Master_Energy_Consump_Sector_v3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Energy Consumption by Sector\n",
    "\n",
    "# Dependencies: Industrials\n",
    "%matplotlib inline\n",
    "\n",
    "import requests\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "\n",
    "# import api_key from config file: Industrials\n",
    "from api_keys import api_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------\n",
      "Data Retrieval Complete for Industrial Consumption\n",
      "-----------------------------\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pk_id</th>\n",
       "      <th>sector</th>\n",
       "      <th>energy_source</th>\n",
       "      <th>frequency</th>\n",
       "      <th>name</th>\n",
       "      <th>source</th>\n",
       "      <th>series_id</th>\n",
       "      <th>unit_long</th>\n",
       "      <th>unit_short</th>\n",
       "      <th>data_for</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consumption</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  pk_id      sector energy_source frequency  \\\n",
       "0     0  Industrial  Total Energy    Annual   \n",
       "\n",
       "                                                name  \\\n",
       "0  Total Energy Consumed by the Industrial Sector...   \n",
       "\n",
       "                                        source        series_id     unit_long  \\\n",
       "0  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A  Trillion Btu   \n",
       "\n",
       "  unit_short     data_for  \n",
       "0        NaN  Consumption  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Industrial\n",
    "series_url = \"http://api.eia.gov/series/?\" + \"api_key=\" + api_key + \"&series_id=\"\n",
    "\n",
    "header_df = pd.DataFrame()\n",
    "# # set up additional columns to hold header information\n",
    "header_df[\"pk_id\"] = \"\"\n",
    "header_df[\"sector\"] = \"\"\n",
    "header_df[\"energy_source\"] = \"\"\n",
    "header_df[\"frequency\"] = \"\"\n",
    "header_df[\"name\"] = \"\"\n",
    "header_df[\"source\"] = \"\"\n",
    "header_df[\"series_id\"] = \"\"\n",
    "header_df[\"unit_long\"] = \"\"\n",
    "header_df[\"unit_short\"] = \"\"\n",
    "\n",
    "header_df[\"data_for\"] = \"\"\n",
    "\n",
    "line_df = pd.DataFrame()\n",
    "# # set up additional columns to hold line information\n",
    "line_df[\"fk_id\"] = \"\"\n",
    "line_df[\"year\"] = \"\"\n",
    "line_df[\"month\"] = \"\"\n",
    "line_df[\"value\"] = \"\"\n",
    "\n",
    "consumption_pre = \"TOTAL.\"\n",
    "consumption_post = \".A\"\n",
    "consumption_source = [\"TEICBUS\"\n",
    "                      ]\n",
    "\n",
    "pkid = 0\n",
    "e_source = \"Total Energy\"\n",
    "    \n",
    "for count, cs in enumerate(consumption_source, start=pkid):\n",
    "    query_url = series_url + consumption_pre + cs + consumption_post\n",
    "    cons_json = requests.get(query_url).json()\n",
    "    header_df = header_df.append({\"pk_id\" : count,\n",
    "                                  \"sector\" : \"Industrial\",\n",
    "                                  \"energy_source\" : e_source,\n",
    "                                  \"frequency\" : \"Annual\",\n",
    "                                  \"name\" : cons_json[\"series\"][0]['name'],\n",
    "                                  \"source\" : \"EIA, U.S. Energy Information Administration\",\n",
    "                                  \"series_id\" : cons_json[\"series\"][0]['series_id'], \n",
    "                                  \"unit_long\" : cons_json[\"series\"][0]['units'],\n",
    "                                  \"data_for\" : \"Consumption\",\n",
    "                                 } , ignore_index=True )\n",
    "    \n",
    "    for dt in cons_json[\"series\"][0][\"data\"]:\n",
    "            line_df = line_df.append({\"fk_id\" : count,\n",
    "                                      \"year\" : dt[0],\n",
    "                                      \"month\" : 13,\n",
    "                                      \"value\" : dt[1]\n",
    "                               } , ignore_index=True )\n",
    "    pkid = count\n",
    "    \n",
    "print(\"-----------------------------\")\n",
    "print(\"Data Retrieval Complete for Industrial Consumption\")\n",
    "print(\"-----------------------------\")\n",
    "\n",
    "header_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>value</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>13</td>\n",
       "      <td>32617.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>32005.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>13</td>\n",
       "      <td>31452.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2015</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "  fk_id  year month      value\n",
       "0     0  2018    13  32617.767\n",
       "1     0  2017    13  32005.673\n",
       "2     0  2016    13  31452.839\n",
       "3     0  2015    13  31471.524\n",
       "4     0  2014    13  31797.941"
      ]
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   ],
   "source": [
    "line_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  pk_id  year month      value\n",
       "0     0  2018    13  32617.767\n",
       "1     0  2017    13  32005.673\n",
       "2     0  2016    13  31452.839\n",
       "3     0  2015    13  31471.524\n",
       "4     0  2014    13  31797.941"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line_df2 = line_df.rename(columns={\"fk_id\": \"pk_id\"})\n",
    "line_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2018</td>\n",
       "      <td>13</td>\n",
       "      <td>32617.767</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
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       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
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       "      <td>31452.839</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consumption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>13</td>\n",
       "      <td>31471.524</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consumption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>13</td>\n",
       "      <td>31797.941</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consumption</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pk_id  year month      value      sector energy_source frequency  \\\n",
       "0     0  2018    13  32617.767  Industrial  Total Energy    Annual   \n",
       "1     0  2017    13  32005.673  Industrial  Total Energy    Annual   \n",
       "2     0  2016    13  31452.839  Industrial  Total Energy    Annual   \n",
       "3     0  2015    13  31471.524  Industrial  Total Energy    Annual   \n",
       "4     0  2014    13  31797.941  Industrial  Total Energy    Annual   \n",
       "\n",
       "                                                name  \\\n",
       "0  Total Energy Consumed by the Industrial Sector...   \n",
       "1  Total Energy Consumed by the Industrial Sector...   \n",
       "2  Total Energy Consumed by the Industrial Sector...   \n",
       "3  Total Energy Consumed by the Industrial Sector...   \n",
       "4  Total Energy Consumed by the Industrial Sector...   \n",
       "\n",
       "                                        source        series_id     unit_long  \\\n",
       "0  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A  Trillion Btu   \n",
       "1  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A  Trillion Btu   \n",
       "2  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A  Trillion Btu   \n",
       "3  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A  Trillion Btu   \n",
       "4  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A  Trillion Btu   \n",
       "\n",
       "  unit_short     data_for  \n",
       "0        NaN  Consumption  \n",
       "1        NaN  Consumption  \n",
       "2        NaN  Consumption  \n",
       "3        NaN  Consumption  \n",
       "4        NaN  Consumption  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_df = pd.merge(line_df2, header_df, on=\"pk_id\")\n",
    "ind_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pk_id</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>value</th>\n",
       "      <th>sector</th>\n",
       "      <th>energy_source</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0</td>\n",
       "      <td>1953</td>\n",
       "      <td>13</td>\n",
       "      <td>18211.754</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consumption</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0</td>\n",
       "      <td>1952</td>\n",
       "      <td>13</td>\n",
       "      <td>17311.362</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consumption</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0</td>\n",
       "      <td>1951</td>\n",
       "      <td>13</td>\n",
       "      <td>17678.532</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consumption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0</td>\n",
       "      <td>1950</td>\n",
       "      <td>13</td>\n",
       "      <td>16241.423</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consumption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>13</td>\n",
       "      <td>14723.587</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Total Energy Consumed by the Industrial Sector...</td>\n",
       "      <td>EIA, U.S. Energy Information Administration</td>\n",
       "      <td>TOTAL.TEICBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consumption</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pk_id  year month      value      sector energy_source frequency  \\\n",
       "65     0  1953    13  18211.754  Industrial  Total Energy    Annual   \n",
       "66     0  1952    13  17311.362  Industrial  Total Energy    Annual   \n",
       "67     0  1951    13  17678.532  Industrial  Total Energy    Annual   \n",
       "68     0  1950    13  16241.423  Industrial  Total Energy    Annual   \n",
       "69     0  1949    13  14723.587  Industrial  Total Energy    Annual   \n",
       "\n",
       "                                                 name  \\\n",
       "65  Total Energy Consumed by the Industrial Sector...   \n",
       "66  Total Energy Consumed by the Industrial Sector...   \n",
       "67  Total Energy Consumed by the Industrial Sector...   \n",
       "68  Total Energy Consumed by the Industrial Sector...   \n",
       "69  Total Energy Consumed by the Industrial Sector...   \n",
       "\n",
       "                                         source        series_id  \\\n",
