e.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)
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  * [ 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.all  #

SeriesGroupBy.  all  (  _ skipna  =  True  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L2207-L2262) #

    

Return True if all values in the group are truthful, else False.

Parameters  :

    

**skipna** bool, default True

    

Flag to ignore nan values during truth testing.

Returns  :

    

Series or DataFrame

    

DataFrame or Series of boolean values, where a value is True if all elements
are True within its respective group, False otherwise.

See also

` Series.groupby  `

    

Apply a function groupby to a Series.

` DataFrame.groupby  `

    

Apply a function groupby to each row or column of a DataFrame.

Examples

For SeriesGroupBy:

    
    
    >>> lst = ['a', 'a', 'b']
    >>> ser = pd.Series([1, 2, 0], index=lst)
    >>> ser
    a    1
    a    2
    b    0
    dtype: int64
    >>> ser.groupby(level=0).all()
    a     True
    b    False
    dtype: bool
    

For DataFrameGroupBy:

    
    
    >>> data = [[1, 0, 3], [1, 5, 6], [7, 8, 9]]
    >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
    ...                   index=["ostrich", "penguin", "parrot"])
    >>> df
             a  b  c
    ostrich  1  0  3
    penguin  1  5  6
    parrot   7  8  9
    >>> df.groupby(by=["a"]).all()
           b      c
    a
    1  False   True
    7   True   True
    

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__ On this page

  * ` SeriesGroupBy.all()  `

[ __ Show Source
](../../_sources/reference/api/pandas.core.groupby.SeriesGroupBy.all.rst.txt)

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