np.where
>>> 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])
np.where
>>> 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])
numpy where
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]
np where and
In [1]: my_array = arange(10)
In [2]: where((my_array > 3) & (my_array < 7))
Out[2]: (array([4, 5, 6]),)
numpy.where
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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