我想从我的 pandas 数据框中创建一个多路列联表并将其存储在一个 xarray 中。在我看来，使用pandas.crosstab后跟DataFrame.to_xarray()应该足够简单，但我在 pandas v1.1.5 中得到“TypeError：无法将'interval [int64]'解释为数据类型”。（v1.0.1 给出“ValueError：所有数组的长度必须相同”）。
In [1]: import numpy as np
...: import pandas as pd
...: pd.__version__
Out[1]: '1.1.5'
In [2]: import xarray as xr
...: xr.__version__
Out[2]: '0.17.0'
In [3]: n = 100
...: np.random.seed(42)
...: x = pd.cut(np.random.uniform(low=0, high=3, size=n), range(5))
...: x
Out[3]:
[(1, 2], (2, 3], (2, 3], (1, 2], (0, 1], ..., (1, 2], (1, 2], (1, 2], (0, 1], (0, 1]]
Length: 100
Categories (4, interval[int64]): [(0, 1] < (1, 2] < (2, 3] < (3, 4]]
In [4]: x.value_counts().sort_index()
Out[4]:
(0, 1] 41
(1, 2] 28
(2, 3] 31
(3, 4] 0
dtype: int64
注意我需要我的表包含空类别，例如 (3, 4]。
In [6]: idx=pd.date_range('2001-01-01', periods=n, freq='8H')
...: df = pd.DataFrame({'x': x}, index=idx)
...: df['xlag'] = df.x.shift(1, 'D')
...: df['h'] = df.index.hour
...: xtab = pd.crosstab([df.h, df.xlag], df.x, dropna=False, normalize='index')
...: xtab
Out[6]:
x (0, 1] (1, 2] (2, 3] (3, 4]
h xlag
0 (0, 1] 0.000000 0.700000 0.300000 0.0
(1, 2] 0.470588 0.411765 0.117647 0.0
(2, 3] 0.500000 0.333333 0.166667 0.0
(3, 4] 0.000000 0.000000 0.000000 0.0
8 (0, 1] 0.588235 0.000000 0.411765 0.0
(1, 2] 1.000000 0.000000 0.000000 0.0
(2, 3] 0.428571 0.142857 0.428571 0.0
(3, 4] 0.000000 0.000000 0.000000 0.0
16 (0, 1] 0.333333 0.250000 0.416667 0.0
(1, 2] 0.444444 0.222222 0.333333 0.0
(2, 3] 0.454545 0.363636 0.181818 0.0
(3, 4] 0.000000 0.000000 0.000000 0.0
没关系，但我的实际应用程序有更多类别和更多维度，所以这似乎是 xarray 的一个明确用例，但我得到一个错误：
In [8]: xtab.to_xarray()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-aaedf730bb97> in <module>
----> 1 xtab.to_xarray()
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/pandas/core/generic.py in to_xarray(self)
2818 return xarray.DataArray.from_series(self)
2819 else:
-> 2820 return xarray.Dataset.from_dataframe(self)
2821
2822 @Substitution(returns=fmt.return_docstring)
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/dataset.py in from_dataframe(cls, dataframe, sparse)
5131 obj._set_sparse_data_from_dataframe(idx, arrays, dims)
5132 else:
-> 5133 obj._set_numpy_data_from_dataframe(idx, arrays, dims)
5134 return obj
5135
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/dataset.py in _set_numpy_data_from_dataframe(self, idx, arrays, dims)
5062 data = np.zeros(shape, values.dtype)
5063 data[indexer] = values
-> 5064 self[name] = (dims, data)
5065
5066 @classmethod
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/dataset.py in __setitem__(self, key, value)
1427 )
1428
-> 1429 self.update({key: value})
1430
1431 def __delitem__(self, key: Hashable) -> None:
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/dataset.py in update(self, other)
3897 Dataset.assign
3898 """
-> 3899 merge_result = dataset_update_method(self, other)
3900 return self._replace(inplace=True, **merge_result._asdict())
3901
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/merge.py in dataset_update_method(dataset, other)
958 priority_arg=1,
959 indexes=indexes,
--> 960 combine_attrs="override",
961 )
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/merge.py in merge_core(objects, compat, join, combine_attrs, priority_arg, explicit_coords, indexes, fill_value)
609 coerced = coerce_pandas_values(objects)
610 aligned = deep_align(
--> 611 coerced, join=join, copy=False, indexes=indexes, fill_value=fill_value
612 )
613 collected = collect_variables_and_indexes(aligned)
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/alignment.py in deep_align(objects, join, copy, indexes, exclude, raise_on_invalid, fill_value)
428 indexes=indexes,
429 exclude=exclude,
--> 430 fill_value=fill_value,
431 )
432
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/alignment.py in align(join, copy, indexes, exclude, fill_value, *objects)
352 if not valid_indexers:
353 # fast path for no reindexing necessary
--> 354 new_obj = obj.copy(deep=copy)
355 else:
356 new_obj = obj.reindex(
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/dataset.py in copy(self, deep, data)
1218 """
1219 if data is None:
-> 1220 variables = {k: v.copy(deep=deep) for k, v in self._variables.items()}
1221 elif not utils.is_dict_like(data):
1222 raise ValueError("Data must be dict-like")
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/dataset.py in <dictcomp>(.0)
1218 """
1219 if data is None:
-> 1220 variables = {k: v.copy(deep=deep) for k, v in self._variables.items()}
1221 elif not utils.is_dict_like(data):
1222 raise ValueError("Data must be dict-like")
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/variable.py in copy(self, deep, data)
2632 """
2633 if data is None:
-> 2634 data = self._data.copy(deep=deep)
2635 else:
2636 data = as_compatible_data(data)
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/indexing.py in copy(self, deep)
1484 # 8000341
1485 array = self.array.copy(deep=True) if deep else self.array
-> 1486 return PandasIndexAdapter(array, self._dtype)
/opt/scitools/environments/default/2021_03_18-1/lib/python3.6/site-packages/xarray/core/indexing.py in __init__(self, array, dtype)
1407 dtype_ = array.dtype
1408 else:
-> 1409 dtype_ = np.dtype(dtype)
1410 self._dtype = dtype_
1411
TypeError: Cannot interpret 'interval[int64]' as a data type
在使用 pandas.crosstab 之前，我可以通过将 x（和 xlag）转换为不同的 dtype 而不是 pandas.Categorical 来避免错误，但是我会丢失任何空类别，我需要将其保留在我的实际应用程序中。