# dask.dataframe.DataFrame.any

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

  Return whether any element is True, potentially over an axis.

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

  Some inconsistencies with the Dask version may exist.

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

  `numpy.any`

    Numpy version of this method.

  `Series.any`

    Return whether any element is True.

  `Series.all`

    Return whether all elements are True.

  `DataFrame.any`

    Return whether any element is True over requested axis.

  `DataFrame.all`

    Return whether all elements are True over requested axis.

  Examples

  Series

  For Series input, the output is a scalar indicating whether any element is True.

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

  DataFrame

  Whether each column contains at least one True element (the default).

      >>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
      >>> df
         A  B  C
      0  1  0  0
      1  2  2  0

      >>> df.any()
      A     True
      B     True
      C    False
      dtype: bool

  Aggregating over the columns.

      >>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
      >>> df
             A  B
      0   True  1
      1  False  2

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

      >>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
      >>> df
             A  B
      0   True  1
      1  False  0

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

  Aggregating over the entire DataFrame with `axis=None`.

      >>> df.any(axis=None)
      True

  any for an empty DataFrame is an empty Series.

      >>> pd.DataFrame([]).any()
      Series([], dtype: bool)