Python Pandas: Split a TimeSerie per month or week

The pd.TimeGrouper is deprecated and will be removed, you can use pd.Grouper instead.

weeks = [g for n, g in df.groupby(pd.Grouper(key='timestamp',freq='W'))]
months = [g for n, g in df.groupby(pd.Grouper(key='timestamp',freq='M'))]

This way you can also avoid setting the timestamp as index.

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Also, if your timestamp is part of a multi index, you can refer to it using using the level parameter (e.g. pd.Grouper(level='timestamp', freq='W')). Than @jtromans for the heads up.

use groupby with pd.TimeGrouper and list comprehensions

weeks = [g for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('W'))]
months = [g for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('M'))]

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You can reset the index if you need

weeks = [g.reset_index()
         for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('W'))]
months = [g.reset_index()
          for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('M'))]

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in a dict

weeks = {n: g.reset_index()
         for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('W'))}
months = {n: g.reset_index()
          for n, g in df.set_index('timestamp').groupby(pd.TimeGrouper('M'))}