{ "cells": [ { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 缺失值处理" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# 读取数据\n", "data = pd.read_csv('./data/IMDB-Movie-Data.csv')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 判断是否有缺失\n", "res = pd.isnull(data) # 判断每个元素是否是NaN\n", "# 判断res中是否有True\n", "np.any(res)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_2 = pd.notnull(data) # 判断每个元素是否不是NaN\n", "# 判断res中是否全部都是True\n", "np.all(res_2)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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RankTitleGenreDescriptionDirectorActorsYearRuntime (Minutes)RatingVotesRevenue (Millions)Metascore
01Guardians of the GalaxyAction,Adventure,Sci-FiA group of intergalactic criminals are forced ...James GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.1757074333.1376.0
12PrometheusAdventure,Mystery,Sci-FiFollowing clues to the origin of mankind, a te...Ridley ScottNoomi Rapace, Logan Marshall-Green, Michael Fa...20121247.0485820126.4665.0
23SplitHorror,ThrillerThree girls are kidnapped by a man with a diag...M. Night ShyamalanJames McAvoy, Anya Taylor-Joy, Haley Lu Richar...20161177.3157606138.1262.0
34SingAnimation,Comedy,FamilyIn a city of humanoid animals, a hustling thea...Christophe LourdeletMatthew McConaughey,Reese Witherspoon, Seth Ma...20161087.260545270.3259.0
45Suicide SquadAction,Adventure,FantasyA secret government agency recruits some of th...David AyerWill Smith, Jared Leto, Margot Robbie, Viola D...20161236.2393727325.0240.0
\n", "
" ], "text/plain": [ " Rank Title Genre \\\n", "0 1 Guardians of the Galaxy Action,Adventure,Sci-Fi \n", "1 2 Prometheus Adventure,Mystery,Sci-Fi \n", "2 3 Split Horror,Thriller \n", "3 4 Sing Animation,Comedy,Family \n", "4 5 Suicide Squad Action,Adventure,Fantasy \n", "\n", " Description Director \\\n", "0 A group of intergalactic criminals are forced ... James Gunn \n", "1 Following clues to the origin of mankind, a te... Ridley Scott \n", "2 Three girls are kidnapped by a man with a diag... M. Night Shyamalan \n", "3 In a city of humanoid animals, a hustling thea... Christophe Lourdelet \n", "4 A secret government agency recruits some of th... David Ayer \n", "\n", " Actors Year Runtime (Minutes) \\\n", "0 Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... 2014 121 \n", "1 Noomi Rapace, Logan Marshall-Green, Michael Fa... 2012 124 \n", "2 James McAvoy, Anya Taylor-Joy, Haley Lu Richar... 2016 117 \n", "3 Matthew McConaughey,Reese Witherspoon, Seth Ma... 2016 108 \n", "4 Will Smith, Jared Leto, Margot Robbie, Viola D... 2016 123 \n", "\n", " Rating Votes Revenue (Millions) Metascore \n", "0 8.1 757074 333.13 76.0 \n", "1 7.0 485820 126.46 65.0 \n", "2 7.3 157606 138.12 62.0 \n", "3 7.2 60545 270.32 59.0 \n", "4 6.2 393727 325.02 40.0 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 第一种处理方式：删除缺失值 把有缺失的行删除\n", "data.dropna().head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RankTitleGenreDescriptionDirectorActorsYearRuntime (Minutes)RatingVotesRevenue (Millions)Metascore
01Guardians of the GalaxyAction,Adventure,Sci-FiA group of intergalactic criminals are forced ...James GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.1757074333.13076.0
12PrometheusAdventure,Mystery,Sci-FiFollowing clues to the origin of mankind, a te...Ridley ScottNoomi Rapace, Logan Marshall-Green, Michael Fa...20121247.0485820126.46065.0
23SplitHorror,ThrillerThree girls are kidnapped by a man with a diag...M. Night ShyamalanJames McAvoy, Anya Taylor-Joy, Haley Lu Richar...20161177.3157606138.12062.0
34SingAnimation,Comedy,FamilyIn a city of humanoid animals, a hustling thea...Christophe LourdeletMatthew McConaughey,Reese Witherspoon, Seth Ma...20161087.260545270.32059.0
45Suicide SquadAction,Adventure,FantasyA secret government agency recruits some of th...David AyerWill Smith, Jared Leto, Margot Robbie, Viola D...20161236.2393727325.02040.0
56The Great WallAction,Adventure,FantasyEuropean mercenaries searching for black powde...Yimou ZhangMatt Damon, Tian Jing, Willem Dafoe, Andy Lau20161036.15603645.13042.0
67La La LandComedy,Drama,MusicA jazz pianist falls for an aspiring actress i...Damien ChazelleRyan Gosling, Emma Stone, Rosemarie DeWitt, J....20161288.3258682151.06093.0
78MindhornComedyA has-been actor best known for playing the ti...Sean FoleyEssie Davis, Andrea Riseborough, Julian Barrat...2016896.4249047.98571.0
89The Lost City of ZAction,Adventure,BiographyA true-life drama, centering on British explor...James GrayCharlie Hunnam, Robert Pattinson, Sienna Mille...20161417.171888.01078.0
910PassengersAdventure,Drama,RomanceA spacecraft traveling to a distant colony pla...Morten TyldumJennifer Lawrence, Chris Pratt, Michael Sheen,...20161167.0192177100.01041.0
1011Fantastic Beasts and Where to Find ThemAdventure,Family,FantasyThe adventures of writer Newt Scamander in New...David YatesEddie Redmayne, Katherine Waterston, Alison Su...20161337.5232072234.02066.0
1112Hidden FiguresBiography,Drama,HistoryThe story of a team of female African-American...Theodore MelfiTaraji P. Henson, Octavia Spencer, Janelle Mon...20161277.893103169.27074.0
1213Rogue OneAction,Adventure,Sci-FiThe Rebel Alliance makes a risky move to steal...Gareth EdwardsFelicity Jones, Diego Luna, Alan Tudyk, Donnie...20161337.9323118532.17065.0
1314MoanaAnimation,Adventure,ComedyIn Ancient Polynesia, when a terrible curse in...Ron ClementsAuli'i Cravalho, Dwayne Johnson, Rachel House,...20161077.7118151248.75081.0
1415ColossalAction,Comedy,DramaGloria is an out-of-work party girl forced to ...Nacho VigalondoAnne Hathaway, Jason Sudeikis, Austin Stowell,...20161096.486122.87070.0
