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python数据分析之pandas

1. 为什么要学习 pandas ？2. 什么是 pandas ？3. pandas 的常用数据类型 ？3.1 pandas之Series3.2 panda 之读取外部数据3.３ panda 之 DataFrame3.3.1 DataFrame创建3.3.2 DataFrame的基础属性和常用方法3.3.3 缺失数据的处理3.3.4 pandas 常用统计方法 3.4 数据合并3.4.1 数据合并之join3.4.２ 数据合并之merge 3.5 数据分组和聚合3.6 索引和复合索引3.7 pandas中的时间序列3.8pandas重采样 上一篇： python数据分析之numpy

1. 为什么要学习 pandas ？

上一篇已经学习numpy，它已经能够帮助我们处理数据，为啥还要学pandas呢？反正多学习点没啥坏处。

2. 什么是 pandas ？

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

3. pandas 的常用数据类型 ？

pandas的常用数据类型

Series 一维，带标签的数组DataFrame 二维，Series容器

3.1 pandas之Series

主要内容有：Ｓeries创建、切片和索引、索引和值 本小节的代码：

# coding=utf-8 ''' pandas的常用数据类型 Series 一维，带标签的数组 DataFrame 二维，Series容器 ''' import pandas as pd # 1、Series创建 t0 = pd.Series([1, 21, 34, 14, 5]) print(t0) print(type(t0)) t1 = pd.Series([1, 6, 3, 2, 7], index=list("abcde")) print(t1) # 通过字典创建 temp_dict = {"name": "xiaoxiong", "age": 30, "tel": 10086} t2 = pd.Series(temp_dict) print(t2) print(t2.dtype) print(t1.astype(float)) # 更改类型 print(t1) # 2、Series切片和索引 print(t2["age"]) # 索引取值 print(t2[1]) # 位置取值 print(t2[:2]) # 取前2行 print(t2[[1,2]]) # 取指定行 print(t2[["age", "tel"]]) # 指定索引 print(t0[t0 > 10]) # 大于10的 # 3、Series的索引和值 # 索引操作 print(t2.index) for i in t2.index: print(i) print(type(t2.index)) print(len(t2.index)) print(list(t2.index)[:2]) # 值操作 print(t2.values) print(t2.values[:2]) print(list(t2.values)[:2]) print(type(t0.values)) print(t2.index)

3.2 panda 之读取外部数据

这些都可以，这里就不多说了，用到的时候可以自行百度。

# coding=utf-8 import pandas as pd df=pd.read_csv('./books.csv') print(df) ''' 可以读取多种格式的文件 txt csv sql mongodb ... '''

3.３ panda 之 DataFrame

3.3.1 DataFrame创建

DataFrame源码，其实好多时候不懂就可以查看源码。

# coding=utf-8 import pandas as pd import numpy as np # 创建DataFrame data = pd.DataFrame(np.arange(12).reshape(3, 4)) print(data) # 创建DataFrame，并为其添加索引 t = pd.DataFrame(np.arange(12).reshape(3, 4), index=list("abc"), columns=list("wxyz")) print(t) # 通过字典创建DataFrme print("*"*100) temp_dict = {"name": ["xiaoxiong", "xiaogan"], "age": [30, 24], "tel": [10086, 10010]} t1 = pd.DataFrame(temp_dict) print(t1) # 通过列表创建DataFrme print("*"*100) d2=[{"name": "xiaoxiong", "age": 30, "tel": 10086},{"name": "xiaoxiong", "age": 30, "tel": 10000},{"name": "xiaoxiong", "age": 30}] print(d2) t2=pd.DataFrame(d2) print(t2)

3.3.2 DataFrame的基础属性和常用方法

# coding=utf-8 import pandas as pd import numpy as np # 创建DataFrame data = pd.DataFrame(np.arange(12).reshape(3, 4)) print(data) # 创建DataFrame，并为其添加索引 t = pd.DataFrame(np.arange(12).reshape(3, 4), index=list("abc"), columns=list("wxyz")) print(t) # 通过字典创建DataFrme print("*"*100) temp_dict = {"name": ["xiaoxiong", "xiaogan"], "age": [30, 24], "tel": [10086, 10010]} t1 = pd.DataFrame(temp_dict) print(t1) # 通过列表创建DataFrme print("*"*100) d2=[{"name": "xiaoxiong", "age": 30, "tel": 10086},{"name": "xiaoxiong", "age": 30, "tel": 10000},{"name": "xiaoxiong", "age": 30}] print(d2) t2=pd.DataFrame(d2) print(t2) # 常用基础属性 print("*"*100) print(t2.shape) print(t2.ndim) print(t2.index) print(t2.columns) print(t2.dtypes) ''' (3, 3) 2 RangeIndex(start=0, stop=3, step=1) Index(['name', 'age', 'tel'], dtype='object') name object age int64 tel int64 dtype: object ''' # 常用方法 print("*"*100) df=pd.read_csv('./books.csv') print(df.head(3)) ''' id ... small_image_url 0 1 ... https://images.gr-assets.com/books/1447303603s... 1 2 ... https://images.gr-assets.com/books/1474154022s... 2 3 ... https://images.gr-assets.com/books/1361039443s... [3 rows x 23 columns] ''' print(df.tail(3)) ''' id ... small_image_url 9997 9998 ... https://images.gr-assets.com/books/1455373531s... 9998 9999 ... https://images.gr-assets.com/books/1279214118s... 9999 10000 ... https://images.gr-assets.com/books/1403194704s... [3 rows x 23 columns] ''' print(df.info) print(df.describe()) ''' id book_id ... ratings_4 ratings_5 count 10000.00000 1.000000e+04 ... 1.000000e+04 1.000000e+04 mean 5000.50000 5.264697e+06 ... 1.996570e+04 2.378981e+04 std 2886.89568 7.575462e+06 ... 5.144736e+04 7.976889e+04 min 1.00000 1.000000e+00 ... 7.500000e+02 7.540000e+02 25% 2500.75000 4.627575e+04 ... 5.405750e+03 5.334000e+03 50% 5000.50000 3.949655e+05 ... 8.269500e+03 8.836000e+03 75% 7500.25000 9.382225e+06 ... 1.602350e+04 1.730450e+04 max 10000.00000 3.328864e+07 ... 1.481305e+06 3.011543e+06 [8 rows x 16 columns] ''' # dataframe中的排序方法sort_values() # df.sort_values # pandas取行或列的注意点 # - 方括号写数组，表示取行索引，对行进行操作 # - 方括号写字符串，表示取列索引，对列进行操作 print("*"*100) print(df[:20]) print(df["books_count"]) print(type(df["books_count"])) ''' pandas之loc df.loc 通过标签索引行数据 ''' print("*"*100) t3=pd.DataFrame(np.arange(12).reshape(3,4),index=list("abc"),columns=list("wxyz")) print(t3) print(t3.loc["a", "z"]) print(type(t3.loc["a", "z"])) print(t3.loc["a"]) # 取行数据 print(t3.loc["a",:]) print(t3.loc[["a","c"]]) # 也可以 t3.loc[["a","c"],:] # 取列数据 print(t3.loc[:,"y"]) print(t3.loc[["a","b"],["y","w"]]) ''' pandas之iloc df.iloc 通过位置获取行数据 ''' print("*"*100) print(t3.iloc[1]) print(t3.iloc[1:]) print(t3.iloc[:,2]) print(t3.iloc[[1,2],[1,2]]) print(t3.iloc[1:,2:]) t3.iloc[1:,2:]=30 print(t3) t3.iloc[1:,2:]=np.nan print(t3) ''' pandas之布尔索引 ''' print("*"*100) print(df[df["books_count"] > 300]) ''' pandas之缺失值处理 对于NaN的数据，在numpy中我们是如何处理的？ 在pandas中我们处理起来非常容易 判断数据是否为NaN：pd.isnull(df),pd.notnull(df) 处理方式1：删除NaN所在的行列dropna (axis=0, how='any', inplace=False) 处理方式2：填充数据，t.fillna(t.mean()),t.fiallna(t.median()),t.fillna(0) 处理为0的数据：t[t==0]=np.nan 当然并不是每次为0的数据都需要处理 计算平均值等情况，nan是不参与计算的，但是0会 ''' print("*"*100) # print(pd.isnull(t3)) print(t3[pd.notnull(t3["w"])]) print(t3) print(t3.dropna(axis=0, how="all",inplace=False)) print(t3.dropna(axis=0, how="any",inplace=False)) print("*"*100) print(t2) print(t2.fillna(0)) print(t2.fillna(10010)) print(t2.fillna(t2.mean())) t2["age"]=t2["age"].fillna(t2["age"].mean()) print(t2) t2["tel"]=t2["tel"].fillna(t2["tel"].mean()) print(t2)

3.3.3 缺失数据的处理

对于NaN的数据，在numpy中我们是如何处理的？ 在pandas中我们处理起来非常容易

判断数据是否为NaN：pd.isnull(df),pd.notnull(df)

处理方式1 删除NaN所在的行列 dropna (axis=0, how=‘any’, inplace=False)处理方式2： 填充数据，t.fillna(t.mean()),t.fillna(t.median()),t.fillna(0)

