I have a numpy array, something like below:
data = np.array([ 1.60130719e-01, 9.93827160e-01, 3.63108206e-04])
and I want to round each element to two decimal places.
How can I do so?
4 Answers
Numpy provides two identical methods to do this. Either use
np.round(data, 2)
or
np.around(data, 2)
as they are equivalent.
See the documentation for more information.
Examples:
>>> import numpy as np
>>> a = np.array([0.015, 0.235, 0.112])
>>> np.round(a, 2)
array([0.02, 0.24, 0.11])
>>> np.around(a, 2)
array([0.02, 0.24, 0.11])
>>> np.round(a, 1)
array([0. , 0.2, 0.1])
2 Comments
ajayramesh
array was not np array, after importing like np.array its working
It is worth noting that the accepted answer will round small floats down to zero as demonstrated below:
>>> import numpy as np
>>> arr = np.asarray([2.92290007e+00, -1.57376965e-03, 4.82011728e-08, 1.92896977e-12])
>>> print(arr)
[ 2.92290007e+00 -1.57376965e-03 4.82011728e-08 1.92896977e-12]
>>> np.round(arr, 2)
array([ 2.92, -0. , 0. , 0. ])
You can use set_printoptions and a custom formatter to fix this and get a more numpy-esque printout with fewer decimal places:
>>> np.set_printoptions(formatter={'float': "{0:0.2e}".format})
>>> print(arr)
[2.92e+00 -1.57e-03 4.82e-08 1.93e-12]
This way, you get the full versatility of format and maintain the precision of numpy's datatypes.
Also note that this only affects printing, not the actual precision of the stored values used for computation.
Comments
If you want the output to be
array([1.6e-01, 9.9e-01, 3.6e-04])
the problem is not really a missing feature of NumPy but rather that this sort of rounding is not a standard thing to do. You can make your own rounding function which achieves this like so:
def my_round(value, N):
exponent = np.ceil(np.log10(value))
return 10**exponent*np.round(value*10**(-exponent), N)
For a general solution handling 0 and negative values as well, you can do something like this:
def my_round(value, N):
value = np.asarray(value).copy()
zero_mask = (value == 0)
value[zero_mask] = 1.0
sign_mask = (value < 0)
value[sign_mask] *= -1
exponent = np.ceil(np.log10(value))
result = 10**exponent*np.round(value*10**(-exponent), N)
result[sign_mask] *= -1
result[zero_mask] = 0.0
return result
1 Comment
user121664
This was more or less what I wanted. Using
np.around instead of round will make it work for multidimensional arrays as well.if you want to update your array named "data" you can do one of the following. Here the digit 2 inside the functions states that the elements are to be rounded to two decimals.
import numpy as np
np.round(data,2,data)
or
import numpy as np
data.round(2,data)
or
import numpy as np
np.around(data,2,data)
np.round(data,2)?np.roundis equivalent tonp.aroundand both work for entire arrays.1.60130719e-01to1.60