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  • Sparse arrays · 1 ✖

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  • CONTRIBUTOR · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
326803603 https://github.com/pydata/xarray/issues/1375#issuecomment-326803603 https://api.github.com/repos/pydata/xarray/issues/1375 MDEyOklzc3VlQ29tbWVudDMyNjgwMzYwMw== rth 630936 2017-09-03T13:01:44Z 2017-09-03T13:01:44Z CONTRIBUTOR

do you have an application that we could use to drive this?

Other examples where labeled sparse arrays would be useful are, * one-hot encoding that are widely used in machine learning. * tokenizing textual data produces large sparse matrices where the column labels correspond to the vocabulary, while row labels correspond to document ids. Here is a minimal example using scikit-learn, ```py import os.path from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import CountVectorizer

 ds = fetch_20newsgroups()
 vect = CountVectorizer()
 X = vect.fit_transform(ds.data)
 print(X)  # Extracted tokens
 # Returns:
 # <11314x130107 sparse matrix of type '<class 'numpy.int64'>'
 #  with 1787565 stored elements in Compressed Sparse Row format>

 column_labels = vect.get_feature_names()
 print(np.asarray(column_labels))
 # Returns:
 # array(['00', '000', '0000', ..., 'íålittin', 'ñaustin', 'ýé'],   dtype='<U180')

 row_labels = [int(os.path.split(el)[1]) for el in ds.filenames]
 print(np.asarray(row_labels))
 # Returns:
 # array([102994,  51861,  51879, ...,  60695,  38319, 104440])
 ```

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