From c749a7d7cdbfa3d7a1ce9a888363c26e8c48d538 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Juan=20Tom=C3=A1s=20Vicens?= <juantvicens@gmail.com>
Date: Tue, 30 Aug 2022 21:23:52 -0300
Subject: [PATCH] =?UTF-8?q?Ejemplo=20de=20Pel=C3=ADculas=20por=20Ariadna?=
=?UTF-8?q?=20Aspit=C3=ADa=20y=20Juan=20Vicens?=
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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "PelÃculas.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# TRABAJANDO CON PELÃCULAS\n",
+ "\n",
+ "En esta ocasión nos encontraremos trabajando con un archivo de datos que contiene información sobre las pelÃculas y series de Netflix, la famosa plataforma de streaming. Los datasets que utilizaremos a continuación fueron descargados de Kaggle:\n",
+ "- https://www.kaggle.com/datasets/shivamb/netflix-shows\n",
+ "- https://www.kaggle.com/datasets/dharmikdonga/academy-awards-dataset-oscars\n",
+ "\n",
+ "Como ya hemos visto en los ejemplos anteriores, para poder comenzar a trabajar con los archivos, primero debemos importar aquellas librerÃas que tienen las funcionalidades que nos interesan. Por ello, importaremos Pandas, y también probaremos por primera vez las herramientas de visualización de gráficos que nos provee Plotly, comparándolas con las ya trabajadas de Matplotlib.\n"
+ ],
+ "metadata": {
+ "id": "9io7UHUBh_GF"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##Preparando el Dataset de Netflix"
+ ],
+ "metadata": {
+ "id": "eOpyY0tcrqaZ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import pandas as pd\n",
+ "import plotly.express as px"
+ ],
+ "metadata": {
+ "id": "4xnbgYwmiHn4"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "En un principio nos concentraremos solamente en el archivo de pelÃculas y series de Netflix. Leemos con pandas el archivo .csv y lo guardamos en la variable netflix_completo.\n",
+ "\n",
+ "A su vez, queremos saber con que datos contamos, y sobre la tabla utilizamos la funcion .head(10) para que se nos muestren las primeras 10 filas que contiene."
+ ],
+ "metadata": {
+ "id": "QCT4n4_8iIMJ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "netflix_completo = pd.read_csv(\"netflix_titles.csv\")\n",
+ "netflix_completo.head(10)"
+ ],
+ "metadata": {
+ "id": "9LzOovpHjlWL",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 580
+ },
+ "outputId": "3710be20-7fe5-4ae6-fc74-ff2b8fba4036"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " show_id type title \\\n",
+ "0 s1 Movie Dick Johnson Is Dead \n",
+ "1 s2 TV Show Blood & Water \n",
+ "2 s3 TV Show Ganglands \n",
+ "3 s4 TV Show Jailbirds New Orleans \n",
+ "4 s5 TV Show Kota Factory \n",
+ "5 s6 TV Show Midnight Mass \n",
+ "6 s7 Movie My Little Pony: A New Generation \n",
+ "7 s8 Movie Sankofa \n",
+ "8 s9 TV Show The Great British Baking Show \n",
+ "9 s10 Movie The Starling \n",
+ "\n",
+ " director \\\n",
+ "0 Kirsten Johnson \n",
+ "1 NaN \n",
+ "2 Julien Leclercq \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "5 Mike Flanagan \n",
+ "6 Robert Cullen, José Luis Ucha \n",
+ "7 Haile Gerima \n",
+ "8 Andy Devonshire \n",
+ "9 Theodore Melfi \n",
+ "\n",
+ " cast \\\n",
+ "0 NaN \n",
+ "1 Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... \n",
+ "2 Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi... \n",
+ "3 NaN \n",
+ "4 Mayur More, Jitendra Kumar, Ranjan Raj, Alam K... \n",
+ "5 Kate Siegel, Zach Gilford, Hamish Linklater, H... \n",
+ "6 Vanessa Hudgens, Kimiko Glenn, James Marsden, ... \n",
+ "7 Kofi Ghanaba, Oyafunmike Ogunlano, Alexandra D... \n",
+ "8 Mel Giedroyc, Sue Perkins, Mary Berry, Paul Ho... \n",
+ "9 Melissa McCarthy, Chris O'Dowd, Kevin Kline, T... \n",
+ "\n",
+ " country date_added \\\n",
+ "0 United States September 25, 2021 \n",
+ "1 South Africa September 24, 2021 \n",
+ "2 NaN September 24, 2021 \n",
+ "3 NaN September 24, 2021 \n",
+ "4 India September 24, 2021 \n",
+ "5 NaN September 24, 2021 \n",
+ "6 NaN September 24, 2021 \n",
+ "7 United States, Ghana, Burkina Faso, United Kin... September 24, 2021 \n",
+ "8 United Kingdom September 24, 2021 \n",
+ "9 United States September 24, 2021 \n",
+ "\n",
+ " release_year rating duration \\\n",
+ "0 2020 PG-13 90 min \n",
+ "1 2021 TV-MA 2 Seasons \n",
+ "2 2021 TV-MA 1 Season \n",
+ "3 2021 TV-MA 1 Season \n",
+ "4 2021 TV-MA 2 Seasons \n",
+ "5 2021 TV-MA 1 Season \n",
+ "6 2021 PG 91 min \n",
+ "7 1993 TV-MA 125 min \n",
+ "8 2021 TV-14 9 Seasons \n",
+ "9 2021 PG-13 104 min \n",
+ "\n",
+ " listed_in \\\n",
+ "0 Documentaries \n",
+ "1 International TV Shows, TV Dramas, TV Mysteries \n",
+ "2 Crime TV Shows, International TV Shows, TV Act... \n",
+ "3 Docuseries, Reality TV \n",
+ "4 International TV Shows, Romantic TV Shows, TV ... \n",
+ "5 TV Dramas, TV Horror, TV Mysteries \n",
+ "6 Children & Family Movies \n",
+ "7 Dramas, Independent Movies, International Movies \n",
+ "8 British TV Shows, Reality TV \n",
+ "9 Comedies, Dramas \n",
+ "\n",
