uVARK/B14r+7O5MN3K2ouTYuXR5V+YWkrRy5zKrwI2DrMh4z+fszYbtqxXq2X9Ww/YkyU+7GItu3Nf1Vu00A+hGqQF/3mTWBKySdT1z3tTZtVzK2ty0v9y3q2ZWBE6qMr5FxEgorNs66FYE8Nc+Plu59kr5FGH2eAxxM6PjPr9DXewgj33qSfkvR11dop8aZZ5kRdPsNXlLhvK2YJ6neX5lJdruoH277xT32049HC/R/MT/a9mskvcL2YZJ+RBjj2nGkwvtolSIU30KoubqxGnBluSgbZ8htL8omqvSxGyHUVmGqy21LN1vb+wP7S9rf9t4Vx1Jjd+BjhGuyiNXcOzs1aLTVFLXGirareNrtCXxY0n1ATW1p2y2j/21vXZ57da+8W9IWts8uY3wGYWDtxuWSXgcsIWl9wrD7uy5t+hGq/d5nOnlcteun1Sqrd/uAh+RFMNMPwsByGTGruY4IvHoKsAJtvDKYCGyqPa8InFSxvyUJyf8EimdAhTY/Jf58S5XHnsDPKrZ9KOHvvjZhHOt2/OeBowj7xXOJIJgDurQ5CPifHr/3nj1aynHnAFs0bD8DOKdCu/PL81nlu1+NLgE6hNfI5wkdcseAxoY2z271GNJ/t2NQYYd2lYO8pjG2HxFxEysQK7QbgfcPoZ9NOj06tNuY8Jq7DvgLcDGwUYX+lie8ti4oj08By3ZpswLhNXchIQz2B1ao0FfP9xngs1X2Nb1/HaFC/xdwS3l9PaE6fXLl32LQP+6oH8SSaZWKx55Xns8l/NKXAf5Yod07G/soF+c7KrR7KPBj4GbgpnLBPbRLm5cT/t13EgLvAcIu0a2vecDbgaOJALm3UVziOrS5klBDXE0I1cuoFqm9FvD0Xm5O07iY31q+72cRbro3A28b0n/pkcDzy+vlgQcNqZ+licnC0eWxO10mGvTgfkgEZ0FExh/X/OjSz8Ly3FP0fsN/9wvlsXWXY2vupOcQK4vajfe/RKxDt75WAlYaxu8zgN+35/sMraPcO37vwLeIcsa17ReW32xzKkTx19uN+gsb4Be/cvkCLiyPA4CVu7T5GLF0fxURIHIj8MkKfS1sse/iIX2uS4jZ98Vl+znAQUPq65GtHl3afLbc2H9VbjrHd7vRNLXv6WIG1q247+zyfAfhbFB7VPKzJ9wpL6D4hBMBQT2nuaj4mQ4mXJSfWx6HAgd3aXMZoZ9eWLYfC/ykzbFPLs89r34Id+SliFXns2v/yQqf6TPAqYS67i1EQOlnKrT7MQ2rVWI1+L0Wx/UVt9LQ/mSmTuxO7NJmA2I1fRJwWu1Roa/K9xliIncZEffTmG7mz8APu/QzpfxmbR8V0nHUHuNkU+jZcGn7k+XlMZJ+QSwfqxiM+9HX9+u7/V/btyiysc6zfbqkz1bo6xmEh8UjS181A/UUg13N84O4YfbKNoS7ZyXDtKSdbP9QTVHADZ5O3bxajmGqYf5omjxvbG9Rnqt4GrXinYRq5rxynj9Kemif5+rGU2xv1LB9mqRLurS5x/Y9kpC0jO2rimvsFDwRP7Gx7QMb3ysuxWd26Kff6P2tSn8PlH4OI1aDH+rS7rG2a/pwbF9e3EWbqTkMtPp9XWF8q7nB5drhiNHt9z2KmI0fTLXULDU+V66P+n2G9hHNPyI8Kfdn8nd1h+12Hlg1bpX0QUKwQsRE/bvcnyq7FI+TUOjZcNngtvkfh0/v8pLe4Q5um4UTCQPmt4g/4G5Us/L/nPDdPoXqf6r/SFqR0KEfLulmBp9bqF/PDwgVzlJU91bq62JWxHdsCKzc5KmzEg0eHS3a9ezPX7jX9n01YVUcAqrcbPrhfknrueS0UaSE6Pab9eN++EbgwKZ9b2qxr47trxCRvzX+Iuk5XfqpsQoTrqQrV2zze0kHAz8kvu+daOFdZvvb5eUptn/b+F6ZEHXjgUaXzyLsuv2+i2x/s8K5mzmHMpEpwuFeSRfRwuuwTEpvU+Rw+0e5L20JPFHS9905duh1hJv3z8r22WXfEkxMlrsyTsFr5xAGsEYvhC/YflqHNgvdYzBPOWYeoaOvBaGdRCz3O17IrfrrRnGhvJuwEexIXFyHuyEYr02782w/tYd+BDzCvflFI+kYItvmqUz20pkS5NXU7hmtLubmfQ3vvYJYlbyc0IXXuAP4se2WniOSLrK9ScP2koRu9vFdxvc54D9E6pTdgXcAV9qunNytKpKeS8TSXEv8nx4JvNn26RXbP5v4X/zaLQIUFTl+XsdEptgaKxE3uue3aNNyRVej24qu9PkZwk4gwga0t+0fd2m3LKFCqcWvnEUkMmw5s27+fdvta9HuxYQqqLZKehawq+22nmyKRIo3A8cy+b/ecgYvaQ3C3vZD4vtvTK74LduP7dDXQiJWZh1iEnocsSLvGtktaUXb/9ftuLbtx0gobEzoZVcmvvxbgTfZbrsMl3QpYdxsVANdanvDdm2mOcZPAb9zBd9tSY8GVm9x43wW8Hc3ZUps0f4zxAyhcqIw9RE9rfCzn4Ltw7q06/difprtcyqMq+7PT+hnIf4X9xE2mY7unEXw70xDehFC8A/0gin/uT2I+JFa9PlV3dRxvayAyix4XVqoJIj/+5SVp6aRVqThHGsSHoAiDJ3/6NamtOuafkLS0wjnhr2ItNc1VgK2bVLHtTvHakykrDjHHVJWlOP/3GJ3S5VsOf6NxEpsU8LOWeN24DB3TnNxkSN55geAu12yuHaasCrSshxMuA2vLWkjwgnjHZ0+15TzjItQqKFSBc0VfKkV2RXXIfSENTXQ32y/t83x081VcwehPrmX8Kqo6fmn+G4X3eOH3ZSrSNKmRGRlp1TSqMfcQqXN1wmjXq/R05VzyEz3Ypa0ARHwt7rtJyiy477c9qfaHN+PP/+MIul021VVMrU2zSugJYj8RG1XQLVVpyM+ZgPCON1yddFwzj1sf6nV+23aPLbYN1oK906TktL+5YT7cMf0E2V1tCVxzX6r4a07gONtt8zIOt3x9YOkV7mH9B6lzXnAl4m04y+z/WdJl7tD6ovS5tWEo8eTyr6ObVqeZ64LhXbL2xqdlrm9qoEkrWn7RvWRq6ZXOv2Yki5zhTxLffR5JeFhUTl6Wj3mkOn3Ym5ofybwfuDbnf74A7g5bU1kPm021A+89KqkTxMr3J8wOXp1yhinswKStAB4JuFpcy4xe73L9o4d2vQksCQdZHvXfiYlDWN8LnBGw+97abv/oKRH9nLdDWB8TyAyrDZGJk9JktjUZg0iJqJyAs1yzG7ECuYIRQqO7W1/pkOb82w/tXFFIemSKqumSecZA6HQcnlbo8oyt48+3w0cafvvfbStlO9c0p9sP7rNOdq+13RcT6H1/Qi7NhdxV6HV68Xc0K6W9Kvxj9/KNvQd27tM4+L/E+G51lN20H7oc1XX8wqoQSWxO5Fi/nMVVBKVBVZTu2XdZAdota9Fu1Y3tk5CYT5RDa35f97x9+2Hcq/ZkhAKvyKyDZxtu2M2A/WZQLOXFXg5/mjCLf9rhFpsD2BT26/t2LCJOe991M9Nf7pqIELVcZKkWwn3r6PdOfFZrd9eqkpdIGkX25PSJUjameGF1vdz8+snhwzAXUV91+vFXKmoj+1dynNPapkG/kaUXZyJWdNr3EWf3YJfSFrB9p2K3F6bAAd2EbQq6rsdCXsJdL8HPL08N04mTPtKaDV+x1Tvmlb7muk1/cThhMDamphZv5God9ARhcvvj4nJXUf7XAOvJpwqLrb9ZkUK/YMrtOs5gWbjChzougIv7EZ4kq1FRDKfRDhI9IaHEIwzigdxoz2WiWjhY4hcPq2OXbM89xys1XSeJxLLwqsI17hux/cScLQ6cTGcQQTiHUB4SpxDhUJA9BdaX6vRfBkRRb2ILtHThOvr60q79YkaDt+qML6TiBvT74kgqu/SJYy/tHsUU4v6TPnN6LOmc0P7pxBuxnvTQ1BUj//ZlxE3sBuIi/jpPbS9lFAbbVRe70n3IkrPJrxYPtjwXX5lwJ9pDSJm5PdEdtpaqootCQN6t/aN6ScuLK/bpp8AFjT+z8vrjt9DOeaRxApjQenrfXRJH8NEipUFxMRQ3a6PcvwZ9F7wagGxQru4Yd9lffweX+i1zZxfKTRwKOFv/5qyvVPZ94LmA23XZpbvcFN6X0VgWNWUvzcTEYq3ECksutFLwNFNRPbM5xBRnQC/tH1axbHVkoLdpaghcAvhgdIWNy1niz7+bV362Z0wht1LfP8nEnlkuvEQ24dI2tP2mcCZxV7QEdvXAs8vRtN5xOfcnrCDNNJvTecanwb+jxDivWQH7YVPA88s/4OnAp8jbtxVWGTbClfdA8t32dITrEbte27YvpaYibdF0sfbnKudGvJFhMfNwwlVRo07CFtIR2zfRfyfqrr+1ozkNxZ16Q2l7279/IX4vj9XViQfI6Lzl+jQ7EJFbMh3iJv2/zG8BJr9rsCb2Y4QeJUZJ6Ew3/ahDdvfk7RXlzYvYKoAeEmLfZOQ9HbiRjSfiKbdxfaVFcbYc8CRw1e9kr96E78ofX2eSIhlqi11G/u+SFGNbQqacH/cxeG736v/fk8Xs8Kr7J3E0vjnxGrhncQf/hJCjdA49unWzHiw7RdO8xzdWGT7KgDb56lanYcadxR1xE7AsxSeQi1rDEv6su29JB1Pa3VpJ5XEnQ2vlyXUNG1TlTtckQ9Tjx43ko7r9H6HMX5KUZvjvcQqdSW617eu9bkOcdPcnggWbFn3uGEMNVXMtySdQKRnubRTm9LuouJg0UvBq36yuLaiSgr3yQ3KEmPOI+kUIgDoiLJrByIA6Hktjn07oWtbD/hTw1sPAn5ru239hdJ+f0Lts3Aa460FHLWtKjUoFMVzuqbw0GRPrlr204fYflGLY68kBOhxhGpg8pSmS0i+wrvnN8AjmLiYP2G75c1B0s+J5G/nEN5iqxIz+D1b/Q6afvDVZ4i8NkOrICfpeibPpt/TuN1pjMWj5XVETePfSFqbqAI2xRNG0pNtLyj/uSmUFUTVMS9DuDxO+U+0OLayo4OkfxJ2nCOI1CLN/6fKY6yCwn1zKSJ78JFl1VSl3VpMeKTVxjbFUaQc2zbFTmnXKU5heWKiVZuYnEgUY5piqNfklNmT3iLyVHVdOU1qNEZCYW3C6v40Yjb0O+KGMcXwVmYWq9JHfhH1Xyqvr3bTQRVrDDcc3+jJtYjIeXNMmz/iHkTk6aOYutqx2wT09EujR1OZFf+L0AG3zNekzsFX7qD+qLWvHFPSL23G1jjIKU4UKq625fUybghyk7S57XMHNb5WKLznzre9fpfjWjo62N65zfFLECv3HQhb3S+BI1wqB3bop6e4ldJmHvABd3DvbNPus8Sq4koml2ht5379AOFQsrC2q+Ftu305ziWI5HxTIs3bHP9npqanaeynt2uxVyPEOD1oqE9Ab7UKDq9y3KDa9fnZfkAIxm8QM/Gv0sWoSHjBdN3X9P43+xzfYUzNUvndDsdf1Gm7Q7tnVNk3Vx6Nn7vX74QJR4LGx2+IIMKHVGhzBWFHe1eFcU6nVskyhF3in8DuXY49k0hceHHDvssr9HFWH9/91cAyPRy/LeHhdCFhs3h0D22Po0uW52E9xsamoP4ykP6SCQm7LGGIvZqm+rct6LlUXr/tyhL0s4QhW1SfsVauMdzA3kQmyG776th+u6QtgPVtH6pIHfAg23/u0tcTPTVLZaecUxspyk9CfAeN5Sg7fR9fZaobZKt9U+hFVTAo1D3Vh9q8brXdTKs65iJKsH6P1sb5rRteLwJucou0GC3o2dGhqKZeSqwW1iES8XVzCFje9vlNBtkq4ztZ0vuYGn/RSVPQU/JHRz3nY4tTxCuAAxRlfD/i7uqwe4iSwic3ja+jY8AgGBuhQB8ZSN2ftw30USpvGu0+R4S5d6pD3IrKNYYlvYRIdbyWpMaMmCvR5QIrKpBNCSPaoYSe/4dEJbVOzJO0qu1/l/M8mA7/R9udvEJajauWTmN+k11hJTp7mNTat1QVEAnahkm3G7vbvG613UzPdcxt/0WRQ+eZZddZxKqhGz05OihSaz+BEFyfsH15hT6gYtxKC2qTxcZypKZzRuC7gIWSekr+SNzgbyNyHq1Nh6y+DfyyPGaccRIKy7vJvbRX3MHbpum4MxXRv+vbPqUYhbreaCrMDlpxUx8CAXqrMXwDscR9OZMD4+6guyfHtoQ/+kXl/DdU9KI5APidIgoTwpX40xXaVWVpQmWxJJPTdN9OtXra29BDnYgB0u1G8PAiuNXwmrK9Vpe2/dQx35NYgddm7IcrUkV8tVNH7r1WyeuJGfEGwB4NM/9uK8F3EtlOHyvp70QxmrZpOxrG13HV0objmJyhtyMKd/IdCPXWKYTr8IWdW9XHd5ikpYnvA6p5LA2EcTI0V85A2tCmsrdNU7tdgF0Jt8X1isvYt9zC06mp3R1MzOaWJpaid7b6wzd4LjybmPH/jMk3947L6n48TYrb550uuZ+KwWsZh+94uzbn295MEykUViDytXSLCq/ld3kuceGf6mpuvT2h/tNp/Jqwp/SdgngYqHssQtvstGXC811CEIgQkDsTq6GX2j6yRZtLiTw9d5btSr+vGmqVlO1VgR3cvVZJZcr/8zO231/GNc9tHA9atF2e8PZa25ELaX1iEvCLLu16Sf74ALGqOpu47ifdbDutMCRtSdjdriN+q0cAb6yivpT0uNpEsh/ng3ESCj17i/TibdPUbiGlKpd7yPfT4jzbAJvZnhLUI+nQqS3quIutpC8knUvUJP6/sl0zDj69Q5v3EZHMLyC8ud4C/KjbTLJ4i03BPdZz6HD+Zp98Ex5Lp9v+YYX2fdWJ6Idp2I367W9l4tr/T4VjLyMqw91Ttpcl3GC75e1Z6D5qlfSKpNPcR54jST8hVsVvcHgtLUcIu407tOk1+eN0BPgC4HU14VO8rI5whdT2kn5JuLsfB7zV9gZdmkxibNRH7qPsoovLn0oOmR6aDqQql+2fSWpZntAl+EptitG0O6eks21v0bQqgWo3mmUbZ8a2/6/MqDp9hi9IegEx63wM8HHbJ3dqU6gZ+SEyflY18lflCy32PRjYSdITbHcrC9mTqmCa9Gs36okiDPahFLBRRJDv10qtI+l7tt9E2InOk3RseWsbIrVJN/oqWdsHFysC345iskG2m4F6PdvbK4oBYftuNVmrW7AvMRk8o7RZqMhe2pJON/0KLNW4GrH9B0ntghPXAW51KRdg+6UKl/HPE7EsPTE2QgF69xYpxshDiOV0L0UpzpT0YcID5gVEINzxFcbXGMwyjzDQdhMmvXrP7Aj9CUngTkmbuGTAlPRkJrxIWlKMif8hgoD+0EVvXGcaRv5KtFOTlRvIAjrUCi43sNe7op/4AOjXbtQrvdQxfyJEAJ2kM4iqbSICQi+u0Fe/JWt75cGEZ1PjaqFKGpP7yuqgJrTWo7tX0aBST1ThQkmHEK7lENd1u0SYx9Dw+YtA2B7YGPh6eb8yYyMU1J+3yJeJXC3HAdi+RFHZrBsfInSxlxE3sl9RLYVEo8tfTV31ilYHqn/vmWMpAkPSMZ5ct7obewFHSaoFo61JfKetxrc0YeDbhnDVmwc8sswod3OPUdpVjfzTxfb93SaE5Zi7JK1cVchNkwuLOuNn9GA36oNe6pgvr3ARrn1ZZ5dnNU4cOvBB4tp4eznHSfSYZqUK7j+dyT6EkHqEpMMJb7k3dWkzqNQTVXg7YUTfg/j+ziJijlqxVO1/Kul/CcePF9i+q6wOe2JshAJ9eovY/lvTTaKrO6vtB4DvKNzoNiTKY3adMfT4B+7Xe6bxw/QUyWj7AkmPZXJZyHYeDx8lDOWPqBn3FF5HXycCdT7WqS+1NvJ3TXlcFbUO/V+VqLncMUq2MJN+4isR7o6NuZZaznYlfZUOs9Mu47tb0haeXMe83UpwLcJDrGWULF1SZ5dr5JvlMTTKNbhnk0H7gG42N9snS7qIiXKce7p7+vJWyR8/2bFFj0h6l+2v2b5X0onuko6lcE2xQT6cuI42LALhcf2MYZyEQk+BJYW/KVJBuMx896BDsq+yFP6q7SuKBD6HECIPlvQ+20e0a1vaf47IIHo3MUvZCNirleHTE5lDv+fevGc6+bFX4TFMVJZ6kiTcOjXGKwkjed0zyfYdkt5BVPXqKBSYLOgWETaGnpa5XVjA5ND/mqH5DGIW1o0Z8xPvcbJQyaWxDbsB32+YPf6bqD/Qij/1acCdbq2SXukpCFLhSv4f27fZvkXSXcSEcgNJX+uywn2pm5I/SnoNHYI7m/o+kohw/iXhjNFqFf8WIl0PhOqoa5AlsZrfjqi+dy1wiqSbidT8HY3dLcdZYYI7q2mYOa1Fj94iiujbA4HnM7HE3dP2LW2Ov8L2huX1XkQCsm0Uycl+3c2zouaRIWlb4o/4bsIbZqMWx7bMaNnwudp5PNzPRCnN5pKNHQ3N6qGylDpXwxpKudCZRj1WvppGPw8n7ETPIH7zs4n/4fVD6q9ex1zSXra/3OKYi7v9n9uce8ZK1pb+LiGuw8YgyDPb/f8UifC2dcTTbEzED+xP2FD+a/utHfqaEm3eal+H9k8hVqo7EOVkp2QWbjzfNH6DZYH/Af7oCh5mzYzDSqE2c1pAj94iZbnYNdClgcZZxAsoMwTb/+juuABMpDbeinAvu7VDu1beM11xj5G/TfRSWcplqd7qAzzQroN+hd1Mo/4qX/XLoVSsBdIwvvmE3r65XnDX2X3NS6XwHsK21kxfgaAutUoGffPvQGMQpIkZc6cgyOVs12xmOxH5tg5QJMlb2KqB+oz4l/RJouZ77bu4lvBeOoHI79SKVcqkcR6wUpNzSiU7k8N9+IJux7VjzguFmtuXInjlHjcFXnVq24c+8j+KlM9/J2Z1O5d2SxKz8m4cL+kqQn30jnJht4yJaOc9M2Tutv2ApEVlNnkz7e0SKxOCuJ3OuR01YfdKIiivpjrbgTC8zxb2pQf3w2nSTy2QWhnKl9JDGcoWtJyVeJopwzVDsRe2vy/pQiaCIF/pzkGQjZ/3uURuL8r/vl2bfiP+X2H7Y1B3Gz2eSOFxtKR2N+0zSz8QxuVG55QqXlXTxyPIwjeMB6HHXrFhe0UiwrlTm4ur7Gt4bwNCyi8E3tSw/0WEMKkyzlWBJcrr5elSWpMIDDua8Kq6tvYY0nf4DWAV4ibzR+Bi4NAh9TUlS2WrfSP8P53X/H+goeTjgPs6hZi1LlEeOxER3p3a9FWGssV5/jqkz/Qn4HEz9FttQbjKQhS+WrfDsQcS7tMHEikxlir71wQu7NLPUj2O6woi19EWRAzOc8t+UaGM56gec36l0EDPgVf0npTtD8CLW+w/kfBEqMLjgHXK6qJG2xoHhBphHyK98XOAN9NmdtcvmgiQe7fDe6unylJ9Ml/So1yKm5RZ+Pwh9VVTAZzhCFJ6pbsvw2fS/bBmXPwS1GuBdItYr1y5TlMDGetvUW2F23y+Zd0l6p8Zir3Q1ISMS9E5IeNehGF2TWALT3jXrUH36oGbSdqXiVio2uqn3Wr6Q8BphNr5cuDZkhYRQv+cbp+tV9p43N3hHnMmzXlDcw1F7dPdPTnw6mu2n9ahzRuI5eOkpGy2f9CuzTTH+AOi2ttCJhfp6GQMX2D7yZpcZOY3tp/Zrk0f46r1UdloNoA+X0zEOdQqXq1DBA5WFa699vd1oqD8RcDm3T6neqh8NQrUY+W6AfR3PuE5cwRwtCdnW211/IH0kbOrj3EtpCRk9ETKmbZOENPs6ypCXbSABtd1t3FMaWorwqX1RcQK/NO2OwaG9jG+64j/w78JgbUKkTH2ZqJsbrvgt0mM00phLyoGXtVw6CMXEDPwKvrI6dJPjYN7ihHsj5LeRdgzHjrgcf1X4efcbEgDhuObb/uEMgN/bNl1lQeYkVTSUwk12z9Lf+9UFKHfk8npktvxGPdXe7qXMX68w9v2RKbRVm/WErfdRvx/h81WwLuAv1CtEHzl2Itpcp9tS6pFJq8w4PM3cpvtX/fTsFzzXymPYXECcGxtYiXphYRm40hCNfzUKicZm5UCgCI3SJXAq+Z2D2WyB8dAkrK16OcoYA8XD42KbZ5CxE6sQgTKrAR83gMsu1hcc59PGAan3KhcIYeLKmZmlPQB258rr19j+6iG9/7XLZID9kNxVXyqJxK5fZGyGiEunC26tD+dmFgcBfzYXcpC9jnG97bYvQLhwPAQ2yu2eL/WtucylD2O7VBgXxfPGUUaiOOIiPk13MF1cybR1ISMOxMxAAO/+Srqdi9BCLbG1U+36O7p9Pl2oib239u5Dzcce6HtTVvtU4sEhW3PMy5CQROpcB9pexdVSIUr6eWES9vDiCXWI4Hfu8QidGi3J6G/vINw2XwS8CF38dgoN5qNgSo1Dprb9pq0r2ckbWT7kj7bVsrM2OSHPUldNUj1lUpMSbHdfI+Yte7m8DKp1I8i/mQ7YsW5EvCTQd10W/T1IGIVszMxszvA9s0djj8TeD/h715Tm1zuAdUAl3SJS/xMUcX+CHiL7d+qpEvv0v5QWgevDSO77wuIFYmI2sYdEzIWz8TDbLcsLNSh3ektdtt9BPn10Of7idn+vYSDStu0/pJOIuK0flx2bU8IyxcTmW0rXVvjpD46lND11WwI1xOzvE750T9JhLmfYvtJmiiK0Y232D5Q0osI4+ibS//d3Pj2rXDuSaj/pH39sFdtGd5IqwtZ/WdmVJvXrbanw9mKCllrENHTzysC4dl0SfJXw/Y/gK+Um8EHiFXUQIVCMQ6+h4iXOQzYpOb40IV+y1AiaUVPpEd/tO0/tTjMijxgawP/C7zEEcm/DJOj0dvReN0tSxRjuqHNsX1Rbu6rFiFwsiIrwZsk/d522xQPjtxW8yUt7R5ydNkeuppO0iuIG3jtu/oC4Rb+fOLa6sTrCKeUnxHX0tll3xJMJEHsyjgJhX5S4f7XEeo+T9I826crEut1o3berQiXzUsq9IX7iz34Mv0l7euHXi7kfjMzdkrDMbBlq+23KWpH3wfcBBxd1GQAXZMEKvLGbE84H/yLmH21Uvf0jaTPE/EaBwH/494K+vRbhhLgt5L+TMz+9yecH5p5GxEEdh9R6vYDRchuT4UgUduTfn9JRxCutwNB0muBbxOZff9ITLh+QARtVQlIvY74Ho5jcm6rKbmGJO1k+4eanK+LTm2a2rdSZd1GuMD+vGn/J4lrqKYOP4KwI/4PETNzULt+HMG4u7d5u5Xgb8k4CYV+UuH+R1FI5iyizODNVJttLShLtXWBvcvSv1MUbyeXQLtLQI/7SNrXDz1eyP1mZtxI0u0Ud8jymrJdpXZtZVwSvxWeIml+zfBcgUOJC/IFDbO2QfNe4j/6UeAjql6CEnooQ1lUq/fZXkSceKOiqz4CeG2rNo6Snc9vOMfLicnJsVSrp9DM+sSqY1B8FHiy7T8p0q6fA7zW9rEV299QHvPovvKpGa/7SUcP8b9+LBM5kl5FxDDsLOk5tvdqOHYpgHIN/RQ4zfany76O7sPFzvQ+wnbWWD6gN/WWZ0GwxHQeRGUwCJ3imURU5+HETGDLLm1XIJZWSxIRoXsQBr5ufdayeq5Sth9MJOYaxuc7mkihfRGRcuF9hOFzJr7bxxCJ0Vq9dyxx4zyZyGe/Rtn/OEpg1Vx/lO/7CeXRU+DSDI5xBeJmtSSwY5tjzqUhSJJYAV5K3PR/OaRx3UFk9K09/wF41QDPf1HT9lX9fn8z8BudBizZsL1k2bcEcGXTsfuW7+pG4BImPCPfAJzcpZ9LiGSPmxHu108mBGdP4x2HlcJ8iLB8hXtp5VS4nqg7uxIViuQ08DRgoe07Je1ECIgD+xl8BXYr516LsJOcRDWXyp5pWNGoPP+D9jlwBpqZcbZRbA/fp6FGrqQ3ukKN3CGPayXi91+LUOucUrbfR9wUDm/RbDmHfQRJuwK7EDaWfxaPmoHj/oo89cJDm9Q5KzZuu7tKp2dbnSLR3M5EuvxGb8VuxvO1COFdq82xAvAwh21jkjbD9r6SDiY0FveWMf6c+G3f0KWfRbannap8znsfSbqWDn7T7hAsI+ltwH6E4fEBukco1tpdSiSOeyKhxzyEiHF4ds8fYEzQNDMzzjY0jRq5Qx7Xz4ngpHOA5xFpU5YmJkEL27Q5jVhFP4KwYTymCIQ1CW+dgQd6lX6fyFRVxkDiFDS5vvoUXErtdmh/HpEA8jhX9N5SuJRfRRhv9yPUdb+3vWeXvnYm1F1nEPeYZxHG+yMIt9/3d2pfFUW09c3EKr7Ru/HWns4zBkLhFkKStkzM1kmKFwPV07qtKFq0u8j2Jorgo7/bPqSqm2MPfUynmMp0+u2ppOlsR9IxRBnKXzsKv1RtNyUqttW+mUaTI9uXIIzga7sUOmrT5iGEWuE+4Brgw0yoJj5i+0dDGOd3iUnTFUzY2zpejzOJpPNsP1UN6anV4Ibbps3FDi/FS20/sRiCT3QFnX0RwJsR96nzPQQ7VXEeaKbrJLeZcVAf/WUaf7RrmKg30At3SNqbqHH7zHJxtiyqPQ0ai6l8gnA1Gyrqr6TpbOebhMvwV8pM73u2r6rQrrlG7k60r5E7k9QDMov64c+dBEI57hYaXGklnUPkBvqs29SK0PRTnG9u+/FdjhklPRXYKtS++/9IegKhXl2nYn/zCHvnksCjiyvwQK8r2wPJ4jsOK4W6pO+j7ZMIY+l5VCzMU9qtQSwhL7D9G0lrE0btTont+mY6n7HHfq4mDOYDSzcxWyjeHDsQaSv+BnwH+KHbRL0r/PHfSdw8RamR6x5rTw8aTRRRAiYVUhpoaupiU2mLu7hXF4F6gIebNqZv1GOBrdLmrYSr9ROJ+8aKwMdtf6tLX7XJVvOqaSC1OSQ91/Zpaqq9UKNXld04CIUn2L68z7bnEwEel9HgUupqaR0eCaxv+5Ti8rdEtxlbvwxaNdWhn18Dr3Fv/vIo4gHWt32ookbEirZbLWVHQlGf7ESs7G4gjLFbELEBWzYd+wrg4ba/XrbPJ5wZDHzA9tEsZqiPCnSKWJrjidn0vUwIrZGq30ZBP5Otck95L/G9d8zQIOkTtvdRRJE307PKbs6rj/oVCIVFtlsGpHRC0i7AroQr6nqEd8G3CMPfXOYuYKEiSKlqSdNeUxfPKJJ+SnhE/QB4mSfyTv1EUZylmQ8w2Xd/acK1b0Xi8y1WQkH9V6D7LiGEJ024hkWZjR9pe4GkL9nuVPwGRar23ZlqCJ/yudQmaK2hTUdPJ/qrH185Q4Ptmmp5v+bJmPooDDXnhcI0Ob246B1Pb9b6dxJGo/PK8X9UJNUbGJoc8La8Jgd5DUxN0MRx9FjSlPB5fxIRR4Gj9u2w3RF74Wu2T2v1hpuShxWWtv23hu2zy//hVg03A+dsZV+mVqBbp0K7v3pIabzbcCHwfkkbUq0U5c8Ir8Hj6S60vkCku/81E6ueXuh5skV/GRqOIdzjGzmamNRUZnEXCrUcPXs37DPtS1DWuNf2fbXfSJF0baB6OA/fz7tVn13VZi2YydTF/bBKC13rbcBlbp1wbtXGDdvvatgcWhGgmUD9lchcZPu27vejKVwl6UdMnXANyiV1N+BXnsho/AsiPuZWompgN+5x9UyqmxCrx5cSs/cjiMp4Va/5fiZblTM0SHosETuxctN/fSX6yBIwNkJB0jOIWU2lqkiKGgUfsv2TPro7U9KHiTQNLwDeQW/Bb7OSorfcn6nF4DsJySMlfZu4+e5CVAz7zlAH2hs7E0vwWobLLYkI3w0k7eepBZXOk7SL7UmfQRHTcv6wBztkPkeo0HqpiNZvBbrliJvYsOopvLNm4FXUVj++nPtLxAp+/y7tDyyqz5PokgbbEf+xEPhQ8VjaAfiqpA9WWQ31Odnah6iP8AhJhxPq2De1OfYxwNZEev3Gms53EIGKPTHnDc011EdVJEln2e45uVwRKDvTkK4XOLiHmcOsRNLZTJT+fBml9GeDzrJdu55SF88kxbXyrbZvKturE26qbyVqQj+h6fiHMlEtrHaDeDKwDLBN7TxzEUm/dZeKaS3azMoKdOV6fzKwGvF7HWD7h+W9rinEJe1P2DyuYbJHUNuYg+JEsR2RJPG/wMfcoa6JpCNtbyfpMlqnEW9pdC/3l1cTabBrGRrOdZd4KklPsz3tMp/jJBTOs12pslBDm48R0cw/YXKmxJ4iAMcF9VH6sxiybvREMZvliMIv183IoLvQ+FnKtgjV0RM6ufpKei6xJIcost7SLjGXUB8lMiU9yfbFffRV2ZDbD2VV+ikif9BlREqSUwkV0sa2O2bCLULliVVcjCW9mXApXZbQ0R/ZRvXY3G5N2zcWT8UpuBQwatO25wlrEVq7MPU778n7aJyEQs9VkdRnBGCvqqq5gqLO9TOJP/5pRMrez9h+TIc2FwJPr11cikCg39p+ygwMuSuSvkFk52zMUHk9UaDmF56BHPmzhX5cFtVnBTpF5btDmOru3U/6+HZ9LFFeLkmoi15ErO7eXWFW/ROipnuVm/sDxOeo2S8m3TQHJeia+ux5wirpd0Td7mZtSbs09q3PM0ZCYcaqIvWjqpoLaGrpz5WBz3VZIi90U5k/dUkXMNNIehURl1ArPHLMXFf1zSTqowJdPyv3mUTSGUQQ2gV0qYKo6Qfy9Wzg72fC2upa7IexEQr9oghXbzasdoxMnu1/+JlE0snAV2sGN0Xw1x62Rx6zUXSzl3bTLy8uSHo48FXCaGlCQO5p+/qK7f+HiOPY3vbSXY59HVFDoashdxS0u9EPciXT0Nef6N3A308/nwJ+Z/tX0zrPuAgFRRqDfYgMhBBZIfdzKQTTps0+hDfK44FfAS8h/NJf3aWvGS/gPUwU1afa0ml5XFzlDifqXItIIfEGty7xOOMUz429G1wXF1uKAP8Rk/M57Wj7BR3a1CrQvZqom/FjYqXVUe3SjyF3XOnTwN8yTXanCasitmkF4p70X/qMaRonoXAMcDlR5xbiD7mR7Zb5QEqby4gU2Bc7qlGtTngRvaxdm9JuxlRVM4GkfxI38yMId75JTulVZk+KCnbykFJ99IsibfRTCHfSRt3swPXAs502qr6OKgdJ5xL/i6PcQ2bPXgy500HSum4Rxdu8r0W7xuDQpYmI4zt7vYFWHGM/Bv6vNmwuS2RLuKjbhHUQjE2cAhEB2Ohx8AlJC7u0udtRzH2RonjJzXQPXGMMjZNrAC8g/K9fB/ySqB1Q1aj4UkrhEZUgJ9v7DWeoPdMxr/5ixr8URaGOKNs7ELP/ttjevM++LiFsU10NudOkryheNwWHStqGiNzuCUnLVnDPXYmIaq4cs2F7Uq3loglpjqlpHktLbyX3mI11nITC3ZK2cKnLWzyE7u7S5kJJqxDBVguA/6NDgJKmWcB7tmL7fiJQ5gRFdtAdgDMUwV1f7dRW0reA5YHnAAcTaoZZE+Rl+0y1SF446nGNiLcAXyPiUEwEoXV0V1R/AY0AqxNRzV0Nuf2gAUfx2v6ZpA9V7Pt8Qo12BCGAOqqGbL+51/G04C7CRtOJxmI9yxJCbgHQkwZjnITC24HDikQVEe7+pk4NPFF671uSTgBWsn1phybTLeA9aynC4KWEQFgH+ArVok+f7ig4cqntT0g6oGK7GUHjm7ywZ4pdpdeb8qFMBDQ+hxLQWKHdsOt/TCuKt0mQzCOSOlbVpW8FvAv4Cx2qPkr6gO3PqU3BLHdONNlYz2IeIZSPand8Od8ktbekRxBR7D0xNjaFGkUNhO3bux1bjh+rSmP9IOkwojj9rwlf9MqZZzVRwepcotTjLcDltrvNamaEokLcDDjPExW2JgW0jTvTvDn1HNDY0HZ1wp4DUW1s4Kok9RnF2xSzsYioxf2dVmMsx+7rEmxWnCuOI8permH7rW36eJnt4yW1rFnuDukvmryjFhHFxCp5iTWcQ4T3XU//9Tm/Umin0mnQbbdV6ajPSmOSPkdEU95NqF02AvZyCbOfg7yeMMJuAOyhieRnVbwXflFUcJ8nAofM7Mp9NPTkhXOAmitkq1Th3binuPb+UdK7iIDGrhmBJW1H/CfOIP5HX5X0fg++HsWfFHnI1qGHKN4eVTqbNAiEJxMeXG+x/duiSmrXx/HluZ/cR1vZ/mDjDkmfbd7X9H6j0J8HbEzYdnpizgsFOqt0ul382xCFK3qtNPZC2x+QtC0RHfsaIuHanBQKtudNo+0ny8tjJP0CWLaTG/AIOFNjmLywF2o3J+Au25NUEJJe06X5XoTNaA8ioPE5RCqJbnwEeEpt5q1IwXAKg69H8XMiivcUGgJJu9HjxM7FiLs28L/AS2xfUVSubVXJ03H1Jhw/mgXAS1rsa6RR6C8inEV+22kMrRgb9ZGkZzR/Aa32Nb3fb6WxK2xvKOk7hM/2CZplUbwzhaRliRvtFkwERH2zgkfGjKAxTV7YD2pRwa/Vvob3liDSnLy/1ftd+mrOOTUPuGTQartuLrXd2pWJ3TZEhoLTW13Dkp4KfBq4j4i7WInIs7Q9oSpteaPux9Vb0tuJ6+lRpa8aDyLSx+zU5XMtTaz4Aa52m3KznRiHlUKNrzLVNa3VvsZlVj/FLwCOL37YdwPvKLOgWXETHAHfJ4x7NS+lHQjXuW4z0BnB9gOEOms2qbRmFEkvIYyja0lqrCGwEjGjbNVmSduLJD1ZkvoQoidIOpEJ99ftCZvVoPmFpK3cexTvUuV5K2JGfava1IywfR5RyxkASS8n8iwdS+R3akc/rt4/Ir6n/YFGb6g73CVRp6QtiTit6wgB9AhJb+zVRjrnVwqSngY8nVjmfqnhrZWAbdtI/o7L3yo6QEUO99tt31/cHFey/Y9exj4OtFohzaZVk8Y0eWEvSNqI0C/vB3y84a07iNnxv1u0ucj2JsWbbH3C86Ux+K+rh1nx8KnlnDrL9rHT+Rxt+qhF8d5XHpWieBVZCbYhJnabEV5Mv/CQ0tc0uHp/nsi00NLVW9KDO52nk2CQtAB4nUstbUkbEEJosau8tjRRP3dJJuv3bid85qdg+zBJTyJcFK9wjzlJ1BCC3jS76JgzaUy5WNLmLknzylK7Zz3mEDmEFskLFydsXwJcIulHfagTHkx4lD2XWF2LCsVyFKmzf1UTHpKWk7SOB5xS3X1WKLT9oeJoUpvY3Qm8YpBjg75cvRcwYQttXrp0qwq5VE0gANj+g6SlOhzfesxzfaVQQ9Ij3SE/edOxHyM8bhYATwX2d1OlrS7tRxaCPtuQ9HvCZ7yWW2htwtvlAWLG1rKQyEyhTF5YRz0Eokm6HvgiE0Kg8QblTl59pf2MpFQvbpc7Auva/qTCN39N210DKBVV1NZhstfSwCZ2moard5/9fZf4rWqRzzsCS/boaTVWQmE+kcFxQyb/4adE80m6gvCMuEvSQ4ATpvNnVQlB7+JNMJaoTQGRGlUF9bDQmCUvnA7qobKepBuJCnWtFO12lzQmrQzAw1ArSvomMQF5ru3HFbXuSd2uZ0k/IDQFC2lwR29nT+zH6K6ow1BTuTXeaKukzu45ZUVZlbyTBpUd8I1evSvHQX1U43CiIMXWwG6E29w/2xx7j+27IGogFM+I6VAlBH1cWRK43va9xdD1ROD7tv8zykE1UFslbNqwz/QY+j8mLGf71GI4/guwr6Tf0Dr6+MZuN/4u/FPSyz05pXrHwjd98tRi+7gYwPa/y6qkG5sCj69qQC8qpp6M7p6Gqzf9paxYEjiwtoorgmyZXjseJ6HwENuHSNqzuHqdKaldds/1GnyI1bTdNT+LWoegHzm94c9ZjgE2lfRoQn9/HOFBsdVIR1Xw+CUvnA69BKJVSWXRid2AwyV9nbhWrgdapoOeJv8tNz9DXWPwQOcmQGRUXgO4sYe+LgZ+Lqlno3uvuL+UFacSXlI1F/vliHoWT++l73ESCjUD2o2KrJ03AA9vc2yzQekLPfbVeHxfIehjxAPFdfGVwJdtf7U2axslkr5se6/yek/bBza89z3bbxrV2EbIXkwNRGt3o55Wbijb1wCba/gp1b9CuIY+VNKnCeeSj1ZotxpwpSIiuWrCvkaje70JM5Pr63rCPtGJZd0Qc2X7/4pnZE+Mk1D4VNHtv5fwmV+J8DqZQqugkV5obC9pNbqkHx5z/itpB+LmUpvd9OzxMAQadbJvBA5s2B6p8XuErGP7AmIm+WaoRzSf13xgN5/4bijyHv0v8DDbL5H0eOBptjv59feM7cOLK+bziNXNNhW9Cffto69BZDuthPpLWXGnpE1q9jJFSo5umaKn9j0uhuaZQNLmwGeIDKyfJKz8qxE/2htsnzDC4Y2EcrHvBpxj+4jiiri97c+MeFwXeyIBXv112W4bxTvOtPrcw/ouFNkCDgU+4ihgtSRRzGrgiQiLcfkRTPYi6upIoIoJ+zSNhIL90hRLtQi4zl1SVihqrP+Y0JIArElciwt66XtsVgrlZrQ7U13MBukR9DXgw0RB+9OIHCjnKnK7H0HkUFmssH2lpPcBGyjqXV89aoFQmFduFvMaXtf05ItVPQX1EdE8AFazfaSkvQGKinHgcSKSPkmkyL+GiRt2V0cC9ZawbzoJBfvlaMIh5n4Io7Gk5WsOMq2wfUG5Fz2G+ExX9RGXMj5CgSh1dwiR7KyKoakl6lxJaUnbJ5Xj9qsFbNm+Sm1C5McdDSi0fgisTHhr1H6Yxpnj4rY8voG4ob2c+E5q3EEbFesAuLO4e9cMwJsDw0iUuB1RdbHXsp+VE/Z5etlO+6Vno7GkdwKH1+IhJK0qaQfb3+il43ESCvfY/kr3w6ai6pWUGoVNs65ucbvR1DiAyBo7KbSeLuUQh43tdUbZ/2zC9iWSLid+p5m6sb2H8ERbT9Jvgfm0yTAwTS6nv7Kf85rURbcQq8q2FMHxQaYG/w3Dvbkfo/Eutr/e0ObfiiJTi61QOFDSPoQ07TVIqVIlJWAjSbcTs8/lymvKds8lAMeEgYTWJ8Ol+Nk/RNLSfcyq++nvIkWhmJoqo6+MnRXYn0i1cjm9lf3sJ2FfLRbqpXSPhZou/RiN5zXGURRX3SoxG5MYG0OzpP2J1BXXMDGjdysprj4rKSVTKd/lA0wztD4ZPpK+TWQNPo7JfvYDqy2uyWUupzBon35FdoJvA5fRsJKv4mGoHhP2aaIK3aUu6VsknWn72Z3a9UM/RmNJnydsqt8iNBe7AX+z/d5e+h6nlcK2wKMqzoL6qqSUtGQ3IrR+DxpC60c6oqQdN5THPIZXZ/xlHd4bhk//v3pRG5cgy9Vt/7YIqFrCvmdJWq/EV7Sjl1ioadGn0fiDRD3yt5c2J9FHyvhxWin8BNi9nVtZ07ELiZtYq0pKC20/bqiDHRNKdOyltrsF1SSzCEkPIlbRPRWXmo1I+iKhNjqOCmpjRXXAD9u+tGn/psA+zZHETcdsTVR5ewQTsVCfcEnlMQgkPdf2ae1WXL2stCRtAexg+529jGGcVgqrA1dJuoDuusW3MVFJ6efABxSFdrYn/lxJBWw/IOkSSWvb/mv3FqNHkdUV4Ou2vzbSwcwwxWX4B0RkLpL+RcTXdCr60m9fMxK8BtTiTzZv2NfJJXWdZoEAYPtCSet06sj2L8rL24ho8GHwbMLdvZVwqpKyfGMiTff2wJ+7Hd/yHGO0Umip16uoW6xVUroYOMTj8qXMAJJOIwKAzmeynnrWZowtrpKb2/7lqMcyk0j6HRFMdnrZ3hL4X9s95cap2NeMBa/1OK4/2X50r++V9x9FRMY/jbBfnAO82/a1Ax7jPODVtivlUysef68lhMEthDH8fbY7ZjBue75xuP+lGmN0TEcYzwSSVgDuLquaDYDHAr8ekifMrEYzUCVPE2U8L7D9FE2OLO+rnnKX/pYBXsXUoNWWGV4lHQGc5qb6KZJ2Jlx2t+/Q17nA15nwWHotobIeeL0OSWfZbpk+u8WxDxBqrZ1t/6nsu9Z9VhccC/XRXFRjjAu2z1TUVFjf9inFl3o2RQyfBTyzRDSfSgRxbU94SS1uXKsoMFXzFNuJUDEMkvMJD6eZCl77eTnvAhrUxh3YCzhW0o5MBPJtSrhubtulrWz/oGH7h4pss8PgZEWmgJ8weQXeKifVqwgBdbqkEwivpb6jacdipQBzU40xDpTgmF2BB9teT1Hd61u2p5Vlc1Bootbw7kQ9gc+pKRfS4kIRjJ9gchGWfd2iRvM0+rjY9pMkbUIYY59ABJjNJ1QiU/T50+zv8n40BJKew0TW0Stsn1ahzWeA/xA3XROTi2WI1cO0kwg29dVKWLvT7L+sirch1EjPJTINHOuShaFy32MkFGa1GmNcKZ5cmwHnNagJLhu17riGIo33O4hqYzsXL7NZM75RoMgm/ICHkM5aE2U8IVxflyEE0L3A/YOMiSj9HQR81fZlgzxvm746rao63rBnGkkPBl5DxDb0FHE9FuojqKsxKmU9rFF0zO8HHslkfeTiWJWrX+61fZ9K7qdiUJxNM409gb2JGdMVxVh4+ojHNBJKQNR3KTEKkm4j4nN6yqLZhSWAFZmqvug5r39FtgDeVG7Y95Z+7SHUBre97qDP2Y42Lqm3AZdVcbsvq5Zvl0dvfY/RSqE56+EzgXZZD2ttLiGi/xYwUaeVAV8kY42kzxFL6jcQWWrfAVxp+yOjHFcyFUmXAu+0/ZuyvQVRw3dgN1DNcFpytakR7iHUBi/2svcAa9vetahKH9PgqjrIvn5JeDnVJjBbAucCGwD7Ndk2BsrYrBToIethA4tsf3MmBjfGfAjYmUgz8DbgV82eHaOk/A8+AGzI8JOYzXbuqAkEANtnSxq0CmlG0gVLWsn27USm15niUGICWXPhvR44Chi4UCBcXh9n+yaox318k6g5fhYTzgIDZ7oF62cTlbMeSnpw0bkdL+kdktas7Sv7k4rYfsD2d2y/xvargd8r6vLOFg4HrgLWJYys1wEXjHJAI+R8Sd+WtKWkZ0v6BnCGpE2KYXgQzJSDwY/K8wLCo2xBw2NYdQ/Ws/05SroL23czPCG4Tk0gFG4GNihqoaG6U4/TSqFV1sNftTl2AaH3rv2g7294z8CsMRjNBQYRRTlEHmL7EEWd5jOBMyUtrs4HG5fnfZr2P50KhWmqMEgPnC79bF2eZ0zPD9wnaTkm3GzXo5obbD/8RpGS46iy/eqybwVCXTs05rxNQdIytu8tr3vKepj0z6CjKIeFpHNtb14mDF8hkpgdbXu9EQ8tGRDF1XZ9JqsHB17kSdILgI8S9RROIuquvMn2GUPoS0Dj/ezsTvbRgWJ7Tj+Ai8rzD/po+05glYbtVYF3jPozzYUHofM8E3h0w75rRz2uFuPcmqjC9gTCaLcAePmoxzWi72JPIombgIOJanQvHPW4pvmZ3krYs/5dft+7iYjlYfX3EKKewtZEffZHztDn3ILI1zX0vsbBprC0osj10yW9svnRpe0utv9T23AE8ewyzMGOEa8C/kFEUX5H0vOYISNjL9j+he3bbF9u+zm2n+wBZrWcY7zFYZx9IfBQ4M3AbKinPR32JNzQ/2L7OUSCvIEXvpH0NEmvBpZw5Mz6K7HyPHvQfTX0ubGkz0q6DvgkYRsbOuNgU9iNSFmwClMzC3bLKjiQSkWLIw7V3LENUZTvBlaX9E36iKIcNJI2JAyDx5XtLxErBoCvuVpFvnGjJrS3Ag51lOmcdYK8R+6xfY+kmir5KkmPGWQHiuI1WwMLgQ8WXf87iCywbxlwX63UsioCb0aY8zaFGpJ2do9peTWgSkVJMJ0oyiGM5Xhgf9u/K9tXAh8jgqheZXubEQ5vJCiq5K1FeGJtRASanWF7pPW0p4OkY4kVz16EofzfRInYrQbYx5VEYa57iv3iBuCJtv84qD4a+hpocru+xjAuQgFA0tOZmi3x+x2On0f41tdUHycBB9u+v12bZG4g6ULbmzZsn2t78/L6bNtbjG50o6H83zcmbD//USSsW8sDzkc0Kkqqm5UZcBZclTKcDdsLPeBsrw3n3pZYKTwdqCW3O9gzGU09LkJB0g+A9YglXu2mbtt7dGm3HBGheHWn45K5haSrbbdUI0j6g+0NZnpMswFJazE1rcvAPXVmCkk/sP36bvum2cd/iICxGs9q3PYQkm4OKrldP4yDTaHGpsDj3YOUUxTX+TxhR1i3+NvvN4wfOZlxbpD0VNvnNe5UpHC+oU2bsUbSZ4lYkitpmDgx+YY319iwcaPYBQetDntF0/YBAz7/FGzfSQReHt6glv0Qoc0YKuO0UjgK2MP2jT20WUBI4TM8keHzUg8hmVYys0jajDDSfY9wvYS4WbyRsHmcP6KhjQxJVxO68GEFXM0YkvYGPgwsB9xV202U2D3I9t6jGttcZ5xWCqsBV0o6n+41mmsssn3b3HfASJqxfb6kpwLvAt5Udl9BlOG8qW3D8eZaYCmGF4U7Y9jev6x8DrY9UA+gxZ1xEgr79tHmckmvA5YoGQ/3AH430FElI8ORC+vjox7HLOIuYKGkU5k8cepod5utOCouDqyUaBKMjfqoH0oq3I8QwTwAJwKfHIfldZI0U4I8p2D7sJkey6AoyRe/Z3txTXI4cOa8UCipf1t9iFqxjZU6tH2N7aO67UuSZHZSYggeQ2S/vZMhFtlp0ff/EoVvDrZ9y7D7mynmvFCYDmpREKTVviQZB4qKdH8ioVtj8rg5mxVYM1hkp0Xf2xBu8BvZfsOw+5spxsmmUBlJLyFC/deS9JWGt1YCFo1mVMlMMK6zu4ocSqTN/hLwHCISeE57Wdj+i6KC3Pq2D1UUVVpxhvr+2Uz0M9MsliuFYpzaGNiPyYbIO4DTS2K8ZAwZ19ldFWqRuZIus/0/Zd9vbD9z1GPrF0n7EDFKj7G9gaSHAUfZfsYQ+ppPJMxch8nBf2Pl/bRYrhRsXwJcIulHxEypFt169SDD45PZx7jO7ipyT0l18UdJ7wL+TmRLnctsS2RGvQjA9g2SHjSkvn5O5CU6hYaa7uPGYikUGng68H3CSCXgEZLeOJfD/pNA0ldp7YAAzF03zGmyF5EQcA8iFfNziGC+ucx9ti2plul4hSH2tbztDw7x/LOCxV0ofJEoMnI11NPWHsHgw+STmWdYdXrnJCX9w3a23w/8H2FPGAeOlPRtYBVJuxCprA8eUl+/kLSV7XZlfseCxdKmUKNVSotMc5GMG5KWtL1I0mnA83rJDzYXUJTJfCGx2j/R9slD6ucOYAUilUZNzdzR7X0usrgLhe8SKoYflF07AkvaHpdZ1GKLpC/b3qvUVZjyJ1+ckh7W3KwlHUDUMj6K8OkHwHanQlSzGkmfbVbptNqXVGdxFwrLEHWaa8WxzwK+kRHNcx9JT7a9oOTYn4LtM2d6TKOiQSgc2rDbTAR6zVnvmTaxRkNb7ZfMys8qm2fY/sUw+hkli7VQSMYfScsCjyZugtfYvmfEQ5pxJF1P2M/EhDCoYdtfHMnApoGktxMlMR8FXNPw1oOA39reaQh9foaoB3142bUDsMD2hwbd1yhZLIWCpCNtbyfpMlqrFtKmMMeRtCQTNXT/AswDHk4EcH1kcXI9lnQj8E1aB6rZ9n4zPKRpI2llYFUiQrvxpnyH7VuH1OelwMa2HyjbSwAXj9v9YnEVCmvavnGUIfLJcJH0JWLW+G7bd5R9KwFfAO62vecoxzeTjHvqlnJzXp3JAWV/HUI/lwJb1oROKX5zRgqFMUXSasAt4+aZsbgi6Y/ABs2/Z7mBXGV7/dGMbOaRdHGtiNS4UYLw9gVuAh4ou4eSEE/SDsBngNOJVdezgL1t/3jQfY2SxVIolJKMnwFuJYJ4fkAU6ZkHvMH2CSMcXjIAOtVhXtxqNEt68LBUKqNG0p+Ap85UHitJaxJ2BQHn2f7HTPQ7k8wb9QBGxNcIffMRwGnAW22vQUj+/Uc5sGRgXClpSm4jSTsBV41gPCNjXAVC4W9EgsOhIemx5XkTYE3g+tLvw8q+sWJxXSkstL1xef17249reG9sl9qLE5LWAn4K3A0sIBwKnkLU9N3W9t9HOLxkQEg6hKin8EsmV5MbmEeVpINs7yrp9BZv2/ZzB9XXbGBxTXPxQMPru5veW/yk5BhSbvpPlfRcYENiuf9r26eOdmTJgPlreSxdHgPH9q7l5UuaXZqLy/NYsbiuFO5nokrTckTtWsr2sraXGtXYkiSZHrW0HkM472JRlGuxXCnYXmLUY0iSpH8knW17i/L6B7Zf3/D2+cDAbtSS1gDWApaT9CQm4j1WIrLOjhWLpVBIkmTO05gi+wlN7w26mtyLgDcRwY8HNJz/duDDA+5r5KRQSJJkLuI2r1ttT68j+zDgMEmvsn3MIM89G0mhkCTJXGQVSdsSbvWrSHpl2S9g5SH1+WRJp9r+D4CkVYH32v7okPobCYuloTlJkrlNU8bXKQwj/X0rd/U0NCdJkswCRlTzZAlJy9RS60taDlhmBOMYKikUkiRJqvFD4NSySjGRgfew0Q5p8KT6KEmSpCKSXgI8j7BdnGT7xBEPaeCkUEiSJEnqpPooSZKxQdKmwI3DyG0l6Q4m3F2XBpYC7rS90qD7GiUpFJIkGSd2B55Y0qNvP8gT235Q47akbYDNBtnHbCDVR0mSjB2SHlSruDfkfs61vfmw+5lJcqWQJMmcRdIzgIW27yy1MjYBDhxGSd2GADmIoLlNGcOsyrlSSJJkzlLqJm8EPJGooHgI8Erbzx5CX40Bc4uA64Dv2L550H2NklwpJEkyl1lk25JeQawQDpH0xkF3Ump7X2r7S4M+92xjcS3HmSTJeHCHpL2BnYBflpv3wOuh2L4fePmgzzsbSfVRkiRzllLr4HXABbZ/I2ltYEvb3x9CX58mku39hCjSBYDtiwbd1yhJ9VGSJHOZd9v+YG3D9l8lbTikvp5envdr2GdgrGo050ohSZI5S5sSmZfafuIQ+nqU7Wu77ZvrpE0hSZI5h6S3S7oMeKykSxsefwYuHVK3R7fYd9SQ+hoZqT5KkmQu8iPg18D+wIca9t9h+9ZBdiTpscCGwMpNsQorAcsOsq/ZQAqFJEnmHLZvK7mI/mcYgWpNPAbYGlgFeFnD/juAXYbc94yTNoUkSeYskg4H9rb91xno62m2zxl2P6MmbQpJksxl1gSukHSqpONqjyH1ta2klSQtVfr7V0mtMVbkSiFJkjmLpJbpLGyfOYS+FtreWNK2wDbAu4HTbW806L5GSdoUkiSZszTf/EuCvNcBAxcKTERKbwUcYftWSUPoZrSkUEiSZE4jaWNCEGwH/Bk4ZkhdHS/pKuBu4B2S5gP3DKmvkZHqoyRJ5hySNgBeC+wA3EKknnif7UcOud9Vgdtt3y9peWAl2/8YZp8zTQqFJEnmHJIeAH4D7Gz7T2XftbYfNeR+nw6sQ4OWZRh5lkZJqo+SJJmLvIpYKZwu6QTgx8BQFfySfgCsBywE7i+7DYyVUMiVQpIkcxZJKxCeQDsQiekOA461fdIQ+vo98HiP+U0z4xSSJJmz2L7T9uG2twYeTsziP9S5Vd9cDqwxpHPPGnKlkCRJUgFJpwMbA+cD99b22x6r4jtpU0iSJKnGvqMewEyQK4UkSZKKSFodeErZPN/2zaMczzBIm0KSJEkFJG1HqI5eQwTKnSfp1aMd1eDJlUKSJEkFJF0CvKC2OigRzaeMW+6jXCkkSZJUY16TuugWxvAemobmJEmSapwg6UTgiLK9PVH9baxI9VGSJElFSjnOLYjo6bNsHzviIQ2cFApJkiQdkPRoYHXbv23a/yzg77avGc3IhsPY6cOSJEkGzJeJeszN3FXeGytSKCRJknRmHduXNu+0fSGRMXWsSKGQJEnSmWU7vLfcjI1ihkihkCRJ0pkLJO3SvFPSzsCCEYxnqKShOUmSpAMltcWxwH1MCIFNgaWBbbPyWpIkyWKIpOcATyibV9g+bZTjGRYpFJIkSZI6aVNIkiRJ6qRQSJIkSeqkUEiSJEnqpFBIkiRJ6qRQSJIkSer8P81nSPzONHz4AAAAAElFTkSuQmCC\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 Revenue: total = 14\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 = 343\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 job_state: total = 37\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 same_state: total = 2\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 python_yn: total = 2\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 R_yn: total = 2\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 spark: total = 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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VkRG3tZx4WU5Llf82F0mq6pFHSZKWhaV2+UiSNEKGgiSpMRQkSY2hIElqltTTR1pcSZ7NzMeIrGXmJcFTwP6qOjrSxiSNjGcKy1SSW5n5bKkAh5h5RyTA+/10Wi1lSV456h6eyHwkdZlK8q/AtVX1rVn1S4AjVbVxNJ1JF5fkZFVdNeo+nqi8fLR8fRt4BvDlWfU13T5pZJJ8bqFdwOrF7GW5MRSWr9cBB5Mc5zufTHsV8EPAq0fVlNRZDWwB/mNWPcA/Ln47y4ehsExV1ceSPJOZv2Gxlpn/bFPAPVV1bqTNSXAX8NSqunf2jiR3L3o3y4j3FCRJjU8fSZIaQ0GS1BgKkqTGUJAkNf8HOqbLRjekIMAAAAAASUVORK5CYII=\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 aws: total = 2\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 excel: total = 2\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 job_simp: total = 7\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 seniority: total = 3\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"
