# 需要导入模块: import xarray [as 别名]# 或者: from xarray importDataArray[as 别名]deftest_ensure_time_as_index_with_change():# Time bounds array doesn't index time initially, which gets fixed.arr = xr.DataArray([-93], dims=[TIME_STR], coords={TIME_STR: [3]}) arr[TIME_STR].a...
"array": [{ "key": 1, "value": "\u0006\u0000\u0000\u0000" }, { "key": 2, ... 来自:开发者社区 项目经验分享:机器学习在智能风控中的应用|社区征文 transaction_data = transaction_data.set_index('Date')market_data['Date'] = pd.to_datetime(market_data['Date'])market_data = ...
还有就是一些程序可能可以用于 numpy.ma.MaskedArray,但含有 NaN 的numpy数组可能并不能工作。...wrf.to_np 函数按照以下流程执行:如果没有缺省值或填充值,那么将直接调用 xarray.DataArray.values 属性返回值如果有缺省值或填充值,那么会用 xarray.DataArray.attrs...属性 _FillValue 值替代 NaN 并返回 numpy....
or_item(data) 209 TODO: remove this (replace with np.asarray) once these issues are fixed 210 """ --> 211 data = np.asarray(data) 212 if data.ndim == 0: 213 if data.dtype.kind == 'M': ~/miniconda3/envs/pangeo/lib/python3.6/site-packages/numpy/core/numeric.py in asarray...
array([time_fill_value,'2023-01-02'],dtype='M8[ns]') # Create a dataset with this one array xr_time_array = xr.DataArray(data=time,dims=['time'],name='time') xr_ds = xr.Dataset(dict(time=xr_time_array)) print("***") print("Created with fill value (NaT)") print(xr_ds...
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self.assertDataArrayEqual(data['extra'], actual['extra'])# verify that the dim argument takes precedence over# concatenating dataset variables of the same namedim = (2* data['dim1']).rename('dim1') datasets = [gfor_, gindata.groupby('dim1', squeeze=False)] ...
返回值: xarray.DataArray 或xarray.Dataset 如果对象是DataFrame,则将pandas结构中的数据转换为Dataset,如果对象是Series,则转换为DataArray。 Notes 请参阅xarray文档 例子 >>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2), ... ('parrot', 'bird', 24.0, 2), ... ('lion', 'mammal',...
Fix default value of combine_attrs in :pyxarray.combine_by_coords (8471) By Gregorio L. Trevisan. Internal Changes :pyDataArray.bfill & :pyDataArray.ffill now use numbagg <https://github.com/numbagg/numbagg>_ by default, which is up to 5x faster where parallelization is possible. (:pu...
(i.e. it will replace original array values with new scaled values), but you can turn it off inopen_datasetwith themask_and_scale=Falseoption:http://xarray.pydata.org/en/stable/generated/xarray.open_dataset.htmlI tried doing this, and then I got identical results with chunked and un...