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 parameter is given, a level of the index of the target object.

If level is passed as a keyword to both Grouper and groupby, the values passed to Grouper take precedence.

Parameters:
*args

Currently unused, reserved for future use.

**kwargs

Dictionary of the keyword arguments to pass to Grouper.

Attributes

key

(str, defaults to None) Groupby key, which selects the grouping column of the target.

level

(name/number, defaults to None) The level for the target index.

freq

(str / 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.

sort

(bool, 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.

origin

(Timestamp 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 - ‘end’: origin is the last value of the timeseries - ‘end_day’: origin is the ceiling midnight of the last day .. versionadded:: 1.3.0

offset

(Timedelta or str, default is None) An offset timedelta added to the origin.

dropna

(bool, 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.
Returns:
Grouper or pandas.api.typing.TimeGrouper

A TimeGrouper is returned if freq is not None. Otherwise, a Grouper is returned.

See also

Series.groupby

Apply a function groupby to a Series.

DataFrame.groupby

Apply a function groupby.

Examples

df.groupby(pd.Grouper(key="Animal")) is equivalent to df.groupby('Animal')

>>> 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: 7min, 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: 17min, 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: 17min, 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: 17min, 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: 17min, 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: 17min, 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: 17min, dtype: int64
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