pandas.Grouper#

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

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.

originTimestamp 或 str，默认 ‘start_day’

用于调整分组的时间戳。origin 的时区必须与索引的时区匹配。如果为字符串，则必须是以下之一：

  • ‘epoch’: origin 为 1970-01-01

  • ‘start’: origin 为时间序列的第一个值

  • ‘start_day’: origin 为时间序列的第一个日期的午夜

  • ‘end’: origin 为时间序列的最后一个值

  • ‘end_day’: origin 为最后一天的午夜上限

在 1.3.0 版本加入.

offsetTimedelta 或 str，默认 None

添加到 origin 的偏移量 timedelta。

dropnabool, default True

如果为 True，并且组键包含 NA 值，则 NA 值将与行/列一起被丢弃。如果为 False，则 NA 值也将被视为组中的键。

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

如果要根据固定时间戳调整 bin 的开始：

>>> 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

如果要使用 offset Timedelta 调整 bin 的开始，以下两行是等效的：

>>> 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
On this page
显示源代码