Sort Dataframe Index That Has A String And Number

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

Solution 1:

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)

Edit 2017 - V1:

To avoid SettingWithCopyWarning:

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

Edit 2017 - V2 for Pandas Version 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)

Solution 2:

Solution without new column with DataFrame.reindex by index of sorted Series :

a = df.index.to_series().str.rsplit('_').str[-1].astype(int).sort_values()
print (a)
Com_Lag_1      1
Com_Lag_3      3
Com_Lag_5      5
Com_Lag_12    12
Com_Lag_24    24
dtype: int32

df = df.reindex(index=a.index)
print (df)
            Age  Year
Com_Lag_1    271991
Com_Lag_3    222001
Com_Lag_5    311997
Com_Lag_12   252004
Com_Lag_24   342009

But if duplicated values is necessary add new column:

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_12'])

print (df)
            Age  Year
Com_Lag_1    27  1991
Com_Lag_12   25  2004
Com_Lag_3    22  2001
Com_Lag_24   34  2009
Com_Lag_12   31  1997

df['indexNumber'] = df.index.str.rsplit('_').str[-1].astype(int)
df = df.sort_values(['indexNumber']).drop('indexNumber', axis=1)
print (df)
            Age  Year
Com_Lag_1    27  1991
Com_Lag_3    22  2001
Com_Lag_12   25  2004
Com_Lag_12   31  1997
Com_Lag_24   34  2009

Solution 3:

Another solution is

    df.sort_index(key=lambda x: (x.to_series().str[8:].astype(int)), inplace=True)

The 8 comes from the position where the numeric values start