pandas.Grouper#

class pandas.Grouper(*args, **kwargs)[source]#

A Grouper allows the user to specify a groupby instruction for an object.

This specification will select a column via the key parameter, or if the level and/or axis parameters are given, a level of the index of the target object.

If axis and/or level are passed as keywords to both Grouper and groupby, the values passed to Grouper take precedence.

Parameters
keystr, defaults to None

Groupby key, which selects the grouping column of the target.

levelname/number, defaults to None

The level for the target index.

freqstr / frequency object, defaults to None

This will groupby the specified frequency if the target selection (via key or level) is a datetime-like object. For full specification of available frequencies, please see here.

axisstr, int, defaults to 0

Number/name of the axis.

sortbool, default to False

Whether to sort the resulting labels.

closed{‘left’ or ‘right’}

Closed end of interval. Only when freq parameter is passed.

label{‘left’ or ‘right’}

Interval boundary to use for labeling. Only when freq parameter is passed.

convention{‘start’, ‘end’, ‘e’, ‘s’}

If grouper is PeriodIndex and freq parameter is passed.

baseint, default 0

Only when freq parameter is passed. For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. For example, for ‘5min’ frequency, base could range from 0 through 4. Defaults to 0.

Deprecated since version 1.1.0: The new arguments that you should use are ‘offset’ or ‘origin’.

loffsetstr, DateOffset, timedelta object

Only when freq parameter is passed.

Deprecated since version 1.1.0: loffset is only working for .resample(...) and not for Grouper (GH28302). However, loffset is also deprecated for .resample(...) See: DataFrame.resample

originTimestamp or str, default ‘start_day’

The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If string, must be one of the following:

  • ‘epoch’: origin is 1970-01-01

  • ‘start’: origin is the first value of the timeseries

  • ‘start_day’: origin is the first day at midnight of the timeseries

New in version 1.1.0.

  • ‘end’: origin is the last value of the timeseries

  • ‘end_day’: origin is the ceiling midnight of the last day

New in version 1.3.0.

offsetTimedelta or str, default is None

An offset timedelta added to the origin.

New in version 1.1.0.

dropnabool, default True

If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

New in version 1.2.0.

Returns
A specification for a groupby instruction

Examples

Syntactic sugar for df.groupby('A')

>>> df = pd.DataFrame(
...     {
...         "Animal": ["Falcon", "Parrot", "Falcon", "Falcon", "Parrot"],
...         "Speed": [100, 5, 200, 300, 15],
...     }
... )
>>> df
   Animal  Speed
0  Falcon    100
1  Parrot      5
2  Falcon    200
3  Falcon    300
4  Parrot     15
>>> df.groupby(pd.Grouper(key="Animal")).mean()
        Speed
Animal
Falcon  200.0
Parrot   10.0

Specify a resample operation on the column ‘Publish date’

>>> df = pd.DataFrame(
...    {
...        "Publish date": [
...             pd.Timestamp("2000-01-02"),
...             pd.Timestamp("2000-01-02"),
...             pd.Timestamp("2000-01-09"),
...             pd.Timestamp("2000-01-16")
...         ],
...         "ID": [0, 1, 2, 3],
...         "Price": [10, 20, 30, 40]
...     }
... )
>>> df
  Publish date  ID  Price
0   2000-01-02   0     10
1   2000-01-02   1     20
2   2000-01-09   2     30
3   2000-01-16   3     40
>>> df.groupby(pd.Grouper(key="Publish date", freq="1W")).mean()
               ID  Price
Publish date
2000-01-02    0.5   15.0
2000-01-09    2.0   30.0
2000-01-16    3.0   40.0

If you want to adjust the start of the bins based on a fixed timestamp:

>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
>>> rng = pd.date_range(start, end, freq='7min')
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00     0
2000-10-01 23:37:00     3
2000-10-01 23:44:00     6
2000-10-01 23:51:00     9
2000-10-01 23:58:00    12
2000-10-02 00:05:00    15
2000-10-02 00:12:00    18
2000-10-02 00:19:00    21
2000-10-02 00:26:00    24
Freq: 7T, dtype: int64

>>> ts.groupby(pd.Grouper(freq='17min')).sum()
2000-10-01 23:14:00     0
2000-10-01 23:31:00     9
2000-10-01 23:48:00    21
2000-10-02 00:05:00    54
2000-10-02 00:22:00    24
Freq: 17T, dtype: int64

>>> ts.groupby(pd.Grouper(freq='17min', origin='epoch')).sum()
2000-10-01 23:18:00     0
2000-10-01 23:35:00    18
2000-10-01 23:52:00    27
2000-10-02 00:09:00    39
2000-10-02 00:26:00    24
Freq: 17T, dtype: int64

>>> ts.groupby(pd.Grouper(freq='17min', origin='2000-01-01')).sum()
2000-10-01 23:24:00     3
2000-10-01 23:41:00    15
2000-10-01 23:58:00    45
2000-10-02 00:15:00    45
Freq: 17T, dtype: int64

If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:

>>> ts.groupby(pd.Grouper(freq='17min', origin='start')).sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17T, dtype: int64

>>> ts.groupby(pd.Grouper(freq='17min', offset='23h30min')).sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17T, dtype: int64

To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2:

>>> ts.groupby(pd.Grouper(freq='17min', offset='2min')).sum()
2000-10-01 23:16:00     0
2000-10-01 23:33:00     9
2000-10-01 23:50:00    36
2000-10-02 00:07:00    39
2000-10-02 00:24:00    24
Freq: 17T, dtype: int64

Attributes

ax

groups