1516The Secret Life of PetsAnimation,Adventure,ComedyThe quiet life of a terrier named Max is upend...Chris RenaudLouis C.K., Eric Stonestreet, Kevin Hart, Lake...2016876.6120259368.31061.0
1617Hacksaw RidgeBiography,Drama,HistoryWWII American Army Medic Desmond T. Doss, who ...Mel GibsonAndrew Garfield, Sam Worthington, Luke Bracey,...20161398.221176067.12071.0
1718Jason BourneAction,ThrillerThe CIA's most dangerous former operative is d...Paul GreengrassMatt Damon, Tommy Lee Jones, Alicia Vikander,V...20161236.7150823162.16058.0
1819LionBiography,DramaA five-year-old Indian boy gets lost on the st...Garth DavisDev Patel, Nicole Kidman, Rooney Mara, Sunny P...20161188.110206151.69069.0
1920ArrivalDrama,Mystery,Sci-FiWhen twelve mysterious spacecraft appear aroun...Denis VilleneuveAmy Adams, Jeremy Renner, Forest Whitaker,Mich...20161168.0340798100.50081.0
2021GoldAdventure,Drama,ThrillerKenny Wells, a prospector desperate for a luck...Stephen GaghanMatthew McConaughey, Edgar Ramírez, Bryce Dall...20161206.7190537.22049.0
2122Manchester by the SeaDramaA depressed uncle is asked to take care of his...Kenneth LonerganCasey Affleck, Michelle Williams, Kyle Chandle...20161377.913421347.70096.0
2223Hounds of LoveCrime,Drama,HorrorA cold-blooded predatory couple while cruising...Ben YoungEmma Booth, Ashleigh Cummings, Stephen Curry,S...20161086.7111547.98572.0
2324TrollsAnimation,Adventure,ComedyAfter the Bergens invade Troll Village, Poppy,...Walt DohrnAnna Kendrick, Justin Timberlake,Zooey Deschan...2016926.538552153.69056.0
2425Independence Day: ResurgenceAction,Adventure,Sci-FiTwo decades after the first Independence Day i...Roland EmmerichLiam Hemsworth, Jeff Goldblum, Bill Pullman,Ma...20161205.3127553103.14032.0
2526Paris pieds nusComedyFiona visits Paris for the first time to assis...Dominique AbelFiona Gordon, Dominique Abel,Emmanuelle Riva, ...2016836.822247.98559.5
2627Bahubali: The BeginningAction,Adventure,DramaIn ancient India, an adventurous and daring ma...S.S. RajamouliPrabhas, Rana Daggubati, Anushka Shetty,Tamann...20151598.3761936.50059.5
2728Dead AwakeHorror,ThrillerA young woman must save herself and her friend...Phillip GuzmanJocelin Donahue, Jesse Bradford, Jesse Borrego...2016994.75230.01059.5
2829Bad MomsComedyWhen three overworked and under-appreciated mo...Jon LucasMila Kunis, Kathryn Hahn, Kristen Bell,Christi...20161006.266540113.08060.0
2930Assassin's CreedAction,Adventure,DramaWhen Callum Lynch explores the memories of his...Justin KurzelMichael Fassbender, Marion Cotillard, Jeremy I...20161155.911281354.65036.0
.......................................
970971Texas Chainsaw 3DHorror,ThrillerA young woman travels to Texas to collect an i...John LuessenhopAlexandra Daddario, Tania Raymonde, Scott East...2013924.83706034.33062.0
971972DisturbiaDrama,Mystery,ThrillerA teen living under house arrest becomes convi...D.J. CarusoShia LaBeouf, David Morse, Carrie-Anne Moss, S...20071056.919349180.05059.5
972973Rock of AgesComedy,Drama,MusicalA small town girl and a city boy meet on the S...Adam ShankmanJulianne Hough, Diego Boneta, Tom Cruise, Alec...20121235.96451338.51047.0
973974Scream 4Horror,MysteryTen years have passed, and Sidney Prescott, wh...Wes CravenNeve Campbell, Courteney Cox, David Arquette, ...20111116.210854438.18052.0
974975Queen of KatweBiography,Drama,SportA Ugandan girl sees her world rapidly change a...Mira NairMadina Nalwanga, David Oyelowo, Lupita Nyong'o...20161247.467538.81073.0
975976My Big Fat Greek Wedding 2Comedy,Family,RomanceA Portokalos family secret brings the beloved ...Kirk JonesNia Vardalos, John Corbett, Michael Constantin...2016946.02096659.57037.0
976977Dark PlacesDrama,Mystery,ThrillerLibby Day was only eight years old when her fa...Gilles Paquet-BrennerCharlize Theron, Nicholas Hoult, Christina Hen...20151136.23163447.98539.0
977978Amateur NightComedyGuy Carter is an award-winning graduate studen...Lisa AddarioJason Biggs, Janet Montgomery,Ashley Tisdale, ...2016925.0222947.98538.0
978979It's Only the End of the WorldDramaLouis (Gaspard Ulliel), a terminally ill write...Xavier DolanNathalie Baye, Vincent Cassel, Marion Cotillar...2016977.01065847.98548.0
979980The Skin I Live InDrama,ThrillerA brilliant plastic surgeon, haunted by past t...Pedro AlmodóvarAntonio Banderas, Elena Anaya, Jan Cornet,Mari...20111207.61087723.19070.0
980981Miracles from HeavenBiography,Drama,FamilyA young girl suffering from a rare digestive d...Patricia RiggenJennifer Garner, Kylie Rogers, Martin Henderso...20161097.01204861.69044.0
981982AnnieComedy,Drama,FamilyA foster kid, who lives with her mean foster m...Will GluckQuvenzhané Wallis, Cameron Diaz, Jamie Foxx, R...20141185.32731285.91033.0
982983Across the UniverseDrama,Fantasy,MusicalThe music of the Beatles and the Vietnam War f...Julie TaymorEvan Rachel Wood, Jim Sturgess, Joe Anderson, ...20071337.49517224.34056.0
983984Let's Be CopsComedyTwo struggling pals dress as police officers f...Luke GreenfieldJake Johnson, Damon Wayans Jr., Rob Riggle, Ni...20141046.511272982.39030.0
984985MaxAdventure,FamilyA Malinois dog that helped American Marines in...Boaz YakinThomas Haden Church, Josh Wiggins, Luke Kleint...20151116.82140542.65047.0
985986Your HighnessAdventure,Comedy,FantasyWhen Prince Fabious's bride is kidnapped, he g...David Gordon GreenDanny McBride, Natalie Portman, James Franco, ...20111025.68790421.56031.0
986987Final Destination 5Horror,ThrillerSurvivors of a suspension-bridge collapse lear...Steven QualeNicholas D'Agosto, Emma Bell, Arlen Escarpeta,...2011925.98800042.58050.0
987988Endless LoveDrama,RomanceThe story of a privileged girl and a charismat...Shana FesteGabriella Wilde, Alex Pettyfer, Bruce Greenwoo...20141046.33368823.39030.0