       "65  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A   \n",
       "66  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A   \n",
       "67  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A   \n",
       "68  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A   \n",
       "69  EIA, U.S. Energy Information Administration  TOTAL.TEICBUS.A   \n",
       "\n",
       "       unit_long unit_short     data_for  \n",
       "65  Trillion Btu        NaN  Consumption  \n",
       "66  Trillion Btu        NaN  Consumption  \n",
       "67  Trillion Btu        NaN  Consumption  \n",
       "68  Trillion Btu        NaN  Consumption  \n",
       "69  Trillion Btu        NaN  Consumption  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>sector</th>\n",
       "      <th>source</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2018</td>\n",
       "      <td>32617.767</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2017</td>\n",
       "      <td>32005.673</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2016</td>\n",
       "      <td>31452.839</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2015</td>\n",
       "      <td>31471.524</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2014</td>\n",
       "      <td>31797.941</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sector        source  year      value         units\n",
       "0  Industrial  Total Energy  2018  32617.767  Trillion Btu\n",
       "1  Industrial  Total Energy  2017  32005.673  Trillion Btu\n",
       "2  Industrial  Total Energy  2016  31452.839  Trillion Btu\n",
       "3  Industrial  Total Energy  2015  31471.524  Trillion Btu\n",
       "4  Industrial  Total Energy  2014  31797.941  Trillion Btu"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_ind_df = pd.DataFrame(ind_df, columns=[\"sector\",\"energy_source\",\"year\",\"value\",\"unit_long\"])\n",
    "format_ind_df = format_ind_df.rename(columns={\"energy_source\": \"source\", \"unit_long\": \"units\"\n",
    "                                         })\n",
    "\n",
    "format_ind_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n"
     ]
    }
   ],
   "source": [
    "# Commercial\n",
    "url = \"http://api.eia.gov/category/?\"\n",
    "query_url=f\"{url}api_key={api_key}&series_id=TOTAL.CLCCBUS.A\"\n",
    "\n",
    "response=requests.get(query_url)\n",
    "print(response.status_code)\n",
    "\n",
    "query_url=f\"{url}api_key={api_key}&category_id=711251\"\n",
    "\n",
    "eia_json = requests.get(query_url).json()\n",
    "\n",
    "url = \"http://api.eia.gov/series/?api_key=\"\n",
    "api_code1=\"&series_id=TOTAL.\"\n",
    "api_code2=\".A\"\n",
    "category=[\"TECCBUS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 1 | TECCBUS\n",
      "-----------------------------\n",
      "Data Retrieval Complete\n",
      "---------------------\n"
     ]
    }
   ],
   "source": [
    "start = []\n",
    "end = []\n",
    "f = []\n",
    "name = []\n",
    "sector = []\n",
    "series_id = []\n",
    "units = []\n",
    "updated = []\n",
    "year = []\n",
    "value = []\n",
    "data = []\n",
    "count_cat = 0\n",
    "\n",
    "for cat in category:\n",
    "        query_url = url + api_key + api_code1 + cat + api_code2\n",
    "        count_cat = count_cat + 1\n",
    "        print(\"Processing Record \" + str(count_cat) + \" | \" + cat)\n",
    "\n",
    "        consumption_response = requests.get(query_url).json()\n",
    "        \n",
    "       \n",
    "        data.append(consumption_response[\"series\"][0][\"data\"])\n",
    "        ln = (len(data[0]))\n",
    "\n",
    "        for i in range(0,ln):\n",
    "            start.append(consumption_response[\"series\"][0][\"start\"])\n",
    "            end.append(consumption_response[\"series\"][0][\"end\"])\n",
    "            f.append(consumption_response[\"series\"][0][\"f\"])\n",
    "            name.append(consumption_response[\"series\"][0][\"name\"])\n",
    "            sector.append(consumption_response[\"series\"][0][\"name\"].split(\" \")[5])\n",
    "            series_id.append(consumption_response[\"series\"][0][\"series_id\"])\n",
    "            units.append(consumption_response[\"series\"][0][\"units\"])\n",
    "            updated.append(consumption_response[\"series\"][0][\"updated\"])\n",
    "            year.append(consumption_response[\"series\"][0][\"data\"][i][0])\n",
    "            value.append(consumption_response[\"series\"][0][\"data\"][i][1])\n",
    "print(\"-----------------------------\")\n",
    "print(\"Data Retrieval Complete\")\n",
    "print(\"---------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>series_id</th>\n",
       "      <th>units</th>\n",
       "      <th>updated</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Commercial Sector...</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>TOTAL.TECCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>2018</td>\n",
       "      <td>18607.551</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Commercial Sector...</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>TOTAL.TECCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>2017</td>\n",
       "      <td>17899.252</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Commercial Sector...</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>TOTAL.TECCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>2016</td>\n",
       "      <td>18030.223</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Commercial Sector...</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>TOTAL.TECCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>2015</td>\n",
       "      <td>18156.728</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Commercial Sector...</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>TOTAL.TECCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>2014</td>\n",
       "      <td>18264.465</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  start   end                                               name      sector  \\\n",
       "0  1949  2018  Total Energy Consumed by the Commercial Sector...  Commercial   \n",
       "1  1949  2018  Total Energy Consumed by the Commercial Sector...  Commercial   \n",
       "2  1949  2018  Total Energy Consumed by the Commercial Sector...  Commercial   \n",
       "3  1949  2018  Total Energy Consumed by the Commercial Sector...  Commercial   \n",
       "4  1949  2018  Total Energy Consumed by the Commercial Sector...  Commercial   \n",
       "\n",
       "         series_id         units                   updated  year      value  \\\n",
       "0  TOTAL.TECCBUS.A  Trillion Btu  2019-06-24T11:59:37-0400  2018  18607.551   \n",
       "1  TOTAL.TECCBUS.A  Trillion Btu  2019-06-24T11:59:37-0400  2017  17899.252   \n",
       "2  TOTAL.TECCBUS.A  Trillion Btu  2019-06-24T11:59:37-0400  2016  18030.223   \n",
       "3  TOTAL.TECCBUS.A  Trillion Btu  2019-06-24T11:59:37-0400  2015  18156.728   \n",
       "4  TOTAL.TECCBUS.A  Trillion Btu  2019-06-24T11:59:37-0400  2014  18264.465   \n",
       "\n",
       "          source  \n",
       "0  Total Energy   \n",
       "1  Total Energy   \n",
       "2  Total Energy   \n",
       "3  Total Energy   \n",
       "4  Total Energy   "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumption_comm_df = pd.DataFrame({\n",
    "   \"start\":start,\n",
    "   \"end\":end,\n",
    "   \"name\": name,\n",
    "   \"sector\": sector,\n",
    "   \"series_id\": series_id,\n",
    "   \"units\": units,\n",
    "   \"updated\": updated,\n",
    "   \"year\": year,\n",
    "   \"value\": value\n",
    "})\n",
    "consumption_comm_df[\"source\"] = consumption_response[\"series\"][0][\"name\"].split(\"Consumed\")[0]\n",
    "consumption_comm_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>series_id</th>\n",
       "      <th>units</th>\n",
       "      <th>updated</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Commercial Sector...</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>TOTAL.TECCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>1953</td>\n",
       "      <td>3770.982</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Commercial Sector...</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>TOTAL.TECCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>1952</td>\n",
       "      <td>3873.385</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Commercial Sector...</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>TOTAL.TECCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>1951</td>\n",
       "      <td>3873.226</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Commercial Sector...</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>TOTAL.TECCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>1950</td>\n",
       "      <td>3893.298</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Commercial Sector...</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>TOTAL.TECCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>1949</td>\n",
       "      <td>3668.816</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start   end                                               name      sector  \\\n",
       "65  1949  2018  Total Energy Consumed by the Commercial Sector...  Commercial   \n",
       "66  1949  2018  Total Energy Consumed by the Commercial Sector...  Commercial   \n",
       "67  1949  2018  Total Energy Consumed by the Commercial Sector...  Commercial   \n",
       "68  1949  2018  Total Energy Consumed by the Commercial Sector...  Commercial   \n",
       "69  1949  2018  Total Energy Consumed by the Commercial Sector...  Commercial   \n",
       "\n",
       "          series_id         units                   updated  year     value  \\\n",
       "65  TOTAL.TECCBUS.A  Trillion Btu  2019-06-24T11:59:37-0400  1953  3770.982   \n",
       "66  TOTAL.TECCBUS.A  Trillion Btu  2019-06-24T11:59:37-0400  1952  3873.385   \n",
       "67  TOTAL.TECCBUS.A  Trillion Btu  2019-06-24T11:59:37-0400  1951  3873.226   \n",
       "68  TOTAL.TECCBUS.A  Trillion Btu  2019-06-24T11:59:37-0400  1950  3893.298   \n",
       "69  TOTAL.TECCBUS.A  Trillion Btu  2019-06-24T11:59:37-0400  1949  3668.816   \n",
       "\n",
       "           source  \n",
       "65  Total Energy   \n",
       "66  Total Energy   \n",
       "67  Total Energy   \n",
       "68  Total Energy   \n",
       "69  Total Energy   "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumption_comm_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2018</td>\n",
       "      <td>18607.551</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2017</td>\n",
