html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1375#issuecomment-326803603,https://api.github.com/repos/pydata/xarray/issues/1375,326803603,MDEyOklzc3VlQ29tbWVudDMyNjgwMzYwMw==,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](https://rasbt.github.io/mlxtend/user_guide/preprocessing/one-hot_encoding/) that are widely used in machine learning. 
  * [tokenizing textual data](http://scikit-learn.org/stable/modules/feature_extraction.html#common-vectorizer-usage) 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])
     ```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,221858543