{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": "In this notebook, we will explore Netflix movies and TV shows with [kotlin/dataframe](https://github.com/Kotlin/dataframe). Also, we will use the [kandy](https://github.com/Kotlin/kandy) library for data visualization." }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents\n", "* [**Imports**](attachment:./#Imports)\n", "* [**Reading and first look**](attachment:./#Reading-and-first-look)\n", "* [**TV Shows and Movies**](attachment:./#TV-Shows-and-Movies)\n", "* [**Lifetimes and Release Times**](attachment:./#Lifetimes-and-Release-Times)\n", "* [**Actors**](attachment:./#Actors)\n", "* [**Countries**](attachment:./#Countries)\n", "* [**Duration**](attachment:./#Duration)\n", "* [**Ratings**](attachment:./#Ratings)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the latest available versions of the libraries, the following line magic is responsible for this:" ] }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-27T14:58:43.005062Z", "start_time": "2025-05-27T14:58:42.961486Z" } }, "cell_type": "code", "source": "%useLatestDescriptors", "outputs": [], "execution_count": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importing dataframe" ] }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-27T14:58:49.424425Z", "start_time": "2025-05-27T14:58:43.009496Z" } }, "cell_type": "code", "source": "%use dataframe@kc25", "outputs": [], "execution_count": 2 }, { "metadata": {}, "cell_type": "markdown", "source": "Importing the visualization library" }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-27T14:58:53.172106Z", "start_time": "2025-05-27T14:58:49.438529Z" } }, "cell_type": "code", "source": "%use kandy@kc25", "outputs": [], "execution_count": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading and first look" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get started, need to read data from csv\n", "\n", "_You can also drag-and-drop the file here!_" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T14:59:40.277036Z", "start_time": "2025-05-27T14:59:37.089993Z" } }, "source": [ "val rawDf = DataFrame.read(\"netflix_titles.csv\")" ], "outputs": [], "execution_count": 5 }, { "cell_type": "markdown", "metadata": {}, "source": "First, let's take a look at the data." }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T14:59:41.412085Z", "start_time": "2025-05-27T14:59:41.280212Z" } }, "source": [ "// taking a look at types and columns\n", "rawDf.schema()" ], "outputs": [ { "data": { "text/plain": [ "show_id: String\n", "type: String\n", "title: String\n", "director: String?\n", "cast: String?\n", "country: String?\n", "date_added: String?\n", "release_year: Int\n", "rating: String?\n", "duration: String\n", "listed_in: String\n", "description: String" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 6 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T14:59:45.979702Z", "start_time": "2025-05-27T14:59:45.875177Z" } }, "source": [ "rawDf.size() // rowsCount x columnsCount" ], "outputs": [ { "data": { "text/plain": [ "7787 x 12" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 7 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T14:59:50.107620Z", "start_time": "2025-05-27T14:59:49.841124Z" } }, "source": "rawDf.head() // return the first five rows", "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
s1TV Show3%nullJoão Miguel, Bianca Comparato, Michel...BrazilAugust 14, 20202020TV-MA4 SeasonsInternational TV Shows, TV Dramas, TV...In a future where the elite inhabit a...
s2Movie7:19Jorge Michel GrauDemián Bichir, Héctor Bonilla, Oscar ...MexicoDecember 23, 20162016TV-MA93 minDramas, International MoviesAfter a devastating earthquake hits M...
s3Movie23:59Gilbert ChanTedd Chan, Stella Chung, Henley Hii, ...SingaporeDecember 20, 20182011R78 minHorror Movies, International MoviesWhen an army recruit is found dead, h...
s4Movie9Shane AckerElijah Wood, John C. Reilly, Jennifer...United StatesNovember 16, 20172009PG-1380 minAction & Adventure, Independent Movie...In a postapocalyptic world, rag-doll ...
s5Movie21Robert LuketicJim Sturgess, Kevin Spacey, Kate Bosw...United StatesJanuary 1, 20202008PG-13123 minDramasA brilliant group of students become ...
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"show_id\",\"type\",\"title\",\"director\",\"cast\",\"country\",\"date_added\",\"release_year\",\"rating\",\"duration\",\"listed_in\",\"description\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":5,\"ncol\":12},\"kotlin_dataframe\":[{\"show_id\":\"s1\",\"type\":\"TV Show\",\"title\":\"3%\",\"director\":null,\"cast\":\"João Miguel, Bianca Comparato, Michel Gomes, Rodolfo Valente, Vaneza Oliveira, Rafael Lozano, Viviane Porto, Mel Fronckowiak, Sergio Mamberti, Zezé Motta, Celso Frateschi\",\"country\":\"Brazil\",\"date_added\":\"August 14, 2020\",\"release_year\":2020,\"rating\":\"TV-MA\",\"duration\":\"4 Seasons\",\"listed_in\":\"International TV Shows, TV Dramas, TV Sci-Fi & Fantasy\",\"description\":\"In a future where the elite inhabit an island paradise far from the crowded slums, you get one chance to join the 3% saved from squalor.\"},{\"show_id\":\"s2\",\"type\":\"Movie\",\"title\":\"7:19\",\"director\":\"Jorge Michel Grau\",\"cast\":\"Demián Bichir, Héctor Bonilla, Oscar Serrano, Azalia Ortiz, Octavio Michel, Carmen Beato\",\"country\":\"Mexico\",\"date_added\":\"December 23, 2016\",\"release_year\":2016,\"rating\":\"TV-MA\",\"duration\":\"93 min\",\"listed_in\":\"Dramas, International Movies\",\"description\":\"After a devastating earthquake hits Mexico City, trapped survivors from all walks of life wait to be rescued while trying desperately to stay alive.\"},{\"show_id\":\"s3\",\"type\":\"Movie\",\"title\":\"23:59\",\"director\":\"Gilbert Chan\",\"cast\":\"Tedd Chan, Stella Chung, Henley Hii, Lawrence Koh, Tommy Kuan, Josh Lai, Mark Lee, Susan Leong, Benjamin Lim\",\"country\":\"Singapore\",\"date_added\":\"December 20, 2018\",\"release_year\":2011,\"rating\":\"R\",\"duration\":\"78 min\",\"listed_in\":\"Horror Movies, International Movies\",\"description\":\"When an army recruit is found dead, his fellow soldiers are forced to confront a terrifying secret that's haunting their jungle island training camp.\"},{\"show_id\":\"s4\",\"type\":\"Movie\",\"title\":\"9\",\"director\":\"Shane Acker\",\"cast\":\"Elijah Wood, John C. Reilly, Jennifer Connelly, Christopher Plummer, Crispin Glover, Martin Landau, Fred Tatasciore, Alan Oppenheimer, Tom Kane\",\"country\":\"United States\",\"date_added\":\"November 16, 2017\",\"release_year\":2009,\"rating\":\"PG-13\",\"duration\":\"80 min\",\"listed_in\":\"Action & Adventure, Independent Movies, Sci-Fi & Fantasy\",\"description\":\"In a postapocalyptic world, rag-doll robots hide in fear from dangerous machines out to exterminate them, until a brave newcomer joins the group.\"},{\"show_id\":\"s5\",\"type\":\"Movie\",\"title\":\"21\",\"director\":\"Robert Luketic\",\"cast\":\"Jim Sturgess, Kevin Spacey, Kate Bosworth, Aaron Yoo, Liza Lapira, Jacob Pitts, Laurence Fishburne, Jack McGee, Josh Gad, Sam Golzari, Helen Carey, Jack Gilpin\",\"country\":\"United States\",\"date_added\":\"January 1, 2020\",\"release_year\":2008,\"rating\":\"PG-13\",\"duration\":\"123 min\",\"listed_in\":\"Dramas\",\"description\":\"A brilliant group of students become card-counting experts with the intent of swindling millions out of Las Vegas casinos by playing blackjack.\"}]}" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 8 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T14:59:55.361508Z", "start_time": "2025-05-27T14:59:54.615410Z" } }, "source": [ "// Getting general statistics and info for each columns\n", "rawDf.describe()" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
nametypecountuniquenullstopfreqmeanstdminp25medianp75max
show_idString778777870s11nullnulls1s2750s4502s6254s999
typeString778720Movie5377nullnullMovieMovieMovieTV ShowTV Show
titleString7787778703%1nullnull#AliveEl club de los insomnesManglehornStronger Than the World최강전사 미니특공대 : 영웅의 탄생
directorString?778740502389Raúl Campos, Jan Suter18nullnullA. L. VijayEleonore PourriatLance BangsRaúl Campos, Jan SuterŞenol Sönmez
castString?77876832718David Attenborough18nullnull'Najite Dede, Jude Chukwuka, Taiwo Ar...Donnie Yen, Zhang Jin, Lynn Hung, Pat...Kay Kay Menon, Ranvir Shorey, Manu Ri...Rafael Ferro, Sol Moreno, Jonathan Sa...Ṣọpẹ́ Dìrísù, Wunmi Mosaku, Matt Smit...
countryString?7787682507United States2555nullnullArgentinaIndiaThailandUnited StatesZimbabwe
date_addedString?7787156610January 1, 2020118nullnull April 15, 2018December 31, 2019July 6, 2019November 1, 2019September 9, 2020
release_yearInt7787730201811212013.9325808.75739519252013.0000002017.0000002018.0000002021
ratingString?7787157TV-MA2863nullnullGTV-14TV-MATV-MAUR
durationString778721601 Season1608nullnull1 Season103 min148 min79 min99 min
listed_inString77874920Documentaries334nullnullAction & AdventureComedies, Dramas, Independent MoviesDocumentaries, Sports MoviesInternational Movies, ThrillersThrillers
descriptionString778777690Multiple women report their husbands ...3nullnull"Brooklyn Nine-Nine" star Chelsea Per...An ad agency CEO is put under investi...In a 1950s orphanage, a young girl re...The isolated life of an extreme intro...Zoe Walker leaves her quiet life behi...
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"name\",\"type\",\"count\",\"unique\",\"nulls\",\"top\",\"freq\",\"mean\",\"std\",\"min\",\"p25\",\"median\",\"p75\",\"max\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Comparable<*>\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Comparable<*>\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Comparable<*>\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Comparable<*>\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Comparable<*>\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Comparable<*>\"}],\"nrow\":12,\"ncol\":14},\"kotlin_dataframe\":[{\"name\":\"show_id\",\"type\":\"String\",\"count\":7787,\"unique\":7787,\"nulls\":0,\"top\":\"s1\",\"freq\":1,\"mean\":null,\"std\":null,\"min\":\"s1\",\"p25\":\"s2750\",\"median\":\"s4502\",\"p75\":\"s6254\",\"max\":\"s999\"},{\"name\":\"type\",\"type\":\"String\",\"count\":7787,\"unique\":2,\"nulls\":0,\"top\":\"Movie\",\"freq\":5377,\"mean\":null,\"std\":null,\"min\":\"Movie\",\"p25\":\"Movie\",\"median\":\"Movie\",\"p75\":\"TV Show\",\"max\":\"TV Show\"},{\"name\":\"title\",\"type\":\"String\",\"count\":7787,\"unique\":7787,\"nulls\":0,\"top\":\"3%\",\"freq\":1,\"mean\":null,\"std\":null,\"min\":\"#Alive\",\"p25\":\"El club de los insomnes\",\"median\":\"Manglehorn\",\"p75\":\"Stronger Than the World\",\"max\":\"최강전사 미니특공대 : 영웅의 탄생\"},{\"name\":\"director\",\"type\":\"String?\",\"count\":7787,\"unique\":4050,\"nulls\":2389,\"top\":\"Raúl Campos, Jan Suter\",\"freq\":18,\"mean\":null,\"std\":null,\"min\":\"A. L. Vijay\",\"p25\":\"Eleonore Pourriat\",\"median\":\"Lance Bangs\",\"p75\":\"Raúl Campos, Jan Suter\",\"max\":\"Şenol Sönmez\"},{\"name\":\"cast\",\"type\":\"String?\",\"count\":7787,\"unique\":6832,\"nulls\":718,\"top\":\"David Attenborough\",\"freq\":18,\"mean\":null,\"std\":null,\"min\":\"'Najite Dede, Jude Chukwuka, Taiwo Arimoro, Odenike Odetola, Funmi Eko, Keppy Ekpenyong\",\"p25\":\"Donnie Yen, Zhang Jin, Lynn Hung, Patrick Tam, Karena Ng, Kent Cheng, Bryan Leung, Louis Cheung, Danny Chan, Mike Tyson, Babyjohn Choi\",\"median\":\"Kay Kay Menon, Ranvir Shorey, Manu Rishi Chadha, Pradhuman Singh, Neha Bhasin, Jackie Shroff, Asrani, Neeraj Vora, Sharat Saxena, Tom Alter\",\"p75\":\"Rafael Ferro, Sol Moreno, Jonathan Sandor, Alejandro Urdapilleta, Julieta Cardinali, Florencia Raggi, Valeria Bertuccelli, Raúl Hochman, Ricardo Merkin, Carlos Piñeyro\",\"max\":\"Ṣọpẹ́ Dìrísù, Wunmi Mosaku, Matt Smith, Malaika Wakoli-Abigaba\"},{\"name\":\"country\",\"type\":\"String?\",\"count\":7787,\"unique\":682,\"nulls\":507,\"top\":\"United States\",\"freq\":2555,\"mean\":null,\"std\":null,\"min\":\"Argentina\",\"p25\":\"India\",\"median\":\"Thailand\",\"p75\":\"United States\",\"max\":\"Zimbabwe\"},{\"name\":\"date_added\",\"type\":\"String?\",\"count\":7787,\"unique\":1566,\"nulls\":10,\"top\":\"January 1, 2020\",\"freq\":118,\"mean\":null,\"std\":null,\"min\":\" April 15, 2018\",\"p25\":\"December 31, 2019\",\"median\":\"July 6, 2019\",\"p75\":\"November 1, 2019\",\"max\":\"September 9, 2020\"},{\"name\":\"release_year\",\"type\":\"Int\",\"count\":7787,\"unique\":73,\"nulls\":0,\"top\":\"2018\",\"freq\":1121,\"mean\":2013.932579940927,\"std\":8.757394618381221,\"min\":\"1925\",\"p25\":\"2013.0\",\"median\":\"2017.0\",\"p75\":\"2018.0\",\"max\":\"2021\"},{\"name\":\"rating\",\"type\":\"String?\",\"count\":7787,\"unique\":15,\"nulls\":7,\"top\":\"TV-MA\",\"freq\":2863,\"mean\":null,\"std\":null,\"min\":\"G\",\"p25\":\"TV-14\",\"median\":\"TV-MA\",\"p75\":\"TV-MA\",\"max\":\"UR\"},{\"name\":\"duration\",\"type\":\"String\",\"count\":7787,\"unique\":216,\"nulls\":0,\"top\":\"1 Season\",\"freq\":1608,\"mean\":null,\"std\":null,\"min\":\"1 Season\",\"p25\":\"103 min\",\"median\":\"148 min\",\"p75\":\"79 min\",\"max\":\"99 min\"},{\"name\":\"listed_in\",\"type\":\"String\",\"count\":7787,\"unique\":492,\"nulls\":0,\"top\":\"Documentaries\",\"freq\":334,\"mean\":null,\"std\":null,\"min\":\"Action & Adventure\",\"p25\":\"Comedies, Dramas, Independent Movies\",\"median\":\"Documentaries, Sports Movies\",\"p75\":\"International Movies, Thrillers\",\"max\":\"Thrillers\"},{\"name\":\"description\",\"type\":\"String\",\"count\":7787,\"unique\":7769,\"nulls\":0,\"top\":\"Multiple women report their husbands as missing but when it appears they are looking for the same man, a police officer traces their cryptic connection.\",\"freq\":3,\"mean\":null,\"std\":null,\"min\":\"\\\"Brooklyn Nine-Nine\\\" star Chelsea Peretti showcases her diverse talents in this dark yet silly reimagining of the comedy special.\",\"p25\":\"An ad agency CEO is put under investigation when his ambitious protégée – and lover – accuses him of sexual harassment, vying for his top position.\",\"median\":\"In a 1950s orphanage, a young girl reveals an astonishing talent for chess and begins an unlikely journey to stardom while grappling with addiction.\",\"p75\":\"The isolated life of an extreme introvert is thrown out of order when his company hires a new employee: a cheery extrovert who's not all she seems.\",\"max\":\"Zoe Walker leaves her quiet life behind to investigate her brother's disappearance in Ibiza, where she quickly heads down a decadent and dangerous path.\"}]}" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data consists of Netflix TV shows and movies up to 2020. Each row contains information about one specific project and consists of:\n", "* `show_id` - unique show number\n", "* `type` - ***TV Show*** or ***Movie***\n", "* `title` - the name of a TV show or movie\n", "* `director` - director's name\n", "* `cast` - cast list\n", "* `country` - the country where the title was released\n", "* `date_added` - when the title was added on netflix\n", "* `release_year` - the year the title was released\n", "* `rating` - rating of the title\n", "* `listed_in` - in which lists/genres the title is present on netflix\n", "* `description` - title description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we get started, let's process the dataframe. It can be seen that `date_added` is of type `String`, let's [convert](https://kotlin.github.io/dataframe/convert.html) it to `LocalDate` for further convenience.\n", "Kotlin DataFrame provides built-in type converters for major types. We will use `String` -> `LocalDate` conversion and specify the date format [pattern](https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html)" ] }, { "cell_type": "code", "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2025-05-27T15:00:44.228460Z", "start_time": "2025-05-27T15:00:43.341608Z" } }, "source": [ "val df = rawDf.dropNulls { date_added } // remove rows where `date_added` is not specified\n", " .convert { date_added }.toLocalDate(\"MMMM d, yyyy\", java.util.Locale.ENGLISH) // convert date_added to LocalDate using date pattern\n", " .sortBy { date_added } // and let's also sort by date for easy operation later\n", "df" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
s7114MovieTo and From New YorkSorin Dan MihalcescuBarbara King, Shaana Diya, John Krisi...United States2008-01-012006TV-MA81 minDramas, Independent Movies, ThrillersWhile covering a story in New York Ci...
s1766TV ShowDinner for FivenullnullUnited States2008-02-042007TV-MA1 SeasonStand-Up Comedy & Talk ShowsIn each episode, four celebrities joi...
s3249MovieJust Another Love StoryOle BornedalAnders W. Berthelsen, Rebecka Hemse, ...Denmark2009-05-052007TV-MA104 minDramas, International MoviesWhen he causes a car accident that le...
s5766MovieSplatterJoe DanteCorey Feldman, Tony Todd, Tara Leigh,...United States2009-11-182009TV-MA29 minHorror MoviesAfter committing suicide, a washed-up...
s3841MovieMad Ron's Prevues from HellJim MonacoNick Pawlow, Jordu Schell, Jay Kushwa...United States2010-11-011987NR84 minCult Movies, Horror MoviesThis collection cherry-picks trailers...
s2042MovieEven the RainIcíar BollaínLuis Tosar, Gael García Bernal, Juan ...Spain, Mexico, France2011-05-172010TV-MA103 minDramas, International MoviesWhile making a film about the incursi...
s3222MovieJoseph: King of DreamsRob LaDuca, Robert C. RamirezBen Affleck, Mark Hamill, Richard Her...United States2011-09-272000TV-PG75 minChildren & Family Movies, Dramas, Fai...With his gift of dream interpretation...
s233MovieA Stoning in Fulham CountyLarry ElikannKen Olin, Jill Eikenberry, Maureen Mu...United States2011-10-011988TV-1495 minDramasAfter reckless teens kill an Amish ch...
s309MovieAdam: His Song ContinuesRobert MarkowitzDaniel J. Travanti, JoBeth Williams, ...United States2011-10-011986TV-MA96 minDramasAfter their child was abducted and mu...
s2623MovieHard LessonsEric LaneuvilleDenzel Washington, Lynn Whitfield, Ri...United States2011-10-011986TV-1494 minDramasThis drama based on real-life events ...
s2963MovieIn Defense of a Married ManJoel OlianskyJudith Light, Michael Ontkean, Jerry ...United States2011-10-011990TV-1494 minDramasA lawyer's husband is having an affai...
s5042MovieQuiet Victory: The Charlie Wedemeyer ...Roy Campanella IIPam Dawber, Michael Nouri, Bess Meyer...United States2011-10-011988TV-PG93 minDramas, Sports MoviesWhen high school football coach Charl...
s5833MovieStrange VoicesArthur Allan SeidelmanNancy McKeon, Valerie Harper, Stephen...United States2011-10-011987TV-PG96 minDramasWhen their college-age daughter sudde...
s6846MovieThe Ryan White StoryJohn HerzfeldJudith Light, Lukas Haas, Michael Bow...United States2011-10-011989TV-PG94 minDramasAfter contracting HIV from a tainted ...
s7150MovieToo Young the HeroBuzz KulikRicky Schroder, Jon DeVries, Debra Mo...United States2011-10-011988TV-MA94 minDramasTwelve-year-old Calvin manages to joi...
s7231MovieTriumph of the HeartRichard MichaelsMario Van Peebles, Susan Ruttan, Lane...United States2011-10-011991TV-PG93 minDramas, Sports MoviesThis drama tells the tale of Ricky Be...
s7363MovieUnspeakable ActsLinda OttoJill Clayburgh, Brad Davis, Sam BehrensUnited States2011-10-011990TV-1495 minDramasLaurie and Joseph are doctors who int...
s7415MovieVictim of BeautyRoger YoungWilliam Devane, Jeri Ryan, Michele Ab...United States2011-10-011991TV-1493 minDramas, ThrillersA beauty pageant winner is stalked by...
s819MovieBeing Elmo: A Puppeteer's JourneyConstance MarksKevin Clash, Whoopi GoldbergUnited States2012-02-212011PG76 minDocumentariesWhoopi Goldberg narrates Elmo creator...
s1230MovieCasa de mi PadreMatt PiedmontWill Ferrell, Gael García Bernal, Die...United States, Mexico2012-11-142012R84 minComediesWill Ferrell stars as a Spanish-speak...
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"show_id\",\"type\",\"title\",\"director\",\"cast\",\"country\",\"date_added\",\"release_year\",\"rating\",\"duration\",\"listed_in\",\"description\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDate\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":7777,\"ncol\":12},\"kotlin_dataframe\":[{\"show_id\":\"s7114\",\"type\":\"Movie\",\"title\":\"To and From New York\",\"director\":\"Sorin Dan Mihalcescu\",\"cast\":\"Barbara King, Shaana Diya, John Krisiukenas, Yoriko Haraguchi, David Crane, Chad Thomas\",\"country\":\"United States\",\"date_added\":\"2008-01-01\",\"release_year\":2006,\"rating\":\"TV-MA\",\"duration\":\"81 min\",\"listed_in\":\"Dramas, Independent Movies, Thrillers\",\"description\":\"While covering a story in New York City, a Seattle-based reporter uncovers a link between two missing women that changes her article entirely.\"},{\"show_id\":\"s1766\",\"type\":\"TV Show\",\"title\":\"Dinner for Five\",\"director\":null,\"cast\":null,\"country\":\"United States\",\"date_added\":\"2008-02-04\",\"release_year\":2007,\"rating\":\"TV-MA\",\"duration\":\"1 Season\",\"listed_in\":\"Stand-Up Comedy & Talk Shows\",\"description\":\"In each episode, four celebrities join host Jon Favreau for dinner and share revealing stories about both show business and their personal lives.\"},{\"show_id\":\"s3249\",\"type\":\"Movie\",\"title\":\"Just Another Love Story\",\"director\":\"Ole Bornedal\",\"cast\":\"Anders W. Berthelsen, Rebecka Hemse, Nikolaj Lie Kaas, Charlotte Fich, Dejan Cukic, Karsten Jansfort, Flemming Enevold, Bent Mejding, Ewa Fröling, Josephine Raahauge, Timm Vladimir, Ditte Hansen\",\"country\":\"Denmark\",\"date_added\":\"2009-05-05\",\"release_year\":2007,\"rating\":\"TV-MA\",\"duration\":\"104 min\",\"listed_in\":\"Dramas, International Movies\",\"description\":\"When he causes a car accident that leaves a young woman with amnesia, a man finds himself telling incredible lies to make her and her family happy.\"},{\"show_id\":\"s5766\",\"type\":\"Movie\",\"title\":\"Splatter\",\"director\":\"Joe Dante\",\"cast\":\"Corey Feldman, Tony Todd, Tara Leigh, Erin Way, Mark Alan, Stuart Pankin\",\"country\":\"United States\",\"date_added\":\"2009-11-18\",\"release_year\":2009,\"rating\":\"TV-MA\",\"duration\":\"29 min\",\"listed_in\":\"Horror Movies\",\"description\":\"After committing suicide, a washed-up rocker returns from the grave to torment the five people who betrayed him in life.\"},{\"show_id\":\"s3841\",\"type\":\"Movie\",\"title\":\"Mad Ron's Prevues from Hell\",\"director\":\"Jim Monaco\",\"cast\":\"Nick Pawlow, Jordu Schell, Jay Kushwara, Michael Townsend Wright, Anthony Kelly, Happy Goldsplatt, Ron Roccia, Fred Pomerleau, Joe Amodei, Nicholas Kitsopoulos\",\"country\":\"United States\",\"date_added\":\"2010-11-01\",\"release_year\":1987,\"rating\":\"NR\",\"duration\":\"84 min\",\"listed_in\":\"Cult Movies, Horror Movies\",\"description\":\"This collection cherry-picks trailers, forgotten scenes and poster art from 47 gory oldies, including Bay of Blood and The Blood Spattered Bride.\"},{\"show_id\":\"s2042\",\"type\":\"Movie\",\"title\":\"Even the Rain\",\"director\":\"Icíar Bollaín\",\"cast\":\"Luis Tosar, Gael García Bernal, Juan Carlos Aduviri, Karra Elejalde, Raúl Arévalo, Carlos Santos, Cassandra Ciangherotti, Milena Soliz, Leónidas Chiri, Vicente Romero, Daniel Currás, Pau Cólera\",\"country\":\"Spain, Mexico, France\",\"date_added\":\"2011-05-17\",\"release_year\":2010,\"rating\":\"TV-MA\",\"duration\":\"103 min\",\"listed_in\":\"Dramas, International Movies\",\"description\":\"While making a film about the incursion of Christopher Columbus in the New World, a director finds the Bolivian locals protesting modern exploitation.\"},{\"show_id\":\"s3222\",\"type\":\"Movie\",\"title\":\"Joseph: King of Dreams\",\"director\":\"Rob LaDuca, Robert C. Ramirez\",\"cast\":\"Ben Affleck, Mark Hamill, Richard Herd, Maureen McGovern, Jodi Benson, Judith Light, James Eckhouse, Richard McGonagle, David Campbell, Steven Weber, Dan Castellaneta, Rene Auberjonois, Ken Hudson Campbell, Tom Virtue, Jeff Bennett\",\"country\":\"United States\",\"date_added\":\"2011-09-27\",\"release_year\":2000,\"rating\":\"TV-PG\",\"duration\":\"75 min\",\"listed_in\":\"Children & Family Movies, Dramas, Faith & Spirituality\",\"description\":\"With his gift of dream interpretation and his brilliantly colored coat, Joseph inspires jealousy in his brothers in this animated Bible story.\"},{\"show_id\":\"s233\",\"type\":\"Movie\",\"title\":\"A Stoning in Fulham County\",\"director\":\"Larry Elikann\",\"cast\":\"Ken Olin, Jill Eikenberry, Maureen Mueller, Gregg Henry, Nicholas Pryor, Noble Willingham, Peter Michael Goetz, Ron Perlman, Theodore Bikel, Olivia Burnette, Bill Allen, Brad Pitt\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1988,\"rating\":\"TV-14\",\"duration\":\"95 min\",\"listed_in\":\"Dramas\",\"description\":\"After reckless teens kill an Amish child, a prosecutor attempts to bring the youths to justice despite the condemnation he faces from the community.\"},{\"show_id\":\"s309\",\"type\":\"Movie\",\"title\":\"Adam: His Song Continues\",\"director\":\"Robert Markowitz\",\"cast\":\"Daniel J. Travanti, JoBeth Williams, Richard Masur, Martha Scott, Paul Regina\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1986,\"rating\":\"TV-MA\",\"duration\":\"96 min\",\"listed_in\":\"Dramas\",\"description\":\"After their child was abducted and murdered, John and Reve Walsh fought to raise national awareness of the problem of missing children.\"},{\"show_id\":\"s2623\",\"type\":\"Movie\",\"title\":\"Hard Lessons\",\"director\":\"Eric Laneuville\",\"cast\":\"Denzel Washington, Lynn Whitfield, Richard Masur, Akosua Busia\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1986,\"rating\":\"TV-14\",\"duration\":\"94 min\",\"listed_in\":\"Dramas\",\"description\":\"This drama based on real-life events tells the story of George McKenna, the tough, determined new principal of a notorious Los Angeles high school.\"},{\"show_id\":\"s2963\",\"type\":\"Movie\",\"title\":\"In Defense of a Married Man\",\"director\":\"Joel Oliansky\",\"cast\":\"Judith Light, Michael Ontkean, Jerry Orbach, Pat Corley, Nicholas Campbell, John Colicos, Cynthia Sikes, Johnny Galecki\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1990,\"rating\":\"TV-14\",\"duration\":\"94 min\",\"listed_in\":\"Dramas\",\"description\":\"A lawyer's husband is having an affair. When his mistress is found dead, he becomes the prime suspect – and his wife must take charge of his defense.\"},{\"show_id\":\"s5042\",\"type\":\"Movie\",\"title\":\"Quiet Victory: The Charlie Wedemeyer Story\",\"director\":\"Roy Campanella II\",\"cast\":\"Pam Dawber, Michael Nouri, Bess Meyer, Peter Berg, James Handy, Kyle Chandler\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1988,\"rating\":\"TV-PG\",\"duration\":\"93 min\",\"listed_in\":\"Dramas, Sports Movies\",\"description\":\"When high school football coach Charlie Wedemeyer is diagnosed with Lou Gehrig's disease, he perseveres in leading his team despite becoming disabled.\"},{\"show_id\":\"s5833\",\"type\":\"Movie\",\"title\":\"Strange Voices\",\"director\":\"Arthur Allan Seidelman\",\"cast\":\"Nancy McKeon, Valerie Harper, Stephen Macht, Tricia Leigh Fisher, Millie Perkins\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1987,\"rating\":\"TV-PG\",\"duration\":\"96 min\",\"listed_in\":\"Dramas\",\"description\":\"When their college-age daughter suddenly begins acting erratically and is diagnosed with schizophrenia, a desperate couple seeks treatment for her.\"},{\"show_id\":\"s6846\",\"type\":\"Movie\",\"title\":\"The Ryan White Story\",\"director\":\"John Herzfeld\",\"cast\":\"Judith Light, Lukas Haas, Michael Bowen, Nikki Cox, George Dzundza, Valerie Landsburg, Sarah Jessica Parker, Mitchell Ryan\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1989,\"rating\":\"TV-PG\",\"duration\":\"94 min\",\"listed_in\":\"Dramas\",\"description\":\"After contracting HIV from a tainted blood treatment, teenaged hemophiliac Ryan White is forced to fight for his right to attend public school.\"},{\"show_id\":\"s7150\",\"type\":\"Movie\",\"title\":\"Too Young the Hero\",\"director\":\"Buzz Kulik\",\"cast\":\"Ricky Schroder, Jon DeVries, Debra Mooney, Mary-Louise Parker, Christopher Curry, Rick Warner, Tom Wood\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1988,\"rating\":\"TV-MA\",\"duration\":\"94 min\",\"listed_in\":\"Dramas\",\"description\":\"Twelve-year-old Calvin manages to join the navy and serves in the battle of Guadalcanal. But when his age is revealed, the boy is sent to the brig.\"},{\"show_id\":\"s7231\",\"type\":\"Movie\",\"title\":\"Triumph of the Heart\",\"director\":\"Richard Michaels\",\"cast\":\"Mario Van Peebles, Susan Ruttan, Lane R. Davis, Lynn Whitfield, Adam Faraizl, Polly Holliday, Jay Pennison, John Schuhmacher\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1991,\"rating\":\"TV-PG\",\"duration\":\"93 min\",\"listed_in\":\"Dramas, Sports Movies\",\"description\":\"This drama tells the tale of Ricky Bell, a pro running back with the Tampa Bay Buccaneers who died of a muscle disease in the prime of his career.\"},{\"show_id\":\"s7363\",\"type\":\"Movie\",\"title\":\"Unspeakable Acts\",\"director\":\"Linda Otto\",\"cast\":\"Jill Clayburgh, Brad Davis, Sam Behrens\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1990,\"rating\":\"TV-14\",\"duration\":\"95 min\",\"listed_in\":\"Dramas\",\"description\":\"Laurie and Joseph are doctors who interview sexually abused children. With a lot of energy they track down a pedophile who has abused many toddlers.\"},{\"show_id\":\"s7415\",\"type\":\"Movie\",\"title\":\"Victim of Beauty\",\"director\":\"Roger Young\",\"cast\":\"William Devane, Jeri Ryan, Michele Abrams, Nick Searcy, Linda Pierce, J. Don Ferguson, Butch Slade, Joyce Bowden-Kirby\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1991,\"rating\":\"TV-14\",\"duration\":\"93 min\",\"listed_in\":\"Dramas, Thrillers\",\"description\":\"A beauty pageant winner is stalked by a serial killer who kidnaps her sister and torments her family in this thriller based on a shocking true story.\"},{\"show_id\":\"s819\",\"type\":\"Movie\",\"title\":\"Being Elmo: A Puppeteer's Journey\",\"director\":\"Constance Marks\",\"cast\":\"Kevin Clash, Whoopi Goldberg\",\"country\":\"United States\",\"date_added\":\"2012-02-21\",\"release_year\":2011,\"rating\":\"PG\",\"duration\":\"76 min\",\"listed_in\":\"Documentaries\",\"description\":\"Whoopi Goldberg narrates Elmo creator Kevin Clash's remarkable journey from a working-class Baltimore neighborhood to Jim Henson's \\\"Sesame Street.\\\"\"},{\"show_id\":\"s1230\",\"type\":\"Movie\",\"title\":\"Casa de mi Padre\",\"director\":\"Matt Piedmont\",\"cast\":\"Will Ferrell, Gael García Bernal, Diego Luna, Genesis Rodriguez, Pedro Armendáriz Jr., Nick Offerman, Efren Ramirez, Adrian Martinez\",\"country\":\"United States, Mexico\",\"date_added\":\"2012-11-14\",\"release_year\":2012,\"rating\":\"R\",\"duration\":\"84 min\",\"listed_in\":\"Comedies\",\"description\":\"Will Ferrell stars as a Spanish-speaking cowboy in this comedy about a Mexican clan trying to rescue their ranch from greedy creditors.\"}]}" }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 10 }, { "cell_type": "code", "source": [ "// let's look at what type of column it turned out\n", "df.date_added.type() " ], "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:00:50.115320Z", "start_time": "2025-05-27T15:00:50.000601Z" } }, "outputs": [ { "data": { "text/plain": [ "kotlinx.datetime.LocalDate" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TV Shows and Movies" ] }, { "cell_type": "markdown", "metadata": {}, "source": "First, let's see what there are more of: shows or films." }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-04-26T14:12:28.499760Z", "start_time": "2025-04-26T14:12:26.827757Z" } }, "source": [ "rawDf\n", " .valueCounts(sort = false) { type }\n", " .plot {\n", " bars {\n", " x(type)\n", " y(\"count\")\n", " fillColor(type) {\n", " scale = categorical(range = listOf(Color.hex(\"#00BCD4\"), Color.hex(\"#009688\")))\n", " }\n", " }\n", "\n", " layout {\n", " title = \"Count of TV Shows and Movies\"\n", " size = 900 to 550\n", " }\n", " }" ], "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", " TV Show\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Movie\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 0\n", " \n", " \n", " \n", " \n", " \n", " \n", " 500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 3,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 3,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 4,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 4,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 5,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 5,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Count of TV Shows and Movies\n", " \n", " \n", " \n", " \n", " count\n", " \n", " \n", " \n", " \n", " type\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " type\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " TV Show\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Movie\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " " ], "application/plot+json": { "output_type": "lets_plot_spec", "output": { "ggtitle": { "text": "Count of TV Shows and Movies" }, "mapping": {}, "data": { "count": [ 2410.0, 5377.0 ], "type": [ "TV Show", "Movie" ] }, "ggsize": { "width": 900.0, "height": 550.0 }, "kind": "plot", "scales": [ { "aesthetic": "x", "discrete": true }, { "aesthetic": "y", "limits": [ null, null ] }, { "aesthetic": "fill", "values": [ "#00BCD4", "#009688" ] } ], "layers": [ { "mapping": { "x": "type", "y": "count", "fill": "type" }, "stat": "identity", "sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar" } ], "data_meta": { "series_annotations": [ { "type": "str", "column": "type" }, { "type": "int", "column": "count" } ] } }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see the number of films on Netflix is about twice the number of TV shows.\n", "But has it always been this way?\n", "To figure this out, let's see if there was a year when the number of TV Shows was higher than that of Movies and let's see the cumulative amount for Movies and TV Shows." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:02:53.326805Z", "start_time": "2025-05-27T15:02:52.577509Z" } }, "source": [ "val df_date_count = df\n", " .convert { date_added }.with { it.year } // converting `date_added` to extract `year`\n", " .groupBy { date_added } // grouping by `year` stored in `date_added`\n", " .aggregate {\n", " count { type == \"TV Show\" } into \"tvshows\" // counting TV Shows into column `tvshows`\n", " count { type == \"Movie\" } into \"movies\" // counting Movies into column `movies`\n", " }\n", "df_date_count" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
date_addedtvshowsmovies
200811
200902
201001
2011013
201203
201356
2014619
20153058
2016185258
2017361864
20184301255
20196561497
20206971312
20212988
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"date_added\",\"tvshows\",\"movies\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}],\"nrow\":14,\"ncol\":3},\"kotlin_dataframe\":[{\"date_added\":2008,\"tvshows\":1,\"movies\":1},{\"date_added\":2009,\"tvshows\":0,\"movies\":2},{\"date_added\":2010,\"tvshows\":0,\"movies\":1},{\"date_added\":2011,\"tvshows\":0,\"movies\":13},{\"date_added\":2012,\"tvshows\":0,\"movies\":3},{\"date_added\":2013,\"tvshows\":5,\"movies\":6},{\"date_added\":2014,\"tvshows\":6,\"movies\":19},{\"date_added\":2015,\"tvshows\":30,\"movies\":58},{\"date_added\":2016,\"tvshows\":185,\"movies\":258},{\"date_added\":2017,\"tvshows\":361,\"movies\":864},{\"date_added\":2018,\"tvshows\":430,\"movies\":1255},{\"date_added\":2019,\"tvshows\":656,\"movies\":1497},{\"date_added\":2020,\"tvshows\":697,\"movies\":1312},{\"date_added\":2021,\"tvshows\":29,\"movies\":88}]}" }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 12 }, { "cell_type": "markdown", "metadata": {}, "source": "Let's see if we can simplify this expression using more advanced operations. First, we can combine the conversion of `date_added` into `year` and grouping using [`map`](https://kotlin.github.io/dataframe/map.html) function within [column selector](https://kotlin.github.io/dataframe/columnselectors.html)." }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-04-26T14:12:29.918587Z", "start_time": "2025-04-26T14:12:29.322522Z" } }, "source": [ "val df_date_count = df\n", " .groupBy { date_added.map { it.year } } // grouping by year added extracted from `date_added`\n", " .aggregate {\n", " count { type == \"TV Show\" } into \"tvshows\" // counting TV Shows into column `tvshows`\n", " count { type == \"Movie\" } into \"movies\" // counting Movies into column `movies`\n", " }\n", "df_date_count" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
date_addedtvshowsmovies
200811
200902
201001
2011013
201203
201356
2014619
20153058
2016185258
2017361864
20184301255
20196561497
20206971312
20212988
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"date_added\",\"tvshows\",\"movies\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}],\"nrow\":14,\"ncol\":3},\"kotlin_dataframe\":[{\"date_added\":2008,\"tvshows\":1,\"movies\":1},{\"date_added\":2009,\"tvshows\":0,\"movies\":2},{\"date_added\":2010,\"tvshows\":0,\"movies\":1},{\"date_added\":2011,\"tvshows\":0,\"movies\":13},{\"date_added\":2012,\"tvshows\":0,\"movies\":3},{\"date_added\":2013,\"tvshows\":5,\"movies\":6},{\"date_added\":2014,\"tvshows\":6,\"movies\":19},{\"date_added\":2015,\"tvshows\":30,\"movies\":58},{\"date_added\":2016,\"tvshows\":185,\"movies\":258},{\"date_added\":2017,\"tvshows\":361,\"movies\":864},{\"date_added\":2018,\"tvshows\":430,\"movies\":1255},{\"date_added\":2019,\"tvshows\":656,\"movies\":1497},{\"date_added\":2020,\"tvshows\":697,\"movies\":1312},{\"date_added\":2021,\"tvshows\":29,\"movies\":88}]}" }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 12 }, { "cell_type": "markdown", "metadata": {}, "source": "Our [groupBy aggregation](https://kotlin.github.io/dataframe/groupby.html#aggregation) adds new columns for \"TV Show\" and \"Movie\". This is exactly what [`pivot`](https://kotlin.github.io/dataframe/pivot.html) does: it generates new columns for every unique value in `type`." }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-04-26T14:12:30.755400Z", "start_time": "2025-04-26T14:12:30.045814Z" } }, "source": [ "df.groupBy { date_added.map { it.year } }\n", " .pivot { type }" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
date_addedtype
MovieTV Show
2008
DataFrame [1 x 12]
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
s7114MovieTo and From New YorkSorin Dan MihalcescuBarbara King, Shaana Diya, John Krisi...United States2008-01-012006TV-MA81 minDramas, Independent Movies, ThrillersWhile covering a story in New York Ci...
DataFrame [1 x 12]
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
s1766TV ShowDinner for FivenullnullUnited States2008-02-042007TV-MA1 SeasonStand-Up Comedy & Talk ShowsIn each episode, four celebrities joi...
2009
DataFrame [2 x 12]
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
s3249MovieJust Another Love StoryOle BornedalAnders W. Berthelsen, Rebecka Hemse, ...Denmark2009-05-052007TV-MA104 minDramas, International MoviesWhen he causes a car accident that le...
s5766MovieSplatterJoe DanteCorey Feldman, Tony Todd, Tara Leigh,...United States2009-11-182009TV-MA29 minHorror MoviesAfter committing suicide, a washed-up...
DataFrame [0 x 0]
2010
DataFrame [1 x 12]
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
s3841MovieMad Ron's Prevues from HellJim MonacoNick Pawlow, Jordu Schell, Jay Kushwa...United States2010-11-011987NR84 minCult Movies, Horror MoviesThis collection cherry-picks trailers...
DataFrame [0 x 0]
2011
DataFrame [13 x 12]
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
s2042MovieEven the RainIcíar BollaínLuis Tosar, Gael García Bernal, Juan ...Spain, Mexico, France2011-05-172010TV-MA103 minDramas, International MoviesWhile making a film about the incursi...
s3222MovieJoseph: King of DreamsRob LaDuca, Robert C. RamirezBen Affleck, Mark Hamill, Richard Her...United States2011-09-272000TV-PG75 minChildren & Family Movies, Dramas, Fai...With his gift of dream interpretation...
s233MovieA Stoning in Fulham CountyLarry ElikannKen Olin, Jill Eikenberry, Maureen Mu...United States2011-10-011988TV-1495 minDramasAfter reckless teens kill an Amish ch...
s309MovieAdam: His Song ContinuesRobert MarkowitzDaniel J. Travanti, JoBeth Williams, ...United States2011-10-011986TV-MA96 minDramasAfter their child was abducted and mu...
s2623MovieHard LessonsEric LaneuvilleDenzel Washington, Lynn Whitfield, Ri...United States2011-10-011986TV-1494 minDramasThis drama based on real-life events ...

... showing only top 5 of 13 rows

DataFrame [0 x 0]
2012
DataFrame [3 x 12]
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s819MovieBeing Elmo: A Puppeteer's JourneyConstance MarksKevin Clash, Whoopi GoldbergUnited States2012-02-212011PG76 minDocumentariesWhoopi Goldberg narrates Elmo creator...
s1230MovieCasa de mi PadreMatt PiedmontWill Ferrell, Gael García Bernal, Die...United States, Mexico2012-11-142012R84 minComediesWill Ferrell stars as a Spanish-speak...
s3467MovieKung Fu Panda: HolidayTim JohnsonJack Black, Angelina Jolie, Dustin Ho...United States2012-12-012010TV-PG26 minChildren & Family Movies, ComediesAs preparations for the Winter Feast ...
DataFrame [0 x 0]
2013
DataFrame [6 x 12]
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
s2949MovieIliza Shlesinger: War PaintJay ChapmanIliza ShlesingerUnited States2013-09-012013NR75 minStand-Up Comedy"Last Comic Standing" winner Iliza Sh...
s5315MovieRussell Peters: NotoriousDave HigbyRussell PetersUnited States2013-10-142013NR72 minStand-Up ComedyGlobal comedy star Russell Peters lea...
s221MovieA Russell Peters ChristmasHenry Sarwer-FonerRussell Peters, Pamela Anderson, Mich...Canada2013-11-012011NR44 minStand-Up ComedyInspired by the variety shows of the ...
s650MovieAziz Ansari: Buried AliveWill Lovelace, Dylan SouthernAziz AnsariUnited States2013-11-012013TV-MA80 minStand-Up Comedy"Parks and Recreation" star Aziz Ansa...
s6865MovieThe Short GameJosh GreenbaumSky Sudberry, Allan Kournikova, Jed D...United States2013-12-122013PG100 minChildren & Family Movies, Documentari...They are fiercely competitive athlete...

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DataFrame [5 x 12]
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s3099TV ShowJack TaylorStuart OrmeIain Glen, Killian Scott, Nora-Jane N...United States, Ireland2013-03-312016TV-MA1 SeasonCrime TV Shows, International TV Show...Ken Bruen's bestselling crime novels ...
s1090TV ShowBreaking BadnullBryan Cranston, Aaron Paul, Anna Gunn...United States2013-08-022013TV-MA5 SeasonsCrime TV Shows, TV Dramas, TV ThrillersA high school chemistry teacher dying...
s6057TV ShowThe 4400nullJoel Gretsch, Jacqueline McKenzie, Pa...United States, United Kingdom2013-09-012007TV-144 SeasonsTV Dramas, TV Mysteries, TV Sci-Fi & ...4400 people who vanished over the cou...
s2504TV ShowGossip GirlnullBlake Lively, Leighton Meester, Penn ...United States2013-10-082012TV-146 SeasonsTV Dramas, Teen TV ShowsA group of hyperprivileged Manhattan ...
s5313TV ShowRussell Peters vs. the WorldnullRussell PetersUnited States2013-10-142013NR1 SeasonDocuseries, Stand-Up Comedy & Talk ShowsA four-part docu-series that's an exc...
2014
DataFrame [19 x 12]
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s6899MovieThe SquareJehane NoujaimAhmed Hassan, Khalid Abdalla, Magdy A...United Kingdom, Egypt, United States2014-01-172013TV-MA105 minDocumentaries, International MoviesThis Emmy-winning, street-level view ...
s4154MovieMittGreg WhiteleyMitt RomneyUnited States2014-01-242014TV-PG93 minDocumentariesThe real Mitt Romney is revealed in t...
s3967MovieMarvel's Iron Man & Hulk: Heroes UnitedLeo Riley, Eric RadomskiAdrian Pasdar, Fred Tatasciore, Dee B...United States2014-02-162013PG72 minAction & Adventure, Sci-Fi & FantasyWhen scientists join Hulk's gamma ene...
s2295MovieFrom One Second to the NextWerner HerzognullUnited States2014-04-152013TV-1435 minDocumentariesWerner Herzog chronicles the devastat...
s6759MovieThe Pirate FairyPeggy HolmesMae Whitman, Christina Hendricks, Tom...United States2014-06-152014G78 minChildren & Family MoviesIn this spritely tale, Tinker Bell an...

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s6158TV ShowThe BorgiasnullJeremy Irons, François Arnaud, Hollid...United States, Hungary, Ireland, Canada2014-02-012013TV-MA3 SeasonsTV DramasFollow the lives of the notorious Bor...
s7521TV ShowWeedsnullMary-Louise Parker, Hunter Parrish, A...United States2014-04-012012TV-MA8 SeasonsTV Comedies, TV DramasA suburban mother starts selling mari...
s2499TV ShowGoosebumpsnullR.L. StineCanada2014-11-191998TV-G5 SeasonsKids' TV, TV ThrillersR.L. Stine's tales take on a life of ...
s6630TV ShowThe Magic School BusnullLily Tomlin, Daniel DeSanto, Erica Lu...Canada, United States2014-11-191997TV-Y4 SeasonsKids' TVJoin Ms. Frizzle as the Magic School ...
s3644TV ShowLilyhammernullSteven Van Zandt, Trond Fausa, Steina...Norway, United States2014-11-212014TV-MA3 SeasonsCrime TV Shows, TV Comedies, TV DramasAfter testifying against the Mafia, e...

