What is boolean masking in NumPy?
An example of an AI chatbot conversation about boolean masking in NumPy.
What is boolean masking in NumPy?
Boolean masking in NumPy allows you to filter arrays using boolean conditions. It creates a mask of True/False values.
Can you show me an example of how to use boolean masking?
Sure! If you have an array arr = np.array([1, 2, 3, 4, 5]), you can create a mask like mask = arr > 3, which results in array([False, False, False, True, True]). You can then apply this mask to filter the array: filtered = arr[mask] gives you array([4, 5]).
What are some common use cases for boolean masking?
Common use cases include filtering data, selecting elements based on conditions, and performing operations on subsets of data.
Are there any performance considerations when using boolean masking?
Yes, boolean masking can be memory-intensive for large arrays, as it creates a new array for the mask. It's important to consider the size of your data.
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