Exemplo n.º 1
0
    def cumsum(self, axis=0, *args, **kwargs):
        """
        Cumulative sum of non-NA/null values.

        When performing the cumulative summation, any non-NA/null values will
        be skipped. The resulting SparseSeries will preserve the locations of
        NaN values, but the fill value will be `np.nan` regardless.

        Parameters
        ----------
        axis : {0}

        Returns
        -------
        cumsum : SparseSeries
        """
        nv.validate_cumsum(args, kwargs)
        if axis is not None:
            axis = self._get_axis_number(axis)

        new_array = self.values.cumsum()

        return self._constructor(
            new_array, index=self.index,
            sparse_index=new_array.sp_index).__finalize__(self)
Exemplo n.º 2
0
    def cumsum(self, axis=0, *args, **kwargs):
        """
        Cumulative sum of non-NA/null values.

        When performing the cumulative summation, any non-NA/null values will
        be skipped. The resulting SparseArray will preserve the locations of
        NaN values, but the fill value will be `np.nan` regardless.

        Parameters
        ----------
        axis : int or None
            Axis over which to perform the cumulative summation. If None,
            perform cumulative summation over flattened array.

        Returns
        -------
        cumsum : SparseArray
        """
        nv.validate_cumsum(args, kwargs)

        if axis is not None and axis >= self.ndim:  # Mimic ndarray behaviour.
            raise ValueError("axis(={axis}) out of bounds".format(axis=axis))

        if not self._null_fill_value:
            return SparseArray(self.to_dense()).cumsum()

        return SparseArray(self.sp_values.cumsum(), sparse_index=self.sp_index,
                           fill_value=self.fill_value)
Exemplo n.º 3
0
    def cumsum(self, axis=0, *args, **kwargs):
        """
        Cumulative sum of non-NA/null values.

        When performing the cumulative summation, any non-NA/null values will
        be skipped. The resulting SparseSeries will preserve the locations of
        NaN values, but the fill value will be `np.nan` regardless.

        Parameters
        ----------
        axis : {0}

        Returns
        -------
        cumsum : SparseSeries
        """
        nv.validate_cumsum(args, kwargs)
        # Validate axis
        if axis is not None:
            self._get_axis_number(axis)

        new_array = self.values.cumsum()

        return self._constructor(
            new_array, index=self.index,
            sparse_index=new_array.sp_index).__finalize__(self)
Exemplo n.º 4
0
Arquivo: array.py Projeto: x997/pandas
    def cumsum(self, axis=0, *args, **kwargs):
        """
        Cumulative sum of non-NA/null values.

        When performing the cumulative summation, any non-NA/null values will
        be skipped. The resulting SparseArray will preserve the locations of
        NaN values, but the fill value will be `np.nan` regardless.

        Parameters
        ----------
        axis : int or None
            Axis over which to perform the cumulative summation. If None,
            perform cumulative summation over flattened array.

        Returns
        -------
        cumsum : SparseArray
        """
        nv.validate_cumsum(args, kwargs)

        if axis is not None and axis >= self.ndim:  # Mimic ndarray behaviour.
            raise ValueError(f"axis(={axis}) out of bounds")

        if not self._null_fill_value:
            return SparseArray(self.to_dense()).cumsum()

        return SparseArray(
            self.sp_values.cumsum(),
            sparse_index=self.sp_index,
            fill_value=self.fill_value,
        )
Exemplo n.º 5
0
    def cumsum(self, axis=0, *args, **kwargs):
        """
        Return SparseDataFrame of cumulative sums over requested axis.

        Parameters
        ----------
        axis : {0, 1}
            0 for row-wise, 1 for column-wise

        Returns
        -------
        y : SparseDataFrame
        """
        nv.validate_cumsum(args, kwargs)
        return self.apply(lambda x: x.cumsum(), axis=axis)
Exemplo n.º 6
0
    def cumsum(self, axis=0, *args, **kwargs):
        """
        Cumulative sum of values. Preserves locations of NaN values

        Returns
        -------
        cumsum : SparseSeries if `self` has a null `fill_value` and a
                 generic Series otherwise
        """
        nv.validate_cumsum(args, kwargs)
        new_array = SparseArray.cumsum(self.values)
        if isinstance(new_array, SparseArray):
            return self._constructor(new_array, index=self.index, sparse_index=new_array.sp_index).__finalize__(self)
        # TODO: gh-12855 - return a SparseSeries here
        return Series(new_array, index=self.index).__finalize__(self)
Exemplo n.º 7
0
    def cumsum(self, axis=0, *args, **kwargs):
        """
        Cumulative sum of values. Preserves locations of NaN values

        Returns
        -------
        cumsum : Series
        """
        nv.validate_cumsum(args, kwargs)

        # TODO: gh-12855 - return a SparseArray here
        if notnull(self.fill_value):
            return self.to_dense().cumsum()

        # TODO: what if sp_values contains NaN??
        return SparseArray(self.sp_values.cumsum(), sparse_index=self.sp_index, fill_value=self.fill_value)
Exemplo n.º 8
0
    def cumsum(self, axis=0, *args, **kwargs):
        """
        Cumulative sum of values. Preserves locations of NaN values

        Returns
        -------
        cumsum : SparseSeries if `self` has a null `fill_value` and a
                 generic Series otherwise
        """
        nv.validate_cumsum(args, kwargs)
        new_array = SparseArray.cumsum(self.values)
        if isinstance(new_array, SparseArray):
            return self._constructor(
                new_array, index=self.index,
                sparse_index=new_array.sp_index).__finalize__(self)
        # TODO: gh-12855 - return a SparseSeries here
        return Series(new_array, index=self.index).__finalize__(self)
Exemplo n.º 9
0
    def cumsum(self, axis=0, *args, **kwargs):
        """
        Cumulative sum of values. Preserves locations of NaN values

        Returns
        -------
        cumsum : Series
        """
        nv.validate_cumsum(args, kwargs)

        # TODO: gh-12855 - return a SparseArray here
        if notnull(self.fill_value):
            return self.to_dense().cumsum()

        # TODO: what if sp_values contains NaN??
        return SparseArray(self.sp_values.cumsum(), sparse_index=self.sp_index,
                           fill_value=self.fill_value)