.reindex

index：array-like，可選（可以按順序指定或as關鍵字）新標籤/索引符合。優選地，Index物件用於避免重複資料

fill_value：scalar，default np.NaN缺失值使用的值。預設為NaN，但可以是任何“相容”值

In [178]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [179]: s
Out[179]: 
a   -1.010924
b   -0.672504
c   -1.139222
d    0.354653
e    0.563622
 

dtype: float64

In [180]: s.reindex(['e', 'b', 'f', 'd'])  # f為新新增的index，預設數值為nan
Out[180]: 
e    0.563622
b   -0.672504
f         NaN
d    0.354653
dtype: float64

例子1

In [181]: df
Out[181]: 
        one     three       two
a -0.626544       NaN -0.351587
b -0.138894 -0.177289  1.136249
 

c  0.011617  0.462215 -0.448789
d       NaN  1.124472 -1.101558

In [182]: df.reindex(index=['c', 'f', 'b'], columns=['three', 'two', 'one'])
Out[182]: 
      three       two       one
c  0.462215 -0.448789  0.011617
f       NaN       NaN       NaN
b -0.177289  1.136249 -0.138894

>>> index = ['Firefox'
 
, 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = pd.DataFrame({
...      'http_status': [200,200,404,404,301],
...      'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
...       index=index)
>>> df
            http_status  response_time
Firefox            200           0.04
Chrome             200           0.02
Safari             404           0.07
IE10               404           0.08
Konqueror          301           1.00

>>> new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
...             'Chrome']
>>> df.reindex(new_index)
               http_status  response_time
Safari                 404           0.07
Iceweasel              NaN            NaN
Comodo Dragon          NaN            NaN
IE10                   404           0.08
Chrome                 200           0.02

>>> df.reindex(new_index, fill_value=0)
               http_status  response_time
Safari                 404           0.07
Iceweasel                0           0.00
Comodo Dragon            0           0.00
IE10                   404           0.08
Chrome                 200           0.02

>>> df.reindex(new_index, fill_value='missing')
              http_status response_time
Safari                404          0.07
Iceweasel         missing       missing
Comodo Dragon     missing       missing
IE10                  404          0.08
Chrome                200          0.02

reindex_like()

In [186]: df2
Out[186]: 
        one       two
a -0.626544 -0.351587
b -0.138894  1.136249
c  0.011617 -0.448789

In [187]: df3
Out[187]: 
        one       two
a -0.375270 -0.463545
b  0.112379  1.024292
c  0.262891 -0.560746

In [188]: df.reindex_like(df2)
Out[188]: 
        one       two
a -0.626544 -0.351587
b -0.138894  1.136249
c  0.011617 -0.448789