Reduce this NamedArray's data by applying ``cumsum`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``cumsum``. For e.g. ``dim="x"`` or ``
(
self,
dim: Dims = None,
*,
skipna: bool | None = None,
**kwargs: Any,
)
| 773 | ) |
| 774 | |
| 775 | def cumsum( |
| 776 | self, |
| 777 | dim: Dims = None, |
| 778 | *, |
| 779 | skipna: bool | None = None, |
| 780 | **kwargs: Any, |
| 781 | ) -> Self: |
| 782 | """ |
| 783 | Reduce this NamedArray's data by applying ``cumsum`` along some dimension(s). |
| 784 | |
| 785 | Parameters |
| 786 | ---------- |
| 787 | dim : str, Iterable of Hashable, "..." or None, default: None |
| 788 | Name of dimension[s] along which to apply ``cumsum``. For e.g. ``dim="x"`` |
| 789 | or ``dim=["x", "y"]``. If "..." or None, will reduce over all dimensions. |
| 790 | skipna : bool or None, optional |
| 791 | If True, skip missing values (as marked by NaN). By default, only |
| 792 | skips missing values for float dtypes; other dtypes either do not |
| 793 | have a sentinel missing value (int) or ``skipna=True`` has not been |
| 794 | implemented (object, datetime64 or timedelta64). |
| 795 | **kwargs : Any |
| 796 | Additional keyword arguments passed on to the appropriate array |
| 797 | function for calculating ``cumsum`` on this object's data. |
| 798 | These could include dask-specific kwargs like ``split_every``. |
| 799 | |
| 800 | Returns |
| 801 | ------- |
| 802 | reduced : NamedArray |
| 803 | New NamedArray with ``cumsum`` applied to its data and the |
| 804 | indicated dimension(s) removed |
| 805 | |
| 806 | See Also |
| 807 | -------- |
| 808 | numpy.cumsum |
| 809 | dask.array.cumsum |
| 810 | Dataset.cumsum |
| 811 | DataArray.cumsum |
| 812 | NamedArray.cumulative |
| 813 | :ref:`agg` |
| 814 | User guide on reduction or aggregation operations. |
| 815 | |
| 816 | Notes |
| 817 | ----- |
| 818 | Non-numeric variables will be removed prior to reducing. |
| 819 | |
| 820 | Note that the methods on the ``cumulative`` method are more performant (with numbagg installed) |
| 821 | and better supported. ``cumsum`` and ``cumprod`` may be deprecated |
| 822 | in the future. |
| 823 | |
| 824 | Examples |
| 825 | -------- |
| 826 | >>> from xarray.namedarray.core import NamedArray |
| 827 | >>> na = NamedArray("x", np.array([1, 2, 3, 0, 2, np.nan])) |
| 828 | >>> na |
| 829 | <xarray.NamedArray (x: 6)> Size: 48B |
| 830 | array([ 1., 2., 3., 0., 2., nan]) |
| 831 | |
| 832 | >>> na.cumsum() |