numpy.nanstd() function compute the standard deviation along the specified axis, while ignoring NaNs.
Syntax : numpy.nanstd(arr, axis = None, dtype = None, out = None, ddof = 0, keepdims)
Parameters :
arr : [array_like] Calculate the standard deviation of the non-NaN values.
axis : [{int, tuple of int, None}, optional] Axis along which the standard deviation is computed.
dtype : [dtype, optional] Type to use in computing the standard deviation. For arrays of integer type, the default is float64, for arrays of float types it is the same as the array type.
out : [ndarray, optional] Alternative output array in which to place the result.
ddof : [int, optional] ddof means Delta Degrees of Freedom. The divisor used in calculations is N – ddof, where N represents the number of non-NaN elements. By default, ddof is zero.
keepdims : [bool, optional] If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original arr.Return : [standard_deviation] If out is None, return a new array containing the standard deviation, otherwise return a reference to the output array.
Code #1 :
# Python program explaining # numpy.nanstd() function # importing numpy as geek import numpy as geek arr = geek.array([[1, 2], [geek.nan, 4]]) gfg = geek.nanstd(arr) print (gfg) |
Output :
1.247219128924647
Code #2 :
# Python program explaining # numpy.nanstd() function # importing numpy as geek import numpy as geek arr = geek.array([[1, 2], [geek.nan, 4]]) gfg = geek.nanstd(arr, axis = 0) print (gfg) |
Output :
[0. 1.]
