y.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.core.... 

#  pandas.core.groupby.DataFrameGroupBy.describe  #

DataFrameGroupBy.  describe  (  _ percentiles  =  None  _ , _ include  =  None
_ , _ exclude  =  None  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L3576-L3612) #

    

Generate descriptive statistics.

Descriptive statistics include those that summarize the central tendency,
dispersion and shape of a datasetâs distribution, excluding ` NaN  ` values.

Analyzes both numeric and object series, as well as ` DataFrame  ` column sets
of mixed data types. The output will vary depending on what is provided. Refer
to the notes below for more detail.

Parameters  :

    

**percentiles** list-like of numbers, optional

    

The percentiles to include in the output. All should fall between 0 and 1. The
default is ` [.25,  .5,  .75]  ` , which returns the 25th, 50th, and 75th
percentiles.

**include** âallâ, list-like of dtypes or None (default), optional

    

A white list of data types to include in the result. Ignored for ` Series  ` .
Here are the options:

  * âallâ : All columns of the input will be included in the output. 

  * A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit ` numpy.number  ` . To limit it instead to object columns submit the ` numpy.object  ` data type. Strings can also be used in the style of ` select_dtypes  ` (e.g. ` df.describe(include=['O'])  ` ). To select pandas categorical columns, use ` 'category'  `

  * None (default) : The result will include all numeric columns. 

**exclude** list-like of dtypes or None (default), optional,

    

A black list of data types to omit from the result. Ignored for ` Series  ` .
Here are the options:

  * A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit ` numpy.number  ` . To exclude object columns submit the data type ` numpy.object  ` . Strings can also be used i