# Ndarray without using np.where() arr = np.array([[12,14,17,19],[13,16,24,27]]) arr2 = arr < 17 print(arr2) # Output: # [[ True True False False] # [ True True False False]]

5. Use NumPy where() without condition expression

In all the previous examples we passed a condition expression as the first argument, which will be evaluated to a bool array. But we can pass a bool array too instead of that,

numpy.where() iterates over the bool array, and for every True , it yields the corresponding element array x , and for every False , it yields the corresponding element from the array y . So, it returns an array of items from x where the condition is True and elements from y elsewhere.

import numpy as np # Use numpy.where() without condition arr2 = np.where([True, False, True], [12,14,17], [13,16,24]) print(arr2) # Output: # [12 16 17]

6. Use numpy.where() with Multiple Conditions

You can use numpy.where() with multiple conditions, where each conditional expression is enclosed in () and & or | is used, the processing is applied to multiple conditions.

# Use numpy.where() to multiple conditions arr2 = np.where((arr > 14) & (arr < 24), -2, 150) print(arr2) # Output: # [[150 150 -2 -2] # [150 -2 150 150]]

Get boolean value ndarray that can be obtained by multiple conditional expressions including ndarray without using np.where() .

# Use bool value arr2 = (arr > 14) & (arr < 24) print(arr2) # Output: # [[False False True True] # [False True False False]]

7. Conclusion

In this article, I have explained how to use Python numpy.where() function how to get element indices from the array when condition meets. Also learned to use both single arguments as well as multiple arguments with this function.

Happy Learning!!

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References

https://numpy.org/doc/stable/reference/generated/numpy.where.html

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I am Mallikarjuna an experienced technical writer with a passion for translating complex Python concepts into clear, concise, and user-friendly documentation. Over the years, I have written hundreds of articles in Pandas, NumPy, Python, and I take pride in my ability to bridge the gap between technical experts and end-users by delivering well-structured, accessible, and informative content.