... showing only top 5 of 6 rows

2015
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s2947MovieIliza Shlesinger: Freezing HotnullIliza Shlesingernull2015-01-232015TV-MA72 minStand-Up ComedySmart and brazen comedian Iliza Shles...
s7435MovieVirunga: Gorillas in PerilnullnullUnited States2015-02-132015TV-1429 minDocumentariesThe team behind the searing documenta...
s5826MovieStop at Nothing: The Lance Armstrong ...Alex HolmesnullAustralia, United Kingdom, United Sta...2015-02-152014NR100 minDocumentaries, International Movies, ...An athlete dupes the world with his t...
s5087MovieRalphie May: UnrulyJohn AsherRalphie MayUnited States2015-02-272015TV-MA83 minStand-Up ComedyFilmed in front of a raucous crowd, c...
s649MovieAziz Ansari Live at Madison Square Ga...Aziz AnsariAziz AnsariUnited States2015-03-062015TV-MA58 minStand-Up ComedyStand-up comedian and TV star Aziz An...

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s4686TV ShowOscar's OasisnullnullFrance2015-03-152011TV-Y1 SeasonKids' TVPlucky lizard Oscar spends his time t...
s3839TV ShowMad MennullJon Hamm, Elisabeth Moss, Vincent Kar...United States2015-03-222014TV-147 SeasonsTV DramasSet in 1960s New York City, this awar...
s5792TV ShowStar-CrossednullAimee Teegarden, Matt Lanter, Grey Da...United States2015-04-162014TV-141 SeasonRomantic TV Shows, TV Dramas, TV Sci-...The passionate love between an alien ...
s3629TV ShowLife on LocationnullDavid AttenboroughUnited States2015-04-222009TV-G1 SeasonBritish TV Shows, Docuseries, Science...Members of the BBC's "Life" productio...

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2016
DataFrame [258 x 12]
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s2843MovieHow to Change the WorldJerry RothwellnullCanada, United Kingdom, Netherlands2016-01-012015NR110 minDocumentaries, International MoviesIn the 1970s, a group of activists wh...
s4943MoviePower Rangers Samurai: Christmas Toge...nullAlex Heartman, Erika Fong, Hector Dav...United States2016-01-012011TV-Y724 minMoviesOn Christmas Eve, as the teens at Shi...
s4944MoviePower Rangers Samurai: Clash of the R...nullAlex Heartman, Erika Fong, Hector Dav...United States2016-01-012011TV-Y746 minChildren & Family MoviesThe Samurai Rangers team up with the ...
s4945MoviePower Rangers Samurai: Party Monsters...nullAlex Heartman, Erika Fong, Hector Dav...United States2016-01-012011TV-Y724 minMoviesOn Halloween night, the creepiest cre...
s4947MoviePower Rangers Super Megaforce: The Le...Charlie Haskell, Koichi SakamotoAndrew M. Gray, Ciara Hanna, John Mar...United States2016-01-012014TV-Y755 minMoviesNew footage augments the tale of the ...

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s900TV ShowBig Bad BeetleborgsnullWesley Barker, Herbie Baez, Elisabeth...United States, France, Japan2016-01-011997TV-G2 SeasonsKids' TV, TV ComediesWhen three teens free a spirit that o...
s4088TV ShowMighty Morphin Alien RangersnullJulia Jordan, Matthew Sakimoto, Sicil...null2016-01-011996TV-Y71 SeasonKids' TVVisitors arrive from space to help Re...
s4089TV ShowMighty Morphin Power RangersnullAustin St. John, Thuy Trang, Walter J...United States, Japan2016-01-012010TV-Y74 SeasonsKids' TVFive average teens are chosen by an i...
s4480TV ShowNinja Turtles: The Next MutationnullJarred Blancard, Mitchell A. Lee Yuen...Canada, United States2016-01-011997TV-G1 SeasonKids' TV, TV ComediesEveryone's favorite teenage mutants a...
s4931TV ShowPower Rangers Dino ThundernullJames Napier, Kevin Duhaney, Emma Lah...United States2016-01-012004TV-Y71 SeasonKids' TVDr. Tommy Oliver returns when his stu...

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2017
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s629MovieAutoheadRohit MittalDeepak Sampat, Ronjini Chakroborty, V...India2017-01-012016TV-MA97 minDramas, International Movies, ThrillersA production crew that thinks it's ma...
s804MovieBee MovieSteve Hickner, Simon J. SmithJerry Seinfeld, Renée Zellweger, Matt...United States, Australia2017-01-012007PG92 minChildren & Family Movies, ComediesBarry, a worker bee stuck in a dead-e...
s2674MovieHeadwindsJalil LespertBenoît Magimel, Isabelle Carré, Antoi...France, Belgium2017-01-012011TV-MA92 minDramas, Independent Movies, Internati...After his wife inexplicably disappear...
s2875MovieHurricane BiancaMatt KugelmanRoy Haylock, Bianca Leigh, Denton Bla...United States2017-01-012016TV-MA85 minComedies, Independent Movies, LGBTQ M...Fired for being gay, schoolteacher Ri...

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s495TV ShowAn Innocent MistakenullJason Wang, Mathilde Lin, Mo Tzu Yi, ...Taiwan2017-01-012012TV-141 SeasonInternational TV Shows, Romantic TV S...Raised by three mothers, a self-assur...
s2057TV ShowEx-BoyfriendnullTony Yang, Nana Lee, Jerry Huang, Lia...China2017-01-012011TV-141 SeasonInternational TV Shows, Romantic TV S...Still haunted by the traumatic event ...
s2607TV ShowHappy 300 DaysnullTammy Chen, Kingone Wang, Hsieh Kun D...Taiwan2017-01-012013TV-141 SeasonInternational TV Shows, Romantic TV S...An unexpected pregnancy sends a woman...
s2961TV ShowIn BetweennullJanel Tsai, Johnny Lu, Darren Chiu, V...Taiwan2017-01-012012TV-141 SeasonInternational TV Shows, Romantic TV S...An author having trouble finishing hi...

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2018
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s275MovieAata PitaUttung ShelarSanjay Narvekar, Bharat Jadhav, Satis...India2018-01-012010TV-14133 minComedies, Dramas, International MoviesA municipal clerk with literary ambit...
s436MovieAlphaGoGreg KohsnullUnited States2018-01-012017TV-G90 minDocumentariesSeemingly simple but deceptively comp...
s700MovieBadhaSumitra Bhave, Sunil SukthankarAmruta Subhash, Rajesh More, Devika D...India2018-01-012006TV-1491 minDramas, International MoviesA young woman who is suspected of pra...
s1189MovieCamp Cool KidsLisa ArnoldConnor Rosen, Logan Shroyer, Michael ...United States2018-01-012017TV-G104 minChildren & Family Movies, Comedies, D...A young boy attends summer camp, wher...

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s2314TV ShowFullmetal Alchemist: BrotherhoodYasuhiro IrieRomi Park, Rie Kugimiya, Megumi Takam...Japan2018-01-012010TV-145 SeasonsAnime Series, International TV ShowsAfter both suffer physical damage – b...
s2874TV ShowHuntik: Secrets and SeekersnullYuri Lowenthal, Drew Aaron, Karen Str...United States, Italy2018-01-012011TV-Y72 SeasonsKids' TVA high school student searching for h...
s4900TV ShowPokémon: Indigo LeaguenullRica Matsumoto, Ikue Otani, Mayumi Ii...Japan2018-01-012000TV-Y71 SeasonAnime Series, Kids' TVExplore the world of Pokémon with 10-...
s6377TV ShowThe Frozen DeadnullCharles Berling, Julia Piaton, Pascal...France2018-01-012017TV-MA1 SeasonCrime TV Shows, International TV Show...A grisly find atop a mountain in the ...

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2019
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s298MovieAcross the UniverseJulie TaymorEvan Rachel Wood, Jim Sturgess, Joe A...United States, United Kingdom2019-01-012007PG-13133 minDramas, Music & Musicals, Romantic Mo...An American girl and a British lad fa...
s667MovieBabelAlejandro G. IñárrituBrad Pitt, Cate Blanchett, Gael Garcí...France, United States, Mexico2019-01-012006R143 minDramas, Independent MoviesWhen an American couple vacationing i...
s680MovieBack to the 90sYanyong KuruaungkoulDan Aaron Ramnarong, Pimchanok Leuwis...Thailand2019-01-012015TV-14111 minComedies, International Movies, Music...When a teenager is transported back t...
s952MovieBlack Hawk DownRidley ScottJosh Hartnett, Ewan McGregor, Tom Siz...United States, United Kingdom2019-01-012001R144 minAction & Adventure, DramasWhen U.S. forces attempt to capture t...

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s1472TV ShowCOMEDIANS of the worldnullNeal Brennan, Chris D'Elia, Nicole By...United States2019-01-012019TV-MA13 SeasonsStand-Up Comedy & Talk Shows, TV Come...This global stand-up comedy series fe...
s6740TV ShowThe PapernullBranka Katić, Trpimir Jurkic, Aleksan...Croatia2019-01-012018TV-MA2 SeasonsInternational TV Shows, TV DramasA construction magnate takes over a s...
s7075TV ShowTidying Up with Marie KondonullMarie KondoUnited States2019-01-012019TV-PG1 SeasonReality TVIn a series of inspiring home makeove...
s1566TV ShowDéjà VunullMandy Wei, Yao Yuan Hao, Wang Si Ping...null2019-01-022013TV-141 SeasonInternational TV Shows, Romantic TV S...After losing everything, a prima ball...

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2020
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s164MovieA Cinderella StoryMark RosmanHilary Duff, Chad Michael Murray, Jen...United States, Canada2020-01-012004PG95 minChildren & Family Movies, ComediesTeen Sam meets the boy of her dreams ...
s434MovieAlpha and Omega: The Legend of the Sa...Richard RichDebi Derryberry, Ben Diskin, Kate Hig...United States, India2020-01-012014TV-Y745 minChildren & Family MoviesWhen a wolf pup named Runt sneaks off...
s456MovieAmerican BeautySam MendesKevin Spacey, Annette Bening, Thora B...United States2020-01-011999R122 minDramasWhile struggling to endure his tightl...
s589MovieArthur ChristmasSarah SmithJames McAvoy, Hugh Laurie, Bill Nighy...United Kingdom, United States2020-01-012011PG98 minChildren & Family Movies, ComediesAt Santa Claus's super-secret toy-mak...

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s1048TV ShowBorder Security: America's Front LinenullnullCanada2020-01-012018TV-141 SeasonCrime TV Shows, International TV Show...This reality series chronicles the be...
s1254TV ShowCells at Work!nullKana Hanazawa, Tomoaki Maeno, Daisuke...Japan2020-01-012018TV-PG1 SeasonAnime Series, Teen TV ShowsOne newcomer red blood cell just want...
s1322TV ShowChhota Bheem Kung Fu Dhamaka SeriesnullPinky Pal Rajput, Sonal Kaushal, Juli...null2020-01-012019TV-Y71 SeasonKids' TVFrom kung fu battles to run-ins with ...
s3683TV ShowLive Up To Your NamenullKim Nam-gil, Kim Ah-joong, Yoo Min-ky...South Korea2020-01-012017TV-MA1 SeasonInternational TV Shows, Korean TV Sho...Pushed together by twists of time, a ...

... showing only top 5 of 697 rows

2021
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s105Movie30 Minutes or LessRuben FleischerJesse Eisenberg, Danny McBride, Aziz ...United States2021-01-012011R83 minAction & Adventure, ComediesTwo crooks planning a bank heist wind...
s693MovieBad Grandpa .5Jeff TremaineJohnny Knoxville, Jackson Nicoll, Gre...United States2021-01-012014TV-MA87 minComediesThe geriatric antics of Irving Zisman...
s1003MovieBlue StreakLes MayfieldMartin Lawrence, Luke Wilson, Peter G...Germany, United States2021-01-011999PG-1394 minAction & Adventure, ComediesA jewel thief returns to his hiding p...
s1042MovieBonnie and ClydeArthur PennWarren Beatty, Faye Dunaway, Michael ...United States2021-01-011967R111 minAction & Adventure, Classic Movies, D...Bonnie Parker and Clyde Barrow are yo...

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s1865TV ShowDream Home MakeovernullnullUnited States2021-01-012021TV-G2 SeasonsReality TVDreams come true for real families lo...
s2673TV ShowHeadspace Guide to MeditationnullAndy PuddicombeUnited States2021-01-012021TV-G1 SeasonDocuseries, Science & Nature TVHeadspace takes a friendly, animated ...
s3148TV ShowJenni Rivera: Mariposa de BarrionullAngélica Celaya, Gabriel Porras, Rosa...United States2021-01-012017TV-MA1 SeasonSpanish-Language TV Shows, TV DramasThis drama follows Mexican-American s...
s4174TV ShowMonarcanullIrene Azuela, Juan Manuel Bernal, Osv...Mexico2021-01-012021TV-MA2 SeasonsInternational TV Shows, Spanish-Langu...After 20 years, Ana María returns to ...

