SeriesGroupBy.resample.html)
    * [ 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.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.hist  #

DataFrameGroupBy.  hist  (  _ column  =  None  _ , _ by  =  None  _ , _ grid
=  True  _ , _ xlabelsize  =  None  _ , _ xrot  =  None  _ , _ ylabelsize  =
None  _ , _ yrot  =  None  _ , _ ax  =  None  _ , _ sharex  =  False  _ , _
sharey  =  False  _ , _ figsize  =  None  _ , _ layout  =  None  _ , _ bins  =
10  _ , _ backend  =  None  _ , _ legend  =  False  _ , _ **  kwargs  _ )  [
[source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/generic.py#L2751-L2790) #

    

Make a histogram of the DataFrameâs columns.

A [ histogram ](https://en.wikipedia.org/wiki/Histogram) is a representation
of the distribution of data. This function calls ` matplotlib.pyplot.hist()  `
, on each series in the DataFrame, resulting in one histogram per column.

Parameters  :

    

**data** DataFrame

    

The pandas object holding the data.

**column** str or sequence, optional

    

If passed, will be used to limit data to a subset of columns.

**by** object, optional

    

If passed, then used to form histograms for separate groups.

**grid** bool, default True

    

Whether to show axis grid lines.

**xlabelsize** int, default None

    

If specified changes the x-axis label size.

**xrot** float, default None

    

Rotation of x axis labels. For example, a value of 90 displays the x labels
rotated 90 degrees clockwise.

**ylabelsize** int, default None

    

If specified changes the y-axis label size.

**yrot** float, default None

    

Rotation of y axis labels. For example, a value of 90 displays the y labels
rotated 90 degrees clockwise.

**ax** Matplotlib axes object, default None

    

The axes to plot the histogram on.

**sharex** bool, default True if ax is None else False

    

In case subplots=True, share x axis and set some x axis labels to invisible;
defaults to True if ax is None otherwise False if an ax is passed in. Note
that passing in both an ax and sharex=True will alte