pandas 稀疏dataFrame到稀疏矩阵，而不在内存中生成密集矩阵 [英] Pandas sparse dataFrame to sparse matrix, without generating a dense matrix in memory

问题描述

是否有一种方法可以将pandas.SparseDataFrame转换为scipy.sparse.csr_matrix，而不会在内存中生成密集矩阵?

Is there a way to convert from a pandas.SparseDataFrame to scipy.sparse.csr_matrix, without generating a dense matrix in memory?

scipy.sparse.csr_matrix(df.values)

不起作用，因为它会生成一个密集矩阵，该矩阵被强制转换为csr_matrix.

doesn't work as it generates a dense matrix which is cast to the csr_matrix.

提前谢谢！

推荐答案

熊猫文档讨论了对稀疏稀疏的实验性转换SparseSeries.to_coo:

Pandas docs talks about an experimental conversion to scipy sparse, SparseSeries.to_coo:

http://pandas -docs.github.io/pandas-docs-travis/sparse.html#interaction-with-scipy-sparse

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edit-这是multiindex的特殊功能，而不是数据框.参见其他答案.注意日期的差异.

edit - this is a special function from a multiindex, not a data frame. See the other answers for that. Note the difference in dates.

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从0.20.0开始，有一个sdf.to_coo()和一个多索引ss.to_coo().由于稀疏矩阵本质上是2d的，因此对于(有效)1d数据序列要求多索引是有意义的.虽然数据框可以表示一个表或2d数组.

As of 0.20.0, there is a sdf.to_coo() and a multiindex ss.to_coo(). Since a sparse matrix is inherently 2d, it makes sense to require multiindex for the (effectively) 1d dataseries. While the dataframe can represent a table or 2d array.

当我第一次回答这个问题时，这个稀疏的数据框/系列功能是实验性的(2015年6月).

When I first responded to this question this sparse dataframe/series feature was experimental (june 2015).

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