       "      <td>17899.252</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2016</td>\n",
       "      <td>18030.223</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2015</td>\n",
       "      <td>18156.728</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2014</td>\n",
       "      <td>18264.465</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sector         source  year      value         units\n",
       "0  Commercial  Total Energy   2018  18607.551  Trillion Btu\n",
       "1  Commercial  Total Energy   2017  17899.252  Trillion Btu\n",
       "2  Commercial  Total Energy   2016  18030.223  Trillion Btu\n",
       "3  Commercial  Total Energy   2015  18156.728  Trillion Btu\n",
       "4  Commercial  Total Energy   2014  18264.465  Trillion Btu"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_comm_df = pd.DataFrame(consumption_comm_df, columns=[\"sector\",\"source\",\"year\",\"value\",\"units\"])\n",
    "format_comm_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transportation\n",
    "url = \"http://api.eia.gov/series/?api_key=\"\n",
    "api_code1=\"&series_id=TOTAL.\"\n",
    "api_code2=\".A\"\n",
    "category=[\"TEACBUS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 1 | TEACBUS\n",
      "-----------------------------\n",
      "Data Retrieval Complete\n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "start = []\n",
    "end = []\n",
    "f = []\n",
    "name = []\n",
    "sector = []\n",
    "series_id = []\n",
    "units = []\n",
    "updated = []\n",
    "year = []\n",
    "value = []\n",
    "data = []\n",
    "count_cat = 0\n",
    "\n",
    "for cat in category:\n",
    "        query_url = url + api_key + api_code1 + cat + api_code2\n",
    "        count_cat = count_cat + 1\n",
    "        print(\"Processing Record \" + str(count_cat) + \" | \" + cat)\n",
    "\n",
    "        consumption_response = requests.get(query_url).json()\n",
    "        \n",
    "       \n",
    "        data.append(consumption_response[\"series\"][0][\"data\"])\n",
    "        ln = (len(data[0]))\n",
    "\n",
    "        for i in range(0,ln):\n",
    "            start.append(consumption_response[\"series\"][0][\"start\"])\n",
    "            end.append(consumption_response[\"series\"][0][\"end\"])\n",
    "            f.append(consumption_response[\"series\"][0][\"f\"])\n",
    "            name.append(consumption_response[\"series\"][0][\"name\"])\n",
    "            sector.append(consumption_response[\"series\"][0][\"name\"].split(\" \")[5])\n",
    "            series_id.append(consumption_response[\"series\"][0][\"series_id\"])\n",
    "            units.append(consumption_response[\"series\"][0][\"units\"])\n",
    "            updated.append(consumption_response[\"series\"][0][\"updated\"])\n",
    "            year.append(consumption_response[\"series\"][0][\"data\"][i][0])\n",
    "            value.append(consumption_response[\"series\"][0][\"data\"][i][1])\n",
    "\n",
    "print(\"-----------------------------\")\n",
    "print(\"Data Retrieval Complete\")\n",
    "print(\"-----------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>series_id</th>\n",
       "      <th>units</th>\n",
       "      <th>updated</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Transportation Se...</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>TOTAL.TEACBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-05-23T15:52:44-0400</td>\n",
       "      <td>2018</td>\n",
       "      <td>28390.532</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Transportation Se...</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>TOTAL.TEACBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-05-23T15:52:44-0400</td>\n",
       "      <td>2017</td>\n",
       "      <td>28014.167</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Transportation Se...</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>TOTAL.TEACBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-05-23T15:52:44-0400</td>\n",
       "      <td>2016</td>\n",
       "      <td>27785.537</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Transportation Se...</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>TOTAL.TEACBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-05-23T15:52:44-0400</td>\n",
       "      <td>2015</td>\n",
       "      <td>27238.037</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Transportation Se...</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>TOTAL.TEACBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-05-23T15:52:44-0400</td>\n",
       "      <td>2014</td>\n",
       "      <td>26868.857</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  start   end                                               name  \\\n",
       "0  1949  2018  Total Energy Consumed by the Transportation Se...   \n",
       "1  1949  2018  Total Energy Consumed by the Transportation Se...   \n",
       "2  1949  2018  Total Energy Consumed by the Transportation Se...   \n",
       "3  1949  2018  Total Energy Consumed by the Transportation Se...   \n",
       "4  1949  2018  Total Energy Consumed by the Transportation Se...   \n",
       "\n",
       "           sector        series_id         units                   updated  \\\n",
       "0  Transportation  TOTAL.TEACBUS.A  Trillion Btu  2019-05-23T15:52:44-0400   \n",
       "1  Transportation  TOTAL.TEACBUS.A  Trillion Btu  2019-05-23T15:52:44-0400   \n",
       "2  Transportation  TOTAL.TEACBUS.A  Trillion Btu  2019-05-23T15:52:44-0400   \n",
       "3  Transportation  TOTAL.TEACBUS.A  Trillion Btu  2019-05-23T15:52:44-0400   \n",
       "4  Transportation  TOTAL.TEACBUS.A  Trillion Btu  2019-05-23T15:52:44-0400   \n",
       "\n",
       "   year      value         source  \n",
       "0  2018  28390.532  Total Energy   \n",
       "1  2017  28014.167  Total Energy   \n",
       "2  2016  27785.537  Total Energy   \n",
       "3  2015  27238.037  Total Energy   \n",
       "4  2014  26868.857  Total Energy   "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumption_trans_df = pd.DataFrame({\n",
    "   \"start\":start,\n",
    "   \"end\":end,\n",
    "   \"name\": name,\n",
    "   \"sector\": sector,\n",
    "   \"series_id\": series_id,\n",
    "   \"units\": units,\n",
    "   \"updated\": updated,\n",
    "   \"year\": year,\n",
    "   \"value\": value\n",
    "})\n",
    "consumption_trans_df[\"source\"] = consumption_response[\"series\"][0][\"name\"].split(\"Consumed\")[0]\n",
    "consumption_trans_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>series_id</th>\n",
       "      <th>units</th>\n",
       "      <th>updated</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Transportation Se...</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>TOTAL.TEACBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-05-23T15:52:44-0400</td>\n",
       "      <td>1953</td>\n",
       "      <td>9123.022</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Transportation Se...</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>TOTAL.TEACBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-05-23T15:52:44-0400</td>\n",
       "      <td>1952</td>\n",
       "      <td>9002.752</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Transportation Se...</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>TOTAL.TEACBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-05-23T15:52:44-0400</td>\n",
       "      <td>1951</td>\n",
       "      <td>9041.892</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Transportation Se...</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>TOTAL.TEACBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-05-23T15:52:44-0400</td>\n",
       "      <td>1950</td>\n",
       "      <td>8492.473</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>Total Energy Consumed by the Transportation Se...</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>TOTAL.TEACBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-05-23T15:52:44-0400</td>\n",
       "      <td>1949</td>\n",
       "      <td>7990.015</td>\n",
       "      <td>Total Energy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start   end                                               name  \\\n",
       "65  1949  2018  Total Energy Consumed by the Transportation Se...   \n",
       "66  1949  2018  Total Energy Consumed by the Transportation Se...   \n",
       "67  1949  2018  Total Energy Consumed by the Transportation Se...   \n",
       "68  1949  2018  Total Energy Consumed by the Transportation Se...   \n",
       "69  1949  2018  Total Energy Consumed by the Transportation Se...   \n",
       "\n",
       "            sector        series_id         units                   updated  \\\n",
       "65  Transportation  TOTAL.TEACBUS.A  Trillion Btu  2019-05-23T15:52:44-0400   \n",
       "66  Transportation  TOTAL.TEACBUS.A  Trillion Btu  2019-05-23T15:52:44-0400   \n",
       "67  Transportation  TOTAL.TEACBUS.A  Trillion Btu  2019-05-23T15:52:44-0400   \n",
       "68  Transportation  TOTAL.TEACBUS.A  Trillion Btu  2019-05-23T15:52:44-0400   \n",
       "69  Transportation  TOTAL.TEACBUS.A  Trillion Btu  2019-05-23T15:52:44-0400   \n",
       "\n",
       "    year     value         source  \n",
       "65  1953  9123.022  Total Energy   \n",
       "66  1952  9002.752  Total Energy   \n",
       "67  1951  9041.892  Total Energy   \n",
       "68  1950  8492.473  Total Energy   \n",
       "69  1949  7990.015  Total Energy   "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumption_trans_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>sector</th>\n",
       "      <th>source</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2018</td>\n",
       "      <td>28390.532</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2017</td>\n",
       "      <td>28014.167</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2016</td>\n",
       "      <td>27785.537</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2015</td>\n",
       "      <td>27238.037</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2014</td>\n",
       "      <td>26868.857</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sector         source  year      value         units\n",
       "0  Transportation  Total Energy   2018  28390.532  Trillion Btu\n",
       "1  Transportation  Total Energy   2017  28014.167  Trillion Btu\n",
       "2  Transportation  Total Energy   2016  27785.537  Trillion Btu\n",
       "3  Transportation  Total Energy   2015  27238.037  Trillion Btu\n",
