ds_intermediate_us_wages_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('Minimum Wage Data.csv',encoding='latin')\n",
    "df.to_csv('minwage.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Table_Data</th>\n",
       "      <th>Footnote</th>\n",
       "      <th>High.Value</th>\n",
       "      <th>Low.Value</th>\n",
       "      <th>CPI.Average</th>\n",
       "      <th>High.2018</th>\n",
       "      <th>Low.2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1968</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1968</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.10000</td>\n",
       "      <td>2.10000</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>15.12</td>\n",
       "      <td>15.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1968</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>18.72 - 26.40/wk(b)</td>\n",
       "      <td>(b)</td>\n",
       "      <td>0.66000</td>\n",
       "      <td>0.46800</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>4.75</td>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1968</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1.25/day(b)</td>\n",
       "      <td>(b)</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1968</td>\n",
       "      <td>California</td>\n",
       "      <td>1.65(b)</td>\n",
       "      <td>(b)</td>\n",
       "      <td>1.65000</td>\n",
       "      <td>1.65000</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>11.88</td>\n",
       "      <td>11.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Year       State           Table_Data Footnote  High.Value  \\\n",
       "0           0  1968     Alabama                  ...      NaN     0.00000   \n",
       "1           1  1968      Alaska                 2.10      NaN     2.10000   \n",
       "2           2  1968     Arizona  18.72 - 26.40/wk(b)      (b)     0.66000   \n",
       "3           3  1968    Arkansas          1.25/day(b)      (b)     0.15625   \n",
       "4           4  1968  California              1.65(b)      (b)     1.65000   \n",
       "\n",
       "   Low.Value  CPI.Average  High.2018  Low.2018  \n",
       "0    0.00000    34.783333       0.00      0.00  \n",
       "1    2.10000    34.783333      15.12     15.12  \n",
       "2    0.46800    34.783333       4.75      3.37  \n",
       "3    0.15625    34.783333       1.12      1.12  \n",
       "4    1.65000    34.783333      11.88     11.88  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('minwage.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>State</th>\n",
       "      <th>Table_Data</th>\n",
       "      <th>Footnote</th>\n",
       "      <th>High.Value</th>\n",
       "      <th>Low.Value</th>\n",
       "      <th>CPI.Average</th>\n",
       "      <th>High.2018</th>\n",
       "      <th>Low.2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>55</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.683333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>110</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.825000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>165</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.491667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>220</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.816667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0    State Table_Data Footnote  High.Value  Low.Value  \\\n",
       "Year                                                                   \n",
       "1968           0  Alabama        ...      NaN         0.0        0.0   \n",
       "1969          55  Alabama        ...      NaN         0.0        0.0   \n",
       "1970         110  Alabama        ...      NaN         0.0        0.0   \n",
       "1971         165  Alabama        ...      NaN         0.0        0.0   \n",
       "1972         220  Alabama        ...      NaN         0.0        0.0   \n",
       "\n",
       "      CPI.Average  High.2018  Low.2018  \n",
       "Year                                    \n",
       "1968    34.783333        0.0       0.0  \n",
       "1969    36.683333        0.0       0.0  \n",
       "1970    38.825000        0.0       0.0  \n",
       "1971    40.491667        0.0       0.0  \n",
       "1972    41.816667        0.0       0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = df.groupby('State')\n",
    "gb.get_group('Alabama').set_index('Year').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Alabama</th>\n",
       "      <th>Alaska</th>\n",
       "      <th>Arizona</th>\n",
       "      <th>Arkansas</th>\n",
       "      <th>California</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Connecticut</th>\n",
       "      <th>Delaware</th>\n",
       "      <th>District of Columbia</th>\n",
       "      <th>Federal (FLSA)</th>\n",
       "      <th>...</th>\n",
       "      <th>Tennessee</th>\n",
       "      <th>Texas</th>\n",
       "      <th>U.S. Virgin Islands</th>\n",
       "      <th>Utah</th>\n",
       "      <th>Vermont</th>\n",
       "      <th>Virginia</th>\n",
       "      <th>Washington</th>\n",
       "      <th>West Virginia</th>\n",
       "      <th>Wisconsin</th>\n",
       "      <th>Wyoming</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.12</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.12</td>\n",
       "      <td>11.88</td>\n",
       "      <td>7.20</td>\n",
       "      <td>10.08</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.20</td>\n",
       "      <td>10.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.52</td>\n",
       "      <td>7.20</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.33</td>\n",
       "      <td>3.19</td>\n",
       "      <td>1.07</td>\n",
       "      <td>11.26</td>\n",
       "      <td>6.83</td>\n",
       "      <td>9.56</td>\n",
       "      <td>8.53</td>\n",
       "      <td>8.53</td>\n",
       "      <td>7.85</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.83</td>\n",
       "      <td>9.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.92</td>\n",
