numpy.nanstd() function - Python Last Updated : 11 Jun, 2020 Comments Improve Suggest changes Like Article Like Report 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 : Python3 # 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 : Python3 # 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.] Create Quiz Comment S sanjoy_62 Follow 0 Improve S sanjoy_62 Follow 0 Improve Article Tags : Python Python-numpy Python numpy-Statistics Functions Explore Python FundamentalsPython Introduction 2 min read Input and Output in Python 4 min read Python Variables 4 min read Python Operators 4 min read Python Keywords 2 min read Python Data Types 8 min read Conditional Statements in Python 3 min read Loops in Python - For, While and Nested Loops 5 min read Python Functions 5 min read Recursion in Python 4 min read Python Lambda Functions 5 min read Python Data StructuresPython String 5 min read Python Lists 4 min read Python Tuples 4 min read Python Dictionary 3 min read Python Sets 6 min read Python Arrays 7 min read List Comprehension in Python 4 min read Advanced PythonPython OOP Concepts 11 min read Python Exception Handling 5 min read File Handling in Python 4 min read Python Database Tutorial 4 min read Python MongoDB Tutorial 3 min read Python MySQL 9 min read Python Packages 10 min read Python Modules 3 min read Python DSA Libraries 15 min read List of Python GUI Library and Packages 3 min read Data Science with PythonNumPy Tutorial - Python Library 3 min read Pandas Tutorial 4 min read Matplotlib Tutorial 5 min read Python Seaborn Tutorial 3 min read StatsModel Library - Tutorial 3 min read Learning Model Building in Scikit-learn 6 min read TensorFlow Tutorial 2 min read PyTorch Tutorial 6 min read Web Development with PythonFlask Tutorial 8 min read Django Tutorial | Learn Django Framework 7 min read Django ORM - Inserting, Updating & Deleting Data 4 min read Templating With Jinja2 in Flask 6 min read Django Templates 5 min read Build a REST API using Flask - Python 3 min read Building a Simple API with Django REST Framework 3 min read Python PracticePython Quiz 1 min read Python Coding Practice 1 min read Python Interview Questions and Answers 15+ min read Like