Python combining two variable statements into one for numpy functions

Question

I have a function:

import numpy as np, random

def simulation(low, high, num_pairs):
    c = np.round(np.random.uniform(low, high, num_pairs), 2)
    n = np.round((0.4*c-0.8), 2) # calculate rounded value
    n = np.where(n < 0, 0, n) # if negative `n`, make zero, else `n`
    return list(zip(c, n))

Calling this:

c_n_pairs = simulation(0, 15, 10)
print(*c_n_pairs, sep = "\n")

Outputs:

(5.69, 1.48)
(11.33, 3.73)
(7.16, 2.06)
(2.93, 0.37)
(8.47, 2.59)
(12.82, 4.33)
(12.84, 4.34)
(7.12, 2.05)
(7.11, 2.04)
(0.07, 0.0)

What I have works, but I'm wondering about the two n statements and if those can be consolidated into one.

    n = np.round((0.4*c-0.8), 2) # calculate rounded value
    n = np.where(n < 0, 0, n) # if negative `n`, make zero, else `n`

Right, I was caught between a couple different thoughts and didn't think that through. — md2614
– md2614, Commented Feb 22, 2020 at 15:55

KyleL · Accepted Answer · 2020-02-22 15:31:43Z

1

You can use np.maximum to take the elementwise maximum for an array. This allows you to check if each element is less than zero and replace it with zero if this is the case, exactly the same as np.where.

def simulation(low, high, num_pairs):
    c = np.round(np.random.uniform(low, high, num_pairs), 2)
    n = np.maximum(np.round((0.4*c-0.8), 2), 0)
    return list(zip(c, n))

edited Feb 22, 2020 at 15:31

answered Feb 22, 2020 at 15:31

KyleL

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1 Comment

Lahiru Jayakody Over a year ago

np.zeros(c.size) is redunded 0 is enogh

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