+ " description \n",
+ "0 As her father nears the end of his life, filmm... \n",
+ "1 After crossing paths at a party, a Cape Town t... \n",
+ "2 To protect his family from a powerful drug lor... \n",
+ "3 Feuds, flirtations and toilet talk go down amo... \n",
+ "4 In a city of coaching centers known to train I... \n",
+ "5 The arrival of a charismatic young priest brin... \n",
+ "6 Equestria's divided. But a bright-eyed hero be... \n",
+ "7 On a photo shoot in Ghana, an American model s... \n",
+ "8 A talented batch of amateur bakers face off in... \n",
+ "9 A woman adjusting to life after a loss contend... "
+ ],
+ "text/html": [
+ "\n",
+ " <div id=\"df-707b8fe4-a3d8-4543-a134-2c11edbaf589\">\n",
+ " <div class=\"colab-df-container\">\n",
+ " <div>\n",
+ "<style scoped>\n",
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+ " <thead>\n",
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+ " <td>September 25, 2021</td>\n",
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+ " <td>Documentaries</td>\n",
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+ " <td>Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...</td>\n",
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+ " <td>September 24, 2021</td>\n",
+ " <td>2021</td>\n",
+ " <td>TV-MA</td>\n",
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+ " <td>International TV Shows, TV Dramas, TV Mysteries</td>\n",
+ " <td>After crossing paths at a party, a Cape Town t...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>September 24, 2021</td>\n",
+ " <td>2021</td>\n",
+ " <td>TV-MA</td>\n",
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+ " <td>Crime TV Shows, International TV Shows, TV Act...</td>\n",
+ " <td>To protect his family from a powerful drug lor...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>1 Season</td>\n",
+ " <td>Docuseries, Reality TV</td>\n",
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+ " <td>Kota Factory</td>\n",
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+ " <td>Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...</td>\n",
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+ " <td>2021</td>\n",
+ " <td>TV-MA</td>\n",
+ " <td>2 Seasons</td>\n",
+ " <td>International TV Shows, Romantic TV Shows, TV ...</td>\n",
+ " <td>In a city of coaching centers known to train I...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>s6</td>\n",
+ " <td>TV Show</td>\n",
+ " <td>Midnight Mass</td>\n",
+ " <td>Mike Flanagan</td>\n",
+ " <td>Kate Siegel, Zach Gilford, Hamish Linklater, H...</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>TV-MA</td>\n",
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+ " <td>TV Dramas, TV Horror, TV Mysteries</td>\n",
+ " <td>The arrival of a charismatic young priest brin...</td>\n",
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+ " <tr>\n",
+ " <th>6</th>\n",
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+ " <td>My Little Pony: A New Generation</td>\n",
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+ " <td>Vanessa Hudgens, Kimiko Glenn, James Marsden, ...</td>\n",
+ " <td>NaN</td>\n",
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+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-707b8fe4-a3d8-4543-a134-2c11edbaf589');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
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+ " docLink.innerHTML = docLinkHtml;\n",
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+ ]
+ },
+ "metadata": {},
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+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Como podemos ver, la tabla contiene las siguientes columnas: \n",
+ " \n",
+ "1) show_id: contiene el identificador único de cada fila. \n",
+ "2) type: corresponde con el tipo de contenido. Si es una **pelÃcula** (Movie) o **serie** (TV Show). \n",
+ "3) title: tÃtulo de la serie/pelÃcula. \n",
+ "4) director: director de la serie/pelÃcula. \n",
+ "5) cast: actores y actrices que forman parte del elenco. \n",
+ "6) country: paÃs de origen de la serie/pelÃcula. \n",
+ "7) date_added: fecha en la que se agregó a la lista de contenido de Netflix. \n",
+ "8) release_year: fecha en la que se estrenó la serie/pelÃcula. \n",
+ "9) rating: clasificación de la serie/pelÃcula de acuerdo a las edades que se recomienda que puedan verlas.\n",
+ "10) duration: duración de las series/pelÃculas. Las pelÃculas se encuentran medidas en minutos, mientras que las series, en cantidad de temporadas. \n",
+ "11) listed_in: genéros del contenido (fantasÃa, horror, comedia, etc). \n",
+ "12) description: breve descripción de la serie/pelÃcula."
+ ],
+ "metadata": {
+ "id": "hjBCtm9-jy5U"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Nos deshacemos de aquellas filas que tienen valores NaN, lo que significa que no hay un valor, para lo cual usamos **dropna** que elimina las filas que contienen este tipo de valores. También vemos el dataframe resultante."
+ ],
+ "metadata": {
+ "id": "GpbtyF-0lgy6"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "netflix_completo_sin_na = netflix_completo.dropna()\n",
+ "netflix_completo_sin_na.head(10)"
+ ],
+ "metadata": {
+ "id": "51mmnhHclgTp",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 580
+ },
+ "outputId": "de6a655e-6312-4324-da66-180097d86f37"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " show_id type title director \\\n",
+ "7 s8 Movie Sankofa Haile Gerima \n",
+ "8 s9 TV Show The Great British Baking Show Andy Devonshire \n",
+ "9 s10 Movie The Starling Theodore Melfi \n",
+ "12 s13 Movie Je Suis Karl Christian Schwochow \n",
+ "24 s25 Movie Jeans S. Shankar \n",
+ "27 s28 Movie Grown Ups Dennis Dugan \n",
+ "28 s29 Movie Dark Skies Scott Stewart \n",
+ "29 s30 Movie Paranoia Robert Luketic \n",
+ "38 s39 Movie Birth of the Dragon George Nolfi \n",
+ "41 s42 Movie Jaws Steven Spielberg \n",
+ "\n",
+ " cast \\\n",
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+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Como se puede ver en el dataframe de arriba, el Ãndice de la tabla quedó sin las filas borradas, dejando huecos a lo largo del Ãndice del Dataframe. para que vuelva a estar ordenado y sin Ãndices faltantes usamos **reset_index**, con el parámetro drop=True que evita que el Ãndice anterior se agregue como columna."
+ ],
+ "metadata": {
+ "id": "Npj9sEjOmEJ_"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "netflix_completo=netflix_completo_sin_na.reset_index(drop=True)\n",
+ "netflix_completo_sin_na.head(10)"
+ ],
+ "metadata": {
+ "id": "ef2OyUBerLt4",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 580
+ },
+ "outputId": "294d4c71-31a7-466e-f241-ec33ac0655c8"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
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+ " <td>Luna Wedler, Jannis Niewöhner, Milan Peschel, ...</td>\n",
+ " <td>Germany, Czech Republic</td>\n",
+ " <td>September 23, 2021</td>\n",
+ " <td>2021</td>\n",
+ " <td>TV-MA</td>\n",
+ " <td>127 min</td>\n",
+ " <td>Dramas, International Movies</td>\n",
+ " <td>After most of her family is murdered in a terr...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>s25</td>\n",
+ " <td>Movie</td>\n",
+ " <td>Jeans</td>\n",
+ " <td>S. Shankar</td>\n",
+ " <td>Prashanth, Aishwarya Rai Bachchan, Sri Lakshmi...</td>\n",
+ " <td>India</td>\n",
+ " <td>September 21, 2021</td>\n",
+ " <td>1998</td>\n",
+ " <td>TV-14</td>\n",
+ " <td>166 min</td>\n",
+ " <td>Comedies, International Movies, Romantic Movies</td>\n",
+ " <td>When the father of the man she loves insists t...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>s28</td>\n",
+ " <td>Movie</td>\n",
+ " <td>Grown Ups</td>\n",
+ " <td>Dennis Dugan</td>\n",
+ " <td>Adam Sandler, Kevin James, Chris Rock, David S...</td>\n",
+ " <td>United States</td>\n",
+ " <td>September 20, 2021</td>\n",
+ " <td>2010</td>\n",
+ " <td>PG-13</td>\n",
+ " <td>103 min</td>\n",
+ " <td>Comedies</td>\n",
+ " <td>Mourning the loss of their beloved junior high...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>s29</td>\n",
+ " <td>Movie</td>\n",
+ " <td>Dark Skies</td>\n",
+ " <td>Scott Stewart</td>\n",
+ " <td>Keri Russell, Josh Hamilton, J.K. Simmons, Dak...</td>\n",
+ " <td>United States</td>\n",
+ " <td>September 19, 2021</td>\n",
+ " <td>2013</td>\n",
+ " <td>PG-13</td>\n",
+ " <td>97 min</td>\n",
+ " <td>Horror Movies, Sci-Fi & Fantasy</td>\n",
+ " <td>A family’s idyllic suburban life shatters when...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>s30</td>\n",
+ " <td>Movie</td>\n",
+ " <td>Paranoia</td>\n",
+ " <td>Robert Luketic</td>\n",
+ " <td>Liam Hemsworth, Gary Oldman, Amber Heard, Harr...</td>\n",
+ " <td>United States, India, France</td>\n",
+ " <td>September 19, 2021</td>\n",
+ " <td>2013</td>\n",
+ " <td>PG-13</td>\n",
+ " <td>106 min</td>\n",
+ " <td>Thrillers</td>\n",
+ " <td>Blackmailed by his company's CEO, a low-level ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>38</th>\n",
+ " <td>s39</td>\n",
+ " <td>Movie</td>\n",
+ " <td>Birth of the Dragon</td>\n",
+ " <td>George Nolfi</td>\n",
+ " <td>Billy Magnussen, Ron Yuan, Qu Jingjing, Terry ...</td>\n",
+ " <td>China, Canada, United States</td>\n",
+ " <td>September 16, 2021</td>\n",
+ " <td>2017</td>\n",
+ " <td>PG-13</td>\n",
+ " <td>96 min</td>\n",
+ " <td>Action & Adventure, Dramas</td>\n",
+ " <td>A young Bruce Lee angers kung fu traditionalis...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41</th>\n",
+ " <td>s42</td>\n",
+ " <td>Movie</td>\n",
+ " <td>Jaws</td>\n",
+ " <td>Steven Spielberg</td>\n",
+ " <td>Roy Scheider, Robert Shaw, Richard Dreyfuss, L...</td>\n",
+ " <td>United States</td>\n",
+ " <td>September 16, 2021</td>\n",
+ " <td>1975</td>\n",
+ " <td>PG</td>\n",
+ " <td>124 min</td>\n",
+ " <td>Action & Adventure, Classic Movies, Dramas</td>\n",
+ " <td>When an insatiable great white shark terrorize...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>\n",
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72387072-0ba7-4b83-b8a6-4860c0ea7045')\"\n",
+ " title=\"Convert this dataframe to an interactive table.\"\n",
+ " style=\"display:none;\">\n",
+ " \n",
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+ " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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+ " </svg>\n",
+ " </button>\n",
+ " \n",
+ " <style>\n",
+ " .colab-df-container {\n",
+ " display:flex;\n",
+ " flex-wrap:wrap;\n",
+ " gap: 12px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-72387072-0ba7-4b83-b8a6-4860c0ea7045 button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-72387072-0ba7-4b83-b8a6-4860c0ea7045');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 114
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Ahora sÃ, una vez limpiados los datos de la tabla, debemos hacernos la pregunta: ¿Qué información podrÃa obtener en base a los datos que me provee?. Nosotros ya nos hicimos esa misma pregunta, y llegamos a los siguientes interrogantes:\n",
+ "\n",
+ "* ¿Cuál es la cantidad de pelÃculas y/o series estrenadas por año, que contiene la plataforma Netflix?\n",
+ "* ¿Cuáles son los 5 directores que dirigieron las pelÃculas más largas en Netflix?\n",
+ "* ¿Cuáles son las series de origen estadounidense, con más temporadas?\n",
+ "\n",
+ "De entre esas, elegimos la primera para resolver."
