Numpy std()


numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=)
Return standard deviation of elements across given axis.
aarray, elements to get the std value
axisInt (optional ), or tuple, default is None, stdvalue among all the elements. If axis given then values across the axis is returned.
outOptional. If given then output to be stored. Must be of same shape as of the output
keepdimsBool ( Optional ), output matches to the input array dimension.
ddofDelta Degrees of Freedom ( default is 0 ) , N - ddof is used where N is the number of elements in computing the standard deviation
We will use these parameters in our examples.

Sample array

You can use randint() to create an array for our examples. Or can use fixed elements to create the array.
import numpy as np
# my_data=np.random.randint(2,high=7,size=(3,3),dtype='int16') 
my_data=np.array([[6, 3, 2], [7, 2, 2], [6, 2, 9]])
[[6 3 2]
 [7 2 2]
 [6 2 9]]


stdimum value of the elements across the axis. Axis of Numpy std array
print("std()       : ", my_data.std())
print("std(axis=0) : ", my_data.std(axis=0))
print("std(axis=1) : ", my_data.std(axis=1))
std()       :  2.5385910352879693
std(axis=0) :  [0.47140452 0.47140452 3.29983165]
std(axis=1) :  [1.69967317 2.3570226  2.86744176]


Alternative output array, must be of same shape as expected output. Let us first check with axis.


If it is set to True ( keepdims=True ) then it will take the dimension of input array.
print("std(keepdims=True) : ", my_data.std(keepdims=True))
print("std(keepdims=False) : ", my_data.std(keepdims=False))
std(keepdims=True) :  [[2.53859104]]
std(keepdims=False) :  2.5385910352879693


ddof = 0 ( default) , this is Population Standard Deviation
ddof = 1 , this is Sample Standard Deviation
Check this code with output.
import numpy as np
print(my_data.std(ddof=0)) # 2.153846153846154
print(my_data.std(ddof=1)) # 2.2417941532712202
Comparison of Standard Deviation using Python, Pandas, Numpy and Statistics library

Numpy mean() sum() max()
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