pandas功能使用rename, reindex, set_index 详解

pandas rename 功能

  • 在使用 pandas 的过程中经常会用到修改列名称的问题，会用到 rename 或者 reindex 等功能，每次都需要去查文档
  • 当然经常也可以使用 df.columns重新赋值为某个列表
  • 用 rename 则可以轻松应对 pandas 中修改列名的问题

导入常用的数据包

import pandas as pd
import numpy as np

构建一个 含有multiIndex的 Series

arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]

tuples = list(zip(*arrays))


index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])

s = pd.Series(np.random.randn(8), index=index)


s.index

MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'], ['one', 'two']],
           labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],
           names=['first', 'second'])

查看 s

s

first  second
bar    one      -0.073094
       two      -0.449141
baz    one       0.109093
       two      -0.033135
foo    one       1.315809
       two      -0.887890
qux    one       2.255328
       two      -0.778246
dtype: float64

使用set_names可以将 index 中的名称进行更改

s.index.set_names(['L1', 'L2'], inplace=True)


s

L1   L2 
bar  one    0.037524
     two   -0.178425
baz  one   -0.778211
     two    1.440168
foo  one    0.314172
     two    0.710597
qux  one    1.197275
     two    0.527058
dtype: float64

s.index

MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'], ['one', 'two']],
           labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],
           names=['L1', 'L2'])

同样可以使用 rename 将Series 修改回来

s.index.rename(['first','second'],inplace= True)

s

first  second
bar    one       0.037524
       two      -0.178425
baz    one      -0.778211
       two       1.440168
foo    one       0.314172
       two       0.710597
qux    one       1.197275
       two       0.527058
dtype: float64

使用reset_index 可以将 index 中的两列转化为正常的列

s.reset_index()
first second 0
0 bar one 0.037524
1 bar two -0.178425
2 baz one -0.778211
3 baz two 1.440168
4 foo one 0.314172
5 foo two 0.710597
6 qux one 1.197275
7 qux two 0.527058

可以使用 pivot_table 恢复成一开始的样子，将两列重新作为 index 展示出来

s.reset_index().pivot_table(index=['first','second'],values=0,aggfunc=lambda x:x)
0
first second
bar one 0.037524
two -0.178425
baz one -0.778211
two 1.440168
foo one 0.314172
two 0.710597
qux one 1.197275
two 0.527058

同样可以使用最简单的方式进行更改 index 中的名称

s.index.names=['first1','second1'] ## 此操作，相当于直接赋值，会更改 s

s.index

MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'], ['one', 'two']],
           labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],
           names=['first1', 'second1'])

s

first1  second1
bar     one        0.037524
        two       -0.178425
baz     one       -0.778211
        two        1.440168
foo     one        0.314172
        two        0.710597
qux     one        1.197275
        two        0.527058
dtype: float64

df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,                    'B' : ['A', 'B', 'C'] * 4,
                 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
                  'D' : np.random.randn(12),
                 'E' : np.random.randn(12)})


df.head()
A B C D E
0 one A foo 0.664180 -0.107764
1 one B foo -0.833609 0.008083
2 two C foo 0.117919 -1.365583
3 three A bar -0.116776 -1.201934
4 one B bar -1.315190 -0.157779 
df.pivot_table(index=['A','C'],values=['D'],columns='B',aggfunc=np.sum,fill_value='unknown')
D
B A B C
A C
one bar 2.71452 -1.31519 0.0231296
foo 0.66418 -0.833609 -0.96451
three bar -0.116776 unknown 0.450891
foo unknown 0.012846 unknown
two bar unknown 0.752643 unknown
foo 0.963631 unknown 0.117919 
df1 =df.pivot_table(index=['A','C'],values=['D'],columns='B',aggfunc=np.sum,fill_value='unknown')

df1.index

MultiIndex(levels=[['one', 'three', 'two'], ['bar', 'foo']],
           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
           names=['A', 'C'])

df1.index.names=['first','second']

df1
D
B A B C
first second
one bar 2.71452 -1.31519 0.0231296
foo 0.66418 -0.833609 -0.96451
three bar -0.116776 unknown 0.450891
foo unknown 0.012846 unknown
two bar unknown 0.752643 unknown
foo 0.963631 unknown 0.117919 
df1_stack=df1.stack()

df1_stack.index.names=['first','second','third']

df1_stack
D
first second third
one bar A 2.71452
B -1.31519
C 0.0231296
foo A 0.66418
B -0.833609
C -0.96451
three bar A -0.116776
B unknown
C 0.450891
foo A unknown
B 0.012846
C unknown
two bar A unknown
B 0.752643
C unknown
foo A 0.963631
B unknown
C 0.117919 
df1_stack.columns=['总和']

df1_stack
总和
first second third
one bar A 2.71452
B -1.31519
C 0.0231296
foo A 0.66418
B -0.833609
C -0.96451
three bar A -0.116776
B unknown
C 0.450891
foo A unknown
B 0.012846
C unknown
two bar A unknown
B 0.752643
C unknown
foo A 0.963631
B unknown
C 0.117919 
df2 = df1_stack.reset_index()

df2.set_index('first')
second third 总和
first
one bar A 2.71452
one bar B -1.31519
one bar C 0.0231296
one foo A 0.66418
one foo B -0.833609
one foo C -0.96451
three bar A -0.116776
three bar B unknown
three bar C 0.450891
three foo A unknown
three foo B 0.012846
three foo C unknown
two bar A unknown
two bar B 0.752643
two bar C unknown
two foo A 0.963631
two foo B unknown
two foo C 0.117919

posted on 2019-02-23 22:51  多一点  阅读(7089)  评论(0)  编辑  收藏  举报

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