处理为0的数据：t[t==0]=np.nan 当然并不是每次为0的数据都需要处理 计算平均值等情况，nan是不参与计算的，但是0会

代码：

# coding=utf-8 import pandas as pd import numpy as np # 创建DataFrame data = pd.DataFrame(np.arange(12).reshape(3, 4)) print(data) # 创建DataFrame，并为其添加索引 t = pd.DataFrame(np.arange(12).reshape(3, 4), index=list("abc"), columns=list("wxyz")) print(t) # 通过字典创建DataFrme print("*"*100) temp_dict = {"name": ["xiaoxiong", "xiaogan"], "age": [30, 24], "tel": [10086, 10010]} t1 = pd.DataFrame(temp_dict) print(t1) # 通过列表创建DataFrme print("*"*100) d2=[{"name": "xiaoxiong", "age": 30, "tel": 10086},{"name": "xiaoxiong", "age": 30, "tel": 10000},{"name": "xiaoxiong", "age": 30}] print(d2) t2=pd.DataFrame(d2) print(t2) # 常用基础属性 print("*"*100) print(t2.shape) print(t2.ndim) print(t2.index) print(t2.columns) print(t2.dtypes) ''' (3, 3) 2 RangeIndex(start=0, stop=3, step=1) Index(['name', 'age', 'tel'], dtype='object') name object age int64 tel int64 dtype: object ''' # 常用方法 print("*"*100) df=pd.read_csv('./books.csv') print(df.head(3)) ''' id ... small_image_url 0 1 ... https://images.gr-assets.com/books/1447303603s... 1 2 ... https://images.gr-assets.com/books/1474154022s... 2 3 ... https://images.gr-assets.com/books/1361039443s... [3 rows x 23 columns] ''' print(df.tail(3)) ''' id ... small_image_url 9997 9998 ... https://images.gr-assets.com/books/1455373531s... 9998 9999 ... https://images.gr-assets.com/books/1279214118s... 9999 10000 ... https://images.gr-assets.com/books/1403194704s... [3 rows x 23 columns] ''' print(df.info) print(df.describe()) ''' id book_id ... ratings_4 ratings_5 count 10000.00000 1.000000e+04 ... 1.000000e+04 1.000000e+04 mean 5000.50000 5.264697e+06 ... 1.996570e+04 2.378981e+04 std 2886.89568 7.575462e+06 ... 5.144736e+04 7.976889e+04 min 1.00000 1.000000e+00 ... 7.500000e+02 7.540000e+02 25% 2500.75000 4.627575e+04 ... 5.405750e+03 5.334000e+03 50% 5000.50000 3.949655e+05 ... 8.269500e+03 8.836000e+03 75% 7500.25000 9.382225e+06 ... 1.602350e+04 1.730450e+04 max 10000.00000 3.328864e+07 ... 1.481305e+06 3.011543e+06 [8 rows x 16 columns] ''' # dataframe中的排序方法sort_values() # df.sort_values # pandas取行或列的注意点 # - 方括号写数组，表示取行索引，对行进行操作 # - 方括号写字符串，表示取列索引，对列进行操作 print("*"*100) print(df[:20]) print(df["books_count"]) print(type(df["books_count"])) ''' pandas之loc df.loc 通过标签索引行数据 ''' print("*"*100) t3=pd.DataFrame(np.arange(12).reshape(3,4),index=list("abc"),columns=list("wxyz")) print(t3) print(t3.loc["a", "z"]) print(type(t3.loc["a", "z"])) print(t3.loc["a"]) # 取行数据 print(t3.loc["a",:]) print(t3.loc[["a","c"]]) # 也可以 t3.loc[["a","c"],:] # 取列数据 print(t3.loc[:,"y"]) print(t3.loc[["a","b"],["y","w"]]) ''' pandas之iloc df.iloc 通过位置获取行数据 ''' print("*"*100) print(t3.iloc[1]) print(t3.iloc[1:]) print(t3.iloc[:,2]) print(t3.iloc[[1,2],[1,2]]) print(t3.iloc[1:,2:]) t3.iloc[1:,2:]=30 print(t3) t3.iloc[1:,2:]=np.nan print(t3) ''' pandas之布尔索引 ''' print("*"*100) print(df[df["books_count"] > 300]) ''' pandas之缺失值处理 对于NaN的数据，在numpy中我们是如何处理的？ 在pandas中我们处理起来非常容易 判断数据是否为NaN：pd.isnull(df),pd.notnull(df) 处理方式1：删除NaN所在的行列dropna (axis=0, how='any', inplace=False) 处理方式2：填充数据，t.fillna(t.mean()),t.fiallna(t.median()),t.fillna(0) 处理为0的数据：t[t==0]=np.nan 当然并不是每次为0的数据都需要处理 计算平均值等情况，nan是不参与计算的，但是0会 ''' print("pandas之缺失值处理") print("*"*100) print(pd.isnull(t3)) print(t3[pd.notnull(t3["w"])]) ''' w x y z a False False False False b False False True True c False False True True w x y z a 0 1 2.0 3.0 b 4 5 NaN NaN c 8 9 NaN NaN ''' print("*"*100) print(t3) print(t3.dropna(axis=0, how="all",inplace=False)) print(t3.dropna(axis=0, how="any",inplace=False)) ''' w x y z a 0 1 2.0 3.0 b 4 5 NaN NaN c 8 9 NaN NaN w x y z a 0 1 2.0 3.0 b 4 5 NaN NaN c 8 9 NaN NaN w x y z a 0 1 2.0 3.0 ''' print("*"*100) print(t2) ''' name age tel 0 xiaoxiong 30 10086.0 1 xiaoxiong 30 10000.0 2 xiaoxiong 30 NaN ''' print(t2.fillna(0)) ''' name age tel 0 xiaoxiong 30 10086.0 1 xiaoxiong 30 10000.0 2 xiaoxiong 30 0.0 ''' print(t2.fillna(10010)) ''' name age tel 0 xiaoxiong 30 10086.0 1 xiaoxiong 30 10000.0 2 xiaoxiong 30 10010.0 ''' print(t2.fillna(t2.mean())) ''' name age tel 0 xiaoxiong 30 10086.0 1 xiaoxiong 30 10000.0 2 xiaoxiong 30 10043.0 ''' print("*"*100) t2["age"]=t2["age"].fillna(t2["age"].mean()) print(t2) t2["tel"]=t2["tel"].fillna(t2["tel"].mean()) print(t2) ''' name age tel 0 xiaoxiong 30 10086.0 1 xiaoxiong 30 10000.0 2 xiaoxiong 30 NaN name age tel 0 xiaoxiong 30 10086.0 1 xiaoxiong 30 10000.0 2 xiaoxiong 30 10043.0 '''

3.3.4 pandas 常用统计方法

假设现在我们有一组从2006年到2016年1000部最流行的电影数据，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？ 数据来源：https://www.kaggle.com/damianpanek/sunday-eda/data 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？

# coding=utf-8 import pandas as pd from matplotlib import pyplot as plt file_path = "./IMDB-Movie-Data.csv" df = pd.read_csv(file_path) # print(df.head(1)) # print(df.info()) # 1、runtime分布情况 # #选择图形，直方图 # #准备数据 runtime_data = df["Runtime (Minutes)"].values # max_runtime = runtime_data.max() min_runtime = runtime_data.min() # # #计算组数 print(max_runtime-min_runtime) num_bin = (max_runtime-min_runtime)//5 # # #设置图形的大小 plt.figure(figsize=(20,8),dpi=80) plt.hist(runtime_data,num_bin) plt.xticks(range(min_runtime,max_runtime+5,5)) plt.savefig("./t_04.png") plt.show()

# coding=utf-8 import pandas as pd from matplotlib import pyplot as plt file_path = "./IMDB-Movie-Data.csv" df = pd.read_csv(file_path) # print(df.head(1)) # print(df.info()) ''' # 1、runtime分布情况 # #选择图形，直方图 # #准备数据 runtime_data = df["Runtime (Minutes)"].values # max_runtime = runtime_data.max() min_runtime = runtime_data.min() # # #计算组数 print(max_runtime-min_runtime) num_bin = (max_runtime-min_runtime)//5 # # #设置图形的大小 plt.figure(figsize=(20,8),dpi=80) plt.hist(runtime_data,num_bin) plt.xticks(range(min_runtime,max_runtime+5,5)) plt.show() plt.savefig("./t_04.png") ''' # 2、rating分布情况 # #选择图形，直方图 # #准备数据 runtime_data = df["Rating"].values # max_runtime = runtime_data.max() min_runtime = runtime_data.min() print(max_runtime) print(min_runtime) # # #计算组数 num_bin_list = [1.9,3.5] i=3.5 while i<=max_runtime: i += 0.5 num_bin_list.append(i) print(num_bin_list) # # #设置图形的大小 plt.figure(figsize=(20,8),dpi=80) plt.hist(runtime_data,num_bin_list) plt.savefig("./t_04_０２.png") plt.show()

