Pandas数据分析实战（Pandas in action）第1章 Pandas 概述

Pandas 数据分析实战

第1章 Pandas 概述

read_csv() 没有设置索引列

read_csv 函数导入 movies.csv 文件，由于没有设置索引，Pandas 会生产一个从 0 开始的数字索引

movies = pd.read_csv('./file/chapter_01/movies.csv')
print(movies)

     Rank                         Title           Studio       Gross  Year
0       1             Avengers: Endgame      Buena Vista  $2,796.30   2019
1       2                        Avatar              Fox  $2,789.70   2009
2       3                       Titanic        Paramount  $2,187.50   1997
3       4  Star Wars: The Force Awakens      Buena Vista  $2,068.20   2015
4       5        Avengers: Infinity War      Buena Vista  $2,048.40   2018
..    ...                           ...              ...         ...   ...
777   778                     Yogi Bear  Warner Brothers    $201.60   2010
778   779           Garfield: The Movie              Fox    $200.80   2004
779   780                   Cats & Dogs  Warner Brothers    $200.70   2001
780   781      The Hunt for Red October        Paramount    $200.50   1990
781   782                      Valkyrie              MGM    $200.30   2008

[782 rows x 5 columns]

为避免显示结果过多导致页面过长，Pandas 仅显示 DataFrame 的前五行和后五行，省略的数据用 ... 代替

read_csv() 指定索引列（index_col）

使用 Title 作为索引

movies = pd.read_csv("./file/chapter_01/movies.csv", index_col="Title")
print(movies)

                              Rank           Studio       Gross  Year
Title
Avengers: Endgame                1      Buena Vista  $2,796.30   2019
Avatar                           2              Fox  $2,789.70   2009
Titanic                          3        Paramount  $2,187.50   1997
Star Wars: The Force Awakens     4      Buena Vista  $2,068.20   2015
Avengers: Infinity War           5      Buena Vista  $2,048.40   2018
...                            ...              ...         ...   ...
Yogi Bear                      778  Warner Brothers    $201.60   2010
Garfield: The Movie            779              Fox    $200.80   2004
Cats & Dogs                    780  Warner Brothers    $200.70   2001
The Hunt for Red October       781        Paramount    $200.50   1990
Valkyrie                       782              MGM    $200.30   2008

[782 rows x 4 columns]

操作 DataFrame

head():

从开头提取几行，默认是 5 行，可以选择传参，比如 head(4)，获取前 4 行数据

movies.head()

                            Rank       Studio       Gross  Year
Title
Avengers: Endgame                1  Buena Vista  $2,796.30   2019
Avatar                           2          Fox  $2,789.70   2009
Titanic                          3    Paramount  $2,187.50   1997
Star Wars: The Force Awakens     4  Buena Vista  $2,068.20   2015
Avengers: Infinity War           5  Buena Vista  $2,048.40   2018

tail()

查看数据集的尾部数据，默认是 5 行，可以选择传参，比如 tail(6)，获取尾部 6 行数据

movies.tail()

                          Rank           Studio     Gross  Year
Title
Yogi Bear                  778  Warner Brothers  $201.60   2010
Garfield: The Movie        779              Fox  $200.80   2004
Cats & Dogs                780  Warner Brothers  $200.70   2001
The Hunt for Red October   781        Paramount  $200.50   1990
Valkyrie                   782              MGM  $200.30   2008

len()

查询 DataFrame 有多少行
```
len(movies)
```
```
782
```
属性：shape

查询 DataFrame 的行数和列数
```
movies.shape
```
```
(782, 5)
```
属性：size

查询单元格总数
```
movies.size
```
```
  3910
```

属性：dtypes

查询列的数据类型，int64 表示整数列，object 表示文本列

movies.dtypes

Rank       int64
Title     object
Studio    object
Gross     object
Year       int64
dtype: object

iloc[]

根据索引位置，提取信息。索引从 0 开始计算。返回一个 Series 对象。

movies.iloc[499]

  Rank                              500
  Title     Maze Runner: The Death Cure
  Studio                            Fox
  Gross                        $288.30
  Year                             2018
  Name: 499, dtype: object

loc[]

使用索引标签提取数据，在导入数据的时候，设置了 Title 作为索引

movies.loc["Forrest Gump"]

Rank            119
Studio    Paramount
Gross      $677.90
Year           1994
Name: Forrest Gump, dtype: object

索引标签可以包含重复项。尽可能保持索引标签的唯一性，唯一的标签集合可以加快 Pandas 定位和提取特定行的速度

movies.loc["101 Dalmatians"]

                Rank       Studio     Gross  Year
Title
101 Dalmatians   425  Buena Vista  $320.70   1996
101 Dalmatians   708  Buena Vista  $215.90   1961

sort_values()

按 Year 列中的值进行排序，取最新上映的 5 部电影。by 指定列名或者索引值， ascending 是否按指定列的数组升序排序，默认为 True，即升序排列

movies.sort_values(by='Year', ascending= False).head()

                                            Rank  ...  Year
Title                                             ...
Avengers: Endgame                              1  ...  2019
John Wick: Chapter 3 - Parabellum            458  ...  2019
The Wandering Earth                          114  ...  2019
Toy Story 4                                  198  ...  2019
How to Train Your Dragon: The Hidden World   199  ...  2019

[5 rows x 4 columns]

还可以根据多列的值对 DateFrame 进行排序，先按 Studio 列的值排序，如果出现重复值，再按 Year 列的值对电影进行排序

movies.sort_values(by=['Studio','Year']).head()

                        Rank       Studio     Gross  Year
Title
The Blair Witch Project   588      Artisan  $248.60   1999
101 Dalmatians            708  Buena Vista  $215.90   1961
The Jungle Book           755  Buena Vista  $205.80   1967
Who Framed Roger Rabbit   410  Buena Vista  $329.80   1988
Dead Poets Society        636  Buena Vista  $235.90   1989

sort_index()

可以按照字母顺序查看电影信息（设置了 Title 为索引）

movies.sort_index().head()

                  Rank           Studio     Gross  Year
Title
10,000 B.C.        536  Warner Brothers  $269.80   2008
101 Dalmatians     708      Buena Vista  $215.90   1961
101 Dalmatians     425      Buena Vista  $320.70   1996
2 Fast 2 Furious   632        Universal  $236.40   2003
2012                93             Sony  $769.70   2009

计算 Series 中的值

从 DataFrame 中提取一列数据作为 Series。Pandas 在 Series 中保存了 DataFrame 的索引，即设置的 Title

movies['Studio']

Title
Avengers: Endgame                   Buena Vista
Avatar                                      Fox
Titanic                               Paramount
Star Wars: The Force Awakens        Buena Vista
Avengers: Infinity War              Buena Vista
                                     ...       
Yogi Bear                       Warner Brothers
Garfield: The Movie                         Fox
Cats & Dogs                     Warner Brothers
The Hunt for Red October              Paramount
Valkyrie                                    MGM
Name: Studio, Length: 782, dtype: object

Series 中有多行数据，Pandas 会截断数据集，只显示前五行和后五行

计算 Series 列中唯一值出现的次数，将结果限制在排名前10的电源公司：

movies['Studio'].value_counts().head(10)

Studio
Warner Brothers    132
Buena Vista        125
Fox                117
Universal          109
Sony                86
Paramount           76
Dreamworks          27
Lionsgate           21
New Line            16
MGM                 11
Name: count, dtype: int64

返回值是另一个 Series 对象。此时，Pandas 使用 Studio 列中的公司名称作为索引标签，他们的计算作为Series 值

根据一个或多个条件筛选列

movies[movies["Studio"] == "Universal"]

