pby.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.fillna  #

DataFrameGroupBy.  fillna  (  _ value  =  None  _ , _ method  =  None  _ , _
axis  =  _NoDefault.no_default  _ , _ inplace  =  False  _ , _ limit  =  None
_ , _ downcast  =  _NoDefault.no_default  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/generic.py#L2378-L2522) #

    

Fill NA/NaN values using the specified method within groups.

Deprecated since version 2.2.0:  This method is deprecated and will be removed
in a future version. Use the [ ` DataFrameGroupBy.ffill()  `
](pandas.core.groupby.DataFrameGroupBy.ffill.html#pandas.core.groupby.DataFrameGroupBy.ffill
"pandas.core.groupby.DataFrameGroupBy.ffill") or [ ` DataFrameGroupBy.bfill()
`
](pandas.core.groupby.DataFrameGroupBy.bfill.html#pandas.core.groupby.DataFrameGroupBy.bfill
"pandas.core.groupby.DataFrameGroupBy.bfill") for forward or backward filling
instead. If you want to fill with a single value, use ` DataFrame.fillna()  `
instead.

Parameters  :

    

**value** scalar, dict, Series, or DataFrame

    

Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of
values specifying which value to use for each index (for a Series) or column
(for a DataFrame). Values not in the dict/Series/DataFrame will not be filled.
This value cannot be a list. Users wanting to use the ` value  ` argument and
not ` method  ` should prefer [ ` DataFrame.fillna()  `
](pandas.DataFrame.fillna.html#pandas.DataFrame.fillna
"pandas.DataFrame.fillna") as this will produce the same result and be more
performant.

**method** {{âbfillâ, âffillâ, None}}, default None

    

Method to use for filling holes. ` 'ffill'  ` will propagate the last valid
observation forward within a group. ` 'bfill'  ` will use next valid
observation to fill the gap.

**axis** {0 or âindexâ, 1 or âcolumnsâ}

    

Axis along which to fill missing values. When the ` DataFrameGroupBy  ` ` axis
` argument is ` 0  ` , using `