{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# National generation and fuel consumption\n", "The data in this notebook is generation and consumption by fuel type for the entire US. These values are larger than what would be calculated by summing facility-level data. Note that the fuel types are somewhat aggregated (coal rather than BIT, SUB, LIG, etc). So when we multiply the fuel consumption by an emissions factor there will be some level of error.\n", "\n", "The code assumes that you have already downloaded an `ELEC.txt` file from [EIA's bulk download website](https://www.eia.gov/opendata/bulkfiles.php)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:00:55.192000Z", "start_time": "2017-10-09T19:00:54.012000Z" }, "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import io, time, json\n", "import pandas as pd\n", "import os\n", "from os.path import join\n", "import numpy as np\n", "import math" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:37:51.582000Z", "start_time": "2017-10-09T19:37:51.577000Z" }, "collapsed": true }, "outputs": [], "source": [ "idx = pd.IndexSlice" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read ELECT.txt file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:01:00.273000Z", "start_time": "2017-10-09T19:00:57.313000Z" }, "collapsed": true }, "outputs": [], "source": [ "cwd = os.getcwd()\n", "path = join(cwd, '..', 'Data storage', 'Raw EIA bulk',\n", " '2017-08-31 ELEC_manual.txt')\n", "with open(path, 'rb') as f:\n", " raw_txt = f.readlines()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Filter lines to only include total generation and fuel use\n", "Only want monthly US data for all sectors\n", "- US-99.M\n", "- ELEC.GEN, ELEC.CONS_TOT_BTU, ELEC.CONS_EG_BTU\n", "- not ALL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fuel codes:\n", "- WWW, wood and wood derived fuels\n", "- WND, wind\n", "- STH, solar thermal\n", "- WAS, other biomass\n", "- TSN, all solar\n", "- SUN, utility-scale solar\n", "- NUC, nuclear\n", "- NG, natural gas\n", "- PEL, petroleum liquids\n", "- SPV, utility-scale solar photovoltaic\n", "- PC, petroluem coke\n", "- OTH, other\n", "- COW, coal,\n", "- DPV, distributed photovoltaic\n", "- OOG, other gases\n", "- HPS, hydro pumped storage\n", "- HYC, conventional hydroelectric\n", "- GEO, geothermal\n", "- AOR, other renewables (total)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:01:33.490000Z", "start_time": "2017-10-09T19:01:33.433000Z" }, "collapsed": true }, "outputs": [], "source": [ "def line_to_df(line):\n", " \"\"\"\n", " Takes in a line (dictionary), returns a dataframe\n", " \"\"\"\n", " for key in ['latlon', 'source', 'copyright', 'description', \n", " 'geoset_id', 'iso3166', 'name', 'state']:\n", " line.pop(key, None)\n", "\n", " # Split the series_id up to extract information\n", " # Example: ELEC.PLANT.GEN.388-WAT-ALL.M\n", " series_id = line['series_id']\n", " series_id_list = series_id.split('.')\n", " # Use the second to last item in list rather than third\n", " plant_fuel_mover = series_id_list[-2].split('-')\n", " line['type'] = plant_fuel_mover[0]\n", "# line['state'] = plant_fuel_mover[1]\n", " line['sector'] = plant_fuel_mover[2]\n", " temp_df = pd.DataFrame(line)\n", "\n", " try:\n", " temp_df['year'] = temp_df.apply(lambda x: x['data'][0][:4], axis=1).astype(int)\n", " temp_df['month'] = temp_df.apply(lambda x: x['data'][0][-2:], axis=1).astype(int)\n", " temp_df['value'] = temp_df.apply(lambda x: x['data'][1], axis=1)\n", " temp_df.drop('data', axis=1, inplace=True)\n", " return temp_df\n", " except:\n", " exception_list.append(line)\n", " pass" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:01:34.486000Z", "start_time": "2017-10-09T19:01:34.477000Z" }, "collapsed": true }, "outputs": [], "source": [ "states = [\"AL\", \"AK\", \"AZ\", \"AR\", \"CA\", \"CO\", \"CT\", \"DE\",\n", " \"FL\", \"GA\", \"HI\", \"ID\", \"IL\", \"IN\", \"IA\", \"KS\",\n", " \"KY\", \"LA\", \"ME\", \"MD\", \"MA\", \"MI\", \"MN\", \"MS\",\n", " \"MO\", \"MT\", \"NE\", \"NV\", \"NH\", \"NJ\", \"NM\", \"NY\",\n", " \"NC\", \"ND\", \"OH\", \"OK\", \"OR\", \"PA\", \"RI\", \"SC\",\n", " \"SD\", \"TN\", \"TX\", \"UT\", \"VT\", \"VA\", \"WA\", \"WV\", \"WI\", \"WY\"]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:07:30.103000Z", "start_time": "2017-10-09T19:07:30.099000Z" }, "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "state_geos = ['USA-{}'.format(state) for state in states]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:04:41.185000Z", "start_time": "2017-10-09T19:04:41.177000Z" } }, "outputs": [ { "data": { "text/plain": [ "dict" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(json.loads(raw_txt[0]))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:05:00.737000Z", "start_time": "2017-10-09T19:05:00.729000Z" } }, "outputs": [ { "data": { "text/plain": [ "u'USA-IL'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "json.loads(raw_txt[0])['geography']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:06:26.421000Z", "start_time": "2017-10-09T19:06:24.572000Z" }, "collapsed": true }, "outputs": [], "source": [ "exception_list = []\n", "gen_rows = [row for row in raw_txt if 'ELEC.GEN' in row \n", " and 'series_id' in row \n", " and '-99.M' in row \n", " and 'ALL' not in row]\n", "\n", "total_fuel_rows = [row for row in raw_txt if 'ELEC.CONS_TOT_BTU' in row \n", " and 'series_id' in row \n", " and '-99.M' in row \n", " and 'ALL' not in row\n", " and 'US-99.m' not in row]\n", "\n", "eg_fuel_rows = [row for row in raw_txt if 'ELEC.CONS_EG_BTU' in row \n", " and 'series_id' in row \n", " and '-99.M' in row \n", " and 'ALL' not in row\n", " and 'US-99.m' not in row]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## All generation by fuel" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:06:29.022000Z", "start_time": "2017-10-09T19:06:28.517000Z" } }, "outputs": [], "source": [ "gen_dicts = [json.loads(row) for row in gen_rows]" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:30.338000Z", "start_time": "2017-10-09T20:06:11.056000Z" } }, "outputs": [], "source": [ "gen_df = pd.concat([line_to_df(x) for x in gen_dicts\n", " if x['geography'] in state_geos])" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:30.385000Z", "start_time": "2017-10-09T20:06:30.341000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endfgeographylast_updatedsectorseries_idstarttypeunitsyearmonthvalue
0201706MUSA-MN2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-MN-99.M200101AORthousand megawatthours201761066.98487
1201706MUSA-MN2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-MN-99.M200101AORthousand megawatthours201751173.59609
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3201706MUSA-MN2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-MN-99.M200101AORthousand megawatthours201731345.11365
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" ], "text/plain": [ " end f geography last_updated sector \\\n", "0 201706 M USA-MN 2017-08-24T11:46:12-04:00 99 \n", "1 201706 M USA-MN 2017-08-24T11:46:12-04:00 99 \n", "2 201706 M USA-MN 2017-08-24T11:46:12-04:00 99 \n", "3 201706 M USA-MN 2017-08-24T11:46:12-04:00 99 \n", "4 201706 M USA-MN 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start type units year month \\\n", "0 ELEC.GEN.AOR-MN-99.M 200101 AOR thousand megawatthours 2017 6 \n", "1 ELEC.GEN.AOR-MN-99.M 200101 AOR thousand megawatthours 2017 5 \n", "2 ELEC.GEN.AOR-MN-99.M 200101 AOR thousand megawatthours 2017 4 \n", "3 ELEC.GEN.AOR-MN-99.M 200101 AOR thousand megawatthours 2017 3 \n", "4 ELEC.GEN.AOR-MN-99.M 200101 AOR thousand megawatthours 2017 2 \n", "\n", " value \n", "0 1066.98487 \n", "1 1173.59609 \n", "2 1250.62552 \n", "3 1345.11365 \n", "4 1222.31074 " ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "gen_df.head()" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:30.440000Z", "start_time": "2017-10-09T20:06:30.389000Z" } }, "outputs": [ { "data": { "text/plain": [ "array([u'USA-MN', u'USA-KY', u'USA-LA', u'USA-CA', u'USA-ME', u'USA-DE',\n", " u'USA-ID', u'USA-ND', u'USA-AR', u'USA-MI', u'USA-MA', u'USA-KS',\n", " u'USA-HI', u'USA-NH', u'USA-FL', u'USA-NC', u'USA-MD', u'USA-AK',\n", " u'USA-CO', u'USA-AZ', u'USA-AL', u'USA-NJ', u'USA-IL', u'USA-CT',\n", " u'USA-MT', u'USA-IA', u'USA-GA', u'USA-OK', u'USA-NY', u'USA-IN',\n", " u'USA-NV', u'USA-NM', u'USA-WA', u'USA-OH', u'USA-TX', u'USA-RI',\n", " u'USA-OR', u'USA-WY', u'USA-SD', u'USA-PA', u'USA-VA', u'USA-VT',\n", " u'USA-UT', u'USA-SC', u'USA-WI', u'USA-WV', u'USA-NE', u'USA-TN',\n", " u'USA-MO', u'USA-MS'], dtype=object)" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gen_df['geography'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Multiply generation values by 1000 and change the units to MWh" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:30.536000Z", "start_time": "2017-10-09T20:06:30.443000Z" }, "collapsed": true }, "outputs": [], "source": [ "gen_df.loc[:,'value'] *= 1000\n", "gen_df.loc[:,'units'] = 'megawatthours'" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:30.626000Z", "start_time": "2017-10-09T20:06:30.539000Z" }, "collapsed": true }, "outputs": [], "source": [ "gen_df.rename_axis({'value':'generation (MWh)'}, axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:30.964000Z", "start_time": "2017-10-09T20:06:30.629000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endfgeographylast_updatedsectorseries_idstarttypeunitsyearmonthgeneration (MWh)
4201706MUSA-AK2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-AK-99.M200101AORmegawatthours20172NaN
5201706MUSA-AK2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-AK-99.M200101AORmegawatthours20171NaN
55201706MUSA-AK2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-AK-99.M200101AORmegawatthours201211NaN
65201706MUSA-AK2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-AK-99.M200101AORmegawatthours20121NaN
0201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20176NaN
1201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20175NaN
2201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20174NaN
3201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20173NaN
4201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20172NaN
5201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20171NaN
6201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours201612NaN
7201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours201611NaN
8201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours201610NaN
9201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20169NaN
10201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20168NaN
11201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20167NaN
12201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20166NaN
13201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20165NaN
14201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20164NaN
15201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20163NaN
17201706MUSA-ID2017-08-24T11:46:12-04:0099ELEC.GEN.COW-ID-99.M200101COWmegawatthours20161NaN
0201706MUSA-AK2017-08-24T11:46:12-04:0099ELEC.GEN.COW-AK-99.M200101COWmegawatthours20176NaN
1201706MUSA-AK2017-08-24T11:46:12-04:0099ELEC.GEN.COW-AK-99.M200101COWmegawatthours20175NaN
2201706MUSA-AK2017-08-24T11:46:12-04:0099ELEC.GEN.COW-AK-99.M200101COWmegawatthours20174NaN
3201706MUSA-AK2017-08-24T11:46:12-04:0099ELEC.GEN.COW-AK-99.M200101COWmegawatthours20173NaN
0201706MUSA-GA2017-08-24T11:46:12-04:0099ELEC.GEN.DPV-GA-99.M201401DPVmegawatthours20176NaN
1201706MUSA-GA2017-08-24T11:46:12-04:0099ELEC.GEN.DPV-GA-99.M201401DPVmegawatthours20175NaN
2201706MUSA-GA2017-08-24T11:46:12-04:0099ELEC.GEN.DPV-GA-99.M201401DPVmegawatthours20174NaN
3201706MUSA-GA2017-08-24T11:46:12-04:0099ELEC.GEN.DPV-GA-99.M201401DPVmegawatthours20173NaN
4201706MUSA-GA2017-08-24T11:46:12-04:0099ELEC.GEN.DPV-GA-99.M201401DPVmegawatthours20172NaN
.......................................
30201702MUSA-WV2017-05-24T14:26:30-04:0099ELEC.GEN.WAS-WV-99.M200101WASmegawatthours20148NaN
31201702MUSA-WV2017-05-24T14:26:30-04:0099ELEC.GEN.WAS-WV-99.M200101WASmegawatthours20147NaN
32201702MUSA-WV2017-05-24T14:26:30-04:0099ELEC.GEN.WAS-WV-99.M200101WASmegawatthours20146NaN
33201702MUSA-WV2017-05-24T14:26:30-04:0099ELEC.GEN.WAS-WV-99.M200101WASmegawatthours20145NaN
34201702MUSA-WV2017-05-24T14:26:30-04:0099ELEC.GEN.WAS-WV-99.M200101WASmegawatthours20144NaN
35201702MUSA-WV2017-05-24T14:26:30-04:0099ELEC.GEN.WAS-WV-99.M200101WASmegawatthours20143NaN
36201702MUSA-WV2017-05-24T14:26:30-04:0099ELEC.GEN.WAS-WV-99.M200101WASmegawatthours20142NaN
37201702MUSA-WV2017-05-24T14:26:30-04:0099ELEC.GEN.WAS-WV-99.M200101WASmegawatthours20141NaN
0201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours201410NaN
1201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours20149NaN
2201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours20148NaN
3201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours20147NaN
4201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours20146NaN
5201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours20145NaN
6201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours20144NaN
7201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours20143NaN
8201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours20142NaN
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10201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours201312NaN
11201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours201311NaN
12201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours201310NaN
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19201410MUSA-AK2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-AK-99.M201101OOGmegawatthours20133NaN
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1588 rows × 12 columns

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" ], "text/plain": [ " end f geography last_updated sector \\\n", "4 201706 M USA-AK 2017-08-24T11:46:12-04:00 99 \n", "5 201706 M USA-AK 2017-08-24T11:46:12-04:00 99 \n", "55 201706 M USA-AK 2017-08-24T11:46:12-04:00 99 \n", "65 201706 M USA-AK 2017-08-24T11:46:12-04:00 99 \n", "0 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "1 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "2 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "3 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "4 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "5 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "6 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "7 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "8 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "9 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "10 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "11 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "12 201706 M USA-ID 2017-08-24T11:46:12-04:00 99 \n", "13 201706 M USA-ID 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148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gen_df.loc[gen_df.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:31.091000Z", "start_time": "2017-10-09T20:06:30.968000Z" }, "collapsed": true }, "outputs": [], "source": [ "gen_df.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:31.182000Z", "start_time": "2017-10-09T20:06:31.094000Z" }, "collapsed": true }, "outputs": [], "source": [ "gen_df.set_index(['type', 'year', 'month', 'geography'], inplace=True)" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:31.271000Z", "start_time": "2017-10-09T20:06:31.185000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitsgeneration (MWh)
typeyearmonthgeography
AOR20176USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-MN-99.M200101megawatthours1066984.87
5USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-MN-99.M200101megawatthours1173596.09
4USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-MN-99.M200101megawatthours1250625.52
3USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-MN-99.M200101megawatthours1345113.65
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" ], "text/plain": [ " end f last_updated sector \\\n", "type year month geography \n", "AOR 2017 6 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 5 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "type year month geography \n", "AOR 2017 6 USA-MN ELEC.GEN.AOR-MN-99.M 200101 megawatthours \n", " 5 USA-MN ELEC.GEN.AOR-MN-99.M 200101 megawatthours \n", " 4 USA-MN ELEC.GEN.AOR-MN-99.M 200101 megawatthours \n", " 3 USA-MN ELEC.GEN.AOR-MN-99.M 200101 megawatthours \n", " 2 USA-MN ELEC.GEN.AOR-MN-99.M 200101 megawatthours \n", "\n", " generation (MWh) \n", "type year month geography \n", "AOR 2017 6 USA-MN 1066984.87 \n", " 5 USA-MN 1173596.09 \n", " 4 USA-MN 1250625.52 \n", " 3 USA-MN 1345113.65 \n", " 2 USA-MN 1222310.74 " ] }, "execution_count": 151, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "gen_df.head()" ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:31.372000Z", "start_time": "2017-10-09T20:06:31.274000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitsgeneration (MWh)
yearmonthgeography
20176USA-NV201706M2017-08-24T11:46:12-04:0099ELEC.GEN.OOG-NV-99.M200210megawatthours0.0
5USA-NV201706M2017-08-24T11:46:12-04:0099ELEC.GEN.OOG-NV-99.M200210megawatthours0.0
4USA-NV201706M2017-08-24T11:46:12-04:0099ELEC.GEN.OOG-NV-99.M200210megawatthours0.0
3USA-NV201706M2017-08-24T11:46:12-04:0099ELEC.GEN.OOG-NV-99.M200210megawatthours0.0
2USA-NV201706M2017-08-24T11:46:12-04:0099ELEC.GEN.OOG-NV-99.M200210megawatthours0.0
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" ], "text/plain": [ " end f last_updated sector \\\n", "year month geography \n", "2017 6 USA-NV 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 5 USA-NV 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-NV 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-NV 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-NV 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "year month geography \n", "2017 6 USA-NV ELEC.GEN.OOG-NV-99.M 200210 megawatthours \n", " 5 USA-NV ELEC.GEN.OOG-NV-99.M 200210 megawatthours \n", " 4 USA-NV ELEC.GEN.OOG-NV-99.M 200210 megawatthours \n", " 3 USA-NV ELEC.GEN.OOG-NV-99.M 200210 megawatthours \n", " 2 USA-NV ELEC.GEN.OOG-NV-99.M 200210 megawatthours \n", "\n", " generation (MWh) \n", "year month geography \n", "2017 6 USA-NV 0.0 \n", " 5 USA-NV 0.0 \n", " 4 USA-NV 0.0 \n", " 3 USA-NV 0.0 \n", " 2 USA-NV 0.0 " ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "gen_df.loc['OOG'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Total fuel consumption" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:13:51.121000Z", "start_time": "2017-10-09T19:13:51.049000Z" }, "collapsed": true }, "outputs": [], "source": [ "total_fuel_dict = [json.loads(row) for row in total_fuel_rows]" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:37.480000Z", "start_time": "2017-10-09T20:06:31.903000Z" } }, "outputs": [], "source": [ "total_fuel_df = pd.concat([line_to_df(x) for x in total_fuel_dict\n", " if x['geography'] in state_geos])" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:37.519000Z", "start_time": "2017-10-09T20:06:37.483000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endfgeographylast_updatedsectorseries_idstarttypeunitsyearmonthvalue
0201706MUSA-CA2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101PCmillion MMBtu201760.0
1201706MUSA-CA2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101PCmillion MMBtu201750.0
2201706MUSA-CA2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101PCmillion MMBtu201740.0
3201706MUSA-CA2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101PCmillion MMBtu201730.0
4201706MUSA-CA2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101PCmillion MMBtu201720.0
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" ], "text/plain": [ " end f geography last_updated sector \\\n", "0 201706 M USA-CA 2017-08-24T11:46:12-04:00 99 \n", "1 201706 M USA-CA 2017-08-24T11:46:12-04:00 99 \n", "2 201706 M USA-CA 2017-08-24T11:46:12-04:00 99 \n", "3 201706 M USA-CA 2017-08-24T11:46:12-04:00 99 \n", "4 201706 M USA-CA 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start type units year month \\\n", "0 ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 PC million MMBtu 2017 6 \n", "1 ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 PC million MMBtu 2017 5 \n", "2 ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 PC million MMBtu 2017 4 \n", "3 ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 PC million MMBtu 2017 3 \n", "4 ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 PC million MMBtu 2017 2 \n", "\n", " value \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 " ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "total_fuel_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Multiply generation values by 1,000,000 and change the units to MMBtu" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:37.574000Z", "start_time": "2017-10-09T20:06:37.524000Z" }, "collapsed": true }, "outputs": [], "source": [ "total_fuel_df.loc[:,'value'] *= 1E6\n", "total_fuel_df.loc[:,'units'] = 'mmbtu'" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:37.670000Z", "start_time": "2017-10-09T20:06:37.577000Z" }, "collapsed": true }, "outputs": [], "source": [ "total_fuel_df.rename_axis({'value':'total fuel (mmbtu)'}, axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:07:12.291000Z", "start_time": "2017-10-09T20:07:12.175000Z" }, "collapsed": true }, "outputs": [], "source": [ "total_fuel_df.set_index(['type', 'year', 'month', 'geography'], inplace=True)" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:37.889000Z", "start_time": "2017-10-09T20:06:37.794000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitstotal fuel (mmbtu)
typeyearmonthgeography
PC20176USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtu0.0
5USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtu0.0
4USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtu0.0
3USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtu0.0
2USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtu0.0
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" ], "text/plain": [ " end f last_updated sector \\\n", "type year month geography \n", "PC 2017 6 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 5 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "type year month geography \n", "PC 2017 6 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 5 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 4 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 3 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 2 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", "\n", " total fuel (mmbtu) \n", "type year month geography \n", "PC 2017 6 USA-CA 0.0 \n", " 5 USA-CA 0.0 \n", " 4 USA-CA 0.0 \n", " 3 USA-CA 0.0 \n", " 2 USA-CA 0.0 " ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "total_fuel_df.head()" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:37.979000Z", "start_time": "2017-10-09T20:06:37.892000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitstotal fuel (mmbtu)
typeyearmonthgeography
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [end, f, last_updated, sector, series_id, start, units, total fuel (mmbtu)]\n", "Index: []" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "total_fuel_df.loc[~(total_fuel_df['total fuel (mmbtu)'] >= 0) &\n", " ~(total_fuel_df['total fuel (mmbtu)'].isnull())]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Drop nans" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:38.240000Z", "start_time": "2017-10-09T20:06:37.983000Z" }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitstotal fuel (mmbtu)
typeyearmonthgeography
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10USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtuNaN
9USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtuNaN
8USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtuNaN
7USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtuNaN
6USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtuNaN
5USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtuNaN
3USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-CA-99.M200101mmbtuNaN
201611USA-IA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-IA-99.M200101mmbtuNaN
10USA-IA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-IA-99.M200101mmbtuNaN
9USA-IA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-IA-99.M200101mmbtuNaN
8USA-IA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-IA-99.M200101mmbtuNaN
6USA-IA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-IA-99.M200101mmbtuNaN
5USA-IA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-IA-99.M200101mmbtuNaN
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NG20166USA-WY201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.NG-WY-99.M200101mmbtuNaN
PC20176USA-GA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-GA-99.M200101mmbtuNaN
5USA-GA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-GA-99.M200101mmbtuNaN
4USA-GA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-GA-99.M200101mmbtuNaN
3USA-GA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-GA-99.M200101mmbtuNaN
2USA-GA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-GA-99.M200101mmbtuNaN
1USA-GA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-GA-99.M200101mmbtuNaN
20145USA-GA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PC-GA-99.M200101mmbtuNaN
COW20176USA-ID201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.COW-ID-99.M200101mmbtuNaN
5USA-ID201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.COW-ID-99.M200101mmbtuNaN
4USA-ID201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.COW-ID-99.M200101mmbtuNaN
3USA-ID201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.COW-ID-99.M200101mmbtuNaN
2USA-ID201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.COW-ID-99.M200101mmbtuNaN
....................................
NG20168USA-VT201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.NG-VT-99.M200101mmbtuNaN
7USA-VT201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.NG-VT-99.M200101mmbtuNaN
6USA-VT201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.NG-VT-99.M200101mmbtuNaN
5USA-VT201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.NG-VT-99.M200101mmbtuNaN
4USA-VT201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.NG-VT-99.M200101mmbtuNaN
3USA-VT201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.NG-VT-99.M200101mmbtuNaN
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1USA-VT201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.NG-VT-99.M200101mmbtuNaN
PEL20173USA-VA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-VA-99.M200101mmbtuNaN
6USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
4USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
3USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
2USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
1USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
201612USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
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10USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
9USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
8USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
7USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
6USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
5USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
4USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
3USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
2USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
1USA-CO201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CO-99.M200101mmbtuNaN
20174USA-CT201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CT-99.M200101mmbtuNaN
201610USA-CT201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CT-99.M200101mmbtuNaN
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3USA-CT201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-CT-99.M200101mmbtuNaN
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356 rows × 8 columns

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" ], "text/plain": [ " end f last_updated sector \\\n", "type year month geography \n", "PC 2012 12 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 11 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 10 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 9 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 8 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 7 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 6 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 5 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2016 11 USA-IA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 10 USA-IA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 9 USA-IA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 8 USA-IA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 6 USA-IA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 5 USA-IA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-IA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 1 USA-IA 201706 M 2017-08-24T11:46:12-04:00 99 \n", "NG 2016 6 USA-WY 201706 M 2017-08-24T11:46:12-04:00 99 \n", "PC 2017 6 USA-GA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 5 USA-GA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-GA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-GA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-GA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 1 USA-GA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2014 5 USA-GA 201706 M 2017-08-24T11:46:12-04:00 99 \n", "COW 2017 6 USA-ID 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 5 USA-ID 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-ID 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-ID 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-ID 201706 M 2017-08-24T11:46:12-04:00 99 \n", "... ... .. ... ... \n", "NG 2016 8 USA-VT 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 7 USA-VT 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 6 USA-VT 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 5 USA-VT 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-VT 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-VT 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-VT 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 1 USA-VT 201706 M 2017-08-24T11:46:12-04:00 99 \n", "PEL 2017 3 USA-VA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 6 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 1 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2016 12 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 11 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 10 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 9 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 8 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 7 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 6 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 5 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 1 USA-CO 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2017 4 USA-CT 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2016 10 USA-CT 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-CT 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-CT 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "type year month geography \n", "PC 2012 12 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 11 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 10 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 9 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 8 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 7 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 6 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 5 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 3 USA-CA ELEC.CONS_TOT_BTU.PC-CA-99.M 200101 mmbtu \n", " 2016 11 USA-IA ELEC.CONS_TOT_BTU.PC-IA-99.M 200101 mmbtu \n", " 10 USA-IA ELEC.CONS_TOT_BTU.PC-IA-99.M 200101 mmbtu \n", " 9 USA-IA ELEC.CONS_TOT_BTU.PC-IA-99.M 200101 mmbtu \n", " 8 USA-IA ELEC.CONS_TOT_BTU.PC-IA-99.M 200101 mmbtu \n", " 6 USA-IA ELEC.CONS_TOT_BTU.PC-IA-99.M 200101 mmbtu \n", " 5 USA-IA ELEC.CONS_TOT_BTU.PC-IA-99.M 200101 mmbtu \n", " 3 USA-IA ELEC.CONS_TOT_BTU.PC-IA-99.M 200101 mmbtu \n", " 1 USA-IA ELEC.CONS_TOT_BTU.PC-IA-99.M 200101 mmbtu \n", "NG 2016 6 USA-WY ELEC.CONS_TOT_BTU.NG-WY-99.M 200101 mmbtu \n", "PC 2017 6 USA-GA ELEC.CONS_TOT_BTU.PC-GA-99.M 200101 mmbtu \n", " 5 USA-GA ELEC.CONS_TOT_BTU.PC-GA-99.M 200101 mmbtu \n", " 4 USA-GA ELEC.CONS_TOT_BTU.PC-GA-99.M 200101 mmbtu \n", " 3 USA-GA ELEC.CONS_TOT_BTU.PC-GA-99.M 200101 mmbtu \n", " 2 USA-GA ELEC.CONS_TOT_BTU.PC-GA-99.M 200101 mmbtu \n", " 1 USA-GA ELEC.CONS_TOT_BTU.PC-GA-99.M 200101 mmbtu \n", " 2014 5 USA-GA ELEC.CONS_TOT_BTU.PC-GA-99.M 200101 mmbtu \n", "COW 2017 6 USA-ID ELEC.CONS_TOT_BTU.COW-ID-99.M 200101 mmbtu \n", " 5 USA-ID ELEC.CONS_TOT_BTU.COW-ID-99.M 200101 mmbtu \n", " 4 USA-ID ELEC.CONS_TOT_BTU.COW-ID-99.M 200101 mmbtu \n", " 3 USA-ID ELEC.CONS_TOT_BTU.COW-ID-99.M 200101 mmbtu \n", " 2 USA-ID ELEC.CONS_TOT_BTU.COW-ID-99.M 200101 mmbtu \n", "... ... ... ... \n", "NG 2016 8 USA-VT ELEC.CONS_TOT_BTU.NG-VT-99.M 200101 mmbtu \n", " 7 USA-VT ELEC.CONS_TOT_BTU.NG-VT-99.M 200101 mmbtu \n", " 6 USA-VT ELEC.CONS_TOT_BTU.NG-VT-99.M 200101 mmbtu \n", " 5 USA-VT ELEC.CONS_TOT_BTU.NG-VT-99.M 200101 mmbtu \n", " 4 USA-VT ELEC.CONS_TOT_BTU.NG-VT-99.M 200101 mmbtu \n", " 3 USA-VT ELEC.CONS_TOT_BTU.NG-VT-99.M 200101 mmbtu \n", " 2 USA-VT ELEC.CONS_TOT_BTU.NG-VT-99.M 200101 mmbtu \n", " 1 USA-VT ELEC.CONS_TOT_BTU.NG-VT-99.M 200101 mmbtu \n", "PEL 2017 3 USA-VA ELEC.CONS_TOT_BTU.PEL-VA-99.M 200101 mmbtu \n", " 6 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 4 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 3 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 2 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 1 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 2016 12 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 11 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 10 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 9 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 8 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 7 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 6 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 5 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 4 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 3 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 2 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 1 USA-CO ELEC.CONS_TOT_BTU.PEL-CO-99.M 200101 mmbtu \n", " 2017 4 USA-CT ELEC.CONS_TOT_BTU.PEL-CT-99.M 200101 mmbtu \n", " 2016 10 USA-CT ELEC.CONS_TOT_BTU.PEL-CT-99.M 200101 mmbtu \n", " 4 USA-CT ELEC.CONS_TOT_BTU.PEL-CT-99.M 200101 mmbtu \n", " 3 USA-CT ELEC.CONS_TOT_BTU.PEL-CT-99.M 200101 mmbtu \n", "\n", " total fuel (mmbtu) \n", "type year month geography \n", "PC 2012 12 USA-CA NaN \n", " 11 USA-CA NaN \n", " 10 USA-CA NaN \n", " 9 USA-CA NaN \n", " 8 USA-CA NaN \n", " 7 USA-CA NaN \n", " 6 USA-CA NaN \n", " 5 USA-CA NaN \n", " 3 USA-CA NaN \n", " 2016 11 USA-IA NaN \n", " 10 USA-IA NaN \n", " 9 USA-IA NaN \n", " 8 USA-IA NaN \n", " 6 USA-IA NaN \n", " 5 USA-IA NaN \n", " 3 USA-IA NaN \n", " 1 USA-IA NaN \n", "NG 2016 6 USA-WY NaN \n", "PC 2017 6 USA-GA NaN \n", " 5 USA-GA NaN \n", " 4 USA-GA NaN \n", " 3 USA-GA NaN \n", " 2 USA-GA NaN \n", " 1 USA-GA NaN \n", " 2014 5 USA-GA NaN \n", "COW 2017 6 USA-ID NaN \n", " 5 USA-ID NaN \n", " 4 USA-ID NaN \n", " 3 USA-ID NaN \n", " 2 USA-ID NaN \n", "... ... \n", "NG 2016 8 USA-VT NaN \n", " 7 USA-VT NaN \n", " 6 USA-VT NaN \n", " 5 USA-VT NaN \n", " 4 USA-VT NaN \n", " 3 USA-VT NaN \n", " 2 USA-VT NaN \n", " 1 USA-VT NaN \n", "PEL 2017 3 USA-VA NaN \n", " 6 USA-CO NaN \n", " 4 USA-CO NaN \n", " 3 USA-CO NaN \n", " 2 USA-CO NaN \n", " 1 USA-CO NaN \n", " 2016 12 USA-CO NaN \n", " 11 USA-CO NaN \n", " 10 USA-CO NaN \n", " 9 USA-CO NaN \n", " 8 USA-CO NaN \n", " 7 USA-CO NaN \n", " 6 USA-CO NaN \n", " 5 USA-CO NaN \n", " 4 USA-CO NaN \n", " 3 USA-CO NaN \n", " 2 USA-CO NaN \n", " 1 USA-CO NaN \n", " 2017 4 USA-CT NaN \n", " 2016 10 USA-CT NaN \n", " 4 USA-CT NaN \n", " 3 USA-CT NaN \n", "\n", "[356 rows x 8 columns]" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total_fuel_df.loc[total_fuel_df.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:06:38.274000Z", "start_time": "2017-10-09T20:06:38.243000Z" }, "collapsed": true }, "outputs": [], "source": [ "total_fuel_df = total_fuel_df.dropna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Electric generation fuel consumption" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T19:17:09.377000Z", "start_time": "2017-10-09T19:17:09.168000Z" }, "collapsed": true }, "outputs": [], "source": [ "eg_fuel_dict = [json.loads(row) for row in eg_fuel_rows]" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:07:11.908000Z", "start_time": "2017-10-09T20:07:06.284000Z" } }, "outputs": [], "source": [ "eg_fuel_df = pd.concat([line_to_df(x) for x in eg_fuel_dict\n", " if x['geography'] in state_geos])" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:07:11.946000Z", "start_time": "2017-10-09T20:07:11.911000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endfgeographylast_updatedsectorseries_idstarttypeunitsyearmonthvalue
0201706MUSA-VA2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.NG-VA-99.M200101NGmillion MMBtu2017635.91854
1201706MUSA-VA2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.NG-VA-99.M200101NGmillion MMBtu2017524.96651
2201706MUSA-VA2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.NG-VA-99.M200101NGmillion MMBtu2017421.21269
3201706MUSA-VA2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.NG-VA-99.M200101NGmillion MMBtu2017328.41358
4201706MUSA-VA2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.NG-VA-99.M200101NGmillion MMBtu2017224.13910
\n", "
" ], "text/plain": [ " end f geography last_updated sector \\\n", "0 201706 M USA-VA 2017-08-24T11:46:12-04:00 99 \n", "1 201706 M USA-VA 2017-08-24T11:46:12-04:00 99 \n", "2 201706 M USA-VA 2017-08-24T11:46:12-04:00 99 \n", "3 201706 M USA-VA 2017-08-24T11:46:12-04:00 99 \n", "4 201706 M USA-VA 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start type units year month \\\n", "0 ELEC.CONS_EG_BTU.NG-VA-99.M 200101 NG million MMBtu 2017 6 \n", "1 ELEC.CONS_EG_BTU.NG-VA-99.M 200101 NG million MMBtu 2017 5 \n", "2 ELEC.CONS_EG_BTU.NG-VA-99.M 200101 NG million MMBtu 2017 4 \n", "3 ELEC.CONS_EG_BTU.NG-VA-99.M 200101 NG million MMBtu 2017 3 \n", "4 ELEC.CONS_EG_BTU.NG-VA-99.M 200101 NG million MMBtu 2017 2 \n", "\n", " value \n", "0 35.91854 \n", "1 24.96651 \n", "2 21.21269 \n", "3 28.41358 \n", "4 24.13910 " ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "eg_fuel_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Multiply generation values by 1,000,000 and change the units to MMBtu" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:07:12.078000Z", "start_time": "2017-10-09T20:07:11.949000Z" }, "collapsed": true }, "outputs": [], "source": [ "eg_fuel_df.loc[:,'value'] *= 1E6\n", "eg_fuel_df.loc[:,'units'] = 'mmbtu'" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:07:12.172000Z", "start_time": "2017-10-09T20:07:12.081000Z" }, "collapsed": true }, "outputs": [], "source": [ "eg_fuel_df.rename_axis({'value':'elec fuel (mmbtu)'}, axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:07:12.291000Z", "start_time": "2017-10-09T20:07:12.175000Z" }, "collapsed": true }, "outputs": [], "source": [ "eg_fuel_df.set_index(['type', 'year', 'month', 'geography'], inplace=True)" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:07:12.398000Z", "start_time": "2017-10-09T20:07:12.294000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitselec fuel (mmbtu)
typeyearmonthgeography
NG20176USA-VA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.NG-VA-99.M200101mmbtu35918540.0
5USA-VA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.NG-VA-99.M200101mmbtu24966510.0
4USA-VA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.NG-VA-99.M200101mmbtu21212690.0
3USA-VA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.NG-VA-99.M200101mmbtu28413580.0
2USA-VA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.NG-VA-99.M200101mmbtu24139100.0
\n", "
" ], "text/plain": [ " end f last_updated sector \\\n", "type year month geography \n", "NG 2017 6 USA-VA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 5 USA-VA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-VA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-VA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-VA 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "type year month geography \n", "NG 2017 6 USA-VA ELEC.CONS_EG_BTU.NG-VA-99.M 200101 mmbtu \n", " 5 USA-VA ELEC.CONS_EG_BTU.NG-VA-99.M 200101 mmbtu \n", " 4 USA-VA ELEC.CONS_EG_BTU.NG-VA-99.M 200101 mmbtu \n", " 3 USA-VA ELEC.CONS_EG_BTU.NG-VA-99.M 200101 mmbtu \n", " 2 USA-VA ELEC.CONS_EG_BTU.NG-VA-99.M 200101 mmbtu \n", "\n", " elec fuel (mmbtu) \n", "type year month geography \n", "NG 2017 6 USA-VA 35918540.0 \n", " 5 USA-VA 24966510.0 \n", " 4 USA-VA 21212690.0 \n", " 3 USA-VA 28413580.0 \n", " 2 USA-VA 24139100.0 " ] }, "execution_count": 167, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "eg_fuel_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I verified on [EIA's website](https://www.eia.gov/opendata/qb.php?category=400&sdid=ELEC.CONS_EG_BTU.PEL-MN-99.M) that the values below are correct." ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:07:12.502000Z", "start_time": "2017-10-09T20:07:12.402000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitselec fuel (mmbtu)
typeyearmonthgeography
PEL200212USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.PEL-MN-99.M200101mmbtu-43000.0
11USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.PEL-MN-99.M200101mmbtu-32000.0
10USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.PEL-MN-99.M200101mmbtu-15000.0
8USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.PEL-MN-99.M200101mmbtu-16000.0
7USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.PEL-MN-99.M200101mmbtu-1000.0
4USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.PEL-MN-99.M200101mmbtu-6000.0
3USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.PEL-MN-99.M200101mmbtu-10000.0
2USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.PEL-MN-99.M200101mmbtu-30000.0
1USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_EG_BTU.PEL-MN-99.M200101mmbtu-34000.0
\n", "
" ], "text/plain": [ " end f last_updated sector \\\n", "type year month geography \n", "PEL 2002 12 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 11 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 10 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 8 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 7 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 1 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "type year month geography \n", "PEL 2002 12 USA-MN ELEC.CONS_EG_BTU.PEL-MN-99.M 200101 mmbtu \n", " 11 USA-MN ELEC.CONS_EG_BTU.PEL-MN-99.M 200101 mmbtu \n", " 10 USA-MN ELEC.CONS_EG_BTU.PEL-MN-99.M 200101 mmbtu \n", " 8 USA-MN ELEC.CONS_EG_BTU.PEL-MN-99.M 200101 mmbtu \n", " 7 USA-MN ELEC.CONS_EG_BTU.PEL-MN-99.M 200101 mmbtu \n", " 4 USA-MN ELEC.CONS_EG_BTU.PEL-MN-99.M 200101 mmbtu \n", " 3 USA-MN ELEC.CONS_EG_BTU.PEL-MN-99.M 200101 mmbtu \n", " 2 USA-MN ELEC.CONS_EG_BTU.PEL-MN-99.M 200101 mmbtu \n", " 1 USA-MN ELEC.CONS_EG_BTU.PEL-MN-99.M 200101 mmbtu \n", "\n", " elec fuel (mmbtu) \n", "type year month geography \n", "PEL 2002 12 USA-MN -43000.0 \n", " 11 USA-MN -32000.0 \n", " 10 USA-MN -15000.0 \n", " 8 USA-MN -16000.0 \n", " 7 USA-MN -1000.0 \n", " 4 USA-MN -6000.0 \n", " 3 USA-MN -10000.0 \n", " 2 USA-MN -30000.0 \n", " 1 USA-MN -34000.0 " ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "eg_fuel_df.loc[~(eg_fuel_df['elec fuel (mmbtu)'] >= 0) &\n", " ~(eg_fuel_df['elec fuel (mmbtu)'].isnull())]" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:07:12.571000Z", "start_time": "2017-10-09T20:07:12.506000Z" }, "collapsed": true }, "outputs": [], "source": [ "eg_fuel_df.dropna(inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combine three datasets\n", "Need to estimate fuel use for OOG, because EIA doesn't include any (this is only ~2% of OOG fuel for electricity in 2015)." ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:08.140000Z", "start_time": "2017-10-09T20:08:07.653000Z" } }, "outputs": [], "source": [ "fuel_df = pd.concat([total_fuel_df, eg_fuel_df['elec fuel (mmbtu)']], axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not sure how this happens in EIA's data, but we do see the negative fuel consumption for electricity generation." ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:08.189000Z", "start_time": "2017-10-09T20:08:08.143000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitstotal fuel (mmbtu)elec fuel (mmbtu)
typeyearmonthgeography
PEL20021USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-MN-99.M200101mmbtu51000.0-34000.0
2USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-MN-99.M200101mmbtu62000.0-30000.0
3USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-MN-99.M200101mmbtu99000.0-10000.0
4USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-MN-99.M200101mmbtu84000.0-6000.0
7USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-MN-99.M200101mmbtu93000.0-1000.0
8USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-MN-99.M200101mmbtu66000.0-16000.0
10USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-MN-99.M200101mmbtu64000.0-15000.0
11USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-MN-99.M200101mmbtu49000.0-32000.0
12USA-MN201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.PEL-MN-99.M200101mmbtu50000.0-43000.0
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" ], "text/plain": [ " end f last_updated sector \\\n", "type year month geography \n", "PEL 2002 1 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 2 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 3 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 4 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 7 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 8 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 10 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 11 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", " 12 USA-MN 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "type year month geography \n", "PEL 2002 1 USA-MN ELEC.CONS_TOT_BTU.PEL-MN-99.M 200101 mmbtu \n", " 2 USA-MN ELEC.CONS_TOT_BTU.PEL-MN-99.M 200101 mmbtu \n", " 3 USA-MN ELEC.CONS_TOT_BTU.PEL-MN-99.M 200101 mmbtu \n", " 4 USA-MN ELEC.CONS_TOT_BTU.PEL-MN-99.M 200101 mmbtu \n", " 7 USA-MN ELEC.CONS_TOT_BTU.PEL-MN-99.M 200101 mmbtu \n", " 8 USA-MN ELEC.CONS_TOT_BTU.PEL-MN-99.M 200101 mmbtu \n", " 10 USA-MN ELEC.CONS_TOT_BTU.PEL-MN-99.M 200101 mmbtu \n", " 11 USA-MN ELEC.CONS_TOT_BTU.PEL-MN-99.M 200101 mmbtu \n", " 12 USA-MN ELEC.CONS_TOT_BTU.PEL-MN-99.M 200101 mmbtu \n", "\n", " total fuel (mmbtu) elec fuel (mmbtu) \n", "type year month geography \n", "PEL 2002 1 USA-MN 51000.0 -34000.0 \n", " 2 USA-MN 62000.0 -30000.0 \n", " 3 USA-MN 99000.0 -10000.0 \n", " 4 USA-MN 84000.0 -6000.0 \n", " 7 USA-MN 93000.0 -1000.0 \n", " 8 USA-MN 66000.0 -16000.0 \n", " 10 USA-MN 64000.0 -15000.0 \n", " 11 USA-MN 49000.0 -32000.0 \n", " 12 USA-MN 50000.0 -43000.0 " ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "fuel_df.loc[~(fuel_df['elec fuel (mmbtu)']>=0)]" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:08.256000Z", "start_time": "2017-10-09T20:08:08.191000Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
endflast_updatedsectorseries_idstartunitstotal fuel (mmbtu)elec fuel (mmbtu)
typeyearmonthgeography
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [end, f, last_updated, sector, series_id, start, units, total fuel (mmbtu), elec fuel (mmbtu)]\n", "Index: []" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "fuel_df.loc[~(fuel_df['total fuel (mmbtu)']>=0)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add CO2 emissions\n", "\n", "The difficulty here is that EIA combines all types of coal fuel consumption together in the bulk download and API. Fortunately the emission factors for different coal types aren't too far off on an energy basis (BIT is 93.3 kg/mmbtu, SUB is 97.2 kg/mmbtu). I'm going to average the BIT and SUB factors rather than trying to do something more complicated. In 2015 BIT represented 45% of coal energy for electricity and SUB represented 48%.\n", "\n", "Same issue with petroleum liquids. Using the average of DFO and RFO, which were the two largest share of petroleum liquids." ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:08.334000Z", "start_time": "2017-10-09T20:08:08.260000Z" }, "collapsed": true }, "outputs": [], "source": [ "path = join(cwd, '..', 'Data storage', 'Final emission factors.csv')\n", "ef = pd.read_csv(path, index_col=0)" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:08.446000Z", "start_time": "2017-10-09T20:08:08.337000Z" } }, "outputs": [ { "data": { "text/plain": [ "Index([u'BIT', u'DFO', u'GEO', u'JF', u'KER', u'LIG', u'MSW', u'NG', u'PC',\n", " u'PG', u'RC', u'RFO', u'SGC', u'SGP', u'SUB', u'TDF', u'WC', u'WO',\n", " u'BFG', u'MSN', u'SC', u'OG', u'AB', u'BLQ', u'LFG', u'MSB', u'NUC',\n", " u'OBG', u'OBL', u'OBS', u'OTH', u'PUR', u'SLW', u'SUN', u'WAT', u'WDL',\n", " u'WDS', u'WH', u'WND'],\n", " dtype='object', name=u'EIA Fuel Code')" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "ef.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Match general types with specific fuel codes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fuel codes:\n", "- WWW, wood and wood derived fuels\n", "- WND, wind\n", "- STH, solar thermal\n", "- WAS, other biomass\n", "- TSN, all solar\n", "- SUN, utility-scale solar\n", "- NUC, nuclear\n", "- NG, natural gas\n", "- PEL, petroleum liquids\n", "- SPV, utility-scale solar photovoltaic\n", "- PC, petroluem coke\n", "- OTH, other\n", "- COW, coal,\n", "- DPV, distributed photovoltaic\n", "- OOG, other gases\n", "- HPS, hydro pumped storage\n", "- HYC, conventional hydroelectric\n", "- GEO, geothermal\n", "- AOR, other renewables (total)" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:08.537000Z", "start_time": "2017-10-09T20:08:08.450000Z" } }, "outputs": [ { "data": { "text/plain": [ "53.07" ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "ef.loc['NG', 'Fossil Factor']" ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:08.637000Z", "start_time": "2017-10-09T20:08:08.540000Z" } }, "outputs": [], "source": [ "fuel_factors = pd.Series({'NG' : ef.loc['NG', 'Fossil Factor'],\n", " 'PEL': ef.loc[['DFO', 'RFO'], 'Fossil Factor'].mean(),\n", " 'PC' : ef.loc['PC', 'Fossil Factor'], \n", " 'COW' : ef.loc[['BIT', 'SUB'], 'Fossil Factor'].mean(),\n", " 'OOG' : ef.loc['OG', 'Fossil Factor']}, name='type')" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:08.739000Z", "start_time": "2017-10-09T20:08:08.639000Z" } }, "outputs": [ { "data": { "text/plain": [ "COW 95.250\n", "NG 53.070\n", "OOG 59.000\n", "PC 102.100\n", "PEL 75.975\n", "Name: type, dtype: float64" ] }, "execution_count": 177, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "fuel_factors" ] }, { "cell_type": "code", "execution_count": 178, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:08.831000Z", "start_time": "2017-10-09T20:08:08.742000Z" } }, "outputs": [ { "data": { "text/plain": [ "Index([u'COW', u'NG', u'PC', u'PEL'], dtype='object', name=u'type')" ] }, "execution_count": 178, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fuel_df.index.get_level_values('type').unique()" ] }, { "cell_type": "code", "execution_count": 179, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:08.964000Z", "start_time": "2017-10-09T20:08:08.836000Z" }, "collapsed": true }, "outputs": [], "source": [ "fuel_df['all fuel CO2 (kg)'] = (fuel_df['total fuel (mmbtu)']\n", " .multiply(fuel_factors, level='type',\n", " fill_value=0))\n", "fuel_df['elec fuel CO2 (kg)'] = (fuel_df['elec fuel (mmbtu)']\n", " .multiply(fuel_factors, level='type',\n", " fill_value=0))" ] }, { "cell_type": "code", "execution_count": 180, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:09.086000Z", "start_time": "2017-10-09T20:08:08.967000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitstotal fuel (mmbtu)elec fuel (mmbtu)all fuel CO2 (kg)elec fuel CO2 (kg)
typeyearmonthgeography
COW20011USA-AK201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.COW-AK-99.M200101mmbtu1120000.0872000.01.066800e+088.305800e+07
USA-AL201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.COW-AL-99.M200101mmbtu67999000.066582000.06.476905e+096.341935e+09
USA-AR201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.COW-AR-99.M200101mmbtu23099000.022700000.02.200180e+092.162175e+09
USA-AZ201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.COW-AZ-99.M200101mmbtu35873000.035483000.03.416903e+093.379756e+09
USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.CONS_TOT_BTU.COW-CA-99.M200101mmbtu3652000.02008000.03.478530e+081.912620e+08
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" ], "text/plain": [ " end f last_updated sector \\\n", "type year month geography \n", "COW 2001 1 USA-AK 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-AL 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-AR 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-AZ 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "type year month geography \n", "COW 2001 1 USA-AK ELEC.CONS_TOT_BTU.COW-AK-99.M 200101 mmbtu \n", " USA-AL ELEC.CONS_TOT_BTU.COW-AL-99.M 200101 mmbtu \n", " USA-AR ELEC.CONS_TOT_BTU.COW-AR-99.M 200101 mmbtu \n", " USA-AZ ELEC.CONS_TOT_BTU.COW-AZ-99.M 200101 mmbtu \n", " USA-CA ELEC.CONS_TOT_BTU.COW-CA-99.M 200101 mmbtu \n", "\n", " total fuel (mmbtu) elec fuel (mmbtu) \\\n", "type year month geography \n", "COW 2001 1 USA-AK 1120000.0 872000.0 \n", " USA-AL 67999000.0 66582000.0 \n", " USA-AR 23099000.0 22700000.0 \n", " USA-AZ 35873000.0 35483000.0 \n", " USA-CA 3652000.0 2008000.0 \n", "\n", " all fuel CO2 (kg) elec fuel CO2 (kg) \n", "type year month geography \n", "COW 2001 1 USA-AK 1.066800e+08 8.305800e+07 \n", " USA-AL 6.476905e+09 6.341935e+09 \n", " USA-AR 2.200180e+09 2.162175e+09 \n", " USA-AZ 3.416903e+09 3.379756e+09 \n", " USA-CA 3.478530e+08 1.912620e+08 " ] }, "execution_count": 180, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fuel_df.head()" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:30.019000Z", "start_time": "2017-10-09T20:08:29.056000Z" } }, "outputs": [], "source": [ "fuel_cols = ['total fuel (mmbtu)', 'elec fuel (mmbtu)',\n", " 'all fuel CO2 (kg)', 'elec fuel CO2 (kg)']\n", "gen_fuel_df = pd.concat([gen_df, fuel_df[fuel_cols]], axis=1)" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:08:30.611000Z", "start_time": "2017-10-09T20:08:30.564000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitsgeneration (MWh)total fuel (mmbtu)elec fuel (mmbtu)all fuel CO2 (kg)elec fuel CO2 (kg)
typeyearmonthgeography
AOR20011USA-AK201706M2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-AK-99.M200101megawatthours87.00NaNNaNNaNNaN
USA-AL201706M2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-AL-99.M200101megawatthours401167.59NaNNaNNaNNaN
USA-AR201706M2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-AR-99.M200101megawatthours136530.37NaNNaNNaNNaN
USA-AZ201706M2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-AZ-99.M200101megawatthours453.00NaNNaNNaNNaN
USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.GEN.AOR-CA-99.M200101megawatthours1717398.41NaNNaNNaNNaN
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" ], "text/plain": [ " end f last_updated sector \\\n", "type year month geography \n", "AOR 2001 1 USA-AK 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-AL 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-AR 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-AZ 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "type year month geography \n", "AOR 2001 1 USA-AK ELEC.GEN.AOR-AK-99.M 200101 megawatthours \n", " USA-AL ELEC.GEN.AOR-AL-99.M 200101 megawatthours \n", " USA-AR ELEC.GEN.AOR-AR-99.M 200101 megawatthours \n", " USA-AZ ELEC.GEN.AOR-AZ-99.M 200101 megawatthours \n", " USA-CA ELEC.GEN.AOR-CA-99.M 200101 megawatthours \n", "\n", " generation (MWh) total fuel (mmbtu) \\\n", "type year month geography \n", "AOR 2001 1 USA-AK 87.00 NaN \n", " USA-AL 401167.59 NaN \n", " USA-AR 136530.37 NaN \n", " USA-AZ 453.00 NaN \n", " USA-CA 1717398.41 NaN \n", "\n", " elec fuel (mmbtu) all fuel CO2 (kg) \\\n", "type year month geography \n", "AOR 2001 1 USA-AK NaN NaN \n", " USA-AL NaN NaN \n", " USA-AR NaN NaN \n", " USA-AZ NaN NaN \n", " USA-CA NaN NaN \n", "\n", " elec fuel CO2 (kg) \n", "type year month geography \n", "AOR 2001 1 USA-AK NaN \n", " USA-AL NaN \n", " USA-AR NaN \n", " USA-AZ NaN \n", " USA-CA NaN " ] }, "execution_count": 182, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gen_fuel_df.head()" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:09:10.436000Z", "start_time": "2017-10-09T20:09:10.296000Z" } }, "outputs": [], "source": [ "years = gen_fuel_df.index.get_level_values('year').astype(str)\n", "months = gen_fuel_df.index.get_level_values('month').astype(str)\n", "datetime = pd.to_datetime(years + '-' + months, format='%Y-%m')\n", "quarters = datetime.quarter\n", "\n", "gen_fuel_df['datetime'] = datetime\n", "gen_fuel_df['quarter'] = quarters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No records with positive fuel use but no generation" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:10:54.441000Z", "start_time": "2017-10-09T20:10:54.435000Z" } }, "outputs": [], "source": [ "gen_fuel_df['generation (MWh)'].fillna(value=0, inplace=True)" ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:11:38.007000Z", "start_time": "2017-10-09T20:11:37.831000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitsgeneration (MWh)total fuel (mmbtu)elec fuel (mmbtu)all fuel CO2 (kg)elec fuel CO2 (kg)datetimequarter
typeyearmonthgeography
COW20161USA-IDNaNNaNNaNNaNNaNNaNNaN0.0203250.033260.01.935956e+073.168015e+062016-01-011
3USA-IDNaNNaNNaNNaNNaNNaNNaN0.0180840.029370.01.722501e+072.797492e+062016-03-011
20173USA-AKNaNNaNNaNNaNNaNNaNNaN0.0907610.0448030.08.644985e+074.267486e+072017-03-011
USA-MENaNNaNNaNNaNNaNNaNNaN0.0259940.056050.02.475928e+075.338762e+062017-03-011
4USA-AKNaNNaNNaNNaNNaNNaNNaN0.0817670.0427170.07.788307e+074.068794e+072017-04-012
5USA-AKNaNNaNNaNNaNNaNNaNNaN0.0841370.0440000.08.014049e+074.191000e+072017-05-012
6USA-AKNaNNaNNaNNaNNaNNaNNaN0.0759460.0315130.07.233856e+073.001613e+072017-06-012
NG20104USA-MTNaNNaNNaNNaNNaNNaNNaN0.046590.09920.02.472531e+065.264544e+052010-04-012
8USA-MTNaNNaNNaNNaNNaNNaNNaN0.056400.08740.02.993148e+064.638318e+052010-08-013
20152USA-NENaNNaNNaNNaNNaNNaNNaN0.066370.066320.03.522256e+063.519602e+062015-02-011
20173USA-NDNaNNaNNaNNaNNaNNaNNaN0.0622640.0511610.03.304350e+072.715114e+072017-03-011
4USA-NDNaNNaNNaNNaNNaNNaNNaN0.0562040.0467050.02.982746e+072.478634e+072017-04-012
5USA-MENaNNaNNaNNaNNaNNaNNaN0.02218070.01345260.01.177130e+087.139295e+072017-05-012
USA-NDNaNNaNNaNNaNNaNNaNNaN0.0368030.0248830.01.953135e+071.320541e+072017-05-012
6USA-NDNaNNaNNaNNaNNaNNaNNaN0.0431120.0285430.02.287954e+071.514777e+072017-06-012
PC20114USA-NYNaNNaNNaNNaNNaNNaNNaN0.05760.05760.05.880960e+055.880960e+052011-04-012
6USA-NYNaNNaNNaNNaNNaNNaNNaN0.070340.070340.07.181714e+067.181714e+062011-06-012
20161USA-MINaNNaNNaNNaNNaNNaNNaN0.0495130.099490.05.055277e+071.015793e+072016-01-011
USA-OHNaNNaNNaNNaNNaNNaNNaN0.0474330.010400.04.842909e+071.061840e+062016-01-011
USA-TXNaNNaNNaNNaNNaNNaNNaN0.0383400.0198590.03.914514e+072.027604e+072016-01-011
2USA-IANaNNaNNaNNaNNaNNaNNaN0.0178520.051380.01.822689e+075.245898e+062016-02-011
4USA-IANaNNaNNaNNaNNaNNaNNaN0.0130230.062060.01.329648e+076.336326e+062016-04-012
7USA-IANaNNaNNaNNaNNaNNaNNaN0.0183010.066640.01.868532e+076.803944e+062016-07-013
12USA-IANaNNaNNaNNaNNaNNaNNaN0.0185790.060730.01.896916e+076.200533e+062016-12-014
20173USA-OHNaNNaNNaNNaNNaNNaNNaN0.01534660.01005180.01.566888e+081.026289e+082017-03-011
PEL20128USA-OKNaNNaNNaNNaNNaNNaNNaN0.0540.0540.04.102650e+044.102650e+042012-08-013
20131USA-RINaNNaNNaNNaNNaNNaNNaN0.01377260.086450.01.046373e+086.568039e+062013-01-011
8USA-RINaNNaNNaNNaNNaNNaNNaN0.08100.04450.06.153975e+053.380888e+052013-08-013
11USA-RINaNNaNNaNNaNNaNNaNNaN0.029880.011310.02.270133e+068.592772e+052013-11-014
12USA-RINaNNaNNaNNaNNaNNaNNaN0.01028040.0130840.07.810534e+079.940569e+062013-12-014
20147USA-RINaNNaNNaNNaNNaNNaNNaN0.011560.04980.08.782710e+053.783555e+052014-07-013
20153USA-RINaNNaNNaNNaNNaNNaNNaN0.021170.010160.01.608391e+067.719060e+052015-03-011
6USA-CONaNNaNNaNNaNNaNNaNNaN0.014220.014210.01.080364e+061.079605e+062015-06-012
20161USA-NHNaNNaNNaNNaNNaNNaNNaN0.043710.016170.03.320867e+061.228516e+062016-01-011
2USA-NHNaNNaNNaNNaNNaNNaNNaN0.059990.017980.04.557740e+061.366030e+062016-02-011
3USA-MENaNNaNNaNNaNNaNNaNNaN0.084780.038880.06.441160e+062.953908e+062016-03-011
USA-WANaNNaNNaNNaNNaNNaNNaN0.064440.08570.04.895829e+066.511058e+052016-03-011
4USA-KSNaNNaNNaNNaNNaNNaNNaN0.038460.038460.02.921998e+062.921998e+062016-04-012
USA-MENaNNaNNaNNaNNaNNaNNaN0.055680.030530.04.230288e+062.319517e+062016-04-012
USA-MSNaNNaNNaNNaNNaNNaNNaN0.013600.012040.01.033260e+069.147390e+052016-04-012
USA-SCNaNNaNNaNNaNNaNNaNNaN0.081930.055810.06.224632e+064.240165e+062016-04-012
USA-WANaNNaNNaNNaNNaNNaNNaN0.067910.07700.05.159462e+065.850075e+052016-04-012
5USA-MENaNNaNNaNNaNNaNNaNNaN0.072780.031530.05.529460e+062.395492e+062016-05-012
USA-WANaNNaNNaNNaNNaNNaNNaN0.0101990.012020.07.748690e+069.132195e+052016-05-012
9USA-MENaNNaNNaNNaNNaNNaNNaN0.054280.020560.04.123923e+061.562046e+062016-09-013
10USA-WANaNNaNNaNNaNNaNNaNNaN0.063460.016980.04.821374e+061.290056e+062016-10-014
11USA-CTNaNNaNNaNNaNNaNNaNNaN0.063950.061130.04.858601e+064.644352e+062016-11-014
USA-MENaNNaNNaNNaNNaNNaNNaN0.062960.034820.04.783386e+062.645450e+062016-11-014
USA-NYNaNNaNNaNNaNNaNNaNNaN0.0212900.0149830.01.617508e+071.138333e+072016-11-014
USA-WANaNNaNNaNNaNNaNNaNNaN0.064270.013500.04.882913e+061.025662e+062016-11-014
20171USA-RINaNNaNNaNNaNNaNNaNNaN0.041220.023340.03.131689e+061.773256e+062017-01-011
2USA-WANaNNaNNaNNaNNaNNaNNaN0.062940.018030.04.781866e+061.369829e+062017-02-011
3USA-WANaNNaNNaNNaNNaNNaNNaN0.061040.09630.04.637514e+067.316392e+052017-03-011
4USA-MENaNNaNNaNNaNNaNNaNNaN0.064280.040760.04.883673e+063.096741e+062017-04-012
USA-SCNaNNaNNaNNaNNaNNaNNaN0.0174000.0156090.01.321965e+071.185894e+072017-04-012
5USA-MENaNNaNNaNNaNNaNNaNNaN0.060470.038450.04.594208e+062.921239e+062017-05-012
USA-WANaNNaNNaNNaNNaNNaNNaN0.091230.013090.06.931199e+069.945127e+052017-05-012
\n", "
" ], "text/plain": [ " end f last_updated sector series_id start units \\\n", "type year month geography \n", "COW 2016 1 USA-ID NaN NaN NaN NaN NaN NaN NaN \n", " 3 USA-ID NaN NaN NaN NaN NaN NaN NaN \n", " 2017 3 USA-AK NaN NaN NaN NaN NaN NaN NaN \n", " USA-ME NaN NaN NaN NaN NaN NaN NaN \n", " 4 USA-AK NaN NaN NaN NaN NaN NaN NaN \n", " 5 USA-AK NaN NaN NaN NaN NaN NaN NaN \n", " 6 USA-AK NaN NaN NaN NaN NaN NaN NaN \n", "NG 2010 4 USA-MT NaN NaN NaN NaN NaN NaN NaN \n", " 8 USA-MT NaN NaN NaN NaN NaN NaN NaN \n", " 2015 2 USA-NE NaN NaN NaN NaN NaN NaN NaN \n", " 2017 3 USA-ND NaN NaN NaN NaN NaN NaN NaN \n", " 4 USA-ND NaN NaN NaN NaN NaN NaN NaN \n", " 5 USA-ME NaN NaN NaN NaN NaN NaN NaN \n", " USA-ND NaN NaN NaN NaN NaN NaN NaN \n", " 6 USA-ND NaN NaN NaN NaN NaN NaN NaN \n", "PC 2011 4 USA-NY NaN NaN NaN NaN NaN NaN NaN \n", " 6 USA-NY NaN NaN NaN NaN NaN NaN NaN \n", " 2016 1 USA-MI NaN NaN NaN NaN NaN NaN NaN \n", " USA-OH NaN NaN NaN NaN NaN NaN NaN \n", " USA-TX NaN NaN NaN NaN NaN NaN NaN \n", " 2 USA-IA NaN NaN NaN NaN NaN NaN NaN \n", " 4 USA-IA NaN NaN NaN NaN NaN NaN NaN \n", " 7 USA-IA NaN NaN NaN NaN NaN NaN NaN \n", " 12 USA-IA NaN NaN NaN NaN NaN NaN NaN \n", " 2017 3 USA-OH NaN NaN NaN NaN NaN NaN NaN \n", "PEL 2012 8 USA-OK NaN NaN NaN NaN NaN NaN NaN \n", " 2013 1 USA-RI NaN NaN NaN NaN NaN NaN NaN \n", " 8 USA-RI NaN NaN NaN NaN NaN NaN NaN \n", " 11 USA-RI NaN NaN NaN NaN NaN NaN NaN \n", " 12 USA-RI NaN NaN NaN NaN NaN NaN NaN \n", " 2014 7 USA-RI NaN NaN NaN NaN NaN NaN NaN \n", " 2015 3 USA-RI NaN NaN NaN NaN NaN NaN NaN \n", " 6 USA-CO NaN NaN NaN NaN NaN NaN NaN \n", " 2016 1 USA-NH NaN NaN NaN NaN NaN NaN NaN \n", " 2 USA-NH NaN NaN NaN NaN NaN NaN NaN \n", " 3 USA-ME NaN NaN NaN NaN NaN NaN NaN \n", " USA-WA NaN NaN NaN NaN NaN NaN NaN \n", " 4 USA-KS NaN NaN NaN NaN NaN NaN NaN \n", " USA-ME NaN NaN NaN NaN NaN NaN NaN \n", " USA-MS NaN NaN NaN NaN NaN NaN NaN \n", " USA-SC NaN NaN NaN NaN NaN NaN NaN \n", " USA-WA NaN NaN NaN NaN NaN NaN NaN \n", " 5 USA-ME NaN NaN NaN NaN NaN NaN NaN \n", " USA-WA NaN NaN NaN NaN NaN NaN NaN \n", " 9 USA-ME NaN NaN NaN NaN NaN NaN NaN \n", " 10 USA-WA NaN NaN NaN NaN NaN NaN NaN \n", " 11 USA-CT NaN NaN NaN NaN NaN NaN NaN \n", " USA-ME NaN NaN NaN NaN NaN NaN NaN \n", " USA-NY NaN NaN NaN NaN NaN NaN NaN \n", " USA-WA NaN NaN NaN NaN NaN NaN NaN \n", " 2017 1 USA-RI NaN NaN NaN NaN NaN NaN NaN \n", " 2 USA-WA NaN NaN NaN NaN NaN NaN NaN \n", " 3 USA-WA NaN NaN NaN NaN NaN NaN NaN \n", " 4 USA-ME NaN NaN NaN NaN NaN NaN NaN \n", " USA-SC NaN NaN NaN NaN NaN NaN NaN \n", " 5 USA-ME NaN NaN NaN NaN NaN NaN NaN \n", " USA-WA NaN NaN NaN NaN NaN NaN NaN \n", "\n", " generation (MWh) total fuel (mmbtu) \\\n", "type year month geography \n", "COW 2016 1 USA-ID 0.0 203250.0 \n", " 3 USA-ID 0.0 180840.0 \n", " 2017 3 USA-AK 0.0 907610.0 \n", " USA-ME 0.0 259940.0 \n", " 4 USA-AK 0.0 817670.0 \n", " 5 USA-AK 0.0 841370.0 \n", " 6 USA-AK 0.0 759460.0 \n", "NG 2010 4 USA-MT 0.0 46590.0 \n", " 8 USA-MT 0.0 56400.0 \n", " 2015 2 USA-NE 0.0 66370.0 \n", " 2017 3 USA-ND 0.0 622640.0 \n", " 4 USA-ND 0.0 562040.0 \n", " 5 USA-ME 0.0 2218070.0 \n", " USA-ND 0.0 368030.0 \n", " 6 USA-ND 0.0 431120.0 \n", "PC 2011 4 USA-NY 0.0 5760.0 \n", " 6 USA-NY 0.0 70340.0 \n", " 2016 1 USA-MI 0.0 495130.0 \n", " USA-OH 0.0 474330.0 \n", " USA-TX 0.0 383400.0 \n", " 2 USA-IA 0.0 178520.0 \n", " 4 USA-IA 0.0 130230.0 \n", " 7 USA-IA 0.0 183010.0 \n", " 12 USA-IA 0.0 185790.0 \n", " 2017 3 USA-OH 0.0 1534660.0 \n", "PEL 2012 8 USA-OK 0.0 540.0 \n", " 2013 1 USA-RI 0.0 1377260.0 \n", " 8 USA-RI 0.0 8100.0 \n", " 11 USA-RI 0.0 29880.0 \n", " 12 USA-RI 0.0 1028040.0 \n", " 2014 7 USA-RI 0.0 11560.0 \n", " 2015 3 USA-RI 0.0 21170.0 \n", " 6 USA-CO 0.0 14220.0 \n", " 2016 1 USA-NH 0.0 43710.0 \n", " 2 USA-NH 0.0 59990.0 \n", " 3 USA-ME 0.0 84780.0 \n", " USA-WA 0.0 64440.0 \n", " 4 USA-KS 0.0 38460.0 \n", " USA-ME 0.0 55680.0 \n", " USA-MS 0.0 13600.0 \n", " USA-SC 0.0 81930.0 \n", " USA-WA 0.0 67910.0 \n", " 5 USA-ME 0.0 72780.0 \n", " USA-WA 0.0 101990.0 \n", " 9 USA-ME 0.0 54280.0 \n", " 10 USA-WA 0.0 63460.0 \n", " 11 USA-CT 0.0 63950.0 \n", " USA-ME 0.0 62960.0 \n", " USA-NY 0.0 212900.0 \n", " USA-WA 0.0 64270.0 \n", " 2017 1 USA-RI 0.0 41220.0 \n", " 2 USA-WA 0.0 62940.0 \n", " 3 USA-WA 0.0 61040.0 \n", " 4 USA-ME 0.0 64280.0 \n", " USA-SC 0.0 174000.0 \n", " 5 USA-ME 0.0 60470.0 \n", " USA-WA 0.0 91230.0 \n", "\n", " elec fuel (mmbtu) all fuel CO2 (kg) \\\n", "type year month geography \n", "COW 2016 1 USA-ID 33260.0 1.935956e+07 \n", " 3 USA-ID 29370.0 1.722501e+07 \n", " 2017 3 USA-AK 448030.0 8.644985e+07 \n", " USA-ME 56050.0 2.475928e+07 \n", " 4 USA-AK 427170.0 7.788307e+07 \n", " 5 USA-AK 440000.0 8.014049e+07 \n", " 6 USA-AK 315130.0 7.233856e+07 \n", "NG 2010 4 USA-MT 9920.0 2.472531e+06 \n", " 8 USA-MT 8740.0 2.993148e+06 \n", " 2015 2 USA-NE 66320.0 3.522256e+06 \n", " 2017 3 USA-ND 511610.0 3.304350e+07 \n", " 4 USA-ND 467050.0 2.982746e+07 \n", " 5 USA-ME 1345260.0 1.177130e+08 \n", " USA-ND 248830.0 1.953135e+07 \n", " 6 USA-ND 285430.0 2.287954e+07 \n", "PC 2011 4 USA-NY 5760.0 5.880960e+05 \n", " 6 USA-NY 70340.0 7.181714e+06 \n", " 2016 1 USA-MI 99490.0 5.055277e+07 \n", " USA-OH 10400.0 4.842909e+07 \n", " USA-TX 198590.0 3.914514e+07 \n", " 2 USA-IA 51380.0 1.822689e+07 \n", " 4 USA-IA 62060.0 1.329648e+07 \n", " 7 USA-IA 66640.0 1.868532e+07 \n", " 12 USA-IA 60730.0 1.896916e+07 \n", " 2017 3 USA-OH 1005180.0 1.566888e+08 \n", "PEL 2012 8 USA-OK 540.0 4.102650e+04 \n", " 2013 1 USA-RI 86450.0 1.046373e+08 \n", " 8 USA-RI 4450.0 6.153975e+05 \n", " 11 USA-RI 11310.0 2.270133e+06 \n", " 12 USA-RI 130840.0 7.810534e+07 \n", " 2014 7 USA-RI 4980.0 8.782710e+05 \n", " 2015 3 USA-RI 10160.0 1.608391e+06 \n", " 6 USA-CO 14210.0 1.080364e+06 \n", " 2016 1 USA-NH 16170.0 3.320867e+06 \n", " 2 USA-NH 17980.0 4.557740e+06 \n", " 3 USA-ME 38880.0 6.441160e+06 \n", " USA-WA 8570.0 4.895829e+06 \n", " 4 USA-KS 38460.0 2.921998e+06 \n", " USA-ME 30530.0 4.230288e+06 \n", " USA-MS 12040.0 1.033260e+06 \n", " USA-SC 55810.0 6.224632e+06 \n", " USA-WA 7700.0 5.159462e+06 \n", " 5 USA-ME 31530.0 5.529460e+06 \n", " USA-WA 12020.0 7.748690e+06 \n", " 9 USA-ME 20560.0 4.123923e+06 \n", " 10 USA-WA 16980.0 4.821374e+06 \n", " 11 USA-CT 61130.0 4.858601e+06 \n", " USA-ME 34820.0 4.783386e+06 \n", " USA-NY 149830.0 1.617508e+07 \n", " USA-WA 13500.0 4.882913e+06 \n", " 2017 1 USA-RI 23340.0 3.131689e+06 \n", " 2 USA-WA 18030.0 4.781866e+06 \n", " 3 USA-WA 9630.0 4.637514e+06 \n", " 4 USA-ME 40760.0 4.883673e+06 \n", " USA-SC 156090.0 1.321965e+07 \n", " 5 USA-ME 38450.0 4.594208e+06 \n", " USA-WA 13090.0 6.931199e+06 \n", "\n", " elec fuel CO2 (kg) datetime quarter \n", "type year month geography \n", "COW 2016 1 USA-ID 3.168015e+06 2016-01-01 1 \n", " 3 USA-ID 2.797492e+06 2016-03-01 1 \n", " 2017 3 USA-AK 4.267486e+07 2017-03-01 1 \n", " USA-ME 5.338762e+06 2017-03-01 1 \n", " 4 USA-AK 4.068794e+07 2017-04-01 2 \n", " 5 USA-AK 4.191000e+07 2017-05-01 2 \n", " 6 USA-AK 3.001613e+07 2017-06-01 2 \n", "NG 2010 4 USA-MT 5.264544e+05 2010-04-01 2 \n", " 8 USA-MT 4.638318e+05 2010-08-01 3 \n", " 2015 2 USA-NE 3.519602e+06 2015-02-01 1 \n", " 2017 3 USA-ND 2.715114e+07 2017-03-01 1 \n", " 4 USA-ND 2.478634e+07 2017-04-01 2 \n", " 5 USA-ME 7.139295e+07 2017-05-01 2 \n", " USA-ND 1.320541e+07 2017-05-01 2 \n", " 6 USA-ND 1.514777e+07 2017-06-01 2 \n", "PC 2011 4 USA-NY 5.880960e+05 2011-04-01 2 \n", " 6 USA-NY 7.181714e+06 2011-06-01 2 \n", " 2016 1 USA-MI 1.015793e+07 2016-01-01 1 \n", " USA-OH 1.061840e+06 2016-01-01 1 \n", " USA-TX 2.027604e+07 2016-01-01 1 \n", " 2 USA-IA 5.245898e+06 2016-02-01 1 \n", " 4 USA-IA 6.336326e+06 2016-04-01 2 \n", " 7 USA-IA 6.803944e+06 2016-07-01 3 \n", " 12 USA-IA 6.200533e+06 2016-12-01 4 \n", " 2017 3 USA-OH 1.026289e+08 2017-03-01 1 \n", "PEL 2012 8 USA-OK 4.102650e+04 2012-08-01 3 \n", " 2013 1 USA-RI 6.568039e+06 2013-01-01 1 \n", " 8 USA-RI 3.380888e+05 2013-08-01 3 \n", " 11 USA-RI 8.592772e+05 2013-11-01 4 \n", " 12 USA-RI 9.940569e+06 2013-12-01 4 \n", " 2014 7 USA-RI 3.783555e+05 2014-07-01 3 \n", " 2015 3 USA-RI 7.719060e+05 2015-03-01 1 \n", " 6 USA-CO 1.079605e+06 2015-06-01 2 \n", " 2016 1 USA-NH 1.228516e+06 2016-01-01 1 \n", " 2 USA-NH 1.366030e+06 2016-02-01 1 \n", " 3 USA-ME 2.953908e+06 2016-03-01 1 \n", " USA-WA 6.511058e+05 2016-03-01 1 \n", " 4 USA-KS 2.921998e+06 2016-04-01 2 \n", " USA-ME 2.319517e+06 2016-04-01 2 \n", " USA-MS 9.147390e+05 2016-04-01 2 \n", " USA-SC 4.240165e+06 2016-04-01 2 \n", " USA-WA 5.850075e+05 2016-04-01 2 \n", " 5 USA-ME 2.395492e+06 2016-05-01 2 \n", " USA-WA 9.132195e+05 2016-05-01 2 \n", " 9 USA-ME 1.562046e+06 2016-09-01 3 \n", " 10 USA-WA 1.290056e+06 2016-10-01 4 \n", " 11 USA-CT 4.644352e+06 2016-11-01 4 \n", " USA-ME 2.645450e+06 2016-11-01 4 \n", " USA-NY 1.138333e+07 2016-11-01 4 \n", " USA-WA 1.025662e+06 2016-11-01 4 \n", " 2017 1 USA-RI 1.773256e+06 2017-01-01 1 \n", " 2 USA-WA 1.369829e+06 2017-02-01 1 \n", " 3 USA-WA 7.316392e+05 2017-03-01 1 \n", " 4 USA-ME 3.096741e+06 2017-04-01 2 \n", " USA-SC 1.185894e+07 2017-04-01 2 \n", " 5 USA-ME 2.921239e+06 2017-05-01 2 \n", " USA-WA 9.945127e+05 2017-05-01 2 " ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "gen_fuel_df.loc[gen_fuel_df['sector'].isnull()]" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:12:20.860000Z", "start_time": "2017-10-09T20:12:20.807000Z" }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitsgeneration (MWh)total fuel (mmbtu)elec fuel (mmbtu)all fuel CO2 (kg)elec fuel CO2 (kg)datetimequarter
yearmonthgeography
20011USA-AK201706M2017-08-24T11:46:12-04:0099ELEC.GEN.COW-AK-99.M200101megawatthours46903.01120000.0872000.01.066800e+088.305800e+072001-01-011
USA-AL201706M2017-08-24T11:46:12-04:0099ELEC.GEN.COW-AL-99.M200101megawatthours6557913.067999000.066582000.06.476905e+096.341935e+092001-01-011
USA-AR201706M2017-08-24T11:46:12-04:0099ELEC.GEN.COW-AR-99.M200101megawatthours2149808.023099000.022700000.02.200180e+092.162175e+092001-01-011
USA-AZ201706M2017-08-24T11:46:12-04:0099ELEC.GEN.COW-AZ-99.M200101megawatthours3418454.035873000.035483000.03.416903e+093.379756e+092001-01-011
USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.GEN.COW-CA-99.M200101megawatthours199857.03652000.02008000.03.478530e+081.912620e+082001-01-011
\n", "
" ], "text/plain": [ " end f last_updated sector \\\n", "year month geography \n", "2001 1 USA-AK 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-AL 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-AR 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-AZ 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "year month geography \n", "2001 1 USA-AK ELEC.GEN.COW-AK-99.M 200101 megawatthours \n", " USA-AL ELEC.GEN.COW-AL-99.M 200101 megawatthours \n", " USA-AR ELEC.GEN.COW-AR-99.M 200101 megawatthours \n", " USA-AZ ELEC.GEN.COW-AZ-99.M 200101 megawatthours \n", " USA-CA ELEC.GEN.COW-CA-99.M 200101 megawatthours \n", "\n", " generation (MWh) total fuel (mmbtu) elec fuel (mmbtu) \\\n", "year month geography \n", "2001 1 USA-AK 46903.0 1120000.0 872000.0 \n", " USA-AL 6557913.0 67999000.0 66582000.0 \n", " USA-AR 2149808.0 23099000.0 22700000.0 \n", " USA-AZ 3418454.0 35873000.0 35483000.0 \n", " USA-CA 199857.0 3652000.0 2008000.0 \n", "\n", " all fuel CO2 (kg) elec fuel CO2 (kg) datetime \\\n", "year month geography \n", "2001 1 USA-AK 1.066800e+08 8.305800e+07 2001-01-01 \n", " USA-AL 6.476905e+09 6.341935e+09 2001-01-01 \n", " USA-AR 2.200180e+09 2.162175e+09 2001-01-01 \n", " USA-AZ 3.416903e+09 3.379756e+09 2001-01-01 \n", " USA-CA 3.478530e+08 1.912620e+08 2001-01-01 \n", "\n", " quarter \n", "year month geography \n", "2001 1 USA-AK 1 \n", " USA-AL 1 \n", " USA-AR 1 \n", " USA-AZ 1 \n", " USA-CA 1 " ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gen_fuel_df.loc['COW',:].head()" ] }, { "cell_type": "code", "execution_count": 191, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:12:33.206000Z", "start_time": "2017-10-09T20:12:33.153000Z" } }, "outputs": [ { "data": { "text/html": [ "
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endflast_updatedsectorseries_idstartunitsgeneration (MWh)total fuel (mmbtu)elec fuel (mmbtu)all fuel CO2 (kg)elec fuel CO2 (kg)datetimequarter
yearmonthgeography
20011USA-AL201706M2017-08-24T11:46:12-04:0099ELEC.GEN.OOG-AL-99.M200101megawatthours25283.0NaNNaNNaNNaN2001-01-011
USA-CA201706M2017-08-24T11:46:12-04:0099ELEC.GEN.OOG-CA-99.M200101megawatthours97569.0NaNNaNNaNNaN2001-01-011
USA-CT201012M2016-12-19T17:19:30-05:0099ELEC.GEN.OOG-CT-99.M200101megawatthours137.0NaNNaNNaNNaN2001-01-011
USA-DE201706M2017-08-24T11:46:12-04:0099ELEC.GEN.OOG-DE-99.M200101megawatthours12552.0NaNNaNNaNNaN2001-01-011
USA-FL201706M2017-08-24T11:46:12-04:0099ELEC.GEN.OOG-FL-99.M200101megawatthours9.0NaNNaNNaNNaN2001-01-011
\n", "
" ], "text/plain": [ " end f last_updated sector \\\n", "year month geography \n", "2001 1 USA-AL 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-CA 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-CT 201012 M 2016-12-19T17:19:30-05:00 99 \n", " USA-DE 201706 M 2017-08-24T11:46:12-04:00 99 \n", " USA-FL 201706 M 2017-08-24T11:46:12-04:00 99 \n", "\n", " series_id start units \\\n", "year month geography \n", "2001 1 USA-AL ELEC.GEN.OOG-AL-99.M 200101 megawatthours \n", " USA-CA ELEC.GEN.OOG-CA-99.M 200101 megawatthours \n", " USA-CT ELEC.GEN.OOG-CT-99.M 200101 megawatthours \n", " USA-DE ELEC.GEN.OOG-DE-99.M 200101 megawatthours \n", " USA-FL ELEC.GEN.OOG-FL-99.M 200101 megawatthours \n", "\n", " generation (MWh) total fuel (mmbtu) elec fuel (mmbtu) \\\n", "year month geography \n", "2001 1 USA-AL 25283.0 NaN NaN \n", " USA-CA 97569.0 NaN NaN \n", " USA-CT 137.0 NaN NaN \n", " USA-DE 12552.0 NaN NaN \n", " USA-FL 9.0 NaN NaN \n", "\n", " all fuel CO2 (kg) elec fuel CO2 (kg) datetime \\\n", "year month geography \n", "2001 1 USA-AL NaN NaN 2001-01-01 \n", " USA-CA NaN NaN 2001-01-01 \n", " USA-CT NaN NaN 2001-01-01 \n", " USA-DE NaN NaN 2001-01-01 \n", " USA-FL NaN NaN 2001-01-01 \n", "\n", " quarter \n", "year month geography \n", "2001 1 USA-AL 1 \n", " USA-CA 1 \n", " USA-CT 1 \n", " USA-DE 1 \n", " USA-FL 1 " ] }, "execution_count": 191, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#drop\n", "gen_fuel_df.loc['OOG'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Export data" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "ExecuteTime": { "end_time": "2017-10-09T20:12:43.480000Z", "start_time": "2017-10-09T20:12:41.305000Z" }, "collapsed": true }, "outputs": [], "source": [ "path = join(cwd, '..', 'Data storage', 'EIA state-level gen fuel CO2.csv')\n", "gen_fuel_df.to_csv(path, index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }