Numpy math round() function to round off numbers to nearest decimal place

Numpy Math Functions

Returns array with radian values.

`a`	Input array with degree
`out`	Optional , ndarray to store result

Examples

We will use the options in our sample scripts.

import numpy as np
ar=np.array([1.57079, 2.0943,  2.792,3.1415])
print(np.round(ar))

Output

[2. 2. 3. 3.]

decimals

We can add decimals=2 to specify the number of places we want the rounding off.

ar=np.array([1.57079, 2.0943,  2.792,3.1415])
print(np.round(ar,2))

Output

[1.57 2.09 2.79 3.14]

Or we can specify like this for three decimal places.

print(np.round(ar,decimals=3))

Output

[1.571 2.094 2.792 3.142]

out

We can store the output in an array. We used shape() to create array (ar_out) of same shape() of our main array ar.
We used ones() to create array of same shape() of our input array with degree values.

import numpy as np
ar=np.array([1.57079, 2.0943,  2.792,3.1415])
ar_out=np.ones(ar.shape)
ar=np.round(ar,out=ar_out)
print(ar)
print(ar_out)

print(ar is ar_out) # True

Output

[2. 2. 3. 3.]
[2. 2. 3. 3.]
True

Rounding off to nearest 100

By using negative decimal places we can round off to nearest hundred or thousands

import numpy as np
ar=np.array([435, 478,  1020,1089,22348])
print(np.round(ar,decimals=-2))

Output

[  400   500  1000  1100 22300]

Rounding off to nearest 1000

import numpy as np
ar=np.array([435, 478,  1020,1889,22348])
print(np.round(ar,decimals=-3))

Output

[    0     0  1000  2000 22000]

Numpy rad2deg() bincount() arange() linspace()

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