# coding=utf-8 import pandas as pd from matplotlib import pyplot as plt import numpy as np file_path = "./IMDB-Movie-Data.csv" df = pd.read_csv(file_path) # print(df.head(1)) print(df["Genre"].head(3)) ''' 0 Action,Adventure,Sci-Fi 1 Adventure,Mystery,Sci-Fi 2 Horror,Thriller Name: Genre, dtype: object ''' # 统计分类的列表 temp_list = df["Genre"].str.split(",").tolist() # [[],[],[]] print(temp_list) ''' [['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Mystery', 'Sci-Fi'], ['Horror', 'Thriller'], ['Animation', 'Comedy', 'Family'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Adventure', 'Fantasy'], ['Comedy', 'Drama', 'Music'], ['Comedy'], ['Action', 'Adventure', 'Biography'], ['Adventure', 'Drama', 'Romance'], ['Adventure', 'Family', 'Fantasy'], ['Biography', 'Drama', 'History'], ['Action', 'Adventure', 'Sci-Fi'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Comedy', 'Drama'], ['Animation', 'Adventure', 'Comedy'], ['Biography', 'Drama', 'History'], ['Action', 'Thriller'], ['Biography', 'Drama'], ['Drama', 'Mystery', 'Sci-Fi'], ['Adventure', 'Drama', 'Thriller'], ['Drama'], ['Crime', 'Drama', 'Horror'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Adventure', 'Sci-Fi'], ['Comedy'], ['Action', 'Adventure', 'Drama'], ['Horror', 'Thriller'], ['Comedy'], ['Action', 'Adventure', 'Drama'], ['Comedy'], ['Drama', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Comedy'], ['Action', 'Horror', 'Sci-Fi'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Drama', 'Sci-Fi'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Adventure', 'Western'], ['Comedy', 'Drama'], ['Animation', 'Adventure', 'Comedy'], ['Drama'], ['Horror'], ['Biography', 'Drama', 'History'], ['Drama'], ['Action', 'Adventure', 'Fantasy'], ['Drama', 'Thriller'], ['Adventure', 'Drama', 'Fantasy'], ['Action', 'Adventure', 'Sci-Fi'], ['Drama'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Adventure', 'Fantasy'], ['Comedy', 'Drama'], ['Action', 'Crime', 'Thriller'], ['Action', 'Crime', 'Drama'], ['Adventure', 'Drama', 'History'], ['Crime', 'Horror', 'Thriller'], ['Drama', 'Romance'], ['Comedy', 'Drama', 'Romance'], ['Biography', 'Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Horror', 'Mystery', 'Thriller'], ['Crime', 'Drama', 'Mystery'], ['Drama', 'Romance', 'Thriller'], ['Drama', 'Mystery', 'Sci-Fi'], ['Action', 'Adventure', 'Comedy'], ['Drama', 'History', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Drama'], ['Action', 'Drama', 'Thriller'], ['Drama', 'History'], ['Action', 'Drama', 'Romance'], ['Drama', 'Fantasy'], ['Drama', 'Romance'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Sci-Fi'], ['Adventure', 'Drama', 'War'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Comedy', 'Fantasy'], ['Action', 'Adventure', 'Sci-Fi'], ['Comedy', 'Drama'], ['Biography', 'Comedy', 'Crime'], ['Crime', 'Drama', 'Mystery'], ['Action', 'Crime', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Crime', 'Drama'], ['Action', 'Adventure', 'Fantasy'], ['Crime', 'Drama', 'Mystery'], ['Action', 'Crime', 'Drama'], ['Crime', 'Drama', 'Mystery'], ['Action', 'Adventure', 'Fantasy'], ['Drama'], ['Comedy', 'Crime', 'Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Comedy', 'Crime'], ['Animation', 'Drama', 'Fantasy'], ['Horror', 'Mystery', 'Sci-Fi'], ['Drama', 'Mystery', 'Thriller'], ['Crime', 'Drama', 'Thriller'], ['Biography', 'Crime', 'Drama'], ['Action', 'Adventure', 'Fantasy'], ['Adventure', 'Drama', 'Sci-Fi'], ['Crime', 'Mystery', 'Thriller'], ['Action', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Thriller'], ['Comedy'], ['Action', 'Adventure', 'Drama'], ['Drama'], ['Drama', 'Mystery', 'Sci-Fi'], ['Action', 'Horror', 'Thriller'], ['Biography', 'Drama', 'History'], ['Romance', 'Sci-Fi'], ['Action', 'Fantasy', 'War'], ['Adventure', 'Drama', 'Fantasy'], ['Comedy'], ['Horror', 'Thriller'], ['Action', 'Biography', 'Drama'], ['Drama', 'Horror', 'Mystery'], ['Animation', 'Adventure', 'Comedy'], ['Adventure', 'Drama', 'Family'], ['Adventure', 'Mystery', 'Sci-Fi'], ['Adventure', 'Comedy', 'Romance'], ['Action'], ['Action', 'Thriller'], ['Adventure', 'Drama', 'Family'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Crime', 'Mystery'], ['Comedy', 'Family', 'Musical'], ['Adventure', 'Drama', 'Thriller'], ['Drama'], ['Adventure', 'Comedy', 'Drama'], ['Drama', 'Horror', 'Thriller'], ['Drama', 'Music'], ['Action', 'Crime', 'Thriller'], ['Crime', 'Drama', 'Thriller'], ['Crime', 'Drama', 'Thriller'], ['Drama', 'Romance'], ['Mystery', 'Thriller'], ['Mystery', 'Thriller', 'Western'], ['Action', 'Adventure', 'Sci-Fi'], ['Comedy', 'Family'], ['Biography', 'Comedy', 'Drama'], ['Drama'], ['Drama', 'Western'], ['Drama', 'Mystery', 'Romance'], ['Comedy', 'Drama'], ['Action', 'Drama', 'Mystery'], ['Comedy'], ['Action', 'Adventure', 'Crime'], ['Adventure', 'Family', 'Fantasy'], ['Adventure', 'Sci-Fi', 'Thriller'], ['Drama'], ['Action', 'Crime', 'Drama'], ['Drama', 'Horror', 'Mystery'], 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'Adventure', 'Comedy'], ['Horror', 'Thriller'], ['Action', 'Thriller'], ['Comedy'], ['Biography', 'Drama'], ['Action', 'Mystery', 'Thriller'], ['Action', 'Mystery', 'Sci-Fi'], ['Crime', 'Drama', 'Thriller'], ['Comedy', 'Romance'], ['Comedy', 'Drama', 'Romance'], ['Biography', 'Drama', 'Thriller'], ['Drama'], ['Action', 'Adventure', 'Family'], ['Animation', 'Comedy', 'Family'], ['Action', 'Crime', 'Drama'], ['Comedy'], ['Comedy', 'Crime', 'Thriller'], ['Comedy', 'Romance'], ['Animation', 'Comedy', 'Drama'], ['Action', 'Crime', 'Thriller'], ['Comedy', 'Romance'], ['Adventure', 'Biography', 'Drama'], ['Animation', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Mystery'], ['Action', 'Comedy', 'Sci-Fi'], ['Comedy', 'Fantasy', 'Horror'], ['Comedy', 'Crime'], ['Animation', 'Action', 'Adventure'], ['Action', 'Drama', 'Thriller'], ['Fantasy', 'Horror'], ['Crime', 'Drama', 'Thriller'], ['Action', 'Adventure', 'Fantasy'], ['Comedy', 'Drama', 'Romance'], ['Biography', 'Drama', 'Romance'], ['Action', 'Drama', 'History'], ['Action', 'Adventure', 'Comedy'], ['Horror', 'Thriller'], ['Horror', 'Mystery', 'Thriller'], ['Comedy', 'Romance'], ['Animation', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Mystery'], ['Crime', 'Drama', 'Mystery'], ['Adventure', 'Biography', 'Drama'], ['Horror', 'Mystery', 'Thriller'], ['Horror', 'Thriller'], ['Drama', 'Romance', 'War'], ['Adventure', 'Fantasy', 'Mystery'], ['Action', 'Adventure', 'Sci-Fi'], ['Biography', 'Drama'], ['Drama', 'Thriller'], ['Horror', 'Thriller'], ['Drama', 'Horror', 'Thriller'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Horror', 'Thriller'], ['Comedy'], ['Drama', 'Sport'], ['Comedy', 'Family'], ['Drama', 'Romance'], ['Action', 'Adventure', 'Comedy'], ['Comedy'], ['Mystery', 'Romance', 'Thriller'], ['Crime', 'Drama'], ['Action', 'Comedy'], ['Crime', 'Drama', 'Mystery'], ['Biography', 'Drama', 'Romance'], ['Comedy', 'Crime'], ['Drama', 'Thriller'], ['Drama'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Thriller'], ['Drama', 'Thriller'], ['Animation', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Mystery'], ['Thriller'], ['Biography', 'Drama', 'Sport'], ['Crime', 'Drama', 'Thriller'], ['Drama', 'Music'], ['Crime', 'Drama', 'Thriller'], ['Drama', 'Romance'], ['Animation', 'Action', 'Adventure'], ['Comedy', 'Drama'], ['Action', 'Adventure', 'Drama'], ['Biography', 'Crime', 'Drama'], ['Horror'], ['Biography', 'Drama', 'Mystery'], ['Drama', 'Romance'], ['Animation', 'Drama', 'Romance'], ['Comedy', 'Family'], ['Drama'], ['Mystery', 'Thriller'], ['Drama', 'Fantasy', 'Horror'], ['Drama', 'Romance'], ['Biography', 'Drama', 'History'], ['Comedy', 'Family'], ['Action', 'Adventure', 'Thriller'], ['Comedy', 'Drama'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Thriller'], ['Drama', 'Romance'], ['Comedy', 'Drama', 'Romance'], ['Drama', 'Horror', 'Sci-Fi'], ['Comedy', 'Horror', 'Romance'], ['Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Adventure', 'Drama'], ['Biography', 'Comedy', 'Drama'], ['Drama', 'Mystery', 'Romance'], ['Animation', 'Adventure', 'Comedy'], ['Drama', 'Romance', 'Sci-Fi'], ['Drama'], ['Drama', 'Fantasy'], ['Drama', 'Romance'], ['Comedy', 'Horror', 'Thriller'], ['Comedy', 'Drama', 'Romance'], ['Crime', 'Drama'], ['Comedy', 'Romance'], ['Action', 'Drama', 'Family'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Thriller', 'War'], ['Action', 'Comedy', 'Horror'], ['Biography', 'Drama', 'Sport'], ['Adventure', 'Comedy', 'Drama'], ['Comedy', 'Romance'], ['Comedy', 'Romance'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Adventure', 'Crime'], ['Comedy', 'Romance'], ['Animation', 'Action', 'Adventure'], ['Action', 'Crime', 'Sci-Fi'], ['Drama'], ['Comedy', 'Drama', 'Romance'], ['Crime', 'Thriller'], ['Comedy', 'Horror', 'Sci-Fi'], ['Drama', 'Thriller'], ['Drama', 'Fantasy', 'Horror'], ['Thriller'], ['Adventure', 'Drama', 'Family'], ['Mystery', 'Sci-Fi', 'Thriller'], ['Biography', 'Crime', 'Drama'], ['Drama', 'Fantasy', 'Horror'], ['Action', 'Adventure', 'Thriller'], ['Crime', 'Drama', 'Horror'], ['Crime', 'Drama', 'Fantasy'], ['Adventure', 'Family', 'Fantasy'], ['Action', 'Adventure', 'Drama'], ['Action', 'Comedy', 'Horror'], ['Comedy', 'Drama', 'Family'], ['Action', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Drama', 'Fantasy'], ['Drama'], ['Drama'], ['Comedy'], ['Drama'], ['Comedy', 'Drama', 'Music'], ['Drama', 'Fantasy', 'Music'], ['Drama'], ['Thriller'], ['Comedy', 'Horror'], ['Action', 'Comedy', 'Sport'], ['Horror'], ['Comedy', 'Drama'], ['Action', 'Drama', 'Thriller'], ['Drama', 'Romance'], ['Horror', 'Mystery'], ['Adventure', 'Drama', 'Fantasy'], ['Thriller'], ['Comedy', 'Romance'], ['Action', 'Sci-Fi', 'Thriller'], ['Fantasy', 'Mystery', 'Thriller'], ['Biography', 'Drama'], ['Crime', 'Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure'], ['Comedy', 'Drama'], ['Comedy', 'Drama'], ['Comedy', 'Drama', 'Romance'], ['Adventure', 'Comedy', 'Drama'], ['Action', 'Sci-Fi', 'Thriller'], ['Comedy', 'Romance'], ['Action', 'Fantasy', 'Horror'], ['Crime', 'Drama', 'Thriller'], ['Action', 'Drama', 'Thriller'], ['Crime', 'Drama', 'Mystery'], ['Crime', 'Drama', 'Mystery'], ['Drama', 'Sci-Fi', 'Thriller'], ['Biography', 'Drama', 'History'], ['Crime', 'Horror', 'Thriller'], ['Drama'], ['Drama', 'Mystery', 'Thriller'], ['Adventure', 'Biography'], ['Adventure', 'Biography', 'Crime'], ['Action', 'Horror', 'Thriller'], ['Action', 'Adventure', 'Western'], ['Horror', 'Thriller'], ['Drama', 'Mystery', 'Thriller'], ['Comedy', 'Drama', 'Musical'], ['Horror', 'Mystery'], ['Biography', 'Drama', 'Sport'], ['Comedy', 'Family', 'Romance'], ['Drama', 'Mystery', 'Thriller'], ['Comedy'], ['Drama'], ['Drama', 'Thriller'], ['Biography', 'Drama', 'Family'], ['Comedy', 'Drama', 'Family'], ['Drama', 'Fantasy', 'Musical'], ['Comedy'], ['Adventure', 'Family'], ['Adventure', 'Comedy', 'Fantasy'], ['Horror', 'Thriller'], ['Drama', 'Romance'], ['Horror'], ['Biography', 'Drama', 'History'], ['Action', 'Adventure', 'Fantasy'], ['Drama', 'Family', 'Music'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Adventure', 'Horror'], ['Comedy'], ['Crime', 'Drama', 'Mystery'], ['Horror'], ['Drama', 'Music', 'Romance'], ['Adventure', 'Comedy'], ['Comedy', 'Family', 'Fantasy']] ['Comedy', 'Adventure', 'Western', 'Sci-Fi', 'Sport', 'Drama', 'Music', 'Biography', 'Fantasy', 'Romance', 'Crime', 'War', 'Action', 'Animation', 'Mystery', 'History', 'Thriller', 'Horror', 'Musical', 'Family'] ''' print("*"*100) genre_list = list(set([i for j in temp_list for i in j])) # 分类列表 print(genre_list) ''' ['Sport', 'Sci-Fi', 'Animation', 'Adventure', 'History', 'Romance', 'Western', 'War', 'Biography', 'Action', 'Crime', 'Horror', 'Mystery', 'Fantasy', 'Music', 'Family', 'Drama', 'Comedy', 'Thriller', 'Musical'] ''' # 构造全为0的数组 zeros_df = pd.DataFrame(np.zeros((df.shape[0], len(genre_list))), columns=genre_list) # print(zeros_df) # 给每个电影出现分类的位置赋值1 for i in range(df.shape[0]): # zeros_df.loc[0,["Sci-fi","Mucical"]] = 1 zeros_df.loc[i, temp_list[i]] = 1 print(zeros_df.head(3)) ''' Mystery Animation Drama Adventure ... Sport Western Biography Music 0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 1 1.0 0.0 0.0 1.0 ... 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 ''' # 统计每个分类的电影的数量和 genre_count = zeros_df.sum(axis=0) print(genre_count) ''' Mystery 106.0 Animation 49.0 Drama 513.0 Adventure 259.0 History 29.0 Action 303.0 Musical 5.0 Fantasy 101.0 Sci-Fi 120.0 Horror 119.0 Thriller 195.0 Comedy 279.0 Family 51.0 War 13.0 Romance 141.0 Crime 150.0 Sport 18.0 Western 7.0 Biography 81.0 Music 16.0 ''' # 排序 genre_count = genre_count.sort_values() _x = genre_count.index _y = genre_count.values # 画图 plt.figure(figsize=(20, 8), dpi=80) plt.bar(range(len(_x)), _y, width=0.4, color="orange") plt.xticks(range(len(_x)), _x) plt.savefig("./t_06.png") plt.show()

