How to group pandas DataFrame entries by date in a non-unique column

I'm using pandas 0.16.2. This has better performance on my large dataset:

data.groupby(data.date.dt.year)

Using the dt option and playing around with weekofyear, dayofweek etc. becomes far easier.

ecatmur's solution will work fine. This will be better performance on large datasets, though:

data.groupby(data['date'].map(lambda x: x.year))

This might be easier to explain with a sample dataset.

Create Sample Data

Let's assume we have a single column of Timestamps, date and another column we would like to perform an aggregation on, a.

df = pd.DataFrame({'date':pd.DatetimeIndex(['2012-1-1', '2012-6-1', '2015-1-1', '2015-2-1', '2015-3-1']),
                   'a':[9,5,1,2,3]}, columns=['date', 'a'])

df

        date  a
0 2012-01-01  9
1 2012-06-01  5
2 2015-01-01  1
3 2015-02-01  2
4 2015-03-01  3

There are several ways to group by year

• Use the dt accessor with year property
• Put date in index and use anonymous function to access year
• Use resample method
• Convert to pandas Period

.dt accessor with year property

When you have a column (and not an index) of pandas Timestamps, you can access many more extra properties and methods with the dt accessor. For instance:

df['date'].dt.year

0    2012
1    2012
2    2015
3    2015
4    2015
Name: date, dtype: int64

We can use this to form our groups and calculate some aggregations on a particular column:

df.groupby(df['date'].dt.year)['a'].agg(['sum', 'mean', 'max'])

      sum  mean  max
date                
2012   14     7    9
2015    6     2    3

put date in index and use anonymous function to access year

If you set the date column as the index, it becomes a DateTimeIndex with the same properties and methods as the dt accessor gives normal columns

df1 = df.set_index('date')
df1.index.year

Int64Index([2012, 2012, 2015, 2015, 2015], dtype='int64', name='date')

Interestingly, when using the groupby method, you can pass it a function. This function will be implicitly passed the DataFrame's index. So, we can get the same result from above with the following:

df1.groupby(lambda x: x.year)['a'].agg(['sum', 'mean', 'max'])

      sum  mean  max
2012   14     7    9
2015    6     2    3

Use the resample method

If your date column is not in the index, you must specify the column with the on parameter. You also need to specify the offset alias as a string.

df.resample('AS', on='date')['a'].agg(['sum', 'mean', 'max'])

             sum  mean  max
date                       
2012-01-01  14.0   7.0  9.0
2013-01-01   NaN   NaN  NaN
2014-01-01   NaN   NaN  NaN
2015-01-01   6.0   2.0  3.0

Convert to pandas Period

You can also convert the date column to a pandas Period object. We must pass in the offset alias as a string to determine the length of the Period.

df['date'].dt.to_period('A')

0   2012
1   2012
2   2015
3   2015
4   2015
Name: date, dtype: object

We can then use this as a group

df.groupby(df['date'].dt.to_period('Y'))['a'].agg(['sum', 'mean', 'max'])


      sum  mean  max
2012   14     7    9
2015    6     2    3

This should work:

data.groupby(lambda x: data['date'][x].year)