Classe « Grouper »

Informations générales

Héritage

builtins.object
    Grouper

Définition

class Grouper(builtins.object):

help(Grouper)

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
----------
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
    <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_.
axis : str, int, defaults to 0
    Number/name of the axis.
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.

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

Constructeur(s)

Signature du constructeur	Description
__new__(cls, args, *kwargs)
__init__(self, key=None, level=None, freq=None, axis: 'Axis \| lib.NoDefault' = <no_default>, sort: 'bool' = False, dropna: 'bool' = True) -> 'None'