{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# World Power Consumption in 2014" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objs as go \n", "from plotly.offline import init_notebook_mode,iplot\n", "init_notebook_mode(connected=True) " ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "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", " \n", " \n", " \n", " \n", " \n", " \n", "
CountryPower Consumption KWHText
0China5.523000e+12China 5,523,000,000,000
1United States3.832000e+12United 3,832,000,000,000
2European2.771000e+12European 2,771,000,000,000
3Russia1.065000e+12Russia 1,065,000,000,000
4Japan9.210000e+11Japan 921,000,000,000
\n", "
" ], "text/plain": [ " Country Power Consumption KWH Text\n", "0 China 5.523000e+12 China 5,523,000,000,000\n", "1 United States 3.832000e+12 United 3,832,000,000,000\n", "2 European 2.771000e+12 European 2,771,000,000,000\n", "3 Russia 1.065000e+12 Russia 1,065,000,000,000\n", "4 Japan 9.210000e+11 Japan 921,000,000,000" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import the dataset\n", "import pandas as pd\n", "df = pd.read_csv('2014_World_Power_Consumption')\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = dict(type = 'choropleth',\n", " colorscale = 'Electric',\n", " reversescale = True,\n", " locations = df['Country'],\n", " locationmode = 'country names',\n", " z = df['Power Consumption KWH'],\n", " text = df['Country'],\n", " colorbar = {'title': '2014 Power Consumption in KWH'})" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layout = dict(title = '2014 Power Consumption in KWH',\n", " geo = dict (showframe = False, projection = {'type': 'natural earth'}))" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": true }, "outputs": [], "source": [ "choromap = go.Figure(data=[data], layout=layout)" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "colorbar": { "title": "2014 Power Consumption in KWH" }, "colorscale": "Electric", "locationmode": "country names", "locations": [ "China", "United States", "European", "Russia", "Japan", "India", "Germany", "Canada", "Brazil", "Korea,", "France", "United Kingdom", "Italy", "Taiwan", "Spain", "Mexico", "Saudi", "Australia", "South", "Turkey", "Iran", "Indonesia", "Ukraine", "Thailand", "Poland", "Egypt", "Sweden", "Norway", "Malaysia", "Argentina", "Netherlands", "Vietnam", "Venezuela", "United Arab Emirates", "Finland", "Belgium", "Kazakhstan", "Pakistan", "Philippines", "Austria", "Chile", "Czechia", "Israel", "Switzerland", "Greece", "Iraq", "Romania", "Kuwait", "Colombia", "Singapore", "Portugal", "Uzbekistan", "Hong", "Algeria", "Bangladesh", "New", "Bulgaria", "Belarus", "Peru", "Denmark", "Qatar", "Slovakia", "Libya", "Serbia", "Morocco", "Syria", "Nigeria", "Ireland", "Hungary", "Oman", "Ecuador", "Puerto", "Azerbaijan", "Croatia", "Iceland", "Cuba", "Korea,", "Dominican", "Jordan", "Tajikistan", "Tunisia", "Slovenia", "Lebanon", "Bosnia", "Turkmenistan", "Bahrain", "Mozambique", "Ghana", "Sri", "Kyrgyzstan", "Lithuania", "Uruguay", "Costa", "Guatemala", "Georgia", "Trinidad", "Zambia", "Paraguay", "Albania", "Burma", "Estonia", "Congo,", "Panama", "Latvia", "Macedonia", "Zimbabwe", "Kenya", "Bolivia", "Luxembourg", "Sudan", "El", "Cameroon", "West", "Ethiopia", "Armenia", "Honduras", "Angola", "Cote", "Tanzania", "Nicaragua", "Moldova", "Cyprus", "Macau", "Namibia", "Mongolia", "Afghanistan", "Yemen", "Brunei", "Cambodia", "Montenegro", "Nepal", "Botswana", "Papua", "Jamaica", "Kosovo", "Laos", "Uganda", "New", "Mauritius", "Senegal", "Bhutan", "Malawi", "Madagascar", "Bahamas,", "Gabon", "Suriname", "Guam", "Liechtenstein", "Swaziland", "Burkina", "Togo", "Curacao", "Mauritania", "Barbados", "Niger", "Aruba", "Benin", "Guinea", "Mali", "Fiji", "Congo,", "Virgin