988989MartyrsHorrorA young woman's quest for revenge against the ...Pascal LaugierMorjana Alaoui, Mylène Jampanoï, Catherine Bég...2008997.16378547.98589.0
989990SelmaBiography,Drama,HistoryA chronicle of Martin Luther King's campaign t...Ava DuVernayDavid Oyelowo, Carmen Ejogo, Tim Roth, Lorrain...20141287.56763752.07059.5
990991Underworld: Rise of the LycansAction,Adventure,FantasyAn origins story centered on the centuries-old...Patrick TatopoulosRhona Mitra, Michael Sheen, Bill Nighy, Steven...2009926.612970845.80044.0
991992Taare Zameen ParDrama,Family,MusicAn eight-year-old boy is thought to be a lazy ...Aamir KhanDarsheel Safary, Aamir Khan, Tanay Chheda, Sac...20071658.51026971.20042.0
992993Take Me Home TonightComedy,Drama,RomanceFour years after graduation, an awkward high s...Michael DowseTopher Grace, Anna Faris, Dan Fogler, Teresa P...2011976.3454196.92059.5
993994Resident Evil: AfterlifeAction,Adventure,HorrorWhile still out to destroy the evil Umbrella C...Paul W.S. AndersonMilla Jovovich, Ali Larter, Wentworth Miller,K...2010975.914090060.13037.0
994995Project XComedy3 high school seniors throw a birthday party t...Nima NourizadehThomas Mann, Oliver Cooper, Jonathan Daniel Br...2012886.716408854.72048.0
995996Secret in Their EyesCrime,Drama,MysteryA tight-knit team of rising investigators, alo...Billy RayChiwetel Ejiofor, Nicole Kidman, Julia Roberts...20151116.22758547.98545.0
996997Hostel: Part IIHorrorThree American college students studying abroa...Eli RothLauren German, Heather Matarazzo, Bijou Philli...2007945.57315217.54046.0
997998Step Up 2: The StreetsDrama,Music,RomanceRomantic sparks occur between two dance studen...Jon M. ChuRobert Hoffman, Briana Evigan, Cassie Ventura,...2008986.27069958.01050.0
998999Search PartyAdventure,ComedyA pair of friends embark on a mission to reuni...Scot ArmstrongAdam Pally, T.J. Miller, Thomas Middleditch,Sh...2014935.6488147.98522.0
9991000Nine LivesComedy,Family,FantasyA stuffy businessman finds himself trapped ins...Barry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Ch...2016875.31243519.64011.0
\n", "

1000 rows × 12 columns

\n", "
" ], "text/plain": [ " Rank Title \\\n", "0 1 Guardians of the Galaxy \n", "1 2 Prometheus \n", "2 3 Split \n", "3 4 Sing \n", "4 5 Suicide Squad \n", "5 6 The Great Wall \n", "6 7 La La Land \n", "7 8 Mindhorn \n", "8 9 The Lost City of Z \n", "9 10 Passengers \n", "10 11 Fantastic Beasts and Where to Find Them \n", "11 12 Hidden Figures \n", "12 13 Rogue One \n", "13 14 Moana \n", "14 15 Colossal \n", "15 16 The Secret Life of Pets \n", "16 17 Hacksaw Ridge \n", "17 18 Jason Bourne \n", "18 19 Lion \n", "19 20 Arrival \n", "20 21 Gold \n", "21 22 Manchester by the Sea \n", "22 23 Hounds of Love \n", "23 24 Trolls \n", "24 25 Independence Day: Resurgence \n", "25 26 Paris pieds nus \n", "26 27 Bahubali: The Beginning \n", "27 28 Dead Awake \n", "28 29 Bad Moms \n", "29 30 Assassin's Creed \n", ".. ... ... \n", "970 971 Texas Chainsaw 3D \n", "971 972 Disturbia \n", "972 973 Rock of Ages \n", "973 974 Scream 4 \n", "974 975 Queen of Katwe \n", "975 976 My Big Fat Greek Wedding 2 \n", "976 977 Dark Places \n", "977 978 Amateur Night \n", "978 979 It's Only the End of the World \n", "979 980 The Skin I Live In \n", "980 981 Miracles from Heaven \n", "981 982 Annie \n", "982 983 Across the Universe \n", "983 984 Let's Be Cops \n", "984 985 Max \n", "985 986 Your Highness \n", "986 987 Final Destination 5 \n", "987 988 Endless Love \n", "988 989 Martyrs \n", "989 990 Selma \n", "990 991 Underworld: Rise of the Lycans \n", "991 992 Taare Zameen Par \n", "992 993 Take Me Home Tonight \n", "993 994 Resident Evil: Afterlife \n", "994 995 Project X \n", "995 996 Secret in Their Eyes \n", "996 997 Hostel: Part II \n", "997 998 Step Up 2: The Streets \n", "998 999 Search Party \n", "999 1000 Nine Lives \n", "\n", " Genre \\\n", "0 Action,Adventure,Sci-Fi \n", "1 Adventure,Mystery,Sci-Fi \n", "2 Horror,Thriller \n", "3 Animation,Comedy,Family \n", "4 Action,Adventure,Fantasy \n", "5 Action,Adventure,Fantasy \n", "6 Comedy,Drama,Music \n", "7 Comedy \n", "8 Action,Adventure,Biography \n", "9 Adventure,Drama,Romance \n", "10 Adventure,Family,Fantasy \n", "11 Biography,Drama,History \n", "12 Action,Adventure,Sci-Fi \n", "13 Animation,Adventure,Comedy \n", "14 Action,Comedy,Drama \n", "15 Animation,Adventure,Comedy \n", "16 Biography,Drama,History \n", "17 Action,Thriller \n", "18 Biography,Drama \n", "19 Drama,Mystery,Sci-Fi \n", "20 Adventure,Drama,Thriller \n", "21 Drama \n", "22 Crime,Drama,Horror \n", "23 Animation,Adventure,Comedy \n", "24 Action,Adventure,Sci-Fi \n", "25 Comedy \n", "26 Action,Adventure,Drama \n", "27 Horror,Thriller \n", "28 Comedy \n", "29 Action,Adventure,Drama \n", ".. ... \n", "970 Horror,Thriller \n", "971 Drama,Mystery,Thriller \n", "972 Comedy,Drama,Musical \n", "973 Horror,Mystery \n", "974 Biography,Drama,Sport \n", "975 Comedy,Family,Romance \n", "976 Drama,Mystery,Thriller \n", "977 Comedy \n", "978 Drama \n", "979 Drama,Thriller \n", "980 Biography,Drama,Family \n", "981 Comedy,Drama,Family \n", "982 Drama,Fantasy,Musical \n", "983 Comedy \n", "984 Adventure,Family \n", "985 Adventure,Comedy,Fantasy \n", "986 Horror,Thriller \n", "987 Drama,Romance \n", "988 Horror \n", "989 Biography,Drama,History \n", "990 Action,Adventure,Fantasy \n", "991 Drama,Family,Music \n", "992 Comedy,Drama,Romance \n", "993 Action,Adventure,Horror \n", "994 Comedy \n", "995 Crime,Drama,Mystery \n", "996 Horror \n", "997 Drama,Music,Romance \n", "998 Adventure,Comedy \n", "999 Comedy,Family,Fantasy \n", "\n", " Description Director \\\n", "0 A group of intergalactic criminals are forced ... James Gunn \n", "1 Following clues to the origin of mankind, a te... Ridley Scott \n", "2 Three girls are kidnapped by a man with a diag... M. Night Shyamalan \n", "3 In a city of humanoid animals, a hustling thea... Christophe Lourdelet \n", "4 A secret government agency recruits some of th... David Ayer \n", "5 European mercenaries searching for black powde... Yimou Zhang \n", "6 A jazz pianist falls for an aspiring actress i... Damien Chazelle \n", "7 A has-been actor best known for playing the ti... Sean Foley \n", "8 A true-life drama, centering on British explor... James Gray \n", "9 A spacecraft traveling to a distant colony pla... Morten Tyldum \n", "10 The adventures of writer Newt Scamander in New... David Yates \n", "11 The story of a team of female African-American... Theodore Melfi \n", "12 The Rebel Alliance makes a risky move to steal... Gareth Edwards \n", "13 In Ancient Polynesia, when a terrible curse in... Ron Clements \n", "14 Gloria is an out-of-work party girl forced to ... Nacho Vigalondo \n", "15 The quiet life of a terrier named Max is upend... Chris Renaud \n", "16 WWII American Army Medic Desmond T. Doss, who ... Mel Gibson \n", "17 The CIA's most dangerous former operative is d... Paul Greengrass \n", "18 A five-year-old Indian boy gets lost on the st... Garth Davis \n", "19 When twelve mysterious spacecraft appear aroun... Denis Villeneuve \n", "20 Kenny Wells, a prospector desperate for a luck... Stephen Gaghan \n", "21 A depressed uncle is asked to take care of his... Kenneth Lonergan \n", "22 A cold-blooded predatory couple while cruising... Ben Young \n", "23 After the Bergens invade Troll Village, Poppy,... Walt Dohrn \n", "24 Two decades after the first Independence Day i... Roland Emmerich \n", "25 Fiona visits Paris for the first time to assis... Dominique Abel \n", "26 In ancient India, an adventurous and daring ma... S.S. Rajamouli \n", "27 A young woman must save herself and her friend... Phillip Guzman \n", "28 When three overworked and under-appreciated mo... Jon Lucas \n", "29 When Callum Lynch explores the memories of his... Justin Kurzel \n", ".. ... ... \n", "970 A young woman travels to Texas to collect an i... John Luessenhop \n", "971 A teen living under house arrest becomes convi... D.J. Caruso \n", "972 A small town girl and a city boy meet on the S... Adam Shankman \n", "973 Ten years have passed, and Sidney Prescott, wh... Wes Craven \n", "974 A Ugandan girl sees her world rapidly change a... Mira Nair \n", "975 A Portokalos family secret brings the beloved ... Kirk Jones \n", "976 Libby Day was only eight years old when her fa... Gilles Paquet-Brenner \n", "977 Guy Carter is an award-winning graduate studen... Lisa Addario \n", "978 Louis (Gaspard Ulliel), a terminally ill write... Xavier Dolan \n", "979 A brilliant plastic surgeon, haunted by past t... Pedro Almodóvar \n", "980 A young girl suffering from a rare digestive d... Patricia Riggen \n", "981 A foster kid, who lives with her mean foster m... Will Gluck \n", "982 The music of the Beatles and the Vietnam War f... Julie Taymor \n", "983 Two struggling pals dress as police officers f... Luke Greenfield \n", "984 A Malinois dog that helped American Marines in... Boaz Yakin \n", "985 When Prince Fabious's bride is kidnapped, he g... David Gordon Green \n", "986 Survivors of a suspension-bridge collapse lear... Steven Quale \n", "987 The story of a privileged girl and a charismat... Shana Feste \n", "988 A young woman's quest for revenge against the ... Pascal Laugier \n", "989 A chronicle of Martin Luther King's campaign t... Ava DuVernay \n", "990 An origins story centered on the centuries-old... Patrick Tatopoulos \n", "991 An eight-year-old boy is thought to be a lazy ... Aamir Khan \n", "992 Four years after graduation, an awkward high s... Michael Dowse \n", "993 While still out to destroy the evil Umbrella C... Paul W.S. Anderson \n", "994 3 high school seniors throw a birthday party t... Nima Nourizadeh \n", "995 A tight-knit team of rising investigators, alo... Billy Ray \n", "996 Three American college students studying abroa... Eli Roth \n", "997 Romantic sparks occur between two dance studen... Jon M. Chu \n", "998 A pair of friends embark on a mission to reuni... Scot Armstrong \n", "999 A stuffy businessman finds himself trapped ins... Barry Sonnenfeld \n", "\n", " Actors Year \\\n", "0 Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... 2014 \n", "1 Noomi Rapace, Logan Marshall-Green, Michael Fa... 2012 \n", "2 James McAvoy, Anya Taylor-Joy, Haley Lu Richar... 2016 \n", "3 Matthew McConaughey,Reese Witherspoon, Seth Ma... 2016 \n", "4 Will Smith, Jared Leto, Margot Robbie, Viola D... 2016 \n", "5 Matt Damon, Tian Jing, Willem Dafoe, Andy Lau 2016 \n", "6 Ryan Gosling, Emma Stone, Rosemarie DeWitt, J.... 2016 \n", "7 Essie Davis, Andrea Riseborough, Julian Barrat... 2016 \n", "8 Charlie Hunnam, Robert Pattinson, Sienna Mille... 2016 \n", "9 Jennifer Lawrence, Chris Pratt, Michael Sheen,... 2016 \n", "10 Eddie Redmayne, Katherine Waterston, Alison Su... 2016 \n", "11 Taraji P. Henson, Octavia Spencer, Janelle Mon... 2016 \n", "12 Felicity Jones, Diego Luna, Alan Tudyk, Donnie... 2016 \n", "13 Auli'i Cravalho, Dwayne Johnson, Rachel House,... 2016 \n", "14 Anne Hathaway, Jason Sudeikis, Austin Stowell,... 2016 \n", "15 Louis C.K., Eric Stonestreet, Kevin Hart, Lake... 2016 \n", "16 Andrew Garfield, Sam Worthington, Luke Bracey,... 2016 \n", "17 Matt Damon, Tommy Lee Jones, Alicia Vikander,V... 2016 \n", "18 Dev Patel, Nicole Kidman, Rooney Mara, Sunny P... 2016 \n", "19 Amy Adams, Jeremy Renner, Forest Whitaker,Mich... 2016 \n", "20 Matthew McConaughey, Edgar Ramírez, Bryce Dall... 2016 \n", "21 Casey Affleck, Michelle Williams, Kyle Chandle... 