Stage 1 - Lesson 5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#conversion to csv\n",
    "import pandas as pd\n",
    "fuel_data = pd.read_csv('https://raw.githubusercontent.com/WalePhenomenon/climate_change/master/fuel_ferc1.csv')\n",
    "fuel_data.to_csv('fuel_ferc1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>utility_id_ferc1</th>\n",
       "      <th>report_year</th>\n",
       "      <th>plant_name_ferc1</th>\n",
       "      <th>fuel_type_code_pudl</th>\n",
       "      <th>fuel_unit</th>\n",
       "      <th>fuel_qty_burned</th>\n",
       "      <th>fuel_mmbtu_per_unit</th>\n",
       "      <th>fuel_cost_per_unit_burned</th>\n",
       "      <th>fuel_cost_per_unit_delivered</th>\n",
       "      <th>fuel_cost_per_mmbtu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29523</td>\n",
       "      <td>29523.000000</td>\n",
       "      <td>29523.000000</td>\n",
       "      <td>29523</td>\n",
       "      <td>29523</td>\n",
       "      <td>29343</td>\n",
       "      <td>2.952300e+04</td>\n",
       "      <td>29523.000000</td>\n",
       "      <td>29523.000000</td>\n",
       "      <td>2.952300e+04</td>\n",
       "      <td>29523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>29523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2315</td>\n",
       "      <td>6</td>\n",
       "      <td>9</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>top</th>\n",
       "      <td>f1_fuel_2005_12_95_1_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>big stone</td>\n",
       "      <td>gas</td>\n",
       "      <td>mcf</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>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156</td>\n",
       "      <td>11486</td>\n",
       "      <td>11354</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>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>118.601836</td>\n",
       "      <td>2005.806050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.622119e+06</td>\n",
       "      <td>8.492111</td>\n",
       "      <td>208.649031</td>\n",
       "      <td>9.175704e+02</td>\n",
       "      <td>19.304354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>74.178353</td>\n",
       "      <td>7.025483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.118004e+06</td>\n",
       "      <td>10.600220</td>\n",
       "      <td>2854.490090</td>\n",
       "      <td>6.877593e+04</td>\n",
       "      <td>2091.540939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-276.080000</td>\n",
       "      <td>-8.749370e+02</td>\n",
       "      <td>-41.501000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.381700e+04</td>\n",
       "      <td>1.024000</td>\n",
       "      <td>5.207000</td>\n",
       "      <td>3.778500e+00</td>\n",
       "      <td>1.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.533220e+05</td>\n",
       "      <td>5.762694</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.737100e+01</td>\n",
       "      <td>4.127000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.424034e+06</td>\n",
       "      <td>17.006000</td>\n",
       "      <td>47.113000</td>\n",
       "      <td>4.213700e+01</td>\n",
       "      <td>7.745000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.558942e+08</td>\n",
       "      <td>341.260000</td>\n",
       "      <td>139358.000000</td>\n",
       "      <td>7.964521e+06</td>\n",
       "      <td>359278.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     record_id  utility_id_ferc1   report_year  \\\n",
       "count                    29523      29523.000000  29523.000000   \n",
       "unique                   29523               NaN           NaN   \n",
       "top     f1_fuel_2005_12_95_1_1               NaN           NaN   \n",
       "freq                         1               NaN           NaN   \n",
       "mean                       NaN        118.601836   2005.806050   \n",
       "std                        NaN         74.178353      7.025483   \n",
       "min                        NaN          1.000000   1994.000000   \n",
       "25%                        NaN         55.000000   2000.000000   \n",
       "50%                        NaN        122.000000   2006.000000   \n",
       "75%                        NaN        176.000000   2012.000000   \n",
       "max                        NaN        514.000000   2018.000000   \n",
       "\n",
       "       plant_name_ferc1 fuel_type_code_pudl fuel_unit  fuel_qty_burned  \\\n",
       "count             29523               29523     29343     2.952300e+04   \n",
       "unique             2315                   6         9              NaN   \n",
       "top           big stone                 gas       mcf              NaN   \n",
       "freq                156               11486     11354              NaN   \n",
       "mean                NaN                 NaN       NaN     2.622119e+06   \n",
       "std                 NaN                 NaN       NaN     9.118004e+06   \n",
       "min                 NaN                 NaN       NaN     1.000000e+00   \n",
       "25%                 NaN                 NaN       NaN     1.381700e+04   \n",
       "50%                 NaN                 NaN       NaN     2.533220e+05   \n",
       "75%                 NaN                 NaN       NaN     1.424034e+06   \n",
       "max                 NaN                 NaN       NaN     5.558942e+08   \n",
       "\n",
       "        fuel_mmbtu_per_unit  fuel_cost_per_unit_burned  \\\n",
       "count          29523.000000               29523.000000   \n",
       "unique                  NaN                        NaN   \n",
       "top                     NaN                        NaN   \n",
       "freq                    NaN                        NaN   \n",
