Python 将 scipy 稀疏 csr 转换为熊猫？
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transform scipy sparse csr to pandas?
提问by KillerSnail
I have used the
我已经使用了
sklearn.preprocessing.OneHotEncoder
to transform some data the output is scipy.sparse.csr.csr_matrixhow can I merge it back into my original dataframe along with the other columns?
转换一些数据输出是scipy.sparse.csr.csr_matrix如何将它与其他列一起合并回我的原始数据帧？
I tried to use pd.concatbut I get
我尝试使用pd.concat但我得到
TypeError: cannot concatenate a non-NDFrame object
Thanks
谢谢
回答by Stefan
If A is csr_matrix, you can use .toarray()(there's also .todense()that produces a numpymatrix, which is also works for the DataFrameconstructor):
如果 A is csr_matrix，您可以使用.toarray()（也.todense()有产生 a numpymatrix，这也适用于DataFrame构造函数）：
df = pd.DataFrame(A.toarray())
You can then use this with pd.concat().
然后，您可以将其与pd.concat().
A = csr_matrix([[1, 0, 2], [0, 3, 0]])
(0, 0) 1
(0, 2) 2
(1, 1) 3
<class 'scipy.sparse.csr.csr_matrix'>
pd.DataFrame(A.todense())
0 1 2
0 1 0 2
1 0 3 0
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 3 columns):
0 2 non-null int64
1 2 non-null int64
2 2 non-null int64
In version 0.20, pandasintroduced sparse data structures, including the SparseDataFrame.
在 0.20 版本中，pandas引入了稀疏数据结构，包括SparseDataFrame.
Alternatively, you can pass sparse matrices to sklearnto avoid running out of memory when converting back to pandas. Just convert your other data to sparse format by passing a numpyarrayto the scipy.sparse.csr_matrixconstructor and use scipy.sparse.hstackto combine (see docs).
或者，您可以将稀疏矩阵传递给sklearn以避免在转换回pandas. 只需通过将 a 传递numpyarray给scipy.sparse.csr_matrix构造函数并使用scipy.sparse.hstackto combine将您的其他数据转换为稀疏格式（请参阅文档）。
回答by scriptator
You could also avoid getting back a sparse matrix in the first place by setting the parameter sparseto Falsewhen creating the Encoder.
您还可以通过在创建编码器时将参数设置为sparse来避免首先取回稀疏矩阵False。
The documentation of the OneHotEncoderstates:
OneHotEncoder的文档指出：
sparse : boolean, default=True
Will return sparse matrix if set True else will return an array.
稀疏：布尔值，默认值=真
如果设置为 True，将返回稀疏矩阵，否则将返回一个数组。
Then you can again call the DataFrame constructor to transform the numpy array to a DataFrame.
然后您可以再次调用 DataFrame 构造函数将 numpy 数组转换为 DataFrame。
