title: Scikit Learn - Working with datasets
body: <p>Reading through some stackoverflow questions and I could not find what I was looking for, at least, I didn't think it was when I read various posts.</p>
  <p>I have some Training data set up like described <a href="http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_files.html#sklearn.datasets.load_files" rel="nofollow noreferrer">here</a>
  So, I am using sklearn.datasets.load_files to read those it as it was a perfect match on set up.</p>
  <p>BUT my files are tsv as bag of words already (aka each line is a word and it's frequency count separated by a tab).</p>
  <p>To be honest, I am not sure how to proceed.  The data pulled in by load_files is set up as a list where each element is the contents of each file, including the new line characters. I am not even 100% sure how the Bunch data type is tracking which files belong to which classifier folder.</p>
  <p>I have worked with scikit-learn before with tsvs, but it was a single tsv file that had all the data so i used pandas to read it in and then used numpy.array to fetch what I needed from it, which is one of the things I attempted to do, but I am not sure how to do it with multiple files where the classifier is the folder name, as in that single tsv file i worked with before, each line of training data was individually</p>
  <p>Some help on getting the data to a format that is useable for training classifiers would be appreciated.</p>