CODEWITHSUNDEEP
pythonnumpymatrix
Ébe Isaac
Ébe Isaac

Reputation: 12341

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'],
        ['sunny', 'hot', 'high', 'TRUE'],
        ['overcast', 'hot', 'high', 'FALSE'],
        ['rainy', 'mild', 'high', 'FALSE'],
        ['rainy', 'cool', 'normal', 'FALSE'],
        ['rainy', 'cool', 'normal', 'TRUE'],
        ['overcast', 'cool', 'normal', 'TRUE'],
        ['sunny', 'mild', 'high', 'FALSE'],
        ['sunny', 'cool', 'normal', 'FALSE'],
        ['rainy', 'mild', 'normal', 'FALSE'],
        ['sunny', 'mild', 'normal', 'TRUE'],
        ['overcast', 'mild', 'high', 'TRUE'],
        ['overcast', 'hot', 'normal', 'FALSE'],
        ['rainy', 'mild', 'high', 'TRUE']], 
       dtype='|S8')

I would like to get the set of all rows that has the first column value as 'rainy' so it tried this

>>> X[X[:,0]=='rainy']

matrix([['rainy', 'rainy', 'rainy', 'rainy', 'rainy']], 
       dtype='|S8')

But I wanted an output like this

matrix([['rainy', 'mild', 'high', 'FALSE'],
        ['rainy', 'cool', 'normal', 'FALSE'],
        ['rainy', 'cool', 'normal', 'TRUE'],
        ['rainy', 'mild', 'normal', 'FALSE'],
        ['rainy', 'mild', 'high', 'TRUE']], 
       dtype='|S8')

How should this be done?

Upvotes: 1

Views: 3429

Answers (2)

Alexander
Alexander

Reputation: 109546

>>> 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')

Upvotes: 3

Hun
Hun

Reputation: 3847

There are more than one way of doing it.

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

Upvotes: 1

Related Questions