", "Lesotho", "South", "Bermuda", "French", "Jersey", "Belize", "Andorra", "Guyana", "Cayman", "Haiti", "Rwanda", "Saint", "Djibouti", "Seychelles", "Somalia", "Antigua", "Greenland", "Cabo", "Eritrea", "Burundi", "Liberia", "Maldives", "Faroe", "Gambia,", "Chad", "Micronesia,", "Grenada", "Central", "Turks", "Gibraltar", "American", "Sierra", "Saint", "Saint", "Timor-Leste", "Equatorial", "Samoa", "Dominica", "Western", "Solomon", "Sao", "British", "Vanuatu", "Guinea-Bissau", "Tonga", "Saint", "Comoros", "Cook", "Kiribati", "Montserrat", "Nauru", "Falkland", "Saint", "Niue", "Gaza", "Malta", "Northern" ], "reversescale": true, "text": [ "China", "United States", "European", "Russia", "Japan", "India", "Germany", "Canada", "Brazil", "Korea,", "France", "United Kingdom", "Italy", "Taiwan", "Spain", "Mexico", "Saudi", "Australia", "South", "Turkey", "Iran", "Indonesia", "Ukraine", "Thailand", "Poland", "Egypt", "Sweden", "Norway", "Malaysia", "Argentina", "Netherlands", "Vietnam", "Venezuela", "United Arab Emirates", "Finland", "Belgium", "Kazakhstan", "Pakistan", "Philippines", "Austria", "Chile", "Czechia", "Israel", "Switzerland", "Greece", "Iraq", "Romania", "Kuwait", "Colombia", "Singapore", "Portugal", "Uzbekistan", "Hong", "Algeria", "Bangladesh", "New", "Bulgaria", "Belarus", "Peru", "Denmark", "Qatar", "Slovakia", "Libya", "Serbia", "Morocco", "Syria", "Nigeria", "Ireland", "Hungary", "Oman", "Ecuador", "Puerto", "Azerbaijan", "Croatia", "Iceland", "Cuba", "Korea,", "Dominican", "Jordan", "Tajikistan", "Tunisia", "Slovenia", "Lebanon", "Bosnia", "Turkmenistan", "Bahrain", "Mozambique", "Ghana", "Sri", "Kyrgyzstan", "Lithuania", "Uruguay", "Costa", "Guatemala", "Georgia", "Trinidad", "Zambia", "Paraguay", "Albania", "Burma", "Estonia", "Congo,", "Panama", "Latvia", "Macedonia", "Zimbabwe", "Kenya", "Bolivia", "Luxembourg", "Sudan", "El", "Cameroon", "West", "Ethiopia", "Armenia", "Honduras", "Angola", "Cote", "Tanzania", "Nicaragua", "Moldova", "Cyprus", "Macau", "Namibia", "Mongolia", "Afghanistan", "Yemen", "Brunei", "Cambodia", "Montenegro", "Nepal", "Botswana", "Papua", "Jamaica", "Kosovo", "Laos", "Uganda", "New", "Mauritius", "Senegal", "Bhutan", "Malawi", "Madagascar", "Bahamas,", "Gabon", "Suriname", "Guam", "Liechtenstein", "Swaziland", "Burkina", "Togo", "Curacao", "Mauritania", "Barbados", "Niger", "Aruba", "Benin", "Guinea", "Mali", "Fiji", "Congo,", "Virgin", "Lesotho", "South", "Bermuda", "French", "Jersey", "Belize", "Andorra", "Guyana", "Cayman", "Haiti", "Rwanda", "Saint", "Djibouti", "Seychelles", "Somalia", "Antigua", "Greenland", "Cabo", "Eritrea", "Burundi", "Liberia", "Maldives", "Faroe", "Gambia,", "Chad", "Micronesia,", "Grenada", "Central", "Turks", "Gibraltar", "American", "Sierra", "Saint", "Saint", "Timor-Leste", "Equatorial", "Samoa", "Dominica", "Western", "Solomon", "Sao", "British", "Vanuatu", "Guinea-Bissau", "Tonga", "Saint", "Comoros", "Cook", "Kiribati", "Montserrat", "Nauru", "Falkland", "Saint", "Niue", "Gaza", "Malta", "Northern" ], "type": "choropleth", "z": [ 5523000000000, 3832000000000, 2771000000000, 1065000000000, 921000000000, 864700000000, 540100000000, 511000000000, 483500000000, 482400000000, 451100000000, 319100000000, 303100000000, 249500000000, 243100000000, 234000000000, 231600000000, 222600000000, 211600000000, 197000000000, 195300000000, 167500000000, 159800000000, 155900000000, 139000000000, 135600000000, 130500000000, 126400000000, 118500000000, 117100000000, 116800000000, 108300000000, 97690000000, 93280000000, 82040000000, 81890000000, 80290000000, 78890000000, 75270000000, 69750000000, 63390000000, 60550000000, 59830000000, 58010000000, 57730000000, 53410000000, 50730000000, 50000000000, 49380000000, 47180000000, 46250000000, 