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Pandas实现Dataframe的重排和旋转

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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:

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。

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。

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

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。

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

到此这篇关于Pandas实现Dataframe的重排和旋转的文章就介绍到这了,更多相关Pandas Dataframe重排和旋转内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家!

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