3.4 数据合并

3.4.1 数据合并之join

先来一段代码

# coding=utf-8 import pandas as pd from matplotlib import pyplot as plt import numpy as np df1=pd.DataFrame(np.ones((2,4)),index=["A","B"],columns=list("abcd")) print(df1) df2=pd.DataFrame(np.ones((3,3)),index=["A","B","C"],columns=list("xyz")) print(df2) # 数据合并join print(df1.join(df2)) ''' a b c d x y z A 1.0 1.0 1.0 1.0 1.0 1.0 1.0 B 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ''' print(df2.join(df1)) ''' x y z a b c d A 1.0 1.0 1.0 1.0 1.0 1.0 1.0 B 1.0 1.0 1.0 1.0 1.0 1.0 1.0 C 1.0 1.0 1.0 NaN NaN NaN NaN ''' # 数据合并merge df3=pd.DataFrame(np.zeros((3,3)),columns=list("fax")) print(df3) ''' f a x 0 0.0 0.0 0.0 1 0.0 0.0 0.0 2 0.0 0.0 0.0 ''' print(df1.merge(df3, on="a")) df3.loc[1,"a"]=1 print(df3) ''' f a x 0 0.0 0.0 0.0 1 0.0 1.0 0.0 2 0.0 0.0 0.0 ''' print(df1.merge(df3, on="a")) ''' a b c d f x 0 1.0 1.0 1.0 1.0 0.0 0.0 1 1.0 1.0 1.0 1.0 0.0 0.0 ''' df3=pd.DataFrame(np.arange(9).reshape((3,3)),columns=list("fax")) print(df3) ''' f a x 0 0 1 2 1 3 4 5 2 6 7 8 ''' print(df1.merge(df3, on="a")) ''' a b c d f x 0 1.0 1.0 1.0 1.0 0 2 1 1.0 1.0 1.0 1.0 0 2 ''' print(df1.merge(df3, on="a",how="outer")) ''' a b c d f x 0 1.0 1.0 1.0 1.0 0 2 1 1.0 1.0 1.0 1.0 0 2 2 4.0 NaN NaN NaN 3 5 3 7.0 NaN NaN NaN 6 8 ''' print(df1.merge(df3, on="a",how="left")) ''' a b c d f x 0 1.0 1.0 1.0 1.0 0 2 1 1.0 1.0 1.0 1.0 0 2 ''' print(df1.merge(df3, on="a",how="right")) ''' a b c d f x 0 1.0 1.0 1.0 1.0 0 2 1 1.0 1.0 1.0 1.0 0 2 2 4.0 NaN NaN NaN 3 5 3 7.0 NaN NaN NaN 6 8 '''

