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)
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)
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)
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,
)
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)
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)
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)
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)
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)