+ ],
+ "metadata": {
+ "id": "q8V9j3COB082"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Pregunta: ¿Cuál es la cantidad de pelÃculas y series estrenadas por año en Netflix?"
+ ],
+ "metadata": {
+ "id": "qRkvdQQHrut9"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Primero separamos las columnas que vamos a usar para nuestro análisis: type (Si es pelÃcula o serie de TV), release_year (Año de estreno) y show_id (Necesario para poder agrupar después)."
+ ],
+ "metadata": {
+ "id": "_MNTjKd5rxAX"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "contenido_por_anio = netflix_completo[[\"show_id\",\"type\",\"release_year\"]]\n",
+ "contenido_por_anio.head(10)"
+ ],
+ "metadata": {
+ "id": "GC2wS31jruWV"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Luego agrupamos los shows por año de estreno y tipo (si es PelÃcula o show de TV). \n",
+ "Luego usamos la columna show_id con **count** para contar la cantidad de elementos en cada subgrupo. \n"
+ ],
+ "metadata": {
+ "id": "ChJbqVrPRIlr"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "contenido_por_anio = contenido_por_anio.groupby([\"release_year\",\"type\"])[\"show_id\"].count()\n",
+ "contenido_por_anio.head(10)"
+ ],
+ "metadata": {
+ "id": "RTMaQfwpHvy-"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Usamos la función **unstack** ya que cuando usamos el groupby esto genera un multiÃndice (un Ãndice con subgrupos) y para graficarlo necesitamos separarlo. \n",
+ "Finalmenete usamos **fillna(0)** para indicar qué valores NaN o null deben ser llenados con 0."
+ ],
+ "metadata": {
+ "id": "4C1oAlMWH1Y_"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "contenido_por_anio = contenido_por_anio.unstack().fillna(0)\n",
+ "contenido_por_anio.head(10)"
+ ],
+ "metadata": {
+ "id": "QvHm3ddZRIKD"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "###Graficando con matplotlib"
+ ],
+ "metadata": {
+ "id": "F6AoD_82r5gy"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "En el caso de matplotlib, usamos la funcion **plot** para obtener un gráfico para el cuál especificamos el tamaño con figsize de 15 pulgadas de ancho y 10 pulgadas de alto. \n",
+ "Le agregamos las etiquetas de los ejes x (Año de Estreno) e y (Cantidad) con los parámetros xlabel e ylabel respectivamente. \n",
+ "También para que se noten los estrenos particulares, agregamos markers con marker=\"o\" que nos da un marker circular.\n"
+ ],
+ "metadata": {
+ "id": "KyIafix6TxZL"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "contenido_por_anio.plot(figsize=(15,10), xlabel=\"Año de estreno\", ylabel=\"Cantidad de estrenos\", marker=\"o\")"
+ ],
+ "metadata": {
+ "id": "FaIuHJVAT1Wl"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "###Graficando con Plotly"
+ ],
+ "metadata": {
+ "id": "BpVpkMZxT1sZ"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "En el caso de Plotly, usamos plotly express, una versión simplificada, muy parecida a MatPlotLib que nos permite tener gráficos interactivos."
