通过传递条件~np.isnan(data),我们可以筛选出非NaN值,并将NaN值替换为缺失值(drop=True表示删除包含NaNs的维度)。 这样,我们就可以得到屏蔽了NaNs的多个xarray.Datasets对象(masked_data1和masked_data2),可以继续进行后续的数据处理和分析。 推荐的腾讯云相关产品:腾讯云服务器(CVM)和腾讯云对象存储(COS)。...
replace NaNs with other values :py:meth:`Dataset.fillna`, :py:meth:`Dataset.ffill`, :py:meth:`Dataset.bfill`, :py:meth:`Dataset.interpolate_na`, :py:meth:`DataArray.fillna`, :py:meth:`DataArray.ffill`, :py:meth:`DataArray.bfill`, :py:meth:`DataArray.interpolate_na` extract the...
问当尝试将xarray WRF横截面保存到netcdf时，TypeErrorEN这部分包含的时 wrf-python 模块中的API，如果...
core.accessor_str.StringAccessor.slice_replace core.accessor_str.StringAccessor.split core.accessor_str.StringAccessor.startswith core.accessor_str.StringAccessor.strip core.accessor_str.StringAccessor.swapcase core.accessor_str.StringAccessor.title core.accessor_str.StringAccessor.translate core.accessor_str...
This is easy so long as xarray can reach in and replace .data Utility functions like as_variable should be moved out of base_variable.py so they can convert BaseVariable objects to/from DataArray or Dataset containing explicitly indexed arrays....
Pull requests181 Discussions Actions Projects5 Security Insights Additional navigation options Files 39b2a37 dask_array_ops.py dataarray.py dataset.py dtypes.py duck_array_ops.py extensions.py formatting.py groupby.py indexing.py merge.py
I think this is another unintended consequence of #648. Consider the following case: In [20]: arr = xr.DataArray(range(3), dims=['abc']) In [21]: new_coord = xr.DataArray([1,2,3], dims=['abc'], coords=[[1,2,3]]) In [22]: arr['abc'] = new...
@@ -903,9 +903,7 @@ def replace(self, pat, repl, n=-1, case=None, flags=0, regex=True): "pattern with regex=False" ) if callable(repl): raise ValueError( "Cannot use a callable replacement when " "regex=False" ) raise ValueError("Cannot use a callable replacement when regex=Fa...
is loaded with specified chunksizes (default: False) decode_cf : bool, optional to decodethese variables, assuming they were savedaccording to CF conventions. mask_and_scale: bool, optional If True, replace array values equal to `_Fill` with NA and scale values according ...
interpolate_na(dim="y", method="nearest") # If we replace the np.nan in [1][1] with a valid value, the exception is gone ds["data"][1][1] = 15 # Data now looks like # data = np.array([[np.nan, np.nan, np.nan, np.nan, np.nan], # [10, 15, np.nan, np.nan, ...