How to filter a NumPy array using value of another NumPy Array

Question

I have one NumPy array like this:

type = array(["A", "B", "A", "B", "B", "A", "A"])

I want to use this array to filter another array like this:

data = array([5,4,5],
             [0,4,3],
             [2,1,6],
             [6,7,9],
             [0,1,4],
             [7,9,8],
             [1,4,9])

I want to be able to filter the data by type, such that:

typeA = array([5,4,5],[2,1,6],[7,9,8],[1,4,9])
typeB = array([0,4,3],[6,7,9],[0,1,4])

Any idea how should I implement it? Since the first array is not boolean, I'm having a lot of trouble to figure out the solution

And what does type = array([[A],[B],[A],[B],[B],[A],[A]) look like exactly? What is .dtype? What is .shape? — juanpa.arrivillaga
– juanpa.arrivillaga, Commented Mar 7, 2018 at 7:19
sorry I made a mistake, 'type = np.array(["A", "B", "A", "B", "B", "A", "A"])', I'll correct that — CLC
– CLC, Commented Mar 7, 2018 at 7:28

John1024 · Accepted Answer · 2018-03-07 07:34:33Z

5

Let's define your arrays:

>>> from numpy import array
>>> data = array(([5,4,5], [0,4,3], [2,1,6], [6,7,9], [0,1,4], [7,9,8], [1,4,9]))
>>> type_ = array(["A", "B", "A", "B", "B", "A", "A"])

Now, let's find your typeA:

>>> A = data[type_=="A"]
>>> A
array([[5, 4, 5],
       [2, 1, 6],
       [7, 9, 8],
       [1, 4, 9]])

And your typeB:

>>> B = data[type_=="B"]
>>> B
array([[0, 4, 3],
       [6, 7, 9],
       [0, 1, 4]])

Here, type_=="A" or type_=="A" are boolean arrays. We use them to select the elements from data that we want.

For more on indexing with boolean or “mask” arrays, see the NumPy docs.

Lastly, type is a python builtin and it is best practices not to overwrite it. So, we used type_ here instead.

edited Mar 7, 2018 at 7:34

answered Mar 7, 2018 at 7:29

John1024

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hanzhichao2000 · Accepted Answer · 2018-03-07 07:33:45Z

0

import numpy as np

type_ = np.array(['A','B','A','B','B','A','A'])
data = np.array([[5,4,5],
                 [0,4,3],
                 [2,1,6],
                 [6,7,9],
                 [0,1,4],
                 [7,9,8],
                 [1,4,9]])

from collections import defaultdict
type_splitted = defaultdict(list)
for type_i, data_i in zip(type_, data):
    type_splitted[type_i].append(data_i)
typeA = np.asarray(type_splitted['A'])
typeB = np.asarray(type_splitted['B'])

edited Mar 7, 2018 at 7:33

answered Mar 7, 2018 at 7:30

hanzhichao2000

213 bronze badges

1 Comment

CLC Over a year ago

Sorry I made a mistake in my original question, the type array should be type = np.array(["A", "B", "A", "B", "B", "A", "A"]) instead. how should i solve this question in that case?

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