+ ],
+ "metadata": {
+ "id": "cOuArF-1UAfJ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "fig= px.line(contenido_por_anio)\n",
+ "fig.show()"
+ ],
+ "metadata": {
+ "id": "ITxkCrivT2CF",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 542
+ },
+ "outputId": "2f5f7426-a3b4-44e4-ea13-2922d3fd04e2"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
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+ "<head><meta charset=\"utf-8\" /></head>\n",
+ "<body>\n",
+ " <div> <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script> <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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+ "source": [
+ "Se ve feo, ¿No? \n",
+ "Para cambiarlo hay que agregar algunos parámetros a line que nos van a ayudar con esto. \n",
+ "Para esto usamos el parámetro _labels_ al cual se le debe pasar un [diccionario](https://www.freecodecamp.org/espanol/news/compresion-de-diccionario-en-python-explicado-con-ejemplos/#:~:text=%C2%BFQu%C3%A9%20es%20un%20diccionario%20en,un%20par%20de%20corchetes%20%7B%7D%20) con los valores anteriores de las etiquetas, y los valores nuevos separados por _:_ (dos puntos)\n",
+ "\n",
+ "* (value -> Cantidad de estrenos)\n",
+ "* (release_year -> Año de estreno)\n",
+ "* (type -> \"\") \n",
+ "Finalmente como en el gráfico de MatPlotLib le colocamos los markes, est\n",
+ "\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "rsMA2coBL-5H"
+ }
+ },
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+ "cell_type": "code",
+ "source": [
+ "fig= px.line(contenido_por_anio,labels={\"value\":\"Cantidad de estrenos\",\"release_year\":\"Año de estreno\",\"type\":\"\"}, markers=True)\n",
+ "fig.show()"
+ ],
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+ "height": 542
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+ "id": "iYx7pfvaL3ca",
+ "outputId": "137c68de-c82b-4af0-a70e-734f838309ed"
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+ "Si hacemos zoom en el gráfico anterior podemos ver que las escalas de los años y las cantidades llegan, por ejemplo, a 112,5 estrenos; para que las escalas se vean correctamente hay que aclarar la unidad de la escala en cada eje, esto lo hacemos con: update_yaxes(dtick=100) y update_yaxes (dtick=100) que para el eje correspondiente aclaran la unidad en 100 y 1 respectivamente."
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+ {
+ "cell_type": "markdown",
+ "source": [
+ "##Preparando el Dataset de los Oscars"
+ ],
+ "metadata": {
+ "id": "LmXTiJ01rbaz"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Para la siguiente demostración vamos a usar el dataset de los Oscars, que está en formato xlsx, el formato de excel, entonces usamos **read_excel**"
+ ],
+ "metadata": {
+ "id": "2iNGXcOyrvrL"
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+ "source": [
+ "oscars = pd.read_excel(\"oscars.xlsx\")\n",
+ "oscars.head(10)"
+ ],
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+ "id": "jOO-_BEcrwBj",
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+ " year_film year_ceremony ceremony Category gender name \\\n",
+ "0 1927 1928 1 Best Actor Male Emil Jannings \n",
+ "1 1927 1928 1 Best Actress Female Janet Gaynor \n",
+ "2 1928 1929 2 Best Actor Male Warner Baxter \n",
+ "3 1928 1929 2 Best Actress Female Mary Pickford \n",
+ "4 1929 1930 3 Best Actor Male George Arliss \n",
+ "5 1929 1930 3 Best Actress Female Norma Shearer \n",
+ "6 1930 1931 4 Best Actor Male Lionel Barrymore \n",
+ "7 1930 1931 4 Best Actress Female Marie Dressler \n",
+ "8 1931 1932 5 Best Actor Male Wallace Beery \n",
+ "9 1931 1932 5 Best Actor Male Fredric March \n",
+ "\n",
+ " Race film winner \n",
+ "0 White The Last Command True \n",
+ "1 White 7th Heaven True \n",
+ "2 White In Old Arizona True \n",
+ "3 White Coquette True \n",
+ "4 White Disraeli True \n",
+ "5 White The Divorcee True \n",
+ "6 White A Free Soul True \n",
+ "7 White Min and Bill True \n",
+ "8 White The Champ True \n",
+ "9 White Dr. Jekyll and Mr. Hyde True "
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+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-f7ea5508-8f89-4204-b411-447e6591ad6b button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-f7ea5508-8f89-4204-b411-447e6591ad6b');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 121
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Como podemos ver, la tabla contiene las siguientes columnas: \n",
+ "\n",
+ "1) year_film: Año de estreno de la pelÃcula. \n",
+ "2) year_ceremony: Año de la ceremonia de premiación en que la pelÃcula calificó. \n",
+ "3) ceremony: Numero de ceremonia en que la pelÃcula calificó. \n",
+ "4) Category: CategorÃa de la nominación (Mejor actriz, mejor pelÃcula, etc). \n",
+ "5) gender: Genero de quien fue nominado/a. \n",
+ "6) name: Nombre del nominado/a. \n",
+ "7) Race: Etnia del nominado/a. \n",
+ "8) film: Nombre de la pelÃcula correspondiente a la nominación. \n",
+ "9) winner: Si ganó o no. "
+ ],
+ "metadata": {
+ "id": "923YIVaprwdb"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Ahora tenemos que ver como con el dataset de Netflix, si hay valores nulos:"
+ ],
+ "metadata": {
+ "id": "5oCuIO3882DF"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "oscars.isnull().values.any()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EBh5_2ujrwtD",
+ "outputId": "a69fbc90-c779-4bf8-ab2f-55ca5e4df302"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 122
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Como hay valores nulos, tenemos que borrar esas filas con **dropna** y arreglar el Ãndice con **reset_index**:"
+ ],
+ "metadata": {
+ "id": "k_euIGiv_NMH"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "oscars = oscars.dropna().reset_index(drop=True)"
+ ],
+ "metadata": {
+ "id": "w5asyE81_UXF"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Revisamos de vuelta para confirmar"
+ ],
+ "metadata": {
+ "id": "md_7xlzzrw97"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "oscars.isnull().values.any()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3ozNl_y3rxXr",
+ "outputId": "425328eb-2d6d-4391-863d-78e8e9c4e1c8"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 124
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##Pregunta: ¿Cuál es la etnia de los ganadores de Oscars año a año?"