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This docuseries explores personal stories and research on near-death experiences, reincarnation and paranormal phenomena.\"},{\"show_id\":\"s3019\",\"type\":\"TV Show\",\"title\":\"Inside the World’s Toughest Prisons\",\"director\":null,\"cast\":\"Paul Connolly\",\"country\":\"United Kingdom\",\"date_added\":\"2021-01-08\",\"release_year\":2021,\"rating\":\"TV-MA\",\"duration\":\"5 Seasons\",\"listed_in\":\"British TV Shows, Crime TV Shows, Docuseries\",\"description\":\"Investigative journalist Paul Connolly becomes a voluntary inmate in the world's most volatile prisons, where intimidation and brutality rule.\"},{\"show_id\":\"s3823\",\"type\":\"TV Show\",\"title\":\"Lupin\",\"director\":null,\"cast\":\"Omar Sy, Ludivine Sagnier, Clotilde Hesme, Nicole Garcia, Hervé Pierre, Soufiane Guerrab, Antoine Gouy, Fargass Assandé, Vincent Londez, Shirine Boutella\",\"country\":\"United States\",\"date_added\":\"2021-01-08\",\"release_year\":2021,\"rating\":\"TV-MA\",\"duration\":\"1 Season\",\"listed_in\":\"Crime TV Shows, International TV Shows, TV Action & Adventure\",\"description\":\"Inspired by the adventures of Arsène Lupin, gentleman thief Assane Diop sets out to avenge his father for an injustice inflicted by a wealthy family.\"},{\"show_id\":\"s4087\",\"type\":\"TV Show\",\"title\":\"Mighty Little Bheem: Kite Festival\",\"director\":null,\"cast\":\"Sumruddhi Shukla, Aranya Kaur, Nishka Raheja\",\"country\":\"India\",\"date_added\":\"2021-01-08\",\"release_year\":2021,\"rating\":\"TV-Y\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV, TV Comedies\",\"description\":\"With winter behind them, Bheem and his townspeople usher in a sunny new season in all their favorite ways during the Makar Sankranti festival.\"},{\"show_id\":\"s4966\",\"type\":\"TV Show\",\"title\":\"Pretend It’s a City\",\"director\":null,\"cast\":\"Fran Lebowitz\",\"country\":\"United States\",\"date_added\":\"2021-01-08\",\"release_year\":2021,\"rating\":\"TV-14\",\"duration\":\"1 Season\",\"listed_in\":\"Docuseries\",\"description\":\"Wander the New York City streets and fascinating mind of wry writer, humorist and raconteur Fran Lebowitz as she sits down with Martin Scorsese.\"}],\"metadata\":{\"kind\":\"FrameColumn\",\"columns\":[\"show_id\",\"type\",\"title\",\"director\",\"cast\",\"country\",\"date_added\",\"release_year\",\"rating\",\"duration\",\"listed_in\",\"description\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDate\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"ncol\":12,\"nrow\":29}}},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"FrameColumn\"},{\"kind\":\"FrameColumn\"}]}}}]}" }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 13 }, { "cell_type": "markdown", "metadata": {}, "source": "After the `type` column is pivoted, we call [`aggregate`](https://kotlin.github.io/dataframe/pivot.html#aggregation) to specify the metrics to be calculated for every data group." }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-04-26T14:12:31.101676Z", "start_time": "2025-04-26T14:12:30.842981Z" } }, "source": [ "df.groupBy { date_added.map { it.year } }\n", " .pivot { type }.aggregate { count() }" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
date_addedtype
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\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"date_added\",\"type\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ColumnGroup\"}],\"nrow\":14,\"ncol\":2},\"kotlin_dataframe\":[{\"date_added\":2008,\"type\":{\"data\":{\"Movie\":1,\"TV Show\":1},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2009,\"type\":{\"data\":{\"Movie\":2,\"TV Show\":null},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2010,\"type\":{\"data\":{\"Movie\":1,\"TV Show\":null},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2011,\"type\":{\"data\":{\"Movie\":13,\"TV Show\":null},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2012,\"type\":{\"data\":{\"Movie\":3,\"TV Show\":null},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2013,\"type\":{\"data\":{\"Movie\":6,\"TV Show\":5},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2014,\"type\":{\"data\":{\"Movie\":19,\"TV Show\":6},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2015,\"type\":{\"data\":{\"Movie\":58,\"TV Show\":30},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2016,\"type\":{\"data\":{\"Movie\":258,\"TV Show\":185},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2017,\"type\":{\"data\":{\"Movie\":864,\"TV Show\":361},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2018,\"type\":{\"data\":{\"Movie\":1255,\"TV Show\":430},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2019,\"type\":{\"data\":{\"Movie\":1497,\"TV Show\":656},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2020,\"type\":{\"data\":{\"Movie\":1312,\"TV Show\":697},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}},{\"date_added\":2021,\"type\":{\"data\":{\"Movie\":88,\"TV Show\":29},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int?\"}]}}}]}" }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 14 }, { "cell_type": "markdown", "metadata": {}, "source": "Simple statistics can also be done without `aggregate`:" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-04-26T14:12:31.653757Z", "start_time": "2025-04-26T14:12:31.463843Z" } }, "source": [ "df.groupBy { date_added.map { it.year } }\n", " .pivot { type }.count()" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
date_addedtype
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\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"date_added\",\"type\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ColumnGroup\"}],\"nrow\":14,\"ncol\":2},\"kotlin_dataframe\":[{\"date_added\":2008,\"type\":{\"data\":{\"Movie\":1,\"TV Show\":1},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2009,\"type\":{\"data\":{\"Movie\":2,\"TV Show\":0},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2010,\"type\":{\"data\":{\"Movie\":1,\"TV Show\":0},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2011,\"type\":{\"data\":{\"Movie\":13,\"TV Show\":0},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2012,\"type\":{\"data\":{\"Movie\":3,\"TV Show\":0},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2013,\"type\":{\"data\":{\"Movie\":6,\"TV Show\":5},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2014,\"type\":{\"data\":{\"Movie\":19,\"TV Show\":6},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2015,\"type\":{\"data\":{\"Movie\":58,\"TV Show\":30},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2016,\"type\":{\"data\":{\"Movie\":258,\"TV Show\":185},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2017,\"type\":{\"data\":{\"Movie\":864,\"TV Show\":361},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2018,\"type\":{\"data\":{\"Movie\":1255,\"TV Show\":430},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2019,\"type\":{\"data\":{\"Movie\":1497,\"TV Show\":656},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2020,\"type\":{\"data\":{\"Movie\":1312,\"TV Show\":697},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2021,\"type\":{\"data\":{\"Movie\":88,\"TV Show\":29},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}}]}" }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the `count` statistic, there is an even shorter API: [`pivotCounts`](https://kotlin.github.io/dataframe/pivot.html#pivotcounts).\n", "\n", "Here is the final version:" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:08:50.920484Z", "start_time": "2025-05-27T15:08:50.504711Z" } }, "source": [ "val df_date_count = df\n", " .groupBy { date_added.map { it.year } }.pivotCounts { type }\n", "df_date_count" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
date_addedtype
MovieTV Show
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2016258185
2017864361
20181255430
20191497656
20201312697
20218829
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"date_added\",\"type\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ColumnGroup\"}],\"nrow\":14,\"ncol\":2},\"kotlin_dataframe\":[{\"date_added\":2008,\"type\":{\"data\":{\"Movie\":1,\"TV Show\":1},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2009,\"type\":{\"data\":{\"Movie\":2,\"TV Show\":0},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2010,\"type\":{\"data\":{\"Movie\":1,\"TV Show\":0},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2011,\"type\":{\"data\":{\"Movie\":13,\"TV Show\":0},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2012,\"type\":{\"data\":{\"Movie\":3,\"TV Show\":0},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2013,\"type\":{\"data\":{\"Movie\":6,\"TV Show\":5},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2014,\"type\":{\"data\":{\"Movie\":19,\"TV Show\":6},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2015,\"type\":{\"data\":{\"Movie\":58,\"TV Show\":30},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2016,\"type\":{\"data\":{\"Movie\":258,\"TV Show\":185},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2017,\"type\":{\"data\":{\"Movie\":864,\"TV Show\":361},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2018,\"type\":{\"data\":{\"Movie\":1255,\"TV Show\":430},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2019,\"type\":{\"data\":{\"Movie\":1497,\"TV Show\":656},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2020,\"type\":{\"data\":{\"Movie\":1312,\"TV Show\":697},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2021,\"type\":{\"data\":{\"Movie\":88,\"TV Show\":29},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}}]}" }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 18 }, { "cell_type": "markdown", "metadata": {}, "source": "Let's plot the results." }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:10:11.915683Z", "start_time": "2025-05-27T15:10:10.890709Z" } }, "source": [ "df_date_count.plot {\n", " x(date_added) { axis.name = \"year\" }\n", "\n", " area {\n", " y(type.`TV Show`) { axis.name = \"count\" }\n", " fillColor = Color.hex(\"#BF360C\")\n", " borderLine.color = Color.hex(\"#BF360C\")\n", " alpha = .5\n", " }\n", "\n", " area {\n", " y(type.Movie)\n", " fillColor = Color.hex(\"#01579B\")\n", " borderLine.color = Color.hex(\"#01579B\")\n", " alpha = .5\n", " }\n", "\n", " layout {\n", " title = \"Number of titles by year\"\n", " size = 800 to 500\n", " style {\n", " panel {\n", " background {\n", " fillColor = Color.hex(\"#ECEFF1\")\n", " borderLineColor = Color.hex(\"#ECEFF1\")\n", " }\n", " grid.lineGlobal { blank = true }\n", " }\n", " }\n", " }\n", "}" ], "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", " 2,008\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,010\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,012\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,014\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,016\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,018\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,020\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 0\n", " \n", " \n", " \n", " \n", " \n", " \n", " 200\n", " \n", " \n", " \n", " \n", " \n", " \n", " 400\n", " \n", " \n", " \n", " \n", " \n", " \n", " 600\n", " \n", " \n", " \n", " \n", " \n", " \n", " 800\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,200\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,400\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", " Number of titles by year\n", " \n", " \n", " \n", " \n", " count\n", " \n", " \n", " \n", " \n", " year\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " " ], "application/plot+json": { "output_type": "lets_plot_spec", "output": { "ggtitle": { "text": "Number of titles by year" }, "mapping": {}, "data": { "date_added": [ 2008.0, 2009.0, 2010.0, 2011.0, 2012.0, 2013.0, 2014.0, 2015.0, 2016.0, 2017.0, 2018.0, 2019.0, 2020.0, 2021.0 ], "Movie": [ 1.0, 2.0, 1.0, 13.0, 3.0, 6.0, 19.0, 58.0, 258.0, 864.0, 1255.0, 1497.0, 1312.0, 88.0 ], "TV Show": [ 1.0, 0.0, 0.0, 0.0, 0.0, 5.0, 6.0, 30.0, 185.0, 361.0, 430.0, 656.0, 697.0, 29.0 ] }, "ggsize": { "width": 800.0, "height": 500.0 }, "kind": "plot", "scales": [ { "aesthetic": "x", "name": "year", "limits": [ null, null ] }, { "aesthetic": "y", "name": "count", "limits": [ null, null ] }, { "aesthetic": "x", "name": "year", "limits": [ null, null ] }, { "aesthetic": "y", "limits": [ null, null ] } ], "layers": [ { "mapping": { "x": "date_added", "y": "TV Show" }, "stat": "identity", "color": "#BF360C", "sampling": "none", "alpha": 0.5, "inherit_aes": false, "position": "identity", "geom": "area", "fill": "#BF360C" }, { "mapping": { "x": "date_added", "y": "Movie" }, "stat": "identity", "color": "#01579B", "sampling": "none", "alpha": 0.5, "inherit_aes": false, "position": "identity", "geom": "area", "fill": "#01579B" } ], "theme": { "panel_background": { "fill": "#ECEFF1", "color": "#ECEFF1", "blank": false }, "axis_ontop": false, "axis_ontop_y": false, "panel_grid": { "blank": true }, "axis_ontop_x": false }, "data_meta": { "series_annotations": [ { "type": "int", "column": "date_added" }, { "type": "int", "column": "TV Show" }, { "type": "int", "column": "Movie" } ] } }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that, compared to shows, more films were added every year.\n", "\n", "The cumulative sum of the movies would clearly then also always be larger than the TV Shows, but let's plot it anyway." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:10:15.506063Z", "start_time": "2025-05-27T15:10:15.303284Z" } }, "source": [ "val df_cumsum_titles = df_date_count\n", " .sortBy { date_added } // sorting by date_added\n", " .cumSum { type.colsOf() } // count cumulative sum for columns `TV Show` and `Movie` that are nested under column `type`\n", "df_cumsum_titles" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
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\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"date_added\",\"type\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ColumnGroup\"}],\"nrow\":14,\"ncol\":2},\"kotlin_dataframe\":[{\"date_added\":2008,\"type\":{\"data\":{\"Movie\":1,\"TV Show\":1},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2009,\"type\":{\"data\":{\"Movie\":3,\"TV Show\":1},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2010,\"type\":{\"data\":{\"Movie\":4,\"TV Show\":1},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2011,\"type\":{\"data\":{\"Movie\":17,\"TV Show\":1},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2012,\"type\":{\"data\":{\"Movie\":20,\"TV Show\":1},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2013,\"type\":{\"data\":{\"Movie\":26,\"TV Show\":6},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2014,\"type\":{\"data\":{\"Movie\":45,\"TV Show\":12},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2015,\"type\":{\"data\":{\"Movie\":103,\"TV Show\":42},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2016,\"type\":{\"data\":{\"Movie\":361,\"TV Show\":227},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2017,\"type\":{\"data\":{\"Movie\":1225,\"TV Show\":588},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2018,\"type\":{\"data\":{\"Movie\":2480,\"TV Show\":1018},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2019,\"type\":{\"data\":{\"Movie\":3977,\"TV Show\":1674},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2020,\"type\":{\"data\":{\"Movie\":5289,\"TV Show\":2371},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}},{\"date_added\":2021,\"type\":{\"data\":{\"Movie\":5377,\"TV Show\":2400},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}]}}}]}" }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 23 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:10:27.682067Z", "start_time": "2025-05-27T15:10:27.311971Z" } }, "source": [ "df_cumsum_titles.plot {\n", " x(date_added) { axis.name = \"year\" }\n", "\n", " area {\n", " y(type.`TV Show`) { axis.name = \"cumulative count\" }\n", " fillColor = Color.hex(\"#BF360C\")\n", " borderLine.color = Color.hex(\"#BF360C\")\n", " alpha = .5\n", " }\n", "\n", " area {\n", " y(type.Movie)\n", " fillColor = Color.hex(\"#01579B\")\n", " borderLine.color = Color.hex(\"#01579B\")\n", " alpha = .5\n", " }\n", "\n", " layout {\n", " title = \"Cumulative count of titles by year\"\n", " size = 800 to 500\n", "\n", " style {\n", " panel {\n", " background {\n", " fillColor = Color.hex(\"#ECEFF1\")\n", " borderLineColor = Color.hex(\"#ECEFF1\")\n", " }\n", " grid.lineGlobal { blank = true }\n", " }\n", " }\n", " }\n", "}" ], "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", " 2,008\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,010\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,012\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,014\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,016\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,018\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,020\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 0\n", " \n", " \n", " \n", " \n", " \n", " \n", " 500\n", " \n", " \n", " \n", " \n", " \n", " 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\n", " \n", " \n", " \n", " \n", "\n", " " ], "application/plot+json": { "output_type": "lets_plot_spec", "output": { "ggtitle": { "text": "Cumulative count of titles by year" }, "mapping": {}, "data": { "date_added": [ 2008.0, 2009.0, 2010.0, 2011.0, 2012.0, 2013.0, 2014.0, 2015.0, 2016.0, 2017.0, 2018.0, 2019.0, 2020.0, 2021.0 ], "Movie": [ 1.0, 3.0, 4.0, 17.0, 20.0, 26.0, 45.0, 103.0, 361.0, 1225.0, 2480.0, 3977.0, 5289.0, 5377.0 ], "TV Show": [ 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 12.0, 42.0, 227.0, 588.0, 1018.0, 1674.0, 2371.0, 2400.0 ] }, "ggsize": { "width": 800.0, "height": 500.0 }, "kind": "plot", "scales": [ { "aesthetic": "x", "name": "year", "limits": [ null, null ] }, { "aesthetic": "y", "name": "cumulative count", "limits": [ null, null ] }, { "aesthetic": "x", "name": "year", "limits": [ null, null ] }, { "aesthetic": "y", "limits": [ null, null ] } ], "layers": [ { "mapping": { "x": "date_added", "y": "TV Show" }, "stat": "identity", "color": "#BF360C", "sampling": "none", "alpha": 0.5, "inherit_aes": false, "position": "identity", "geom": "area", "fill": "#BF360C" }, { "mapping": { "x": "date_added", "y": "Movie" }, "stat": "identity", "color": "#01579B", "sampling": "none", "alpha": 0.5, "inherit_aes": false, "position": "identity", "geom": "area", "fill": "#01579B" } ], "theme": { "panel_background": { "fill": "#ECEFF1", "color": "#ECEFF1", "blank": false }, "axis_ontop": false, "axis_ontop_y": false, "panel_grid": { "blank": true }, "axis_ontop_x": false }, "data_meta": { "series_annotations": [ { "type": "int", "column": "date_added" }, { "type": "int", "column": "TV Show" }, { "type": "int", "column": "Movie" } ] } }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lifetimes and Release Times" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the distribution of the lifetime of titles on the platform.\n", "To do this, we find the most recently uploaded title and calculate the difference between the date it was added and the most recent date found." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:11:24.176184Z", "start_time": "2025-05-27T15:11:24.151665Z" } }, "source": [ "import kotlinx.datetime.*" ], "outputs": [], "execution_count": 25 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:11:30.296090Z", "start_time": "2025-05-27T15:11:29.842288Z" } }, "source": [ "val maxDate = df.date_added.max()\n", "val df_days = df.add {\n", " \"days_on_platform\" from { date_added.daysUntil(maxDate) } // adding column for number of days on the platform\n", " \"months_on_platform\" from { date_added.monthsUntil(maxDate) } // adding column for number of months on the platform\n", " \"years_on_platform\" from { date_added.yearsUntil(maxDate) } // adding column for number of years on the platform\n", "}" ], "outputs": [], "execution_count": 26 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:11:48.413091Z", "start_time": "2025-05-27T15:11:47.600291Z" } }, "source": [ "val p1 = df_days.select { type and days_on_platform }.plot {\n", " histogram(days_on_platform, binsOption = BinsOption.byNumber(30)) {\n", " y(Stat.density)\n", " fillColor = Color.hex(\"#ef0b0b\")\n", " borderLine.color = Color.hex(\"#ECEFF1\")\n", " }\n", "\n", " statBin(days_on_platform, binsOption = BinsOption.byNumber(30)) {\n", " area {\n", " x(Stat.x)\n", " y(Stat.density)\n", " alpha = .5\n", " fillColor = Color.hex(\"#0befef\")\n", " }\n", " }\n", "\n", " layout {\n", " xAxisLabel = \"days\"\n", " title = \"Age distribution (in days) on Netflix\"\n", " }\n", "}\n", "\n", "val p2 = df_days.select { type and days_on_platform }.plot {\n", " boxplot(x = type, y = days_on_platform) {\n", " boxes {\n", " fillColor(Stat.x) {\n", " scale = categorical(range = listOf(Color.hex(\"#792020\"), Color.hex(\"#207979\")))\n", " }\n", " }\n", " }\n", " layout {\n", " yAxisLabel = \"days\"\n", " title = \"Boxplot for age (in days) by type\"\n", " }\n", "}\n", "\n", "plotBunch {\n", " add(p1, 0, 0, 500, 450)\n", " add(p2, 500, 0, 500, 450)\n", "}" ], "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", " 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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", " \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", " Movie\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " TV Show\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 0\n", " \n", " \n", " \n", " \n", " \n", " \n", " 500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 3,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 3,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 4,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 4,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 5,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Boxplot for age (in days) by type\n", " \n", " \n", " \n", " \n", " days\n", " \n", " \n", " \n", " \n", " type\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " x\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Movie\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " TV Show\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " " ], "application/plot+json": { "output_type": "lets_plot_spec", "output": { "kind": "ggbunch", "items": [ { "feature_spec": { "ggtitle": { "text": "Age distribution (in days) on Netflix" }, "mapping": {}, "guides": { "x": { "title": "days" } }, "data": {}, "kind": "plot", "scales": [ { "aesthetic": "x", "name": "days_on_platform", "limits": [ null, null ] }, { "aesthetic": "x", "limits": [ null, null ] }, { "aesthetic": "y", "limits": [ null, null ] }, { "aesthetic": "x", "limits": [ null, null ] }, { "aesthetic": "y", "limits": [ null, null ] } ], "layers": [ { "mapping": { "x": "x", "y": "density" }, "stat": "identity", "data": { "density": [ 7.190355595473184E-4, 6.987924413168196E-4, 8.728832580991097E-4, 6.631645532311417E-4, 7.141772111719988E-4, 6.453506091883027E-4, 5.157946525131101E-4, 4.655917193014731E-4, 4.2753465702813527E-4, 2.744966832055641E-4, 1.1417118682001341E-4, 9.149889440185471E-5, 3.238898916879813E-5, 1.9433393501278876E-5, 1.1336146209079345E-5, 3.2388989168798126E-6, 9.716696750639438E-6, 1.6194494584399063E-6, 1.6194494584399063E-6, 0.0, 8.097247292199532E-7, 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} ] }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The age distribution of titles on the platform is similar for movies and TV shows. But you can see in the second graph that there are more old titles among the movies compared to the shows.\n", " Let's take a closer look at this.\n", " To do this, let's build a graph of the duration in years of being on the platform for films and shows." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:13:00.111413Z", "start_time": "2025-05-27T15:12:59.851850Z" } }, "source": [ "df_days.valueCounts(sort = false) { type and years_on_platform }.plot {\n", " bars {\n", " x(years_on_platform) { axis.name = \"years\" }\n", " y(\"count\")\n", " fillColor(type) {\n", " scale = categorical(range = listOf(Color.hex(\"#bc3076\"), Color.hex(\"#30bc76\")))\n", " }\n", " position = Position.dodge()\n", " }\n", " layout {\n", " title = \"Years of Movies and TV Shows on Netflix\"\n", " size = 900 to 500\n", " }\n", "}" ], "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", " 0\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 4\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 6\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 8\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 10\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 12\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 14\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 0\n", " \n", " \n", " \n", " \n", " \n", " \n", " 200\n", " \n", " \n", " \n", " \n", " \n", " \n", " 400\n", " \n", " \n", " \n", " \n", " \n", " \n", " 600\n", " \n", " \n", " \n", " \n", " \n", " \n", " 800\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,200\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,400\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,600\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Years of Movies and TV Shows on Netflix\n", " \n", " \n", " \n", " \n", " count\n", " \n", " \n", " \n", " \n", " years\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " type\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Movie\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " TV Show\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " " ], "application/plot+json": { "output_type": "lets_plot_spec", "output": { "ggtitle": { "text": "Years of Movies and TV Shows on Netflix" }, "mapping": {}, "data": { "count": [ 1.0, 1.0, 2.0, 1.0, 13.0, 3.0, 5.0, 6.0, 19.0, 6.0, 70.0, 56.0, 289.0, 169.0, 362.0, 903.0, 1257.0, 441.0, 1552.0, 668.0, 1261.0, 692.0 ], "years_on_platform": [ 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 7.0, 6.0, 6.0, 5.0, 5.0, 4.0, 4.0, 3.0, 3.0, 2.0, 2.0, 1.0, 1.0, 0.0, 0.0 ], "type": [ "Movie", "TV Show", "Movie", "Movie", "Movie", "Movie", "TV Show", "Movie", "Movie", "TV Show", "Movie", "TV Show", "Movie", "TV Show", "TV Show", "Movie", "Movie", "TV Show", "Movie", "TV Show", "Movie", "TV Show" ] }, "ggsize": { "width": 900.0, "height": 500.0 }, "kind": "plot", "scales": [ { "aesthetic": "x", "name": "years", "limits": [ null, null ] }, { "aesthetic": "y", "limits": [ null, null ] }, { "aesthetic": "fill", "values": [ "#bc3076", "#30bc76" ] } ], "layers": [ { "mapping": { "x": "years_on_platform", "y": "count", "fill": "type" }, "stat": "identity", "sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar" } ], "data_meta": { "series_annotations": [ { "type": "int", "column": "years_on_platform" }, { "type": "int", "column": "count" }, { "type": "str", "column": "type" } ] } }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, movies are usually older than TV shows.\n", "After that, you might ask yourself: how quickly were titles added to Netflix after their release? Well, finding the answer to it will be quite simple." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:13:19.491765Z", "start_time": "2025-05-27T15:13:19.240341Z" } }, "source": [ "val df_years = df\n", " // adding a new column of the difference between the year of release and the year of addition\n", " .add(\"years_off_platform\") {\n", " date_added.year - release_year\n", " }\n", " // dropping negative values and equal to zero\n", " .filter { \"years_off_platform\"() > 0 }\n", "df_years" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescriptionyears_off_platform