       "mean               8.492111                 208.649031   \n",
       "std               10.600220                2854.490090   \n",
       "min                0.000001                -276.080000   \n",
       "25%                1.024000                   5.207000   \n",
       "50%                5.762694                  26.000000   \n",
       "75%               17.006000                  47.113000   \n",
       "max              341.260000              139358.000000   \n",
       "\n",
       "        fuel_cost_per_unit_delivered  fuel_cost_per_mmbtu  \n",
       "count                   2.952300e+04         29523.000000  \n",
       "unique                           NaN                  NaN  \n",
       "top                              NaN                  NaN  \n",
       "freq                             NaN                  NaN  \n",
       "mean                    9.175704e+02            19.304354  \n",
       "std                     6.877593e+04          2091.540939  \n",
       "min                    -8.749370e+02           -41.501000  \n",
       "25%                     3.778500e+00             1.940000  \n",
       "50%                     1.737100e+01             4.127000  \n",
       "75%                     4.213700e+01             7.745000  \n",
       "max                     7.964521e+06        359278.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading data and description\n",
    "import pandas as pd\n",
    "fuel_data = pd.read_csv('fuel_ferc1.csv')\n",
    "fuel_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "record_id                         0\n",
       "utility_id_ferc1                  0\n",
       "report_year                       0\n",
       "plant_name_ferc1                  0\n",
       "fuel_type_code_pudl               0\n",
       "fuel_unit                       180\n",
       "fuel_qty_burned                   0\n",
       "fuel_mmbtu_per_unit               0\n",
       "fuel_cost_per_unit_burned         0\n",
       "fuel_cost_per_unit_delivered      0\n",
       "fuel_cost_per_mmbtu               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values\n",
    "fuel_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use groupby to count the sum of each unique value in the fuel unit column\n",
    "fuel_data.groupby('fuel_unit')['fuel_unit'].count()\n",
    "fuel_data[['fuel_unit']] = fuel_data[['fuel_unit']].fillna(value='mcf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "report_year\n",
       "1994    1235\n",
       "1995    1201\n",
       "1996    1088\n",
       "1997    1094\n",
       "1998    1107\n",
       "1999    1050\n",
       "2000    1373\n",
       "2001    1356\n",
       "2002    1205\n",
       "2003    1211\n",
       "2004    1192\n",
       "2005    1269\n",
       "2006    1243\n",
       "2007    1264\n",
       "2008    1228\n",
       "2009    1222\n",
       "2010    1261\n",
       "2011    1240\n",
       "2012    1243\n",
       "2013    1199\n",
       "2014    1171\n",
       "2015    1093\n",
       "2016    1034\n",
       "2017     993\n",
       "2018     951\n",
       "Name: report_year, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if missing values have been filled\n",
    "fuel_data.isnull().sum()\n",
    "\n",
    "fuel_data.groupby('report_year')['report_year'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>utility_id_ferc1</th>\n",
       "      <th>report_year</th>\n",
       "      <th>plant_name_ferc1</th>\n",
       "      <th>fuel_unit</th>\n",
       "      <th>fuel_qty_burned</th>\n",
       "      <th>fuel_mmbtu_per_unit</th>\n",
       "      <th>fuel_cost_per_unit_burned</th>\n",
       "      <th>fuel_cost_per_unit_delivered</th>\n",
       "      <th>fuel_cost_per_mmbtu</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel_type_code_pudl</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>coal</th>\n",
       "      <td>f1_fuel_1994_12_1_0_7</td>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>rockport</td>\n",
       "      <td>ton</td>\n",
       "      <td>5377489.0</td>\n",
       "      <td>16.590000</td>\n",
       "      <td>18.590</td>\n",
       "      <td>18.530</td>\n",
       "      <td>1.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas</th>\n",
       "      <td>f1_fuel_1994_12_2_0_10</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>chickasaw</td>\n",
       "      <td>mcf</td>\n",
       "      <td>40533.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.770</td>\n",
       "      <td>2.770</td>\n",
       "      <td>2.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nuclear</th>\n",
       "      <td>f1_fuel_1994_12_2_1_1</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>joseph m. farley</td>\n",
       "      <td>kgU</td>\n",
       "      <td>2260.0</td>\n",
       "      <td>0.064094</td>\n",
       "      <td>28.770</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oil</th>\n",
       "      <td>f1_fuel_1994_12_6_0_2</td>\n",
       "      <td>6</td>\n",
       "      <td>1994</td>\n",
       "      <td>clinch river</td>\n",
       "      <td>bbl</td>\n",
       "      <td>6510.0</td>\n",
       "      <td>5.875338</td>\n",
       "      <td>32.130</td>\n",
       "      <td>23.444</td>\n",
       "      <td>5.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>f1_fuel_1994_12_11_0_6</td>\n",
       "      <td>11</td>\n",
       "      <td>1994</td>\n",
       "      <td>w.f. wyman</td>\n",
       "      <td>bbl</td>\n",
       "      <td>55652.0</td>\n",
       "      <td>0.149719</td>\n",
       "      <td>14.685</td>\n",
       "      <td>15.090</td>\n",
       "      <td>2.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waste</th>\n",
       "      <td>f1_fuel_1994_12_9_0_3</td>\n",
       "      <td>9</td>\n",
       "      <td>1994</td>\n",
