Formatting an array of float values in Numpy Python

Question

The function values[(values<=0).argmin():] = 0 turns from the nth element to the last elements values into zeroes if the number is below 0 as seen in the Int array output. however when the array elements are floats the arrays are automatically converted into zeroes in the float array output. How can I fix this?

Float array

Numbers = np.array([123.6 , 123.6 ,  123.6, 110.3748 ,  111.6992976, 102.3165566,  97.81462811])
Numbers[(Numbers>=-50).argmin():] = 0

Output

[ 10   3 -20   0   0   0]

Int array

import numpy as np
values = np.array([123 , 123 , -123.6,  110,  111])
values[(values<=0).argmin():] = 0

Output:

[123 , 123 , 0,  0,  0]

what are you trying to achieve and what does the first output represent? — Krishna Chaurasia
– Krishna Chaurasia, Commented Feb 16, 2021 at 7:34

Akshay Sehgal · Accepted Answer · 2021-02-17 00:15:29Z

3

EDIT:

Try these 2 methods, one is in place assignment to the NumPy view and the other creates a fresh array to be assigned to another variable -

#Method 1 (Inplace assignment)
Numbers[(Numbers<=0).cumsum(dtype=bool)] = 0

#OR

#Method 2 (Not inplace)
np.where(~(Numbers<=0).cumsum(dtype=bool), Numbers, 0)

Explanation -

The bool array that returns [F, F, F, T, F, F, F] can be seen as an array of 1s and 0s. Doing a cumsum ends up propogating the first T to the subsequent elements.
This, therefore, turns the array as [F, F, F, T, T, T, T] which can now be used with just boolean indexing and set the view to 0 OR np.where to fetch original elements or 0 based on reversing the boolean with ~
Advantage here is that if your array is just composed of False, meaning no element meets the condition, it just returns the original Numbers itself, instead of setting them to 0.

Running tests -

With a value that meets condition

Numbers = np.array([123.6 , 123.6 ,  -123.6, 110.3748 ,  111.6992976, 102.3165566,  97.81462811])

Numbers[(Numbers<=0).cumsum(dtype=bool)] = 0

#array([123.6, 123.6,   0. ,   0. ,   0. ,   0. ,   0. ])

With no values meeting the condition

Numbers = np.array([123.6 , 123.6 ,  123.6, 110.3748 ,  111.6992976, 102.3165566,  97.81462811])

Numbers[(Numbers<=0).cumsum(dtype=bool)] = 0

#array([123.6 , 123.6 , 123.6 , 110.3748 ,111.6992976 , 102.3165566 ,  97.81462811])

edited Feb 17, 2021 at 0:15

answered Feb 16, 2021 at 7:39

Akshay Sehgal

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6 Comments

ege Selcuk Over a year ago

I found out that the problem with the function is if none of the elements meets the criteria it just turns all the numbers into zeroes.

ege Selcuk Over a year ago

I ma de another issue about this if you could take a look I would appreciate it: stackoverflow.com/questions/66233741/…

Akshay Sehgal Over a year ago

Updated my answer, the alternate thread as well

Akshay Sehgal Over a year ago

Does this work as you expect it? were you able to understand the logic behind it? do let me know.

ege Selcuk Over a year ago

Yes it works thank you, sorry for the late response

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