Apply Function on DataFrame Index

Question

What is the best way to apply a function over the index of a Pandas DataFrame? Currently I am using this verbose approach:

pd.DataFrame({\"Month\":          


        

Answer 1

A lot of answers are returning the Index as an array, which loses information about the index name etc (though you could do pd.Series(index.map(myfunc), name=index.name)). It also won't work for a MultiIndex.

The way that I worked with this is to use "rename":

mix = pd.MultiIndex.from_tuples([[1, 'hi'], [2, 'there'], [3, 'dude']], names=['num', 'name'])
data = np.random.randn(3)
df = pd.Series(data, index=mix)
print(df)
num  name 
1    hi       1.249914
2    there   -0.414358
3    dude     0.987852
dtype: float64

# Define a few dictionaries to denote the mapping
rename_dict = {i: i*100 for i in df.index.get_level_values('num')}
rename_dict.update({i: i+'_yeah!' for i in df.index.get_level_values('name')})
df = df.rename(index=rename_dict)
print(df)
num  name       
100  hi_yeah!       1.249914
200  there_yeah!   -0.414358
300  dude_yeah!     0.987852
dtype: float64

The only trick with this is that your index needs to have unique labels b/w different multiindex levels, but maybe someone more clever than me knows how to get around that. For my purposes this works 95% of the time.