       "      <td>b.l. england</td>\n",
       "      <td>ton</td>\n",
       "      <td>2438.0</td>\n",
       "      <td>0.015939</td>\n",
       "      <td>34.180</td>\n",
       "      <td>34.180</td>\n",
       "      <td>1.072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  record_id  utility_id_ferc1  report_year  \\\n",
       "fuel_type_code_pudl                                                          \n",
       "coal                  f1_fuel_1994_12_1_0_7                 1         1994   \n",
       "gas                  f1_fuel_1994_12_2_0_10                 2         1994   \n",
       "nuclear               f1_fuel_1994_12_2_1_1                 2         1994   \n",
       "oil                   f1_fuel_1994_12_6_0_2                 6         1994   \n",
       "other                f1_fuel_1994_12_11_0_6                11         1994   \n",
       "waste                 f1_fuel_1994_12_9_0_3                 9         1994   \n",
       "\n",
       "                     plant_name_ferc1 fuel_unit  fuel_qty_burned  \\\n",
       "fuel_type_code_pudl                                                \n",
       "coal                         rockport       ton        5377489.0   \n",
       "gas                         chickasaw       mcf          40533.0   \n",
       "nuclear              joseph m. farley       kgU           2260.0   \n",
       "oil                      clinch river       bbl           6510.0   \n",
       "other                      w.f. wyman       bbl          55652.0   \n",
       "waste                    b.l. england       ton           2438.0   \n",
       "\n",
       "                     fuel_mmbtu_per_unit  fuel_cost_per_unit_burned  \\\n",
       "fuel_type_code_pudl                                                   \n",
       "coal                           16.590000                     18.590   \n",
       "gas                             1.000000                      2.770   \n",
       "nuclear                         0.064094                     28.770   \n",
       "oil                             5.875338                     32.130   \n",
       "other                           0.149719                     14.685   \n",
       "waste                           0.015939                     34.180   \n",
       "\n",
       "                     fuel_cost_per_unit_delivered  fuel_cost_per_mmbtu  \n",
       "fuel_type_code_pudl                                                     \n",
       "coal                                       18.530                1.121  \n",
       "gas                                         2.770                2.570  \n",
       "nuclear                                     0.000                0.450  \n",
       "oil                                        23.444                5.469  \n",
       "other                                      15.090                2.335  \n",
       "waste                                      34.180                1.072  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group by the fuel type code year and print the first entries in all the groups formed\n",
    "\n",
    "fuel_data.groupby('fuel_type_code_pudl').first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fuel_df1 = fuel_data.iloc[0:1900].reset_index(drop=True)\n",
    "fuel_df2 = fuel_data.iloc[1900:].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check that the length of both dataframes sum to the expected length\n",
    "assert len(fuel_data) == (len(fuel_df1) + len(fuel_df2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr 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>record_id</th>\n",
       "      <th>utility_id_ferc1</th>\n",
       "      <th>report_year</th>\n",
       "      <th>plant_name_ferc1</th>\n",
       "      <th>fuel_type_code_pudl</th>\n",
       "      <th>fuel_unit</th>\n",
       "      <th>fuel_qty_burned</th>\n",
       "      <th>fuel_mmbtu_per_unit</th>\n",
       "      <th>fuel_cost_per_unit_burned</th>\n",
       "      <th>fuel_cost_per_unit_delivered</th>\n",
       "      <th>fuel_cost_per_mmbtu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [record_id, utility_id_ferc1, report_year, plant_name_ferc1, fuel_type_code_pudl, fuel_unit, fuel_qty_burned, fuel_mmbtu_per_unit, fuel_cost_per_unit_burned, fuel_cost_per_unit_delivered, fuel_cost_per_mmbtu]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#outer merge returns all rows in both dataframes\n",
    "pd.merge(fuel_df1, fuel_df2, how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>utility_id_ferc1</th>\n",
       "      <th>report_year</th>\n",
       "      <th>plant_name_ferc1</th>\n",
       "      <th>fuel_type_code_pudl</th>\n",
       "      <th>fuel_unit</th>\n",
       "      <th>fuel_qty_burned</th>\n",
       "      <th>fuel_mmbtu_per_unit</th>\n",
       "      <th>fuel_cost_per_unit_burned</th>\n",
       "      <th>fuel_cost_per_unit_delivered</th>\n",
       "      <th>fuel_cost_per_mmbtu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f1_fuel_1994_12_1_0_7</td>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>rockport</td>\n",
       "      <td>coal</td>\n",
       "      <td>ton</td>\n",
       "      <td>5377489.0</td>\n",
       "      <td>16.590</td>\n",
       "      <td>18.59</td>\n",
       "      <td>18.53</td>\n",
       "      <td>1.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f1_fuel_1994_12_1_0_10</td>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>rockport total plant</td>\n",
       "      <td>coal</td>\n",
       "      <td>ton</td>\n",
       "      <td>10486945.0</td>\n",
       "      <td>16.592</td>\n",
       "      <td>18.58</td>\n",
       "      <td>18.53</td>\n",
       "      <td>1.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f1_fuel_1994_12_2_0_1</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>gorgas</td>\n",
       "      <td>coal</td>\n",
       "      <td>ton</td>\n",
       "      <td>2978683.0</td>\n",
       "      <td>24.130</td>\n",
       "      <td>39.72</td>\n",
       "      <td>38.12</td>\n",
       "      <td>1.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f1_fuel_1994_12_2_0_7</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>barry</td>\n",
       "      <td>coal</td>\n",
       "      <td>ton</td>\n",
       "      <td>3739484.0</td>\n",
       "      <td>23.950</td>\n",
       "      <td>47.21</td>\n",
       "      <td>45.99</td>\n",
       "      <td>1.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f1_fuel_1994_12_2_0_10</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>chickasaw</td>\n",
       "      <td>gas</td>\n",
       "      <td>mcf</td>\n",
       "      <td>40533.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29518</th>\n",
       "      <td>f1_fuel_2018_12_12_0_13</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>neil simpson ct #1</td>\n",
       "      <td>gas</td>\n",
       "      <td>mcf</td>\n",
       "      <td>18799.0</td>\n",
       "      <td>1.059</td>\n",
       "      <td>4.78</td>\n",
       "      <td>4.78</td>\n",
       "      <td>9.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29519</th>\n",
       "      <td>f1_fuel_2018_12_12_1_1</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>cheyenne prairie 58%</td>\n",
       "      <td>gas</td>\n",
       "      <td>mcf</td>\n",
       "      <td>806730.0</td>\n",
       "      <td>1.050</td>\n",
       "      <td>3.65</td>\n",
       "      <td>3.65</td>\n",
       "      <td>6.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29520</th>\n",
       "      <td>f1_fuel_2018_12_12_1_10</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>lange ct facility</td>\n",
       "      <td>gas</td>\n",
       "      <td>mcf</td>\n",
       "      <td>104554.0</td>\n",
       "      <td>1.060</td>\n",
       "      <td>4.77</td>\n",
       "      <td>4.77</td>\n",
       "      <td>8.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29521</th>\n",
       "      <td>f1_fuel_2018_12_12_1_13</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>wygen 3 bhp 52%</td>\n",
       "      <td>coal</td>\n",
       "      <td>ton</td>\n",
       "      <td>315945.0</td>\n",
       "      <td>16.108</td>\n",
       "      <td>3.06</td>\n",
       "      <td>14.76</td>\n",
       "      <td>1.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29522</th>\n",
       "      <td>f1_fuel_2018_12_12_1_14</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "      <td>wygen 3 bhp 52%</td>\n",
       "      <td>gas</td>\n",
       "      <td>mcf</td>\n",
       "      <td>17853.0</td>\n",
       "      <td>1.059</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29523 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     record_id  utility_id_ferc1  report_year  \\\n",
       "0        f1_fuel_1994_12_1_0_7                 1         1994   \n",
       "1       f1_fuel_1994_12_1_0_10                 1         1994   \n",
       "2        f1_fuel_1994_12_2_0_1                 2         1994   \n",
       "3        f1_fuel_1994_12_2_0_7                 2         1994   \n",
       "4       f1_fuel_1994_12_2_0_10                 2         1994   \n",
       "...                        ...               ...          ...   \n",
       "29518  f1_fuel_2018_12_12_0_13                12         2018   \n",
       "29519   f1_fuel_2018_12_12_1_1                12         2018   \n",
       "29520  f1_fuel_2018_12_12_1_10                12         2018   \n",
       "29521  f1_fuel_2018_12_12_1_13                12         2018   \n",
       "29522  f1_fuel_2018_12_12_1_14                12         2018   \n",
       "\n",
       "           plant_name_ferc1 fuel_type_code_pudl fuel_unit  fuel_qty_burned  \\\n",
       "0                  rockport                coal       ton        5377489.0   \n",
       "1      rockport total plant                coal       ton       10486945.0   \n",
       "2                    gorgas                coal       ton        2978683.0   \n",
       "3                     barry                coal       ton        3739484.0   \n",
       "4                 chickasaw                 gas       mcf          40533.0   \n",
       "...                     ...                 ...       ...              ...   \n",
       "29518    neil simpson ct #1                 gas       mcf          18799.0   \n",
       "29519  cheyenne prairie 58%                 gas       mcf         806730.0   \n",
       "29520     lange ct facility                 gas       mcf         104554.0   \n",
       "29521       wygen 3 bhp 52%                coal       ton         315945.0   \n",
       "29522       wygen 3 bhp 52%                 gas       mcf          17853.0   \n",
       "\n",
       "       fuel_mmbtu_per_unit  fuel_cost_per_unit_burned  \\\n",
       "0                   16.590                      18.59   \n",
       "1                   16.592                      18.58   \n",
       "2                   24.130                      39.72   \n",
       "3                   23.950                      47.21   \n",
       "4                    1.000                       2.77   \n",
       "...                    ...                        ...   \n",
       "29518                1.059                       4.78   \n",
       "29519                1.050                       3.65   \n",
       "29520                1.060                       4.77   \n",
       "29521               16.108                       3.06   \n",
       "29522                1.059                       0.00   \n",
       "\n",
       "       fuel_cost_per_unit_delivered  fuel_cost_per_mmbtu  \n",
       "0                             18.53                1.121  \n",
       "1                             18.53                1.120  \n",
       "2                             38.12                1.650  \n",
       "3                             45.99                1.970  \n",
       "4                              2.77                2.570  \n",
       "...                             ...                  ...  \n",
       "29518                          4.78                9.030  \n",
       "29519                          3.65                6.950  \n",
       "29520                          4.77                8.990  \n",
       "29521                         14.76                1.110  \n",
       "29522                          0.00               11.680  \n",
       "\n",
       "[29523 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#outer merge returns all rows in both dataframes\n",
    "pd.merge(fuel_df1, fuel_df2, how=\"outer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>utility_id_ferc1</th>\n",
       "      <th>report_year</th>\n",
       "      <th>plant_name_ferc1</th>\n",
       "      <th>fuel_type_code_pudl</th>\n",
       "      <th>fuel_unit</th>\n",
       "      <th>fuel_qty_burned</th>\n",
       "      <th>fuel_mmbtu_per_unit</th>\n",
       "      <th>fuel_cost_per_unit_burned</th>\n",
       "      <th>fuel_cost_per_unit_delivered</th>\n",
       "      <th>fuel_cost_per_mmbtu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f1_fuel_1994_12_1_0_7</td>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>rockport</td>\n",
       "      <td>coal</td>\n",
       "      <td>ton</td>\n",
       "      <td>5377489.0</td>\n",
       "      <td>16.590000</td>\n",
       "      <td>18.590</td>\n",
       "      <td>18.530</td>\n",
       "      <td>1.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f1_fuel_1994_12_1_0_10</td>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>rockport total plant</td>\n",
       "      <td>coal</td>\n",
       "      <td>ton</td>\n",
       "      <td>10486945.0</td>\n",
       "      <td>16.592000</td>\n",
       "      <td>18.580</td>\n",
       "      <td>18.530</td>\n",
       "      <td>1.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f1_fuel_1994_12_2_0_1</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>gorgas</td>\n",
       "      <td>coal</td>\n",
       "      <td>ton</td>\n",
       "      <td>2978683.0</td>\n",
       "      <td>24.130000</td>\n",
       "      <td>39.720</td>\n",
       "      <td>38.120</td>\n",
       "      <td>1.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f1_fuel_1994_12_2_0_7</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>barry</td>\n",
       "      <td>coal</td>\n",
       "      <td>ton</td>\n",
       "      <td>3739484.0</td>\n",
       "      <td>23.950000</td>\n",
       "      <td>47.210</td>\n",
       "      <td>45.990</td>\n",
       "      <td>1.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f1_fuel_1994_12_2_0_10</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>chickasaw</td>\n",
       "      <td>gas</td>\n",
       "      <td>mcf</td>\n",
       "      <td>40533.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.770</td>\n",
       "      <td>2.770</td>\n",
       "      <td>2.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>f1_fuel_1995_12_39_0_3</td>\n",
       "      <td>39</td>\n",
       "      <td>1995</td>\n",
       "      <td>montville</td>\n",
       "      <td>gas</td>\n",
       "      <td>mcf</td>\n",
       "      <td>1733652.0</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>2.511</td>\n",
       "      <td>2.474</td>\n",
       "      <td>2.438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>f1_fuel_1995_12_39_0_6</td>\n",
       "      <td>39</td>\n",
       "      <td>1995</td>\n",
       "      <td>devon</td>\n",
       "      <td>gas</td>\n",
       "      <td>mcf</td>\n",
       "      <td>13468921.0</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>1.969</td>\n",
       "      <td>1.969</td>\n",
       "      <td>1.955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>f1_fuel_1995_12_39_2_1</td>\n",
       "      <td>39</td>\n",
       "      <td>1995</td>\n",
       "      <td>milstone no. 1 &amp; 2</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>gramsU</td>\n",
       "      <td>504938.0</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>f1_fuel_1995_12_39_2_4</td>\n",
       "      <td>39</td>\n",
       "      <td>1995</td>\n",
       "      <td>millstone no. 3</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>gramsU</td>\n",
       "      <td>713219.0</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>f1_fuel_1995_12_39_2_7</td>\n",
       "      <td>39</td>\n",
       "      <td>1995</td>\n",
       "      <td>seabrook no. 1</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>gramsU</td>\n",
       "      <td>54719.0</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1900 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   record_id  utility_id_ferc1  report_year  \\\n",
       "0      f1_fuel_1994_12_1_0_7                 1         1994   \n",
       "1     f1_fuel_1994_12_1_0_10                 1         1994   \n",
       "2      f1_fuel_1994_12_2_0_1                 2         1994   \n",
       "3      f1_fuel_1994_12_2_0_7                 2         1994   \n",
       "4     f1_fuel_1994_12_2_0_10                 2         1994   \n",
       "...                      ...               ...          ...   \n",
       "1895  f1_fuel_1995_12_39_0_3                39         1995   \n",
       "1896  f1_fuel_1995_12_39_0_6                39         1995   \n",
       "1897  f1_fuel_1995_12_39_2_1                39         1995   \n",
       "1898  f1_fuel_1995_12_39_2_4                39         1995   \n",
       "1899  f1_fuel_1995_12_39_2_7                39         1995   \n",
       "\n",
       "          plant_name_ferc1 fuel_type_code_pudl fuel_unit  fuel_qty_burned  \\\n",
       "0                 rockport                coal       ton        5377489.0   \n",
       "1     rockport total plant                coal       ton       10486945.0   \n",
       "2                   gorgas                coal       ton        2978683.0   \n",
       "3                    barry                coal       ton        3739484.0   \n",
       "4                chickasaw                 gas       mcf          40533.0   \n",
       "...                    ...                 ...       ...              ...   \n",
       "1895             montville                 gas       mcf        1733652.0   \n",
       "1896                 devon                 gas       mcf       13468921.0   \n",
       "1897    milstone no. 1 & 2             nuclear    gramsU         504938.0   \n",
       "1898       millstone no. 3             nuclear    gramsU         713219.0   \n",
       "1899        seabrook no. 1             nuclear    gramsU          54719.0   \n",
       "\n",
       "      fuel_mmbtu_per_unit  fuel_cost_per_unit_burned  \\\n",
       "0               16.590000                     18.590   \n",
       "1               16.592000                     18.580   \n",
       "2               24.130000                     39.720   \n",
       "3               23.950000                     47.210   \n",
       "4                1.000000                      2.770   \n",
       "...                   ...                        ...   \n",
       "1895             1.030000                      2.511   \n",
       "1896             1.010000                      1.969   \n",
       "1897             0.000006                      0.000   \n",
       "1898             0.000006                      0.000   \n",
       "1899             0.000006                      0.000   \n",
       "\n",
       "      fuel_cost_per_unit_delivered  fuel_cost_per_mmbtu  \n",
       "0                           18.530                1.121  \n",
       "1                           18.530                1.120  \n",
       "2                           38.120                1.650  \n",
       "3                           45.990                1.970  \n",
       "4                            2.770                2.570  \n",
       "...                            ...                  ...  \n",
       "1895                         2.474                2.438  \n",
       "1896                         1.969                1.955  \n",
       "1897                         0.000                0.000  \n",
       "1898                         0.000                0.000  \n",
       "1899                         0.000                0.000  \n",
       "\n",
       "[1900 rows x 11 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removes rows from the right dataframe that don not have a match with the left\n",
    "#and keeps all rows from the left\n",
    "pd.merge(fuel_df1, fuel_df2, how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicate rows\n",
    "fuel_data.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Text(0.5, 0, 'Fuel Unit'),)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Import plotting library\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.xticks(rotation=90)\n",
    "fuel_unit = pd.DataFrame({'unit':['BBL', 'GAL', 'GRAMSU', 'KGU', 'MCF', 'MMBTU', 'MWDTH', 'MWHTH', 'TON'],\n",
    "                          'count':[7998, 84, 464, 110, 11354, 180, 95, 100, 8958]\n",
    "                                          })\n",
    "                        \n",
    "sns.barplot(data=fuel_unit, x='unit', y='count')\n",
    "plt.xlabel('Fuel Unit'),\n",
    "\n",
    "#Because of the extreme range of the values for the fuel unit, we can plot the barchart by taking the logarithm of the y-axis as follows:\n",
    "g = sns.barplot(data=fuel_unit, x='unit', y='count')\n",
    "g.set_yscale(\"log\")\n",
    "g.set_ylim(1, 12000)\n",
    "plt.xlabel('Fuel Unit'),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcc54300b88>"
      ]
     },
     "execution_count": 34,
     "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": [
    "import pandas as pd\n",
    "# Import plotting library\n",
    "import seaborn as sns\n",
    "fuel_data =  pd.read_csv('fuel_ferc1.csv')\n",
    "\n",
    "# Box plot\n",
    "sns.boxplot(x=\"fuel_type_code_pudl\", y=\"utility_id_ferc1\",\n",
    "            palette=[\"m\", \"g\"], data=fuel_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcc49418a08>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select a sample of the dataset\n",
    "sample_df = fuel_data.sample(n=50, random_state=4)\n",
    "sns.regplot(x=sample_df[\"utility_id_ferc1\"], y=sample_df[\"fuel_cost_per_mmbtu\"], fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'sample_df')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Qqi7BJYVTRKQWcJaqLlVVBd4GrirOiZi87dkDq1aFTxVRTt27Q0qKmybDGOOfYJJBHWBrjueZ3raAZVT1OLAfqF7AMTMLOCYAIjJSRNJEJG3Xrl1BhGtyW7LEVcf43aU0kG7d3P3Mmf7GYUysCyYZBKrL1yKUKVJ5VZ2oqsmqmlyjRo18DmnysngxlC8PF13kdyS/dd55rvrKqoqM8VcwySATqJfjeV1ge15lRKQsUBXYW8Ax6xZwTBMiixe7RFC+vN+RBNa9O6xZA+vW+R2JMbErmGSQCjQSkQYiUh4YAszJVWYOMNx7PBhY4LUFBKSqO4ADItLe60V0E/BBoaM3BTp0CFauhGbN/I4kb127upHRNubAGP8UmAy8NoDRwDxgPTBLVdeKyFgRGeAVmwRUF5F04F7gVPdTEckAngVGiEhmjp5IdwJ/B9KBH4FPQnNKJqfly934gubN/Y4kb9WqufaM6dNd24YxpvSVDaaQqs4F5uba9miOx0eAa/LYNyGP7WlAUrCBmqJZvNgN8Aqn8QWBdO8O48fD11+HZ68nY6KdjUCOcosXu6kfwmE+ovx06gRxcTYAzRi/WDKIYseOwdKl4d1ekK1qVXdFMGuWVRUZ4wdLBlFs1Sq3fkEkJANwK6Bt3OgavI0xpcuSQRRbvNjdR0oy6NQJypa1qiJj/GDJIIr9+99QqxbEx/sdSXCsqsgY/1gyiFKqLhk0aeJ3JIXTpQts2gQrVvgdiTGxxZJBlMrMhB07wr9LaW5WVWSMPywZRKmlS919pCWDs86yqiJj/GDJIEotXQoVKsAFF/gdSeF17QoZGZCW5nckxsQOSwZRaulSaNzYrW4WaayqyJjSZ8kgCmVluWkdEnOvRxchqlSBNm2sqsiY0mTJIAqtXAlHj0Zee0FOXbvC5s2Qmup3JMbEBksGUSi78ThSrwwAOna0qiJjSpMlgyi0bJlbQax6fguPhrkqVSA52SUDqyoypuRZMohCkTjYLJAuXayqyJjSYskgyuzYAdu2RUcyyO5VNGuW35EYE/0sGUSZ7F/RF1/sbxyhULmyVRUZU1osGUSZ1FS3nnDDhn5HEhpdu8KWLZCS4nckxkQ3SwZRJjUVEhLgjDP8jiQ0OnZ0A+esqsiYkmXJIIqoumQQDVVE2XJWFZ086Xc0xkQvSwZRZNMm2LsXLrrI70hCq2tX2LrVqoqMKUmWDKJINDUe59Shg1UVGVPSLBlEkdRUKF8+MmcqzU/lytC2rVUVGVOSLBlEkZQUuPBC1zc/2nTt6hbsWb7c70iMiU6WDKLEiRNugrpoqyLKdumlVlVkTEkKKhmISB8R2SAi6SLyYIDXK4jITO/15SKSkOO1h7ztG0Skd47t94jIWhH5VkSmi0jFUJxQrFq/Hg4dir7G42yVK0O7dlZVZExJKTAZiEgc8DLQF0gEhopI7vkwbwV+VtWGwHPAOG/fRGAI0BToA7wiInEiUgcYAySrahIQ55UzRRStjcc5deniptpYtszvSIyJPsFcGbQD0lV1o6oeBWYAA3OVGQi85T2eDfQQEfG2z1DVLFXdBKR7xwMoC5whImWBM4HtxTuV2JaaCmeeCfXq+R1JyenQwTWQz5zpdyTGRJ9gkkEdYGuO55netoBlVPU4sB+onte+qroNGA9sAXYA+1X1s0BvLiIjRSRNRNJ27doVRLixKSXFLXNZJopbgSpVclVFs2a5NhJjTOgE89UhAbblnjYsrzIBt4vIObirhgZAbaCSiNwY6M1VdaKqJqtqco0aNYIIN/ZkZcHq1dHbXpDT5ZfDf/4DCxb4HYkx0SWYZJAJ5Kx8qMtvq3ROlfGqfaoCe/PZ93Jgk6ruUtVjwHtAh6KcgHGJ4Nix6Ji2uiCXXuquEKZN8zsSY6JLMMkgFWgkIg1EpDyuoXdOrjJzgOHe48HAAlVVb/sQr7dRA6ARkIKrHmovImd6bQs9gPXFP53YlN14HAtXBuXLw2WXwXvvweHDfkdjTPQoMBl4bQCjgXm4L+xZqrpWRMaKyACv2CSguoikA/cCD3r7rgVmAeuAT4FRqnpCVZfjGppXAmu8OCaG9MxiSGoqnH021KzpdySl4/LL4cAB+PBDvyMxJnqIRtCqIcnJyZqWluZ3GGGnaVO3ZvDTT/sdSek4cQKGDHG9iz74wO9ojAl/IrJCVZPzKxPFfU9iw4EDbsBZNI8vyC0uDrp1g08+cbO0GmOKz5JBhFu50q1jEAvtBTldfrlrNH/nHb8jMSY6WDKIcLEw8jiQRo3g/POtV5ExoWLJIMKlprqG43PO8TuS0iXirg4WL4bNm/2OxpjIZ8kgwqWkxF4VUbYePdz99On+xmFMNLBkEMF274aMjNirIspWqxYkJVlVkTGhYMkggmX3so3VKwNwVwfffutGYRtjis6SQQRLTXV1540b+x2Jf7p1cyu7TZnidyTGRDZLBhEsNRXq13cLv8SqqlXhkktg6lQ4ftzvaIyJXJYMIpTq6WmrY12fPm4m008/9TsSYyKXJYMItW0b7NwZu43HObVv77rWvvmm35EYE7ksGUSoWB1sFkjZsm7MwZw5YOsfGVM0lgwiVEqK+xJs2NDvSMJD376uzcC6mRpTNJYMIlRqKjRo4Ob3N+7f4uKLYfJk155ijCkcSwYR6ORJN8YglscXBNK3L6xZc7oKzRgTPEsGESg9Hfbvt/aC3Hr0gIoVYaItk2RMoVkyiECxtMxlYVSqBN27u7mKfvnF72iMiSyWDCJQaipUqODqyc2vXXEF/Pe/8I9/+B2JMZHFkkEESk11g83i4vyOJPxcfLHrYfX669aQbExhWDKIMMePw9dfWxVRXkSgf39Yter0RH7GmIJZMogwa9fC4cOWDPJz+eVwxhnw8st+R2JM5LBkEGFs5HHBKleGnj1hxgwbkWxMsCwZRJjUVKhSBerU8TuS8Hb11ZCVBZMm+R2JMZHBkkGEyZ6pVMTvSMJbQgK0bg2vvGJTWxsTDEsGEeTwYTfC1toLgnPVVbB1K3z4od+RGBP+gkoGItJHRDaISLqIPBjg9QoiMtN7fbmIJOR47SFv+wYR6Z1j+9kiMltEvhOR9SJyaShOKJqtWgUnTlh7QbA6dICaNeGFF/yOxJjwV2AyEJE44GWgL5AIDBWRxFzFbgV+VtWGwHPAOG/fRGAI0BToA7ziHQ/gBeBTVb0YaAGsL/7pRDdrPC6cuDgYNAj+9S9YscLvaIwJb8FcGbQD0lV1o6oeBWYAA3OVGQi85T2eDfQQEfG2z1DVLFXdBKQD7UTkLKAzMAlAVY+q6r7in050S0mB+Hh3M8Hp189NU/G3v/kdiTHhLZhkUAfYmuN5prctYBlVPQ7sB6rns+8FwC7gTRH5WkT+LiKVAr25iIwUkTQRSdsV4/0Ely2DJk2s8bgwKld2g9BmzYItW/yOxpjwFUwyCPTVk3ugf15l8tpeFmgNvKqqrYBDwG/aIgBUdaKqJqtqco0aNYIINzrt3g0//uiSgSmcQYPcvbUdGJO3YJJBJlAvx/O6wPa8yohIWaAqsDeffTOBTFVd7m2fjUsOJg/LvX8pSwaFV7MmdOkCb7wB+6wy0piAgkkGqUAjEWkgIuVxDcJzcpWZAwz3Hg8GFqiqetuHeL2NGgCNgBRV/Q+wVUSyO0n2ANYV81yi2vLlUKaMdSstqqFD4cABeOklvyMxJjwVmAy8NoDRwDxcj59ZqrpWRMaKyACv2CSguoikA/fiVfmo6lpgFu6L/lNglKqe8Pb5f8A0EVkNtASeCt1pRZ9ly+CCC9ycO6bwGjaESy+F556Dgwf9jsaY8CMaQfP8Jicna1oMTkV58iRUqwadO8O99/odTeRatw5GjYJnnoH77vM7GmNKj4isUNXk/MrYCOQIsGGDW+YyMffoDlMoiYnQpg2MH+9GcxtjTrNkEAGWLXP31nhcfDfeCDt3usZkY8xplgwiwPLlrr98vXoFlzX5a9ECWraEp56CQ4f8jsaY8GHJIAIsW+amoChjn1axicCtt7qrgxdf9DsaY8KHfb2EuUOH3EylVkUUOklJrmfRuHE27sCYbJYMwlxamutNZMkgtG65xSWC8eP9jsSY8GDJIMxljzy2nkSh1bAhdO8Ozz4LmZl+R2OM/ywZhLlly6BuXaha1e9Ios9tt7n1If74R78jMcZ/lgzCmOrpxmMTerVqweDBMGXK6bUijIlVlgzCWGYm7NhhVUQl6frr3ejue+5xydeYWGXJIIzZTKUlr1Il15j81Vcwfbrf0RjjH0sGYWzZMihfHi680O9IolufPq4q7t57raupiV2WDMLY8uXQqBGUK+d3JNEtLs4lgl27rDHZxC5LBmHq2DE3xsCqiEpHo0ZuRbTXXjtdPWdMLLFkEKbWrIEjR6zxuDTdfDPEx7vpKrKy/I7GmNJlySBM/fvf7t6SQek580xXXbR2Lfz5z35HY0zpsmQQphYvdmv31qzpdySxpX176NvXzVuUPXW4MbHAkkEYUnXJICnJ70hi0113QY0acNNNNs21iR2WDMJQRoYbbNasmd+RxKbKleGBByA9HcaM8TsaY0qHJYMwtGSJu7dk4J/WreGGG2DyZJg2ze9ojCl5lgzC0JIl7tdpQoLfkcS2ESNcQr7jDvj+e7+jMaZkWTIIQ0uWQNOmtrKZ3+Li4JFH3P1VV8Evv/gdkTElx75uwsyePbBunVURhYsaNeCxx9yVwY03uoWGjIlGlgzCTPb4AksG4aNVK9fD6MMP4dFH/Y7GmJIRVDIQkT4iskFE0kXkwQCvVxCRmd7ry0UkIcdrD3nbN4hI71z7xYnI1yLyUXFPJFosXuzmIrI1DMLL1VdDv37w5JMwaZLf0RgTegUmAxGJA14G+gKJwFARyT0u9lbgZ1VtCDwHjPP2TQSGAE2BPsAr3vGy/R5YX9yTiCaLFsFFF7nZSk34EHFrHrRtC7ffDh9/7HdExoRWMFcG7YB0Vd2oqkeBGcDAXGUGAm95j2cDPUREvO0zVDVLVTcB6d7xEJG6QH/g78U/jehw4ICbnK5lS78jMYGULeumqWjYEK699nSVnjHRIJhkUAfYmuN5prctYBlVPQ7sB6oXsO/zwANAvk1yIjJSRNJEJG3Xrl1BhBu5lixxa/JaMghfZ5wBTz3lVkfr29eWyzTRI5hkIAG25V4gMK8yAbeLyBXAT6q6oqA3V9WJqpqsqsk1atQoONoItnCh+/XZtKnfkZj8VKsGzz7rxoL06gUrV/odkTHFF0wyyATq5XheF9ieVxkRKQtUBfbms29HYICIZOCqnbqLyNQixB9VFi506xdUrOh3JKYgNWrA3/4GFSpA9+5WZWQiXzDJIBVoJCINRKQ8rkF4Tq4yc4Dh3uPBwAJVVW/7EK+3UQOgEZCiqg+pal1VTfCOt0BVbwzB+USs/fvdL0yrIooc550HL7wAVapAz57w+ed+R2RM0RWYDLw2gNHAPFzPn1mqulZExorIAK/YJKC6iKQD9wIPevuuBWYB64BPgVGqeiL0pxH5lixxA5osGUSWmjVdQqhdG/r3h7ff9jsiY4pG3A/4yJCcnKxpaWl+h1Ei7rsPJkxwA5sqVPA7GlNYBw+6kcorV7r7xx5z3VGNCQciskJVk/MrYyOQw8SXX7pVzSwRRKbKleHpp6FPH9f99LrrXIIwJlJYMggD+/bB119DixZ+R2KKo1w5tw7C7bfDu+/CJZfYbKcmclgyCAPz57v2guR8L+JMJBCBIUPgr3+FbdvcZzo15vvJmUhgySAMfPYZVKrkupWa6NCmDbz+uluTYtgwd9u/3++ojMmbJQOfqcK8ea4XUdmyfkdjQqlmTXjuObdIzj/+4QYTfvKJ31EZE5glA5+lp8PmzW4CNBN94uJg+HB46SXXptCvH9x0E+zc6XdkxvyaJQOfzZvn7q29ILo1aeKqjYYNg+nT3cy0L74Ix475HZkxjiUDn332mRuwVCf31H8m6pQvD7fc4tZDaNgQxoxxVUfvv++qC43xkyUDHx096uYjsquC2FK/PjzzjFso5+hRGDQI2rVzAw4tKRi/WDLw0bJlbmCStRfEHhHo0MFdJdx3n+uGOmCAG2syeTIcOeJ3hCbWWDLw0SefuAZGm48odsXFnZ7T6Oq7qeIAABGCSURBVA9/gEOH4NZboV49t7La6tV+R2hihc1N5KOkJFeP/OyzfkdiwoWqG43+wQduWuzjx127wu9+B1dd5a4cythPOFNIwcxNZMnAJxs3woUXwqhRMHiw39GYcLR/vxudvmgRrFnjRqnHx0O3btCxo6tebN7czYtkTH6CSQY2zMknH37o7i+91N84TPiqWtU1Lg8aBD//DCkpblbURYvgnXdOl6tZ0/2wyL7VqgXnnANnn+3uzznHrblQubJbttNmUzWBWDLwyZw5bqoC61JqgnHOOdC7t7sB7N4N330HGRmwfTvs2OHGrPz0U/7HEXFTn1Sq5JJD5crucZUqLnnUrg1167qkctFF7r5cuRI/PRMGLBn4YN8+9+vu2mv9jsREqvh46NTJ3XI6etRVLx044G4HD7r7w4ddD6XDh399y97288+wbh3s2vXrnkwVK0Lr1m4G1p49oUsXOPPM0j1XUzosGfjg009dw2CHDn5HYqJN+fJufeYaNYq2v6pLIJmZsGWLmy5lwwZ45RU3z1KFCu7q5Prr4corLTFEE0