s7114MovieTo and From New YorkSorin Dan MihalcescuBarbara King, Shaana Diya, John Krisi...United States2008-01-012006TV-MA81 minDramas, Independent Movies, ThrillersWhile covering a story in New York Ci...2
s1766TV ShowDinner for FivenullnullUnited States2008-02-042007TV-MA1 SeasonStand-Up Comedy & Talk ShowsIn each episode, four celebrities joi...1
s3249MovieJust Another Love StoryOle BornedalAnders W. Berthelsen, Rebecka Hemse, ...Denmark2009-05-052007TV-MA104 minDramas, International MoviesWhen he causes a car accident that le...2
s3841MovieMad Ron's Prevues from HellJim MonacoNick Pawlow, Jordu Schell, Jay Kushwa...United States2010-11-011987NR84 minCult Movies, Horror MoviesThis collection cherry-picks trailers...23
s2042MovieEven the RainIcíar BollaínLuis Tosar, Gael García Bernal, Juan ...Spain, Mexico, France2011-05-172010TV-MA103 minDramas, International MoviesWhile making a film about the incursi...1
s3222MovieJoseph: King of DreamsRob LaDuca, Robert C. RamirezBen Affleck, Mark Hamill, Richard Her...United States2011-09-272000TV-PG75 minChildren & Family Movies, Dramas, Fai...With his gift of dream interpretation...11
s233MovieA Stoning in Fulham CountyLarry ElikannKen Olin, Jill Eikenberry, Maureen Mu...United States2011-10-011988TV-1495 minDramasAfter reckless teens kill an Amish ch...23
s309MovieAdam: His Song ContinuesRobert MarkowitzDaniel J. Travanti, JoBeth Williams, ...United States2011-10-011986TV-MA96 minDramasAfter their child was abducted and mu...25
s2623MovieHard LessonsEric LaneuvilleDenzel Washington, Lynn Whitfield, Ri...United States2011-10-011986TV-1494 minDramasThis drama based on real-life events ...25
s2963MovieIn Defense of a Married ManJoel OlianskyJudith Light, Michael Ontkean, Jerry ...United States2011-10-011990TV-1494 minDramasA lawyer's husband is having an affai...21
s5042MovieQuiet Victory: The Charlie Wedemeyer ...Roy Campanella IIPam Dawber, Michael Nouri, Bess Meyer...United States2011-10-011988TV-PG93 minDramas, Sports MoviesWhen high school football coach Charl...23
s5833MovieStrange VoicesArthur Allan SeidelmanNancy McKeon, Valerie Harper, Stephen...United States2011-10-011987TV-PG96 minDramasWhen their college-age daughter sudde...24
s6846MovieThe Ryan White StoryJohn HerzfeldJudith Light, Lukas Haas, Michael Bow...United States2011-10-011989TV-PG94 minDramasAfter contracting HIV from a tainted ...22
s7150MovieToo Young the HeroBuzz KulikRicky Schroder, Jon DeVries, Debra Mo...United States2011-10-011988TV-MA94 minDramasTwelve-year-old Calvin manages to joi...23
s7231MovieTriumph of the HeartRichard MichaelsMario Van Peebles, Susan Ruttan, Lane...United States2011-10-011991TV-PG93 minDramas, Sports MoviesThis drama tells the tale of Ricky Be...20
s7363MovieUnspeakable ActsLinda OttoJill Clayburgh, Brad Davis, Sam BehrensUnited States2011-10-011990TV-1495 minDramasLaurie and Joseph are doctors who int...21
s7415MovieVictim of BeautyRoger YoungWilliam Devane, Jeri Ryan, Michele Ab...United States2011-10-011991TV-1493 minDramas, ThrillersA beauty pageant winner is stalked by...20
s819MovieBeing Elmo: A Puppeteer's JourneyConstance MarksKevin Clash, Whoopi GoldbergUnited States2012-02-212011PG76 minDocumentariesWhoopi Goldberg narrates Elmo creator...1
s3467MovieKung Fu Panda: HolidayTim JohnsonJack Black, Angelina Jolie, Dustin Ho...United States2012-12-012010TV-PG26 minChildren & Family Movies, ComediesAs preparations for the Winter Feast ...2
s6057TV ShowThe 4400nullJoel Gretsch, Jacqueline McKenzie, Pa...United States, United Kingdom2013-09-012007TV-144 SeasonsTV Dramas, TV Mysteries, TV Sci-Fi & ...4400 people who vanished over the cou...6
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"show_id\",\"type\",\"title\",\"director\",\"cast\",\"country\",\"date_added\",\"release_year\",\"rating\",\"duration\",\"listed_in\",\"description\",\"years_off_platform\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDate\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}],\"nrow\":4940,\"ncol\":13},\"kotlin_dataframe\":[{\"show_id\":\"s7114\",\"type\":\"Movie\",\"title\":\"To and From New York\",\"director\":\"Sorin Dan Mihalcescu\",\"cast\":\"Barbara King, Shaana Diya, John Krisiukenas, Yoriko Haraguchi, David Crane, Chad Thomas\",\"country\":\"United States\",\"date_added\":\"2008-01-01\",\"release_year\":2006,\"rating\":\"TV-MA\",\"duration\":\"81 min\",\"listed_in\":\"Dramas, Independent Movies, Thrillers\",\"description\":\"While covering a story in New York City, a Seattle-based reporter uncovers a link between two missing women that changes her article entirely.\",\"years_off_platform\":2},{\"show_id\":\"s1766\",\"type\":\"TV Show\",\"title\":\"Dinner for Five\",\"director\":null,\"cast\":null,\"country\":\"United States\",\"date_added\":\"2008-02-04\",\"release_year\":2007,\"rating\":\"TV-MA\",\"duration\":\"1 Season\",\"listed_in\":\"Stand-Up Comedy & Talk Shows\",\"description\":\"In each episode, four celebrities join host Jon Favreau for dinner and share revealing stories about both show business and their personal lives.\",\"years_off_platform\":1},{\"show_id\":\"s3249\",\"type\":\"Movie\",\"title\":\"Just Another Love Story\",\"director\":\"Ole Bornedal\",\"cast\":\"Anders W. Berthelsen, Rebecka Hemse, Nikolaj Lie Kaas, Charlotte Fich, Dejan Cukic, Karsten Jansfort, Flemming Enevold, Bent Mejding, Ewa Fröling, Josephine Raahauge, Timm Vladimir, Ditte Hansen\",\"country\":\"Denmark\",\"date_added\":\"2009-05-05\",\"release_year\":2007,\"rating\":\"TV-MA\",\"duration\":\"104 min\",\"listed_in\":\"Dramas, International Movies\",\"description\":\"When he causes a car accident that leaves a young woman with amnesia, a man finds himself telling incredible lies to make her and her family happy.\",\"years_off_platform\":2},{\"show_id\":\"s3841\",\"type\":\"Movie\",\"title\":\"Mad Ron's Prevues from Hell\",\"director\":\"Jim Monaco\",\"cast\":\"Nick Pawlow, Jordu Schell, Jay Kushwara, Michael Townsend Wright, Anthony Kelly, Happy Goldsplatt, Ron Roccia, Fred Pomerleau, Joe Amodei, Nicholas Kitsopoulos\",\"country\":\"United States\",\"date_added\":\"2010-11-01\",\"release_year\":1987,\"rating\":\"NR\",\"duration\":\"84 min\",\"listed_in\":\"Cult Movies, Horror Movies\",\"description\":\"This collection cherry-picks trailers, forgotten scenes and poster art from 47 gory oldies, including Bay of Blood and The Blood Spattered Bride.\",\"years_off_platform\":23},{\"show_id\":\"s2042\",\"type\":\"Movie\",\"title\":\"Even the Rain\",\"director\":\"Icíar Bollaín\",\"cast\":\"Luis Tosar, Gael García Bernal, Juan Carlos Aduviri, Karra Elejalde, Raúl Arévalo, Carlos Santos, Cassandra Ciangherotti, Milena Soliz, Leónidas Chiri, Vicente Romero, Daniel Currás, Pau Cólera\",\"country\":\"Spain, Mexico, France\",\"date_added\":\"2011-05-17\",\"release_year\":2010,\"rating\":\"TV-MA\",\"duration\":\"103 min\",\"listed_in\":\"Dramas, International Movies\",\"description\":\"While making a film about the incursion of Christopher Columbus in the New World, a director finds the Bolivian locals protesting modern exploitation.\",\"years_off_platform\":1},{\"show_id\":\"s3222\",\"type\":\"Movie\",\"title\":\"Joseph: King of Dreams\",\"director\":\"Rob LaDuca, Robert C. Ramirez\",\"cast\":\"Ben Affleck, Mark Hamill, Richard Herd, Maureen McGovern, Jodi Benson, Judith Light, James Eckhouse, Richard McGonagle, David Campbell, Steven Weber, Dan Castellaneta, Rene Auberjonois, Ken Hudson Campbell, Tom Virtue, Jeff Bennett\",\"country\":\"United States\",\"date_added\":\"2011-09-27\",\"release_year\":2000,\"rating\":\"TV-PG\",\"duration\":\"75 min\",\"listed_in\":\"Children & Family Movies, Dramas, Faith & Spirituality\",\"description\":\"With his gift of dream interpretation and his brilliantly colored coat, Joseph inspires jealousy in his brothers in this animated Bible story.\",\"years_off_platform\":11},{\"show_id\":\"s233\",\"type\":\"Movie\",\"title\":\"A Stoning in Fulham County\",\"director\":\"Larry Elikann\",\"cast\":\"Ken Olin, Jill Eikenberry, Maureen Mueller, Gregg Henry, Nicholas Pryor, Noble Willingham, Peter Michael Goetz, Ron Perlman, Theodore Bikel, Olivia Burnette, Bill Allen, Brad Pitt\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1988,\"rating\":\"TV-14\",\"duration\":\"95 min\",\"listed_in\":\"Dramas\",\"description\":\"After reckless teens kill an Amish child, a prosecutor attempts to bring the youths to justice despite the condemnation he faces from the community.\",\"years_off_platform\":23},{\"show_id\":\"s309\",\"type\":\"Movie\",\"title\":\"Adam: His Song Continues\",\"director\":\"Robert Markowitz\",\"cast\":\"Daniel J. Travanti, JoBeth Williams, Richard Masur, Martha Scott, Paul Regina\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1986,\"rating\":\"TV-MA\",\"duration\":\"96 min\",\"listed_in\":\"Dramas\",\"description\":\"After their child was abducted and murdered, John and Reve Walsh fought to raise national awareness of the problem of missing children.\",\"years_off_platform\":25},{\"show_id\":\"s2623\",\"type\":\"Movie\",\"title\":\"Hard Lessons\",\"director\":\"Eric Laneuville\",\"cast\":\"Denzel Washington, Lynn Whitfield, Richard Masur, Akosua Busia\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1986,\"rating\":\"TV-14\",\"duration\":\"94 min\",\"listed_in\":\"Dramas\",\"description\":\"This drama based on real-life events tells the story of George McKenna, the tough, determined new principal of a notorious Los Angeles high school.\",\"years_off_platform\":25},{\"show_id\":\"s2963\",\"type\":\"Movie\",\"title\":\"In Defense of a Married Man\",\"director\":\"Joel Oliansky\",\"cast\":\"Judith Light, Michael Ontkean, Jerry Orbach, Pat Corley, Nicholas Campbell, John Colicos, Cynthia Sikes, Johnny Galecki\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1990,\"rating\":\"TV-14\",\"duration\":\"94 min\",\"listed_in\":\"Dramas\",\"description\":\"A lawyer's husband is having an affair. When his mistress is found dead, he becomes the prime suspect – and his wife must take charge of his defense.\",\"years_off_platform\":21},{\"show_id\":\"s5042\",\"type\":\"Movie\",\"title\":\"Quiet Victory: The Charlie Wedemeyer Story\",\"director\":\"Roy Campanella II\",\"cast\":\"Pam Dawber, Michael Nouri, Bess Meyer, Peter Berg, James Handy, Kyle Chandler\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1988,\"rating\":\"TV-PG\",\"duration\":\"93 min\",\"listed_in\":\"Dramas, Sports Movies\",\"description\":\"When high school football coach Charlie Wedemeyer is diagnosed with Lou Gehrig's disease, he perseveres in leading his team despite becoming disabled.\",\"years_off_platform\":23},{\"show_id\":\"s5833\",\"type\":\"Movie\",\"title\":\"Strange Voices\",\"director\":\"Arthur Allan Seidelman\",\"cast\":\"Nancy McKeon, Valerie Harper, Stephen Macht, Tricia Leigh Fisher, Millie Perkins\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1987,\"rating\":\"TV-PG\",\"duration\":\"96 min\",\"listed_in\":\"Dramas\",\"description\":\"When their college-age daughter suddenly begins acting erratically and is diagnosed with schizophrenia, a desperate couple seeks treatment for her.\",\"years_off_platform\":24},{\"show_id\":\"s6846\",\"type\":\"Movie\",\"title\":\"The Ryan White Story\",\"director\":\"John Herzfeld\",\"cast\":\"Judith Light, Lukas Haas, Michael Bowen, Nikki Cox, George Dzundza, Valerie Landsburg, Sarah Jessica Parker, Mitchell Ryan\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1989,\"rating\":\"TV-PG\",\"duration\":\"94 min\",\"listed_in\":\"Dramas\",\"description\":\"After contracting HIV from a tainted blood treatment, teenaged hemophiliac Ryan White is forced to fight for his right to attend public school.\",\"years_off_platform\":22},{\"show_id\":\"s7150\",\"type\":\"Movie\",\"title\":\"Too Young the Hero\",\"director\":\"Buzz Kulik\",\"cast\":\"Ricky Schroder, Jon DeVries, Debra Mooney, Mary-Louise Parker, Christopher Curry, Rick Warner, Tom Wood\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1988,\"rating\":\"TV-MA\",\"duration\":\"94 min\",\"listed_in\":\"Dramas\",\"description\":\"Twelve-year-old Calvin manages to join the navy and serves in the battle of Guadalcanal. But when his age is revealed, the boy is sent to the brig.\",\"years_off_platform\":23},{\"show_id\":\"s7231\",\"type\":\"Movie\",\"title\":\"Triumph of the Heart\",\"director\":\"Richard Michaels\",\"cast\":\"Mario Van Peebles, Susan Ruttan, Lane R. Davis, Lynn Whitfield, Adam Faraizl, Polly Holliday, Jay Pennison, John Schuhmacher\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1991,\"rating\":\"TV-PG\",\"duration\":\"93 min\",\"listed_in\":\"Dramas, Sports Movies\",\"description\":\"This drama tells the tale of Ricky Bell, a pro running back with the Tampa Bay Buccaneers who died of a muscle disease in the prime of his career.\",\"years_off_platform\":20},{\"show_id\":\"s7363\",\"type\":\"Movie\",\"title\":\"Unspeakable Acts\",\"director\":\"Linda Otto\",\"cast\":\"Jill Clayburgh, Brad Davis, Sam Behrens\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1990,\"rating\":\"TV-14\",\"duration\":\"95 min\",\"listed_in\":\"Dramas\",\"description\":\"Laurie and Joseph are doctors who interview sexually abused children. With a lot of energy they track down a pedophile who has abused many toddlers.\",\"years_off_platform\":21},{\"show_id\":\"s7415\",\"type\":\"Movie\",\"title\":\"Victim of Beauty\",\"director\":\"Roger Young\",\"cast\":\"William Devane, Jeri Ryan, Michele Abrams, Nick Searcy, Linda Pierce, J. Don Ferguson, Butch Slade, Joyce Bowden-Kirby\",\"country\":\"United States\",\"date_added\":\"2011-10-01\",\"release_year\":1991,\"rating\":\"TV-14\",\"duration\":\"93 min\",\"listed_in\":\"Dramas, Thrillers\",\"description\":\"A beauty pageant winner is stalked by a serial killer who kidnaps her sister and torments her family in this thriller based on a shocking true story.\",\"years_off_platform\":20},{\"show_id\":\"s819\",\"type\":\"Movie\",\"title\":\"Being Elmo: A Puppeteer's Journey\",\"director\":\"Constance Marks\",\"cast\":\"Kevin Clash, Whoopi Goldberg\",\"country\":\"United States\",\"date_added\":\"2012-02-21\",\"release_year\":2011,\"rating\":\"PG\",\"duration\":\"76 min\",\"listed_in\":\"Documentaries\",\"description\":\"Whoopi Goldberg narrates Elmo creator Kevin Clash's remarkable journey from a working-class Baltimore neighborhood to Jim Henson's \\\"Sesame Street.\\\"\",\"years_off_platform\":1},{\"show_id\":\"s3467\",\"type\":\"Movie\",\"title\":\"Kung Fu Panda: Holiday\",\"director\":\"Tim Johnson\",\"cast\":\"Jack Black, Angelina Jolie, Dustin Hoffman, Jackie Chan, Seth Rogen, Lucy Liu, David Cross, James Hong, Dan Fogler\",\"country\":\"United States\",\"date_added\":\"2012-12-01\",\"release_year\":2010,\"rating\":\"TV-PG\",\"duration\":\"26 min\",\"listed_in\":\"Children & Family Movies, Comedies\",\"description\":\"As preparations for the Winter Feast build, Po is caught between his obligations as the Dragon Warrior and his family holiday traditions.\",\"years_off_platform\":2},{\"show_id\":\"s6057\",\"type\":\"TV Show\",\"title\":\"The 4400\",\"director\":null,\"cast\":\"Joel Gretsch, Jacqueline McKenzie, Patrick John Flueger, Conchita Campbell, Chad Faust, Richard Kahan, Mahershala Ali, Billy Campbell\",\"country\":\"United States, United Kingdom\",\"date_added\":\"2013-09-01\",\"release_year\":2007,\"rating\":\"TV-14\",\"duration\":\"4 Seasons\",\"listed_in\":\"TV Dramas, TV Mysteries, TV Sci-Fi & Fantasy\",\"description\":\"4400 people who vanished over the course of five decades suddenly reappear. None have aged, and slowly it's revealed that they have mysterious powers.\",\"years_off_platform\":6}]}" }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We dropped negative values because it happens that titles are added to the platform while it is still in production. Also dropped the zero values as they are of no interest. " ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:13:35.998769Z", "start_time": "2025-05-27T15:13:35.720763Z" } }, "source": [ "df_years.valueCounts(false) { years_off_platform }.plot {\n", " x(years_off_platform) { axis.name = \"years\" }\n", " points {\n", " y(\"count\")\n", " size = 7.5\n", " color(years_off_platform) {\n", " scale = continuous(range = Color.hex(\"#97a6d9\")..Color.hex(\"#00256e\"))\n", " }\n", " }\n", " layout {\n", " title = \"How long does it take for a title to be added to Netflix?\"\n", " size = 1000 to 500\n", " }\n", "}" ], "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", " 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"mapping": { "x": "years_off_platform", "y": "count", "color": "years_off_platform" }, "stat": "identity", "size": 7.5, "sampling": "none", "inherit_aes": false, "position": "identity", "geom": "point" } ], "data_meta": { "series_annotations": [ { "type": "int", "column": "years_off_platform" }, { "type": "int", "column": "count" } ] } }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, let's build the informal top charts for the oldest and newest movies and TV shows." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* ***Top 5 movies with the most days on Netflix***" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:13:43.947786Z", "start_time": "2025-05-27T15:13:43.752015Z" } }, "source": [ "// Top 5 oldest movies\n", "df_days\n", " .filter { type == \"Movie\" } // filtering by type\n", " .sortByDesc { days_on_platform } // sorting by number of days on Netflix\n", " .select { cols(type, title, country, date_added, release_year, duration) } // selecting required columns\n", " .head() // taking first five rows" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
typetitlecountrydate_addedrelease_yearduration
MovieTo and From New YorkUnited States2008-01-01200681 min
MovieJust Another Love StoryDenmark2009-05-052007104 min
MovieSplatterUnited States2009-11-18200929 min
MovieMad Ron's Prevues from HellUnited States2010-11-01198784 min
MovieEven the RainSpain, Mexico, France2011-05-172010103 min
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"type\",\"title\",\"country\",\"date_added\",\"release_year\",\"duration\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDate\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":5,\"ncol\":6},\"kotlin_dataframe\":[{\"type\":\"Movie\",\"title\":\"To and From New York\",\"country\":\"United States\",\"date_added\":\"2008-01-01\",\"release_year\":2006,\"duration\":\"81 min\"},{\"type\":\"Movie\",\"title\":\"Just Another Love Story\",\"country\":\"Denmark\",\"date_added\":\"2009-05-05\",\"release_year\":2007,\"duration\":\"104 min\"},{\"type\":\"Movie\",\"title\":\"Splatter\",\"country\":\"United States\",\"date_added\":\"2009-11-18\",\"release_year\":2009,\"duration\":\"29 min\"},{\"type\":\"Movie\",\"title\":\"Mad Ron's Prevues from Hell\",\"country\":\"United States\",\"date_added\":\"2010-11-01\",\"release_year\":1987,\"duration\":\"84 min\"},{\"type\":\"Movie\",\"title\":\"Even the Rain\",\"country\":\"Spain, Mexico, France\",\"date_added\":\"2011-05-17\",\"release_year\":2010,\"duration\":\"103 min\"}]}" }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* ***Top 5 movies recently added on Netflix***" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:13:56.719564Z", "start_time": "2025-05-27T15:13:56.454244Z" } }, "source": [ "// Top 5 newest movies\n", "df_days\n", " .filter { type == \"Movie\" }\n", " .sortBy { days_on_platform }\n", " .select { cols(type, title, country, date_added, release_year, duration) }\n", " .head()" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
typetitlecountrydate_addedrelease_yearduration
MovieA Monster CallsUnited Kingdom, Spain, United States2021-01-162016108 min
MovieDeath of MeUnited States, Thailand2021-01-16202094 min
MovieRadium GirlsUnited States2021-01-162018103 min
MovieDouble DadBrazil2021-01-152020105 min
MovieHookUnited States2021-01-151991142 min
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"type\",\"title\",\"country\",\"date_added\",\"release_year\",\"duration\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDate\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":5,\"ncol\":6},\"kotlin_dataframe\":[{\"type\":\"Movie\",\"title\":\"A Monster Calls\",\"country\":\"United Kingdom, Spain, United States\",\"date_added\":\"2021-01-16\",\"release_year\":2016,\"duration\":\"108 min\"},{\"type\":\"Movie\",\"title\":\"Death of Me\",\"country\":\"United States, Thailand\",\"date_added\":\"2021-01-16\",\"release_year\":2020,\"duration\":\"94 min\"},{\"type\":\"Movie\",\"title\":\"Radium Girls\",\"country\":\"United States\",\"date_added\":\"2021-01-16\",\"release_year\":2018,\"duration\":\"103 min\"},{\"type\":\"Movie\",\"title\":\"Double Dad\",\"country\":\"Brazil\",\"date_added\":\"2021-01-15\",\"release_year\":2020,\"duration\":\"105 min\"},{\"type\":\"Movie\",\"title\":\"Hook\",\"country\":\"United States\",\"date_added\":\"2021-01-15\",\"release_year\":1991,\"duration\":\"142 min\"}]}" }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* ***Top 5 TV Shows with most days on Netflix***" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:13:59.973881Z", "start_time": "2025-05-27T15:13:59.741559Z" } }, "source": [ "// Top 5 oldest shows\n", "df_days\n", " .filter { type == \"TV Show\" }\n", " .sortByDesc { days_on_platform }\n", " .select { cols(type, title, country, date_added, release_year, duration) }\n", " .head()" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
typetitlecountrydate_addedrelease_yearduration
TV ShowDinner for FiveUnited States2008-02-0420071 Season
TV ShowJack TaylorUnited States, Ireland2013-03-3120161 Season
TV ShowBreaking BadUnited States2013-08-0220135 Seasons
TV ShowThe 4400United States, United Kingdom2013-09-0120074 Seasons
TV ShowGossip GirlUnited States2013-10-0820126 Seasons
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"type\",\"title\",\"country\",\"date_added\",\"release_year\",\"duration\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDate\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":5,\"ncol\":6},\"kotlin_dataframe\":[{\"type\":\"TV Show\",\"title\":\"Dinner for Five\",\"country\":\"United States\",\"date_added\":\"2008-02-04\",\"release_year\":2007,\"duration\":\"1 Season\"},{\"type\":\"TV Show\",\"title\":\"Jack Taylor\",\"country\":\"United States, Ireland\",\"date_added\":\"2013-03-31\",\"release_year\":2016,\"duration\":\"1 Season\"},{\"type\":\"TV Show\",\"title\":\"Breaking Bad\",\"country\":\"United States\",\"date_added\":\"2013-08-02\",\"release_year\":2013,\"duration\":\"5 Seasons\"},{\"type\":\"TV Show\",\"title\":\"The 4400\",\"country\":\"United States, United Kingdom\",\"date_added\":\"2013-09-01\",\"release_year\":2007,\"duration\":\"4 Seasons\"},{\"type\":\"TV Show\",\"title\":\"Gossip Girl\",\"country\":\"United States\",\"date_added\":\"2013-10-08\",\"release_year\":2012,\"duration\":\"6 Seasons\"}]}" }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "* ***Top 5 TV Shows recently added on Netflix***" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:14:03.492706Z", "start_time": "2025-05-27T15:14:03.286641Z" } }, "source": [ "// Top 5 newest shows\n", "df_days\n", " .filter { type == \"TV Show\" }\n", " .sortBy { days_on_platform }\n", " .select { cols(type, title, country, date_added, release_year, duration) }\n", " .head()" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
typetitlecountrydate_addedrelease_yearduration
TV ShowBling Empirenull2021-01-1520211 Season
TV ShowCarmen SandiegoUnited States2021-01-1520214 Seasons
TV ShowDisenchantmentUnited States2021-01-1520213 Seasons
TV ShowHenry DangerUnited States2021-01-1520163 Seasons
TV ShowKuroko's BasketballJapan2021-01-1520131 Season
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"type\",\"title\",\"country\",\"date_added\",\"release_year\",\"duration\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDate\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":5,\"ncol\":6},\"kotlin_dataframe\":[{\"type\":\"TV Show\",\"title\":\"Bling Empire\",\"country\":null,\"date_added\":\"2021-01-15\",\"release_year\":2021,\"duration\":\"1 Season\"},{\"type\":\"TV Show\",\"title\":\"Carmen Sandiego\",\"country\":\"United States\",\"date_added\":\"2021-01-15\",\"release_year\":2021,\"duration\":\"4 Seasons\"},{\"type\":\"TV Show\",\"title\":\"Disenchantment\",\"country\":\"United States\",\"date_added\":\"2021-01-15\",\"release_year\":2021,\"duration\":\"3 Seasons\"},{\"type\":\"TV Show\",\"title\":\"Henry Danger\",\"country\":\"United States\",\"date_added\":\"2021-01-15\",\"release_year\":2016,\"duration\":\"3 Seasons\"},{\"type\":\"TV Show\",\"title\":\"Kuroko's Basketball\",\"country\":\"Japan\",\"date_added\":\"2021-01-15\",\"release_year\":2013,\"duration\":\"1 Season\"}]}" }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 34 }, { "cell_type": "markdown", "metadata": {}, "source": "You might be interested in what months titles are added most often?" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:14:28.253554Z", "start_time": "2025-05-27T15:14:27.646437Z" } }, "source": [ "val df_split_date = df\n", " // splitting dates into four columns\n", " .split { date_added }.by { listOf(it, it.dayOfWeek, it.month, it.year) }\n", " .into(\"date\", \"day\", \"month\", \"year\")\n", " .sortBy(\"month\") // sorting by month\n", "df_split_date" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
show_idtypetitledirectorcastcountrydatedaymonthyearrelease_yearratingdurationlisted_indescription
s7114MovieTo and From New YorkSorin Dan MihalcescuBarbara King, Shaana Diya, John Krisi...United States2008-01-01TUESDAYJANUARY20082006TV-MA81 minDramas, Independent Movies, ThrillersWhile covering a story in New York Ci...
s6899MovieThe SquareJehane NoujaimAhmed Hassan, Khalid Abdalla, Magdy A...United Kingdom, Egypt, United States2014-01-17FRIDAYJANUARY20142013TV-MA105 minDocumentaries, International MoviesThis Emmy-winning, street-level view ...
s4154MovieMittGreg WhiteleyMitt RomneyUnited States2014-01-24FRIDAYJANUARY20142014TV-PG93 minDocumentariesThe real Mitt Romney is revealed in t...
s2947MovieIliza Shlesinger: Freezing HotnullIliza Shlesingernull2015-01-23FRIDAYJANUARY20152015TV-MA72 minStand-Up ComedySmart and brazen comedian Iliza Shles...
s900TV ShowBig Bad BeetleborgsnullWesley Barker, Herbie Baez, Elisabeth...United States, France, Japan2016-01-01FRIDAYJANUARY20161997TV-G2 SeasonsKids' TV, TV ComediesWhen three teens free a spirit that o...
s2843MovieHow to Change the WorldJerry RothwellnullCanada, United Kingdom, Netherlands2016-01-01FRIDAYJANUARY20162015NR110 minDocumentaries, International MoviesIn the 1970s, a group of activists wh...
s4088TV ShowMighty Morphin Alien RangersnullJulia Jordan, Matthew Sakimoto, Sicil...null2016-01-01FRIDAYJANUARY20161996TV-Y71 SeasonKids' TVVisitors arrive from space to help Re...
s4089TV ShowMighty Morphin Power RangersnullAustin St. John, Thuy Trang, Walter J...United States, Japan2016-01-01FRIDAYJANUARY20162010TV-Y74 SeasonsKids' TVFive average teens are chosen by an i...
s4480TV ShowNinja Turtles: The Next MutationnullJarred Blancard, Mitchell A. Lee Yuen...Canada, United States2016-01-01FRIDAYJANUARY20161997TV-G1 SeasonKids' TV, TV ComediesEveryone's favorite teenage mutants a...
s4931TV ShowPower Rangers Dino ThundernullJames Napier, Kevin Duhaney, Emma Lah...United States2016-01-01FRIDAYJANUARY20162004TV-Y71 SeasonKids' TVDr. Tommy Oliver returns when his stu...