2016 \n", "22 Emma Booth, Ashleigh Cummings, Stephen Curry,S... 2016 \n", "23 Anna Kendrick, Justin Timberlake,Zooey Deschan... 2016 \n", "24 Liam Hemsworth, Jeff Goldblum, Bill Pullman,Ma... 2016 \n", "25 Fiona Gordon, Dominique Abel,Emmanuelle Riva, ... 2016 \n", "26 Prabhas, Rana Daggubati, Anushka Shetty,Tamann... 2015 \n", "27 Jocelin Donahue, Jesse Bradford, Jesse Borrego... 2016 \n", "28 Mila Kunis, Kathryn Hahn, Kristen Bell,Christi... 2016 \n", "29 Michael Fassbender, Marion Cotillard, Jeremy I... 2016 \n", ".. ... ... \n", "970 Alexandra Daddario, Tania Raymonde, Scott East... 2013 \n", "971 Shia LaBeouf, David Morse, Carrie-Anne Moss, S... 2007 \n", "972 Julianne Hough, Diego Boneta, Tom Cruise, Alec... 2012 \n", "973 Neve Campbell, Courteney Cox, David Arquette, ... 2011 \n", "974 Madina Nalwanga, David Oyelowo, Lupita Nyong'o... 2016 \n", "975 Nia Vardalos, John Corbett, Michael Constantin... 2016 \n", "976 Charlize Theron, Nicholas Hoult, Christina Hen... 2015 \n", "977 Jason Biggs, Janet Montgomery,Ashley Tisdale, ... 2016 \n", "978 Nathalie Baye, Vincent Cassel, Marion Cotillar... 2016 \n", "979 Antonio Banderas, Elena Anaya, Jan Cornet,Mari... 2011 \n", "980 Jennifer Garner, Kylie Rogers, Martin Henderso... 2016 \n", "981 Quvenzhané Wallis, Cameron Diaz, Jamie Foxx, R... 2014 \n", "982 Evan Rachel Wood, Jim Sturgess, Joe Anderson, ... 2007 \n", "983 Jake Johnson, Damon Wayans Jr., Rob Riggle, Ni... 2014 \n", "984 Thomas Haden Church, Josh Wiggins, Luke Kleint... 2015 \n", "985 Danny McBride, Natalie Portman, James Franco, ... 2011 \n", "986 Nicholas D'Agosto, Emma Bell, Arlen Escarpeta,... 2011 \n", "987 Gabriella Wilde, Alex Pettyfer, Bruce Greenwoo... 2014 \n", "988 Morjana Alaoui, Mylène Jampanoï, Catherine Bég... 2008 \n", "989 David Oyelowo, Carmen Ejogo, Tim Roth, Lorrain... 2014 \n", "990 Rhona Mitra, Michael Sheen, Bill Nighy, Steven... 2009 \n", "991 Darsheel Safary, Aamir Khan, Tanay Chheda, Sac... 2007 \n", "992 Topher Grace, Anna Faris, Dan Fogler, Teresa P... 2011 \n", "993 Milla Jovovich, Ali Larter, Wentworth Miller,K... 2010 \n", "994 Thomas Mann, Oliver Cooper, Jonathan Daniel Br... 2012 \n", "995 Chiwetel Ejiofor, Nicole Kidman, Julia Roberts... 2015 \n", "996 Lauren German, Heather Matarazzo, Bijou Philli... 2007 \n", "997 Robert Hoffman, Briana Evigan, Cassie Ventura,... 2008 \n", "998 Adam Pally, T.J. Miller, Thomas Middleditch,Sh... 2014 \n", "999 Kevin Spacey, Jennifer Garner, Robbie Amell,Ch... 2016 \n", "\n", " Runtime (Minutes) Rating Votes Revenue (Millions) Metascore \n", "0 121 8.1 757074 333.130 76.0 \n", "1 124 7.0 485820 126.460 65.0 \n", "2 117 7.3 157606 138.120 62.0 \n", "3 108 7.2 60545 270.320 59.0 \n", "4 123 6.2 393727 325.020 40.0 \n", "5 103 6.1 56036 45.130 42.0 \n", "6 128 8.3 258682 151.060 93.0 \n", "7 89 6.4 2490 47.985 71.0 \n", "8 141 7.1 7188 8.010 78.0 \n", "9 116 7.0 192177 100.010 41.0 \n", "10 133 7.5 232072 234.020 66.0 \n", "11 127 7.8 93103 169.270 74.0 \n", "12 133 7.9 323118 532.170 65.0 \n", "13 107 7.7 118151 248.750 81.0 \n", "14 109 6.4 8612 2.870 70.0 \n", "15 87 6.6 120259 368.310 61.0 \n", "16 139 8.2 211760 67.120 71.0 \n", "17 123 6.7 150823 162.160 58.0 \n", "18 118 8.1 102061 51.690 69.0 \n", "19 116 8.0 340798 100.500 81.0 \n", "20 120 6.7 19053 7.220 49.0 \n", "21 137 7.9 134213 47.700 96.0 \n", "22 108 6.7 1115 47.985 72.0 \n", "23 92 6.5 38552 153.690 56.0 \n", "24 120 5.3 127553 103.140 32.0 \n", "25 83 6.8 222 47.985 59.5 \n", "26 159 8.3 76193 6.500 59.5 \n", "27 99 4.7 523 0.010 59.5 \n", "28 100 6.2 66540 113.080 60.0 \n", "29 115 5.9 112813 54.650 36.0 \n", ".. ... ... ... ... ... \n", "970 92 4.8 37060 34.330 62.0 \n", "971 105 6.9 193491 80.050 59.5 \n", "972 123 5.9 64513 38.510 47.0 \n", "973 111 6.2 108544 38.180 52.0 \n", "974 124 7.4 6753 8.810 73.0 \n", "975 94 6.0 20966 59.570 37.0 \n", "976 113 6.2 31634 47.985 39.0 \n", "977 92 5.0 2229 47.985 38.0 \n", "978 97 7.0 10658 47.985 48.0 \n", "979 120 7.6 108772 3.190 70.0 \n", "980 109 7.0 12048 61.690 44.0 \n", "981 118 5.3 27312 85.910 33.0 \n", "982 133 7.4 95172 24.340 56.0 \n", "983 104 6.5 112729 82.390 30.0 \n", "984 111 6.8 21405 42.650 47.0 \n", "985 102 5.6 87904 21.560 31.0 \n", "986 92 5.9 88000 42.580 50.0 \n", "987 104 6.3 33688 23.390 30.0 \n", "988 99 7.1 63785 47.985 89.0 \n", "989 128 7.5 67637 52.070 59.5 \n", "990 92 6.6 129708 45.800 44.0 \n", "991 165 8.5 102697 1.200 42.0 \n", "992 97 6.3 45419 6.920 59.5 \n", "993 97 5.9 140900 60.130 37.0 \n", "994 88 6.7 164088 54.720 48.0 \n", "995 111 6.2 27585 47.985 45.0 \n", "996 94 5.5 73152 17.540 46.0 \n", "997 98 6.2 70699 58.010 50.0 \n", "998 93 5.6 4881 47.985 22.0 \n", "999 87 5.3 12435 19.640 11.0 \n", "\n", "[1000 rows x 12 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 第二种处理方式：填充（替换）缺失值\n", "data.fillna(value=data.median())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# 有特殊标记的缺失 # 1057013\n", "wis = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# 把有特殊标记的缺失值替换成np.nan\n", "wis_2 = wis.replace(to_replace='?', value=np.nan)\n", "\n", "# 删除缺失值\n", "wis_2.dropna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 数据离散化" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# 读取数据\n", "data = pd.read_csv('./data/stock_day.csv')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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openhighcloselowvolumeprice_changep_changema5ma10ma20v_ma5v_ma10v_ma20turnover
2018-02-2723.5325.8824.1623.5395578.030.632.6822.94222.14222.87553782.6446738.6555576.112.39
2018-02-2622.8023.7823.5322.8060985.110.693.0222.40621.95522.94240827.5242736.3456007.501.53
