tml)
    * [ pandas.core.groupby.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
    * [ pandas.core.groupby.SeriesGroupBy.sample ](pandas.core.groupby.SeriesGroupBy.sample.html)
    * [ pandas.core.groupby.SeriesGroupBy.sem ](pandas.core.groupby.SeriesGroupBy.sem.html)
    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
    * [ pandas.core.groupby.SeriesGroupBy.size ](pandas.core.groupby.SeriesGroupBy.size.html)
    * [ pandas.core.groupby.SeriesGroupBy.skew ](pandas.core.groupby.SeriesGroupBy.skew.html)
    * [ pandas.core.groupby.SeriesGroupBy.std ](pandas.core.groupby.SeriesGroupBy.std.html)
    * [ pandas.core.groupby.SeriesGroupBy.sum ](pandas.core.groupby.SeriesGroupBy.sum.html)
    * [ pandas.core.groupby.SeriesGroupBy.var ](pandas.core.groupby.SeriesGroupBy.var.html)
    * [ pandas.core.groupby.SeriesGroupBy.tail ](pandas.core.groupby.SeriesGroupBy.tail.html)
    * [ pandas.core.groupby.SeriesGroupBy.take ](pandas.core.groupby.SeriesGroupBy.take.html)
    * [ pandas.core.groupby.SeriesGroupBy.value_counts ](pandas.core.groupby.SeriesGroupBy.value_counts.html)
    * [ pandas.core.groupby.DataFrameGroupBy.boxplot ](pandas.core.groupby.DataFrameGroupBy.boxplot.html)
    * [ pandas.core.groupby.DataFrameGroupBy.hist ](pandas.core.groupby.DataFrameGroupBy.hist.html)
    * [ pandas.core.groupby.SeriesGroupBy.hist ](pandas.core.groupby.SeriesGroupBy.hist.html)
    * [ pandas.core.groupby.DataFrameGroupBy.plot ](pandas.core.groupby.DataFrameGroupBy.plot.html)
    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.Grouper 

#  pandas.Grouper  #

_ class  _ pandas.  Grouper  (  _ *  args  _ , _ **  kwargs  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/grouper.py#L66-L485) #

    

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** {âleftâ or ârightâ}

    

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

**label** {âleftâ or ârightâ}

    

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

**convention** {âstartâ, âendâ, âeâ, âsâ}

    

If grouper is PeriodIndex and  freq  parameter is passed.

**origin** Timestamp or str, default âstart_dayâ

    

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:

  * âepochâ:  origin  is 1970-01-01 

  * âstartâ:  origin  is the first value of the timeseries 

  * âstart_dayâ:  origin  is the first day at midn