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NumPy's mean() and nanmean() Methods

In this tutorial, we will learn about the NumPy's mean() and nanmean() Methods with the help of Python programs. By Pranit Sharma Last updated : October 09, 2023

NumPy is an abbreviated form of Numerical Python. It is used for different types of scientific operations in python. Numpy is a vast library in python which is used for almost every kind of scientific or mathematical operation. It is itself an array which is a collection of various methods and functions for processing the arrays.

numpy.mean() and numpy.nanmean() methods

Both methods are used to calculate the mean of the values of the numpy array. The numpy.mean() computes the arithmetic mean along the specified axis. It returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs.

While on the other hand, the numpy.nanmean() computes the arithmetic mean along the specified axis, ignoring NaNs.

Let us understand with the help of an example,

Python program to demonstrate the example of NumPy's mean() and nanmean() Methods

# Import numpy
import numpy as np

# Creating numpy array
arr = np.array([[1, np.nan], [3, 4]])

# Using nanmean method
res = np.nanmean(arr)

# Display nan mean result
print("Nanmean Result:\n",res,"\n")

# Creating array for mean
arr = np.array([[1, 2], [3, 4]])

# Using mean
res1 = np.mean(arr)

# Display mean result
print("Mean Result:\n",res1,"\n")

Output

The output of the above program is:

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