{ "cells": [ { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# 读取数据\n", "data = pd.read_csv('./data/IMDB-Movie-Data.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RankTitleGenreDescriptionDirectorActorsYearRuntime (Minutes)RatingVotesRevenue (Millions)Metascore
01Guardians of the GalaxyAction,Adventure,Sci-FiA group of intergalactic criminals are forced ...James GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe S...20141218.1757074333.1376.0
12PrometheusAdventure,Mystery,Sci-FiFollowing clues to the origin of mankind, a te...Ridley ScottNoomi Rapace, Logan Marshall-Green, Michael Fa...20121247.0485820126.4665.0
23SplitHorror,ThrillerThree girls are kidnapped by a man with a diag...M. Night ShyamalanJames McAvoy, Anya Taylor-Joy, Haley Lu Richar...20161177.3157606138.1262.0
34SingAnimation,Comedy,FamilyIn a city of humanoid animals, a hustling thea...Christophe LourdeletMatthew McConaughey,Reese Witherspoon, Seth Ma...20161087.260545270.3259.0
45Suicide SquadAction,Adventure,FantasyA secret government agency recruits some of th...David AyerWill Smith, Jared Leto, Margot Robbie, Viola D...20161236.2393727325.0240.0
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" ], "text/plain": [ " Rank Title Genre \\\n", "0 1 Guardians of the Galaxy Action,Adventure,Sci-Fi \n", "1 2 Prometheus Adventure,Mystery,Sci-Fi \n", "2 3 Split Horror,Thriller \n", "3 4 Sing Animation,Comedy,Family \n", "4 5 Suicide Squad Action,Adventure,Fantasy \n", "\n", " Description Director \\\n", "0 A group of intergalactic criminals are forced ... James Gunn \n", "1 Following clues to the origin of mankind, a te... Ridley Scott \n", "2 Three girls are kidnapped by a man with a diag... M. Night Shyamalan \n", "3 In a city of humanoid animals, a hustling thea... Christophe Lourdelet \n", "4 A secret government agency recruits some of th... David Ayer \n", "\n", " Actors Year Runtime (Minutes) \\\n", "0 Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... 2014 121 \n", "1 Noomi Rapace, Logan Marshall-Green, Michael Fa... 2012 124 \n", "2 James McAvoy, Anya Taylor-Joy, Haley Lu Richar... 2016 117 \n", "3 Matthew McConaughey,Reese Witherspoon, Seth Ma... 2016 108 \n", "4 Will Smith, Jared Leto, Margot Robbie, Viola D... 2016 123 \n", "\n", " Rating Votes Revenue (Millions) Metascore \n", "0 8.1 757074 333.13 76.0 \n", "1 7.0 485820 126.46 65.0 \n", "2 7.3 157606 138.12 62.0 \n", "3 7.2 60545 270.32 59.0 \n", "4 6.2 393727 325.02 40.0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 问题1：我们想知道这些电影数据中评分的平均分，导演的人数等信息，我们应该怎么获取？" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "644" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 获取评分的平均分\n", "data['Rating'].mean()\n", "\n", "# 获取导演人数\n", "np.unique(data['Director']).size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 问题2：对于这一组电影数据，如果我们想rating，runtime的分布情况，应该如何呈现数据？" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 绘制直方图\n", "# 创建画布\n", "plt.figure(figsize=(20,8))\n", "# 绘制图像\n", "res = plt.hist(data['Rating'], bins=20)\n", "# 设置x轴刻度\n", "plt.xticks(res[1])\n", "# 显示图像\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 问题3：对于这一组电影数据，如果我们希望统计电影分类(genre)的情况，应该如何处理数据？" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# 遍历所有的电影类型数据，累加对应类型的数量\n", "# 1.拿到所有的电影类型\n", "genre_temp = [i.split(',') for i in data['Genre']]\n", "genre_list = np.unique([i for j in genre_temp for i in j])\n", "# 2.构建一个记录电影类型数量的容器 Series\n", "genre_s = pd.Series(np.zeros((len(genre_list),)), index=genre_list)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# 遍历所有的电影，统计电影类型的数量\n", "for i in genre_temp:\n", " for j in i:\n", " genre_s[j] += 1" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 绘制柱状图\n", "genre_s.plot(kind='bar', figsize=(20,8))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }