.nanmean()

In NumPy, the .nanmean() function computes the arithmetic mean of the elements in an array over a specified axis, while ignoring NaN (Not a Number) values. This is useful when working with arrays that contain missing or undefined values represented as NaN.

Syntax

numpy.nanmean(a, axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>)

Parameters:

• a: The data for which the mean is computed. It can be a NumPy array or any array-like object (e.g., list).
• axis (Optional): Used to specify the axis along which the mean is calculated. If None (default), the mean is computed over the flattened array.
• dtype (Optional): The data type used for the calculation. If not specified, the result will have the same data type as the input (unless a is an integer, where the result will be float64 by default).
• out (Optional): A location where the result is stored. The array must have the same shape as the expected output. If not provided, a new array is created.
• keepdims (Optional): If set to True, the reduced axes are retained in the result with size one. This can be useful for broadcasting purposes.
• where (Optional): A condition that allows the function to compute the mean only on elements where the condition is True. It can be a boolean array or a condition (usually True).

Return value:

The .nanmean() function returns the mean of the array, ignoring NaN values:

• If axis is None or if no axis is specified, it returns a single value representing the mean of the entire array.
• If axis is specified, it returns an array of mean values along the specified axis of the data a.

Example 1: Basic Usage of .nanmean()

The example below shows how to calculate the mean value from an array:

import numpy as np

# Creating a numpy array with NaN values
arr = np.array([6, 13, 1, np.nan, 7])

# Computing the mean of the array, ignoring NaNs
result = np.nanmean(arr)

print(result)

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The example code above results in the following output:

6.75

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Example 2: Using .nanmean() Along an Axis

This example shows how to use .nanmean() function along a specific axis:

import numpy as np

# Creating a 2D numpy array with NaN values
arr = np.array([[8, np.nan, 3], [4, 2, 9]])

# Computing the mean along axis 0 (columns)
result = np.nanmean(arr, axis=0)

print(result)

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The example code above results in the following output:

[6. 2. 6.]

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Codebyte Example: Using .nanmean() with keepdims

In this codebyte example, the .nanmean() method computes the mean of the elements in the array, demonstrating the use of keepdims parameter:

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