diff --git a/Seaborn/1. Loading Datasets.ipynb b/Seaborn/1. Loading Datasets.ipynb deleted file mode 100644 index 23aa8c9..0000000 --- a/Seaborn/1. Loading Datasets.ipynb +++ /dev/null @@ -1,4256 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "
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Prepared by Asif Bhat

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Data Visualization With Seaborn

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Follow Me on - LinkedIn  Twitter  Instagram  Facebook

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#NameType 1Type 2HPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendaryTotal
01BulbasaurGrassPoison4549496565451False318
12IvysaurGrassPoison6062638080601False405
23VenusaurGrassPoison808283100100801False525
33VenusaurMega VenusaurGrassPoison80100123122120801False625
44CharmanderFireNaN3952436050651False309
55CharmeleonFireNaN5864588065801False405
66CharizardFireFlying788478109851001False534
76CharizardMega Charizard XFireDragon78130111130851001False634
86CharizardMega Charizard YFireFlying78104781591151001False634
97SquirtleWaterNaN4448655064431False314
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genderrace/ethnicityparental level of educationlunchtest preparation coursemath scorereading scorewriting score
0femalegroup Bbachelor's degreestandardnone727274
1femalegroup Csome collegestandardcompleted699088
2femalegroup Bmaster's degreestandardnone909593
3malegroup Aassociate's degreefree/reducednone475744
4malegroup Csome collegestandardnone767875
5femalegroup Bassociate's degreestandardnone718378
6femalegroup Bsome collegestandardcompleted889592
7malegroup Bsome collegefree/reducednone404339
8malegroup Dhigh schoolfree/reducedcompleted646467
9femalegroup Bhigh schoolfree/reducednone386050
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CountryConfirmedRecoveredDeaths
Date
2020-01-22Afghanistan000
2020-01-22Albania000
2020-01-22Algeria000
2020-01-22Andorra000
2020-01-22Angola000
2020-01-22Antigua and Barbuda000
2020-01-22Argentina000
2020-01-22Armenia000
2020-01-22Australia000
2020-01-22Austria000
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" - ], - "text/plain": [ - " Country Confirmed Recovered Deaths\n", - "Date \n", - "2020-01-22 Afghanistan 0 0 0\n", - "2020-01-22 Albania 0 0 0\n", - "2020-01-22 Algeria 0 0 0\n", - "2020-01-22 Andorra 0 0 0\n", - "2020-01-22 Angola 0 0 0\n", - "2020-01-22 Antigua and Barbuda 0 0 0\n", - "2020-01-22 Argentina 0 0 0\n", - "2020-01-22 Armenia 0 0 0\n", - "2020-01-22 Australia 0 0 0\n", - "2020-01-22 Austria 0 0 0" - ] - }, - "execution_count": 677, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "corona = pd.read_csv('C:/Users/DELL/Documents/GitHub/Public/COVID-19/covid/data/countries-aggregated.csv' ,\n", - " index_col='Date' , parse_dates=True)\n", - "corona.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 678, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Shape of YouDespacitoSomething Just Like ThisHUMBLE.Unforgettable
Date
2017-01-0612287078NaNNaNNaNNaN
2017-01-0713190270NaNNaNNaNNaN
2017-01-0813099919NaNNaNNaNNaN
2017-01-0914506351NaNNaNNaNNaN
2017-01-1014275628NaNNaNNaNNaN
2017-01-1114372699NaNNaNNaNNaN
2017-01-1214148108NaNNaNNaNNaN
2017-01-1314536236275178.0NaNNaNNaN
2017-01-14141733111144886.0NaNNaNNaN
2017-01-15128898491288198.0NaNNaNNaN