                                Rank     Studio       Gross  Year
Title                                                            
Jurassic World                     6  Universal  $1,671.70   2015
Furious 7                          8  Universal  $1,516.00   2015
Jurassic World: Fallen Kingdom    13  Universal  $1,309.50   2018
The Fate of the Furious           17  Universal  $1,236.00   2017
Minions                           19  Universal  $1,159.40   2015
...                              ...        ...         ...   ...
The Break-Up                     763  Universal    $205.00   2006
Everest                          766  Universal    $203.40   2015
Patch Adams                      772  Universal    $202.30   1998
Kindergarten Cop                 775  Universal    $202.00   1990
Straight Outta Compton           776  Universal    $201.60   2015

[109 rows x 4 columns]

可以将过滤条件赋给一个变量

filter_by_universal = movies["Studio"] == "Universal"
movies[filter_by_universal]

                                Rank     Studio       Gross  Year
Title                                                            
Jurassic World                     6  Universal  $1,671.70   2015
Furious 7                          8  Universal  $1,516.00   2015
Jurassic World: Fallen Kingdom    13  Universal  $1,309.50   2018
The Fate of the Furious           17  Universal  $1,236.00   2017
Minions                           19  Universal  $1,159.40   2015

还可以根据多个条件过滤DataFrame行，获取2015年由 Universal 发行的所有电影

filter_by_universal = movies["Studio"] == "Universal"
filter_by_year = movies['Year'] == 2015
movies[filter_by_universal & filter_by_year]

                        Rank     Studio       Gross  Year
Title                                                    
Jurassic World             6  Universal  $1,671.70   2015
Furious 7                  8  Universal  $1,516.00   2015
Minions                   19  Universal  $1,159.40   2015
Fifty Shades of Grey     165  Universal    $571.00   2015
Pitch Perfect 2          504  Universal    $287.50   2015
Ted 2                    702  Universal    $216.70   2015
Everest                  766  Universal    $203.40   2015
Straight Outta Compton   776  Universal    $201.60   2015

若要筛选符合以下两个条件中任意一个的电源：Universal 发行的电源或2015年发行的电影

filter_by_universal = movies["Studio"] == "Universal"
filter_by_year = movies['Year'] == 2015
movies[filter_by_universal | filter_by_year]

                                Rank       Studio       Gross  Year
Title                                                              
Star Wars: The Force Awakens       4  Buena Vista  $2,068.20   2015
Jurassic World                     6    Universal  $1,671.70   2015
Furious 7                          8    Universal  $1,516.00   2015
Avengers: Age of Ultron            9  Buena Vista  $1,405.40   2015
Jurassic World: Fallen Kingdom    13    Universal  $1,309.50   2018
...                              ...          ...         ...   ...
The Break-Up                     763    Universal    $205.00   2006
Everest                          766    Universal    $203.40   2015
Patch Adams                      772    Universal    $202.30   1998
Kindergarten Cop                 775    Universal    $202.00   1990
Straight Outta Compton           776    Universal    $201.60   2015

[140 rows x 4 columns]

可以将小于或大于特定值的列值作为筛选条件。以1975年之前发行的电影为筛选条件

filter_by_1975 = movies['Year'] < 1975
movies[filter_by_1975]

                    Rank           Studio     Gross  Year
Title                                                    
The Exorcist         252  Warner Brothers  $441.30   1973
Gone with the Wind   288              MGM  $402.40   1939
Bambi                540              RKO  $267.40   1942
The Godfather        604        Paramount  $245.10   1972
101 Dalmatians       708      Buena Vista  $215.90   1961
The Jungle Book      755      Buena Vista  $205.80   1967

指定一个范围，所有值必须在该范围之内。筛选1983-1986年发行的电影

year__between = movies["Year"].between(1983, 1986)
print(movies[year__between])

                                      Rank     Studio     Gross  Year
Title                                                                
Return of the Jedi                     222        Fox  $475.10   1983
Back to the Future                     311  Universal  $381.10   1985
Top Gun                                357  Paramount  $356.80   1986
Indiana Jones and the Temple of Doom   403  Paramount  $333.10   1984
Crocodile Dundee                       413  Paramount  $328.20   1986
Beverly Hills Cop                      432  Paramount  $316.40   1984
Rocky IV                               467        MGM  $300.50   1985
Rambo: First Blood Part II             469    TriStar  $300.40   1985
Ghostbusters                           485   Columbia  $295.20   1984
Out of Africa                          662  Universal  $227.50   1985

使用 DataFrame 索引来过滤。查找标题中带有“dark"一词的所有电影。先将索引的电影标题中的大写字母转换为小写字母。

str_lower__str_contains = movies.index.str.lower().str.contains("dark")
print(movies[str_lower__str_contains])

                                Rank           Studio       Gross  Year
Title                                                                  
Transformers: Dark of the Moon    23        Paramount  $1,123.80   2011
The Dark Knight Rises             27  Warner Brothers  $1,084.90   2012
The Dark Knight                   39  Warner Brothers  $1,004.90   2008
Thor: The Dark World             132      Buena Vista    $644.60   2013
Star Trek Into Darkness          232        Paramount    $467.40   2013
Fifty Shades Darker              309        Universal    $381.50   2017
Dark Shadows                     600  Warner Brothers    $245.50   2012
Dark Phoenix                     603              Fox    $245.10   2019

对数据分组

筛选出总票房最高的电影公司，首先按电影公司名称汇总 Gross 列的值。注意 Gross 列的值存储为文本而不是数字。Pandas 将列的值作为文本导入，以保留原始 CSV 中的美元符号和逗号符合，需要删除美元符号和逗号符合，然后将列的值转换为数字

movies['Gross'].str.replace("$", "", regex=False).str.replace(",", "", regex=False)

Title
Avengers: Endgame               2796.30 
Avatar                          2789.70 
Titanic                         2187.50 
Star Wars: The Force Awakens    2068.20 
Avengers: Infinity War          2048.40 
                                  ...   
Yogi Bear                        201.60 
Garfield: The Movie              200.80 
Cats & Dogs                      200.70 
The Hunt for Red October         200.50 
Valkyrie                         200.30 
Name: Gross, Length: 782, dtype: object

可以看到 dtype 类型为 object ，可以转换为浮点数

movies['Gross'].str.replace("$", "", regex=False).str.replace(",", "", regex=False).astype(float)

Title
Avengers: Endgame               2796.3
Avatar                          2789.7
Titanic                         2187.5
Star Wars: The Force Awakens    2068.2
Avengers: Infinity War          2048.4
                                 ...  
Yogi Bear                        201.6
Garfield: The Movie              200.8
Cats & Dogs                      200.7
The Hunt for Red October         200.5
Valkyrie                         200.3
Name: Gross, Length: 782, dtype: float64

以上操作都是在原始数据结构的副本上进行的操作，需要修改原始数据，需要进行覆盖操作

gross_series = movies['Gross'].str.replace("$", "", regex=False).str.replace(",", "", regex=False).astype(float)
movies['Gross'] = gross_series
print(movies.dtypes)

Rank        int64
Studio     object
Gross     float64
Year        int64
dtype: object

计算电影的平均票房收入

movies['Gross'].mean()

439.0308184143222

计算每个电影公司的总票房收入

先对电影公司进行分组

studios = movies.groupby("Studio")

对电影出票数量由多到少的顺序排序

studios['Gross'].count().sort_values(ascending=False).head()

Studio
Warner Brothers    132
Buena Vista        125
Fox                117
Universal          109
Sony                86
Name: Gross, dtype: int64

计算每个电影公司的总票房，找出票房最高的5家公司

studios['Gross'].sum().sort_values(ascending=False).head()

Studio
Buena Vista        73585.0
Warner Brothers    58643.8
Fox                50420.8
Universal          44302.3
Sony               32822.5
Name: Gross, dtype: float64

posted @ 2023-12-16 21:03 熠然阅读(56) 评论(0) 编辑收藏举报

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Pandas数据分析实战（Pandas in action）第1章 Pandas 概述

Pandas 数据分析实战

第1章 Pandas 概述

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