3.4.２ 数据合并之merge

刚刚我们学会了数据分合并，那么接下来，我们按照电影分类(genre)信息把数据呈现出来。 代码

# coding=utf-8 import pandas as pd from matplotlib import pyplot as plt import numpy as np file_path = "./IMDB-Movie-Data.csv" df = pd.read_csv(file_path) # print(df.head(1)) print(df["Genre"].head(3)) ''' 0 Action,Adventure,Sci-Fi 1 Adventure,Mystery,Sci-Fi 2 Horror,Thriller Name: Genre, dtype: object ''' # 统计分类的列表 temp_list = df["Genre"].str.split(",").tolist() # [[],[],[]] print(temp_list) ''' [['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Mystery', 'Sci-Fi'], ['Horror', 'Thriller'], ['Animation', 'Comedy', 'Family'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Adventure', 'Fantasy'], ['Comedy', 'Drama', 'Music'], ['Comedy'], ['Action', 'Adventure', 'Biography'], ['Adventure', 'Drama', 'Romance'], ['Adventure', 'Family', 'Fantasy'], ['Biography', 'Drama', 'History'], ['Action', 'Adventure', 'Sci-Fi'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Comedy', 'Drama'], ['Animation', 'Adventure', 'Comedy'], ['Biography', 'Drama', 'History'], ['Action', 'Thriller'], ['Biography', 'Drama'], ['Drama', 'Mystery', 'Sci-Fi'], ['Adventure', 'Drama', 'Thriller'], ['Drama'], ['Crime', 'Drama', 'Horror'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Adventure', 'Sci-Fi'], ['Comedy'], ['Action', 'Adventure', 'Drama'], ['Horror', 'Thriller'], ['Comedy'], ['Action', 'Adventure', 'Drama'], ['Comedy'], ['Drama', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Comedy'], ['Action', 'Horror', 'Sci-Fi'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Drama', 'Sci-Fi'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Adventure', 'Western'], ['Comedy', 'Drama'], ['Animation', 'Adventure', 'Comedy'], ['Drama'], ['Horror'], ['Biography', 'Drama', 'History'], ['Drama'], ['Action', 'Adventure', 'Fantasy'], ['Drama', 'Thriller'], ['Adventure', 'Drama', 'Fantasy'], ['Action', 'Adventure', 'Sci-Fi'], ['Drama'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Adventure', 'Fantasy'], ['Comedy', 'Drama'], ['Action', 'Crime', 'Thriller'], ['Action', 'Crime', 'Drama'], ['Adventure', 'Drama', 'History'], ['Crime', 'Horror', 'Thriller'], ['Drama', 'Romance'], ['Comedy', 'Drama', 'Romance'], ['Biography', 'Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Horror', 'Mystery', 'Thriller'], ['Crime', 'Drama', 'Mystery'], ['Drama', 'Romance', 'Thriller'], ['Drama', 'Mystery', 'Sci-Fi'], ['Action', 'Adventure', 'Comedy'], ['Drama', 'History', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Drama'], ['Action', 'Drama', 'Thriller'], ['Drama', 'History'], ['Action', 'Drama', 'Romance'], ['Drama', 'Fantasy'], ['Drama', 'Romance'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Sci-Fi'], ['Adventure', 'Drama', 'War'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Comedy', 'Fantasy'], ['Action', 'Adventure', 'Sci-Fi'], ['Comedy', 'Drama'], ['Biography', 'Comedy', 'Crime'], ['Crime', 'Drama', 'Mystery'], ['Action', 'Crime', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Crime', 'Drama'], ['Action', 'Adventure', 'Fantasy'], ['Crime', 'Drama', 'Mystery'], ['Action', 'Crime', 'Drama'], ['Crime', 'Drama', 'Mystery'], ['Action', 'Adventure', 'Fantasy'], ['Drama'], ['Comedy', 'Crime', 'Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Comedy', 'Crime'], ['Animation', 'Drama', 'Fantasy'], ['Horror', 'Mystery', 'Sci-Fi'], ['Drama', 'Mystery', 'Thriller'], ['Crime', 'Drama', 'Thriller'], ['Biography', 'Crime', 'Drama'], ['Action', 'Adventure', 'Fantasy'], ['Adventure', 'Drama', 'Sci-Fi'], ['Crime', 'Mystery', 'Thriller'], ['Action', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Thriller'], ['Comedy'], ['Action', 'Adventure', 'Drama'], ['Drama'], ['Drama', 'Mystery', 'Sci-Fi'], ['Action', 'Horror', 'Thriller'], ['Biography', 'Drama', 'History'], ['Romance', 'Sci-Fi'], ['Action', 'Fantasy', 'War'], ['Adventure', 'Drama', 'Fantasy'], ['Comedy'], ['Horror', 'Thriller'], ['Action', 'Biography', 'Drama'], ['Drama', 'Horror', 'Mystery'], ['Animation', 'Adventure', 'Comedy'], ['Adventure', 'Drama', 'Family'], ['Adventure', 'Mystery', 'Sci-Fi'], ['Adventure', 'Comedy', 'Romance'], ['Action'], ['Action', 'Thriller'], ['Adventure', 'Drama', 'Family'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Crime', 'Mystery'], ['Comedy', 'Family', 'Musical'], ['Adventure', 'Drama', 'Thriller'], ['Drama'], ['Adventure', 'Comedy', 'Drama'], ['Drama', 'Horror', 'Thriller'], ['Drama', 'Music'], ['Action', 'Crime', 'Thriller'], ['Crime', 'Drama', 'Thriller'], ['Crime', 'Drama', 'Thriller'], ['Drama', 'Romance'], ['Mystery', 'Thriller'], ['Mystery', 'Thriller', 'Western'], ['Action', 'Adventure', 'Sci-Fi'], ['Comedy', 'Family'], ['Biography', 'Comedy', 'Drama'], ['Drama'], ['Drama', 'Western'], ['Drama', 'Mystery', 'Romance'], ['Comedy', 'Drama'], ['Action', 'Drama', 'Mystery'], ['Comedy'], ['Action', 'Adventure', 'Crime'], ['Adventure', 'Family', 'Fantasy'], ['Adventure', 'Sci-Fi', 'Thriller'], ['Drama'], ['Action', 'Crime', 'Drama'], ['Drama', 'Horror', 'Mystery'], ['Action', 'Horror', 'Sci-Fi'], ['Action', 'Adventure', 'Sci-Fi'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Comedy', 'Fantasy'], ['Action', 'Comedy', 'Mystery'], ['Thriller', 'War'], ['Action', 'Comedy', 'Crime'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Crime'], ['Action', 'Adventure', 'Thriller'], ['Drama', 'Fantasy', 'Romance'], ['Action', 'Adventure', 'Comedy'], ['Biography', 'Drama', 'History'], ['Action', 'Drama', 'History'], ['Action', 'Adventure', 'Thriller'], ['Crime', 'Drama', 'Thriller'], ['Animation', 'Adventure', 'Family'], ['Adventure', 'Horror'], ['Drama', 'Romance', 'Sci-Fi'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Adventure', 'Family'], ['Action', 'Adventure', 'Drama'], ['Action', 'Comedy'], ['Horror', 'Mystery', 'Thriller'], ['Action', 'Adventure', 'Comedy'], ['Comedy', 'Romance'], ['Horror', 'Mystery'], ['Drama', 'Family', 'Fantasy'], ['Sci-Fi'], ['Drama', 'Thriller'], ['Drama', 'Romance'], ['Drama', 'War'], ['Drama', 'Fantasy', 'Horror'], ['Crime', 'Drama'], ['Comedy', 'Drama', 'Romance'], ['Drama', 'Romance'], ['Drama'], ['Crime', 'Drama', 'History'], ['Horror', 'Sci-Fi', 'Thriller'], ['Action', 'Drama', 'Sport'], ['Action', 'Adventure', 'Sci-Fi'], ['Crime', 'Drama', 'Thriller'], ['Adventure', 'Biography', 'Drama'], ['Biography', 'Drama', 'Thriller'], ['Action', 'Comedy', 'Crime'], ['Action', 'Adventure', 'Sci-Fi'], ['Drama', 'Fantasy', 'Horror'], ['Biography', 'Drama', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Mystery'], ['Action', 'Adventure', 'Sci-Fi'], ['Drama', 'Horror'], ['Comedy', 'Drama', 'Romance'], ['Comedy', 'Romance'], ['Drama', 'Horror', 'Thriller'], ['Action', 'Adventure', 'Drama'], ['Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Drama', 'Mystery'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Comedy'], ['Drama', 'Horror'], ['Action', 'Comedy'], ['Action', 'Adventure', 'Sci-Fi'], ['Animation', 'Adventure', 'Comedy'], ['Horror', 'Mystery'], ['Crime', 'Drama', 'Mystery'], ['Comedy', 'Crime'], ['Drama'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Family'], ['Horror', 'Sci-Fi', 'Thriller'], ['Drama', 'Fantasy', 'War'], ['Crime', 'Drama', 'Thriller'], ['Action', 'Adventure', 'Drama'], ['Action', 'Adventure', 'Thriller'], ['Action', 'Adventure', 'Drama'], ['Drama', 'Romance'], ['Biography', 'Drama', 'History'], ['Drama', 'Horror', 'Thriller'], ['Adventure', 'Comedy', 'Drama'], ['Action', 'Adventure', 'Romance'], ['Action', 'Drama', 'War'], ['Animation', 'Adventure', 'Comedy'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Family', 'Fantasy'], ['Drama', 'Musical', 'Romance'], ['Drama', 'Sci-Fi', 'Thriller'], ['Comedy', 'Drama'], ['Action', 'Comedy', 'Crime'], ['Biography', 'Comedy', 'Drama'], ['Comedy', 'Drama', 'Romance'], ['Drama', 'Thriller'], ['Biography', 'Drama', 'History'], ['Action', 'Adventure', 'Sci-Fi'], ['Horror', 'Mystery', 'Thriller'], ['Comedy'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Drama', 'Sci-Fi'], ['Horror'], ['Drama', 'Thriller'], ['Comedy', 'Drama', 'Romance'], ['Drama', 'Thriller'], ['Comedy', 'Drama'], ['Drama'], ['Action', 'Adventure', 'Comedy'], ['Drama', 'Horror', 'Thriller'], ['Comedy'], ['Drama', 'Sci-Fi'], ['Action', 'Adventure', 'Sci-Fi'], ['Horror'], ['Action', 'Adventure', 'Thriller'], ['Adventure', 'Fantasy'], ['Action', 'Comedy', 'Crime'], ['Comedy', 'Drama', 'Music'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Adventure', 'Mystery'], ['Action', 'Comedy', 'Crime'], ['Crime', 'Drama', 'History'], ['Comedy'], ['Action', 'Adventure', 'Sci-Fi'], ['Crime', 'Mystery', 'Thriller'], ['Action', 'Adventure', 'Crime'], ['Thriller'], ['Biography', 'Drama', 'Romance'], ['Action', 'Adventure'], ['Action', 'Fantasy'], ['Action', 'Comedy'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Comedy', 'Crime'], ['Thriller'], ['Action', 'Drama', 'Horror'], ['Comedy', 'Music', 'Romance'], ['Comedy'], ['Drama'], ['Action', 'Adventure', 'Fantasy'], ['Drama', 'Romance'], ['Animation', 'Adventure', 'Comedy'], ['Comedy', 'Drama'], ['Biography', 'Crime', 'Drama'], ['Drama', 'History'], ['Action', 'Crime', 'Thriller'], ['Action', 'Biography', 'Drama'], ['Horror'], ['Comedy', 'Romance'], ['Comedy', 'Romance'], ['Comedy', 'Crime', 'Drama'], ['Adventure', 'Family', 'Fantasy'], ['Crime', 'Drama', 'Thriller'], ['Action', 'Crime', 'Thriller'], ['Comedy', 'Romance'], ['Biography', 'Drama', 'Sport'], ['Drama', 'Romance'], ['Drama', 'Horror'], ['Adventure', 'Fantasy'], ['Adventure', 'Family', 'Fantasy'], ['Action', 'Drama', 'Sci-Fi'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Horror'], ['Comedy', 'Horror', 'Thriller'], ['Action', 'Crime', 'Thriller'], ['Crime', 'Drama', 'Music'], ['Drama'], ['Action', 'Crime', 'Thriller'], ['Action', 'Sci-Fi', 'Thriller'], ['Biography', 'Drama'], ['Action', 'Adventure', 'Fantasy'], ['Drama', 'Horror', 'Sci-Fi'], ['Biography', 'Comedy', 'Drama'], ['Crime', 'Horror', 'Thriller'], ['Crime', 'Drama', 'Mystery'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Biography', 'Drama'], ['Biography', 'Drama'], ['Biography', 'Drama', 'History'], ['Action', 'Biography', 'Drama'], ['Drama', 'Fantasy', 'Horror'], ['Comedy', 'Drama', 'Romance'], ['Drama', 