NumPy array boolean indexing allows you to select elements from an array based on a condition expressed as a boolean expression. Boolean indexing can be a powerful tool for filtering, masking, and modifying arrays.
Here are some tips for using NumPy array boolean indexing:
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Understand boolean expressions: Boolean expressions are expressions that evaluate to either True or False. In NumPy, you can use boolean expressions to create masks, which are arrays of the same shape as the input array, where each element is either True or False depending on whether the corresponding element in the input array satisfies the condition.
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Use boolean expressions to create masks: You can create a mask by applying a boolean expression to an array. For example,
mask = arr > 0creates a mask where each element is True if the corresponding element inarris greater than 0, and False otherwise. -
Use masks for filtering: You can use a mask to select elements from an array that satisfy the condition. For example,
arr[mask]selects all elements ofarrthat are greater than 0. -
Use masks for masking: You can use a mask to mask or remove elements from an array that do not satisfy the condition. For example,
arr[mask] = 0sets all elements ofarrthat are greater than 0 to 0. -
Combine masks with logical operators: You can combine masks using logical operators, such as
&(and),|(or), and~(not), to create more complex conditions. For example,mask = (arr > 0) & (arr < 10)creates a mask where each element is True if the corresponding element inarris between 0 and 10. -
Use
numpy.wherefor conditional operations: You can use thenumpy.wherefunction to perform conditional operations based on a mask. For example,np.where(mask, arr1, arr2)returns an array where each element is taken fromarr1if the corresponding element inmaskis True, and fromarr2otherwise.
By using boolean indexing, you can efficiently filter, mask, and modify arrays based on conditions expressed as boolean expressions.