Pandas高级教程之:Dataframe的重排和旋转

ゞ浴缸里的玫瑰 2022-10-07 00:46 147阅读 0赞

### 文章目录 ###

*  简介
 *  使用Pivot
 *  使用Stack
 *  使用melt
 *  使用Pivot tables
 *  使用crosstab
 *  get\_dummies

# 简介 #

使用Pandas的pivot方法可以将DF进行旋转变换，本文将会详细讲解pivot的秘密。

# 使用Pivot #

pivot用来重组DF，使用指定的index，columns和values来对现有的DF进行重构。

看一个Pivot的例子：

![20201227143354173.png][]

通过pivot变化，新的DF使用foo中的值作为index，使用bar的值作为columns，zoo作为对应的value。

再看一个时间变化的例子：

In [1]: df
    Out[1]: 
             date variable     value
    0  2000-01-03        A  0.469112
    1  2000-01-04        A -0.282863
    2  2000-01-05        A -1.509059
    3  2000-01-03        B -1.135632
    4  2000-01-04        B  1.212112
    5  2000-01-05        B -0.173215
    6  2000-01-03        C  0.119209
    7  2000-01-04        C -1.044236
    8  2000-01-05        C -0.861849
    9  2000-01-03        D -2.104569
    10 2000-01-04        D -0.494929
    11 2000-01-05        D  1.071804

In [3]: df.pivot(index='date', columns='variable', values='value')
    Out[3]: 
    variable           A         B         C         D
    date                                              
    2000-01-03  0.469112 -1.135632  0.119209 -2.104569
    2000-01-04 -0.282863  1.212112 -1.044236 -0.494929
    2000-01-05 -1.509059 -0.173215 -0.861849  1.071804

如果剩余的value，多于一列的话，每一列都会有相应的columns值：

In [4]: df['value2'] = df['value'] * 2
    
    In [5]: pivoted = df.pivot(index='date', columns='variable')
    
    In [6]: pivoted
    Out[6]: 
                   value                                  value2                              
    variable           A         B         C         D         A         B         C         D
    date                                                                                      
    2000-01-03  0.469112 -1.135632  0.119209 -2.104569  0.938225 -2.271265  0.238417 -4.209138
    2000-01-04 -0.282863  1.212112 -1.044236 -0.494929 -0.565727  2.424224 -2.088472 -0.989859
    2000-01-05 -1.509059 -0.173215 -0.861849  1.071804 -3.018117 -0.346429 -1.723698  2.143608

通过选择value2，可以得到相应的子集：

In [7]: pivoted['value2']
    Out[7]: 
    variable           A         B         C         D
    date                                              
    2000-01-03  0.938225 -2.271265  0.238417 -4.209138
    2000-01-04 -0.565727  2.424224 -2.088472 -0.989859
    2000-01-05 -3.018117 -0.346429 -1.723698  2.143608

# 使用Stack #

Stack是对DF进行转换，将列转换为新的内部的index。

![20201227144301304.png][]

上面我们将列A，B转成了index。

unstack是stack的反向操作，是将最内层的index转换为对应的列。

![20201227144642726.png][]

举个具体的例子：

In [8]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
       ...:                      'foo', 'foo', 'qux', 'qux'],
       ...:                     ['one', 'two', 'one', 'two',
       ...:                      'one', 'two', 'one', 'two']]))
       ...: 
    
    In [9]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
    
    In [10]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
    
    In [11]: df2 = df[:4]
    
    In [12]: df2
    Out[12]: 
                         A         B
    first second                    
    bar   one     0.721555 -0.706771
          two    -1.039575  0.271860
    baz   one    -0.424972  0.567020
          two     0.276232 -1.087401

In [13]: stacked = df2.stack()
    
    In [14]: stacked
    Out[14]: 
    first  second   
    bar    one     A    0.721555
                   B   -0.706771
           two     A   -1.039575
                   B    0.271860
    baz    one     A   -0.424972
                   B    0.567020
           two     A    0.276232
                   B   -1.087401
    dtype: float64

默认情况下unstack是unstack最后一个index，我们还可以指定特定的index值：

In [15]: stacked.unstack()
    Out[15]: 
                         A         B
    first second                    
    bar   one     0.721555 -0.706771
          two    -1.039575  0.271860
    baz   one    -0.424972  0.567020
          two     0.276232 -1.087401
    
    In [16]: stacked.unstack(1)
    Out[16]: 
    second        one       two
    first                      
    bar   A  0.721555 -1.039575
          B -0.706771  0.271860
    baz   A -0.424972  0.276232
          B  0.567020 -1.087401
    
    In [17]: stacked.unstack(0)
    Out[17]: 
    first          bar       baz
    second                      
    one    A  0.721555 -0.424972
           B -0.706771  0.567020
    two    A -1.039575  0.276232
           B  0.271860 -1.087401

![20201227144908787.png][]

默认情况下stack只会stack一个level，还可以传入多个level：

In [23]: columns = pd.MultiIndex.from_tuples([
       ....:     ('A', 'cat', 'long'), ('B', 'cat', 'long'),
       ....:     ('A', 'dog', 'short'), ('B', 'dog', 'short')],
       ....:     names=['exp', 'animal', 'hair_length']
       ....: )
       ....: 
    
    In [24]: df = pd.DataFrame(np.random.randn(4, 4), columns=columns)
    
    In [25]: df
    Out[25]: 
    exp                 A         B         A         B
    animal            cat       cat       dog       dog
    hair_length      long      long     short     short
    0            1.075770 -0.109050  1.643563 -1.469388
    1            0.357021 -0.674600 -1.776904 -0.968914
    2           -1.294524  0.413738  0.276662 -0.472035
    3           -0.013960 -0.362543 -0.006154 -0.923061
    
    In [26]: df.stack(level=['animal', 'hair_length'])
    Out[26]: 
    exp                          A         B
      animal hair_length                    
    0 cat    long         1.075770 -0.109050
      dog    short        1.643563 -1.469388
    1 cat    long         0.357021 -0.674600
      dog    short       -1.776904 -0.968914
    2 cat    long        -1.294524  0.413738
      dog    short        0.276662 -0.472035
    3 cat    long        -0.013960 -0.362543
      dog    short       -0.006154 -0.923061

上面等价于：

In [27]: df.stack(level=[1, 2])

# 使用melt #

melt指定特定的列作为标志变量，其他的列被转换为行的数据。并放置在新的两个列：variable和value中。

![20201227145722872.png][]

上面例子中我们指定了两列first和last，这两列是不变的，height和weight被变换成为行数据。

举个例子：

In [41]: cheese = pd.DataFrame({'first': ['John', 'Mary'],
       ....:                        'last': ['Doe', 'Bo'],
       ....:                        'height': [5.5, 6.0],
       ....:                        'weight': [130, 150]})
       ....: 
    
    In [42]: cheese
    Out[42]: 
      first last  height  weight
    0  John  Doe     5.5     130
    1  Mary   Bo     6.0     150
    
    In [43]: cheese.melt(id_vars=['first', 'last'])
    Out[43]: 
      first last variable  value
    0  John  Doe   height    5.5
    1  Mary   Bo   height    6.0
    2  John  Doe   weight  130.0
    3  Mary   Bo   weight  150.0
    
    In [44]: cheese.melt(id_vars=['first', 'last'], var_name='quantity')
    Out[44]: 
      first last quantity  value
    0  John  Doe   height    5.5
    1  Mary   Bo   height    6.0
    2  John  Doe   weight  130.0
    3  Mary   Bo   weight  150.0

# 使用Pivot tables #

虽然Pivot可以进行DF的轴转置，Pandas还提供了 pivot\_table() 在转置的同时可以进行数值的统计。

pivot\_table() 接收下面的参数：

data: 一个df对象

values:一列或者多列待聚合的数据。

Index: index的分组对象

Columns: 列的分组对象

Aggfunc: 聚合的方法。

先创建一个df：

In [59]: import datetime
    
    In [60]: df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 6,
       ....:                    'B': ['A', 'B', 'C'] * 8,
       ....:                    'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4,
       ....:                    'D': np.random.randn(24),
       ....:                    'E': np.random.randn(24),
       ....:                    'F': [datetime.datetime(2013, i, 1) for i in range(1, 13)]
       ....:                    + [datetime.datetime(2013, i, 15) for i in range(1, 13)]})
       ....: 
    
    In [61]: df
    Out[61]: 
            A  B    C         D         E          F
    0     one  A  foo  0.341734 -0.317441 2013-01-01
    1     one  B  foo  0.959726 -1.236269 2013-02-01
    2     two  C  foo -1.110336  0.896171 2013-03-01
    3   three  A  bar -0.619976 -0.487602 2013-04-01
    4     one  B  bar  0.149748 -0.082240 2013-05-01
    ..    ... ..  ...       ...       ...        ...
    19  three  B  foo  0.690579 -2.213588 2013-08-15
    20    one  C  foo  0.995761  1.063327 2013-09-15
    21    one  A  bar  2.396780  1.266143 2013-10-15
    22    two  B  bar  0.014871  0.299368 2013-11-15
    23  three  C  bar  3.357427 -0.863838 2013-12-15
    
    [24 rows x 6 columns]

下面是几个聚合的例子：

In [62]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
    Out[62]: 
    C             bar       foo
    A     B                    
    one   A  1.120915 -0.514058
          B -0.338421  0.002759
          C -0.538846  0.699535
    three A -1.181568       NaN
          B       NaN  0.433512
          C  0.588783       NaN
    two   A       NaN  1.000985
          B  0.158248       NaN
          C       NaN  0.176180
    
    In [63]: pd.pivot_table(df, values='D', index=['B'], columns=['A', 'C'], aggfunc=np.sum)
    Out[63]: 
    A       one               three                 two          
    C       bar       foo       bar       foo       bar       foo
    B                                                            
    A  2.241830 -1.028115 -2.363137       NaN       NaN  2.001971
    B -0.676843  0.005518       NaN  0.867024  0.316495       NaN
    C -1.077692  1.399070  1.177566       NaN       NaN  0.352360
    
    In [64]: pd.pivot_table(df, values=['D', 'E'], index=['B'], columns=['A', 'C'],
       ....:                aggfunc=np.sum)
       ....: 
    Out[64]: 
              D                                                           E                                                  
    A       one               three                 two                 one               three                 two          
    C       bar       foo       bar       foo       bar       foo       bar       foo       bar       foo       bar       foo
    B                                                                                                                        
    A  2.241830 -1.028115 -2.363137       NaN       NaN  2.001971  2.786113 -0.043211  1.922577       NaN       NaN  0.128491
    B -0.676843  0.005518       NaN  0.867024  0.316495       NaN  1.368280 -1.103384       NaN -2.128743 -0.194294       NaN
    C -1.077692  1.399070  1.177566       NaN       NaN  0.352360 -1.976883  1.495717 -0.263660       NaN       NaN  0.872482

添加margins=True会添加一个All列，表示对所有的列进行聚合：

In [69]: df.pivot_table(index=['A', 'B'], columns='C', margins=True, aggfunc=np.std)
    Out[69]: 
                    D                             E                    
    C             bar       foo       All       bar       foo       All
    A     B                                                            
    one   A  1.804346  1.210272  1.569879  0.179483  0.418374  0.858005
          B  0.690376  1.353355  0.898998  1.083825  0.968138  1.101401
          C  0.273641  0.418926  0.771139  1.689271  0.446140  1.422136
    three A  0.794212       NaN  0.794212  2.049040       NaN  2.049040
          B       NaN  0.363548  0.363548       NaN  1.625237  1.625237
          C  3.915454       NaN  3.915454  1.035215       NaN  1.035215
    two   A       NaN  0.442998  0.442998       NaN  0.447104  0.447104
          B  0.202765       NaN  0.202765  0.560757       NaN  0.560757
          C       NaN  1.819408  1.819408       NaN  0.650439  0.650439
    All      1.556686  0.952552  1.246608  1.250924  0.899904  1.059389

# 使用crosstab #

Crosstab 用来统计表格中元素的出现次数。

In [70]: foo, bar, dull, shiny, one, two = 'foo', 'bar', 'dull', 'shiny', 'one', 'two'
    
    In [71]: a = np.array([foo, foo, bar, bar, foo, foo], dtype=object)
    
    In [72]: b = np.array([one, one, two, one, two, one], dtype=object)
    
    In [73]: c = np.array([dull, dull, shiny, dull, dull, shiny], dtype=object)
    
    In [74]: pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
    Out[74]: 
    b    one        two      
    c   dull shiny dull shiny
    a                        
    bar    1     0    0     1
    foo    2     1    1     0

crosstab可以接收两个Series:

In [75]: df = pd.DataFrame({'A': [1, 2, 2, 2, 2], 'B': [3, 3, 4, 4, 4],
       ....:                    'C': [1, 1, np.nan, 1, 1]})
       ....: 
    
    In [76]: df
    Out[76]: 
       A  B    C
    0  1  3  1.0
    1  2  3  1.0
    2  2  4  NaN
    3  2  4  1.0
    4  2  4  1.0
    
    In [77]: pd.crosstab(df['A'], df['B'])
    Out[77]: 
    B  3  4
    A      
    1  1  0
    2  1  3

还可以使用normalize来指定比例值：

In [82]: pd.crosstab(df['A'], df['B'], normalize=True)
    Out[82]: 
    B    3    4
    A          
    1  0.2  0.0
    2  0.2  0.6

还可以normalize行或者列：

In [83]: pd.crosstab(df['A'], df['B'], normalize='columns')
    Out[83]: 
    B    3    4
    A          
    1  0.5  0.0
    2  0.5  1.0

可以指定聚合方法：

In [84]: pd.crosstab(df['A'], df['B'], values=df['C'], aggfunc=np.sum)
    Out[84]: 
    B    3    4
    A          
    1  1.0  NaN
    2  1.0  2.0

# get\_dummies #

get\_dummies可以将DF中的一列转换成为k列的0和1组合：

df = pd.DataFrame({'key': list('bbacab'), 'data1': range(6)})
    
    df
    Out[9]: 
       data1 key
    0      0   b
    1      1   b
    2      2   a
    3      3   c
    4      4   a
    5      5   b
    
    pd.get_dummies(df['key'])
    Out[10]: 
       a  b  c
    0  0  1  0
    1  0  1  0
    2  1  0  0
    3  0  0  1
    4  1  0  0
    5  0  1  0

get\_dummies 和 cut 可以进行结合用来统计范围内的元素：

In [95]: values = np.random.randn(10)
    
    In [96]: values
    Out[96]: 
    array([ 0.4082, -1.0481, -0.0257, -0.9884,  0.0941,  1.2627,  1.29  ,
            0.0824, -0.0558,  0.5366])
    
    In [97]: bins = [0, 0.2, 0.4, 0.6, 0.8, 1]
    
    In [98]: pd.get_dummies(pd.cut(values, bins))
    Out[98]: 
       (0.0, 0.2]  (0.2, 0.4]  (0.4, 0.6]  (0.6, 0.8]  (0.8, 1.0]
    0           0           0           1           0           0
    1           0           0           0           0           0
    2           0           0           0           0           0
    3           0           0           0           0           0
    4           1           0           0           0           0
    5           0           0           0           0           0
    6           0           0           0           0           0
    7           1           0           0           0           0
    8           0           0           0           0           0
    9           0           0           1           0           0

get\_dummies还可以接受一个DF参数：

In [99]: df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'],
       ....:                    'C': [1, 2, 3]})
       ....: 
    
    In [100]: pd.get_dummies(df)
    Out[100]: 
       C  A_a  A_b  B_b  B_c
    0  1    1    0    0    1
    1  2    0    1    0    1
    2  3    1    0    1    0

> 本文已收录于 [http://www.flydean.com/05-python-pandas-reshaping-pivot/][http_www.flydean.com_05-python-pandas-reshaping-pivot]
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[20201227143354173.png]: /images/20221005/5be48c35d3dd431fabeeb90a288624ed.png
[20201227144301304.png]: /images/20221005/088cd2a47d2947a8bf09e1187b97cbe9.png
[20201227144642726.png]: /images/20221005/ca8ad068bf4e40e8bf4fba61d7a04e6a.png
[20201227144908787.png]: /images/20221005/dff38daea49a442ba63599d136e3bd05.png
[20201227145722872.png]: /images/20221005/aec413be341746e8b33e1f1d0a22d05a.png
[http_www.flydean.com_05-python-pandas-reshaping-pivot]: http://www.flydean.com/05-python-pandas-reshaping-pivot/