基于pandas,matplotlib,numpy的电影案例分析

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

movie = pd.read_csv("./data/IMDB-Movie-Data.csv")

movie.head()

前言

数据来源:kaggle

	Rank	Title	Genre	Description	Director	Actors	Year	Runtime (Minutes)	Rating	Votes	Revenue (Millions)	Metascore
0	1	Guardians of the Galaxy	Action,Adventure,Sci-Fi	A group of intergalactic criminals are forced ...	James Gunn	Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...	2014	121	8.1	757074	333.13	76.0
1	2	Prometheus	Adventure,Mystery,Sci-Fi	Following clues to the origin of mankind, a te...	Ridley Scott	Noomi Rapace, Logan Marshall-Green, Michael Fa...	2012	124	7.0	485820	126.46	65.0
2	3	Split	Horror,Thriller	Three girls are kidnapped by a man with a diag...	M. Night Shyamalan	James McAvoy, Anya Taylor-Joy, Haley Lu Richar...	2016	117	7.3	157606	138.12	62.0
3	4	Sing	Animation,Comedy,Family	In a city of humanoid animals, a hustling thea...	Christophe Lourdelet	Matthew McConaughey,Reese Witherspoon, Seth Ma...	2016	108	7.2	60545	270.32	59.0
4	5	Suicide Squad	Action,Adventure,Fantasy	A secret government agency recruits some of th...	David Ayer	Will Smith, Jared Leto, Margot Robbie, Viola D...	2016	123	6.2	393727	325.02	40.0

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？

movie["Rating"].mean()

6.723199999999999

np.unique(movie["Director"]).shape[0]

644

对于这一组电影数据，如果我们想Rating，Runtime (Minutes)的分布情况，应该如何呈现数据？

用pandas画图

# Rating分布
# pandas画图已经无法满足我们的要求,只能完成一些简单的画图,如下
movie["Rating"].plot(kind="hist")

用matplotlib画图

# Rating分布

# 1.创建画布
plt.figure(figsize=(20, 8), dpi=100)

# 2.绘制图像
plt.hist(movie["Rating"].values, bins=20)

# 2.1 添加刻度
max_ = movie["Rating"].max()
min_ = movie["Rating"].min()

t1 = np.linspace(min_, max_, num=21)

plt.xticks(t1)

# 2.2 添加网格
plt.grid()

# 3.显示
plt.show()

# Runtime (Minutes)分布

# 1.创建画布
plt.figure(figsize=(20, 8), dpi=100)

# 2.绘制图像
plt.hist(movie["Runtime (Minutes)"].values, bins=20)

# 2.1 添加刻度
max_ = movie["Runtime (Minutes)"].max()
min_ = movie["Runtime (Minutes)"].min()

t1 = np.linspace(min_, max_, num=21)

plt.xticks(t1)

# 2.2 添加网格
plt.grid()

# 3.显示
plt.show()

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？

# 取出类别,用逗号分隔,形成二维数组
temp_list = [i.split(",") for i in movie["Genre"]]

# 取出类别
#用嵌套循环列表生成,先循环二维数组的每一维, 在每个数组中进行循环
# 最后去重
genre_list = np.unique([i for j in temp_list for i in j])
genre_list

array(['Action', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime',
       'Drama', 'Family', 'Fantasy', 'History', 'Horror', 'Music',
       'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Sport', 'Thriller',
       'War', 'Western'], dtype='

# 用movie行数, 类别数 形成 零数组的行列数
zeros = np.zeros([movie.shape[0], genre_list.shape[0]])

# 将1列索引更改为字符串
temp_movie = pd.DataFrame(zeros, columns=genre_list)

temp_movie.head()

	Action	Adventure	Animation	Biography	Comedy	Crime	Drama	Family	Fantasy	History	Horror	Music	Musical	Mystery	Romance	Sci-Fi	Sport	Thriller	War	Western
0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

# 用loc的索引方式,在两个表中分别取对应的值(temp_movie 和temp_list)
# 若相等则表示该电影类别取 1
for i in range(1000):
    temp_movie.loc[i, temp_list[i]] = 1

# 升序排列, 求出对应类别电影的和
genre = temp_movie.sum().sort_values(ascending=False)
genre

Drama        513.0
Action       303.0
Comedy       279.0
Adventure    259.0
Thriller     195.0
Crime        150.0
Romance      141.0
Sci-Fi       120.0
Horror       119.0
Mystery      106.0
Fantasy      101.0
Biography     81.0
Family        51.0
Animation     49.0
History       29.0
Sport         18.0
Music         16.0
War           13.0
Western        7.0
Musical        5.0
dtype: float64

# 用pandas的画图方法
genre.plot(kind="bar", colormap="cool", figsize=(20, 8), fontsize=16)