Consider a csv file:

a string,2/5/11 9:16am,1.0
a string,3/5/11 10:44pm,2.0
a string,4/22/11 12:07pm,3.0
a string,4/22/11 12:10pm,4.0
a string,4/29/11 11:59am,1.0
a string,5/2/11 1:41pm,2.0
a string,5/2/11 2:02pm,3.0
a string,5/2/11 2:56pm,4.0
a string,5/2/11 3:00pm,5.0
a string,5/2/14 3:02pm,6.0
a string,5/2/14 3:18pm,7.0

I can read this in, and reformat the date column into datetime format:

b['date']=pd.to_datetime(b['date'],format="%m/%d/%y %I:%M%p")

I have been trying to group the data by month. It seems like there should be an obvious way of accessing the month and grouping by that. But I can’t seem to do it. Does anyone know how?

What I am currently trying is re-indexing by the date:


I can access the month like so:


However I can’t seem to find a function to lump together by month.

Managed to do it:

b = pd.read_csv('b.dat')
b.index = pd.to_datetime(b['date'],format="%m/%d/%y %I:%M%p")
b.groupby(by=[b.index.month, b.index.year])


b.groupby(pd.Grouper(freq='M'))  # update for v0.21+

(update: 2018)

Note that pd.Timegrouper is depreciated and will be removed. Use instead:


One solution which avoids MultiIndex is to create a new datetime column setting day = 1. Then group by this column.

Normalise day of month

df = pd.DataFrame({'Date': pd.to_datetime(['2017-10-05', '2017-10-20', '2017-10-01', '2017-09-01']),
                   'Values': [5, 10, 15, 20]})

# normalize day to beginning of month, 4 alternative methods below
df['YearMonth'] = df['Date'] + pd.offsets.MonthEnd(-1) + pd.offsets.Day(1)
df['YearMonth'] = df['Date'] - pd.to_timedelta(df['Date'], unit="D")
df['YearMonth'] = df['Date'].map(lambda dt: dt.replace(day=1))
df['YearMonth'] = df['Date'].dt.normalize().map(pd.tseries.offsets.MonthBegin().rollback)

Then use groupby as normal:

g = df.groupby('YearMonth')

res = g['Values'].sum()

# YearMonth
# 2017-09-01    20
# 2017-10-01    30
# Name: Values, dtype: int64

Comparison with pd.Grouper

The subtle benefit of this solution is, unlike pd.Grouper, the grouper index is normalized to the beginning of each month rather than the end, and therefore you can easily extract groups via get_group:

some_group = g.get_group('2017-10-01')

Calculating the last day of October is slightly more cumbersome. pd.Grouper, as of v0.23, does support a convention parameter, but this is only applicable for a PeriodIndex grouper.

Comparison with string conversion

An alternative to the above idea is to convert to a string, e.g. convert datetime 2017-10-XX to string '2017-10'. However, this is not recommended since you lose all the efficiency benefits of a datetime series (stored internally as numerical data in a contiguous memory block) versus an object series of strings (stored as an array of pointers).

To groupby time-series data you can use the method resample. For example, to groupby by month:

df.resample(rule="M", on='date')['Values'].sum()

The list with offset aliases you can find here.

Slightly alternative solution to @jpp’s but outputting a YearMonth string:

df['YearMonth'] = pd.to_datetime(df['Date']).apply(lambda x: '{year}-{month}'.format(year=x.year, month=x.month))

res = df.groupby('YearMonth')['Values'].sum()