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)