       "      <td>6.83</td>\n",
       "      <td>8.53</td>\n",
       "      <td>8.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13.54</td>\n",
       "      <td>3.02</td>\n",
       "      <td>7.09</td>\n",
       "      <td>10.64</td>\n",
       "      <td>6.45</td>\n",
       "      <td>10.32</td>\n",
       "      <td>8.06</td>\n",
       "      <td>10.32</td>\n",
       "      <td>8.38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.45</td>\n",
       "      <td>10.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.32</td>\n",
       "      <td>6.45</td>\n",
       "      <td>8.38</td>\n",
       "      <td>8.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.99</td>\n",
       "      <td>2.89</td>\n",
       "      <td>6.80</td>\n",
       "      <td>10.20</td>\n",
       "      <td>6.18</td>\n",
       "      <td>9.89</td>\n",
       "      <td>7.73</td>\n",
       "      <td>9.89</td>\n",
       "      <td>8.04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.18</td>\n",
       "      <td>9.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.89</td>\n",
       "      <td>6.18</td>\n",
       "      <td>8.04</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.57</td>\n",
       "      <td>2.80</td>\n",
       "      <td>7.19</td>\n",
       "      <td>9.88</td>\n",
       "      <td>5.99</td>\n",
       "      <td>11.08</td>\n",
       "      <td>9.58</td>\n",
       "      <td>9.58</td>\n",
       "      <td>9.58</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.19</td>\n",
       "      <td>9.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.58</td>\n",
       "      <td>7.19</td>\n",
       "      <td>8.68</td>\n",
       "      <td>8.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Alabama  Alaska  Arizona  Arkansas  California  Colorado  Connecticut  \\\n",
       "Year                                                                          \n",
       "1968      0.0   15.12     3.37      1.12       11.88      7.20        10.08   \n",
       "1969      0.0   14.33     3.19      1.07       11.26      6.83         9.56   \n",
       "1970      0.0   13.54     3.02      7.09       10.64      6.45        10.32   \n",
       "1971      0.0   12.99     2.89      6.80       10.20      6.18         9.89   \n",
       "1972      0.0   12.57     2.80      7.19        9.88      5.99        11.08   \n",
       "\n",
       "      Delaware  District of Columbia  Federal (FLSA)  ...  Tennessee  Texas  \\\n",
       "Year                                                  ...                     \n",
       "1968      9.00                  9.00            8.28  ...        0.0   0.00   \n",
       "1969      8.53                  8.53            7.85  ...        0.0   0.00   \n",
       "1970      8.06                 10.32            8.38  ...        0.0   0.00   \n",
       "1971      7.73                  9.89            8.04  ...        0.0   0.00   \n",
       "1972      9.58                  9.58            9.58  ...        0.0   8.38   \n",
       "\n",
       "      U.S. Virgin Islands  Utah  Vermont  Virginia  Washington  West Virginia  \\\n",
       "Year                                                                            \n",
       "1968                  NaN  7.20    10.08       0.0       11.52           7.20   \n",
       "1969                  NaN  6.83     9.56       0.0       10.92           6.83   \n",
       "1970                  NaN  6.45    10.32       0.0       10.32           6.45   \n",
       "1971                  NaN  6.18     9.89       0.0        9.89           6.18   \n",
       "1972                  NaN  7.19     9.58       0.0        9.58           7.19   \n",
       "\n",
       "      Wisconsin  Wyoming  \n",
       "Year                      \n",
       "1968       9.00     8.64  \n",
       "1969       8.53     8.19  \n",
       "1970       8.38     8.38  \n",
       "1971       8.04     8.04  \n",
       "1972       8.68     8.98  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "act_min_wage = pd.DataFrame()\n",
    "# gb = df.groupby('State')\n",
    "for name,group in gb:\n",
    "    if act_min_wage.empty:\n",
    "        act_min_wage = group.set_index('Year')[['Low.2018']].rename(columns={'Low.2018':name})\n",
    "    else:\n",
    "        act_min_wage = act_min_wage.join(group.set_index('Year')[['Low.2018']].rename(columns={'Low.2018':name}))\n",
    "\n",
    "act_min_wage.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alabama</th>\n",
       "      <th>Alaska</th>\n",
       "      <th>Arizona</th>\n",
       "      <th>Arkansas</th>\n",
       "      <th>California</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Connecticut</th>\n",
       "      <th>Delaware</th>\n",
       "      <th>District of Columbia</th>\n",
       "      <th>Federal (FLSA)</th>\n",
       "      <th>...</th>\n",
       "      <th>Tennessee</th>\n",
       "      <th>Texas</th>\n",
       "      <th>U.S. Virgin Islands</th>\n",
       "      <th>Utah</th>\n",
       "      <th>Vermont</th>\n",
       "      <th>Virginia</th>\n",
       "      <th>Washington</th>\n",
       "      <th>West Virginia</th>\n",
       "      <th>Wisconsin</th>\n",
       "      <th>Wyoming</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.583800</td>\n",
       "      <td>2.316200</td>\n",
       "      <td>6.781600</td>\n",
       "      <td>8.632800</td>\n",
       "      <td>6.389800</td>\n",
       "      <td>8.823600</td>\n",
       "      <td>7.498800</td>\n",
       "      <td>8.343200</td>\n",
       "      <td>7.773200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.541200</td>\n",
       "      <td>6.643590</td>\n",
       "      <td>6.836600</td>\n",
       "      <td>8.657400</td>\n",
       "      <td>5.897400</td>\n",
       "      <td>8.301800</td>\n",
       "      <td>7.166400</td>\n",
       "      <td>7.694200</td>\n",
       "      <td>5.149200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.845176</td>\n",
       "      <td>3.474504</td>\n",
       "      <td>1.341223</td>\n",
       "      <td>1.187508</td>\n",
       "      <td>1.521052</td>\n",
       "      <td>1.023613</td>\n",
       "      <td>1.265436</td>\n",
       "      <td>1.476133</td>\n",
       "      <td>0.887354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.202229</td>\n",
       "      <td>1.590176</td>\n",
       "      <td>0.819367</td>\n",
       "      <td>0.984647</td>\n",
       "      <td>2.726151</td>\n",
       "      <td>1.868613</td>\n",
       "      <td>0.793673</td>\n",
       "      <td>0.762675</td>\n",
       "      <td>1.998928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>6.420000</td>\n",
       "      <td>3.840000</td>\n",
       "      <td>6.820000</td>\n",
       "      <td>4.410000</td>\n",
       "      <td>5.510000</td>\n",
       "      <td>6.220000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.390000</td>\n",
       "      <td>4.790000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.410000</td>\n",
       "      <td>5.580000</td>\n",
       "      <td>6.420000</td>\n",
       "      <td>2.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.252500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.522500</td>\n",
       "      <td>7.695000</td>\n",
       "      <td>5.072500</td>\n",
       "      <td>7.865000</td>\n",
       "      <td>7.015000</td>\n",
       "      <td>7.097500</td>\n",
       "      <td>7.172500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.762500</td>\n",
       "      <td>5.105000</td>\n",
       "      <td>6.317500</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>5.892500</td>\n",
       "      <td>7.227500</td>\n",
       "      <td>6.630000</td>\n",
       "      <td>7.107500</td>\n",
       "      <td>3.270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.890000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.920000</td>\n",
       "      <td>8.670000</td>\n",
       "      <td>6.375000</td>\n",
       "      <td>9.075000</td>\n",
       "      <td>7.845000</td>\n",
       "      <td>8.510000</td>\n",
       "      <td>7.655000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.070000</td>\n",
       "      <td>6.770000</td>\n",
       "      <td>6.930000</td>\n",
       "      <td>8.885000</td>\n",
       "      <td>6.855000</td>\n",
       "      <td>9.055000</td>\n",
       "      <td>7.075000</td>\n",
       "      <td>7.580000</td>\n",
       "      <td>5.445000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.492500</td>\n",
       "      <td>3.147500</td>\n",
       "      <td>7.290000</td>\n",
       "      <td>9.307500</td>\n",
       "      <td>7.677500</td>\n",
       "      <td>9.455000</td>\n",
       "      <td>8.327500</td>\n",
       "      <td>9.140000</td>\n",
       "      <td>8.242500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.105000</td>\n",
       "      <td>7.830000</td>\n",
       "      <td>7.470000</td>\n",
       "      <td>9.372500</td>\n",
       "      <td>7.620000</td>\n",
       "      <td>9.672500</td>\n",
       "      <td>7.612500</td>\n",
       "      <td>8.145000</td>\n",
       "      <td>6.607500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.120000</td>\n",
       "      <td>10.220000</td>\n",
       "      <td>8.680000</td>\n",
       "      <td>11.880000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>11.080000</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.010000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.380000</td>\n",
       "      <td>10.010000</td>\n",
       "      <td>8.330000</td>\n",
       "      <td>10.320000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>11.520000</td>\n",
       "      <td>9.130000</td>\n",
       "      <td>9.660000</td>\n",
       "      <td>8.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Alabama     Alaska    Arizona   Arkansas  California   Colorado  \\\n",
       "count     50.0  50.000000  50.000000  50.000000   50.000000  50.000000   \n",
       "mean       0.0   9.583800   2.316200   6.781600    8.632800   6.389800   \n",
       "std        0.0   1.845176   3.474504   1.341223    1.187508   1.521052   \n",
       "min        0.0   7.380000   0.000000   1.070000    6.420000   3.840000   \n",
       "25%        0.0   8.252500   0.000000   6.522500    7.695000   5.072500   \n",
       "50%        0.0   8.890000   0.000000   6.920000    8.670000   6.375000   \n",
       "75%        0.0  10.492500   3.147500   7.290000    9.307500   7.677500   \n",
       "max        0.0  15.120000  10.220000   8.680000   11.880000   9.500000   \n",
       "\n",
       "       Connecticut   Delaware  District of Columbia  Federal (FLSA)  ...  \\\n",
       "count    50.000000  50.000000             50.000000       50.000000  ...   \n",
       "mean      8.823600   7.498800              8.343200        7.773200  ...   \n",
       "std       1.023613   1.265436              1.476133        0.887354  ...   \n",
       "min       6.820000   4.410000              5.510000        6.220000  ...   \n",
       "25%       7.865000   7.015000              7.097500        7.172500  ...   \n",
       "50%       9.075000   7.845000              8.510000        7.655000  ...   \n",
       "75%       9.455000   8.327500              9.140000        8.242500  ...   \n",
       "max      11.080000   9.580000             12.000000       10.010000  ...   \n",
       "\n",
       "       Tennessee      Texas  U.S. Virgin Islands       Utah    Vermont  \\\n",
       "count       50.0  50.000000            39.000000  50.000000  50.000000   \n",
       "mean         0.0   5.541200             6.643590   6.836600   8.657400   \n",
       "std          0.0   2.202229             1.590176   0.819367   0.984647   \n",
       "min          0.0   0.000000             4.390000   4.790000   6.800000   \n",
       "25%          0.0   4.762500             5.105000   6.317500   7.800000   \n",
       "50%          0.0   6.070000             6.770000   6.930000   8.885000   \n",
       "75%          0.0   7.105000             7.830000   7.470000   9.372500   \n",
       "max          0.0   8.380000            10.010000   8.330000  10.320000   \n",
       "\n",
       "        Virginia  Washington  West Virginia  Wisconsin    Wyoming  \n",
       "count  50.000000   50.000000      50.000000  50.000000  50.000000  \n",
       "mean    5.897400    8.301800       7.166400   7.694200   5.149200  \n",
       "std     2.726151    1.868613       0.793673   0.762675   1.998928  \n",
       "min     0.000000    4.410000       5.580000   6.420000   2.260000  \n",
       "25%     5.892500    7.227500       6.630000   7.107500   3.270000  \n",
       "50%     6.855000    9.055000       7.075000   7.580000   5.445000  \n",
       "75%     7.620000    9.672500       7.612500   8.145000   6.607500  \n",
       "max     8.800000   11.520000       9.130000   9.660000   8.980000  \n",
       "\n",
       "[8 rows x 55 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "act_min_wage.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alabama</th>\n",
       "      <th>Alaska</th>\n",
       "      <th>Arizona</th>\n",
       "      <th>Arkansas</th>\n",
       "      <th>California</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Connecticut</th>\n",
       "      <th>Delaware</th>\n",
       "      <th>District of Columbia</th>\n",
       "      <th>Federal (FLSA)</th>\n",
       "      <th>...</th>\n",
       "      <th>Tennessee</th>\n",
       "      <th>Texas</th>\n",
       "      <th>U.S. Virgin Islands</th>\n",
       "      <th>Utah</th>\n",
       "      <th>Vermont</th>\n",
       "      <th>Virginia</th>\n",
       "      <th>Washington</th>\n",
       "      <th>West Virginia</th>\n",
       "      <th>Wisconsin</th>\n",
       "      <th>Wyoming</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018638</td>\n",
       "      <td>-0.377934</td>\n",
       "      <td>0.717653</td>\n",
       "      <td>-0.129332</td>\n",
       "      <td>0.680886</td>\n",
       "      <td>0.258997</td>\n",
       "      <td>0.321785</td>\n",
       "      <td>0.637679</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.522472</td>\n",
       "      <td>0.277016</td>\n",
       "      <td>-0.020462</td>\n",
       "      <td>0.663558</td>\n",
       "      <td>-0.601640</td>\n",
       "      <td>0.411593</td>\n",
       "      <td>0.044814</td>\n",
       "      <td>0.702570</td>\n",
       "      <td>0.727932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018638</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089395</td>\n",
       "      <td>0.498994</td>\n",
       "      <td>0.742527</td>\n",
       "      <td>0.458163</td>\n",
       "      <td>0.429836</td>\n",
       "      <td>0.590848</td>\n",
       "      <td>-0.049914</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.369900</td>\n",
       "      <td>-0.758607</td>\n",
       "      <td>0.439344</td>\n",
       "      <td>0.517960</td>\n",
       "      <td>0.047405</td>\n",
       "      <td>0.587975</td>\n",
       "      <td>0.584596</td>\n",
       "      <td>0.131588</td>\n",
       "      <td>0.346528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.377934</td>\n",
       "      <td>0.089395</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.234367</td>\n",
       "      <td>0.135749</td>\n",
       "      <td>0.047580</td>\n",
       "      <td>0.016125</td>\n",
       "      <td>0.266889</td>\n",
       "      <td>0.117245</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.503242</td>\n",
       "      <td>-0.204485</td>\n",
       "      <td>0.194680</td>\n",
       "      <td>0.087429</td>\n",
       "      <td>0.582192</td>\n",
       "      <td>-0.072343</td>\n",
       "      <td>0.420819</td>\n",
       "      <td>0.000470</td>\n",
       "      <td>-0.250592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.717653</td>\n",
       "      <td>0.498994</td>\n",
       "      <td>-0.234367</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.483313</td>\n",
       "      <td>0.876215</td>\n",
       "      <td>0.479197</td>\n",
       "      <td>0.596865</td>\n",
       "      <td>0.371966</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.239533</td>\n",
       "      <td>-0.417782</td>\n",
       "      <td>0.392898</td>\n",
       "      <td>0.877922</td>\n",
       "      <td>-0.326364</td>\n",
       "      <td>0.754085</td>\n",
       "      <td>0.371765</td>\n",
       "      <td>0.584067</td>\n",
       "      <td>0.722617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Alabama    Alaska   Arizona  Arkansas  California  Colorado  \\\n",
       "Alabama         NaN       NaN       NaN       NaN         NaN       NaN   \n",
       "Alaska          NaN  1.000000  0.018638 -0.377934    0.717653 -0.129332   \n",
       "Arizona         NaN  0.018638  1.000000  0.089395    0.498994  0.742527   \n",
       "Arkansas        NaN -0.377934  0.089395  1.000000   -0.234367  0.135749   \n",
       "California      NaN  0.717653  0.498994 -0.234367    1.000000  0.483313   \n",
       "\n",
       "            Connecticut  Delaware  District of Columbia  Federal (FLSA)  ...  \\\n",
       "Alabama             NaN       NaN                   NaN             NaN  ...   \n",
       "Alaska         0.680886  0.258997              0.321785        0.637679  ...   \n",
       "Arizona        0.458163  0.429836              0.590848       -0.049914  ...   \n",
       "Arkansas       0.047580  0.016125              0.266889        0.117245  ...   \n",
       "California     0.876215  0.479197              0.596865        0.371966  ...   \n",
       "\n",
       "            Tennessee     Texas  U.S. Virgin Islands      Utah   Vermont  \\\n",
       "Alabama           NaN       NaN                  NaN       NaN       NaN   \n",
       "Alaska            NaN -0.522472             0.277016 -0.020462  0.663558   \n",
       "Arizona           NaN  0.369900            -0.758607  0.439344  0.517960   \n",
       "Arkansas          NaN  0.503242            -0.204485  0.194680  0.087429   \n",
       "California        NaN -0.239533            -0.417782  0.392898  0.877922   \n",
       "\n",
       "            Virginia  Washington  West Virginia  Wisconsin   Wyoming  \n",
       "Alabama          NaN         NaN            NaN        NaN       NaN  \n",
       "Alaska     -0.601640    0.411593       0.044814   0.702570  0.727932  \n",
       "Arizona     0.047405    0.587975       0.584596   0.131588  0.346528  \n",
       "Arkansas    0.582192   -0.072343       0.420819   0.000470 -0.250592  \n",
       "California -0.326364    0.754085       0.371765   0.584067  0.722617  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "act_min_wage.corr().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Alabama', 'Florida', 'Georgia', 'Illinois', 'Iowa', 'Kansas',\n",
       "       'Louisiana', 'Mississippi', 'Missouri', 'Montana',\n",
       "       'South Carolina', 'Tennessee', 'Texas', 'Virginia', 'Arizona'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issue_df = df[df['Low.2018']==0 ]\n",
    "issue_df['State'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Alaska</th>\n",
       "      <th>Arkansas</th>\n",
       "      <th>California</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Connecticut</th>\n",
       "      <th>Delaware</th>\n",
       "      <th>District of Columbia</th>\n",
       "      <th>Federal (FLSA)</th>\n",
       "      <th>Guam</th>\n",
       "      <th>Hawaii</th>\n",
       "      <th>...</th>\n",
       "      <th>Pennsylvania</th>\n",
       "      <th>Puerto Rico</th>\n",
       "      <th>Rhode Island</th>\n",
       "      <th>South Dakota</th>\n",
       "      <th>Utah</th>\n",
       "      <th>Vermont</th>\n",
       "      <th>Washington</th>\n",
       "      <th>West Virginia</th>\n",
       "      <th>Wisconsin</th>\n",
       "      <th>Wyoming</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.377934</td>\n",
       "      <td>0.717653</td>\n",
       "      <td>-0.129332</td>\n",
       "      <td>0.680886</td>\n",
       "      <td>0.258997</td>\n",
       "      <td>0.321785</td>\n",
       "      <td>0.637679</td>\n",
       "      <td>0.787915</td>\n",
       "      <td>0.672620</td>\n",
       "      <td>...</td>\n",
       "      <td>0.610814</td>\n",
       "      <td>-0.038118</td>\n",
       "      <td>0.652353</td>\n",
       "      <td>-0.326316</td>\n",
       "      <td>-0.020462</td>\n",
       "      <td>0.663558</td>\n",
       "      <td>0.411593</td>\n",
       "      <td>0.044814</td>\n",
       "      <td>0.702570</td>\n",
       "      <td>0.727932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>-0.377934</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.234367</td>\n",
       "      <td>0.135749</td>\n",
       "      <td>0.047580</td>\n",
       "      <td>0.016125</td>\n",
       "      <td>0.266889</td>\n",
       "      <td>0.117245</td>\n",
       "      <td>0.039593</td>\n",
       "      <td>0.204801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159923</td>\n",
       "      <td>0.232186</td>\n",
       "      <td>0.003498</td>\n",
       "      <td>0.800116</td>\n",
       "      <td>0.194680</td>\n",
       "      <td>0.087429</td>\n",
       "      <td>-0.072343</td>\n",
       "      <td>0.420819</td>\n",
       "      <td>0.000470</td>\n",
       "      <td>-0.250592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>0.717653</td>\n",
       "      <td>-0.234367</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.483313</td>\n",
       "      <td>0.876215</td>\n",
       "      <td>0.479197</td>\n",
       "      <td>0.596865</td>\n",
       "      <td>0.371966</td>\n",
       "      <td>0.492052</td>\n",
       "      <td>0.519241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429061</td>\n",
       "      <td>0.512712</td>\n",
       "      <td>0.780916</td>\n",
       "      <td>-0.036787</td>\n",
       "      <td>0.392898</td>\n",
       "      <td>0.877922</td>\n",
       "      <td>0.754085</td>\n",
       "      <td>0.371765</td>\n",
       "      <td>0.584067</td>\n",
       "      <td>0.722617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>-0.129332</td>\n",
       "      <td>0.135749</td>\n",
       "      <td>0.483313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.402020</td>\n",
       "      <td>0.566304</td>\n",
       "      <td>0.673371</td>\n",
       "      <td>-0.232035</td>\n",
       "      <td>-0.192616</td>\n",
       "      <td>0.069800</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.136195</td>\n",
       "      <td>0.657364</td>\n",
       "      <td>0.429852</td>\n",
       "      <td>0.399137</td>\n",
       "      <td>0.622330</td>\n",
       "      <td>0.448485</td>\n",
       "      <td>0.612637</td>\n",
       "      <td>0.533623</td>\n",
       "      <td>0.011501</td>\n",
       "      <td>0.130053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>0.680886</td>\n",
       "      <td>0.047580</td>\n",
       "      <td>0.876215</td>\n",
       "      <td>0.402020</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.552613</td>\n",
       "      <td>0.652488</td>\n",
       "      <td>0.487750</td>\n",
       "      <td>0.632073</td>\n",
       "      <td>0.621503</td>\n",
       "      <td>...</td>\n",
       "      <td>0.531769</td>\n",
       "      <td>0.626712</td>\n",
       "      <td>0.802485</td>\n",
       "      <td>0.105707</td>\n",
       "      <td>0.302538</td>\n",
       "      <td>0.898469</td>\n",
       "      <td>0.715691</td>\n",
       "      <td>0.400099</td>\n",
       "      <td>0.585790</td>\n",
       "      <td>0.814971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Alaska  Arkansas  California  Colorado  Connecticut  Delaware  \\\n",
       "Alaska       1.000000 -0.377934    0.717653 -0.129332     0.680886  0.258997   \n",
       "Arkansas    -0.377934  1.000000   -0.234367  0.135749     0.047580  0.016125   \n",
       "California   0.717653 -0.234367    1.000000  0.483313     0.876215  0.479197   \n",
       "Colorado    -0.129332  0.135749    0.483313  1.000000     0.402020  0.566304   \n",
       "Connecticut  0.680886  0.047580    0.876215  0.402020     1.000000  0.552613   \n",
       "\n",
       "             District of Columbia  Federal (FLSA)      Guam    Hawaii  ...  \\\n",
       "Alaska                   0.321785        0.637679  0.787915  0.672620  ...   \n",
       "Arkansas                 0.266889        0.117245  0.039593  0.204801  ...   \n",
       "California               0.596865        0.371966  0.492052  0.519241  ...   \n",
       "Colorado                 0.673371       -0.232035 -0.192616  0.069800  ...   \n",
       "Connecticut              0.652488        0.487750  0.632073  0.621503  ...   \n",
       "\n",
       "             Pennsylvania  Puerto Rico  Rhode Island  South Dakota      Utah  \\\n",
       "Alaska           0.610814    -0.038118      0.652353     -0.326316 -0.020462   \n",
       "Arkansas         0.159923     0.232186      0.003498      0.800116  0.194680   \n",
       "California       0.429061     0.512712      0.780916     -0.036787  0.392898   \n",
       "Colorado        -0.136195     0.657364      0.429852      0.399137  0.622330   \n",
       "Connecticut      0.531769     0.626712      0.802485      0.105707  0.302538   \n",
       "\n",
       "              Vermont  Washington  West Virginia  Wisconsin   Wyoming  \n",
       "Alaska       0.663558    0.411593       0.044814   0.702570  0.727932  \n",
       "Arkansas     0.087429   -0.072343       0.420819   0.000470 -0.250592  \n",
       "California   0.877922    0.754085       0.371765   0.584067  0.722617  \n",
       "Colorado     0.448485    0.612637       0.533623   0.011501  0.130053  \n",
       "Connecticut  0.898469    0.715691       0.400099   0.585790  0.814971  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "act_min_wage.replace(0,np.NaN).dropna(axis=1).corr().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_wage_corr = act_min_wage.replace(0,np.NaN).dropna(axis=1).corr().head()\n",
    "\n",
    "for problem in iss['State']unqiue():\n",
    "    if problem`in min_wage_corr:\n",
    "        print('We are missing something here')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Table_Data</th>\n",
       "      <th>Footnote</th>\n",
       "      <th>High.Value</th>\n",
       "      <th>Low.Value</th>\n",
       "      <th>CPI.Average</th>\n",
       "      <th>High.2018</th>\n",
       "      <th>Low.2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1968</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>1969</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.683333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>110</td>\n",
       "      <td>1970</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.825000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>165</td>\n",
       "      <td>1971</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.491667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>220</td>\n",
       "      <td>1972</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.816667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Year    State Table_Data Footnote  High.Value  Low.Value  \\\n",
       "0             0  1968  Alabama        ...      NaN         0.0        0.0   \n",
       "55           55  1969  Alabama        ...      NaN         0.0        0.0   \n",
       "110         110  1970  Alabama        ...      NaN         0.0        0.0   \n",
       "165         165  1971  Alabama        ...      NaN         0.0        0.0   \n",
       "220         220  1972  Alabama        ...      NaN         0.0        0.0   \n",
       "\n",
       "     CPI.Average  High.2018  Low.2018  \n",
       "0      34.783333        0.0       0.0  \n",
       "55     36.683333        0.0       0.0  \n",
       "110    38.825000        0.0       0.0  \n",
       "165    40.491667        0.0       0.0  \n",
       "220    41.816667        0.0       0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_issues = issue_df.groupby('State')\n",
    "\n",
    "grouped_issues.get_group('Alabama').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_issues.get_group('Alabama')['Low.2018'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for state,data in grouped_issues:\n",
    "    \n",
    "    if data['Low.2018'].sum() != 0.0:\n",
    "        print('We missed something')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>State</th>\n",
       "      <th>Table_Data</th>\n",
       "      <th>Footnote</th>\n",
       "      <th>High.Value</th>\n",
       "      <th>Low.Value</th>\n",
       "      <th>CPI.Average</th>\n",
       "      <th>High.2018</th>\n",
       "      <th>Low.2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1968</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1968</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.10000</td>\n",
       "      <td>2.10000</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>15.12</td>\n",
       "      <td>15.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1968</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>18.72 - 26.40/wk(b)</td>\n",
       "      <td>(b)</td>\n",
       "      <td>0.66000</td>\n",
       "      <td>0.46800</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>4.75</td>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1968</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1.25/day(b)</td>\n",
       "      <td>(b)</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1968</td>\n",
       "      <td>California</td>\n",
       "      <td>1.65(b)</td>\n",
       "      <td>(b)</td>\n",
       "      <td>1.65000</td>\n",
       "      <td>1.65000</td>\n",
       "      <td>34.783333</td>\n",
       "      <td>11.88</td>\n",
       "      <td>11.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Year       State           Table_Data Footnote  High.Value  \\\n",
       "0           0  1968     Alabama                  ...      NaN     0.00000   \n",
       "1           1  1968      Alaska                 2.10      NaN     2.10000   \n",
       "2           2  1968     Arizona  18.72 - 26.40/wk(b)      (b)     0.66000   \n",
       "3           3  1968    Arkansas          1.25/day(b)      (b)     0.15625   \n",
       "4           4  1968  California              1.65(b)      (b)     1.65000   \n",
       "\n",
       "   Low.Value  CPI.Average  High.2018  Low.2018  \n",
       "0    0.00000    34.783333       0.00      0.00  \n",
       "1    2.10000    34.783333      15.12     15.12  \n",
       "2    0.46800    34.783333       4.75      3.37  \n",
       "3    0.15625    34.783333       1.12      1.12  \n",
       "4    1.65000    34.783333      11.88     11.88  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('minwage.csv')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Alaska</th>\n",
       "      <th>Arkansas</th>\n",
       "      <th>California</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Connecticut</th>\n",
       "      <th>Delaware</th>\n",
       "      <th>District of Columbia</th>\n",
       "      <th>Federal (FLSA)</th>\n",
       "      <th>Guam</th>\n",
       "      <th>Hawaii</th>\n",
       "      <th>...</th>\n",
       "      <th>Pennsylvania</th>\n",
       "      <th>Puerto Rico</th>\n",
       "      <th>Rhode Island</th>\n",
       "      <th>South Dakota</th>\n",
       "      <th>Utah</th>\n",
       "      <th>Vermont</th>\n",
       "      <th>Washington</th>\n",
       "      <th>West Virginia</th>\n",
       "      <th>Wisconsin</th>\n",
       "      <th>Wyoming</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.377934</td>\n",
       "      <td>0.717653</td>\n",
       "      <td>-0.129332</td>\n",
       "      <td>0.680886</td>\n",
       "      <td>0.258997</td>\n",
       "      <td>0.321785</td>\n",
       "      <td>0.637679</td>\n",
       "      <td>0.787915</td>\n",
       "      <td>0.672620</td>\n",
       "      <td>...</td>\n",
       "      <td>0.610814</td>\n",
       "      <td>-0.038118</td>\n",
       "      <td>0.652353</td>\n",
       "      <td>-0.326316</td>\n",
       "      <td>-0.020462</td>\n",
       "      <td>0.663558</td>\n",
       "      <td>0.411593</td>\n",
       "      <td>0.044814</td>\n",
       "      <td>0.702570</td>\n",
       "      <td>0.727932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>-0.377934</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.234367</td>\n",
       "      <td>0.135749</td>\n",
       "      <td>0.047580</td>\n",
       "      <td>0.016125</td>\n",
       "      <td>0.266889</td>\n",
       "      <td>0.117245</td>\n",
       "      <td>0.039593</td>\n",
       "      <td>0.204801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159923</td>\n",
       "      <td>0.232186</td>\n",
       "      <td>0.003498</td>\n",
       "      <td>0.800116</td>\n",
       "      <td>0.194680</td>\n",
       "      <td>0.087429</td>\n",
       "      <td>-0.072343</td>\n",
       "      <td>0.420819</td>\n",
       "      <td>0.000470</td>\n",
       "      <td>-0.250592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>0.717653</td>\n",
       "      <td>-0.234367</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.483313</td>\n",
       "      <td>0.876215</td>\n",
       "      <td>0.479197</td>\n",
       "      <td>0.596865</td>\n",
       "      <td>0.371966</td>\n",
       "      <td>0.492052</td>\n",
       "      <td>0.519241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429061</td>\n",
       "      <td>0.512712</td>\n",
       "      <td>0.780916</td>\n",
       "      <td>-0.036787</td>\n",
       "      <td>0.392898</td>\n",
       "      <td>0.877922</td>\n",
       "      <td>0.754085</td>\n",
       "      <td>0.371765</td>\n",
       "      <td>0.584067</td>\n",
       "      <td>0.722617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>-0.129332</td>\n",
       "      <td>0.135749</td>\n",
       "      <td>0.483313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.402020</td>\n",
       "      <td>0.566304</td>\n",
       "      <td>0.673371</td>\n",
       "      <td>-0.232035</td>\n",
       "      <td>-0.192616</td>\n",
       "      <td>0.069800</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.136195</td>\n",
       "      <td>0.657364</td>\n",
       "      <td>0.429852</td>\n",
       "      <td>0.399137</td>\n",
       "      <td>0.622330</td>\n",
       "      <td>0.448485</td>\n",
       "      <td>0.612637</td>\n",
       "      <td>0.533623</td>\n",
       "      <td>0.011501</td>\n",
       "      <td>0.130053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>0.680886</td>\n",
       "      <td>0.047580</td>\n",
       "      <td>0.876215</td>\n",
       "      <td>0.402020</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.552613</td>\n",
       "      <td>0.652488</td>\n",
       "      <td>0.487750</td>\n",
       "      <td>0.632073</td>\n",
       "      <td>0.621503</td>\n",
       "      <td>...</td>\n",
       "      <td>0.531769</td>\n",
       "      <td>0.626712</td>\n",
       "      <td>0.802485</td>\n",
       "      <td>0.105707</td>\n",
       "      <td>0.302538</td>\n",
       "      <td>0.898469</td>\n",
       "      <td>0.715691</td>\n",
       "      <td>0.400099</td>\n",
       "      <td>0.585790</td>\n",
       "      <td>0.814971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Alaska  Arkansas  California  Colorado  Connecticut  Delaware  \\\n",
       "Alaska       1.000000 -0.377934    0.717653 -0.129332     0.680886  0.258997   \n",
       "Arkansas    -0.377934  1.000000   -0.234367  0.135749     0.047580  0.016125   \n",
       "California   0.717653 -0.234367    1.000000  0.483313     0.876215  0.479197   \n",
       "Colorado    -0.129332  0.135749    0.483313  1.000000     0.402020  0.566304   \n",
       "Connecticut  0.680886  0.047580    0.876215  0.402020     1.000000  0.552613   \n",
       "\n",
       "             District of Columbia  Federal (FLSA)      Guam    Hawaii  ...  \\\n",
       "Alaska                   0.321785        0.637679  0.787915  0.672620  ...   \n",
       "Arkansas                 0.266889        0.117245  0.039593  0.204801  ...   \n",
       "California               0.596865        0.371966  0.492052  0.519241  ...   \n",
       "Colorado                 0.673371       -0.232035 -0.192616  0.069800  ...   \n",
       "Connecticut              0.652488        0.487750  0.632073  0.621503  ...   \n",
       "\n",
       "             Pennsylvania  Puerto Rico  Rhode Island  South Dakota      Utah  \\\n",
       "Alaska           0.610814    -0.038118      0.652353     -0.326316 -0.020462   \n",
       "Arkansas         0.159923     0.232186      0.003498      0.800116  0.194680   \n",
       "California       0.429061     0.512712      0.780916     -0.036787  0.392898   \n",
       "Colorado        -0.136195     0.657364      0.429852      0.399137  0.622330   \n",
       "Connecticut      0.531769     0.626712      0.802485      0.105707  0.302538   \n",
       "\n",
       "              Vermont  Washington  West Virginia  Wisconsin   Wyoming  \n",
       "Alaska       0.663558    0.411593       0.044814   0.702570  0.727932  \n",
       "Arkansas     0.087429   -0.072343       0.420819   0.000470 -0.250592  \n",
       "California   0.877922    0.754085       0.371765   0.584067  0.722617  \n",
       "Colorado     0.448485    0.612637       0.533623   0.011501  0.130053  \n",
       "Connecticut  0.898469    0.715691       0.400099   0.585790  0.814971  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "act_min_wage = pd.DataFrame()\n",
    "# gb = df.groupby('State')\n",
    "for name,group in gb:\n",
    "    if act_min_wage.empty:\n",
    "        act_min_wage = group.set_index('Year')[['Low.2018']].rename(columns={'Low.2018':name})\n",
    "    else:\n",
    "        act_min_wage = act_min_wage.join(group.set_index('Year')[['Low.2018']].rename(columns={'Low.2018':name}))\n",
    "\n",
    "\n",
    "min_wage_corr = act_min_wage.replace(0,np.NaN).dropna(axis=1).corr()\n",
    "\n",
    "min_wage_corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [c[:2] for c in min_wage_corr.columns]\n",
    "\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.matshow(min_wage_corr,cmap=plt.cm.RdYlGn)\n",
    "\n",
    "ax.set_xticks(np.arange(len(labels)))\n",
    "ax.set_yticks(np.arange(len(labels)))\n",
    "\n",
    "ax.set_xticklabels(labels);\n",
    "ax.set_yticklabels(labels);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "web_access = requests.get('https://www.infoplease.com/us/postal-information/state-abbreviations-and-state-postal-codes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_web = pd.read_html(web_access.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for df in data_web:\n",
    "#     print(data.head())\n",
    "\n",
    "state_abbv = data_web[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_abbv.to_csv('state_abbv.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>Postal Code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State/District</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>Ala.</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>Ariz.</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>Ark.</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>Calif.</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Abbreviation Postal Code\n",
       "State/District                         \n",
       "Alabama                Ala.          AL\n",
       "Alaska               Alaska          AK\n",
       "Arizona               Ariz.          AZ\n",
       "Arkansas               Ark.          AR\n",
       "California           Calif.          CA"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_abbv = pd.read_csv('state_abbv.csv', index_col=0)\n",
    "state_abbv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "abbv_dict = state_abbv[['Postal Code']].to_dict()\n",
    "abbv_dict = abbv_dict['Postal Code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Puerto Rico'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-69-cfc5393f894f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mabbv_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmin_wage_corr\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[0m\u001b[0;32m      2\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-69-cfc5393f894f>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mabbv_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmin_wage_corr\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[0m\u001b[0;32m      2\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Puerto Rico'"
     ]
    }
   ],
   "source": [
    "labels = [abbv_dict[c] for c in min_wage_corr.columns]\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "abbv_dict['Federal (FLSA)'] = 'FLSA'\n",
    "abbv_dict['Guam'] = 'GU'\n",
    "abbv_dict['Puerto Rico'] = 'PR'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AK',\n",
       " 'AR',\n",
       " 'CA',\n",
       " 'CO',\n",
       " 'CT',\n",
       " 'DE',\n",
       " 'DC',\n",
       " 'FLSA',\n",
       " 'GU',\n",
       " 'HI',\n",
       " 'ID',\n",
       " 'IN',\n",
       " 'KY',\n",
       " 'ME',\n",
       " 'MD',\n",
       " 'MA',\n",
       " 'MI',\n",
       " 'MN',\n",
       " 'NE',\n",
       " 'NV',\n",
       " 'NH',\n",
       " 'NJ',\n",
       " 'NM',\n",
       " 'NY',\n",
       " 'NC',\n",
       " 'ND',\n",
       " 'OH',\n",
       " 'OK',\n",
       " 'OR',\n",
       " 'PA',\n",
       " 'PR',\n",
       " 'RI',\n",
       " 'SD',\n",
       " 'UT',\n",
       " 'VT',\n",
       " 'WA',\n",
       " 'WV',\n",
       " 'WI',\n",
       " 'WY']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [abbv_dict[c] for c in min_wage_corr.columns]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [abbv_dict[c] for c in min_wage_corr.columns]\n",
    "\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.matshow(min_wage_corr,cmap=plt.cm.RdYlGn)\n",
    "\n",
    "ax.set_xticks(np.arange(len(labels)))\n",
    "ax.set_yticks(np.arange(len(labels)))\n",
    "\n",
    "ax.set_xticklabels(labels);\n",
    "ax.set_yticklabels(labels);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}