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" - ], - "text/plain": [ - " Shape of You Despacito Something Just Like This HUMBLE. \\\n", - "Date \n", - "2017-01-06 12287078 NaN NaN NaN \n", - "2017-01-07 13190270 NaN NaN NaN \n", - "2017-01-08 13099919 NaN NaN NaN \n", - "2017-01-09 14506351 NaN NaN NaN \n", - "2017-01-10 14275628 NaN NaN NaN \n", - "2017-01-11 14372699 NaN NaN NaN \n", - "2017-01-12 14148108 NaN NaN NaN \n", - "2017-01-13 14536236 275178.0 NaN NaN \n", - "2017-01-14 14173311 1144886.0 NaN NaN \n", - "2017-01-15 12889849 1288198.0 NaN NaN \n", - "\n", - " Unforgettable \n", - "Date \n", - "2017-01-06 NaN \n", - "2017-01-07 NaN \n", - "2017-01-08 NaN \n", - "2017-01-09 NaN \n", - "2017-01-10 NaN \n", - "2017-01-11 NaN \n", - "2017-01-12 NaN \n", - "2017-01-13 NaN \n", - "2017-01-14 NaN \n", - "2017-01-15 NaN " - ] - }, - "execution_count": 678, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spotify = pd.read_csv(\"spotify.csv\" , index_col=\"Date\")\n", - "spotify.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 679, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
20635-121.0939.4825.01665.0374.0845.0330.01.560378100.0INLAND
20636-121.2139.4918.0697.0150.0356.0114.02.556877100.0INLAND
20637-121.2239.4317.02254.0485.01007.0433.01.700092300.0INLAND
20638-121.3239.4318.01860.0409.0741.0349.01.867284700.0INLAND
20639-121.2439.3716.02785.0616.01387.0530.02.388689400.0INLAND
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" - ], - "text/plain": [ - " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", - "20635 -121.09 39.48 25.0 1665.0 374.0 \n", - "20636 -121.21 39.49 18.0 697.0 150.0 \n", - "20637 -121.22 39.43 17.0 2254.0 485.0 \n", - "20638 -121.32 39.43 18.0 1860.0 409.0 \n", - "20639 -121.24 39.37 16.0 2785.0 616.0 \n", - "\n", - " population households median_income median_house_value \\\n", - "20635 845.0 330.0 1.5603 78100.0 \n", - "20636 356.0 114.0 2.5568 77100.0 \n", - "20637 1007.0 433.0 1.7000 92300.0 \n", - "20638 741.0 349.0 1.8672 84700.0 \n", - "20639 1387.0 530.0 2.3886 89400.0 \n", - "\n", - " ocean_proximity \n", - "20635 INLAND \n", - "20636 INLAND \n", - "20637 INLAND \n", - "20638 INLAND \n", - "20639 INLAND " - ] - }, - "execution_count": 679, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "housing = pd.read_csv('C:/Users/DELL/Documents/GitHub/Data-Visualization/housing.csv')\n", - "housing.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 680, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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agesexbmichildrensmokerregioncharges
019female27.9000yessouthwest16884.92400
118male33.7701nosoutheast1725.55230
228male33.0003nosoutheast4449.46200
333male22.7050nonorthwest21984.47061
432male28.8800nonorthwest3866.85520
531female25.7400nosoutheast3756.62160
646female33.4401nosoutheast8240.58960
737female27.7403nonorthwest7281.50560
837male29.8302nonortheast6406.41070
960female25.8400nonorthwest28923.13692
\n", - "
" - ], - "text/plain": [ - " age sex bmi children smoker region charges\n", - "0 19 female 27.900 0 yes southwest 16884.92400\n", - "1 18 male 33.770 1 no southeast 1725.55230\n", - "2 28 male 33.000 3 no southeast 4449.46200\n", - "3 33 male 22.705 0 no northwest 21984.47061\n", - "4 32 male 28.880 0 no northwest 3866.85520\n", - "5 31 female 25.740 0 no southeast 3756.62160\n", - "6 46 female 33.440 1 no southeast 8240.58960\n", - "7 37 female 27.740 3 no northwest 7281.50560\n", - "8 37 male 29.830 2 no northeast 6406.41070\n", - "9 60 female 25.840 0 no northwest 28923.13692" - ] - }, - "execution_count": 680, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "insurance = pd.read_csv('C:/Users/DELL/Documents/GitHub/Data-Visualization/insurance.csv')\n", - "insurance.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 681, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AgeGenderPeriodUnemployed