s4932TV ShowPower Rangers in SpacenullTracy Lynn Cruz, Patricia Ja Lee, Chr...United States, France, Japan2016-01-01FRIDAYJANUARY20161998TV-Y71 SeasonKids' TVWith the Power Chamber destroyed, for...
s4933TV ShowPower Rangers Jungle FurynullJason Smith, Aljin Abella, Anna Hutch...United States2016-01-01FRIDAYJANUARY20162008TV-Y71 SeasonKids' TVThe Power Rangers travel to Californi...
s4934TV ShowPower Rangers Lightspeed RescuenullMichael Chaturantabut, Sean CW Johnso...France, Japan, United States2016-01-01FRIDAYJANUARY20162000TV-Y71 SeasonKids' TVAs demons rumble from their graves be...
s4935TV ShowPower Rangers Lost GalaxynullArchie Kao, Reggie Rolle, Danny Slavi...United States, France, Japan2016-01-01FRIDAYJANUARY20161999TV-Y71 SeasonKids' TVFive teenagers, transformed by the my...
s4936TV ShowPower Rangers Mystic ForcenullFirass Dirani, Angie Diaz, Richard Br...United States2016-01-01FRIDAYJANUARY20162006TV-Y71 SeasonKids' TVWhen the wicked Undead Army is unleas...
s4938TV ShowPower Rangers Ninja StormnullPua Magasiva, Sally Martin, Glenn McM...United States, New Zealand2016-01-01FRIDAYJANUARY20162003TV-Y71 SeasonKids' TVWhen the elite warriors from the Wind...
s4939TV ShowPower Rangers Operation OverdrivenullJames Maclurcan, Caitlin Murphy, Samu...United States, New Zealand, Japan2016-01-01FRIDAYJANUARY20162007TV-Y71 SeasonKids' TVTo keep powerful jewels from falling ...
s4940TV ShowPower Rangers RPMnullEka Darville, Ari Boyland, Rose McIve...United States2016-01-01FRIDAYJANUARY20162009TV-Y71 SeasonKids' TVThe Power Rangers' new member, Dillon...
s4941TV ShowPower Rangers S.P.D.nullBrandon Jay McLaren, Chris Violette, ...United States, New Zealand2016-01-01FRIDAYJANUARY20162005TV-Y71 SeasonKids' TVWhen the Troobian Empire attacks Eart...
s4942TV ShowPower Rangers SamurainullAlex Heartman, Erika Fong, Hector Dav...United States2016-01-01FRIDAYJANUARY20162011TV-Y71 SeasonKids' TVA new generation of Power Rangers mus...
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"show_id\",\"type\",\"title\",\"director\",\"cast\",\"country\",\"date\",\"day\",\"month\",\"year\",\"release_year\",\"rating\",\"duration\",\"listed_in\",\"description\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDate\"},{\"kind\":\"ValueColumn\",\"type\":\"java.time.DayOfWeek\"},{\"kind\":\"ValueColumn\",\"type\":\"java.time.Month\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":7777,\"ncol\":15},\"kotlin_dataframe\":[{\"show_id\":\"s7114\",\"type\":\"Movie\",\"title\":\"To and From New York\",\"director\":\"Sorin Dan Mihalcescu\",\"cast\":\"Barbara King, Shaana Diya, John Krisiukenas, Yoriko Haraguchi, David Crane, Chad Thomas\",\"country\":\"United States\",\"date\":\"2008-01-01\",\"day\":\"TUESDAY\",\"month\":\"JANUARY\",\"year\":2008,\"release_year\":2006,\"rating\":\"TV-MA\",\"duration\":\"81 min\",\"listed_in\":\"Dramas, Independent Movies, Thrillers\",\"description\":\"While covering a story in New York City, a Seattle-based reporter uncovers a link between two missing women that changes her article entirely.\"},{\"show_id\":\"s6899\",\"type\":\"Movie\",\"title\":\"The Square\",\"director\":\"Jehane Noujaim\",\"cast\":\"Ahmed Hassan, Khalid Abdalla, Magdy Ashour, Ramy Essam, Bothaina Kamel, Dina Abdullah, Aida Elkashef, Ragia Omran, Pierre Seyoufr, Dina Amer, Sherif Boray, Khaled Nagy, Salma Saied, Ahmed Saleh, Alaa Seif\",\"country\":\"United Kingdom, Egypt, United States\",\"date\":\"2014-01-17\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2014,\"release_year\":2013,\"rating\":\"TV-MA\",\"duration\":\"105 min\",\"listed_in\":\"Documentaries, International Movies\",\"description\":\"This Emmy-winning, street-level view of the 2011 Egyptian Revolution captures the astonishing uprising that led to the collapse of two governments.\"},{\"show_id\":\"s4154\",\"type\":\"Movie\",\"title\":\"Mitt\",\"director\":\"Greg Whiteley\",\"cast\":\"Mitt Romney\",\"country\":\"United States\",\"date\":\"2014-01-24\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2014,\"release_year\":2014,\"rating\":\"TV-PG\",\"duration\":\"93 min\",\"listed_in\":\"Documentaries\",\"description\":\"The real Mitt Romney is revealed in this documentary that goes beyond the sound bites with unprecedented access to his 2012 presidential campaign.\"},{\"show_id\":\"s2947\",\"type\":\"Movie\",\"title\":\"Iliza Shlesinger: Freezing Hot\",\"director\":null,\"cast\":\"Iliza Shlesinger\",\"country\":null,\"date\":\"2015-01-23\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2015,\"release_year\":2015,\"rating\":\"TV-MA\",\"duration\":\"72 min\",\"listed_in\":\"Stand-Up Comedy\",\"description\":\"Smart and brazen comedian Iliza Shlesinger applies her fresh, laugh-out-loud perspective to the universal struggles between men and women.\"},{\"show_id\":\"s900\",\"type\":\"TV Show\",\"title\":\"Big Bad Beetleborgs\",\"director\":null,\"cast\":\"Wesley Barker, Herbie Baez, Elisabeth Lund, Shannon Chandler, Billy Forester, Frank Addela, Kyle Jordan, Dave 'Foots' Footman, Derek Stephen Prince, Vivian Smallwood\",\"country\":\"United States, France, Japan\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":1997,\"rating\":\"TV-G\",\"duration\":\"2 Seasons\",\"listed_in\":\"Kids' TV, TV Comedies\",\"description\":\"When three teens free a spirit that offers to grant each a wish, they choose to become their favorite superheroes, launching a series of adventures.\"},{\"show_id\":\"s2843\",\"type\":\"Movie\",\"title\":\"How to Change the World\",\"director\":\"Jerry Rothwell\",\"cast\":null,\"country\":\"Canada, United Kingdom, Netherlands\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2015,\"rating\":\"NR\",\"duration\":\"110 min\",\"listed_in\":\"Documentaries, International Movies\",\"description\":\"In the 1970s, a group of activists who gathered to protest nuclear testing formed the iconic Greenpeace environmental organization.\"},{\"show_id\":\"s4088\",\"type\":\"TV Show\",\"title\":\"Mighty Morphin Alien Rangers\",\"director\":null,\"cast\":\"Julia Jordan, Matthew Sakimoto, Sicily Johnson, Michael O'Laskey II, Michael R. Gotto, Justin Timsit, Rajia Baroudi, David Bacon, Jim Gray, Karim Prince, Alan Palmer, Tom Wyner, Barbara Goodson, Robert Axelrod, Kerrigan Mahan\",\"country\":null,\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":1996,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"Visitors arrive from space to help Red Ranger and his squad – temporarily stripped of their powers – fight against Lord Zedd and Rita Repulsa.\"},{\"show_id\":\"s4089\",\"type\":\"TV Show\",\"title\":\"Mighty Morphin Power Rangers\",\"director\":null,\"cast\":\"Austin St. John, Thuy Trang, Walter Jones, Amy Jo Johnson, David Yost, Jason David Frank, Johnny Yong Bosch, Karan Ashley, Steve Cardenas, Catherine Sutherland, Paul Schrier, Jason Narvy, David Fielding, Richard Steven Horvitz, Barbara Goodson\",\"country\":\"United States, Japan\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2010,\"rating\":\"TV-Y7\",\"duration\":\"4 Seasons\",\"listed_in\":\"Kids' TV\",\"description\":\"Five average teens are chosen by an intergalactic wizard to become the Power Rangers, who must use their new powers to fight the evil Rita Repulsa.\"},{\"show_id\":\"s4480\",\"type\":\"TV Show\",\"title\":\"Ninja Turtles: The Next Mutation\",\"director\":null,\"cast\":\"Jarred Blancard, Mitchell A. Lee Yuen, Dean Choe, Richard Yee, Gabe Khouth, Nicole Parker, Kirby Morrow, Matt Hill, Jason Gray-Stanford, Michael Dobson, Lalainia Lindbjerg, Shishir Inocalla, Fiona Scott, Stephen Mendel, Larry Lam, Geoff Wong\",\"country\":\"Canada, United States\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":1997,\"rating\":\"TV-G\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV, TV Comedies\",\"description\":\"Everyone's favorite teenage mutants are back in live action to fight crime with help from a new ninja turtle – a female named Venus De Milo.\"},{\"show_id\":\"s4931\",\"type\":\"TV Show\",\"title\":\"Power Rangers Dino Thunder\",\"director\":null,\"cast\":\"James Napier, Kevin Duhaney, Emma Lahana, Jason David Frank, Jeffrey Parazzo, Katrina Devine, Tom Hern, Miriama Smith, Latham Gaines, James Gaylyn\",\"country\":\"United States\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2004,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"Dr. Tommy Oliver returns when his students discover the Dino Gems that morph them into the Dino Power Rangers, enabling them to stop the evil Mesogog.\"},{\"show_id\":\"s4932\",\"type\":\"TV Show\",\"title\":\"Power Rangers in Space\",\"director\":null,\"cast\":\"Tracy Lynn Cruz, Patricia Ja Lee, Christopher Khayman Lee, Justin Nimmo, Roger Velasco, Selwyn Ward, Melody Perkins, Paul Schrier, Jason Narvy, Mioko Fujiwara, Soichiro Hoshi, Yuji Kishimoto, Sanae Kobayashi, Tsuyoshi Takishita, Lex Lang\",\"country\":\"United States, France, Japan\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":1998,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"With the Power Chamber destroyed, former Turbo Power Rangers T.J., Cassie, Carlos and Ashley search for Zordon, who's been abducted by Dark Specter.\"},{\"show_id\":\"s4933\",\"type\":\"TV Show\",\"title\":\"Power Rangers Jungle Fury\",\"director\":null,\"cast\":\"Jason Smith, Aljin Abella, Anna Hutchison, David de Lautour, Nikolai Nikolaeff, Sarah Thomson, Bede Skinner, Holly Shanahan, Kelson Henderson, Richard Simpson, Cameron Rhodes, Elisabeth Easther, Mark Wright, Derek Judge\",\"country\":\"United States\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2008,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"The Power Rangers travel to California in search of their new master and find him running a pizza parlor – so they join the staff to be trained.\"},{\"show_id\":\"s4934\",\"type\":\"TV Show\",\"title\":\"Power Rangers Lightspeed Rescue\",\"director\":null,\"cast\":\"Michael Chaturantabut, Sean CW Johnson, Alison MacInnis, Keith Robinson, Sasha Craig, Monica Louwerens, Rhett Fisher, Ron Roggé, Jennifer L. Yen, Kim Strauss\",\"country\":\"France, Japan, United States\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2000,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"As demons rumble from their graves beneath Mariner Bay, a government organization called Lightspeed recruits five civilians to defend the city.\"},{\"show_id\":\"s4935\",\"type\":\"TV Show\",\"title\":\"Power Rangers Lost Galaxy\",\"director\":null,\"cast\":\"Archie Kao, Reggie Rolle, Danny Slavin, Valerie Vernon, Cerina Vincent, Russell Lawrence, Amy Miller, Melody Perkins, Kazuki Maehara, Eri Tanaka, Terence J. Rotolo, Hiroshi Tsuchida, Tom Whyte, Bob Papenbrook, Mari Maruta\",\"country\":\"United States, France, Japan\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":1999,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"Five teenagers, transformed by the mystical powers of the Quasar Sabers into the Galaxy Power Rangers, battle the evil Scorpius and Captain Mutiny.\"},{\"show_id\":\"s4936\",\"type\":\"TV Show\",\"title\":\"Power Rangers Mystic Force\",\"director\":null,\"cast\":\"Firass Dirani, Angie Diaz, Richard Brancatisano, Melanie Vallejo, Nic Sampson, John Tui, Peta Rutter, Antonia Prebble, Barnie Duncan, Kelson Henderson, Donogh Rees, Geoff Dolan, Holly Shanahan\",\"country\":\"United States\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2006,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"When the wicked Undead Army is unleashed, sorceress Udonna empowers five teenagers – the Mystic Force Power Rangers – to save mankind.\"},{\"show_id\":\"s4938\",\"type\":\"TV Show\",\"title\":\"Power Rangers Ninja Storm\",\"director\":null,\"cast\":\"Pua Magasiva, Sally Martin, Glenn McMillan, Adam Tuominen, Jorgito Vargas Jr., Jason Chan, Katrina Browne, Katrina Devine, Grant McFarland, Peter Rowley, Megan Nicol, Bruce Hopkins\",\"country\":\"United States, New Zealand\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2003,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"When the elite warriors from the Wind Ninja Academy are captured by the evil Lothor, Shane, Tori and Waldo become Wind Power Rangers to fight back.\"},{\"show_id\":\"s4939\",\"type\":\"TV Show\",\"title\":\"Power Rangers Operation Overdrive\",\"director\":null,\"cast\":\"James Maclurcan, Caitlin Murphy, Samuell Benta, Rhoda Montemayor, Gareth Yuen, Dwayne Cameron, Rod Lousich, David Weatherley, Kelson Henderson, Gerald Urquhart, Ria Vandervis, Mark Ferguson\",\"country\":\"United States, New Zealand, Japan\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2007,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"To keep powerful jewels from falling into the hands of two wicked brothers, five young Power Rangers set out on a dangerous global adventure.\"},{\"show_id\":\"s4940\",\"type\":\"TV Show\",\"title\":\"Power Rangers RPM\",\"director\":null,\"cast\":\"Eka Darville, Ari Boyland, Rose McIver, Milo Cawthorne, Daniel Ewing, Mike Ginn, Li Ming Hu, Olivia Tennet, James Gaylyn, Adelaide Kane, Andrew Laing, Charlie McDermott, Mark Mitchinson\",\"country\":\"United States\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2009,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"The Power Rangers' new member, Dillon, isn't sure he wants to be on the team until he has a dream that sets the Rangers into action.\"},{\"show_id\":\"s4941\",\"type\":\"TV Show\",\"title\":\"Power Rangers S.P.D.\",\"director\":null,\"cast\":\"Brandon Jay McLaren, Chris Violette, Matt Austin, Monica May, Alycia Purrott, John Tui, Rene Naufahu, Michelle Langstone, Barnie Duncan, Kelson Henderson, Jim McLarty, Olivia James-Baird, Josephine Davison, Derek Judge, James Gaylyn\",\"country\":\"United States, New Zealand\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2005,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"When the Troobian Empire attacks Earth, the B-Squad Power Rangers Space Patrol Delta morphs into action to squash the alien threat and save the world.\"},{\"show_id\":\"s4942\",\"type\":\"TV Show\",\"title\":\"Power Rangers Samurai\",\"director\":null,\"cast\":\"Alex Heartman, Erika Fong, Hector David Jr., Najee De-Tiege, Brittany Anne Pirtle, Rene Naufahu, Jeff Szusterman, Felix Ryan, Paul Schrier\",\"country\":\"United States\",\"date\":\"2016-01-01\",\"day\":\"FRIDAY\",\"month\":\"JANUARY\",\"year\":2016,\"release_year\":2011,\"rating\":\"TV-Y7\",\"duration\":\"1 Season\",\"listed_in\":\"Kids' TV\",\"description\":\"A new generation of Power Rangers must master the Symbols of Samurai Power, which give them control over the Earth's elements to battle dark forces.\"}]}" }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 35 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:14:35.525410Z", "start_time": "2025-05-27T15:14:35.173895Z" } }, "source": [ "df_split_date\n", " .valueCounts(false) { year and month }\n", " .plot {\n", " tiles {\n", " x(year)\n", " y(month)\n", " width = .9\n", " height = .9\n", " fillColor(\"count\") 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"execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 36 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Actors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section, let's take a look at the actors and directors who make the content.\n", "First, let's determine the average number of actors in titles." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:14:53.171440Z", "start_time": "2025-05-27T15:14:51.938131Z" } }, "source": [ "// splitting cast and couting number of actors\n", "val cast_df = df\n", " .split { cast }.by(',').inplace()\n", " .add(\"size_cast\") { \"cast\">().size }\n", " // Since we need the time in milliseconds since epoch for the plots, let's convert date_added to an Instant\n", " .convert { date_added }.with { it.atStartOfDayIn(TimeZone.UTC) }\n", "cast_df" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescriptionsize_cast
s7114MovieTo and From New YorkSorin Dan Mihalcescu[Barbara King, Shaana Diya, John Kris...United States2008-01-01T00:00:00Z2006TV-MA81 minDramas, Independent Movies, ThrillersWhile covering a story in New York Ci...6
s1766TV ShowDinner for Fivenull[ ]United States2008-02-04T00:00:00Z2007TV-MA1 SeasonStand-Up Comedy & Talk ShowsIn each episode, four celebrities joi...0
s3249MovieJust Another Love StoryOle Bornedal[Anders W. Berthelsen, Rebecka Hemse,...Denmark2009-05-05T00:00:00Z2007TV-MA104 minDramas, International MoviesWhen he causes a car accident that le...12
s5766MovieSplatterJoe Dante[Corey Feldman, Tony Todd, Tara Leigh...United States2009-11-18T00:00:00Z2009TV-MA29 minHorror MoviesAfter committing suicide, a washed-up...6
s3841MovieMad Ron's Prevues from HellJim Monaco[Nick Pawlow, Jordu Schell, Jay Kushw...United States2010-11-01T00:00:00Z1987NR84 minCult Movies, Horror MoviesThis collection cherry-picks trailers...10
s2042MovieEven the RainIcíar Bollaín[Luis Tosar, Gael García Bernal, Juan...Spain, Mexico, France2011-05-17T00:00:00Z2010TV-MA103 minDramas, International MoviesWhile making a film about the incursi...12
s3222MovieJoseph: King of DreamsRob LaDuca, Robert C. Ramirez[Ben Affleck, Mark Hamill, Richard He...United States2011-09-27T00:00:00Z2000TV-PG75 minChildren & Family Movies, Dramas, Fai...With his gift of dream interpretation...15
s233MovieA Stoning in Fulham CountyLarry Elikann[Ken Olin, Jill Eikenberry, Maureen M...United States2011-10-01T00:00:00Z1988TV-1495 minDramasAfter reckless teens kill an Amish ch...12
s309MovieAdam: His Song ContinuesRobert Markowitz[Daniel J. Travanti, JoBeth Williams,...United States2011-10-01T00:00:00Z1986TV-MA96 minDramasAfter their child was abducted and mu...5
s2623MovieHard LessonsEric Laneuville[Denzel Washington, Lynn Whitfield, R...United States2011-10-01T00:00:00Z1986TV-1494 minDramasThis drama based on real-life events ...4
s2963MovieIn Defense of a Married ManJoel Oliansky[Judith Light, Michael Ontkean, Jerry...United States2011-10-01T00:00:00Z1990TV-1494 minDramasA lawyer's husband is having an affai...8
s5042MovieQuiet Victory: The Charlie Wedemeyer ...Roy Campanella II[Pam Dawber, Michael Nouri, Bess Meye...United States2011-10-01T00:00:00Z1988TV-PG93 minDramas, Sports MoviesWhen high school football coach Charl...6
s5833MovieStrange VoicesArthur Allan Seidelman[Nancy McKeon, Valerie Harper, Stephe...United States2011-10-01T00:00:00Z1987TV-PG96 minDramasWhen their college-age daughter sudde...5
s6846MovieThe Ryan White StoryJohn Herzfeld[Judith Light, Lukas Haas, Michael Bo...United States2011-10-01T00:00:00Z1989TV-PG94 minDramasAfter contracting HIV from a tainted ...8
s7150MovieToo Young the HeroBuzz Kulik[Ricky Schroder, Jon DeVries, Debra M...United States2011-10-01T00:00:00Z1988TV-MA94 minDramasTwelve-year-old Calvin manages to joi...7
s7231MovieTriumph of the HeartRichard Michaels[Mario Van Peebles, Susan Ruttan, Lan...United States2011-10-01T00:00:00Z1991TV-PG93 minDramas, Sports MoviesThis drama tells the tale of Ricky Be...8
s7363MovieUnspeakable ActsLinda Otto[Jill Clayburgh, Brad Davis, Sam Behr...United States2011-10-01T00:00:00Z1990TV-1495 minDramasLaurie and Joseph are doctors who int...3
s7415MovieVictim of BeautyRoger Young[William Devane, Jeri Ryan, Michele A...United States2011-10-01T00:00:00Z1991TV-1493 minDramas, ThrillersA beauty pageant winner is stalked by...8
s819MovieBeing Elmo: A Puppeteer's JourneyConstance Marks[Kevin Clash, Whoopi Goldberg]United States2012-02-21T00:00:00Z2011PG76 minDocumentariesWhoopi Goldberg narrates Elmo creator...2
s1230MovieCasa de mi PadreMatt Piedmont[Will Ferrell, Gael García Bernal, Di...United States, Mexico2012-11-14T00:00:00Z2012R84 minComediesWill Ferrell stars as a Spanish-speak...8
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" Number of people on cast\n", " \n", " \n", " \n", " \n", " count\n", " \n", " \n", " \n", " \n", " actors\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " " ], "application/plot+json": { "output_type": "lets_plot_spec", "output": { "ggtitle": { "text": "Number of people on cast" }, "mapping": {}, "guides": { "x": { "title": "actors" } }, "data": {}, "ggsize": { "width": 950.0, "height": 650.0 }, "kind": "plot", "scales": [ { "aesthetic": "x", "name": "size_cast", "limits": [ null, null ] }, { "aesthetic": "x", "limits": [ null, null ] }, { "aesthetic": "y", "limits": [ null, null ] }, { "aesthetic": "fill", "scale_mapper_kind": "color_gradient", "high": "#006064", "low": "#E0F7FA", "limits": [ null, null ], "guide": "none" } ], "layers": [ { "mapping": { "x": "x", "y": "count", "fill": "count" }, "stat": "identity", "data": { "x": [ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 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content? Let's take a look at these actors and how many times they took part in movies and shows." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:15:51.843715Z", "start_time": "2025-05-27T15:15:51.552318Z" } }, "source": [ "// counting the participation of each actor\n", "val actors_df = cast_df.cast.explode().valueCounts()\n", "actors_df" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
castcount
Anupam Kher42
Shah Rukh Khan35
Om Puri30
Naseeruddin Shah30
Takahiro Sakurai29
Akshay Kumar29
Yuki Kaji27
Amitabh Bachchan27
Boman Irani27
Paresh Rawal27
Kareena Kapoor25
Andrea Libman24
John Cleese24
Vincent Tong24
Tara Strong22
Ashleigh Ball22
Nawazuddin Siddiqui21
Ajay Devgn21
Daisuke Ono20
Salman Khan20
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"cast\",\"count\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}],\"nrow\":32847,\"ncol\":2},\"kotlin_dataframe\":[{\"cast\":\"Anupam Kher\",\"count\":42},{\"cast\":\"Shah Rukh Khan\",\"count\":35},{\"cast\":\"Om Puri\",\"count\":30},{\"cast\":\"Naseeruddin Shah\",\"count\":30},{\"cast\":\"Takahiro Sakurai\",\"count\":29},{\"cast\":\"Akshay Kumar\",\"count\":29},{\"cast\":\"Yuki Kaji\",\"count\":27},{\"cast\":\"Amitabh Bachchan\",\"count\":27},{\"cast\":\"Boman Irani\",\"count\":27},{\"cast\":\"Paresh Rawal\",\"count\":27},{\"cast\":\"Kareena Kapoor\",\"count\":25},{\"cast\":\"Andrea Libman\",\"count\":24},{\"cast\":\"John Cleese\",\"count\":24},{\"cast\":\"Vincent Tong\",\"count\":24},{\"cast\":\"Tara Strong\",\"count\":22},{\"cast\":\"Ashleigh Ball\",\"count\":22},{\"cast\":\"Nawazuddin Siddiqui\",\"count\":21},{\"cast\":\"Ajay Devgn\",\"count\":21},{\"cast\":\"Daisuke Ono\",\"count\":20},{\"cast\":\"Salman Khan\",\"count\":20}]}" }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 39 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:15:58.050840Z", "start_time": "2025-05-27T15:15:57.721613Z" } }, "source": [ "actors_df.take(30).plot { \n", " barsH {\n", " y(cast) { scale = categorical() }\n", " x(count)\n", " fillColor(cast) { \n", " scale = categoricalColorHue()\n", " legend {\n", " type = LegendType.None\n", " }\n", " }\n", " }\n", " layout.title = \"Top 30 actors\"\n", " layout.size = 950 to 900\n", "}" ], "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", " \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", " 0\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 5\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 10\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 15\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 20\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 25\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 30\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 35\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 40\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Anupam Kher\n", " \n", " \n", " \n", " \n", " \n", " \n", " Shah Rukh Khan\n", " \n", " \n", " \n", " \n", " \n", " \n", " Om Puri\n", " \n", " \n", " \n", " \n", " \n", " \n", " Naseeruddin Shah\n", " \n", " \n", " \n", " \n", " \n", " \n", " Takahiro Sakurai\n", " \n", " \n", " \n", " \n", " \n", " \n", " Akshay Kumar\n", " \n", " \n", " \n", " \n", " \n", " \n", " Yuki Kaji\n", " \n", " \n", " \n", " \n", " \n", " \n", " Amitabh Bachchan\n", " \n", " \n", " \n", " \n", " \n", " \n", " Boman Irani\n", " \n", " \n", " \n", " \n", " \n", " \n", " Paresh Rawal\n", " \n", " \n", " \n", " \n", " \n", " \n", " Kareena Kapoor\n", " \n", " \n", " \n", " \n", " \n", " \n", " Andrea Libman\n", " \n", " \n", " \n", " \n", " \n", " \n", " John Cleese\n", " \n", " \n", " \n", " \n", " \n", " \n", " Vincent Tong\n", " \n", " \n", " \n", " \n", " \n", " \n", " Tara Strong\n", " \n", " \n", " \n", " \n", " \n", " \n", " Ashleigh Ball\n", " \n", " \n", " \n", " \n", " \n", " \n", " Nawazuddin Siddiqui\n", " \n", " \n", " \n", " \n", " \n", " \n", " Ajay Devgn\n", " \n", " \n", " \n", " \n", " \n", " \n", " Daisuke Ono\n", " \n", " \n", " \n", " \n", " \n", " \n", " Salman Khan\n", " \n", " \n", " \n", " \n", " \n", " \n", " Nicolas Cage\n", " \n", " \n", " \n", " \n", " \n", " \n", " Kay Kay Menon\n", " \n", " \n", " \n", " \n", " \n", " \n", " Samuel L. Jackson\n", " \n", " \n", " \n", " \n", " \n", " \n", " Fred Tatasciore\n", " \n", " \n", " \n", " \n", " \n", " \n", " Adam Sandler\n", " \n", " \n", " \n", " \n", " \n", " \n", " Gulshan Grover\n", " \n", " \n", " \n", " \n", " \n", " \n", " David Attenborough\n", " \n", " \n", " \n", " \n", " \n", " \n", " Erin Fitzgerald\n", " \n", " \n", " \n", " \n", " \n", " \n", " Debi Derryberry\n", " \n", " \n", " \n", " \n", " \n", " \n", " Ai Kayano\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Top 30 actors\n", " \n", " \n", " \n", " \n", " cast\n", " \n", " \n", " \n", " \n", " count\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " " ], "application/plot+json": { "output_type": "lets_plot_spec", "output": { "ggtitle": { "text": "Top 30 actors" }, "mapping": {}, "data": { "cast": [ "Anupam Kher", "Shah Rukh Khan", "Om Puri", "Naseeruddin Shah", "Takahiro Sakurai", "Akshay Kumar", "Yuki Kaji", "Amitabh Bachchan", "Boman Irani", "Paresh Rawal", "Kareena Kapoor", "Andrea Libman", "John Cleese", "Vincent Tong", "Tara Strong", "Ashleigh Ball", "Nawazuddin Siddiqui", "Ajay Devgn", "Daisuke Ono", "Salman Khan", "Nicolas Cage", "Kay Kay Menon", "Samuel L. Jackson", "Fred Tatasciore", "Adam Sandler", "Gulshan Grover", "David Attenborough", "Erin Fitzgerald", "Debi Derryberry", "Ai Kayano" ], "count": [ 42.0, 35.0, 30.0, 30.0, 29.0, 29.0, 27.0, 27.0, 27.0, 27.0, 25.0, 24.0, 24.0, 24.0, 22.0, 22.0, 21.0, 21.0, 20.0, 20.0, 20.0, 20.0, 20.0, 19.0, 19.0, 19.0, 18.0, 18.0, 18.0, 18.0 ] }, "ggsize": { "width": 950.0, "height": 900.0 }, "kind": "plot", "scales": [ { "aesthetic": "y", "discrete": true }, { "aesthetic": "x", "limits": [ null, null ] }, { "aesthetic": "fill", "scale_mapper_kind": "color_hue", "guide": "none" } ], "layers": [ { "mapping": { "y": "cast", "x": "count", "fill": "cast" }, "stat": "identity", "orientation": "y", "sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar" } ], "data_meta": { "series_annotations": [ { "type": "str", "column": "cast" }, { "type": "int", "column": "count" } ] } }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Anupam Kher is definitely in the lead with 42 titles. Now we will split the castes for participation in movies or shows." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:16:10.583597Z", "start_time": "2025-05-27T15:16:10.242910Z" } }, "source": [ "val actors = cast_df.pivot { type }.aggregate {\n", " cast.explode().valueCounts()\n", "}\n", "actors" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
MovieTV Show
DataFrame [23049 x 2]
castcount
Anupam Kher41
Shah Rukh Khan35
Om Puri30
Naseeruddin Shah30
Akshay Kumar29