sGPpgzx132X3yx35EY82sirsqoSRN3y66Wyspy3VxTUtxCTHPmuDaNW25xnSAuvNDXsE0IWCe1UpaVBXPnusvuuDi/ozEmOBUquN5Lo0bBjBmuO3SbNm5+pUaNYOhQ14ZhIpclg1I2b56r0+3a1e9IjCmauDho1QoeecRNujdkiBsX0bQp3Hwz/Oc/fkdoisKSQSmbORPOOsv9qjIm0sXHw8iRMG2aGxg3dSo0bgzjx9uMrJHGkkEpOnzY1bVedpktZGOiyznnwF13wZtvupH1998P7dvDN9/4HZkJliWDUjR3ruup0a2b35EYUzLq1oWnnoLHH4dNm9yMvP/7v3DihN+RmYJYMihFM2e6X1A2MZ2Jdl26uKuEzp3h0UehRw/XXdWEL0sGpeTgQfjoI1dFZL2ITCyoWhX+9Cc3G2tKiptkb/58v6MyeQkqGYhIHxHZICLpIvJggNcriMhM7/XlIpKQ47WHvO0bRKS3t62eiCwUkfUislZEfh+qEwpXH3zg2gy6d/c7EmNKjwj06eOW/DzrLDdu4fnnbRGfcFRgMhCROOBloC+QCAwVkcRcxW4FflbVhsBzwDhv30RgCNAU6AO84h3vOPA/qtoEaA+MCnDMqDJpkpv3pVkzvyMxpvTVqwcvv+wale+5x3VBtQV8wkswVwbtgHRV3aiqR4EZwMBcZQYCb3mPZwM9RES87TNUNUtVNwHpQDtV3aGqKwFU9QCwHojaKds2bnTLW/bpY3PRm9h15pkwdiyMGAFvveWqTK0dIXwE89VUB9ia43kmv/3iPlVGVY8D+4HqwezrVSm1ApYHenMRGSkiaSKStmvXriDCDT9vvumSQJ8+fkdijL/KlIHhw10Po3Xr3Eh8634aHoJJBoFmP89d45dXmXz3FZHKwLvA3ar6S6A3V9WJqpqsqsk1ijr7lo9OnHDJoG3bok8eZky06dTJTWVx7Ji7QrCGZf8FkwwygXo5ntcFtudVRkTKAlWBvfntKyLlcIlgmqq+V5TgI8Hnn7vFzvv29TsSY8LLBRe4doT4eHfVPGWK3xHFtmCSQSrQSEQaiEh5XIPwnFxl5gDDvceDgQXq1tOcAwzxehs1ABoBKV57wiRgvapG9QrAEye6RUNsumpjfqtGDZgwwXWsuOkm+MtfrKeRXwpMBl4bwGhgHq6hd5aqrhWRsSIywCs2CaguIunAvcCD3r5rgVnAOuBTYJSqngA6AsOA7iKyyrv1C/G5+W7TJteltF8/Wy3KmLxUrgzjxrmBaX/8o5sZ1UYsl76gZshR1bnA3FzbHs3x+AhwTR77Pgk8mWvbEgK3J0SVCRNcg9lVV/kdiTHhrVw5lwji4+HVV93ynVOnupXWTOmw6dJKyP79bmxB167WcGxMMMqUgTvugOrV3cpqe/bAP//pRjKbkme93kvIpElu7dnBg/2OxJjIcs017iphyRI3x9GOHX5HFBssGZSA48ddFVHz5nDRRX5HY0zk6dkTnnwSvv8eOnaEH37wO6LoZ8mgBEyZAps3w7XX+h2JMZGrXTv429/g559dQkhL8zui6GbJIMSOHoU//9ktdm/dSY0pniZN3FV2XJxbB+Tzz/2OKHpZMgixSZPcVcHNN7sZG40xxVOvnhutXLMm9O8PM2b4HVF0smQQQocPwxNPuAE0bdv6HY0x0SM+3k19nZgIQ4fCCy/4HVH0sWQQQi++CNu3wy232FWBMaFWuTL89a9uLqO774bf/9511jChYckgRDIz3fS8HTrYspbGlJTy5eGxx1yX7QkTYMAA+CXgFJemsCwZhMg997hfKaNH+x2JMdEtLs5NWXHPPfDZZ66nUUaG31FFPksGITBvHsyeDTfeCLVq+R2NMbFhwAA3p1FGhlsXYelSvyOKbJYMiunAAbjrLtfjwcYVGFO62rRx02CXK+emfnn5ZZv1tKgsGRTTqFHul8l997n6TGNM6apf3yWB1q1dNe2QIdaOUBSWDIph6lQ32njYMDf1hDHGH1WruukrRo6Ed9+F5GRYvdrvqCKLJYMi2rAB7rzTjSkYNszvaIwxZcq4MQjZU1hccgm89ppVGwXLkkER7NzplukrVw4eftj1bjDGhIcWLeD116FpU/eD7fLLYeNGv6MKf5YMCunQIbjiCjet7pNPuiHyxpjwUq2aG6B2772wfLm7gn/hBVtBLT+WDArh0CEYNAhWroRHHnGTaBljwlOZMnDllTB5smvTu/tu6NwZvv3W78jCkyWDIO3bB717wxdfuJ5DHTv6HZExJhjnngtPPQUPPeQSQYsWrhfg7t1+RxZeLBkEYdMm14d5+XJ49FHo29fviIwxhSECvXrB22/DwIGuTeGCC9zUFvv2+R1deLBkUID334dWrVwD1FNPuWX4jDGRqWpVGDPGTTXfqpWbT6xBA/jTn1zHkFhmySAPu3a52UcHDXJTTLz+uk1LbUy0OP98twjVxImQlOR+6J1/vvubT02Nze6oohF01snJyZpWwmvfHT4Mb7zhLh8PHnSLc48YYaOLjYlmW7a4+cW++MJ9B7RsCddd52ZHbdjQ7+iKT0RWqGpyvmUsGTg7d7peB88/Dz/95Ia2jxnjfi0YY2LDoUMuIcybB+vXu20tWrikMHCgG7tQJgLrUywZFGDHDvehv/OOuz9xwlUF3XCD64pmC9QYE7t27oTFi2HRIlizxm075xzo1Ml1Ub3sMtfuEAm1BiFLBiLSB3gBiAP+rqpP53q9AvA20AbYA1ynqhneaw8BtwIngDGqOi+YYwZSnGSwd6+bQuLbb12d4NKlp/sbn3uuG6XYq5ddCRhjfmvXLlixws139O23sHWr2x4XB40auSuG7FtCAtSt6wakhsvsBCFJBiISB3wP9AQygVRgqKquy1HmLqC5qt4hIkOAq1X1OhFJBKYD7YDawBdAY2+3fI8ZSFGTQatWsGrV6ednnQUXXeTqBdu1c13MIvHSzxjjjz173NVCejps3uxu27bByZOny8TFwXnnQZ06bkR01apw9tnuvkoVN51NuXJQtqy7V4WsLDhyxN1nZbnZV/fscXMtffJJ0b+ngkkGZYM4TjsgXVU3egedAQwEcn5xDwQe9x7PBl4SEfG2z1DVLGCTiKR7xyOIY4bM0KHu0u78892tTh2rAjLGFF3t2u7Wu/fpbVlZriH6p59O33btcrctW1yHlIMH3Roox44V/B5ly7p1n886y11lHD4MlSqV3DkFkwzqAFtzPM8ELsmrjKoeF5H9QHVv+7Jc+9bxHhd0TABEZCQw0nt6UEQ2BBFzYcQDsTgWMRbP2845dkT8eR8/7gbE7dvnkknlygXukt85F1gBHkwyCPQbOnfdUl5l8toe6GInYH2Vqk4EJuYXYHGISFpBl0/RKBbP2845dsTieRf3nIOpgcoE6uV4XhfYnlcZESkLVAX25rNvMMc0xhhTSoJJBqlAIxFpICLlgSHAnFxl5gDDvceDgQXqWqbnAENEpIKINAAaASlBHtMYY0wpKbCayGsDGA3Mw3UDnayqa0VkLJCmqnOAScAUr4F4L+7LHa/cLFzD8HFglKqeAAh0zNCfXlBKrAoqzMXieds5x45YPO9inXNEDTozxhhTMqx3vTHGGEsGxhhjYjgZiMjjIrJNRFZ5t345XntIRNJFZIOI9M7vOJFGRPp455UuIg/6HU9JEZEMEVnjfbZp3rZqIvK5iPzg3Z/jd5zFJSKTReQnEfk2x7aA5ynOBO+zXy0irf2LvOjyOOeo/nsWkXoislBE1ovIWhH5vbc9dJ+1qsbkDTdi+r4A2xOBb4AKQAPgRyDO73hDdM5x3vlcAJT3zjPR77hK6FwzgPhc2/4KPOg9fhAY53ecITjPzkBr4NuCzhPoB3yCG//THljud/whPOeo/nsGagGtvcdVcNP5JIbys47ZK4N8nJpCQ1U3ATmn0Ih0p6YWUdWjQPY0ILFiIPCW9/gt4CofYwkJVV2E68GXU17nORB4W51lwNkiUqt0Ig2dPM45L1Hx96yqO1R1pff4ALAeN5tDyD7rWE8Go71LqMk5qgwCTb9R57e7RqRoPrfcFPhMRFZ4U5oA1FTVHeD+uIBzfYuuZOV1ntH++cfE37OIJACtgOWE8LOO6mQgIl+IyLcBbgOBV4ELgZbADuBv2bsFOFS09L+N5nPLraOqtgb6AqNEpLPfAYWBaP78Y+LvWUQqA+8Cd6vqL/kVDbAt3/MOZm6iiKWqlwdTTkTeAD7ynkbzVBnRfG6/oqrbvfufROR9XNXAThGppao7vEvmn3wNsuTkdZ5R+/mr6qnl7KP171lEyuESwTRVfc/bHLLPOqqvDPKTq/7saiC7Z0JeU2hEg5iYBkREKolIlezHQC/c55tz2pThwAf+RFji8jrPOcBNXk+T9sD+7CqGSBftf88iIriZHtar6rM5XgrdZ+13K7mPrfNTgDXAau8frlaO1x7G9TrYAPT1O9YQn3c/XE+EH4GH/Y6nhM7xAlwPkm+AtdnniZtWfT7wg3dfze9YQ3Cu03HVIsdwvwZvzes8cVUHL3uf/Rog2e/4Q3jOUf33DHTCVfOsBlZ5t36h/KxtOgpjjDGxW01kjDHmNEsGxhhjLBkYY4yxZGCMMQZLBsYYY7BkYIwxBksGxoSUiHwpIsnFPEZXEfnIe1zBm1ZllYhcF5oojfmtqJ6Owpgo0Aoop6ot/Q7ERDe7MjBRz5ue4mMR+cabqPA6EXlURFK95xO94f7Zv+yfE5FF3kIibUXkPW/xkCe8Mgki8p2IvOXNkjlbRM4M8L69RGSpiKwUkXe8ScbyirGPd8wlwCBv27nAVKCld2VwYYn8AxmDJQMTG/oA21W1haomAZ8CL6lqW+/5GcAVOcofVdXOwGu4uV5GAUnACBGp7pW5CJioqs2BX4C7cr6hiMQDfwIuVzd7ahpwb6DgRKQi8AZwJXAZcB64SfaA24DFqtpSVX8s5r+DMXmyZGBiwRrgchEZJyKXqep+oJuILBeRNUB3oGmO8nNy7LdW3cIiWcBGTs8EuVVVv/IeT8XNHZNTe9xKVF+JyCrcJGLn5xHfxcAmVf1B3fwwU4t+qsYUjbUZmKinqt+LSBvcxF5/EZHPcL/2k1V1q4g8DlTMsUuWd38yx+Ps59l/M7kn9cr9XIDPVXVosGEGWc6YEmFXBibqiUht4L+qOhUYj1s/F2C3V48/uAiHrS8il3qPhwJLcr2+DOgoIg29GM4UkcZ5HOs7oEGONoFgE4gxIWNXBiYWNAOeEZGTuGmP78StFbsGyMCt81BY64HhIvI6bvrgV3O+qKq7RGQEMF1EKnib/4SbPpxcZY94S3N+LCK7cYklqQgxGVNkNoW1MYXkrUH7kdf4bExUsGoiY4wxdmVgTGny1mNukGvzH1R1nh/xGJPNkoExxhirJjLGGGPJwBhjDJYMjDHGYMnAGGMM8P8Bzh2GpIDpA5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "fuel_data = pd.read_csv('fuel_ferc1.csv')\n",
    "sample_df = pd.DataFrame(fuel_data.sample(n=50, random_state=7), columns=['fuel_cost_per_unit_burned'])\n",
    "\n",
    "# KDE plot\n",
    "sns.kdeplot(sample_df['fuel_cost_per_unit_burned'], shade=True, color=\"b\")\n",
    "plt.xlabel('sample_df')"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}