016 to 19 yearsMen2005-01-0191000
120 to 24 yearsMen2005-01-01175000
225 to 34 yearsMen2005-01-01194000
335 to 44 yearsMen2005-01-01201000
445 to 54 yearsMen2005-01-01207000
555 to 64 yearsMen2005-01-01101000
665 years and overMen2005-01-0133000
716 to 19 yearsWomen2005-01-0138000
820 to 24 yearsWomen2005-01-0190000
925 to 34 yearsWomen2005-01-01142000
\n", - "
" - ], - "text/plain": [ - " Age Gender Period Unemployed\n", - "0 16 to 19 years Men 2005-01-01 91000\n", - "1 20 to 24 years Men 2005-01-01 175000\n", - "2 25 to 34 years Men 2005-01-01 194000\n", - "3 35 to 44 years Men 2005-01-01 201000\n", - "4 45 to 54 years Men 2005-01-01 207000\n", - "5 55 to 64 years Men 2005-01-01 101000\n", - "6 65 years and over Men 2005-01-01 33000\n", - "7 16 to 19 years Women 2005-01-01 38000\n", - "8 20 to 24 years Women 2005-01-01 90000\n", - "9 25 to 34 years Women 2005-01-01 142000" - ] - }, - "execution_count": 681, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "employment = pd.read_excel(\"unemployment.xlsx\")\n", - "employment.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 683, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ticketrequestorRequestorSeniorityITOwnerFiledAgainstTicketTypeSeverityPrioritydaysOpenSatisfaction
0119291 - Junior50SystemsIssue2 - Normal0 - Unassigned31 - Unsatisfied
1215872 - Regular15SoftwareRequest1 - Minor1 - Low51 - Unsatisfied
239252 - Regular15Access/LoginRequest2 - Normal0 - Unassigned00 - Unknown
344134 - Management22SystemsRequest2 - Normal0 - Unassigned200 - Unknown
453181 - Junior22Access/LoginRequest2 - Normal1 - Low11 - Unsatisfied
568584 - Management38Access/LoginRequest2 - Normal3 - High00 - Unknown
6719783 - Senior10SystemsRequest2 - Normal3 - High90 - Unknown
7812094 - Management1SoftwareRequest2 - Normal0 - Unassigned150 - Unknown
898872 - Regular14SoftwareRequest2 - Normal2 - Medium61 - Unsatisfied
91017803 - Senior46Access/LoginRequest2 - Normal1 - Low11 - Unsatisfied
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" - ], - "text/plain": [ - " ticket requestor RequestorSeniority ITOwner FiledAgainst TicketType \\\n", - "0 1 1929 1 - Junior 50 Systems Issue \n", - "1 2 1587 2 - Regular 15 Software Request \n", - "2 3 925 2 - Regular 15 Access/Login Request \n", - "3 4 413 4 - Management 22 Systems Request \n", - "4 5 318 1 - Junior 22 Access/Login Request \n", - "5 6 858 4 - Management 38 Access/Login Request \n", - "6 7 1978 3 - Senior 10 Systems Request \n", - "7 8 1209 4 - Management 1 Software Request \n", - "8 9 887 2 - Regular 14 Software Request \n", - "9 10 1780 3 - Senior 46 Access/Login Request \n", - "\n", - " Severity Priority daysOpen Satisfaction \n", - "0 2 - Normal 0 - Unassigned 3 1 - Unsatisfied \n", - "1 1 - Minor 1 - Low 5 1 - Unsatisfied \n", - "2 2 - Normal 0 - Unassigned 0 0 - Unknown \n", - "3 2 - Normal 0 - Unassigned 20 0 - Unknown \n", - "4 2 - Normal 1 - Low 1 1 - Unsatisfied \n", - "5 2 - Normal 3 - High 0 0 - Unknown \n", - "6 2 - Normal 3 - High 9 0 - Unknown \n", - "7 2 - Normal 0 - Unassigned 15 0 - Unknown \n", - "8 2 - Normal 2 - Medium 6 1 - Unsatisfied \n", - "9 2 - Normal 1 - Low 1 1 - Unsatisfied " - ] - }, - "execution_count": 683, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "helpdesk = pd.read_csv(\"helpdesk.csv\")\n", - "helpdesk.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 684, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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SpeciesWeightLength1Length2Length3HeightWidth
0Bream242.023.225.430.011.52004.0200
1Bream290.024.026.331.212.48004.3056
2Bream340.023.926.531.112.37784.6961
3Bream363.026.329.033.512.73004.4555
4Bream430.026.529.034.012.44405.1340
5Bream450.026.829.734.713.60244.9274
6Bream500.026.829.734.514.17955.2785
7Bream390.027.630.035.012.67004.6900
8Bream450.027.630.035.114.00494.8438
9Bream500.028.530.736.214.22664.9594
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" - ], - "text/plain": [ - " Species Weight Length1 Length2 Length3 Height Width\n", - "0 Bream 242.0 23.2 25.4 30.0 11.5200 4.0200\n", - "1 Bream 290.0 24.0 26.3 31.2 12.4800 4.3056\n", - "2 Bream 340.0 23.9 26.5 31.1 12.3778 4.6961\n", - "3 Bream 363.0 26.3 29.0 33.5 12.7300 4.4555\n", - "4 Bream 430.0 26.5 29.0 34.0 12.4440 5.1340\n", - "5 Bream 450.0 26.8 29.7 34.7 13.6024 4.9274\n", - "6 Bream 500.0 26.8 29.7 34.5 14.1795 5.2785\n", - "7 Bream 390.0 27.6 30.0 35.0 12.6700 4.6900\n", - "8 Bream 450.0 27.6 30.0 35.1 14.0049 4.8438\n", - "9 Bream 500.0 28.5 30.7 36.2 14.2266 4.9594" - ] - }, - "execution_count": 684, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fish= pd.read_csv(\"Fish.csv\")\n", - "fish.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 685, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iddietpulsetimekind
01low fat851 minrest
11low fat8515 minrest
21low fat8830 minrest
32low fat901 minrest
42low fat9215 minrest
52low fat9330 minrest
63low fat971 minrest
73low fat9715 minrest
83low fat9430 minrest
94low fat801 minrest
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" - ], - "text/plain": [ - " id diet pulse time kind\n", - "0 1 low fat 85 1 min rest\n", - "1 1 low fat 85 15 min rest\n", - "2 1 low fat 88 30 min rest\n", - "3 2 low fat 90 1 min rest\n", - "4 2 low fat 92 15 min rest\n", - "5 2 low fat 93 30 min rest\n", - "6 3 low fat 97 1 min rest\n", - "7 3 low fat 97 15 min rest\n", - "8 3 low fat 94 30 min rest\n", - "9 4 low fat 80 1 min rest" - ] - }, - "execution_count": 685, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exercise = pd.read_csv(\"exercise.csv\")\n", - "exercise.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 686, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countryyearsexagesuicides_nopopulationsuicides/100k popcountry-yearHDI for yeargdp_for_year ($)gdp_per_capita ($)generation
0Albania1987male15-24 years213129006.71Albania1987NaN2,156,624,900796Generation X
1Albania1987male35-54 years163080005.19Albania1987NaN2,156,624,900796Silent
2Albania1987female15-24 years142897004.83Albania1987NaN2,156,624,900796Generation X
3Albania1987male75+ years1218004.59Albania1987NaN2,156,624,900796G.I. Generation
4Albania1987male25-34 years92743003.28Albania1987NaN2,156,624,900796Boomers
5Albania1987female75+ years1356002.81Albania1987NaN2,156,624,900796G.I. Generation