... showing only top 5 of 23049 rows

DataFrame [13538 x 2]
castcount
Takahiro Sakurai22
Yuki Kaji17
Daisuke Ono16
Ai Kayano16
Junichi Suwabe15

... showing only top 5 of 13538 rows

\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"FrameColumn\"},{\"kind\":\"FrameColumn\"}],\"nrow\":1,\"ncol\":2},\"kotlin_dataframe\":[{\"Movie\":{\"data\":[{\"cast\":\"Anupam Kher\",\"count\":41},{\"cast\":\"Shah Rukh Khan\",\"count\":35},{\"cast\":\"Om Puri\",\"count\":30},{\"cast\":\"Naseeruddin Shah\",\"count\":30},{\"cast\":\"Akshay Kumar\",\"count\":29},{\"cast\":\"Amitabh Bachchan\",\"count\":27},{\"cast\":\"Boman Irani\",\"count\":27},{\"cast\":\"Paresh Rawal\",\"count\":27},{\"cast\":\"Kareena Kapoor\",\"count\":25},{\"cast\":\"Ajay Devgn\",\"count\":21},{\"cast\":\"Salman Khan\",\"count\":20},{\"cast\":\"Nawazuddin Siddiqui\",\"count\":20},{\"cast\":\"Kay Kay Menon\",\"count\":20},{\"cast\":\"Adam Sandler\",\"count\":19},{\"cast\":\"John Cleese\",\"count\":19},{\"cast\":\"Nicolas Cage\",\"count\":19},{\"cast\":\"Gulshan Grover\",\"count\":19},{\"cast\":\"Samuel L. Jackson\",\"count\":19},{\"cast\":\"James Franco\",\"count\":18},{\"cast\":\"Yashpal Sharma\",\"count\":18}],\"metadata\":{\"kind\":\"FrameColumn\",\"columns\":[\"cast\",\"count\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}],\"ncol\":2,\"nrow\":23049}},\"TV Show\":{\"data\":[{\"cast\":\"Takahiro Sakurai\",\"count\":22},{\"cast\":\"Yuki Kaji\",\"count\":17},{\"cast\":\"Daisuke Ono\",\"count\":16},{\"cast\":\"Ai Kayano\",\"count\":16},{\"cast\":\"Junichi Suwabe\",\"count\":15},{\"cast\":\"Yuichi Nakamura\",\"count\":14},{\"cast\":\"Yoshimasa Hosoya\",\"count\":14},{\"cast\":\"David Attenborough\",\"count\":13},{\"cast\":\"Jun Fukuyama\",\"count\":13},{\"cast\":\"Vincent Tong\",\"count\":13},{\"cast\":\"Kana Hanazawa\",\"count\":13},{\"cast\":\"Hiroshi Kamiya\",\"count\":13},{\"cast\":\"Ashleigh Ball\",\"count\":12},{\"cast\":\"Tomokazu Sugita\",\"count\":12},{\"cast\":\"Natsuki Hanae\",\"count\":11},{\"cast\":\"Mamoru Miyano\",\"count\":11},{\"cast\":\"Tay Ping Hui\",\"count\":11},{\"cast\":\"Ryohei Kimura\",\"count\":10},{\"cast\":\"Katsuyuki Konishi\",\"count\":10},{\"cast\":\"Miyuki Sawashiro\",\"count\":10}],\"metadata\":{\"kind\":\"FrameColumn\",\"columns\":[\"cast\",\"count\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"}],\"ncol\":2,\"nrow\":13538}}}]}" }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 41 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:16:13.874039Z", "start_time": "2025-05-27T15:16:13.114939Z" } }, "source": [ "val p1 = actors.`TV Show`.take(30).plot {\n", " barsH {\n", " x(count)\n", " y(cast)\n", " fillColor(cast) {\n", " scale = continuous(Color.hex(\"#263238\")..Color.hex(\"#ECEFF1\"))\n", " legend { type = LegendType.None }\n", " }\n", " }\n", " layout.title = \"Top 30 actors in Shows\"\n", "}\n", "\n", "val p2 = actors.Movie.take(30).plot {\n", " barsH {\n", " x(count)\n", " y(cast)\n", " fillColor(cast) {\n", " scale = continuousColorGradientN(listOf(Color.hex(\"#006064\"), Color.hex(\"#E0F7FA\")))\n", " legend { type = LegendType.None }\n", " }\n", " }\n", " layout.title = \"Top 30 actors in Movies\"\n", "}\n", "\n", "plotBunch {\n", " add(p1, 0, 0, 500, 700)\n", " add(p2, 500, 0, 500, 700)\n", "}" ], "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", " 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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", " 0\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 10\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 20\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 30\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 40\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Anupam Kher\n", " \n", " \n", " \n", " \n", " \n", " \n", " Shah Rukh Khan\n", " \n", " \n", " \n", " \n", " \n", " \n", " Om Puri\n", " \n", " \n", " \n", " \n", " \n", " \n", " Naseeruddin Shah\n", " \n", " \n", " \n", " \n", " \n", " \n", " Akshay Kumar\n", " \n", " \n", " \n", " \n", " \n", " \n", " Amitabh Bachchan\n", " \n", " \n", " \n", " \n", " \n", " \n", " Boman Irani\n", " \n", " \n", " \n", " \n", " \n", " \n", " Paresh Rawal\n", " \n", " \n", " \n", " \n", " \n", " \n", " Kareena Kapoor\n", " \n", " \n", " \n", " \n", " \n", " \n", " Ajay Devgn\n", " \n", " \n", " \n", " \n", " \n", " \n", " Salman Khan\n", " \n", " \n", " \n", " \n", " \n", " \n", " Nawazuddin Siddiqui\n", " \n", " \n", " \n", " \n", " \n", " \n", " Kay Kay Menon\n", " \n", " \n", " \n", " \n", " \n", " \n", " Adam Sandler\n", " \n", " \n", " \n", " \n", " \n", " \n", " John Cleese\n", " \n", " \n", " \n", " \n", " \n", " \n", " Nicolas Cage\n", " \n", " \n", " \n", " \n", " \n", " \n", " Gulshan Grover\n", " \n", " \n", " \n", " \n", " \n", " \n", " Samuel L. Jackson\n", " \n", " \n", " \n", " \n", " \n", " \n", " James Franco\n", " \n", " \n", " \n", " \n", " \n", " \n", " Yashpal Sharma\n", " \n", " \n", " \n", " \n", " \n", " \n", " Anil Kapoor\n", " \n", " \n", " \n", " \n", " \n", " \n", " Jackie Shroff\n", " \n", " \n", " \n", " \n", " \n", " \n", " Asrani\n", " \n", " \n", " \n", " \n", " \n", " \n", " Andrea Libman\n", " \n", " \n", " \n", " \n", " \n", " \n", " Amrish Puri\n", " \n", " \n", " \n", " \n", " \n", " \n", " Aamir Khan\n", " \n", " \n", " \n", " \n", " \n", " \n", " Saif Ali Khan\n", " \n", " \n", " \n", " \n", " \n", " \n", " Rajpal Yadav\n", " \n", " \n", " \n", " \n", " \n", " \n", " Manoj Joshi\n", " \n", " \n", " \n", " \n", " \n", " \n", " Priyanka Chopra\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Top 30 actors in Movies\n", " \n", " \n", " \n", " \n", " cast\n", " \n", " \n", " \n", " \n", " count\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " " ], "application/plot+json": { "output_type": "lets_plot_spec", "output": { "kind": "ggbunch", "items": [ { "feature_spec": { "ggtitle": { "text": "Top 30 actors in Shows" }, "mapping": {}, "data": { "cast": [ "Takahiro Sakurai", "Yuki Kaji", "Daisuke Ono", "Ai Kayano", "Junichi Suwabe", "Yuichi Nakamura", "Yoshimasa Hosoya", "David Attenborough", "Jun Fukuyama", "Vincent Tong", "Kana Hanazawa", "Hiroshi Kamiya", "Ashleigh Ball", "Tomokazu Sugita", "Natsuki Hanae", "Mamoru Miyano", "Tay Ping Hui", "Ryohei Kimura", "Katsuyuki Konishi", "Miyuki Sawashiro", "Nobuhiko Okamoto", "Johnny Yong Bosch", "Saori Hayami", "Kaito Ishikawa", "Yoko Hikasa", "Takehito Koyasu", "Sayaka Ohara", "Daisuke Namikawa", "Kenjiro Tsuda", "Eri Kitamura" ], "count": [ 22.0, 17.0, 16.0, 16.0, 15.0, 14.0, 14.0, 13.0, 13.0, 13.0, 13.0, 13.0, 12.0, 12.0, 11.0, 11.0, 11.0, 10.0, 10.0, 10.0, 10.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0 ] }, "kind": "plot", "scales": [ { "aesthetic": "x", "limits": [ null, null ] }, { "aesthetic": "y", "discrete": true }, { "aesthetic": "fill", "scale_mapper_kind": "color_gradient", "high": "#ECEFF1", "low": "#263238", "limits": [ null, null ], "guide": "none" } ], "layers": [ { "mapping": { "x": "count", "y": "cast", "fill": "cast" }, "stat": "identity", "orientation": "y", "sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar" } ], "data_meta": { "series_annotations": [ { "type": "int", "column": "count" }, { "type": "str", "column": "cast" } ] } }, "x": 0, "y": 0, "width": 500, "height": 700 }, { "feature_spec": { "ggtitle": { "text": "Top 30 actors in Movies" }, "mapping": {}, "data": { "cast": [ "Anupam Kher", "Shah Rukh Khan", "Om Puri", "Naseeruddin Shah", "Akshay Kumar", "Amitabh Bachchan", "Boman Irani", "Paresh Rawal", "Kareena Kapoor", "Ajay Devgn", "Salman Khan", "Nawazuddin Siddiqui", "Kay Kay Menon", "Adam Sandler", "John Cleese", "Nicolas Cage", "Gulshan Grover", "Samuel L. Jackson", "James Franco", "Yashpal Sharma", "Anil Kapoor", "Jackie Shroff", "Asrani", "Andrea Libman", "Amrish Puri", "Aamir Khan", "Saif Ali Khan", "Rajpal Yadav", "Manoj Joshi", "Priyanka Chopra" ], "count": [ 41.0, 35.0, 30.0, 30.0, 29.0, 27.0, 27.0, 27.0, 25.0, 21.0, 20.0, 20.0, 20.0, 19.0, 19.0, 19.0, 19.0, 19.0, 18.0, 18.0, 18.0, 17.0, 17.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0 ] }, "kind": "plot", "scales": [ { "aesthetic": "x", "limits": [ null, null ] }, { "aesthetic": "y", "discrete": true }, { "aesthetic": "fill", "scale_mapper_kind": "color_gradientn", "limits": [ null, null ], "guide": "none", "colors": [ "#006064", "#E0F7FA" ] } ], "layers": [ { "mapping": { "x": "count", "y": "cast", "fill": "cast" }, "stat": "identity", "orientation": "y", "sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar" } ], "data_meta": { "series_annotations": [ { "type": "int", "column": "count" }, { "type": "str", "column": "cast" } ] } }, "x": 500, "y": 0, "width": 500, "height": 700 } ] }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 42 }, { "cell_type": "markdown", "metadata": {}, "source": "How about directors? Let's see the top 10 directors with more appearance in the Netflix catalog." }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:16:30.284329Z", "start_time": "2025-05-27T15:16:29.964176Z" } }, "source": [ "val directors_df = df.valueCounts { director }" ], "outputs": [], "execution_count": 43 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:16:30.700808Z", "start_time": "2025-05-27T15:16:30.429424Z" } }, "source": [ "directors_df.take(10).plot {\n", " barsH {\n", " x(count)\n", " y(director) { axis.name = \"Name\" }\n", " fillColor(director) { \n", " scale = categoricalColorHue()\n", " legend { type = LegendType.None }\n", " }\n", " }\n", " layout.title = \"Top 10 directors\"\n", " layout.size = 850 to 500\n", "}" ], "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", " 0\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 4\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 6\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 8\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 10\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 12\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 14\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 16\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 18\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Raúl Campos, Jan Suter\n", " \n", " \n", " \n", " \n", " \n", " \n", " Marcus Raboy\n", " \n", " \n", " \n", " \n", " \n", " \n", " Jay Karas\n", " \n", " \n", " \n", " \n", " \n", " \n", " Cathy Garcia-Molina\n", " \n", " \n", " \n", " \n", " \n", " \n", " Jay Chapman\n", " \n", " \n", " \n", " \n", " \n", " \n", " Martin Scorsese\n", " \n", " \n", " \n", " \n", " \n", " \n", " Youssef Chahine\n", " \n", " \n", " \n", " \n", " \n", " \n", " Steven Spielberg\n", " \n", " \n", " \n", " \n", " \n", " \n", " David Dhawan\n", " \n", " \n", " \n", " \n", " \n", " \n", " Lance Bangs\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Top 10 directors\n", " \n", " \n", " \n", " \n", " Name\n", " \n", " \n", " \n", " \n", " count\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " " ], "application/plot+json": { "output_type": "lets_plot_spec", "output": { "ggtitle": { "text": "Top 10 directors" }, "mapping": {}, "data": { "director": [ "Raúl Campos, Jan Suter", "Marcus Raboy", "Jay Karas", "Cathy Garcia-Molina", "Jay Chapman", "Martin Scorsese", "Youssef Chahine", "Steven Spielberg", "David Dhawan", "Lance Bangs" ], "count": [ 18.0, 16.0, 14.0, 13.0, 12.0, 12.0, 12.0, 10.0, 9.0, 8.0 ] }, "ggsize": { "width": 850.0, "height": 500.0 }, "kind": "plot", "scales": [ { "aesthetic": "x", "limits": [ null, null ] }, { "aesthetic": "y", "discrete": true, "name": "Name" }, { "aesthetic": "fill", "scale_mapper_kind": "color_hue", "guide": "none" } ], "layers": [ { "mapping": { "x": "count", "y": "director", "fill": "director" }, "stat": "identity", "orientation": "y", "sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar" } ], "data_meta": { "series_annotations": [ { "type": "int", "column": "count" }, { "type": "str", "column": "director" } ] } }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 44 }, { "cell_type": "markdown", "metadata": {}, "source": [ "These people work very productively." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Countries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This section focuses on analyzing how content is distributed across various countries.\n", "\n", "To do so, we will need to import libraries that work with geospatial data and maps and then perform the necessary manipulations to render the maps.\n", "However, let's prepare the data first." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's add another dataframe with country labels." ] }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:16:53.385188Z", "start_time": "2025-05-27T15:16:52.996464Z" } }, "cell_type": "code", "source": [ "val countriesCodes = DataFrame.readCsv(\"country_codes.csv\")\n", "countriesCodes.head()" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
countrycodeisolatlon
United StatesUSUSA39.783730-100.445883
IndiaININD22.35111578.667743
United KingdomGBGBR54.702355-3.276575
nullUnknownUnknown51.14613912.233285
CanadaCACAN61.066692-107.991707
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"country\",\"code\",\"iso\",\"lat\",\"lon\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}],\"nrow\":5,\"ncol\":5},\"kotlin_dataframe\":[{\"country\":\"United States\",\"code\":\"US\",\"iso\":\"USA\",\"lat\":39.7837304,\"lon\":-100.4458825},{\"country\":\"India\",\"code\":\"IN\",\"iso\":\"IND\",\"lat\":22.3511148,\"lon\":78.6677428},{\"country\":\"United Kingdom\",\"code\":\"GB\",\"iso\":\"GBR\",\"lat\":54.7023545,\"lon\":-3.2765753},{\"country\":null,\"code\":\"Unknown\",\"iso\":\"Unknown\",\"lat\":51.1461391,\"lon\":12.233285},{\"country\":\"Canada\",\"code\":\"CA\",\"iso\":\"CAN\",\"lat\":61.0666922,\"lon\":-107.991707}]}" }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 45 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:16:55.753324Z", "start_time": "2025-05-27T15:16:55.362166Z" } }, "cell_type": "code", "source": [ "// counting the number of titles by county and joining them with country codes in the dataframe\n", "val dfCountry = df.valueCounts { country }.join(countriesCodes)\n", "dfCountry" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
countrycountcodeisolatlon
United States2549USUSA39.783730-100.445883
India923ININD22.35111578.667743
United Kingdom396GBGBR54.702355-3.276575
Japan225JPJPN36.574844139.239418
South Korea183KRKOR36.638392127.696119
Canada177CACAN61.066692-107.991707
Spain134ESESP39.326069-4.837979
France115FRFRA46.6033541.888334
Egypt101EGEGY26.25404929.267547
Mexico100MXMEX22.500049-100.000038
Turkey100TRTUR38.95975934.924965
Australia82AUAUS-24.776109134.755000
Taiwan78TWTWN23.973937120.982018
Brazil72BRBRA-10.333333-53.200000
Philippines71PHPHL12.750349122.731210
Nigeria70NGNGA9.6000367.999972
Indonesia70IDIDN-2.483383117.890285
Germany61DEDEU51.08342010.423447
China57CNCHN35.000074104.999927
Thailand57THTHA14.897192100.832730
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"country\",\"count\",\"code\",\"iso\",\"lat\",\"lon\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}],\"nrow\":68,\"ncol\":6},\"kotlin_dataframe\":[{\"country\":\"United States\",\"count\":2549,\"code\":\"US\",\"iso\":\"USA\",\"lat\":39.7837304,\"lon\":-100.4458825},{\"country\":\"India\",\"count\":923,\"code\":\"IN\",\"iso\":\"IND\",\"lat\":22.3511148,\"lon\":78.6677428},{\"country\":\"United Kingdom\",\"count\":396,\"code\":\"GB\",\"iso\":\"GBR\",\"lat\":54.7023545,\"lon\":-3.2765753},{\"country\":\"Japan\",\"count\":225,\"code\":\"JP\",\"iso\":\"JPN\",\"lat\":36.5748441,\"lon\":139.2394179},{\"country\":\"South Korea\",\"count\":183,\"code\":\"KR\",\"iso\":\"KOR\",\"lat\":36.638392,\"lon\":127.6961188},{\"country\":\"Canada\",\"count\":177,\"code\":\"CA\",\"iso\":\"CAN\",\"lat\":61.0666922,\"lon\":-107.991707},{\"country\":\"Spain\",\"count\":134,\"code\":\"ES\",\"iso\":\"ESP\",\"lat\":39.3260685,\"lon\":-4.8379791},{\"country\":\"France\",\"count\":115,\"code\":\"FR\",\"iso\":\"FRA\",\"lat\":46.603354,\"lon\":1.8883335},{\"country\":\"Egypt\",\"count\":101,\"code\":\"EG\",\"iso\":\"EGY\",\"lat\":26.2540493,\"lon\":29.2675469},{\"country\":\"Mexico\",\"count\":100,\"code\":\"MX\",\"iso\":\"MEX\",\"lat\":22.5000485,\"lon\":-100.0000375},{\"country\":\"Turkey\",\"count\":100,\"code\":\"TR\",\"iso\":\"TUR\",\"lat\":38.9597594,\"lon\":34.9249653},{\"country\":\"Australia\",\"count\":82,\"code\":\"AU\",\"iso\":\"AUS\",\"lat\":-24.7761086,\"lon\":134.755},{\"country\":\"Taiwan\",\"count\":78,\"code\":\"TW\",\"iso\":\"TWN\",\"lat\":23.9739374,\"lon\":120.9820179},{\"country\":\"Brazil\",\"count\":72,\"code\":\"BR\",\"iso\":\"BRA\",\"lat\":-10.3333333,\"lon\":-53.2},{\"country\":\"Philippines\",\"count\":71,\"code\":\"PH\",\"iso\":\"PHL\",\"lat\":12.7503486,\"lon\":122.7312101},{\"country\":\"Nigeria\",\"count\":70,\"code\":\"NG\",\"iso\":\"NGA\",\"lat\":9.6000359,\"lon\":7.9999721},{\"country\":\"Indonesia\",\"count\":70,\"code\":\"ID\",\"iso\":\"IDN\",\"lat\":-2.4833826,\"lon\":117.8902853},{\"country\":\"Germany\",\"count\":61,\"code\":\"DE\",\"iso\":\"DEU\",\"lat\":51.0834196,\"lon\":10.4234469},{\"country\":\"China\",\"count\":57,\"code\":\"CN\",\"iso\":\"CHN\",\"lat\":35.000074,\"lon\":104.999927},{\"country\":\"Thailand\",\"count\":57,\"code\":\"TH\",\"iso\":\"THA\",\"lat\":14.8971921,\"lon\":100.83273}]}" }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 46 }, { "cell_type": "markdown", "metadata": {}, "source": "The result clearly shows where the content is mainly produced and gets to Netflix. Let's take a closer look at the top of such countries." }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:18:46.490919Z", "start_time": "2025-05-27T15:18:46.358386Z" } }, "source": [ "dfCountry[0..9].sortByDesc { count }.plot {\n", " bars {\n", " x(country)\n", " y(count)\n", " fillColor = Color.hex(\"#00796B\")\n", " }\n", " layout.title = \"Top 10 Countries\"\n", " layout.size = 900 to 450\n", "}" ], "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", " United States\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " India\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " United Kingdom\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Japan\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " South Korea\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Canada\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Spain\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " France\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Egypt\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Mexico\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 0\n", " \n", " \n", " \n", " \n", " \n", " \n", " 500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 1,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,000\n", " \n", " \n", " \n", " \n", " \n", " \n", " 2,500\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Top 10 Countries\n", " \n", " \n", " \n", " \n", " count\n", " \n", " \n", " \n", " \n", " country\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " " ], "application/plot+json": { "output_type": "lets_plot_spec", "output": { "ggtitle": { "text": "Top 10 Countries" }, "mapping": {}, "data": { "country": [ "United States", "India", "United Kingdom", "Japan", "South Korea", "Canada", "Spain", "France", "Egypt", "Mexico" ], "count": [ 2549.0, 923.0, 396.0, 225.0, 183.0, 177.0, 134.0, 115.0, 101.0, 100.0 ] }, "ggsize": { "width": 900.0, "height": 450.0 }, "kind": "plot", "scales": [ { "aesthetic": "x", "discrete": true }, { "aesthetic": "y", "limits": [ null, null ] } ], "layers": [ { "mapping": { "x": "country", "y": "count" }, "stat": "identity", "sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar", "fill": "#00796B" } ], "data_meta": { "series_annotations": [ { "type": "str", "column": "country" }, { "type": "int", "column": "count" } ] } }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Duration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How long is the content? How much time would you need to spend to watch it all?\n", "\n", "Let's get the number of minutes for films and the number of seasons for shows." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:20:19.228662Z", "start_time": "2025-05-27T15:20:18.976319Z" } }, "source": [ "val df_dur = df\n", " .split { duration }.by(\" \").inward(\"duration_num\", \"duration_scale\") // splitting duration by time and scale inward\n", " .convert { \"duration\"[\"duration_num\"] }.toInt() // converting by column path\n", " .update { \"duration\"[\"duration_scale\"] }.with { if (it == \"Seasons\") \"Season\" else it }\n", "df_dur.head()" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
duration_numduration_scale
s7114MovieTo and From New YorkSorin Dan MihalcescuBarbara King, Shaana Diya, John Krisi...United States2008-01-012006TV-MA81minDramas, Independent Movies, ThrillersWhile covering a story in New York Ci...
s1766TV ShowDinner for FivenullnullUnited States2008-02-042007TV-MA1SeasonStand-Up Comedy & Talk ShowsIn each episode, four celebrities joi...
s3249MovieJust Another Love StoryOle BornedalAnders W. Berthelsen, Rebecka Hemse, ...Denmark2009-05-052007TV-MA104minDramas, International MoviesWhen he causes a car accident that le...
s5766MovieSplatterJoe DanteCorey Feldman, Tony Todd, Tara Leigh,...United States2009-11-182009TV-MA29minHorror MoviesAfter committing suicide, a washed-up...
s3841MovieMad Ron's Prevues from HellJim MonacoNick Pawlow, Jordu Schell, Jay Kushwa...United States2010-11-011987NR84minCult Movies, Horror MoviesThis collection cherry-picks trailers...
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"show_id\",\"type\",\"title\",\"director\",\"cast\",\"country\",\"date_added\",\"release_year\",\"rating\",\"duration\",\"listed_in\",\"description\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String?\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDate\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ColumnGroup\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":5,\"ncol\":12},\"kotlin_dataframe\":[{\"show_id\":\"s7114\",\"type\":\"Movie\",\"title\":\"To and From New York\",\"director\":\"Sorin Dan Mihalcescu\",\"cast\":\"Barbara King, Shaana Diya, John Krisiukenas, Yoriko Haraguchi, David Crane, Chad Thomas\",\"country\":\"United States\",\"date_added\":\"2008-01-01\",\"release_year\":2006,\"rating\":\"TV-MA\",\"duration\":{\"data\":{\"duration_num\":81,\"duration_scale\":\"min\"},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"duration_num\",\"duration_scale\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}]}},\"listed_in\":\"Dramas, Independent Movies, Thrillers\",\"description\":\"While covering a story in New York City, a Seattle-based reporter uncovers a link between two missing women that changes her article entirely.\"},{\"show_id\":\"s1766\",\"type\":\"TV Show\",\"title\":\"Dinner for Five\",\"director\":null,\"cast\":null,\"country\":\"United States\",\"date_added\":\"2008-02-04\",\"release_year\":2007,\"rating\":\"TV-MA\",\"duration\":{\"data\":{\"duration_num\":1,\"duration_scale\":\"Season\"},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"duration_num\",\"duration_scale\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}]}},\"listed_in\":\"Stand-Up Comedy & Talk Shows\",\"description\":\"In each episode, four celebrities join host Jon Favreau for dinner and share revealing stories about both show business and their personal lives.\"},{\"show_id\":\"s3249\",\"type\":\"Movie\",\"title\":\"Just Another Love Story\",\"director\":\"Ole Bornedal\",\"cast\":\"Anders W. Berthelsen, Rebecka Hemse, Nikolaj Lie Kaas, Charlotte Fich, Dejan Cukic, Karsten Jansfort, Flemming Enevold, Bent Mejding, Ewa Fröling, Josephine Raahauge, Timm Vladimir, Ditte Hansen\",\"country\":\"Denmark\",\"date_added\":\"2009-05-05\",\"release_year\":2007,\"rating\":\"TV-MA\",\"duration\":{\"data\":{\"duration_num\":104,\"duration_scale\":\"min\"},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"duration_num\",\"duration_scale\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}]}},\"listed_in\":\"Dramas, International Movies\",\"description\":\"When he causes a car accident that leaves a young woman with amnesia, a man finds himself telling incredible lies to make her and her family happy.\"},{\"show_id\":\"s5766\",\"type\":\"Movie\",\"title\":\"Splatter\",\"director\":\"Joe Dante\",\"cast\":\"Corey Feldman, Tony Todd, Tara Leigh, Erin Way, Mark Alan, Stuart Pankin\",\"country\":\"United States\",\"date_added\":\"2009-11-18\",\"release_year\":2009,\"rating\":\"TV-MA\",\"duration\":{\"data\":{\"duration_num\":29,\"duration_scale\":\"min\"},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"duration_num\",\"duration_scale\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}]}},\"listed_in\":\"Horror Movies\",\"description\":\"After committing suicide, a washed-up rocker returns from the grave to torment the five people who betrayed him in life.\"},{\"show_id\":\"s3841\",\"type\":\"Movie\",\"title\":\"Mad Ron's Prevues from Hell\",\"director\":\"Jim Monaco\",\"cast\":\"Nick Pawlow, Jordu Schell, Jay Kushwara, Michael Townsend Wright, Anthony Kelly, Happy Goldsplatt, Ron Roccia, Fred Pomerleau, Joe Amodei, Nicholas Kitsopoulos\",\"country\":\"United States\",\"date_added\":\"2010-11-01\",\"release_year\":1987,\"rating\":\"NR\",\"duration\":{\"data\":{\"duration_num\":84,\"duration_scale\":\"min\"},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"duration_num\",\"duration_scale\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}]}},\"listed_in\":\"Cult Movies, Horror Movies\",\"description\":\"This collection cherry-picks trailers, forgotten scenes and poster art from 47 gory oldies, including Bay of Blood and The Blood Spattered Bride.\"}]}" }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 50 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:20:21.526788Z", "start_time": "2025-05-27T15:20:21.054889Z" } }, "source": [ "val durations = df_dur.pivot { type }.values { duration }\n", "durations" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
MovieTV Show
DataFrame [5377 x 2]
duration_numduration_scale
81min
104min
29min
84min
103min