    }
   ],
   "source": [
    "for i in df_cat.columns:\n",
    "    cat_num = df_cat[i].value_counts()\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": 68,
   "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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\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] #take the top 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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<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_simp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>1686.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mle</th>\n",
       "      <td>1264.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>1175.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data engineer</th>\n",
       "      <td>1054.033613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>848.532609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manager</th>\n",
       "      <td>840.227273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analyst</th>\n",
       "      <td>658.578431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 avg_salary\n",
       "job_simp                   \n",
       "director        1686.071429\n",
       "mle             1264.318182\n",
       "data scientist  1175.645161\n",
       "data engineer   1054.033613\n",
       "na               848.532609\n",
       "manager          840.227273\n",
       "analyst          658.578431"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values = \"avg_salary\", index = \"job_simp\").sort_values(by = [\"avg_salary\"], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg_salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_simp</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>565.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>611.554054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>790.925926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">data engineer</th>\n",
       "      <th>na</th>\n",
       "      <td>967.012195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>1246.891892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">data scientist</th>\n",
       "      <th>jr</th>\n",
       "      <td>1065.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>1070.430108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>1389.565217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <th>na</th>\n",
       "      <td>1686.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manager</th>\n",
       "      <th>na</th>\n",
       "      <td>840.227273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">mle</th>\n",
       "      <th>na</th>\n",
       "      <td>1191.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>1420.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">na</th>\n",
       "      <th>na</th>\n",
       "      <td>739.881890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>senior</th>\n",
       "      <td>1090.614035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           avg_salary\n",
       "job_simp       seniority             \n",
       "analyst        jr          565.000000\n",
       "               na          611.554054\n",
       "               senior      790.925926\n",
       "data engineer  na          967.012195\n",
       "               senior     1246.891892\n",
       "data scientist jr         1065.000000\n",
       "               na         1070.430108\n",
       "               senior     1389.565217\n",
       "director       na         1686.071429\n",
       "manager        na          840.227273\n",
       "mle            na         1191.333333\n",
       "               senior     1420.714286\n",
       "na             na          739.881890\n",
       "               senior     1090.614035"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values = \"avg_salary\", index = [\"job_simp\", \"seniority\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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",
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       "</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>CA</th>\n",
       "      <td>1235.131579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <td>1166.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>1101.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>1062.815534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>1045.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>1002.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RI</th>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>986.527778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <td>984.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <td>973.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KY</th>\n",
       "      <td>970.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>956.219512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UT</th>\n",
       "      <td>941.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>931.904762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>925.606061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>921.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>903.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>893.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>875.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <td>871.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KS</th>\n",
       "      <td>870.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>856.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN</th>\n",
       "      <td>855.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>847.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>806.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IA</th>\n",
       "      <td>789.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>776.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>770.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NM</th>\n",
       "      <td>743.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>733.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>711.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <td>605.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>579.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>562.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>522.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NE</th>\n",
       "      <td>410.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            avg_salary\n",
       "job_state             \n",
       "CA         1235.131579\n",
       "IL         1166.625000\n",
       "DC         1101.818182\n",
       "MA         1062.815534\n",
       "NJ         1045.588235\n",
       "MI         1002.500000\n",
       "RI         1000.000000\n",
       "NY          986.527778\n",
       "NC          984.523810\n",
       "MD          973.571429\n",
       "KY          970.000000\n",
       "VA          956.219512\n",
       "UT          941.500000\n",
       "WA          931.904762\n",
       "PA          925.606061\n",
       "OR          921.250000\n",
       "IN          903.000000\n",
       "TX          893.928571\n",
       "OH          875.714286\n",
       "TN          871.153846\n",
       "KS          870.000000\n",
       "AZ          856.666667\n",
       "MN          855.000000\n",
       "MO          847.222222\n",
       "GA          806.666667\n",
       "IA          789.000000\n",
       "FL          776.250000\n",
       "CO          770.454545\n",
       "NM          743.333333\n",
       "WI          733.000000\n",
       "CT          711.000000\n",
       "SC          605.000000\n",
       "AL          579.375000\n",
       "ID          562.500000\n",
       "LA          522.500000\n",
       "NE          410.000000\n",
       "DE          275.000000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values = \"avg_salary\", index = \"job_state\").sort_values(by = [\"avg_salary\"], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Option damit alle angezeigt werden:\n",
    "pd.options.display.max_rows\n",
    "pd.set_option(\"display.max_rows\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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_simp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <th>na</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">CA</th>\n",
       "      <th>na</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data engineer</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <th>analyst</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <th>na</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <th>analyst</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <th>na</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <th>na</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <th>manager</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">PA</th>\n",