'Sport'], ['Drama', 'Romance'], ['Comedy', 'Romance'], ['Action', 'Crime', 'Thriller'], ['Action', 'Crime', 'Drama'], ['Action', 'Drama', 'Thriller'], ['Adventure', 'Family', 'Fantasy'], ['Action', 'Adventure'], ['Action', 'Adventure', 'Romance'], ['Adventure', 'Family', 'Fantasy'], ['Crime', 'Drama'], ['Comedy', 'Horror'], ['Comedy', 'Fantasy', 'Romance'], ['Drama'], ['Drama'], ['Comedy', 'Drama'], ['Comedy', 'Drama', 'Romance'], ['Adventure', 'Sci-Fi', 'Thriller'], ['Action', 'Adventure', 'Fantasy'], ['Comedy', 'Drama'], ['Biography', 'Drama', 'Romance'], ['Comedy', 'Fantasy'], ['Comedy', 'Drama', 'Fantasy'], ['Comedy'], ['Horror', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Comedy', 'Horror'], ['Comedy', 'Mystery'], ['Drama'], ['Adventure', 'Drama', 'Fantasy'], ['Drama', 'Sport'], ['Action', 'Adventure'], ['Action', 'Adventure', 'Drama'], ['Action', 'Drama', 'Sci-Fi'], ['Action', 'Mystery', 'Sci-Fi'], ['Action', 'Crime', 'Drama'], ['Action', 'Crime', 'Fantasy'], ['Biography', 'Comedy', 'Drama'], ['Action', 'Crime', 'Thriller'], ['Biography', 'Crime', 'Drama'], ['Drama', 'Sport'], ['Adventure', 'Comedy', 'Drama'], ['Action', 'Adventure', 'Thriller'], ['Comedy', 'Fantasy', 'Horror'], ['Drama', 'Sport'], ['Horror', 'Thriller'], ['Drama', 'History', 'Thriller'], ['Animation', 'Action', 'Adventure'], ['Action', 'Adventure', 'Drama'], ['Action', 'Comedy', 'Family'], ['Action', 'Adventure', 'Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Comedy'], ['Action', 'Crime', 'Drama'], ['Biography', 'Drama'], ['Comedy', 'Romance'], ['Comedy'], ['Drama', 'Fantasy', 'Romance'], ['Action', 'Adventure', 'Sci-Fi'], ['Comedy'], ['Comedy', 'Sci-Fi'], ['Comedy', 'Drama'], ['Animation', 'Action', 'Adventure'], ['Horror'], ['Action', 'Biography', 'Crime'], ['Animation', 'Adventure', 'Comedy'], ['Drama', 'Romance'], ['Drama', 'Mystery', 'Thriller'], ['Drama', 'History', 'Thriller'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Comedy'], ['Action', 'Thriller'], ['Comedy', 'Music'], ['Animation', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Thriller'], ['Action', 'Adventure', 'Crime'], ['Comedy', 'Drama', 'Horror'], ['Drama'], ['Drama', 'Mystery', 'Romance'], ['Adventure', 'Family', 'Fantasy'], ['Drama'], ['Action', 'Drama', 'Thriller'], ['Drama'], ['Action', 'Horror', 'Romance'], ['Action', 'Drama', 'Fantasy'], ['Action', 'Crime', 'Drama'], ['Drama', 'Fantasy', 'Romance'], ['Action', 'Crime', 'Thriller'], ['Action', 'Mystery', 'Thriller'], ['Horror', 'Mystery', 'Thriller'], ['Action', 'Horror', 'Sci-Fi'], ['Comedy', 'Drama'], ['Comedy'], ['Action', 'Adventure', 'Horror'], ['Action', 'Adventure', 'Thriller'], ['Action', 'Crime', 'Drama'], ['Comedy', 'Crime', 'Drama'], ['Drama', 'Romance'], ['Drama', 'Thriller'], ['Action', 'Comedy', 'Crime'], ['Comedy'], ['Adventure', 'Family', 'Fantasy'], ['Drama', 'Romance'], ['Animation', 'Family', 'Fantasy'], ['Drama', 'Romance'], ['Thriller'], ['Adventure', 'Horror', 'Mystery'], ['Action', 'Sci-Fi'], ['Adventure', 'Comedy', 'Drama'], ['Animation', 'Action', 'Adventure'], ['Drama', 'Horror'], ['Action', 'Adventure', 'Sci-Fi'], ['Comedy', 'Drama'], ['Action', 'Horror', 'Mystery'], ['Action', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Drama'], ['Comedy', 'Drama', 'Romance'], ['Comedy', 'Crime'], ['Comedy', 'Romance'], ['Drama', 'Romance'], ['Crime', 'Drama', 'Thriller'], ['Horror', 'Mystery', 'Thriller'], ['Biography', 'Drama'], ['Drama', 'Mystery', 'Sci-Fi'], ['Adventure', 'Comedy', 'Family'], ['Action', 'Adventure', 'Crime'], ['Action', 'Crime', 'Mystery'], ['Mystery', 'Thriller'], ['Action', 'Sci-Fi', 'Thriller'], ['Action', 'Comedy', 'Crime'], ['Biography', 'Crime', 'Drama'], ['Biography', 'Drama', 'History'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Family', 'Fantasy'], ['Biography', 'Drama', 'History'], ['Biography', 'Comedy', 'Drama'], ['Drama', 'Thriller'], ['Horror', 'Thriller'], ['Drama'], ['Drama', 'War'], ['Comedy', 'Drama', 'Romance'], ['Drama', 'Romance', 'Sci-Fi'], ['Action', 'Crime', 'Drama'], ['Comedy', 'Drama'], ['Animation', 'Action', 'Adventure'], ['Adventure', 'Comedy', 'Drama'], ['Comedy', 'Drama', 'Family'], ['Drama', 'Romance', 'Thriller'], ['Comedy', 'Crime', 'Drama'], ['Animation', 'Comedy', 'Family'], ['Drama', 'Horror', 'Sci-Fi'], ['Action', 'Adventure', 'Drama'], ['Action', 'Horror', 'Sci-Fi'], ['Action', 'Crime', 'Sport'], ['Drama', 'Horror', 'Sci-Fi'], ['Drama', 'Horror', 'Sci-Fi'], ['Action', 'Adventure', 'Comedy'], ['Mystery', 'Sci-Fi', 'Thriller'], ['Crime', 'Drama', 'Thriller'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Sci-Fi', 'Thriller'], ['Drama', 'Romance'], ['Crime', 'Drama', 'Thriller'], ['Comedy', 'Drama', 'Music'], ['Drama', 'Fantasy', 'Romance'], ['Crime', 'Drama', 'Thriller'], ['Crime', 'Drama', 'Thriller'], ['Comedy', 'Drama', 'Romance'], ['Comedy', 'Romance'], ['Drama', 'Sci-Fi', 'Thriller'], ['Drama', 'War'], ['Action', 'Crime', 'Drama'], ['Sci-Fi', 'Thriller'], ['Adventure', 'Drama', 'Horror'], ['Comedy', 'Drama', 'Music'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Adventure', 'Drama'], ['Action', 'Crime', 'Drama'], ['Adventure', 'Fantasy'], ['Drama', 'Romance'], ['Biography', 'History', 'Thriller'], ['Crime', 'Drama', 'Thriller'], ['Action', 'Drama', 'History'], ['Biography', 'Comedy', 'Drama'], ['Crime', 'Drama', 'Thriller'], ['Action', 'Biography', 'Drama'], ['Action', 'Drama', 'Sci-Fi'], ['Adventure', 'Horror'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Mystery'], ['Comedy', 'Drama', 'Romance'], ['Horror', 'Thriller'], ['Action', 'Sci-Fi', 'Thriller'], ['Action', 'Sci-Fi', 'Thriller'], ['Biography', 'Drama'], ['Action', 'Crime', 'Drama'], ['Action', 'Crime', 'Mystery'], ['Action', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Thriller'], ['Crime', 'Drama'], ['Mystery', 'Thriller'], ['Mystery', 'Sci-Fi', 'Thriller'], ['Action', 'Mystery', 'Sci-Fi'], ['Drama', 'Romance'], ['Drama', 'Thriller'], ['Drama', 'Mystery', 'Sci-Fi'], ['Comedy', 'Drama'], ['Adventure', 'Family', 'Fantasy'], ['Biography', 'Drama', 'Sport'], ['Drama'], ['Comedy', 'Drama', 'Romance'], ['Biography', 'Drama', 'Romance'], ['Action', 'Adventure', 'Sci-Fi'], ['Drama', 'Sci-Fi', 'Thriller'], ['Drama', 'Romance', 'Thriller'], ['Mystery', 'Thriller'], ['Mystery', 'Thriller'], ['Action', 'Drama', 'Fantasy'], ['Action', 'Adventure', 'Biography'], ['Adventure', 'Comedy', 'Sci-Fi'], ['Action', 'Adventure', 'Thriller'], ['Fantasy', 'Horror'], ['Horror', 'Mystery'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Adventure', 'Drama'], ['Adventure', 'Family', 'Fantasy'], ['Action', 'Adventure', 'Sci-Fi'], ['Comedy', 'Drama'], ['Comedy', 'Drama'], ['Crime', 'Drama', 'Thriller'], ['Comedy', 'Romance'], ['Animation', 'Comedy', 'Family'], ['Comedy', 'Drama'], ['Comedy', 'Drama'], ['Biography', 'Drama', 'Sport'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Drama', 'History'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Mystery'], ['Crime', 'Drama', 'Mystery'], ['Action'], ['Action', 'Adventure', 'Family'], ['Comedy', 'Romance'], ['Comedy', 'Drama', 'Romance'], ['Biography', 'Drama', 'Sport'], ['Action', 'Fantasy', 'Thriller'], ['Biography', 'Drama', 'Sport'], ['Action', 'Drama', 'Fantasy'], ['Adventure', 'Sci-Fi', 'Thriller'], ['Animation', 'Adventure', 'Comedy'], ['Drama', 'Mystery', 'Thriller'], ['Drama', 'Romance'], ['Crime', 'Drama', 'Mystery'], ['Comedy', 'Romance', 'Sport'], ['Comedy', 'Family'], ['Drama', 'Horror', 'Mystery'], ['Action', 'Drama', 'Sport'], ['Action', 'Adventure', 'Comedy'], ['Drama', 'Mystery', 'Sci-Fi'], ['Animation', 'Action', 'Comedy'], ['Action', 'Crime', 'Drama'], ['Action', 'Crime', 'Drama'], ['Comedy', 'Drama', 'Romance'], ['Animation', 'Action', 'Adventure'], ['Crime', 'Drama'], ['Drama'], ['Drama'], ['Comedy', 'Crime'], ['Drama'], ['Action', 'Adventure', 'Fantasy'], ['Drama', 'Fantasy', 'Romance'], ['Comedy', 'Drama'], ['Drama', 'Fantasy', 'Thriller'], ['Biography', 'Crime', 'Drama'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Crime', 'Drama'], ['Sci-Fi'], ['Action', 'Biography', 'Drama'], ['Action', 'Comedy', 'Romance'], ['Adventure', 'Comedy', 'Drama'], ['Comedy', 'Crime', 'Drama'], ['Action', 'Fantasy', 'Horror'], ['Drama', 'Horror'], ['Horror'], ['Action', 'Thriller'], ['Action', 'Adventure', 'Mystery'], ['Action', 'Adventure', 'Fantasy'], ['Comedy', 'Drama', 'Romance'], ['Crime', 'Drama', 'Mystery'], ['Adventure', 'Comedy', 'Family'], ['Comedy', 'Drama', 'Romance'], ['Comedy'], ['Comedy', 'Drama', 'Horror'], ['Drama', 'Horror', 'Thriller'], ['Animation', 'Adventure', 'Family'], ['Comedy', 'Romance'], ['Mystery', 'Romance', 'Sci-Fi'], ['Crime', 'Drama'], ['Drama', 'Horror', 'Mystery'], ['Comedy'], ['Biography', 'Drama'], ['Comedy', 'Drama', 'Thriller'], ['Comedy', 'Western'], ['Drama', 'History', 'War'], ['Drama', 'Horror', 'Sci-Fi'], ['Drama'], ['Comedy', 'Drama'], ['Fantasy', 'Horror', 'Thriller'], ['Drama', 'Romance'], ['Action', 'Comedy', 'Fantasy'], ['Drama', 'Horror', 'Musical'], ['Crime', 'Drama', 'Mystery'], ['Horror', 'Mystery', 'Thriller'], ['Comedy', 'Music'], ['Drama'], ['Biography', 'Crime', 'Drama'], ['Drama'], ['Action', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Mystery'], ['Drama'], ['Action', 'Comedy', 'Crime'], ['Comedy', 'Drama', 'Romance'], ['Crime', 'Drama', 'Mystery'], ['Action', 'Comedy', 'Crime'], ['Drama'], ['Drama', 'Romance'], ['Crime', 'Drama', 'Mystery'], ['Adventure', 'Comedy', 'Romance'], ['Comedy', 'Crime', 'Drama'], ['Adventure', 'Drama', 'Thriller'], ['Biography', 'Crime', 'Drama'], ['Crime', 'Drama', 'Thriller'], ['Drama', 'History', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Comedy'], ['Horror'], ['Action', 'Crime', 'Mystery'], ['Comedy', 'Romance'], ['Comedy'], ['Action', 'Drama', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Drama', 'Mystery', 'Thriller'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Fantasy', 'Horror'], ['Drama', 'Romance'], ['Biography', 'Drama'], ['Biography', 'Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Animation', 'Adventure', 'Comedy'], ['Drama', 'Mystery', 'Thriller'], ['Action', 'Horror', 'Sci-Fi'], ['Drama', 'Romance'], ['Biography', 'Drama'], ['Action', 'Adventure', 'Drama'], ['Adventure', 'Drama', 'Fantasy'], ['Drama', 'Family'], ['Comedy', 'Drama', 'Romance'], ['Drama', 'Romance', 'Sci-Fi'], ['Action', 'Adventure', 'Thriller'], ['Comedy', 'Romance'], ['Crime', 'Drama', 'Horror'], ['Comedy', 'Fantasy'], ['Action', 'Comedy', 'Crime'], ['Adventure', 'Drama', 'Romance'], ['Action', 'Crime', 'Drama'], ['Crime', 'Horror', 'Thriller'], ['Romance', 'Sci-Fi', 'Thriller'], ['Comedy', 'Drama', 'Romance'], ['Crime', 'Drama'], ['Crime', 'Drama', 'Mystery'], ['Action', 'Adventure', 'Sci-Fi'], ['Animation', 'Fantasy'], ['Animation', 'Adventure', 'Comedy'], ['Drama', 'Mystery', 'War'], ['Comedy', 'Romance'], ['Animation', 'Comedy', 'Family'], ['Comedy'], ['Horror', 'Mystery', 'Thriller'], ['Action', 'Adventure', 'Drama'], ['Comedy'], ['Drama'], ['Adventure', 'Biography', 'Drama'], ['Comedy'], ['Horror', 'Thriller'], ['Action', 'Drama', 'Family'], ['Comedy', 'Fantasy', 'Horror'], ['Comedy', 'Romance'], ['Drama', 'Mystery', 'Romance'], ['Action', 'Adventure', 'Comedy'], ['Thriller'], ['Comedy'], ['Adventure', 'Comedy', 'Sci-Fi'], ['Comedy', 'Drama', 'Fantasy'], ['Mystery', 'Thriller'], ['Comedy', 'Drama'], ['Adventure', 'Drama', 'Family'], ['Horror', 'Thriller'], ['Action', 'Drama', 'Romance'], ['Drama', 'Romance'], ['Action', 'Adventure', 'Fantasy'], ['Comedy'], ['Action', 'Biography', 'Drama'], ['Drama', 'Mystery', 'Romance'], ['Adventure', 'Drama', 'Western'], ['Drama', 'Music', 'Romance'], ['Comedy', 'Romance', 'Western'], ['Thriller'], ['Comedy', 'Drama', 'Romance'], ['Horror', 'Thriller'], ['Adventure', 'Family', 'Fantasy'], ['Crime', 'Drama', 'Mystery'], ['Horror', 'Mystery'], ['Comedy', 'Crime', 'Drama'], ['Action', 'Comedy', 'Romance'], ['Biography', 'Drama', 'History'], ['Adventure', 'Drama'], ['Drama', 'Thriller'], ['Drama'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Biography', 'Drama'], ['Drama', 'Music'], ['Comedy', 'Drama'], ['Drama', 'Thriller', 'War'], ['Action', 'Mystery', 'Thriller'], ['Horror', 'Sci-Fi', 'Thriller'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Sci-Fi'], ['Action', 'Adventure', 'Fantasy'], ['Drama', 'Mystery', 'Romance'], ['Drama'], ['Action', 'Adventure', 'Thriller'], ['Action', 'Crime', 'Thriller'], ['Animation', 'Action', 'Adventure'], ['Drama', 'Fantasy', 'Mystery'], ['Drama', 'Sci-Fi'], ['Animation', 'Adventure', 'Comedy'], ['Horror', 'Thriller'], ['Action', 'Thriller'], ['Comedy'], ['Biography', 'Drama'], ['Action', 'Mystery', 'Thriller'], ['Action', 'Mystery', 'Sci-Fi'], ['Crime', 'Drama', 'Thriller'], ['Comedy', 'Romance'], ['Comedy', 'Drama', 'Romance'], ['Biography', 'Drama', 'Thriller'], ['Drama'], ['Action', 'Adventure', 'Family'], ['Animation', 'Comedy', 'Family'], ['Action', 'Crime', 'Drama'], ['Comedy'], ['Comedy', 'Crime', 'Thriller'], ['Comedy', 'Romance'], ['Animation', 'Comedy', 'Drama'], ['Action', 'Crime', 'Thriller'], ['Comedy', 'Romance'], ['Adventure', 'Biography', 'Drama'], ['Animation', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Mystery'], ['Action', 'Comedy', 'Sci-Fi'], ['Comedy', 'Fantasy', 'Horror'], ['Comedy', 'Crime'], ['Animation', 'Action', 'Adventure'], ['Action', 'Drama', 'Thriller'], ['Fantasy', 'Horror'], ['Crime', 'Drama', 'Thriller'], ['Action', 'Adventure', 'Fantasy'], ['Comedy', 'Drama', 'Romance'], ['Biography', 'Drama', 'Romance'], ['Action', 'Drama', 'History'], ['Action', 'Adventure', 'Comedy'], ['Horror', 'Thriller'], ['Horror', 'Mystery', 'Thriller'], ['Comedy', 'Romance'], ['Animation', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Mystery'], ['Crime', 'Drama', 'Mystery'], ['Adventure', 'Biography', 'Drama'], ['Horror', 'Mystery', 'Thriller'], ['Horror', 'Thriller'], ['Drama', 'Romance', 'War'], ['Adventure', 'Fantasy', 'Mystery'], ['Action', 'Adventure', 'Sci-Fi'], ['Biography', 'Drama'], ['Drama', 'Thriller'], ['Horror', 'Thriller'], ['Drama', 'Horror', 'Thriller'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Horror', 'Thriller'], ['Comedy'], ['Drama', 'Sport'], ['Comedy', 'Family'], ['Drama', 'Romance'], ['Action', 'Adventure', 'Comedy'], ['Comedy'], ['Mystery', 'Romance', 'Thriller'], ['Crime', 'Drama'], ['Action', 'Comedy'], ['Crime', 'Drama', 'Mystery'], ['Biography', 'Drama', 'Romance'], ['Comedy', 'Crime'], ['Drama', 'Thriller'], ['Drama'], ['Animation', 'Adventure', 'Comedy'], ['Action', 'Thriller'], ['Drama', 'Thriller'], ['Animation', 'Adventure', 'Comedy'], ['Crime', 'Drama', 'Mystery'], ['Thriller'], ['Biography', 'Drama', 'Sport'], ['Crime', 'Drama', 'Thriller'], ['Drama', 'Music'], ['Crime', 'Drama', 'Thriller'], ['Drama', 'Romance'], ['Animation', 'Action', 'Adventure'], ['Comedy', 'Drama'], ['Action', 'Adventure', 'Drama'], ['Biography', 'Crime', 'Drama'], ['Horror'], ['Biography', 'Drama', 'Mystery'], ['Drama', 'Romance'], ['Animation', 'Drama', 'Romance'], ['Comedy', 'Family'], ['Drama'], ['Mystery', 'Thriller'], ['Drama', 'Fantasy', 'Horror'], ['Drama', 'Romance'], ['Biography', 'Drama', 'History'], ['Comedy', 'Family'], ['Action', 'Adventure', 'Thriller'], ['Comedy', 'Drama'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Thriller'], ['Drama', 'Romance'], ['Comedy', 'Drama', 'Romance'], ['Drama', 'Horror', 'Sci-Fi'], ['Comedy', 'Horror', 'Romance'], ['Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Action', 'Adventure', 'Fantasy'], ['Action', 'Adventure', 'Drama'], ['Biography', 'Comedy', 'Drama'], ['Drama', 'Mystery', 'Romance'], ['Animation', 'Adventure', 'Comedy'], ['Drama', 'Romance', 'Sci-Fi'], ['Drama'], ['Drama', 'Fantasy'], ['Drama', 'Romance'], ['Comedy', 'Horror', 'Thriller'], ['Comedy', 'Drama', 'Romance'], ['Crime', 'Drama'], ['Comedy', 'Romance'], ['Action', 'Drama', 'Family'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Thriller', 'War'], ['Action', 'Comedy', 'Horror'], ['Biography', 'Drama', 'Sport'], ['Adventure', 'Comedy', 'Drama'], ['Comedy', 'Romance'], ['Comedy', 'Romance'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Adventure', 'Crime'], ['Comedy', 'Romance'], ['Animation', 'Action', 'Adventure'], ['Action', 'Crime', 'Sci-Fi'], ['Drama'], ['Comedy', 'Drama', 'Romance'], ['Crime', 'Thriller'], ['Comedy', 'Horror', 'Sci-Fi'], ['Drama', 'Thriller'], ['Drama', 'Fantasy', 'Horror'], ['Thriller'], ['Adventure', 'Drama', 'Family'], ['Mystery', 'Sci-Fi', 'Thriller'], ['Biography', 'Crime', 'Drama'], ['Drama', 'Fantasy', 'Horror'], ['Action', 'Adventure', 'Thriller'], ['Crime', 'Drama', 'Horror'], ['Crime', 'Drama', 'Fantasy'], ['Adventure', 'Family', 'Fantasy'], ['Action', 'Adventure', 'Drama'], ['Action', 'Comedy', 'Horror'], ['Comedy', 'Drama', 'Family'], ['Action', 'Thriller'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure', 'Drama', 'Fantasy'], ['Drama'], ['Drama'], ['Comedy'], ['Drama'], ['Comedy', 'Drama', 'Music'], ['Drama', 'Fantasy', 'Music'], ['Drama'], ['Thriller'], ['Comedy', 'Horror'], ['Action', 'Comedy', 'Sport'], ['Horror'], ['Comedy', 'Drama'], ['Action', 'Drama', 'Thriller'], ['Drama', 'Romance'], ['Horror', 'Mystery'], ['Adventure', 'Drama', 'Fantasy'], ['Thriller'], ['Comedy', 'Romance'], ['Action', 'Sci-Fi', 'Thriller'], ['Fantasy', 'Mystery', 'Thriller'], ['Biography', 'Drama'], ['Crime', 'Drama'], ['Action', 'Adventure', 'Sci-Fi'], ['Adventure'], ['Comedy', 'Drama'], ['Comedy', 'Drama'], ['Comedy', 'Drama', 'Romance'], ['Adventure', 'Comedy', 'Drama'], ['Action', 'Sci-Fi', 'Thriller'], ['Comedy', 'Romance'], ['Action', 'Fantasy', 'Horror'], ['Crime', 'Drama', 'Thriller'], ['Action', 'Drama', 'Thriller'], ['Crime', 'Drama', 'Mystery'], ['Crime', 'Drama', 'Mystery'], ['Drama', 'Sci-Fi', 'Thriller'], ['Biography', 'Drama', 'History'], ['Crime', 'Horror', 'Thriller'], ['Drama'], ['Drama', 'Mystery', 'Thriller'], ['Adventure', 'Biography'], ['Adventure', 'Biography', 'Crime'], ['Action', 'Horror', 'Thriller'], ['Action', 'Adventure', 'Western'], ['Horror', 'Thriller'], ['Drama', 'Mystery', 'Thriller'], ['Comedy', 'Drama', 'Musical'], ['Horror', 'Mystery'], ['Biography', 'Drama', 'Sport'], ['Comedy', 'Family', 'Romance'], ['Drama', 'Mystery', 'Thriller'], ['Comedy'], ['Drama'], ['Drama', 'Thriller'], ['Biography', 'Drama', 'Family'], ['Comedy', 'Drama', 'Family'], ['Drama', 'Fantasy', 'Musical'], ['Comedy'], ['Adventure', 'Family'], ['Adventure', 'Comedy', 'Fantasy'], ['Horror', 'Thriller'], ['Drama', 'Romance'], ['Horror'], ['Biography', 'Drama', 'History'], ['Action', 'Adventure', 'Fantasy'], ['Drama', 'Family', 'Music'], ['Comedy', 'Drama', 'Romance'], ['Action', 'Adventure', 'Horror'], ['Comedy'], ['Crime', 'Drama', 'Mystery'], ['Horror'], ['Drama', 'Music', 'Romance'], ['Adventure', 'Comedy'], ['Comedy', 'Family', 'Fantasy']] ['Comedy', 'Adventure', 'Western', 'Sci-Fi', 'Sport', 'Drama', 'Music', 'Biography', 'Fantasy', 'Romance', 'Crime', 'War', 'Action', 'Animation', 'Mystery', 'History', 'Thriller', 'Horror', 'Musical', 'Family'] ''' print("*"*100) genre_list = list(set([i for j in temp_list for i in j])) # 分类列表 print(genre_list) ''' ['Sport', 'Sci-Fi', 'Animation', 'Adventure', 'History', 'Romance', 'Western', 'War', 'Biography', 'Action', 'Crime', 'Horror', 'Mystery', 'Fantasy', 'Music', 'Family', 'Drama', 'Comedy', 'Thriller', 'Musical'] ''' # 构造全为0的数组 zeros_df = pd.DataFrame(np.zeros((df.shape[0], len(genre_list))), columns=genre_list) # print(zeros_df) # 给每个电影出现分类的位置赋值1 for i in range(df.shape[0]): # zeros_df.loc[0,["Sci-fi","Mucical"]] = 1 zeros_df.loc[i, temp_list[i]] = 1 print(zeros_df.head(3)) ''' Mystery Animation Drama Adventure ... Sport Western Biography Music 0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 1 1.0 0.0 0.0 1.0 ... 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 ''' # 统计每个分类的电影的数量和 genre_count = zeros_df.sum(axis=0) print(genre_count) ''' Mystery 106.0 Animation 49.0 Drama 513.0 Adventure 259.0 History 29.0 Action 303.0 Musical 5.0 Fantasy 101.0 Sci-Fi 120.0 Horror 119.0 Thriller 195.0 Comedy 279.0 Family 51.0 War 13.0 Romance 141.0 Crime 150.0 Sport 18.0 Western 7.0 Biography 81.0 Music 16.0 ''' # 排序 genre_count = genre_count.sort_values() _x = genre_count.index _y = genre_count.values # 画图 plt.figure(figsize=(20, 8), dpi=80) plt.bar(range(len(_x)), _y, width=0.4, color="orange") plt.xticks(range(len(_x)), _x) plt.savefig("./t_06.png") plt.show()

3.5 数据分组和聚合

在pandas中类似的分组的操作我们有很简单的方式来完成 df.groupby(by=“columns_name”) 那么问题来了，调用groupby方法之后返回的是什么内容？

# coding=utf-8 import pandas as pd from matplotlib import pyplot as plt from matplotlib import font_manager my_font = font_manager.FontProperties(fname="/usr/share/fonts/truetype/arphic/ukai.ttc") file_path = "./starbucks_store_worldwide.csv" df = pd.read_csv(file_path) ''' # 1、使用matplotlib呈现出店铺总数排名前10的国家 #准备数据 data1 = df.groupby(by="Country").count()["Brand"].sort_values(ascending=False)[:10] _x = data1.index _y = data1.values #画图 plt.figure(figsize=(20,8),dpi=80) plt.bar(range(len(_x)),_y) plt.xticks(range(len(_x)),_x) plt.show() plt.savefig("./t_09.png") ''' # 2、使用matplotlib呈现出每个中国每个城市的店铺数量 df=df[df["Country"]=="CN"] #准备数据 data1 = df.groupby(by="City").count()["Brand"].sort_values(ascending=False)[:25] _x = data1.index _y = data1.values #画图 plt.figure(figsize=(20,8),dpi=80) plt.bar(range(len(_x)),_y,width=0.3,color="orange") plt.xticks(range(len(_x)),_x,fontproperties=my_font) plt.savefig("./t_09_02.png") plt.show()

# coding=utf-8 import pandas as pd import numpy as np file_path = "./starbucks_store_worldwide.csv" df = pd.read_csv(file_path) print(df.head(1)) print(df.info()) # 分组和聚合 grouped=df.groupby(by="Country") print(grouped) # DataFrameGroupBy # 可以进行遍历 for i,j in grouped: print(i) print("-"*100) print(j,type(j)) print("*"*100) # print(grouped.count()) # 调用聚合方法 country_count=grouped["Brand"].count() print(country_count["US"]) print(country_count["CN"]) # 统计中国每个省份店铺的数量 china_data=df[df["Country"]=="CN"] grouped=china_data.groupby(by="State/Province").count()["Brand"] print(grouped) # 数据按照多个条件进行分组,返回Series grouped=df["Brand"].groupby(by=[df["Country"],df["State/Province"]]).count() print(grouped) print(type(grouped)) ''' <class 'pandas.core.series.Series'> ''' # 数据按照多个条件进行分组,返回DataFrame grouped1=df[["Brand"]].groupby(by=[df["Country"],df["State/Province"]]).count() grouped2=df.groupby(by=[df["Country"],df["State/Province"]])[["Brand"]].count() grouped3=df.groupby(by=[df["Country"],df["State/Province"]]).count()[["Brand"]] print(grouped1,type(grouped1)) print(grouped2,type(grouped2)) print(grouped3,type(grouped3)) # 索引的方法和属性 print(grouped1.index) a = pd.DataFrame({'a': range(7),'b': range(7, 0, -1),'c': ['one','one','one','two','two','two', 'two'],'d': list("hjklmno")}) print(a) b=a.set_index(["c","d"]) print(b) c=b["a"] print(c,type(c)) print(c["one"]["j"]) d=a.set_index(["d","c"])["a"] print(d) print(d.index) print(d.swaplevel()["one"]) ''' h 0 j 1 k 2 ''' print(b.loc["one"].loc["h"]) ''' a 0 b 7 ''' print(b.swaplevel().loc["h"])

3.6 索引和复合索引

动手

使用matplotlib呈现出店铺总数排名前10的国家使用matplotlib呈现出每个中国每个城市的店铺数量

代码：

# coding=utf-8 import pandas as pd from matplotlib import pyplot as plt from matplotlib import font_manager my_font = font_manager.FontProperties(fname="/usr/share/fonts/truetype/arphic/ukai.ttc") file_path = "./starbucks_store_worldwide.csv" df = pd.read_csv(file_path) # 1、使用matplotlib呈现出店铺总数排名前10的国家 #准备数据 data1 = df.groupby(by="Country").count()["Brand"].sort_values(ascending=False)[:10] _x = data1.index _y = data1.values #画图 plt.figure(figsize=(20,8),dpi=80) plt.bar(range(len(_x)),_y) plt.xticks(range(len(_x)),_x) plt.savefig("./t_09.png") plt.show() # 2、使用matplotlib呈现出每个中国每个城市的店铺数量 df=df[df["Country"]=="CN"] #准备数据 data1 = df.groupby(by="City").count()["Brand"].sort_values(ascending=False)[:25] _x = data1.index _y = data1.values #画图 plt.figure(figsize=(20,8),dpi=80) plt.bar(range(len(_x)),_y,width=0.3,color="orange") plt.xticks(range(len(_x)),_x,fontproperties=my_font) plt.savefig("./t_09_02.png") plt.show()

# coding=utf-8 import pandas as pd from matplotlib import pyplot as plt file_path="./books.csv" df = pd.read_csv(file_path) # print(df.head(2)) # print(df.info()) # data1 = df[pd.notnull(df["original_publication_year"])] # grouped = data1.groupby(by="original_publication_year").count()["title"] # print(grouped) ''' -1750.0 1 -762.0 1 -750.0 2 -720.0 1 -560.0 1 ... 2013.0 518 2014.0 437 2015.0 306 2016.0 198 2017.0 11 ''' #不同年份书的平均评分情况 #去除original_publication_year列中nan的行 data1 = df[pd.notnull(df["original_publication_year"])] grouped = data1["average_rating"].groupby(by=data1["original_publication_year"]).mean() print(grouped) ''' -1750.0 3.630000 -762.0 4.030000 -750.0 4.005000 -720.0 3.730000 -560.0 4.050000 ... 2013.0 4.012297 2014.0 3.985378 2015.0 3.954641 2016.0 4.027576 2017.0 4.100909 ''' _x = grouped.index _y = grouped.values # #画图 plt.figure(figsize=(20,8),dpi=80) plt.plot(range(len(_x)),_y) print(len(_x)) # plt.xticks(range(len(_x)),_y) plt.xticks(list(range(len(_x)))[::10],_x[::10].astype(int),rotation=45) plt.savefig('./t_10.png') plt.show()

# coding=utf-8 import pandas as pd import numpy as np from matplotlib import pyplot as plt df=pd.read_csv("./911.csv") print(df.head(5)) # 获取分类 # print()df["title"].str.split(": ") temp_list = df["title"].str.split(": ").tolist() cate_list = [i[0] for i in temp_list] # cate_df=pd.DataFrame(np.array(cate_list).reshape((df.shape[0],1)),columns=["cate"]) # print(cate_df) df["cate"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0],1))) print(df.head(5)) print(df.groupby(by="cate").count()["title"]) ''' [5 rows x 10 columns] cate EMS 47755 Fire 14558 Traffic 34801 Name: title, dtype: int64 '''

3.7 pandas中的时间序列

# coding=utf-8 import numpy as np import pandas as pd ''' pandas之时间序列 ''' print(pd.date_range(start="20171230", end="20191231", freq="D")) ''' DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08', ... '2019-12-22', '2019-12-23', '2019-12-24', '2019-12-25', '2019-12-26', '2019-12-27', '2019-12-28', '2019-12-29', '2019-12-30', '2019-12-31'], dtype='datetime64[ns]', length=732, freq='D') ''' print(pd.date_range(start="20171230", periods=10, freq="M")) ''' DatetimeIndex(['2017-12-31', '2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30', '2018-05-31', '2018-06-30', '2018-07-31', '2018-08-31', '2018-09-30'], dtype='datetime64[ns]', freq='M') ''' print(pd.date_range(start="20171230", periods=10, freq="H")) ''' DatetimeIndex(['2017-12-30 00:00:00', '2017-12-30 01:00:00', '2017-12-30 02:00:00', '2017-12-30 03:00:00', '2017-12-30 04:00:00', '2017-12-30 05:00:00', '2017-12-30 06:00:00', '2017-12-30 07:00:00', '2017-12-30 08:00:00', '2017-12-30 09:00:00'], dtype='datetime64[ns]', freq='H') ''' print(pd.date_range(start="20171230", periods=10, freq="MS")) ''' DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01', '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01', '2018-09-01', '2018-10-01'], dtype='datetime64[ns]', freq='MS')DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01', '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01', '2018-09-01', '2018-10-01'], dtype='datetime64[ns]', freq='MS') ''' print(pd.date_range(start="2017-12-30 10:10:30", periods=10, freq="H"))

# coding=utf-8 import pandas as pd import numpy as np from matplotlib import pyplot as plt print("*"*100) df = pd.read_csv("./911.csv") print(df.head(5)) print("*"*100) df["timeStamp"] = pd.to_datetime(df["timeStamp"]) df.set_index("timeStamp", inplace=True) # 重新设置索引 print(df.head()) # 统计出911数据中不同月份电话次数的 print("*"*100) count_by_month=df.resample("M").count()["title"] print(count_by_month) #画图 print("*"*100) _x = count_by_month.index _y = count_by_month.values # for i in _x: # print(dir(i)) # break _x = [i.strftime("%Y%m%d") for i in _x] plt.plot(range(len(_x)),_y) plt.xticks(range(len(_x)),_x,rotation=45) plt.show() plt.savefig("./t_13.png")

3.8pandas重采样

统计出911数据中不同月份电话次数的变化情况 统计出911数据中不同月份不同类型的电话的次数的变化情况

# coding=utf-8 # 911数据中不同月份不同类型的电话的次数的变化情况 import pandas as pd import numpy as np from matplotlib import pyplot as plt # 把时间字符串转为时间类型设置为索引 df = pd.read_csv("./911.csv") df["timeStamp"] = pd.to_datetime(df["timeStamp"]) # 添加列，表示分类 temp_list = df["title"].str.split(": ").tolist() cate_list = [i[0] for i in temp_list] print(cate_list) # print(np.array(cate_list).reshape((df.shape[0],1))) df["cate"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0], 1))) df.set_index("timeStamp", inplace=True) print(df.head(1)) plt.figure(figsize=(20, 8), dpi=80) # 分组 for group_name, group_data in df.groupby(by="cate"): # 对不同的分类都进行绘图 count_by_month = group_data.resample("M").count()["title"] # 画图 _x = count_by_month.index print(_x) _y = count_by_month.values _x = [i.strftime("%Y%m%d") for i in _x] plt.plot(range(len(_x)), _y, label=group_name) plt.xticks(range(len(_x)), _x, rotation=45) plt.legend(loc="best") plt.savefig("./t_13.png") plt.show()

# coding=utf-8 import pandas as pd from matplotlib import pyplot as plt file_path = "./PM2.5/BeijingPM20100101_20151231.csv" df = pd.read_csv(file_path) # print(df.head()) # print(df.info()) # 把分开的时间字符串通过periodIndex的方法转化为pandas的时间类型 period = pd.PeriodIndex(year=df["year"], month=df["month"], day=df["day"], hour=df["hour"], freq="H") # print(period) # print(type(period)) df["datetime"] = period # print(df.head(10)) # 把datetime 设置为索引 df.set_index("datetime", inplace=True) # 进行降采样 df = df.resample("7D").mean() # print(df.shape) # print(df.head()) # 处理缺失数据，删除缺失数据 # print(df["PM_US Post"]) data = df["PM_US Post"] data_china = df["PM_Dongsi"] print(data_china.index) # 画图 _x=data.index _x = [i.strftime("%Y%m%d") for i in _x] _x_china = [i.strftime("%Y%m%d") for i in data_china.index] print(len(_x),len(_x_china)) _y=data.values _y_china=data_china.values plt.figure(figsize=(20,8),dpi=80) plt.plot(range(len(_x)),_y,label="US_POST") plt.plot(range(len(_x_china)),_y_china,label="CN_POST") plt.xticks(range(0,len(_x),10),list(_x)[::10],rotation=45) plt.legend(loc="best") plt.savefig("./pm2.5.png") plt.show()

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