Mastering numpy.nanmean(): Ignore NaNs with Ease Computing the arithmetic mean while ignoring NaNs (Not a Number) is a breeze with numpy.nanmean(). Learn its syntax, arguments, and return value in this comprehensive guide, complete with examples to get you started.

Unlock the Power of numpy.nanmean(): A Comprehensive Guide

Ignoring NaNs with Ease

When working with numerical data, encountering NaNs (Not a Number) is a common occurrence. Fortunately, the numpy library provides a powerful function to compute the arithmetic mean while ignoring these pesky NaNs: numpy.nanmean(). In this article, we’ll dive into the world of numpy.nanmean() and explore its syntax, arguments, and return value.

The Syntax of numpy.nanmean()

The syntax of numpy.nanmean() is straightforward:

numpy.nanmean(array, axis=None, dtype=None, out=None, keepdims=False, where=True)

Understanding the Arguments

• array: The input array containing numbers whose mean is desired. It can be an array-like object.
• axis: The axis or axes along which the means are computed. It’s an optional argument that defaults to None, meaning the mean of the entire array is taken.
• dtype: The datatype to use in the calculation of the mean. It’s an optional argument that defaults to None, which means the datatype of the array elements is used.
• out: The output array in which to place the result. It’s an optional argument that defaults to None, meaning the result is stored only if assigned to a variable.
• keepdims: A boolean that specifies whether to preserve the shape of the original array. It defaults to False.
• where: An array of booleans that specifies which elements to include in the mean. It defaults to True.

Return Value: The Arithmetic Mean

The numpy.nanmean() function returns the arithmetic mean of the array, ignoring NaNs. If all elements are NaN, the function returns NaN as the output.

Examples Galore!

Let’s explore some examples to see numpy.nanmean() in action:

Example 1: Finding the Mean of a ndArray


import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.nanmean(arr)) # Output: 3.0


Example 2: Specifying Datatype of Mean


import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.nanmean(arr, dtype=np.float64)) # Output: 3.0


Example 3: Using Optional keepdims Argument


import numpy as np
arr = np.array([[1, 2], [3, 4]])
print(np.nanmean(arr, axis=0, keepdims=True)) # Output: [[2.], [3.]]


Example 4: Using Optional where Argument


import numpy as np
arr = np.array([1, 2, 3, 4, 5])
mask = np.array([True, False, True, False, True])
print(np.nanmean(arr, where=mask)) # Output: 2.5


Example 5: Using Optional out Argument


import numpy as np
arr = np.array([1, 2, 3, 4, 5])
out = np.empty(())
np.nanmean(arr, out=out)
print(out) # Output: 3.0


With these examples, you’re now equipped to harness the power of numpy.nanmean() to compute means while ignoring NaNs. Remember to use the optional arguments to tailor the function to your specific needs.