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numpy where

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icy_milktea27 answered on April 17, 2020 Popularity 10/10 Helpfulness 3/10

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import numpy as np

# Return elements chosen from x or y depending on condition.
a = np.arange(10) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
single_where = np.where((a<5),-1,a) # [-1, -1, -1, -1, -1,  5,  6,  7,  8,  9]
multiple_where = np.where((a<5),-1,np.where((a>5),0,a)) # [-1, -1, -1, -1, -1,  5,  0,  0,  0,  0]

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Contributed on Jun 19 2020

icy_milktea27

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np.where

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>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.where(a < 5, a, 10*a)
array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])

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Source: docs.scipy.org

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Contributed on Apr 17 2020

Difficult Dunlin

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numpy.where

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Parameters:
condition : When True, yield x, otherwise yield y.
x, y : Values from which to choose. x, y and condition need to be broadcastable to some shape.

Returns:
out : [ndarray or tuple of ndarrays] If both x and y are specified, the output array contains elements of x where condition is True, and elements from y elsewhere.

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Contributed on May 03 2020

Breakable Beetle

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np.where

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  predictions = np.where(A2 > 0.5, 1, 0)
  
  np.where to classify the predictions. If the activation is greater than 0.5, the prediction is set to 1; 
  otherwise, it is set to 0.

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Contributed on Jun 03 2023

Niransha

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np.where

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np.where([[True, False], [True, True]],
...          [[1, 2], [3, 4]],
...          [[9, 8], [7, 6]])
array([[1, 8],
       [3, 4]])

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Source: numpy.org

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Contributed on Sep 07 2022

Mohit Singh

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numpy.where

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-2

>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.where(a < 5, a, 10*a)
array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])

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Source: Grepper

Tags: python py

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Contributed on Apr 17 2022

Alert Alligator

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