+ ],
+ "metadata": {
+ "id": "hJh2Da1P_wmu"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Primero separamos las columnas necesarias: la etnia, el año de la ceremonia y si ganó o no."
+ ],
+ "metadata": {
+ "id": "7Y40qzDNKrtt"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataframe_race_year = oscars[[\"Race\",\"year_ceremony\",\"winner\"]]\n",
+ "dataframe_race_year.head(10)"
+ ],
+ "metadata": {
+ "id": "yPYSkkGkK09A",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 363
+ },
+ "outputId": "48f58e0b-4000-427e-f6c0-65a80e329e36"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " Race year_ceremony winner\n",
+ "0 White 1928 True\n",
+ "1 White 1928 True\n",
+ "2 White 1929 True\n",
+ "3 White 1929 True\n",
+ "4 White 1930 True\n",
+ "5 White 1930 True\n",
+ "6 White 1931 True\n",
+ "7 White 1931 True\n",
+ "8 White 1932 True\n",
+ "9 White 1932 True"
+ ],
+ "text/html": [
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
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+ " <th>9</th>\n",
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+ " <td>True</td>\n",
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+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 125
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Luego filtramos para quedarnos solo con los ganadores."
+ ],
+ "metadata": {
+ "id": "yjcPjb10K18Y"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataframe_winners = dataframe_race_year[dataframe_race_year.winner == True]\n",
+ "dataframe_winners.head(10)"
+ ],
+ "metadata": {
+ "id": "fdvlIna0K6TC",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 363
+ },
+ "outputId": "407ce8d4-b626-42e3-e410-0b26ddcde44e"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " Race year_ceremony winner\n",
+ "0 White 1928 True\n",
+ "1 White 1928 True\n",
+ "2 White 1929 True\n",
+ "3 White 1929 True\n",
+ "4 White 1930 True\n",
+ "5 White 1930 True\n",
+ "6 White 1931 True\n",
+ "7 White 1931 True\n",
+ "8 White 1932 True\n",
+ "9 White 1932 True"
+ ],
+ "text/html": [
+ "\n",
+ " <div id=\"df-eb0c00d2-ef32-4287-8eb2-2bc0902f1096\">\n",
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Race</th>\n",
+ " <th>year_ceremony</th>\n",
+ " <th>winner</th>\n",
+ " </tr>\n",
+ " </thead>\n",
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+ " <th>1</th>\n",
+ " <td>White</td>\n",
+ " <td>1928</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>White</td>\n",
+ " <td>1929</td>\n",
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+ " <th>3</th>\n",
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+ " <td>1929</td>\n",
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+ " <td>1930</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>White</td>\n",
+ " <td>1931</td>\n",
+ " <td>True</td>\n",
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+ " <tr>\n",
+ " <th>7</th>\n",
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+ " <td>1931</td>\n",
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+ " <td>1932</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>White</td>\n",
+ " <td>1932</td>\n",
+ " <td>True</td>\n",
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+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-eb0c00d2-ef32-4287-8eb2-2bc0902f1096 button.colab-df-convert');\n",
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+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-eb0c00d2-ef32-4287-8eb2-2bc0902f1096');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 126
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Ahora agrupamos los datos por el año y la etnia con **groupby** y usamos la columna winner para contar la cantidad de elementos de cada subgrupo. \n",
+ "\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "DbHfZhl2LJXv"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "group_race_year= dataframe_winners.groupby([\"year_ceremony\",\"Race\"])[\"winner\"].count()\n",
+ "group_race_year.head(10)"
+ ],
+ "metadata": {
+ "id": "M0bw2mp9LAeN",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "1862cbfc-8479-4c78-d228-4016bd37c0ca"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "year_ceremony Race \n",
+ "1928 White 12\n",
+ "1929 White 7\n",
+ "1930 White 8\n",
+ "1931 White 8\n",
+ "1932 White 12\n",
+ "1933 White 12\n",
+ "1935 White 16\n",
+ "1936 White 17\n",
+ "1937 White 20\n",
+ "1938 White 20\n",
+ "Name: winner, dtype: int64"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 127
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Una vez hecho esto usamos **unstack** para deshacernos del multiÃndice generado con el groupby y el fillna para llenar los casos donde una etnia no ganó premios ese año."
+ ],
+ "metadata": {
+ "id": "j94YTV0TLiPh"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "group_race_year= group_race_year.unstack().fillna(0)\n",
+ "group_race_year.head(10)"
+ ],
+ "metadata": {
+ "id": "WRxCzbesLir5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 394
+ },
+ "outputId": "85488bb4-5859-4a3a-9218-6995c8113571"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "Race Asian Black Hispanic White\n",
+ "year_ceremony \n",
+ "1928 0.0 0.0 0.0 12.0\n",
+ "1929 0.0 0.0 0.0 7.0\n",
+ "1930 0.0 0.0 0.0 8.0\n",
+ "1931 0.0 0.0 0.0 8.0\n",
+ "1932 0.0 0.0 0.0 12.0\n",
+ "1933 0.0 0.0 0.0 12.0\n",
+ "1935 0.0 0.0 0.0 16.0\n",
+ "1936 0.0 0.0 0.0 17.0\n",
+ "1937 0.0 0.0 0.0 20.0\n",
+ "1938 0.0 0.0 0.0 20.0"
+ ],
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+ " <div class=\"colab-df-container\">\n",
+ " <div>\n",
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th>Race</th>\n",
+ " <th>Asian</th>\n",
+ " <th>Black</th>\n",
+ " <th>Hispanic</th>\n",
+ " <th>White</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>year_ceremony</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1928</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>12.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1929</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>7.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1930</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1931</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1932</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>12.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1933</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>12.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1935</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>16.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1936</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>17.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1937</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>20.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1938</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>20.0</td>\n",
+ " </tr>\n",
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+ " </svg>\n",
+ " </button>\n",
+ " \n",
+ " <style>\n",
+ " .colab-df-container {\n",
+ " display:flex;\n",
+ " flex-wrap:wrap;\n",
+ " gap: 12px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-9eba2673-4198-4f44-a6af-24196d8d3a3c button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-9eba2673-4198-4f44-a6af-24196d8d3a3c');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 128
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "###Graficando con MatPlotLib"
+ ],
+ "metadata": {
+ "id": "Bowx9cWaMEGq"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Para graficar esto con MatPlotLib usamos **plot** y cambiamos las etiquetas de los ejes por etiquetas más explicativas."