... showing only top 5 of 5377 rows

DataFrame [2400 x 2]
duration_numduration_scale
1Season
1Season
5Season
4Season
6Season

... showing only top 5 of 2400 rows

\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"FrameColumn\"},{\"kind\":\"FrameColumn\"}],\"nrow\":1,\"ncol\":2},\"kotlin_dataframe\":[{\"Movie\":{\"data\":[{\"duration_num\":81,\"duration_scale\":\"min\"},{\"duration_num\":104,\"duration_scale\":\"min\"},{\"duration_num\":29,\"duration_scale\":\"min\"},{\"duration_num\":84,\"duration_scale\":\"min\"},{\"duration_num\":103,\"duration_scale\":\"min\"},{\"duration_num\":75,\"duration_scale\":\"min\"},{\"duration_num\":95,\"duration_scale\":\"min\"},{\"duration_num\":96,\"duration_scale\":\"min\"},{\"duration_num\":94,\"duration_scale\":\"min\"},{\"duration_num\":94,\"duration_scale\":\"min\"},{\"duration_num\":93,\"duration_scale\":\"min\"},{\"duration_num\":96,\"duration_scale\":\"min\"},{\"duration_num\":94,\"duration_scale\":\"min\"},{\"duration_num\":94,\"duration_scale\":\"min\"},{\"duration_num\":93,\"duration_scale\":\"min\"},{\"duration_num\":95,\"duration_scale\":\"min\"},{\"duration_num\":93,\"duration_scale\":\"min\"},{\"duration_num\":76,\"duration_scale\":\"min\"},{\"duration_num\":84,\"duration_scale\":\"min\"},{\"duration_num\":26,\"duration_scale\":\"min\"}],\"metadata\":{\"kind\":\"FrameColumn\",\"columns\":[\"duration_num\",\"duration_scale\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"ncol\":2,\"nrow\":5377}},\"TV Show\":{\"data\":[{\"duration_num\":1,\"duration_scale\":\"Season\"},{\"duration_num\":1,\"duration_scale\":\"Season\"},{\"duration_num\":5,\"duration_scale\":\"Season\"},{\"duration_num\":4,\"duration_scale\":\"Season\"},{\"duration_num\":6,\"duration_scale\":\"Season\"},{\"duration_num\":1,\"duration_scale\":\"Season\"},{\"duration_num\":3,\"duration_scale\":\"Season\"},{\"duration_num\":8,\"duration_scale\":\"Season\"},{\"duration_num\":5,\"duration_scale\":\"Season\"},{\"duration_num\":4,\"duration_scale\":\"Season\"},{\"duration_num\":3,\"duration_scale\":\"Season\"},{\"duration_num\":5,\"duration_scale\":\"Season\"},{\"duration_num\":4,\"duration_scale\":\"Season\"},{\"duration_num\":1,\"duration_scale\":\"Season\"},{\"duration_num\":7,\"duration_scale\":\"Season\"},{\"duration_num\":1,\"duration_scale\":\"Season\"},{\"duration_num\":1,\"duration_scale\":\"Season\"},{\"duration_num\":1,\"duration_scale\":\"Season\"},{\"duration_num\":1,\"duration_scale\":\"Season\"},{\"duration_num\":2,\"duration_scale\":\"Season\"}],\"metadata\":{\"kind\":\"FrameColumn\",\"columns\":[\"duration_num\",\"duration_scale\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"ncol\":2,\"nrow\":2400}}}]}" }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 51 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:20:24.792411Z", "start_time": "2025-05-27T15:20:23.997865Z" } }, "source": [ "val p1 = durations.Movie.plot {\n", " histogram(duration_num, binsOption = BinsOption.byNumber(100)) {\n", " y(Stat.density)\n", " fillColor = Color.hex(\"#00BCD4\")\n", " }\n", "\n", " statBin(duration_num, binsOption = BinsOption.byNumber(25)) {\n", " line {\n", " x(Stat.x) { axis.name = \"minutes\" }\n", " y(Stat.density) { axis.name = \"density\" }\n", " alpha = 1.0\n", " width = 1.0\n", " color = Color.hex(\"#d41900\")\n", " }\n", " }\n", "\n", " layout.title = \"Distribution of movies duration in minutes\"\n", "}\n", "\n", "val p2 = durations.`TV Show`.plot {\n", " statBin(duration_num, binsOption = BinsOption.byNumber(15)) {\n", " bars {\n", " x(Stat.x)\n", " y(Stat.count)\n", " fillColor = Color.hex(\"#00BCD4\")\n", " 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Black Mirror: BandersnatchUnited States2018-12-282018312min
The School of MischiefEgypt2020-05-211973253min
No Longer kidsEgypt2020-05-211979237min
Lock Your Girls Innull2020-05-211982233min
Raya and Sakinanull2020-05-211984230min
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NCISUnited States2018-07-01201715Season
SupernaturalUnited States, Canada2020-06-05201915Season
COMEDIANS of the worldUnited States2019-01-01201913Season
Criminal MindsUnited States, Canada2017-06-30201712Season
\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"title\",\"country\",\"date_added\",\"release_year\",\"duration_num\",\"duration_scale\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlinx.datetime.LocalDate\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Int\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":5,\"ncol\":6},\"kotlin_dataframe\":[{\"title\":\"Grey's Anatomy\",\"country\":\"United States\",\"date_added\":\"2020-05-09\",\"release_year\":2019,\"duration_num\":16,\"duration_scale\":\"Season\"},{\"title\":\"NCIS\",\"country\":\"United States\",\"date_added\":\"2018-07-01\",\"release_year\":2017,\"duration_num\":15,\"duration_scale\":\"Season\"},{\"title\":\"Supernatural\",\"country\":\"United States, Canada\",\"date_added\":\"2020-06-05\",\"release_year\":2019,\"duration_num\":15,\"duration_scale\":\"Season\"},{\"title\":\"COMEDIANS of the world\",\"country\":\"United States\",\"date_added\":\"2019-01-01\",\"release_year\":2019,\"duration_num\":13,\"duration_scale\":\"Season\"},{\"title\":\"Criminal Minds\",\"country\":\"United States, Canada\",\"date_added\":\"2017-06-30\",\"release_year\":2017,\"duration_num\":12,\"duration_scale\":\"Season\"}]}" }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 54 }, { "cell_type": "markdown", "metadata": {}, "source": "And in the top content-producing countries, how long are movies and TV shows?" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:21:52.668525Z", "start_time": "2025-05-27T15:21:52.049473Z" } }, "source": [ "val list_top_countries = dfCountry.country.take(10).toSet()\n", "\n", "val df_cntr = df_dur\n", " .filter { country in list_top_countries }\n", " .pivot { type }.aggregate { \n", " groupBy { country }.mean { duration.duration_num }\n", " }\n", "df_cntr" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
MovieTV Show
DataFrame [10 x 2]
countryduration_num
United States89.393514
Canada82.372881
Mexico86.830769
Spain99.932584
United Kingdom84.378238

... showing only top 5 of 10 rows

DataFrame [10 x 2]
countryduration_num
United States2.276109
Canada2.491525
France1.326087
United Kingdom1.847291
Japan1.570513

... showing only top 5 of 10 rows

\n", " \n", " \n", " " ], "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"Movie\",\"TV Show\"],\"types\":[{\"kind\":\"FrameColumn\"},{\"kind\":\"FrameColumn\"}],\"nrow\":1,\"ncol\":2},\"kotlin_dataframe\":[{\"Movie\":{\"data\":[{\"country\":\"United States\",\"duration_num\":89.39351351351351},{\"country\":\"Canada\",\"duration_num\":82.37288135593221},{\"country\":\"Mexico\",\"duration_num\":86.83076923076923},{\"country\":\"Spain\",\"duration_num\":99.93258426966293},{\"country\":\"United Kingdom\",\"duration_num\":84.37823834196891},{\"country\":\"France\",\"duration_num\":93.23188405797102},{\"country\":\"India\",\"duration_num\":127.41431924882629},{\"country\":\"Japan\",\"duration_num\":96.20289855072464},{\"country\":\"South Korea\",\"duration_num\":116.94444444444444},{\"country\":\"Egypt\",\"duration_num\":109.34831460674157}],\"metadata\":{\"kind\":\"FrameColumn\",\"columns\":[\"country\",\"duration_num\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}],\"ncol\":2,\"nrow\":10}},\"TV Show\":{\"data\":[{\"country\":\"United States\",\"duration_num\":2.2761087267525038},{\"country\":\"Canada\",\"duration_num\":2.4915254237288136},{\"country\":\"France\",\"duration_num\":1.326086956521739},{\"country\":\"United Kingdom\",\"duration_num\":1.8472906403940887},{\"country\":\"Japan\",\"duration_num\":1.5705128205128205},{\"country\":\"South Korea\",\"duration_num\":1.163265306122449},{\"country\":\"Spain\",\"duration_num\":1.7333333333333334},{\"country\":\"Mexico\",\"duration_num\":1.2857142857142858},{\"country\":\"India\",\"duration_num\":1.1549295774647887},{\"country\":\"Egypt\",\"duration_num\":1.0}],\"metadata\":{\"kind\":\"FrameColumn\",\"columns\":[\"country\",\"duration_num\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}],\"ncol\":2,\"nrow\":10}}}]}" }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 55 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:21:56.691990Z", "start_time": "2025-05-27T15:21:55.999638Z" } }, "source": [ "val p1 = df_cntr.Movie.sortBy { duration_num }.plot {\n", " bars {\n", " x(country) { axis.name = \"Name\" }\n", " y(duration_num) { axis.name = \"Minute\" }\n", " fillColor(duration_num) {\n", " scale = continuous(Color.hex(\"#ECEFF1\")..Color.hex(\"#263238\"))\n", " legend.type = LegendType.None\n", " }\n", " }\n", " layout.title = \"Top 10 cast on Movies by country\"\n", "}\n", "\n", "val p2 = df_cntr.`TV Show`.sortBy { duration_num }.plot {\n", " bars {\n", " x(country) { axis.name = \"Name\" }\n", " y(duration_num) { axis.name = \"Season\" }\n", " fillColor(duration_num) {\n", " scale = continuous(Color.hex(\"#E0F7FA\")..Color.hex(\"#006064\"))\n", " legend.type = LegendType.None\n", " }\n", " }\n", " layout.title = \"Top 10 cast on TV Shows by country\"\n", "}\n", "\n", "plotBunch {\n", " add(p1, 0, 0, 900, 550)\n", " add(p2, 0, 550, 900, 550)\n", "}" ], "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", 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}, "kind": "plot", "scales": [ { "aesthetic": "x", "discrete": true, "name": "Name" }, { "aesthetic": "y", "name": "Minute", "limits": [ null, null ] }, { "aesthetic": "fill", "scale_mapper_kind": "color_gradient", "high": "#263238", "low": "#ECEFF1", "limits": [ null, null ], "guide": "none" } ], "layers": [ { "mapping": { "x": "country", "y": "duration_num", "fill": "duration_num" }, "stat": "identity", "sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar" } ], "data_meta": { "series_annotations": [ { "type": "str", "column": "country" }, { "type": "float", "column": "duration_num" } ] } }, "x": 0, "y": 0, "width": 900, "height": 550 }, { "feature_spec": { "ggtitle": { "text": "Top 10 cast on TV Shows by country" }, "mapping": {}, "data": { "country": [ "Egypt", "India", "South Korea", "Mexico", "France", "Japan", "Spain", "United Kingdom", "United States", "Canada" ], "duration_num": [ 1.0, 1.1549295774647887, 1.163265306122449, 1.2857142857142858, 1.326086956521739, 1.5705128205128205, 1.7333333333333334, 1.8472906403940887, 2.2761087267525038, 2.4915254237288136 ] }, "kind": "plot", "scales": [ { "aesthetic": "x", "discrete": true, "name": "Name" }, { "aesthetic": "y", "name": "Season", "limits": [ null, null ] }, { "aesthetic": "fill", "scale_mapper_kind": "color_gradient", "high": "#006064", "low": "#E0F7FA", "limits": [ null, null ], "guide": "none" } ], "layers": [ { "mapping": { "x": "country", "y": "duration_num", "fill": "duration_num" }, "stat": "identity", "sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar" } ], "data_meta": { "series_annotations": [ { "type": "str", "column": "country" }, { "type": "float", "column": "duration_num" } ] } }, "x": 0, "y": 550, "width": 900, "height": 550 } ] }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 56 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ratings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's take a look at the rating column.\n", "Here we will find out what is the most commonly assigned rating for films and shows." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:22:08.804677Z", "start_time": "2025-05-27T15:22:08.384819Z" } }, "source": [ "val dfInstants = df.convert { date_added }.with { it.atStartOfDayIn(TimeZone.UTC) }" ], "outputs": [], "execution_count": 57 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:22:09.381925Z", "start_time": "2025-05-27T15:22:09.146211Z" } }, "source": [ "dfInstants.valueCounts(false) { rating }.sortBy(\"count\").plot {\n", " bars {\n", " x(rating)\n", " y(\"count\")\n", " fillColor(rating) { \n", " scale = categoricalColorHue()\n", " legend.type = LegendType.None\n", " }\n", " }\n", " layout.title = \"Rating of Titles\"\n", " layout.size = 950 to 500\n", "}" ], "outputs": 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"sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar" } ], "data_meta": { "series_annotations": [ { "type": "str", "column": "rating" }, { "type": "int", "column": "count" } ] } }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 58 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:22:13.482608Z", "start_time": "2025-05-27T15:22:13.130646Z" } }, "cell_type": "code", "source": [ "dfInstants.valueCounts(sort = false) { rating and type }.plot {\n", " bars {\n", " x(rating)\n", " y(\"count\")\n", " fillColor(type) { scale = categorical(listOf(Color.hex(\"#607D8B\"), Color.hex(\"#00BCD4\"))) }\n", " position = Position.dodge()\n", " }\n", " layout.title = \"Rating of Titles\"\n", " layout.size = 950 to 500\n", "}" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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"TV-14", "NR", "TV-G", "G", "TV-G", "TV-Y", "TV-Y", "TV-PG", "TV-Y7-FV", "TV-Y7", "PG-13", "TV-Y7", "TV-Y7-FV", "NC-17", "R", "UR" ], "count": [ 1845.0, 1016.0, 79.0, 505.0, 1272.0, 247.0, 663.0, 656.0, 4.0, 111.0, 39.0, 83.0, 162.0, 117.0, 299.0, 1.0, 175.0, 386.0, 95.0, 5.0, 3.0, 2.0, 5.0 ], "type": [ "Movie", "TV Show", "Movie", "Movie", "Movie", "Movie", "Movie", "TV Show", "TV Show", "Movie", "Movie", "TV Show", "TV Show", "Movie", "TV Show", "TV Show", "TV Show", "Movie", "Movie", "Movie", "Movie", "TV Show", "Movie" ] }, "ggsize": { "width": 950.0, "height": 500.0 }, "kind": "plot", "scales": [ { "aesthetic": "x", "discrete": true }, { "aesthetic": "y", "limits": [ null, null ] }, { "aesthetic": "fill", "values": [ "#607D8B", "#00BCD4" ] } ], "layers": [ { "mapping": { "x": "rating", "y": "count", "fill": "type" }, "stat": "identity", "sampling": "none", "inherit_aes": false, "position": "dodge", "geom": "bar" } ], "data_meta": { "series_annotations": [ { "type": "str", "column": "rating" }, { "type": "int", "column": "count" }, { "type": "str", "column": "type" } ] } }, "apply_color_scheme": true, "swing_enabled": true } }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 59 }, { "metadata": {}, "cell_type": "markdown", "source": "Finally, use the experimental Kandy-Geo integration to plot some results on a map!" }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:23:22.413034Z", "start_time": "2025-05-27T15:22:58.986318Z" } }, "cell_type": "code", "source": [ "// Dataframe & Kandy geo extensions\n", "%use kandy-geo@kc25" ], "outputs": [], "execution_count": 60 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:23:32.717335Z", "start_time": "2025-05-27T15:23:25.640194Z" } }, "cell_type": "code", "source": [ "val worldGeo = with(\"naturalearth_lowres\") {\n", " val url = \"https://raw.githubusercontent.com/JetBrains/lets-plot-kotlin/master/docs/examples/shp/${this}/${this}.shp\"\n", " GeoDataFrame.readShapefile(url)\n", "}" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2025-05-27T15:23:28.575890Z Execution of code 'val worldGeo = with(...' ERROR Log4j2 could not find a logging implementation. Please add log4j-core to the classpath. Using SimpleLogger to log to the console...\n" ] } ], "execution_count": 61 }, { "metadata": { "ExecuteTime": { "end_time": "2025-05-27T15:24:11.780959Z", "start_time": "2025-05-27T15:24:10.926365Z" } }, "cell_type": "code", "source": [ "val geoCountriesCount = worldGeo.modify {\n", " join(dfCountry) {\n", " iso_a3 match right.iso\n", " }\n", "}\n", "geoCountriesCount.df" ], "outputs": [ { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", "
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326625791North AmericaUnited States of AmericaUSA18560000.000000MULTIPOLYGON (((-122.84000000000003 4...United States2549US39.783730-100.445883
260580739AsiaIndonesiaIDN3028000.000000MULTIPOLYGON (((141.00021040259185 -2...Indonesia70ID-2.483383117.890285
44293293South AmericaArgentinaARG879400.000000MULTIPOLYGON (((-68.63401022758323 -5...Argentina50AR-34.996496-64.967282
17789267South AmericaChileCHL436100.000000MULTIPOLYGON (((-68.63401022758323 -5...Chile14CL-31.761337-71.318770
47615739AfricaKenyaKEN152700.000000MULTIPOLYGON (((39.20222 -4.67677, 37...Kenya2KE1.44196838.431398
142257519EuropeRussiaRUS3745000.000000MULTIPOLYGON (((178.7253 71.0988, 180...Russia16RU64.68631497.745306
54841552AfricaSouth AfricaZAF739100.000000MULTIPOLYGON (((16.344976840895242 -2...South Africa25ZA-28.81662424.991639
124574795North AmericaMexicoMEX2307000.000000MULTIPOLYGON (((-117.12775999999985 3...Mexico100MX22.500049-100.000038
3360148South AmericaUruguayURY73250.000000MULTIPOLYGON (((-57.62513342958296 -3...Uruguay3UY-32.875555-56.020153
207353391South AmericaBrazilBRA3081000.000000MULTIPOLYGON (((-53.373661668498244 -...Brazil72BR-10.333333-53.200000
31036656South AmericaPeruPER410400.000000MULTIPOLYGON (((-69.89363521999663 -4...Peru4PE-6.869970-75.045852
47698524South AmericaColombiaCOL688000.000000MULTIPOLYGON (((-66.87632585312258 1....Colombia31CO2.889443-73.783892
15460732North AmericaGuatemalaGTM131800.000000MULTIPOLYGON (((-92.22775000686983 14...Guatemala1GT15.635609-89.898809
31304016South AmericaVenezuelaVEN468600.000000MULTIPOLYGON (((-60.73357418480372 5....Venezuela1VE8.001871-66.110932
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2484780AfricaNamibiaNAM25990.000000MULTIPOLYGON (((19.895767856534434 -2...Namibia1null-23.23355017.323111
14668522AfricaSenegalSEN39720.000000MULTIPOLYGON (((-16.71372880702347 13...Senegal1SN14.465177-14.765341
190632261AfricaNigeriaNGA1089000.000000MULTIPOLYGON (((2.6917016943562544 6....Nigeria70NG9.6000367.999972
27499924AfricaGhanaGHA120800.000000MULTIPOLYGON (((0.0238025244237008 11...Ghana3GH7.857371-1.084098
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