Converting Index Into Multiindex (hierarchical Index) In Pandas

In the data I am working with the index is compound - i.e. it has both item name and a timestamp, e.g. name@domain.com|2013-05-07 05:52:51 +0200. I want to do hierarchical indexing

Solution 1:

Once we have a DataFrame

import pandas as pd
df = pd.read_csv("input.csv", index_col=0)  # or from another source

and a function mapping each index to a tuple (below, it is for the example from this question)

defprocess_index(k):
    returntuple(k.split("|"))

we can create a hierarchical index in the following way:

df.index = pd.MultiIndex.from_tuples([process_index(k) for k,v in df.iterrows()])

An alternative approach is to create two columns then set them as the index (the original index will be dropped):

df['e-mail'] = [x.split("|")[0] for x in df.index] 
df['date'] = [x.split("|")[1] for x in df.index]
df = df.set_index(['e-mail', 'date'])

or even shorter

df['e-mail'], df['date'] = zip(*map(process_index, df.index))
df = df.set_index(['e-mail', 'date'])

Solution 2:

In pandas>=0.16.0, we can use the .str accessor on indices. This makes the following possible:

df.index = pd.MultiIndex.from_tuples(df.index.str.split('|').tolist())

(Note: I tried the more intuitive: pd.MultiIndex.from_arrays(df.index.str.split('|')) but for some reason that gives me errors.)

Solution 3:

My preference would be to initially read this in as a column (i.e. not as an index), then you can use the str split method:

csv ='\n'.join(['name@domain.com|2013-05-07 05:52:51 +0200, 42'] *3)
df = pd.read_csv(StringIO(csv), header=None)

In [13]: df[0].str.split('|')
Out[13]:
0    [name@domain.com, 2013-05-0705:52:51+0200]
1    [name@domain.com, 2013-05-0705:52:51+0200]
2    [name@domain.com, 2013-05-0705:52:51+0200]
Name: 0, dtype: object

And then feed this into a MultiIndex (perhaps this can be done cleaner?):

m = pd.MultiIndex.from_arrays(zip(*df[0].str.split('|')))

Delete the 0th column and set the index to the new MultiIndex:

deldf[0]df.index=mIn [17]:dfOut[17]:1name@domain.com2013-05-07 05:52:51+0200422013-05-07 05:52:51+0200422013-05-07 05:52:51+020042