# dask.dataframe.DataFrame.all

DataFrame.all(axis=None, skipna=True, split_every=False, out=None)

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

  This docstring was copied from pandas.core.frame.DataFrame.all.

  Some inconsistencies with the Dask version may exist.

  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 (Not supported in Dask)

      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 DataFrame

      If level is specified, then, DataFrame is returned; otherwise, Series is returned.

  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