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.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 
  * [ 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.SeriesGroupBy.plot  #

_ property  _ SeriesGroupBy.  plot  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/generic.py#L1148-L1152) #

    

Make plots of Series or DataFrame.

Uses the backend specified by the option ` plotting.backend  ` . By default,
matplotlib is used.

Parameters  :

    

**data** Series or DataFrame

    

The object for which the method is called.

**x** label or position, default None

    

Only used if data is a DataFrame.

**y** label, position or list of label, positions, default None

    

Allows plotting of one column versus another. Only used if data is a
DataFrame.

**kind** str

    

The kind of plot to produce:

  * âlineâ : line plot (default) 

  * âbarâ : vertical bar plot 

  * âbarhâ : horizontal bar plot 

  * âhistâ : histogram 

  * âboxâ : boxplot 

  * âkdeâ : Kernel Density Estimation plot 

  * âdensityâ : same as âkdeâ 

  * âareaâ : area plot 

  * âpieâ : pie plot 

  * âscatterâ : scatter plot (DataFrame only) 

  * âhexbinâ : hexbin plot (DataFrame only) 

**ax** matplotlib axes object, default None

    

An axes of the current figure.

**subplots** bool or sequence of iterables, default False

    

Whether to group columns into subplots:

  * ` False  ` : No subplots will be used 

  * ` True  ` : Make separate subplots for each column. 

  * sequence of iterables of column labels: Create a subplot for each group of columns. For example  [(âaâ, âcâ), (âbâ, âdâ)]  will create 2 subplots: one with columns âaâ and âcâ, and one with columns âbâ and âdâ. Remaining columns that arenât specified will be plotted in additional subplots (one per column). 

New in version 1.5.0.

**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