       "4  Transportation  Total Energy   2014  26868.857  Trillion Btu"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_trans_df = pd.DataFrame(consumption_trans_df, columns=[\"sector\",\"source\",\"year\",\"value\",\"units\"])\n",
    "format_trans_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Residential\n",
    "#http://api.eia.gov/series/?api_key=YOUR_API_KEY_HERE&series_id=TOTAL.TERCBUS.A\n",
    "\n",
    "url = \"http://api.eia.gov/series/?api_key=\"\n",
    "api_code1 = \"&series_id=TOTAL.\"\n",
    "api_energy_source = \"TERCBUS\"\n",
    "api_period = \".A\"    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------\n",
      "Data Retrieval Complete\n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "start = []\n",
    "end = []\n",
    "f = []\n",
    "name = []\n",
    "sector = []\n",
    "sec = \"Residential\"\n",
    "source = []\n",
    "energy_source = \"Total Energy\"\n",
    "series_id = []\n",
    "units = []\n",
    "updated = []\n",
    "year = []\n",
    "value = []\n",
    "count_st = 0\n",
    "\n",
    "query_url = url + api_key + api_code1 + api_energy_source + api_period\n",
    "consume_res_resp = requests.get(query_url).json()\n",
    "     \n",
    "data = []\n",
    "data.append(consume_res_resp[\"series\"][0][\"data\"])\n",
    "ln = (len(data[0]))\n",
    "\n",
    "for i in range(0, ln):\n",
    "\n",
    "    start.append(consume_res_resp[\"series\"][0][\"start\"])\n",
    "    end.append(consume_res_resp[\"series\"][0][\"end\"])\n",
    "    f.append(consume_res_resp[\"series\"][0][\"f\"])\n",
    "    name.append(consume_res_resp[\"series\"][0][\"name\"])\n",
    "    source.append(energy_source)\n",
    "    sector.append(sec)\n",
    "    series_id.append(consume_res_resp[\"series\"][0][\"series_id\"])\n",
    "    units.append(consume_res_resp[\"series\"][0][\"units\"])\n",
    "    updated.append(consume_res_resp[\"series\"][0][\"updated\"])\n",
    "    year.append(consume_res_resp[\"series\"][0][\"data\"][i][0])\n",
    "    value.append(consume_res_resp[\"series\"][0][\"data\"][i][1])\n",
    "        \n",
    "print(\"-----------------------------\")\n",
    "print(\"Data Retrieval Complete\")      \n",
    "print(\"-----------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>f</th>\n",
       "      <th>name</th>\n",
       "      <th>source</th>\n",
       "      <th>sector</th>\n",
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       "      <th>units</th>\n",
       "      <th>updated</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Energy Consumed by the Residential Secto...</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Residential</td>\n",
       "      <td>TOTAL.TERCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>2018</td>\n",
       "      <td>21621.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Energy Consumed by the Residential Secto...</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Residential</td>\n",
       "      <td>TOTAL.TERCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>2017</td>\n",
       "      <td>19889.581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Energy Consumed by the Residential Secto...</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Residential</td>\n",
       "      <td>TOTAL.TERCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>2016</td>\n",
       "      <td>20181.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Energy Consumed by the Residential Secto...</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Residential</td>\n",
       "      <td>TOTAL.TERCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>2015</td>\n",
       "      <td>20616.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Energy Consumed by the Residential Secto...</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Residential</td>\n",
       "      <td>TOTAL.TERCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>2014</td>\n",
       "      <td>21444.938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  start   end  f                                               name  \\\n",
       "0  1949  2018  A  Total Energy Consumed by the Residential Secto...   \n",
       "1  1949  2018  A  Total Energy Consumed by the Residential Secto...   \n",
       "2  1949  2018  A  Total Energy Consumed by the Residential Secto...   \n",
       "3  1949  2018  A  Total Energy Consumed by the Residential Secto...   \n",
       "4  1949  2018  A  Total Energy Consumed by the Residential Secto...   \n",
       "\n",
       "         source       sector        series_id         units  \\\n",
       "0  Total Energy  Residential  TOTAL.TERCBUS.A  Trillion Btu   \n",
       "1  Total Energy  Residential  TOTAL.TERCBUS.A  Trillion Btu   \n",
       "2  Total Energy  Residential  TOTAL.TERCBUS.A  Trillion Btu   \n",
       "3  Total Energy  Residential  TOTAL.TERCBUS.A  Trillion Btu   \n",
       "4  Total Energy  Residential  TOTAL.TERCBUS.A  Trillion Btu   \n",
       "\n",
       "                    updated  year      value  \n",
       "0  2019-06-24T11:59:37-0400  2018  21621.074  \n",
       "1  2019-06-24T11:59:37-0400  2017  19889.581  \n",
       "2  2019-06-24T11:59:37-0400  2016  20181.113  \n",
       "3  2019-06-24T11:59:37-0400  2015  20616.830  \n",
       "4  2019-06-24T11:59:37-0400  2014  21444.938  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create DataFrame\n",
    "energy_consume_resi_df = pd.DataFrame({\n",
    "    \"start\": start,\n",
    "    \"end\": end,\n",
    "    \"f\": f,\n",
    "    \"name\": name,\n",
    "    \"source\": source,\n",
    "    \"sector\": sector,\n",
    "    \"series_id\": series_id,\n",
    "    \"units\": units,\n",
    "    \"updated\": updated,\n",
    "    \"year\": year,\n",
    "    \"value\": value\n",
    "    \n",
    "})\n",
    "energy_consume_resi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>f</th>\n",
       "      <th>name</th>\n",
       "      <th>source</th>\n",
       "      <th>sector</th>\n",
       "      <th>series_id</th>\n",
       "      <th>units</th>\n",
       "      <th>updated</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Energy Consumed by the Residential Secto...</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Residential</td>\n",
       "      <td>TOTAL.TERCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>1953</td>\n",
       "      <td>6558.748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Energy Consumed by the Residential Secto...</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Residential</td>\n",
       "      <td>TOTAL.TERCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>1952</td>\n",
       "      <td>6560.243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Energy Consumed by the Residential Secto...</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Residential</td>\n",
       "      <td>TOTAL.TERCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>1951</td>\n",
       "      <td>6380.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Energy Consumed by the Residential Secto...</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Residential</td>\n",
       "      <td>TOTAL.TERCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>1950</td>\n",
       "      <td>5988.553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1949</td>\n",
       "      <td>2018</td>\n",
       "      <td>A</td>\n",
       "      <td>Total Energy Consumed by the Residential Secto...</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>Residential</td>\n",
       "      <td>TOTAL.TERCBUS.A</td>\n",
       "      <td>Trillion Btu</td>\n",
       "      <td>2019-06-24T11:59:37-0400</td>\n",
       "      <td>1949</td>\n",
       "      <td>5599.250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start   end  f                                               name  \\\n",
       "65  1949  2018  A  Total Energy Consumed by the Residential Secto...   \n",
       "66  1949  2018  A  Total Energy Consumed by the Residential Secto...   \n",
       "67  1949  2018  A  Total Energy Consumed by the Residential Secto...   \n",
       "68  1949  2018  A  Total Energy Consumed by the Residential Secto...   \n",
       "69  1949  2018  A  Total Energy Consumed by the Residential Secto...   \n",
       "\n",
       "          source       sector        series_id         units  \\\n",
       "65  Total Energy  Residential  TOTAL.TERCBUS.A  Trillion Btu   \n",
       "66  Total Energy  Residential  TOTAL.TERCBUS.A  Trillion Btu   \n",
       "67  Total Energy  Residential  TOTAL.TERCBUS.A  Trillion Btu   \n",
       "68  Total Energy  Residential  TOTAL.TERCBUS.A  Trillion Btu   \n",
       "69  Total Energy  Residential  TOTAL.TERCBUS.A  Trillion Btu   \n",
       "\n",
       "                     updated  year     value  \n",
       "65  2019-06-24T11:59:37-0400  1953  6558.748  \n",
       "66  2019-06-24T11:59:37-0400  1952  6560.243  \n",
       "67  2019-06-24T11:59:37-0400  1951  6380.193  \n",
       "68  2019-06-24T11:59:37-0400  1950  5988.553  \n",
       "69  2019-06-24T11:59:37-0400  1949  5599.250  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_consume_resi_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>sector</th>\n",
       "      <th>source</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2018</td>\n",
       "      <td>21621.074</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2017</td>\n",
       "      <td>19889.581</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2016</td>\n",
       "      <td>20181.113</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2015</td>\n",
       "      <td>20616.830</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2014</td>\n",
       "      <td>21444.938</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sector        source  year      value         units\n",
       "0  Residential  Total Energy  2018  21621.074  Trillion Btu\n",
       "1  Residential  Total Energy  2017  19889.581  Trillion Btu\n",
       "2  Residential  Total Energy  2016  20181.113  Trillion Btu\n",
       "3  Residential  Total Energy  2015  20616.830  Trillion Btu\n",
       "4  Residential  Total Energy  2014  21444.938  Trillion Btu"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_resi_df = pd.DataFrame(energy_consume_resi_df, columns=[\"sector\",\"source\",\"year\",\"value\",\"units\"])\n",
    "format_resi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\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>sector</th>\n",
       "      <th>source</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2018</td>\n",
       "      <td>32617.767</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2017</td>\n",
       "      <td>32005.673</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2016</td>\n",
       "      <td>31452.839</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2015</td>\n",
       "      <td>31471.524</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2014</td>\n",
       "      <td>31797.941</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2013</td>\n",
       "      <td>31627.437</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2012</td>\n",
       "      <td>31060.724</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2011</td>\n",
       "      <td>30981.322</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2010</td>\n",
       "      <td>30670.436</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2009</td>\n",
       "      <td>28465.439</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2008</td>\n",
       "      <td>31333.358</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2007</td>\n",
       "      <td>32384.515</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2006</td>\n",
       "      <td>32390.355</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2005</td>\n",
       "      <td>32441.115</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2004</td>\n",
       "      <td>33515.280</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2003</td>\n",
       "      <td>32552.715</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2002</td>\n",
       "      <td>32660.366</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2001</td>\n",
       "      <td>32718.499</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2000</td>\n",
       "      <td>34662.130</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1999</td>\n",
       "      <td>34762.524</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1998</td>\n",
       "      <td>34841.110</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1997</td>\n",
       "      <td>35198.990</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1996</td>\n",
       "      <td>34902.978</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1995</td>\n",
       "      <td>33969.227</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1994</td>\n",
       "      <td>33519.691</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1993</td>\n",
       "      <td>32618.544</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1992</td>\n",
       "      <td>32570.887</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1991</td>\n",
       "      <td>31399.342</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1990</td>\n",
       "      <td>31801.602</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1989</td>\n",
       "      <td>31327.767</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1978</td>\n",
       "      <td>16132.287</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1977</td>\n",
       "      <td>15661.713</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1976</td>\n",
       "      <td>15410.259</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1975</td>\n",
       "      <td>14813.400</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1974</td>\n",
       "      <td>14654.336</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1973</td>\n",
       "      <td>14897.351</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1972</td>\n",
       "      <td>14857.045</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1971</td>\n",
       "      <td>14246.200</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1970</td>\n",
       "      <td>13765.758</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1969</td>\n",
       "      <td>13169.156</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1968</td>\n",
       "      <td>12336.290</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1967</td>\n",
       "      <td>11639.275</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1966</td>\n",
       "      <td>11168.630</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1965</td>\n",
       "      <td>10639.387</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1964</td>\n",
       "      <td>10240.574</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1963</td>\n",
       "      <td>9988.479</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1962</td>\n",
       "      <td>9782.420</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1961</td>\n",
       "      <td>9286.024</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1960</td>\n",
       "      <td>9039.418</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1959</td>\n",
       "      <td>8411.866</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1958</td>\n",
       "      <td>8200.674</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1957</td>\n",
       "      <td>7712.000</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1956</td>\n",
       "      <td>7662.725</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1955</td>\n",
       "      <td>7277.988</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1954</td>\n",
       "      <td>6846.058</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1953</td>\n",
       "      <td>6558.748</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1952</td>\n",
       "      <td>6560.243</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1951</td>\n",
       "      <td>6380.193</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1950</td>\n",
       "      <td>5988.553</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1949</td>\n",
       "      <td>5599.250</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         sector        source  year      value         units\n",
       "0    Industrial  Total Energy  2018  32617.767  Trillion Btu\n",
       "1    Industrial  Total Energy  2017  32005.673  Trillion Btu\n",
       "2    Industrial  Total Energy  2016  31452.839  Trillion Btu\n",
       "3    Industrial  Total Energy  2015  31471.524  Trillion Btu\n",
       "4    Industrial  Total Energy  2014  31797.941  Trillion Btu\n",
       "5    Industrial  Total Energy  2013  31627.437  Trillion Btu\n",
       "6    Industrial  Total Energy  2012  31060.724  Trillion Btu\n",
       "7    Industrial  Total Energy  2011  30981.322  Trillion Btu\n",
       "8    Industrial  Total Energy  2010  30670.436  Trillion Btu\n",
       "9    Industrial  Total Energy  2009  28465.439  Trillion Btu\n",
       "10   Industrial  Total Energy  2008  31333.358  Trillion Btu\n",
       "11   Industrial  Total Energy  2007  32384.515  Trillion Btu\n",
       "12   Industrial  Total Energy  2006  32390.355  Trillion Btu\n",
       "13   Industrial  Total Energy  2005  32441.115  Trillion Btu\n",
       "14   Industrial  Total Energy  2004  33515.280  Trillion Btu\n",
       "15   Industrial  Total Energy  2003  32552.715  Trillion Btu\n",
       "16   Industrial  Total Energy  2002  32660.366  Trillion Btu\n",
       "17   Industrial  Total Energy  2001  32718.499  Trillion Btu\n",
       "18   Industrial  Total Energy  2000  34662.130  Trillion Btu\n",
       "19   Industrial  Total Energy  1999  34762.524  Trillion Btu\n",
       "20   Industrial  Total Energy  1998  34841.110  Trillion Btu\n",
       "21   Industrial  Total Energy  1997  35198.990  Trillion Btu\n",
       "22   Industrial  Total Energy  1996  34902.978  Trillion Btu\n",
       "23   Industrial  Total Energy  1995  33969.227  Trillion Btu\n",
       "24   Industrial  Total Energy  1994  33519.691  Trillion Btu\n",
       "25   Industrial  Total Energy  1993  32618.544  Trillion Btu\n",
       "26   Industrial  Total Energy  1992  32570.887  Trillion Btu\n",
       "27   Industrial  Total Energy  1991  31399.342  Trillion Btu\n",
       "28   Industrial  Total Energy  1990  31801.602  Trillion Btu\n",
       "29   Industrial  Total Energy  1989  31327.767  Trillion Btu\n",
       "..          ...           ...   ...        ...           ...\n",
       "40  Residential  Total Energy  1978  16132.287  Trillion Btu\n",
       "41  Residential  Total Energy  1977  15661.713  Trillion Btu\n",
       "42  Residential  Total Energy  1976  15410.259  Trillion Btu\n",
       "43  Residential  Total Energy  1975  14813.400  Trillion Btu\n",
       "44  Residential  Total Energy  1974  14654.336  Trillion Btu\n",
       "45  Residential  Total Energy  1973  14897.351  Trillion Btu\n",
       "46  Residential  Total Energy  1972  14857.045  Trillion Btu\n",
       "47  Residential  Total Energy  1971  14246.200  Trillion Btu\n",
       "48  Residential  Total Energy  1970  13765.758  Trillion Btu\n",
       "49  Residential  Total Energy  1969  13169.156  Trillion Btu\n",
       "50  Residential  Total Energy  1968  12336.290  Trillion Btu\n",
       "51  Residential  Total Energy  1967  11639.275  Trillion Btu\n",
       "52  Residential  Total Energy  1966  11168.630  Trillion Btu\n",
       "53  Residential  Total Energy  1965  10639.387  Trillion Btu\n",
       "54  Residential  Total Energy  1964  10240.574  Trillion Btu\n",
       "55  Residential  Total Energy  1963   9988.479  Trillion Btu\n",
       "56  Residential  Total Energy  1962   9782.420  Trillion Btu\n",
       "57  Residential  Total Energy  1961   9286.024  Trillion Btu\n",
       "58  Residential  Total Energy  1960   9039.418  Trillion Btu\n",
       "59  Residential  Total Energy  1959   8411.866  Trillion Btu\n",
       "60  Residential  Total Energy  1958   8200.674  Trillion Btu\n",
       "61  Residential  Total Energy  1957   7712.000  Trillion Btu\n",
       "62  Residential  Total Energy  1956   7662.725  Trillion Btu\n",
       "63  Residential  Total Energy  1955   7277.988  Trillion Btu\n",
       "64  Residential  Total Energy  1954   6846.058  Trillion Btu\n",
       "65  Residential  Total Energy  1953   6558.748  Trillion Btu\n",
       "66  Residential  Total Energy  1952   6560.243  Trillion Btu\n",
       "67  Residential  Total Energy  1951   6380.193  Trillion Btu\n",
       "68  Residential  Total Energy  1950   5988.553  Trillion Btu\n",
       "69  Residential  Total Energy  1949   5599.250  Trillion Btu\n",
       "\n",
       "[280 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# master consumption DataFrame\n",
    "master_consum_df = pd.DataFrame()\n",
    "master_consum_df[\"sector\"] = []\n",
    "master_consum_df[\"source\"] = []\n",
    "master_consum_df[\"year\"] = []\n",
    "master_consum_df[\"value\"] = []\n",
    "master_consum_df[\"units\"] = []\n",
    "\n",
    "master_consum_df = master_consum_df.append(format_ind_df)\n",
    "master_consum_df = master_consum_df.append(format_comm_df)\n",
    "master_consum_df = master_consum_df.append(format_trans_df)\n",
    "master_consum_df = master_consum_df.append(format_resi_df)\n",
    "master_consum_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(280, 5)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_consum_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sector     object\n",
       "source     object\n",
       "year       object\n",
       "value     float64\n",
       "units      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_consum_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 280 entries, 0 to 69\n",
      "Data columns (total 5 columns):\n",
      "sector    280 non-null object\n",
      "source    280 non-null object\n",
      "year      280 non-null object\n",
      "value     280 non-null float64\n",
      "units     280 non-null object\n",
      "dtypes: float64(1), object(4)\n",
      "memory usage: 13.1+ KB\n"
     ]
    }
   ],
   "source": [
    "master_consum_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>280.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18746.007143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8485.537439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3668.816000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11630.982750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18138.573500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26287.001750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35198.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              value\n",
       "count    280.000000\n",
       "mean   18746.007143\n",
       "std     8485.537439\n",
       "min     3668.816000\n",
       "25%    11630.982750\n",
       "50%    18138.573500\n",
       "75%    26287.001750\n",
       "max    35198.990000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_consum_df.describe()"
   ]
  },
  {
   "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>sector</th>\n",
       "      <th>source</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1949</td>\n",
       "      <td>3668.816</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1949</td>\n",
       "      <td>7990.015</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1949</td>\n",
       "      <td>14723.587</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1949</td>\n",
       "      <td>5599.250</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1950</td>\n",
       "      <td>16241.423</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1950</td>\n",
       "      <td>8492.473</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1950</td>\n",
       "      <td>3893.298</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1950</td>\n",
       "      <td>5988.553</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1951</td>\n",
       "      <td>3873.226</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1951</td>\n",
       "      <td>17678.532</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1951</td>\n",
       "      <td>9041.892</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1951</td>\n",
       "      <td>6380.193</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1952</td>\n",
       "      <td>17311.362</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1952</td>\n",
       "      <td>6560.243</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1952</td>\n",
       "      <td>3873.385</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1952</td>\n",
       "      <td>9002.752</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1953</td>\n",
       "      <td>6558.748</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1953</td>\n",
       "      <td>18211.754</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1953</td>\n",
       "      <td>3770.982</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1953</td>\n",
       "      <td>9123.022</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1954</td>\n",
       "      <td>8902.617</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1954</td>\n",
       "      <td>3732.921</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1954</td>\n",
       "      <td>17157.695</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1954</td>\n",
       "      <td>6846.058</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1955</td>\n",
       "      <td>19484.608</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1955</td>\n",
       "      <td>9550.205</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1955</td>\n",
       "      <td>3895.140</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1955</td>\n",
       "      <td>7277.988</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1956</td>\n",
       "      <td>7662.725</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>1956</td>\n",
       "      <td>4022.859</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2011</td>\n",
       "      <td>17981.378</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2011</td>\n",
       "      <td>30981.322</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2012</td>\n",
       "      <td>17421.351</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2012</td>\n",
       "      <td>31060.724</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2012</td>\n",
       "      <td>19869.589</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2012</td>\n",
       "      <td>26125.738</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2013</td>\n",
       "      <td>26611.709</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2013</td>\n",
       "      <td>31627.437</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2013</td>\n",
       "      <td>21051.185</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2013</td>\n",
       "      <td>17929.446</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2014</td>\n",
       "      <td>18264.465</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2014</td>\n",
       "      <td>31797.941</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2014</td>\n",
       "      <td>26868.857</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2014</td>\n",
       "      <td>21444.938</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2015</td>\n",
       "      <td>27238.037</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2015</td>\n",
       "      <td>20616.830</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2015</td>\n",
       "      <td>18156.728</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2015</td>\n",
       "      <td>31471.524</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2016</td>\n",
       "      <td>20181.113</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2016</td>\n",
       "      <td>18030.223</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2016</td>\n",
       "      <td>31452.839</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2016</td>\n",
       "      <td>27785.537</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2017</td>\n",
       "      <td>28014.167</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2017</td>\n",
       "      <td>32005.673</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2017</td>\n",
       "      <td>17899.252</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2017</td>\n",
       "      <td>19889.581</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Residential</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2018</td>\n",
       "      <td>21621.074</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2018</td>\n",
       "      <td>28390.532</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2018</td>\n",
       "      <td>18607.551</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Total Energy</td>\n",
       "      <td>2018</td>\n",
       "      <td>32617.767</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            sector         source  year      value         units\n",
       "69      Commercial  Total Energy   1949   3668.816  Trillion Btu\n",
       "69  Transportation  Total Energy   1949   7990.015  Trillion Btu\n",
       "69      Industrial   Total Energy  1949  14723.587  Trillion Btu\n",
       "69     Residential   Total Energy  1949   5599.250  Trillion Btu\n",
       "68      Industrial   Total Energy  1950  16241.423  Trillion Btu\n",
       "68  Transportation  Total Energy   1950   8492.473  Trillion Btu\n",
       "68      Commercial  Total Energy   1950   3893.298  Trillion Btu\n",
       "68     Residential   Total Energy  1950   5988.553  Trillion Btu\n",
       "67      Commercial  Total Energy   1951   3873.226  Trillion Btu\n",
       "67      Industrial   Total Energy  1951  17678.532  Trillion Btu\n",
       "67  Transportation  Total Energy   1951   9041.892  Trillion Btu\n",
       "67     Residential   Total Energy  1951   6380.193  Trillion Btu\n",
       "66      Industrial   Total Energy  1952  17311.362  Trillion Btu\n",
       "66     Residential   Total Energy  1952   6560.243  Trillion Btu\n",
       "66      Commercial  Total Energy   1952   3873.385  Trillion Btu\n",
       "66  Transportation  Total Energy   1952   9002.752  Trillion Btu\n",
       "65     Residential   Total Energy  1953   6558.748  Trillion Btu\n",
       "65      Industrial   Total Energy  1953  18211.754  Trillion Btu\n",
       "65      Commercial  Total Energy   1953   3770.982  Trillion Btu\n",
       "65  Transportation  Total Energy   1953   9123.022  Trillion Btu\n",
       "64  Transportation  Total Energy   1954   8902.617  Trillion Btu\n",
       "64      Commercial  Total Energy   1954   3732.921  Trillion Btu\n",
       "64      Industrial   Total Energy  1954  17157.695  Trillion Btu\n",
       "64     Residential   Total Energy  1954   6846.058  Trillion Btu\n",
       "63      Industrial   Total Energy  1955  19484.608  Trillion Btu\n",
       "63  Transportation  Total Energy   1955   9550.205  Trillion Btu\n",
       "63      Commercial  Total Energy   1955   3895.140  Trillion Btu\n",
       "63     Residential   Total Energy  1955   7277.988  Trillion Btu\n",
       "62     Residential   Total Energy  1956   7662.725  Trillion Btu\n",
       "62      Commercial  Total Energy   1956   4022.859  Trillion Btu\n",
       "..             ...            ...   ...        ...           ...\n",
       "7       Commercial  Total Energy   2011  17981.378  Trillion Btu\n",
       "7       Industrial   Total Energy  2011  30981.322  Trillion Btu\n",
       "6       Commercial  Total Energy   2012  17421.351  Trillion Btu\n",
       "6       Industrial   Total Energy  2012  31060.724  Trillion Btu\n",
       "6      Residential   Total Energy  2012  19869.589  Trillion Btu\n",
       "6   Transportation  Total Energy   2012  26125.738  Trillion Btu\n",
       "5   Transportation  Total Energy   2013  26611.709  Trillion Btu\n",
       "5       Industrial   Total Energy  2013  31627.437  Trillion Btu\n",
       "5      Residential   Total Energy  2013  21051.185  Trillion Btu\n",
       "5       Commercial  Total Energy   2013  17929.446  Trillion Btu\n",
       "4       Commercial  Total Energy   2014  18264.465  Trillion Btu\n",
       "4       Industrial   Total Energy  2014  31797.941  Trillion Btu\n",
       "4   Transportation  Total Energy   2014  26868.857  Trillion Btu\n",
       "4      Residential   Total Energy  2014  21444.938  Trillion Btu\n",
       "3   Transportation  Total Energy   2015  27238.037  Trillion Btu\n",
       "3      Residential   Total Energy  2015  20616.830  Trillion Btu\n",
       "3       Commercial  Total Energy   2015  18156.728  Trillion Btu\n",
       "3       Industrial   Total Energy  2015  31471.524  Trillion Btu\n",
       "2      Residential   Total Energy  2016  20181.113  Trillion Btu\n",
       "2       Commercial  Total Energy   2016  18030.223  Trillion Btu\n",
       "2       Industrial   Total Energy  2016  31452.839  Trillion Btu\n",
       "2   Transportation  Total Energy   2016  27785.537  Trillion Btu\n",
       "1   Transportation  Total Energy   2017  28014.167  Trillion Btu\n",
       "1       Industrial   Total Energy  2017  32005.673  Trillion Btu\n",
       "1       Commercial  Total Energy   2017  17899.252  Trillion Btu\n",
       "1      Residential   Total Energy  2017  19889.581  Trillion Btu\n",
       "0      Residential   Total Energy  2018  21621.074  Trillion Btu\n",
       "0   Transportation  Total Energy   2018  28390.532  Trillion Btu\n",
       "0       Commercial  Total Energy   2018  18607.551  Trillion Btu\n",
       "0       Industrial   Total Energy  2018  32617.767  Trillion Btu\n",
       "\n",
       "[280 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_consum_df = master_consum_df.sort_values(by='year',ascending=True)\n",
    "master_consum_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_ind = master_consum_df[master_consum_df.sector==\"Industrial\"].year[-10:]\n",
    "x_comm = master_consum_df[master_consum_df.sector==\"Commercial\"].year[-10:]\n",
    "x_trans = master_consum_df[master_consum_df.sector==\"Transportation\"].year[-10:]\n",
    "x_resi = master_consum_df[master_consum_df.sector==\"Residential\"].year[-10:]\n",
    "\n",
    "\n",
    "y_ind = master_consum_df[master_consum_df.sector==\"Industrial\"].value[-10:]\n",
    "y_comm = master_consum_df[master_consum_df.sector==\"Commercial\"].value[-10:]\n",
    "y_trans = master_consum_df[master_consum_df.sector==\"Transportation\"].value[-10:]\n",
    "y_resi = master_consum_df[master_consum_df.sector==\"Residential\"].value[-10:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "y_columns = [\"Industrial\", \"Transportation\", \"Residential\", \"Commercial\"]\n",
    "\n",
    "plt.plot(x_ind, y_ind, color=\"red\", linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "plt.plot(x_trans, y_trans, color=\"green\", linestyle=(0, (.5,.5)),marker=\"s\")\n",
    "plt.plot(x_resi, y_resi, color=\"blue\", linestyle=(0, (.5,.5)),marker=\"d\")\n",
    "plt.plot(x_comm, y_comm, color=\"black\", linestyle=(0, (.5,.5)),marker=\"^\")\n",
    "\n",
    "plt.legend(y_columns)\n",
    "\n",
    "plt.title(\"Total Energy Consumption by Sector\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Total Energy Consumed (Trillion Btu)\")\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "# Save the Figure\n",
    "plt.savefig('Total Energy_Energy Consumption by Sector.png') \n",
    "# Show the Figure\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Industrial had a CAGR of 1.37% from 2009 to 2018\n",
      "Commercial had a CAGR of 0.40% from 2009 to 2018\n",
      "Transportation had a CAGR of 0.66% from 2009 to 2018\n",
      "Residential had a CAGR of 0.25% from 2009 to 2009\n"
     ]
    }
   ],
   "source": [
    "# Compound Annual Growth\n",
    "\n",
    "ind_yr_f = master_consum_df[master_consum_df.sector==\"Industrial\"].year[-10:-9]\n",
    "comm_yr_f = master_consum_df[master_consum_df.sector==\"Commercial\"].year[-10:-9]\n",
    "trans_yr_f = master_consum_df[master_consum_df.sector==\"Transportation\"].year[-10:-9]\n",
    "resi_yr_f = master_consum_df[master_consum_df.sector==\"Residential\"].year[-10:-9]\n",
    "\n",
    "ind_yr_l = master_consum_df[master_consum_df.sector==\"Industrial\"].year[-1:]\n",
    "comm_yr_l = master_consum_df[master_consum_df.sector==\"Commercial\"].year[-1:]\n",
    "trans_yr_l = master_consum_df[master_consum_df.sector==\"Transportation\"].year[-1:]\n",
    "resi_yr_l = master_consum_df[master_consum_df.sector==\"Residential\"].year[-1:]\n",
    "\n",
    "ind_years = int(ind_yr_l) - int(ind_yr_f) + 1\n",
    "comm_years = int(comm_yr_l) - int(comm_yr_f) + 1\n",
    "trans_years = int(trans_yr_l) - int(trans_yr_f) + 1\n",
    "resi_years = int(resi_yr_l) - int(resi_yr_f) + 1\n",
    "\n",
    "ind_f = master_consum_df[master_consum_df.sector==\"Industrial\"].value[-10:-9]\n",
    "comm_f = master_consum_df[master_consum_df.sector==\"Commercial\"].value[-10:-9]\n",
    "trans_f = master_consum_df[master_consum_df.sector==\"Transportation\"].value[-10:-9]\n",
    "resi_f = master_consum_df[master_consum_df.sector==\"Residential\"].value[-10:-9]\n",
    "\n",
    "ind_l = master_consum_df[master_consum_df.sector==\"Industrial\"].value[-1:]\n",
    "comm_l = master_consum_df[master_consum_df.sector==\"Commercial\"].value[-1:]\n",
    "trans_l = master_consum_df[master_consum_df.sector==\"Transportation\"].value[-1:]\n",
    "resi_l = master_consum_df[master_consum_df.sector==\"Residential\"].value[-1:]\n",
    "\n",
    "cagr_ind = (int(ind_l) / int(ind_f))**(1/ind_years) - 1\n",
    "cagr_comm = (int(comm_l) / int(comm_f))**(1/comm_years) - 1\n",
    "cagr_trans = (int(trans_l) / int(trans_f))**(1/trans_years) - 1\n",
    "cagr_resi = (int(resi_l) / int(resi_f))**(1/resi_years) - 1\n",
    "\n",
    "print(\"Industrial had a CAGR of {:.2%}\".format(cagr_ind) + \" from \" + str(int(ind_yr_f)) + \" to \" + str(int(ind_yr_l)))\n",
    "print('Commercial had a CAGR of {:.2%} '.format(cagr_comm) + \"from \" + str(int(comm_yr_f)) + \" to \" + str(int(comm_yr_l)))\n",
    "print('Transportation had a CAGR of {:.2%} '.format(cagr_trans) + \"from \" + str(int(trans_yr_f)) + \" to \" + str(int(trans_yr_l)))\n",
    "print('Residential had a CAGR of {:.2%} '.format(cagr_resi) + \"from \" + str(int(resi_yr_f)) + \" to \" + str(int(resi_yr_f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "a = [\"Industrial\", \"Transportation\", \"Residential\", \"Commercial\"]\n",
    "b = [(cagr_ind*100), (cagr_trans*100), (cagr_resi*100), (cagr_comm*100)]\n",
    "\n",
    "color = [\"red\", \"green\", \"blue\", \"black\"]\n",
    "\n",
    "time_frame = (\" from \" + str(int(ind_yr_f)) + \" to \" + str(int(ind_yr_l)))\n",
    "\n",
    "\n",
    "# Create horizontal bars\n",
    "plt.barh(a, b, color=color)\n",
    "\n",
    "y_i = 0\n",
    "y_t = 1\n",
    "y_r = 2\n",
    "y_c = 3\n",
    "\n",
    "\n",
    "\n",
    "text_i = (str(round(cagr_ind*100,2)) +\"%\")\n",
    "text_t = (str(round(cagr_trans*100,2))  +\"%\")\n",
    "text_r = (str(round(cagr_resi*100,2)) +\"%\")\n",
    "text_c = (str(round(cagr_comm*100,2)) +\"%\")\n",
    "\n",
    "\n",
    "plt.text(cagr_ind*100-.17, y_i, text_i, color=\"white\")\n",
    "plt.text(cagr_trans*100-.17, y_t, text_t, color=\"white\")\n",
    "plt.text(cagr_resi*100-.17, y_r, text_r, color=\"white\")\n",
    "plt.text(cagr_comm*100-.17, y_c, text_c, color=\"white\")\n",
    "\n",
    "\n",
    "plt.title(\"Compound Annual Growth Rate (CAGR) % \\n\"  + time_frame)\n",
    "plt.xlabel(\"%\")\n",
    "plt.ylabel(\"Sector\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the Figure\n",
    "plt.savefig('Compound Annual Growth Rate by Sector.png') \n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_ind_df = pd.DataFrame({\"year\": x_ind,\n",
    "                                \"value\": y_ind  \n",
    "                                })\n",
    "analysis_ind_df[\"sector\"] = \"Industrial\"\n",
    "analysis_ind_df[\"units\"] = \"Trillion Btu\"\n",
    "\n",
    "\n",
    "analysis_comm_df = pd.DataFrame({\"year\": x_comm,\n",
    "                                \"value\": y_comm  \n",
    "                                })\n",
    "analysis_comm_df[\"sector\"] = \"Commercial\"\n",
    "analysis_comm_df[\"units\"] = \"Trillion Btu\"\n",
    "\n",
    "\n",
    "analysis_trans_df = pd.DataFrame({\"year\": x_trans,\n",
    "                                \"value\": y_trans  \n",
    "                                })\n",
    "analysis_trans_df[\"sector\"] = \"Transportation\"\n",
    "analysis_trans_df[\"units\"] = \"Trillion Btu\"\n",
    "\n",
    "\n",
    "analysis_resi_df = pd.DataFrame({\"year\": x_resi,\n",
    "                                \"value\": y_resi  \n",
    "                                })\n",
    "analysis_resi_df[\"sector\"] = \"Residential\"\n",
    "analysis_resi_df[\"units\"] = \"Trillion Btu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>year</th>\n",
       "      <th>value</th>\n",
       "      <th>sector</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>28465.439</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>30670.436</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011</td>\n",
       "      <td>30981.322</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>31060.724</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>31627.437</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>31797.941</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>31471.524</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>31452.839</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>32005.673</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>32617.767</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>17887.561</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>18058.632</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011</td>\n",
       "      <td>17981.378</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>17421.351</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>17929.446</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>18264.465</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>18156.728</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>18030.223</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>17899.252</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>18607.551</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>26588.888</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>26978.400</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011</td>\n",
       "      <td>26598.624</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>26125.738</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>26611.709</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>26868.857</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>27238.037</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>27785.537</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>28014.167</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>28390.532</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>21081.368</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>21894.159</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011</td>\n",
       "      <td>21381.247</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>19869.589</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>21051.185</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>21444.938</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>20616.830</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>20181.113</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>19889.581</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>21621.074</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Trillion Btu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      value          sector         units\n",
       "9  2009  28465.439      Industrial  Trillion Btu\n",
       "8  2010  30670.436      Industrial  Trillion Btu\n",
       "7  2011  30981.322      Industrial  Trillion Btu\n",
       "6  2012  31060.724      Industrial  Trillion Btu\n",
       "5  2013  31627.437      Industrial  Trillion Btu\n",
       "4  2014  31797.941      Industrial  Trillion Btu\n",
       "3  2015  31471.524      Industrial  Trillion Btu\n",
       "2  2016  31452.839      Industrial  Trillion Btu\n",
       "1  2017  32005.673      Industrial  Trillion Btu\n",
       "0  2018  32617.767      Industrial  Trillion Btu\n",
       "9  2009  17887.561      Commercial  Trillion Btu\n",
       "8  2010  18058.632      Commercial  Trillion Btu\n",
       "7  2011  17981.378      Commercial  Trillion Btu\n",
       "6  2012  17421.351      Commercial  Trillion Btu\n",
       "5  2013  17929.446      Commercial  Trillion Btu\n",
       "4  2014  18264.465      Commercial  Trillion Btu\n",
       "3  2015  18156.728      Commercial  Trillion Btu\n",
       "2  2016  18030.223      Commercial  Trillion Btu\n",
       "1  2017  17899.252      Commercial  Trillion Btu\n",
       "0  2018  18607.551      Commercial  Trillion Btu\n",
       "9  2009  26588.888  Transportation  Trillion Btu\n",
       "8  2010  26978.400  Transportation  Trillion Btu\n",
       "7  2011  26598.624  Transportation  Trillion Btu\n",
       "6  2012  26125.738  Transportation  Trillion Btu\n",
       "5  2013  26611.709  Transportation  Trillion Btu\n",
       "4  2014  26868.857  Transportation  Trillion Btu\n",
       "3  2015  27238.037  Transportation  Trillion Btu\n",
       "2  2016  27785.537  Transportation  Trillion Btu\n",
       "1  2017  28014.167  Transportation  Trillion Btu\n",
       "0  2018  28390.532  Transportation  Trillion Btu\n",
       "9  2009  21081.368     Residential  Trillion Btu\n",
       "8  2010  21894.159     Residential  Trillion Btu\n",
       "7  2011  21381.247     Residential  Trillion Btu\n",
       "6  2012  19869.589     Residential  Trillion Btu\n",
       "5  2013  21051.185     Residential  Trillion Btu\n",
       "4  2014  21444.938     Residential  Trillion Btu\n",
       "3  2015  20616.830     Residential  Trillion Btu\n",
       "2  2016  20181.113     Residential  Trillion Btu\n",
       "1  2017  19889.581     Residential  Trillion Btu\n",
       "0  2018  21621.074     Residential  Trillion Btu"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df = pd.DataFrame()\n",
    "anova_df[\"year\"] = []\n",
    "anova_df[\"value\"] = []\n",
    "anova_df[\"sector\"] = []\n",
    "anova_df[\"units\"] = []\n",
    "\n",
    "anova_df = anova_df.append(analysis_ind_df)\n",
    "anova_df = anova_df.append(analysis_comm_df)\n",
    "anova_df = anova_df.append(analysis_trans_df)\n",
    "anova_df = anova_df.append(analysis_resi_df)\n",
    "anova_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f5e7270710>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a boxplot to compare means\n",
    "anova_df.boxplot(\"value\", by=\"sector\", figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract individual groups\n",
    "group1 = anova_df[anova_df[\"sector\"] == \"Commercial\"][\"value\"]\n",
    "group2 = anova_df[anova_df[\"sector\"] == \"Industrial\"][\"value\"]\n",
    "group3 = anova_df[anova_df[\"sector\"] == \"Residential\"][\"value\"]\n",
    "group4 = anova_df[anova_df[\"sector\"] == \"Transportation\"][\"value\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=595.2020331471848, pvalue=1.0236119476134707e-30)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform the ANOVA\n",
    "stats.f_oneway(group1, group2, group3, group4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0236119476134707e-30\n"
     ]
    }
   ],
   "source": [
    "p_test = stats.f_oneway(group1, group2, group3, group4)[1]\n",
    "print(p_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The differences between some of the means are statistically significant\n"
     ]
    }
   ],
   "source": [
    "if p_test < .05:\n",
    "    print(\"The differences between some of the means are statistically significant\")\n",
    "else:\n",
    "    print(\"The differences between the means are not statistically significant\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31215.110200000003\n",
      "18023.6587\n",
      "27120.0489\n",
      "20903.1084\n"
     ]
    }
   ],
   "source": [
    "# Calculate Mean\n",
    "ind_val = anova_df[anova_df[\"sector\"]==\"Industrial\"].value\n",
    "comm_val = anova_df[anova_df[\"sector\"]==\"Commercial\"].value\n",
    "trans_val = anova_df[anova_df[\"sector\"]==\"Transportation\"].value\n",
    "resi_val = anova_df[anova_df[\"sector\"]==\"Residential\"].value\n",
    "\n",
    "ind_avg = ind_val.mean()\n",
    "comm_avg = comm_val.mean()\n",
    "trans_avg = trans_val.mean()\n",
    "resi_avg = resi_val.mean()\n",
    "\n",
    "print(ind_avg)\n",
    "print(comm_avg)\n",
    "print(trans_avg)\n",
    "print(resi_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=36.147992778512126, pvalue=3.2825166921803944e-12)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(ind_val, comm_val, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=9.738232302731404, pvalue=5.310723781756896e-08)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(ind_val, trans_val, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=24.508854744679795, pvalue=8.039249850013244e-14)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(ind_val, resi_val, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-36.53217616093951, pvalue=1.0647296195642882e-13)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(comm_val, trans_val, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-11.546039402752209, pvalue=7.285710924812013e-08)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(comm_val, resi_val, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=19.108406151817306, pvalue=2.1135540794463932e-13)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(trans_val, resi_val, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_vs_comm_p = stats.ttest_ind(ind_val, comm_val, equal_var=False)[1]\n",
    "ind_vs_trans_p = stats.ttest_ind(ind_val, trans_val, equal_var=False)[1]\n",
    "ind_vs_resi_p = stats.ttest_ind(ind_val, resi_val, equal_var=False)[1]\n",
    "comm_vs_trans_p = stats.ttest_ind(comm_val, trans_val, equal_var=False)[1]\n",
    "comm_vs_resi_p = stats.ttest_ind(comm_val, resi_val, equal_var=False)[1]\n",
    "trans_vs_resi_p = stats.ttest_ind(trans_val, resi_val, equal_var=False)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ind_vs_comm: \n",
      "  The differences between some of the means are statistically significant\n",
      "ind_vs_trans: \n",
      "  The differences between some of the means are statistically significant\n",
      "ind_vs_resi: \n",
      "  The differences between some of the means are statistically significant\n",
      "comm_vs_trans: \n",
      "  The differences between some of the means are statistically significant\n",
      "comm_vs_resi: \n",
      "  The differences between some of the means are statistically significant\n",
      "trans_vs_resi: \n",
      "  The differences between some of the means are statistically significant\n"
     ]
    }
   ],
   "source": [
    "if ind_vs_comm_p < .05:\n",
    "    print(\"ind_vs_comm: \\n  The differences between some of the means are statistically significant\")\n",
    "else:\n",
    "    print(\"ind_vs_comm: \\n  The differences between the means are not statistically significant\")\n",
    "    \n",
    "\n",
    "if ind_vs_trans_p < .05:\n",
    "    print(\"ind_vs_trans: \\n  The differences between some of the means are statistically significant\")\n",
    "else:\n",
    "    print(\"ind_vs_trans: \\n  The differences between the means are not statistically significant\")    \n",
    "   \n",
    "\n",
    "if ind_vs_resi_p < .05:\n",
    "    print(\"ind_vs_resi: \\n  The differences between some of the means are statistically significant\")\n",
    "else:\n",
    "    print(\"ind_vs_resi: \\n  The differences between the means are not statistically significant\") \n",
    "    \n",
    "    \n",
    "if comm_vs_trans_p < .05:\n",
    "    print(\"comm_vs_trans: \\n  The differences between some of the means are statistically significant\")\n",
    "else:\n",
    "    print(\"comm_vs_trans: \\n  The differences between the means are not statistically significant\")  \n",
    "\n",
    "    \n",
    "if comm_vs_resi_p < .05:\n",
    "    print(\"comm_vs_resi: \\n  The differences between some of the means are statistically significant\")\n",
    "else:\n",
    "    print(\"comm_vs_resi: \\n  The differences between the means are not statistically significant\")  \n",
    "\n",
    "       \n",
    "if trans_vs_resi_p < .05:\n",
    "    print(\"trans_vs_resi: \\n  The differences between some of the means are statistically significant\")\n",
    "else:\n",
    "    print(\"trans_vs_resi: \\n  The differences between the means are not statistically significant\")      \n"
   ]
  }
 ],
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