6Albania1987female35-54 years62788002.15Albania1987NaN2,156,624,900796Silent
7Albania1987female25-34 years42572001.56Albania1987NaN2,156,624,900796Boomers
8Albania1987male55-74 years11375000.73Albania1987NaN2,156,624,900796G.I. Generation
9Albania1987female5-14 years03110000.00Albania1987NaN2,156,624,900796Generation X
\n", - "
" - ], - "text/plain": [ - " country year sex age suicides_no population \\\n", - "0 Albania 1987 male 15-24 years 21 312900 \n", - "1 Albania 1987 male 35-54 years 16 308000 \n", - "2 Albania 1987 female 15-24 years 14 289700 \n", - "3 Albania 1987 male 75+ years 1 21800 \n", - "4 Albania 1987 male 25-34 years 9 274300 \n", - "5 Albania 1987 female 75+ years 1 35600 \n", - "6 Albania 1987 female 35-54 years 6 278800 \n", - "7 Albania 1987 female 25-34 years 4 257200 \n", - "8 Albania 1987 male 55-74 years 1 137500 \n", - "9 Albania 1987 female 5-14 years 0 311000 \n", - "\n", - " suicides/100k pop country-year HDI for year gdp_for_year ($) \\\n", - "0 6.71 Albania1987 NaN 2,156,624,900 \n", - "1 5.19 Albania1987 NaN 2,156,624,900 \n", - "2 4.83 Albania1987 NaN 2,156,624,900 \n", - "3 4.59 Albania1987 NaN 2,156,624,900 \n", - "4 3.28 Albania1987 NaN 2,156,624,900 \n", - "5 2.81 Albania1987 NaN 2,156,624,900 \n", - "6 2.15 Albania1987 NaN 2,156,624,900 \n", - "7 1.56 Albania1987 NaN 2,156,624,900 \n", - "8 0.73 Albania1987 NaN 2,156,624,900 \n", - "9 0.00 Albania1987 NaN 2,156,624,900 \n", - "\n", - " gdp_per_capita ($) generation \n", - "0 796 Generation X \n", - "1 796 Silent \n", - "2 796 Generation X \n", - "3 796 G.I. Generation \n", - "4 796 Boomers \n", - "5 796 G.I. Generation \n", - "6 796 Silent \n", - "7 796 Boomers \n", - "8 796 G.I. Generation \n", - "9 796 Generation X " - ] - }, - "execution_count": 686, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "suicide = pd.read_csv(\"suicide.csv\") \n", - "suicide.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 687, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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TypeCoverageOdNameAREAAreaNameREGRegNameDEVDevName1980...2004200520062007200820092010201120122013
0ImmigrantsForeignersAfghanistan935Asia5501Southern Asia902Developing regions16...2978343630092652211117461758220326352004
1ImmigrantsForeignersAlbania908Europe925Southern Europe901Developed regions1...14501223856702560716561539620603
2ImmigrantsForeignersAlgeria903Africa912Northern Africa902Developing regions80...3616362648073623400553934752432537744331
3ImmigrantsForeignersAmerican Samoa909Oceania957Polynesia902Developing regions0...0010000000
4ImmigrantsForeignersAndorra908Europe925Southern Europe901Developed regions0...0011000011
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5 rows × 43 columns

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CountryYearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeasles...PolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
0Afghanistan2015Developing65.0263.0620.0171.27962465.01154...6.08.1665.00.1584.25921033736494.017.217.30.47910.1
1Afghanistan2014Developing59.9271.0640.0173.52358262.0492...58.08.1862.00.1612.696514327582.017.517.50.47610.0
2Afghanistan2013Developing59.9268.0660.0173.21924364.0430...62.08.1364.00.1631.74497631731688.017.717.70.4709.9
3Afghanistan2012Developing59.5272.0690.0178.18421567.02787...67.08.5267.00.1669.9590003696958.017.918.00.4639.8
4Afghanistan2011Developing59.2275.0710.017.09710968.03013...68.07.8768.00.163.5372312978599.018.218.20.4549.5
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5 rows × 22 columns

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" - ], - "text/plain": [ - " Country Year Status Life expectancy Adult Mortality \\\n", - "0 Afghanistan 2015 Developing 65.0 263.0 \n", - "1 Afghanistan 2014 Developing 59.9 271.0 \n", - "2 Afghanistan 2013 Developing 59.9 268.0 \n", - "3 Afghanistan 2012 Developing 59.5 272.0 \n", - "4 Afghanistan 2011 Developing 59.2 275.0 \n", - "\n", - " infant deaths Alcohol percentage expenditure Hepatitis B Measles \\\n", - "0 62 0.01 71.279624 65.0 1154 \n", - "1 64 0.01 73.523582 62.0 492 \n", - "2 66 0.01 73.219243 64.0 430 \n", - "3 69 0.01 78.184215 67.0 2787 \n", - "4 71 0.01 7.097109 68.0 3013 \n", - "\n", - " ... Polio Total expenditure Diphtheria HIV/AIDS GDP \\\n", - "0 ... 6.0 8.16 65.0 0.1 584.259210 \n", - "1 ... 58.0 8.18 62.0 0.1 612.696514 \n", - "2 ... 62.0 8.13 64.0 0.1 631.744976 \n", - "3 ... 67.0 8.52 67.0 0.1 669.959000 \n", - "4 ... 68.0 7.87 68.0 0.1 63.537231 \n", - "\n", - " Population thinness 1-19 years thinness 5-9 years \\\n", - "0 33736494.0 17.2 17.3 \n", - "1 327582.0 17.5 17.5 \n", - "2 31731688.0 17.7 17.7 \n", - "3 3696958.0 17.9 18.0 \n", - "4 2978599.0 18.2 18.2 \n", - "\n", - " Income composition of resources Schooling \n", - "0 0.479 10.1 \n", - "1 0.476 10.0 \n", - "2 0.470 9.9 \n", - "3 0.463 9.8 \n", - "4 0.454 9.5 \n", - "\n", - "[5 rows x 22 columns]" - ] - }, - "execution_count": 695, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "led = pd.read_csv(\"Life Expectancy Data.csv\")\n", - "led.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 696, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ageworkclassfnlwgteducationeducation.nummarital.statusoccupationrelationshipracesexcapital.gaincapital.losshours.per.weeknative.countryincome
090?77053HS-grad9Widowed?Not-in-familyWhiteFemale0435640United-States<=50K
182Private132870HS-grad9WidowedExec-managerialNot-in-familyWhiteFemale0435618United-States<=50K
266?186061Some-college10Widowed?UnmarriedBlackFemale0435640United-States<=50K
354Private1403597th-8th4DivorcedMachine-op-inspctUnmarriedWhiteFemale0390040United-States<=50K
441Private264663Some-college10SeparatedProf-specialtyOwn-childWhiteFemale0390040United-States<=50K
534Private216864HS-grad9DivorcedOther-serviceUnmarriedWhiteFemale0377045United-States<=50K
638Private15060110th6SeparatedAdm-clericalUnmarriedWhiteMale0377040United-States<=50K
774State-gov88638Doctorate16Never-marriedProf-specialtyOther-relativeWhiteFemale0368320United-States>50K
868Federal-gov422013HS-grad9DivorcedProf-specialtyNot-in-familyWhiteFemale0368340United-States<=50K
941Private70037Some-college10Never-marriedCraft-repairUnmarriedWhiteMale0300460?>50K
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" - ], - "text/plain": [ - " age workclass fnlwgt education education.num marital.status \\\n", - "0 90 ? 77053 HS-grad 9 Widowed \n", - "1 82 Private 132870 HS-grad 9 Widowed \n", - "2 66 ? 186061 Some-college 10 Widowed \n", - "3 54 Private 140359 7th-8th 4 Divorced \n", - "4 41 Private 264663 Some-college 10 Separated \n", - "5 34 Private 216864 HS-grad 9 Divorced \n", - "6 38 Private 150601 10th 6 Separated \n", - "7 74 State-gov 88638 Doctorate 16 Never-married \n", - "8 68 Federal-gov 422013 HS-grad 9 Divorced \n", - "9 41 Private 70037 Some-college 10 Never-married \n", - "\n", - " occupation relationship race sex capital.gain \\\n", - "0 ? Not-in-family White Female 0 \n", - "1 Exec-managerial Not-in-family White Female 0 \n", - "2 ? Unmarried Black Female 0 \n", - "3 Machine-op-inspct Unmarried White Female 0 \n", - "4 Prof-specialty Own-child White Female 0 \n", - "5 Other-service Unmarried White Female 0 \n", - "6 Adm-clerical Unmarried White Male 0 \n", - "7 Prof-specialty Other-relative White Female 0 \n", - "8 Prof-specialty Not-in-family White Female 0 \n", - "9 Craft-repair Unmarried White Male 0 \n", - "\n", - " capital.loss hours.per.week native.country income \n", - "0 4356 40 United-States <=50K \n", - "1 4356 18 United-States <=50K \n", - "2 4356 40 United-States <=50K \n", - "3 3900 40 United-States <=50K \n", - "4 3900 40 United-States <=50K \n", - "5 3770 45 United-States <=50K \n", - "6 3770 40 United-States <=50K \n", - "7 3683 20 United-States >50K \n", - "8 3683 40 United-States <=50K \n", - "9 3004 60 ? >50K " - ] - }, - "execution_count": 696, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adult = pd.read_csv(\"adult.csv\")\n", - "adult.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 697, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ageworkclassfnlwgteducationeducation.nummarital.statusoccupationrelationshipracesexcapital.gaincapital.losshours.per.weeknative.countryincome
182Private132870HS-grad9WidowedExec-managerialNot-in-familyWhiteFemale0435618United-States<=50K
354Private1403597th-8th4DivorcedMachine-op-inspctUnmarriedWhiteFemale0390040United-States<=50K
441Private264663Some-college10SeparatedProf-specialtyOwn-childWhiteFemale0390040United-States<=50K
534Private216864HS-grad9DivorcedOther-serviceUnmarriedWhiteFemale0377045United-States<=50K
638Private15060110th6SeparatedAdm-clericalUnmarriedWhiteMale0377040United-States<=50K
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "0 5.1 3.5 1.4 0.2 setosa\n", - "1 4.9 3.0 1.4 0.2 setosa\n", - "2 4.7 3.2 1.3 0.2 setosa\n", - "3 4.6 3.1 1.5 0.2 setosa\n", - "4 5.0 3.6 1.4 0.2 setosa" - ] - }, - "execution_count": 698, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris = sns.load_dataset(\"iris\")\n", - "iris.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 700, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mpgcylinderscubicincheshpweightlbstime-to-60yearbrand
014.083501654209121972US.
131.9489711925141980Europe.
217.083021403449111971US.
315.084001503761101971US.
430.5498632051171978US.
523.083501253900171980US.
613.083511584363131974US.
714.08440215431291971US.
825.45183773530201980Europe.
937.7489622050171982Japan.
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" - ], - "text/plain": [ - " mpg cylinders cubicinches hp weightlbs time-to-60 year brand\n", - "0 14.0 8 350 165 4209 12 1972 US.\n", - "1 31.9 4 89 71 1925 14 1980 Europe.\n", - "2 17.0 8 302 140 3449 11 1971 US.\n", - "3 15.0 8 400 150 3761 10 1971 US.\n", - "4 30.5 4 98 63 2051 17 1978 US.\n", - "5 23.0 8 350 125 3900 17 1980 US.\n", - "6 13.0 8 351 158 4363 13 1974 US.\n", - "7 14.0 8 440 215 4312 9 1971 US.\n", - "8 25.4 5 183 77 3530 20 1980 Europe.\n", - "9 37.7 4 89 62 2050 17 1982 Japan." - ] - }, - "execution_count": 700, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cars = pd.read_csv(\"cars.csv\")\n", - "cars.head(10)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}