45210000000, 44210000000, 42870000000, 41520000000, 40300000000, 37990000000, 37880000000, 35690000000, 31960000000, 30530000000, 28360000000, 27540000000, 26910000000, 26700000000, 25700000000, 24780000000, 24240000000, 21550000000, 20360000000, 19020000000, 18620000000, 17790000000, 16970000000, 16940000000, 16200000000, 16000000000, 15140000000, 14560000000, 14420000000, 13310000000, 13020000000, 12940000000, 12560000000, 11750000000, 11690000000, 11280000000, 10580000000, 10170000000, 9943000000, 9664000000, 9559000000, 8987000000, 8915000000, 8468000000, 8365000000, 8327000000, 8125000000, 7793000000, 7765000000, 7417000000, 7292000000, 7144000000, 7141000000, 6960000000, 6831000000, 6627000000, 6456000000, 6108000000, 5665000000, 5665000000, 5535000000, 5312000000, 5227000000, 5043000000, 5036000000, 4842000000, 4731000000, 4545000000, 4412000000, 4305000000, 4296000000, 4291000000, 4238000000, 4204000000, 3893000000, 3838000000, 3766000000, 3553000000, 3465000000, 3239000000, 3213000000, 3116000000, 3008000000, 2887000000, 2874000000, 2821000000, 2716000000, 2658000000, 2586000000, 2085000000, 2027000000, 1883000000, 1716000000, 1680000000, 1572000000, 1566000000, 1360000000, 1295000000, 985500000, 976000000, 968000000, 962600000, 938000000, 930200000, 920700000, 911000000, 903000000, 882600000, 777600000, 740000000, 723500000, 707000000, 694100000, 664200000, 652900000, 630100000, 605000000, 562400000, 558000000, 545900000, 452000000, 365500000, 336400000, 311600000, 293900000, 293000000, 293000000, 292000000, 285500000, 284000000, 282900000, 276900000, 267100000, 261300000, 218600000, 190700000, 178600000, 178000000, 168300000, 167400000, 160000000, 146000000, 134900000, 130200000, 127400000, 125300000, 93000000, 90400000, 89750000, 83700000, 79050000, 60450000, 51150000, 49290000, 46500000, 44640000, 39990000, 39990000, 28950000, 24180000, 23250000, 23250000, 11160000, 7440000, 2790000, 202000, 174700, 48300 ] } ], "layout": { "geo": { "projection": { "type": "natural earth" }, "showframe": false }, "title": "2014 Power Consumption in KWH" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iplot(choromap,validate=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "China and USA were the major power consuming nations in 2014. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2012 General Election Voting Data" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": true }, "outputs": [], "source": [ "elect = pd.read_csv('2012_Election_Data')" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "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", " \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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearICPSR State CodeAlphanumeric State CodeStateVEP Total Ballots CountedVEP Highest OfficeVAP Highest OfficeTotal Ballots CountedHighest OfficeVoting-Eligible Population (VEP)Voting-Age Population (VAP)% Non-citizenPrisonProbationParoleTotal Ineligible FelonState Abv
02012411AlabamaNaN58.6%56.0%NaN2,074,3383,539,2173707440.02.6%32,23257,9938,61671,584AL
12012812Alaska58.9%58.7%55.3%301,694300,495511,792543763.03.8%5,6337,1731,88211,317AK
22012613Arizona53.0%52.6%46.5%2,323,5792,306,5594,387,9004959270.09.9%35,18872,4527,46081,048AZ
32012424Arkansas51.1%50.7%47.7%1,078,5481,069,4682,109,8472242740.03.5%14,47130,12223,37253,808AR
42012715California55.7%55.1%45.1%13,202,15813,038,54723,681,83728913129.017.4%119,455089,287208,742CA
\n", "
