对具有字符串和数字的DataFrame索引进行排序 [英] Sort DataFrame index that has a string and number

问题描述

我的df DataFrame索引如下:

My df DataFrame index looks like this:

Com_Lag_01
Com_Lag_02
Com_Lag_03
Com_Lag_04
Com_Lag_05
Com_Lag_06
Com_Lag_07
Com_Lag_08
Com_Lag_09
Com_Lag_10
Com_Lag_101
Com_Lag_102
Com_Lag_103
...
Com_Lag_11
Com_Lag_111
Com_Lag_112
Com_Lag_113
Com_Lag_114
...
Com_Lag_12
Com_Lag_120
...
Com_Lag_13
Com_Lag_14
Com_Lag_15

我想对这个索引进行排序，以便数字从Com_Lag_1到Com_Lag_120.如果使用df.sort_index()，我将获得与上述相同的结果.关于如何正确排序此索引的任何建议?

I want to sort this index so that the numbers go from Com_Lag_1 to Com_Lag_120. If I use df.sort_index() I will get the same thing as above. Any suggestion on how to sort this index properly?

推荐答案

人们可以通过对索引的编号版本进行排序来尝试类似的操作

One could try something like this, by performing a sort on a numbered version of the index

import pandas as pd
# Create a DataFrame example
df = pd.DataFrame(\
    {'Year': [1991 ,2004 ,2001 ,2009 ,1997],\
    'Age': [27 ,25 ,22 ,34 ,31],\
    },\
    index = ['Com_Lag_1' ,'Com_Lag_12' ,'Com_Lag_3' ,'Com_Lag_24' ,'Com_Lag_5'])

# Add of a column containing a numbered version of the index
df['indexNumber'] = [int(i.split('_')[-1]) for i in df.index]
# Perform sort of the rows
df.sort(['indexNumber'], ascending = [True], inplace = True)
# Deletion of the added column
df.drop('indexNumber', 1, inplace = True)

编辑2017-V1 :

为避免SettingWithCopyWarning:

To avoid SettingWithCopyWarning:

df = df.assign(indexNumber=[int(i.split('_')[-1]) for i in df.index])

编辑2017-V2 for Pandas版本0.21.0

import pandas as pd
print(pd.__version__)
# Create a DataFrame example
df = pd.DataFrame(\
    {'Year': [1991 ,2004 ,2001 ,2009 ,1997],\
    'Age': [27 ,25 ,22 ,34 ,31],\
    },\
    index = ['Com_Lag_1' ,'Com_Lag_12' ,'Com_Lag_3' ,'Com_Lag_24' ,'Com_Lag_5'])

df.reindex(index=df.index.to_series().str.rsplit('_').str[-1].astype(int).sort_values().index)

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