       "      <th>na</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analyst</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mle</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <th>na</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <th>director</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IL</th>\n",
       "      <th>na</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <th>analyst</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <th>analyst</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <th>na</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <th>analyst</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <th>mle</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <th>manager</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <th>na</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <th>analyst</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <th>analyst</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NE</th>\n",
       "      <th>na</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UT</th>\n",
       "      <th>analyst</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <th>analyst</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KY</th>\n",
       "      <th>na</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IN</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <th>na</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NM</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IA</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <th>na</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <th>manager</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <th>na</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">CO</th>\n",
       "      <th>na</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data engineer</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">UT</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <th>analyst</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">WI</th>\n",
       "      <th>analyst</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <th>analyst</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <th>na</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <th>analyst</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MO</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KS</th>\n",
       "      <th>mle</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <th>analyst</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <th>analyst</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <th>mle</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <th>manager</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <th>na</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <th>analyst</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KY</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <th>manager</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IA</th>\n",
       "      <th>analyst</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <th>mle</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <th>mle</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th>analyst</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <th>director</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <th>na</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <th>na</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <th>analyst</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN</th>\n",
       "      <th>analyst</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AZ</th>\n",
       "      <th>analyst</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data scientist</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <th>na</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <th>manager</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <th>mle</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <th>data scientist</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <th>na</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RI</th>\n",
       "      <th>analyst</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LA</th>\n",
       "      <th>analyst</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>na</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <th>data engineer</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          avg_salary\n",
       "job_state job_simp                  \n",
       "CA        data scientist          68\n",
       "MA        na                      46\n",
       "NY        data scientist          40\n",
       "MA        data scientist          30\n",
       "CA        na                      27\n",
       "          data engineer           25\n",
       "VA        data scientist          18\n",
       "CA        analyst                 18\n",
       "WA        data scientist          17\n",
       "MD        na                      16\n",
       "IL        data scientist          15\n",
       "NY        analyst                 14\n",
       "MD        data scientist          13\n",
       "TX        data scientist          13\n",
       "NC        na                      12\n",
       "MA        data engineer           12\n",
       "NY        na                      12\n",
       "VA        data engineer           10\n",
       "TN        data engineer           10\n",
       "CA        manager                  9\n",
       "IL        data engineer            8\n",
       "TX        data engineer            8\n",
       "PA        na                       8\n",
       "          analyst                  8\n",
       "          mle                      7\n",
       "TX        na                       7\n",
       "FL        data scientist           7\n",
       "OH        data scientist           7\n",
       "NC        data engineer            6\n",
       "MA        director                 6\n",
       "IL        na                       6\n",
       "          director                 6\n",
       "NJ        analyst                  6\n",
       "FL        analyst                  6\n",
       "DE        na                       6\n",
       "PA        data scientist           6\n",
       "VA        analyst                  6\n",
       "DC        data scientist           5\n",
       "CA        mle                      5\n",
       "AZ        data engineer            5\n",
       "MA        manager                  5\n",
       "VA        na                       5\n",
       "AL        analyst                  5\n",
       "NY        data engineer            4\n",
       "OH        analyst                  4\n",
       "NJ        data scientist           4\n",
       "NE        na                       4\n",
       "UT        analyst                  4\n",
       "MI        data scientist           4\n",
       "MA        analyst                  4\n",
       "KY        na                       4\n",
       "DC        data engineer            4\n",
       "IN        data engineer            4\n",
       "          na                       4\n",
       "WI        na                       4\n",
       "MD        data engineer            3\n",
       "NM        data scientist           3\n",
       "IA        data engineer            3\n",
       "GA        data scientist           3\n",
       "OH        na                       3\n",
       "OR        data scientist           3\n",
       "FL        data engineer            3\n",
       "PA        manager                  3\n",
       "CT        na                       3\n",
       "CO        na                       3\n",
       "          data scientist           3\n",
       "          data engineer            3\n",
       "UT        data engineer            3\n",
       "          data scientist           3\n",
       "WA        analyst                  3\n",
       "WI        analyst                  3\n",
       "          data scientist           3\n",
       "IL        analyst                  3\n",
       "GA        na                       3\n",
       "MD        analyst                  3\n",
       "MO        data scientist           3\n",
       "          na                       3\n",
       "KS        mle                      3\n",
       "NC        data scientist           3\n",
       "TN        analyst                  2\n",
       "MO        analyst                  2\n",
       "VA        mle                      2\n",
       "CT        data scientist           2\n",
       "NJ        manager                  2\n",
       "AL        na                       2\n",
       "DC        analyst                  2\n",
       "LA        data engineer            2\n",
       "IN        data scientist           2\n",
       "KY        data scientist           2\n",
       "MI        manager                  2\n",
       "NJ        data engineer            2\n",
       "IA        analyst                  2\n",
       "NY        mle                      2\n",
       "IL        mle                      2\n",
       "ID        analyst                  2\n",
       "NJ        director                 2\n",
       "AZ        na                       2\n",
       "WA        na                       1\n",
       "CO        analyst                  1\n",
       "MN        analyst                  1\n",
       "AL        data engineer            1\n",
       "AZ        analyst                  1\n",
       "          data scientist           1\n",
       "NJ        na                       1\n",
       "MN        data engineer            1\n",
       "MO        manager                  1\n",
       "CO        mle                      1\n",
       "TN        data scientist           1\n",
       "SC        na                       1\n",
       "RI        analyst                  1\n",
       "PA        data engineer            1\n",
       "LA        analyst                  1\n",
       "          na                       1\n",
       "OR        data engineer            1"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values = \"avg_salary\", index = [\"job_state\", \"job_simp\"], aggfunc = \"count\").sort_values(by = [\"avg_salary\"], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>avg_salary</th>\n",
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       "      <th>job_state</th>\n",
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       "      <th>DC</th>\n",
       "      <td>1490.000000</td>\n",
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       "      <th>CA</th>\n",
       "      <td>1425.220588</td>\n",
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       "      <th>UT</th>\n",
       "      <td>1405.000000</td>\n",
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       "      <td>1172.333333</td>\n",
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       "      <th>NC</th>\n",
       "      <td>1170.000000</td>\n",
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       "      <th>NY</th>\n",
       "      <td>1152.500000</td>\n",
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       "      <th>MA</th>\n",
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       "      <th>WI</th>\n",
       "      <td>1135.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>1133.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MD</th>\n",
       "      <td>1091.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>1086.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>1084.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>1068.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>1066.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OH</th>\n",
       "      <td>1052.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>1007.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>997.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>973.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <td>960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN</th>\n",
       "      <td>845.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KY</th>\n",
       "      <td>840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>813.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NM</th>\n",
       "      <td>743.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>695.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            avg_salary\n",
       "job_state             \n",
       "DC         1490.000000\n",
       "CA         1425.220588\n",
       "UT         1405.000000\n",
       "MO         1276.666667\n",
       "IL         1172.333333\n",
       "NC         1170.000000\n",
       "NY         1152.500000\n",
       "MA         1137.500000\n",
       "WI         1135.000000\n",
       "PA         1133.333333\n",
       "MD         1091.153846\n",
       "CO         1086.666667\n",
       "VA         1084.166667\n",
       "NJ         1068.750000\n",
       "MI         1066.250000\n",
       "OH         1052.857143\n",
       "TX         1007.307692\n",
       "WA          997.647059\n",
       "OR          985.000000\n",
       "FL          973.571429\n",
       "TN          960.000000\n",
       "IN          845.000000\n",
       "KY          840.000000\n",
       "CT          840.000000\n",
       "GA          813.333333\n",
       "NM          743.333333\n",
       "AZ          695.000000"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df[df.job_simp == \"data scientist\"], values = \"avg_salary\", index =  \"job_state\").sort_values(by = [\"avg_salary\"], ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rating\n",
      "         avg_salary\n",
      "Rating             \n",
      "-10     1361.818182\n",
      " 50     1347.000000\n",
      " 25     1200.000000\n",
      " 45     1177.142857\n",
      " 41     1163.684211\n",
      " 39     1130.714286\n",
      " 26     1090.833333\n",
      " 44     1086.060606\n",
      " 32     1055.000000\n",
      " 40     1053.404255\n",
      " 35     1048.979592\n",
      " 37     1041.311475\n",
      " 47     1039.838710\n",
      " 43     1024.687500\n",
      " 33     1018.589744\n",
      " 42     1004.423077\n",
      " 22      977.500000\n",
      " 46      972.500000\n",
      " 38      968.114754\n",
      " 21      959.000000\n",
      " 34      930.113636\n",
      " 19      875.000000\n",
      " 31      865.800000\n",
      " 30      855.882353\n",
      " 36      854.673913\n",
      " 28      839.285714\n",
      " 27      832.500000\n",
      " 23      815.000000\n",
      " 29      811.388889\n",
      " 48      802.777778\n",
      " 24      602.142857\n",
      "Industry\n",
      "                                           avg_salary\n",
      "Industry                                             \n",
      "Other Retail Stores                       1635.000000\n",
      "Motion Picture Production & Distribution  1460.000000\n",
      "Financial Analytics & Research            1451.250000\n",
      "Health, Beauty, & Fitness                 1395.000000\n",
      "Telecommunications Services               1315.000000\n",
      "Brokerage Services                        1290.000000\n",
      "Auctions & Galleries                      1280.000000\n",
      "Internet                                  1238.103448\n",
      "Investment Banking & Asset Management     1184.000000\n",
      "TV Broadcast & Cable Networks             1177.500000\n",
      "Enterprise Software & Network Solutions   1153.690476\n",
      "Computer Hardware & Software              1151.949153\n",
      "-1                                        1146.500000\n",
      "Accounting                                1135.000000\n",
      "Biotech & Pharmaceuticals                 1117.678571\n",
      "Insurance Agencies & Brokerages           1092.500000\n",
      "K-12 Education                            1088.750000\n",
      "Consulting                                1086.896552\n",
      "Transportation Management                 1075.000000\n",
      "Metals Brokers                            1070.000000\n",
      "Staffing & Outsourcing                    1062.000000\n",
      "Video Games                               1061.666667\n",
      "Insurance Carriers                        1056.269841\n",
      "Real Estate                               1047.500000\n",
      "Transportation Equipment Manufacturing    1045.000000\n",
      "Advertising & Marketing                   1043.000000\n",
      "Wholesale                                 1031.666667\n",
      "IT Services                               1028.400000\n",
      "Colleges & Universities                   1026.875000\n",
      "Aerospace & Defense                        990.600000\n",
      "Logistics & Supply Chain                   982.500000\n",
      "Gas Stations                               980.000000\n",
      "Mining                                     980.000000\n",
      "Consumer Products Manufacturing            953.500000\n",
      "Security Services                          935.000000\n",
      "Department, Clothing, & Shoe Stores        926.666667\n",
      "Stock Exchanges                            870.000000\n",
      "Industrial Manufacturing                   867.500000\n",
      "Consumer Product Rental                    866.666667\n",
      "Beauty & Personal Accessories Stores       855.000000\n",
      "Sporting Goods Stores                      850.000000\n",
      "Lending                                    830.000000\n",
      "Farm Support Services                      805.000000\n",
      "Energy                                     804.642857\n",
      "Education Training Services                795.000000\n",
      "Trucking                                   790.000000\n",
      "Religious Organizations                    788.333333\n",
      "Federal Agencies                           785.454545\n",
      "Research & Development                     783.947368\n",
      "Financial Transaction Processing           765.000000\n",
      "Health Care Products Manufacturing         730.000000\n",
      "Banks & Credit Unions                      706.250000\n",
      "Travel Agencies                            695.000000\n",
      "Health Care Services & Hospitals           676.224490\n",
      "Food & Beverage Manufacturing              532.500000\n",
      "Architectural & Engineering Services       505.000000\n",
      "Gambling                                   485.000000\n",
      "Social Assistance                          481.666667\n",
      "Telecommunications Manufacturing           440.000000\n",
      "Construction                               265.000000\n",
      "Sector\n",
      "                                     avg_salary\n",
      "Sector                                         \n",
      "Media                               1166.666667\n",
      "-1                                  1146.500000\n",
      "Accounting & Legal                  1135.000000\n",
      "Information Technology              1131.916667\n",
      "Biotech & Pharmaceuticals           1117.678571\n",
      "Insurance                           1059.420290\n",
      "Real Estate                         1047.500000\n",
      "Mining & Metals                     1040.000000\n",
      "Telecommunications                  1023.333333\n",
      "Education                           1007.391304\n",
      "Consumer Services                    998.750000\n",
      "Retail                               996.666667\n",
      "Transportation & Logistics           993.125000\n",
      "Aerospace & Defense                  990.600000\n",
      "Business Services                    977.010309\n",
      "Finance                              973.690476\n",
      "Manufacturing                        840.441176\n",
      "Agriculture & Forestry               805.000000\n",
      "Oil, Gas, Energy & Utilities         804.642857\n",
      "Government                           785.454545\n",
      "Travel & Tourism                     695.000000\n",
      "Non-Profit                           686.111111\n",
      "Health Care                          676.224490\n",
      "Arts, Entertainment & Recreation     485.000000\n",
      "Construction, Repair & Maintenance   265.000000\n",
      "Revenue\n",
      "                                   avg_salary\n",
      "Revenue                                      \n",
      "$5 to $10 million (USD)           1261.111111\n",
      "$1 to $5 million (USD)            1193.125000\n",
      "-1                                1175.000000\n",
      "$10+ billion (USD)                1151.653226\n",
      "Less than $1 million (USD)        1086.250000\n",
      "$1 to $2 billion (USD)            1045.333333\n",
      "Unknown / Non-Applicable          1043.103448\n",
      "$50 to $100 million (USD)         1005.652174\n",
      "$10 to $25 million (USD)          1003.593750\n",
      "$2 to $5 billion (USD)             947.307692\n",
      "$5 to $10 billion (USD)            941.842105\n",
      "$500 million to $1 billion (USD)   869.912281\n",
      "$100 to $500 million (USD)         831.538462\n",
      "$25 to $50 million (USD)           828.375000\n",
      "num_comp\n",
      "           avg_salary\n",
      "num_comp             \n",
      "2         1068.658537\n",
      "1         1062.083333\n",
      "3         1055.043860\n",
      "0          976.021739\n",
      "4          565.000000\n",
      "hourly\n",
      "         avg_salary\n",
      "hourly             \n",
      "0       1031.538997\n",
      "1        250.000000\n",
      "employer_provided\n",
      "                    avg_salary\n",
      "employer_provided             \n",
      "1                  1199.705882\n",
      "0                  1001.724138\n",
      "python_yn\n",
      "            avg_salary\n",
      "python_yn             \n",
      "1          1126.530612\n",
      "0           871.557143\n",
      "R_yn\n",
      "       avg_salary\n",
      "R_yn             \n",
      "0     1007.067568\n",
      "1      707.500000\n",
      "spark\n",
      "        avg_salary\n",
      "spark             \n",
      "1      1133.473054\n",
      "0       969.313043\n",
      "aws\n",
      "      avg_salary\n",
      "aws             \n",
      "1    1125.596591\n",
      "0     969.151943\n",
      "excel\n",
      "        avg_salary\n",
      "excel             \n",
      "0      1029.533898\n",
      "1       985.025773\n",
      "Type of ownership\n",
      "                                 avg_salary\n",
      "Type of ownership                          \n",
      "-1                              1175.000000\n",
      "Company - Public                1108.937824\n",
      "Subsidiary or Business Segment  1105.735294\n",
      "College / University            1076.153846\n",
      "Company - Private               1017.768293\n",
      "Government                       843.000000\n",
      "School / School District         777.500000\n",
      "Other Organization               775.000000\n",
      "Nonprofit Organization           682.818182\n",
      "Hospital                         540.000000\n",
      "Unknown                          395.000000\n",
      "avg_salary\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Grouper for 'avg_salary' not 1-dimensional",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-86-ffbf8dbbfab7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf_pivots\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_pivots\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'avg_salary'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'avg_salary'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mascending\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\pivot.py\u001b[0m in \u001b[0;36mpivot_table\u001b[1;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)\u001b[0m\n\u001b[0;32m    109\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m     \u001b[0mgrouped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobserved\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m     \u001b[0magged\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdropna\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magged\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magged\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[1;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[0;32m   6509\u001b[0m         \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6510\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6511\u001b[1;33m         return DataFrameGroupBy(\n\u001b[0m\u001b[0;32m   6512\u001b[0m             \u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6513\u001b[0m             \u001b[0mkeys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mby\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[0;32m    523\u001b[0m             \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_grouper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    524\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 525\u001b[1;33m             grouper, exclusions, obj = get_grouper(\n\u001b[0m\u001b[0;32m    526\u001b[0m                 \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    527\u001b[0m                 \u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[1;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[0;32m    796\u001b[0m         \u001b[1;31m# allow us to passing the actual Grouping as the gpr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m         ping = (\n\u001b[1;32m--> 798\u001b[1;33m             Grouping(\n\u001b[0m\u001b[0;32m    799\u001b[0m                 \u001b[0mgroup_axis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    800\u001b[0m                 \u001b[0mgpr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, index, grouper, obj, name, level, sort, observed, in_axis, dropna)\u001b[0m\n\u001b[0;32m    515\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ndim\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    516\u001b[0m                     \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 517\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Grouper for '{t}' not 1-dimensional\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    518\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m                 if not (\n",
      "\u001b[1;31mValueError\u001b[0m: Grouper for 'avg_salary' not 1-dimensional"
     ]
    }
   ],
   "source": [
    "for i in df_pivots.columns:\n",
    "    print(i)\n",
    "    print(pd.pivot_table(df_pivots,index =i, values = 'avg_salary').sort_values('avg_salary', ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df_pivots, index = 'Revenue', columns = 'python_yn', values = 'avg_salary', aggfunc = 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting wordcloud\n",
      "  Downloading wordcloud-1.8.1-cp38-cp38-win_amd64.whl (155 kB)\n",