" ], "text/plain": [ " Year ICPSR State Code Alphanumeric State Code State \\\n", "0 2012 41 1 Alabama \n", "1 2012 81 2 Alaska \n", "2 2012 61 3 Arizona \n", "3 2012 42 4 Arkansas \n", "4 2012 71 5 California \n", "\n", " VEP Total Ballots Counted VEP Highest Office VAP Highest Office \\\n", "0 NaN 58.6% 56.0% \n", "1 58.9% 58.7% 55.3% \n", "2 53.0% 52.6% 46.5% \n", "3 51.1% 50.7% 47.7% \n", "4 55.7% 55.1% 45.1% \n", "\n", " Total Ballots Counted Highest Office Voting-Eligible Population (VEP) \\\n", "0 NaN 2,074,338 3,539,217 \n", "1 301,694 300,495 511,792 \n", "2 2,323,579 2,306,559 4,387,900 \n", "3 1,078,548 1,069,468 2,109,847 \n", "4 13,202,158 13,038,547 23,681,837 \n", "\n", " Voting-Age Population (VAP) % Non-citizen Prison Probation Parole \\\n", "0 3707440.0 2.6% 32,232 57,993 8,616 \n", "1 543763.0 3.8% 5,633 7,173 1,882 \n", "2 4959270.0 9.9% 35,188 72,452 7,460 \n", "3 2242740.0 3.5% 14,471 30,122 23,372 \n", "4 28913129.0 17.4% 119,455 0 89,287 \n", "\n", " Total Ineligible Felon State Abv \n", "0 71,584 AL \n", "1 11,317 AK \n", "2 81,048 AZ \n", "3 53,808 AR \n", "4 208,742 CA " ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "elect.head()" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = dict(type='choropleth',\n", " colorscale = 'Viridis',\n", " reversescale = True,\n", " locations = elect['State Abv'],\n", " z = elect['Voting-Age Population (VAP)'],\n", " locationmode = 'USA-states',\n", " text = elect['State'],\n", " marker = dict(line = dict(color = 'rgb(12,12,12)',width = 1)),\n", " colorbar = {'title':\"Voting-Age Population (VAP)\"}\n", " ) " ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layout = dict(title = '2012 General Election Voting Data',\n", " geo = dict(scope='usa',showlakes = True, lakecolor = 'rgb(85,173,240)'))" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": true }, "outputs": [], "source": [ "choromap1 = go.Figure(data = [data],layout = layout)" ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "colorbar": { "title": "Voting-Age Population (VAP)" }, "colorscale": "Viridis", "locationmode": "USA-states", "locations": [ "AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE", "District of Columbia", "FL", "GA", "HI", "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD", "MA", "MI", "MN", "MS", "MO", "MT", "NE", "NV", "NH", "NJ", "NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC", "SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY" ], "marker": { "line": { "color": "rgb(12,12,12)", "width": 1 } }, "reversescale": true, "text": [ "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delaware", "District of Columbia", "Florida", "Georgia", "Hawaii", "Idaho", "Illinois", "Indiana", "Iowa", "Kansas", "Kentucky", "Louisiana", "Maine", "Maryland", "Massachusetts", "Michigan", "Minnesota", "Mississippi", "Missouri", "Montana", "Nebraska", "Nevada", "New Hampshire", "New Jersey", "New Mexico", "New York", "North Carolina", "North Dakota", "Ohio", "Oklahoma", "Oregon", "Pennsylvania", "Rhode Island", "South Carolina", "South Dakota", "Tennessee", "Texas", "Utah", "Vermont", "Virginia", "Washington", "West Virginia", "Wisconsin", "Wyoming" ], "type": "choropleth", "z": [ 3707440, 543763, 4959270, 2242740, 28913129, 3981208, 2801375, 715708, 528848, 15380947, 7452696, 1088335, 1173727, 9827043, 4960376, 2356209, 2162442, 3368684, 3495847, 1064779, 4553853, 5263550, 7625576, 4114820, 2246931, 4628500, 785454, 1396507, 2105976, 1047978, 6847503, 1573400, 15344671, 7496980, 549955, 8896930, 2885093, 3050747, 10037099, 834983, 3662322, 631472, 4976284, 19185395, 1978956, 502242, 6348827, 5329782, 1472642, 4417273, 441726 ] } ], "layout": { "geo": { "lakecolor": "rgb(85,173,240)", "scope": "usa", "showlakes": true }, "title": "2012 General Election Voting Data" } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iplot(choromap1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see California, Texas, New York, and Florida are the major states where voting population resides." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }