How to Remove NaN (Not-a-Number) Values from a NumPy Array?

Removing NaN Values from a NumPy Array

NumPy arrays often contain missing or invalid data represented as NaN (Not-a-Number). Removing these values is essential for data manipulation or analysis. Here's how to accomplish this using NumPy:

Using Numpy.isnan and Array Indexing

To remove NaN values from an array x:

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

Explanation:

1. numpy.isnan(x): This function creates a logical array where True represents NaN values in x.
2. Logical-NOT Operator (~): The tilde (~) flips the True/False values, resulting in an array with True for non-NaN values.
3. Array Indexing with the Resulting Array: Using this logical array to index x, we retrieve the elements corresponding to True values, effectively removing the NaN values.

Example:

<code class="python">array = [1, 2, NaN, 4, NaN, 8]

# Remove NaN values
array_cleaned = array[~numpy.isnan(array)]

print(array_cleaned)  # Output: [1, 2, 4, 8]</code>

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