Replace all elements of NumPy array that are greater than some value

Maximizing efficiency: optimized operations and performance wins

In-place operations and clip

You like speed, and NumPy loves giving it to you. Use clip to keep all values within a range, right in-place:

arr.clip(max=3, out=arr)  # 3, playing the role of the caped superhero, swoops in to save the day!
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Now, anything that tries to go beyond 3 is firmly told, "Nope, you stay at 3."

Conditional replacements with np.where

np.where is more than just a question. It plays hide 'n' seek with array elements:

arr = np.where(arr > 3, 0, arr)  # "Old man 3 called. He wants his >3 grandkids replaced with 0s."
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This does give you a new array though. For Hulk-sized arrays, heap space might flex its muscles.

Check your engine: performance profiling

How do you know you've won the race? You time it! Here's how you can use timeit with large matrices:

import timeit

# Assume 'large_arr' is a big, fat 2D array
timeit.timeit('large_arr[large_arr > 255] = 0', globals=globals(), number=1000)
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Put that stopwatch to work!

Keep it at bay: np.minimum/maximum

You don't want values running around like headless chickens. Use np.minimum and np.maximum to keep them within limits:

arr = np.minimum(arr, 3)  # "Everyone in the pool! But you can only go as far as 3m."
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Now everyone's in the pool, but no one's drowning. Isn't safety beautiful?

Optimizations you didn't know existed

np.putmask: the in-place savior

np.putmask gets rid of any pretense and replaces values right in-place:

np.putmask(arr, arr > 3, 0)  # "Arr matey, all ye greater than 3 be 0 now."
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Fast as a fox, np.putmask delivers. Especially handy when your arrays start resembling the cosmos.

Mastering np.clip's in-place modifications

You want your data in-place, top of the tables & within limits? np.clip has your back:

np.clip(arr, None, 3, out=arr)  # "Conga line, humans! And remember, no one's taller than 3."
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It's in-place, maintains array structure, and scores +1 on efficiency!

Boolean indexing power play

What's faster than an F1 car and operates directly on arrays? Boolean indexing:

arr[arr > 3] = np.minimum(arr, 3)  # "Faster than a supercar. And well within speed limits."
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Runs directly on arrays and gives speed demons a run for their money. What's not to love?