How To Get A Subset Of Rows From A Numpy Matrix Based On A Condition?

How to return a set of rows of a NumPy Matrix that would match a given condition? This is a Numpy Matrix object >>> X matrix([['sunny', 'hot', 'high', 'FALSE'], [

Solution 1:

>>> X[(X[:, 0] == 'rainy').ravel(), :]
matrix([['rainy', 'mild', 'high', 'FALSE'],
        ['rainy', 'cool', 'normal', 'FALSE'],
        ['rainy', 'cool', 'normal', 'TRUE'],
        ['rainy', 'mild', 'normal', 'FALSE'],
        ['rainy', 'mild', 'high', 'TRUE']], 
       dtype='|S8')

If you look at the result of your comparison:

>>> X[:, 0] == 'rainy'array([[False],
       [False],
       [False],
       [ True],
       [ True],
       [ True],
       [False],
       [False],
       [False],
       [ True],
       [False],
       [False],
       [False],
       [ True]], dtype=bool)

This needs to be flattened into a vector using ravel:

(X[:, 0] == 'rainy').ravel()
array([False, False, False,  True,  True,  True, False, False, False,
        True, False, False, False,  True], dtype=bool)

For additional constraints, this works:

X[(X[:, 0] == 'rainy').ravel() & (X[:, 1] == 'cool').ravel(), :]
matrix([['rainy', 'cool', 'normal', 'FALSE'],
        ['rainy', 'cool', 'normal', 'TRUE']], 
       dtype='|S8')

Solution 2:

There are more than one way of doing it.

foo = np.where(X[:, 0] == 'rainy') # get the index
X[foo, :]                          # The result you want.