轉換為稀疏矩陣時出現類型錯誤

Question

這是 dataframe 的數據類型。我想將其轉換為稀疏的 dataframe，但由於某種原因它返回錯誤。 我的代碼有什么問題？

>X[nums].dtypes

bin_1         float64
bin_2         float64
bin_3         float64
bin_4         float64
ord_0         float64
ord_1         float64
ord_2         float64
ord_3         float64
ord_4         float64
day_sin       float64
day_cos       float64
month_sin     float64
month_cos     float64
ord_5         float64
dtype: object

這是我嘗試將其轉換為稀疏矩陣時的錯誤。 我也用 pandas sparse 嘗試過它，它返回了相同的結果。 為什么說 TypeError: no supported conversion for types: (dtype('O'),)

>scipy.sparse.csr_matrix(X[nums].values)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/scipy/sparse/base.py in asformat(self, format, copy)
    326             try:
--> 327                 return convert_method(copy=copy)
    328             except TypeError:

/opt/conda/lib/python3.6/site-packages/scipy/sparse/coo.py in tocsr(self, copy)
    399             indices = np.empty_like(col, dtype=idx_dtype)
--> 400             data = np.empty_like(self.data, dtype=upcast(self.dtype))
    401 

/opt/conda/lib/python3.6/site-packages/scipy/sparse/sputils.py in upcast(*args)
     51 
---> 52     raise TypeError('no supported conversion for types: %r' % (args,))
     53 

TypeError: no supported conversion for types: (dtype('O'),)

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-102-20bd81b47238> in <module>
----> 1 scipy.sparse.csr_matrix(X[notToDummy].values)

/opt/conda/lib/python3.6/site-packages/scipy/sparse/compressed.py in __init__(self, arg1, shape, dtype, copy)
     81                                  "".format(self.format))
     82             from .coo import coo_matrix
---> 83             self._set_self(self.__class__(coo_matrix(arg1, dtype=dtype)))
     84 
     85         # Read matrix dimensions given, if any

/opt/conda/lib/python3.6/site-packages/scipy/sparse/compressed.py in __init__(self, arg1, shape, dtype, copy)
     30                 arg1 = arg1.copy()
     31             else:
---> 32                 arg1 = arg1.asformat(self.format)
     33             self._set_self(arg1)
     34 

/opt/conda/lib/python3.6/site-packages/scipy/sparse/base.py in asformat(self, format, copy)
    327                 return convert_method(copy=copy)
    328             except TypeError:
--> 329                 return convert_method()
    330 
    331     ###################################################################

/opt/conda/lib/python3.6/site-packages/scipy/sparse/coo.py in tocsr(self, copy)
    398             indptr = np.empty(M + 1, dtype=idx_dtype)
    399             indices = np.empty_like(col, dtype=idx_dtype)
--> 400             data = np.empty_like(self.data, dtype=upcast(self.dtype))
    401 
    402             coo_tocsr(M, N, self.nnz, row, col, self.data,

/opt/conda/lib/python3.6/site-packages/scipy/sparse/sputils.py in upcast(*args)
     50             return t
     51 
---> 52     raise TypeError('no supported conversion for types: %r' % (args,))
     53 
     54 

TypeError: no supported conversion for types: (dtype('O'),)

Answer 1

正如@bglbrt 在此評論中指定的那樣，使用：

scipy.sparse.csr_matrix(X[nums].values.astype(float))

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_{這個答案是在 CC BY-SA 4.0 下由 OP Elizabeth McBeth 轉換為稀疏矩陣時作為對問題 TypeError的編輯發布的。}