      "Requirement already satisfied: pillow in c:\\users\\besitzer\\anaconda3\\lib\\site-packages (from wordcloud) (8.0.1)\n",
      "Requirement already satisfied: numpy>=1.6.1 in c:\\users\\besitzer\\appdata\\roaming\\python\\python38\\site-packages (from wordcloud) (1.19.5)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\besitzer\\anaconda3\\lib\\site-packages (from wordcloud) (3.3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\besitzer\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\besitzer\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\besitzer\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (1.3.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\besitzer\\appdata\\roaming\\python\\python38\\site-packages (from matplotlib->wordcloud) (2020.12.5)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\besitzer\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (2.4.7)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\besitzer\\appdata\\roaming\\python\\python38\\site-packages (from python-dateutil>=2.1->matplotlib->wordcloud) (1.15.0)\n",
      "Installing collected packages: wordcloud\n",
      "Successfully installed wordcloud-1.8.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Word cloud for job description\n",
    "from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "LookupError",
     "evalue": "\n**********************************************************************\n  Resource \u001b[93mstopwords\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('stopwords')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mcorpora/stopwords\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\Besitzer/nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\anaconda3\\\\nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\anaconda3\\\\share\\\\nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\anaconda3\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\AppData\\\\Roaming\\\\nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n**********************************************************************\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLookupError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py\u001b[0m in \u001b[0;36m__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     82\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m                     \u001b[0mroot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{}/{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubdir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzip_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\nltk\\data.py\u001b[0m in \u001b[0;36mfind\u001b[1;34m(resource_name, paths)\u001b[0m\n\u001b[0;32m    584\u001b[0m     \u001b[0mresource_not_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"\\n%s\\n%s\\n%s\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 585\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresource_not_found\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    586\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mLookupError\u001b[0m: \n**********************************************************************\n  Resource \u001b[93mstopwords\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('stopwords')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mcorpora/stopwords.zip/stopwords/\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\Besitzer/nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\anaconda3\\\\nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\anaconda3\\\\share\\\\nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\anaconda3\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\AppData\\\\Roaming\\\\nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n**********************************************************************\n",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mLookupError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-91-c77528177ed4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mwords_filtered\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpunctuation_stop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mtext\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\" \"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mele\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mele\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mwords_filtered\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-91-c77528177ed4>\u001b[0m in \u001b[0;36mpunctuation_stop\u001b[1;34m(text)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;34m\"\"\"remove punctuation and stop words\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mfiltered\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mstop_words\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstopwords\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'english'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mword_tokens\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mword_tokenize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mword_tokens\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, attr)\u001b[0m\n\u001b[0;32m    118\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"LazyCorpusLoader object has no attribute '__bases__'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__load\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    121\u001b[0m         \u001b[1;31m# This looks circular, but its not, since __load() changes our\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    122\u001b[0m         \u001b[1;31m# __class__ to something new:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py\u001b[0m in \u001b[0;36m__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     83\u001b[0m                     \u001b[0mroot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{}/{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubdir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzip_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 85\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m         \u001b[1;31m# Load the corpus.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py\u001b[0m in \u001b[0;36m__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     78\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 80\u001b[1;33m                 \u001b[0mroot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{}/{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubdir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     81\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mLookupError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\nltk\\data.py\u001b[0m in \u001b[0;36mfind\u001b[1;34m(resource_name, paths)\u001b[0m\n\u001b[0;32m    583\u001b[0m     \u001b[0msep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"*\"\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m70\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    584\u001b[0m     \u001b[0mresource_not_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"\\n%s\\n%s\\n%s\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 585\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresource_not_found\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    586\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mLookupError\u001b[0m: \n**********************************************************************\n  Resource \u001b[93mstopwords\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('stopwords')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mcorpora/stopwords\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\Besitzer/nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\anaconda3\\\\nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\anaconda3\\\\share\\\\nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\anaconda3\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\Besitzer\\\\AppData\\\\Roaming\\\\nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n**********************************************************************\n"
     ]
    }
   ],
   "source": [
    "words = \" \".join(df['Job Description'])\n",
    "\n",
    "def punctuation_stop(text):\n",
    "    \"\"\"remove punctuation and stop words\"\"\"\n",
    "    filtered = []\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    word_tokens = word_tokenize(text)\n",
    "    for w in word_tokens:\n",
    "        if w not in stop_words and w.isalpha():\n",
    "            filtered.append(w.lower())\n",
    "    return filtered\n",
    "\n",
    "\n",
    "words_filtered = punctuation_stop(words)\n",
    "\n",
    "text = \" \".join([ele for ele in words_filtered])\n",
    "\n",
    "wc= WordCloud(background_color=\"white\", random_state=1,stopwords=STOPWORDS, max_words = 2000, width =800, height = 1500)\n",
    "wc.generate(text)\n",
    "\n",
    "plt.figure(figsize=[10,10])\n",
    "plt.imshow(wc,interpolation=\"bilinear\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  }
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