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# pandas.Series.all

Series.all(*, axis=0, bool_only=False, skipna=True, **kwargs)

  Return whether all elements are True, potentially over an axis.

  Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

  Parameters:
    axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

      Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.

      - 0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.

      - 1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.

      - None : reduce all axes, return a scalar.

    bool_only bool, default False

      Include only boolean columns. Not implemented for Series.

    skipna bool, default True

      Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

    **kwargs any, default None

      Additional keywords have no effect but might be accepted for compatibility with NumPy.

  Returns:
    Series or scalar

      If axis=None, then a scalar boolean is returned. Otherwise a Series is returned with index matching the index argument.

  See also

  `Series.all`

    Return True if all elements are True.

  `DataFrame.any`

    Return True if one (or more) elements are True.

  Examples

  Series

      >>> pd.Series([True, True]).all()
      True
      >>> pd.Series([True, False]).all()
      False
      >>> pd.Series([], dtype="float64").all()
      True
      >>> pd.Series([np.nan]).all()
      True
      >>> pd.Series([np.nan]).all(skipna=False)
      True

  DataFrames

  Create a DataFrame from a dictionary.

      >>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
      >>> df
         col1   col2
      0  True   True
      1  True  False

  Default behaviour checks if values in each column all return True.

      >>> df.all()
      col1     True
      col2    False
      dtype: bool

  Specify `axis='columns'` to check if values in each row all return True.

      >>> df.all(axis='columns')
      0     True
      1    False
      dtype: bool

  Or `axis=None` for whether every value is True.

      >>> df.all(axis=None)
      False