import dascore as dc
# load an example patch which has some NaN values.
patch = dc.get_example_patch("patch_with_null")
# drop all time labels that have a single null value
out = patch.dropna("time", how="any")
# drop all distance labels that have all null values
out = patch.dropna("distance", how="all")dropna
dropna(
patch: Patch ,
dim ,
how: Literal[‘any’, ‘all’] = any,
include_inf = True,
)-> ‘PatchType’
Return a patch with nullish values dropped along dimension.
Parameters
| Parameter | Description |
|---|---|
| patch | The patch which may contain nullish values. |
| dim | The dimension along which to drop nullish values. |
| how |
“any” or “all”. If “any” drop label if any null values. If “all” drop label if all values are nullish. |
| include_inf | If True, drop all non-finite values. |
Note
When include_inf is False, “nullish” is defined by pandas.isnull. When include_inf is True (default), “nullish” includes non-finite values (NaN, inf, -inf) as determined by numpy.isfinite