How to Exclude NaN Values from NumPy Arrays?

Excluding NaN Values from NumPy Arrays

NaN (Not-a-Number) values can arise in numerical computations, leading to undesired results. Removing NaN values from NumPy arrays is crucial for data integrity and further analysis.

How to Remove NaN Values:

To effectively eliminate NaN values from a NumPy array named x, leverage the following code snippet:

<code class="python">x = x[~numpy.isnan(x)]</code>

Explanation:

• The numpy.isnan() function generates a Boolean array indicating True for NaN values and False otherwise.
• Using the logical NOT operator ~, we invert the array, resulting in True for non-NaN values.
• Subsequently, we employ the inverted array as an indexing mask for the original array x.
• This indexing operation isolates only the elements corresponding to True values, effectively removing NaN values from the array.

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