Truncating decimal digits numpy array of floats

Question

I want to truncate the float values within the numpy array, for .e.g.

2.34341232 --> 2.34

I read the post truncate floating point but its for one float. I don't want to run a loop on the numpy array, it will be quite expensive. Is there any inbuilt method within numpy which can do this easily? I do need output as a float not string.

Numpy provides the around method. The syntax is np.around(numpy_array, num_decimals). For example: a = np.array([2.3434]), np.around(a, 2) --> produces array([2.34]). Is this what are you looking for? — Cristian Ramon-Cortes
– Cristian Ramon-Cortes, Commented Feb 3, 2017 at 10:28
this looks like a dupe: stackoverflow.com/questions/21008858/… — EdChum
– EdChum, Commented Feb 3, 2017 at 10:31
Do you actually want to discard the data after the 2nd decimal place, or do you just want to change how the data is displayed? If the former, use the numpy.around or numpy.round method. — PM 2Ring
– PM 2Ring, Commented Feb 3, 2017 at 10:37
@AlonAlexander num = ((num*100)//1)/100 this logic only works for positive values. For example to truncate negative value to one decimal , x = -2.134 then (-2.134*10//1)10 gives -3, which is not an acceptable answer.! — Girish Kumar Chandora
– Girish Kumar Chandora, Commented Apr 13, 2021 at 9:50

Wolphyrus Imperius · Accepted Answer · 2019-06-04 19:15:27Z

44

Try out this modified version of numpy.trunc().

import numpy as np
def trunc(values, decs=0):
    return np.trunc(values*10**decs)/(10**decs)

Sadly, numpy.trunc function doesn't allow decimal truncation. Luckily, multiplying the argument and dividing it's result by a power of ten give the expected results.

vec = np.array([-4.79, -0.38, -0.001, 0.011, 0.4444, 2.34341232, 6.999])

trunc(vec, decs=2)

which returns:

>>> array([-4.79, -0.38, -0.  ,  0.01,  0.44,  2.34,  6.99])

edited Jun 4, 2019 at 19:15

answered Sep 3, 2017 at 5:39

Wolphyrus Imperius

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1 Comment

Milo Wielondek Over a year ago

Happy to report this seems to run in only about half the time as np.around for anyone switching their rounding -> truncating logic.

shx2 · Accepted Answer · 2017-02-03 10:53:04Z

17

Use numpy.round:

import numpy as np
a = np.arange(4) ** np.pi
a
=> array([  0.        ,   1.        ,   8.82497783,  31.5442807 ])
a.round(decimals=2)
=> array([  0.  ,   1.  ,   8.82,  31.54])

answered Feb 3, 2017 at 10:53

shx2

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3 Comments

John Y Over a year ago

How is this the answer when the question is for truncation, not rounding? (I know, I know, the asker accepted it. So I'm really mystified at them, even more so than this answer.)

Wolphyrus Imperius Over a year ago

Rounding might be subtle when not carefull. Please see my answer for real truncation.

Julio CamPlaz Over a year ago

I think this answer is not correct to the question. Casually, the third decimal is less than 5 in all array elements and for this reason the output is successful, however if you change the decimal numbers ( 'a.round(decimals=3)') to 1 or 3, it outputs 8.825, what is wrong, and if you want to truncate, it should output 8.254

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