2018-02-2322.8823.3722.8222.7152914.010.542.4221.93821.92923.02235119.5841871.9756372.851.32
2018-02-2222.2522.7622.2822.0236105.010.361.6421.44621.90923.13735397.5839904.7860149.600.90
2018-02-1421.4921.9921.9221.4823331.040.442.0521.36621.92323.25333590.2142935.7461716.110.58
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" ], "text/plain": [ " open high close low volume price_change p_change \\\n", "2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 \n", "2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 \n", "2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 \n", "2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 \n", "2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 \n", "\n", " ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover \n", "2018-02-27 22.942 22.142 22.875 53782.64 46738.65 55576.11 2.39 \n", "2018-02-26 22.406 21.955 22.942 40827.52 42736.34 56007.50 1.53 \n", "2018-02-23 21.938 21.929 23.022 35119.58 41871.97 56372.85 1.32 \n", "2018-02-22 21.446 21.909 23.137 35397.58 39904.78 60149.60 0.90 \n", "2018-02-14 21.366 21.923 23.253 33590.21 42935.74 61716.11 0.58 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# 取出p_change这一列数据\n", "p_change = data['p_change']" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2018-02-27 (1.49, 10.03]\n", "2018-02-26 (1.49, 10.03]\n", "2018-02-23 (1.49, 10.03]\n", "2018-02-22 (1.49, 10.03]\n", "2018-02-14 (1.49, 10.03]\n", "Name: p_change, dtype: category\n", "Categories (3, interval[float64]): [(-10.030999999999999, -1.04] < (-1.04, 1.49] < (1.49, 10.03]]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 进行数据离散化 labels: 离散化之后每个类别的标记\n", "pd.qcut(p_change, 3).head() # 把样本平均分到指定数量的类别中\n", "# res.value_counts() # 统计各个类别的数量" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "# 把区间平均分成指定数量的份数，再对落在每个区间内的样本打上相应区间的标签\n", "res = pd.cut(p_change, 3)\n", "# res.value_counts() # 统计各个类别的数量" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2018-02-27 (-3.343, 3.343]\n", "2018-02-26 (-3.343, 3.343]\n", "2018-02-23 (-3.343, 3.343]\n", "2018-02-22 (-3.343, 3.343]\n", "2018-02-14 (-3.343, 3.343]\n", "2018-02-13 (-3.343, 3.343]\n", "2018-02-12 (3.343, 10.03]\n", "2018-02-09 (-10.05, -3.343]\n", "2018-02-08 (-3.343, 3.343]\n", "2018-02-07 (-3.343, 3.343]\n", "2018-02-06 (-10.05, -3.343]\n", "2018-02-05 (-3.343, 3.343]\n", "2018-02-02 (-3.343, 3.343]\n", "2018-02-01 (-10.05, -3.343]\n", "2018-01-31 (-3.343, 3.343]\n", "2018-01-30 (-3.343, 3.343]\n", "2018-01-29 (-3.343, 3.343]\n", "2018-01-26 (-3.343, 3.343]\n", "2018-01-25 (-10.05, -3.343]\n", "2018-01-24 (-3.343, 3.343]\n", "2018-01-23 (-3.343, 3.343]\n", "2018-01-22 (-3.343, 3.343]\n", "2018-01-19 (-3.343, 3.343]\n", "2018-01-18 (-3.343, 3.343]\n", "2018-01-17 (-3.343, 3.343]\n", "2018-01-16 (3.343, 10.03]\n", "2018-01-15 (-3.343, 3.343]\n", "2018-01-12 (-3.343, 3.343]\n", "2018-01-11 (-3.343, 3.343]\n", "2018-01-10 (-3.343, 3.343]\n", " ... \n", "2015-04-13 (3.343, 10.03]\n", "2015-04-10 (-3.343, 3.343]\n", "2015-04-09 (3.343, 10.03]\n", "2015-04-08 (3.343, 10.03]\n", "2015-04-07 (3.343, 10.03]\n", "2015-04-03 (-3.343, 3.343]\n", "2015-04-02 (-3.343, 3.343]\n", "2015-04-01 (-3.343, 3.343]\n", "2015-03-31 (-3.343, 3.343]\n", "2015-03-30 (3.343, 10.03]\n", "2015-03-27 (3.343, 10.03]\n", "2015-03-26 (-3.343, 3.343]\n", "2015-03-25 (-3.343, 3.343]\n", "2015-03-24 (-3.343, 3.343]\n", "2015-03-23 (-3.343, 3.343]\n", "2015-03-20 (-3.343, 3.343]\n", "2015-03-19 (-3.343, 3.343]\n", "2015-03-18 (-3.343, 3.343]\n", "2015-03-17 (-3.343, 3.343]\n", "2015-03-16 (-3.343, 3.343]\n", "2015-03-13 (-3.343, 3.343]\n", "2015-03-12 (-3.343, 3.343]\n", "2015-03-11 (-3.343, 3.343]\n", "2015-03-10 (-3.343, 3.343]\n", "2015-03-09 (-3.343, 3.343]\n", "2015-03-06 (3.343, 10.03]\n", "2015-03-05 (-3.343, 3.343]\n", "2015-03-04 (-3.343, 3.343]\n", "2015-03-03 (-3.343, 3.343]\n", "2015-03-02 (-3.343, 3.343]\n", "Name: p_change, Length: 643, dtype: category\n", "Categories (3, interval[float64]): [(-10.05, -3.343] < (-3.343, 3.343] < (3.343, 10.03]]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "# one_hot编码\n", "res = pd.get_dummies(res, prefix='abc')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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openhighcloselowvolumeprice_changep_changema5ma10ma20v_ma5v_ma10v_ma20turnover
2018-02-2723.5325.8824.1623.5395578.030.632.6822.94222.14222.87553782.6446738.6555576.112.39
2018-02-2622.8023.7823.5322.8060985.110.693.0222.40621.95522.94240827.5242736.3456007.501.53
2018-02-2322.8823.3722.8222.7152914.010.542.4221.93821.92923.02235119.5841871.9756372.851.32
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2018-02-1421.4921.9921.9221.4823331.040.442.0521.36621.92323.25333590.2142935.7461716.110.58
\n", "
" ], "text/plain": [ " open high close low volume price_change p_change \\\n", "2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 \n", "2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 \n", "2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 \n", "2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 \n", "2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 \n", "\n", " ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover \n", "2018-02-27 22.942 22.142 22.875 53782.64 46738.65 55576.11 2.39 \n", "2018-02-26 22.406 21.955 22.942 40827.52 42736.34 56007.50 1.53 \n", "2018-02-23 21.938 21.929 23.022 35119.58 41871.97 56372.85 1.32 \n", "2018-02-22 21.446 21.909 23.137 35397.58 39904.78 60149.60 0.90 \n", "2018-02-14 21.366 21.923 23.253 33590.21 42935.74 61716.11 0.58 " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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abc_(-10.05, -3.343]abc_(-3.343, 3.343]abc_(3.343, 10.03]
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openhighcloselowvolumeprice_changep_changema5ma10ma20v_ma5v_ma10v_ma20turnoverabc_(-10.05, -3.343]abc_(-3.343, 3.343]abc_(3.343, 10.03]
2018-02-2723.5325.8824.1623.5395578.030.632.6822.94222.14222.87553782.6446738.6555576.112.39010
2018-02-2622.8023.7823.5322.8060985.110.693.0222.40621.95522.94240827.5242736.3456007.501.53010
2018-02-2322.8823.3722.8222.7152914.010.542.4221.93821.92923.02235119.5841871.9756372.851.32010
2018-02-2222.2522.7622.2822.0236105.010.361.6421.44621.90923.13735397.5839904.7860149.600.90010
2018-02-1421.4921.9921.9221.4823331.040.442.0521.36621.92323.25333590.2142935.7461716.110.58010
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" ], "text/plain": [ " open high close low volume price_change p_change \\\n", "2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 \n", "2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 \n", "2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 \n", "2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 \n", "2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 \n", "\n", " ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover \\\n", "2018-02-27 22.942 22.142 22.875 53782.64 46738.65 55576.11 2.39 \n", "2018-02-26 22.406 21.955 22.942 40827.52 42736.34 56007.50 1.53 \n", "2018-02-23 21.938 21.929 23.022 35119.58 41871.97 56372.85 1.32 \n", "2018-02-22 21.446 21.909 23.137 35397.58 39904.78 60149.60 0.90 \n", "2018-02-14 21.366 21.923 23.253 33590.21 42935.74 61716.11 0.58 \n", "\n", " abc_(-10.05, -3.343] abc_(-3.343, 3.343] abc_(3.343, 10.03] \n", "2018-02-27 0 1 0 \n", "2018-02-26 0 1 0 \n", "2018-02-23 0 1 0 \n", "2018-02-22 0 1 0 \n", "2018-02-14 0 1 0 " ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 把one_hot编码后的数据合并到数据集中\n", "pd.concat([data, res], axis=1).head() # 按列合并数据集" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## merge合并数据集" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\n", " 'key2': ['K0', 'K1', 'K0', 'K1'],\n", " 'A': ['A0', 'A1', 'A2', 'A3'],\n", " 'B': ['B0', 'B1', 'B2', 'B3']})\n", "\n", "right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\n", " 'key2': ['K0', 'K0', 'K0', 'K0'],\n", " 'C': ['C0', 'C1', 'C2', 'C3'],\n", " 'D': ['D0', 'D1', 'D2', 'D3']})" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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key1key2AB
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\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", "
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" ], "text/plain": [ " key1 key2 A B C D\n", "0 K0 K0 A0 B0 C0 D0\n", "1 K1 K0 A2 B2 C1 D1\n", "2 K1 K0 A2 B2 C2 D2" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# inner连接合并数据集 left:左数据集， right：右数据集， on：合并的共有字段， how：合并方式\n", "pd.merge(left=left, right=right, on=['key1', 'key2'], how='inner')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 交叉表和透视表" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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openhighcloselowvolumeprice_changep_changema5ma10ma20v_ma5v_ma10v_ma20turnoverweekrise
2018-02-2723.5325.8824.1623.5395578.030.632.6822.94222.14222.87553782.6446738.6555576.112.3911
2018-02-2622.8023.7823.5322.8060985.110.693.0222.40621.95522.94240827.5242736.3456007.501.5301
2018-02-2322.8823.3722.8222.7152914.010.542.4221.93821.92923.02235119.5841871.9756372.851.3241
2018-02-2222.2522.7622.2822.0236105.010.361.6421.44621.90923.13735397.5839904.7860149.600.9031
2018-02-1421.4921.9921.9221.4823331.040.442.0521.36621.92323.25333590.2142935.7461716.110.5821
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" ], "text/plain": [ " open high close low volume price_change p_change \\\n", "2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 \n", "2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 \n", "2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 \n", "2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 \n", "2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 \n", "\n", " ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover \\\n", "2018-02-27 22.942 22.142 22.875 53782.64 46738.65 55576.11 2.39 \n", "2018-02-26 22.406 21.955 22.942 40827.52 42736.34 56007.50 1.53 \n", "2018-02-23 21.938 21.929 23.022 35119.58 41871.97 56372.85 1.32 \n", "2018-02-22 21.446 21.909 23.137 35397.58 39904.78 60149.60 0.90 \n", "2018-02-14 21.366 21.923 23.253 33590.21 42935.74 61716.11 0.58 \n", "\n", " week rise \n", "2018-02-27 1 1 \n", "2018-02-26 0 1 \n", "2018-02-23 4 1 \n", "2018-02-22 3 1 \n", "2018-02-14 2 1 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "# 探究股票的涨跌与星期几有关\n", "# 获取每一行是周几的数据，生成新的一列数据\n", "data['week'] = pd.to_datetime(data.index).weekday" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "# 创建涨跌特征列\n", "data['rise'] = np.where(data['p_change'] > 0, 1, 0)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "rise 0 1\n", "week \n", "0 63 62\n", "1 55 76\n", "2 61 71\n", "3 63 65\n", "4 59 68" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 建立交叉表 两列离散的数据\n", "temp = pd.crosstab(data['week'], data['rise'])\n", "temp" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "week\n", "0 125\n", "1 131\n", "2 132\n", "3 128\n", "4 127\n", "dtype: int64" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 按行求和\n", "sum_day = temp.sum(1)\n", "sum_day" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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rise01
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" ], "text/plain": [ "rise 0 1\n", "week \n", "0 0.504000 0.496000\n", "1 0.419847 0.580153\n", "2 0.462121 0.537879\n", "3 0.492188 0.507812\n", "4 0.464567 0.535433" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 计算上涨下跌的概率\n", "res = temp.div(sum_day, axis=0)\n", "res" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 绘制柱状图\n", "res.plot(kind='bar', stacked=True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " rise\n", "week \n", "0 0.496000\n", "1 0.580153\n", "2 0.537879\n", "3 0.507812\n", "4 0.535433" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 用透视表计算涨跌概率 分组聚合\n", "pd.pivot_table(data=data, index='week', values='rise', aggfunc='mean')" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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openhighcloselowvolumeprice_changep_changema5ma10ma20v_ma5v_ma10v_ma20turnoverweekrise
2018-02-2723.5325.8824.1623.5395578.030.632.6822.94222.14222.87553782.6446738.6555576.112.3911
2018-02-2622.8023.7823.5322.8060985.110.693.0222.40621.95522.94240827.5242736.3456007.501.5301
2018-02-2322.8823.3722.8222.7152914.010.542.4221.93821.92923.02235119.5841871.9756372.851.3241
2018-02-2222.2522.7622.2822.0236105.010.361.6421.44621.90923.13735397.5839904.7860149.600.9031
2018-02-1421.4921.9921.9221.4823331.040.442.0521.36621.92323.25333590.2142935.7461716.110.5821
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" ], "text/plain": [ " open high close low volume price_change p_change \\\n", "2018-02-27 23.53 25.88 24.16 23.53 95578.03 0.63 2.68 \n", "2018-02-26 22.80 23.78 23.53 22.80 60985.11 0.69 3.02 \n", "2018-02-23 22.88 23.37 22.82 22.71 52914.01 0.54 2.42 \n", "2018-02-22 22.25 22.76 22.28 22.02 36105.01 0.36 1.64 \n", "2018-02-14 21.49 21.99 21.92 21.48 23331.04 0.44 2.05 \n", "\n", " ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 turnover \\\n", "2018-02-27 22.942 22.142 22.875 53782.64 46738.65 55576.11 2.39 \n", "2018-02-26 22.406 21.955 22.942 40827.52 42736.34 56007.50 1.53 \n", "2018-02-23 21.938 21.929 23.022 35119.58 41871.97 56372.85 1.32 \n", "2018-02-22 21.446 21.909 23.137 35397.58 39904.78 60149.60 0.90 \n", "2018-02-14 21.366 21.923 23.253 33590.21 42935.74 61716.11 0.58 \n", "\n", " week rise \n", "2018-02-27 1 1 \n", "2018-02-26 0 1 \n", "2018-02-23 4 1 \n", "2018-02-22 3 1 \n", "2018-02-14 2 1 " ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 分组与聚合" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "col =pd.DataFrame({'color': ['white','red','green','red','green'], 'object': ['pen','pencil','pencil','ashtray','pen'],'price1':[5.56,4.20,1.30,0.56,2.75],'price2':[4.75,4.12,1.60,0.75,3.15]})\n" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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colorobjectprice1price2
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1redpencil4.204.12
2greenpencil1.301.60
3redashtray0.560.75
4greenpen2.753.15
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" ], "text/plain": [ " color object price1 price2\n", "0 white pen 5.56 4.75\n", "1 red pencil 4.20 4.12\n", "2 green pencil 1.30 1.60\n", "3 red ashtray 0.56 0.75\n", "4 green pen 2.75 3.15" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "col" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "color\n", "green 2\n", "red 2\n", "white 1\n", "Name: price1, dtype: int64" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 按照color进形分组, 再进行count聚合\n", "col.groupby('color')['price1'].count()" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "color\n", "green 2\n", "red 2\n", "white 1\n", "Name: price1, dtype: int64" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 第二种分组聚合的方式\n", "col['price1'].groupby(col['color']).count()" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "# 读取星巴克店铺数据\n", "data = pd.read_csv('./data/directory.csv')" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BrandStore NumberStore NameOwnership TypeStreet AddressCityState/ProvinceCountryPostcodePhone NumberTimezoneLongitudeLatitude
0Starbucks47370-257954Meritxell, 96LicensedAv. Meritxell, 96Andorra la Vella7ADAD500376818720GMT+1:00 Europe/Andorra1.5342.51
1Starbucks22331-212325Ajman Drive ThruLicensed1 Street 69, Al JarfAjmanAJAENaNNaNGMT+04:00 Asia/Dubai55.4725.42
2Starbucks47089-256771Dana MallLicensedSheikh Khalifa Bin Zayed St.AjmanAJAENaNNaNGMT+04:00 Asia/Dubai55.4725.39
3Starbucks22126-218024Twofour 54LicensedAl Salam StreetAbu DhabiAZAENaNNaNGMT+04:00 Asia/Dubai54.3824.48
4Starbucks17127-178586Al Ain TowerLicensedKhaldiya Area, Abu Dhabi IslandAbu DhabiAZAENaNNaNGMT+04:00 Asia/Dubai54.5424.51
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" ], "text/plain": [ " Brand Store Number Store Name Ownership Type \\\n", "0 Starbucks 47370-257954 Meritxell, 96 Licensed \n", "1 Starbucks 22331-212325 Ajman Drive Thru Licensed \n", "2 Starbucks 47089-256771 Dana Mall Licensed \n", "3 Starbucks 22126-218024 Twofour 54 Licensed \n", "4 Starbucks 17127-178586 Al Ain Tower Licensed \n", "\n", " Street Address City State/Province Country \\\n", "0 Av. Meritxell, 96 Andorra la Vella 7 AD \n", "1 1 Street 69, Al Jarf Ajman AJ AE \n", "2 Sheikh Khalifa Bin Zayed St. Ajman AJ AE \n", "3 Al Salam Street Abu Dhabi AZ AE \n", "4 Khaldiya Area, Abu Dhabi Island Abu Dhabi AZ AE \n", "\n", " Postcode Phone Number Timezone Longitude Latitude \n", "0 AD500 376818720 GMT+1:00 Europe/Andorra 1.53 42.51 \n", "1 NaN NaN GMT+04:00 Asia/Dubai 55.47 25.42 \n", "2 NaN NaN GMT+04:00 Asia/Dubai 55.47 25.39 \n", "3 NaN NaN GMT+04:00 Asia/Dubai 54.38 24.48 \n", "4 NaN NaN GMT+04:00 Asia/Dubai 54.54 24.51 " ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "# 统计星巴克各个国家的店铺数量\n", "# 按照Country进行分组， 再按照count\n", "res = data.groupby('Country')['Brand'].count()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 绘制柱状图\n", "res.plot(kind='bar', figsize=(20, 8))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }