Python：當我嘗試在日期之間插入 xarray 時，為什么會出現錯誤？

Question

我正在嘗試插入一個名為pop的xarray的值

pop

我正在使用 function xarray.interp

dates = pd.date_range('1990-01-01', '2020-01-01', freq='1Y')
popI = pop.interp(time=dates, kwargs={"fill_value": "extrapolate"})

但我收到以下錯誤

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-75-1393bc257da7> in <module>
----> 1 popI = pop.interp(time=dates, kwargs={"fill_value": "extrapolate"})

/usr/lib/python3/dist-packages/xarray/core/dataset.py in interp(self, coords, method, assume_sorted, kwargs, method_non_numeric, **coords_kwargs)
   3163         if method in ["linear", "nearest"]:
   3164             for k, v in validated_indexers.items():
-> 3165                 obj, newidx = missing._localize(obj, {k: v})
   3166                 validated_indexers[k] = newidx[k]
   3167 

/usr/lib/python3/dist-packages/xarray/core/missing.py in _localize(var, indexes_coords)
    561     indexes = {}
    562     for dim, [x, new_x] in indexes_coords.items():
--> 563         minval = np.nanmin(new_x.values)
    564         maxval = np.nanmax(new_x.values)
    565         index = x.to_index()

<__array_function__ internals> in nanmin(*args, **kwargs)

/usr/lib/python3/dist-packages/numpy/lib/nanfunctions.py in nanmin(a, axis, out, keepdims)
    319         # which do not implement isnan (gh-9009), or fmin correctly (gh-8975)
    320         res = np.fmin.reduce(a, axis=axis, out=out, **kwargs)
--> 321         if np.isnan(res).any():
    322             warnings.warn("All-NaN slice encountered", RuntimeWarning,
    323                           stacklevel=3)

TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

Answer 1

您在Dataset上調用 interp ，它將始終將函數應用於所有數據變量。 您的數據變量之一是字符串數組mollewide 。 這不能插值。 因此，您可以將其設置為坐標：

popI = pop.set_coords('mollewide').interp(time=dates, kwargs={"fill_value": "extrapolate"})

或者您只能對popDensity數據變量進行操作：

popI = pop["popDensity"].interp(time=dates, kwargs={"fill_value": "extrapolate"})