+ ],
+ "metadata": {
+ "id": "ufaKzwwJMTJM"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "group_race_year.plot(figsize=(15,10),grid=True,xlabel=\"Año de la ceremonia\",ylabel=\"Cantidad de ganadores\")"
+ ],
+ "metadata": {
+ "id": "6-_3E8ZkMEpv",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 624
+ },
+ "outputId": "a8568e0b-6b27-4c2c-b5a9-cf2073c2eac7"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7f212715c8d0>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 129
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 1080x720 with 1 Axes>"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "###Graficando con Plotly"
+ ],
+ "metadata": {
+ "id": "P12K2WAdME7X"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "En el caso de Plotly es muy parecido. Cambiamos las etiquetas y como en el caso anterior usamos unidades apropiadas para las escalas.\n"
+ ],
+ "metadata": {
+ "id": "VwTtF_NtMWM3"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "fig = px.line(group_race_year,labels={\"value\":\"Cantidad de Oscars\",\"year_ceremony\":\"Año de la ceremonia\"},markers=True)\n",
+ "fig.update_yaxes(dtick=1)\n",
+ "fig.update_xaxes(dtick=10)\n",
+ "fig.show()"
+ ],
+ "metadata": {
+ "id": "POXuVDkdMFPG",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 542
+ },
+ "outputId": "c43005b5-cbec-4f40-9d4a-f61bd9866de8"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "<html>\n",
+ "<head><meta charset=\"utf-8\" /></head>\n",
+ "<body>\n",
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+ " if (!display || display === 'none') {{\n",
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+ " Plotly.purge(gd);\n",
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+ "</body>\n",
+ "</html>"
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##Pregunta: ¿Cuáles son las 10 pelÃculas de Netflix con más Oscars ganados?"
+ ],
+ "metadata": {
+ "id": "agsfWSSevGNs"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Este análisis es más complicado que los anteriores, ya que usamos dos datasets distintos y los unimos para sacar conclusiones más complejas que con uno solo."
+ ],
+ "metadata": {
+ "id": "ASE8yFKaJSIT"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Primero separamos las columnas del dataset de oscars que necesitamos para el análisis, film (Las pelÃculas) y winner (si ganó o no) y elegimos solo a los que ganaron."
+ ],
+ "metadata": {
+ "id": "nOxnqY-ZzvVk"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "oscars_de_peliculas = oscars[[\"film\",\"winner\"]]\n",
+ "oscars_de_peliculas = oscars_de_peliculas[oscars_de_peliculas[\"winner\"] == True]\n",
+ "oscars_de_peliculas.head(10)"
+ ],
+ "metadata": {
+ "id": "9NQHBLVQzv1N",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 363
+ },
+ "outputId": "675ce3f9-8fa1-481c-be16-613cf21e9472"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " film winner\n",
+ "0 The Last Command True\n",
+ "1 7th Heaven True\n",
+ "2 In Old Arizona True\n",
+ "3 Coquette True\n",
+ "4 Disraeli True\n",
+ "5 The Divorcee True\n",
+ "6 A Free Soul True\n",
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+ ]
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+ "metadata": {},
+ "execution_count": 131
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Hacemos lo mismo con los shows de netflix, con las columnas type (si es pelÃcula o TV) y title (los shows) y elegimos solo las pelÃculas preguntando por el tipo."
+ ],
+ "metadata": {
+ "id": "cnPTZbqnK6uy"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "peliculas = netflix_completo[[\"type\",\"title\"]]\n",
+ "peliculas = peliculas[peliculas[\"type\"] == \"Movie\"]\n",
+ "peliculas.head(10)"
+ ],
+ "metadata": {
+ "id": "v2xXRFPJLMfA",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 363
+ },
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+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " type title\n",
+ "0 Movie Sankofa\n",
+ "2 Movie The Starling\n",
+ "3 Movie Je Suis Karl\n",
+ "4 Movie Jeans\n",
+ "5 Movie Grown Ups\n",
+ "6 Movie Dark Skies\n",
+ "7 Movie Paranoia\n",
+ "8 Movie Birth of the Dragon\n",
+ "9 Movie Jaws\n",
+ "10 Movie Jaws 2"
+ ],
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+ " const element = document.querySelector('#df-814a70c9-ec8a-4d51-9056-f9319a6e9e1f');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 132
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Ahora, ya que ya usamos las columnas winner del dataset de oscars y type del de Netflix y ya no aportan nada, los borramos."
+ ],
+ "metadata": {
+ "id": "WrhobWF9LhkJ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "oscars_de_peliculas= oscars_de_peliculas[[\"film\"]]\n",
+ "oscars_de_peliculas.head(10)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 363
+ },
+ "id": "FwVSn3FuLyy1",
+ "outputId": "a4020009-61a3-4929-92a6-181ef62c2be5"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " film\n",
+ "0 The Last Command\n",
+ "1 7th Heaven\n",
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+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>Dr. Jekyll and Mr. Hyde</td>\n",
+ " </tr>\n",
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+ "</table>\n",
+ "</div>\n",
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+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 133
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "peliculas = netflix_completo[[\"title\"]]\n",
+ "peliculas.head(10)"
+ ],
+ "metadata": {
+ "colab": {
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+ "height": 363
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+ " title\n",
+ "0 Sankofa\n",
+ "1 The Great British Baking Show\n",
+ "2 The Starling\n",
+ "3 Je Suis Karl\n",
+ "4 Jeans\n",
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+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
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+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
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+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 134
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "El siguiente paso es unir las tablas para quedarnos solo con las pelÃculas que ganaron oscars y están en netflix; para luego contar la cantidad de apariciones de cada una."
+ ],
+ "metadata": {
+ "id": "zXvHR0umL5YY"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Para hacer la unión de las tablas usamos **join** sobre el dataset de Netflix, esto nos permite unir las tablas, los parámetros que le pasamos son el otro dataframe con el index que vamos a usar, _on_ la columna del otro dataset por la que los vamos a unir y how que indica como se va a hacer, en este caso se aclara inner, que solo deja filas que coincidan el mismo tÃtulo en ambos datasets."
+ ],
+ "metadata": {
+ "id": "mjbNxoFSMbH3"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "peliculas_con_oscars = peliculas.join(oscars_de_peliculas.set_index(\"film\"), on = \"title\", how = \"inner\")\n",
+ "peliculas_con_oscars.head(10)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 363
+ },
+ "id": "aBNLh-CvN5wT",
+ "outputId": "a9d23f97-7fd4-45e5-b6b5-8105608b380d"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " title\n",
+ "9 Jaws\n",
+ "9 Jaws\n",
+ "9 Jaws\n",
+ "14 Training Day\n",
+ "47 Cold Mountain\n",
+ "82 The Guns of Navarone\n",
+ "84 The Nutty Professor\n",
+ "118 The Piano\n",
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+ "118 The Piano"
+ ],
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+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
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+ " const buttonEl =\n",
+ " document.querySelector('#df-db7790a3-8ed2-41b5-a3c5-fbc9f39fe8b2 button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-db7790a3-8ed2-41b5-a3c5-fbc9f39fe8b2');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 135
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Ahora lo que queda por hacer es agrupar las pelÃculas según su tÃtulo:\n",
+ "```\n",
+ "# peliculas_con_oscars.groupby(['title'])\n",
+ "```\n",
+ "Encontrar el tamaño de cada grupo:\n",
+ "```\n",
+ "# .size()\n",
+ "```\n",
+ "Ordenarlos de mayor a menor:\n",
+ "```\n",
+ "# sort_values(ascending = False)\n",
+ "```\n",
+ "Y obtener los 10 primeros:\n",
+ "```\n",
+ "# .head(10)\n",
+ "```\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "B-r4LLe9PO5j"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "peliculas_con_oscars = peliculas_con_oscars.groupby(['title']).size().sort_values(ascending = False).head(10)\n",
+ "peliculas_con_oscars.head(10)"
+ ],
+ "metadata": {
+ "id": "nnLTGCcOOPkj",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "39761e0d-f042-46c7-c59c-ed38a60d0a29"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "title\n",
+ "The Lord of the Rings: The Return of the King 11\n",
+ "Gigi 9\n",
+ "My Fair Lady 8\n",
+ "Schindler's List 7\n",
+ "American Beauty 5\n",
+ "Doctor Zhivago 5\n",
+ "The Artist 5\n",
+ "Hugo 5\n",
+ "The Matrix 4\n",
+ "Ordinary People 4\n",
+ "dtype: int64"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 136
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "###Graficando con Plotly"
+ ],
+ "metadata": {
+ "id": "Mnjc0Cu0P67k"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "fig = px.bar(peliculas_con_oscars,labels={'x': 'TÃtulo', 'y':'Cantidad de Oscars'}, title=\"10 pelÃculas en Nextflix con más Oscars ganados.\")\n",
+ "fig.update_yaxes(dtick=1)\n",
+ "fig.show()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 542
+ },
+ "id": "Qpmom8_-QJNz",
+ "outputId": "cd38059e-808d-4bef-85f3-3b19a8db50ef"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "<html>\n",
+ "<head><meta charset=\"utf-8\" /></head>\n",
+ "<body>\n",
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+ "###Graficando con MatPlotLib"
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+ "peliculas_con_oscars.plot(kind=\"bar\",xlabel= \"TÃtulo\",ylabel= \"Cantidad de Oscars\", legend= False, title = \"10 pelÃculas en Nextflix con más Oscars ganados.\", figsize = (15, 10))"
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+ "<matplotlib.axes._subplots.AxesSubplot at 0x7f2126ed5b90>"
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+ "metadata": {},
+ "execution_count": 138
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+ "data": {
+ "text/plain": [
+ "<Figure size 1080x720 with 1 Axes>"
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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
--
GitLab