Numpy Array Initialization (Fill With Identical Values)

Other Ways: We Got Plenty!

Empty, but filled? [Yeah, kind of schrödinger thingy 😉]

For large arrays, np.empty() combined with fill() lands a pretty fast way to get your arrays filled up:

# Hey look, treasure troves everywhere! Oops, they’re empty! 
large_array = np.empty((10, 10))
# No worries, fill 'em up with 7s you said? Okay then! 
large_array.fill(7)
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Here we first allocate memory using np.empty() without initializing it and then fill it up. Faster and furious(er)?

Slices all over: [No, not pizzas. Arrays, man!]

# Trazan call! Empty array coming up! Assemble!
array = np.empty((2, 2))
# All right my sliced friends, stand in line and take your 9s, now!
array[:] = 9
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Just another efficient way to initialize your moderate-sized arrays. Take a slice, have a byte!

None shall pass: [Unless you're None]

For those none-believers - ahem, I mean None values. We have something for them too:

# None, you say? Alright, everyone make room for None!
array_with_none = np.full((3, 3), None, dtype=object)
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This trick ensures None values are handled correctly. No None left behind!

Speed Talk: Performance Tips

Broadcasting for the win: number-crunching, literally!

# Just broadcasting some zeros, wait, now they're eights, magic!
array_broadcasted = np.zeros((5, 5)) + 8
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This exploitation of broadcasting can leapfrog performance without resorting to explicit loops.

Use performance-tooling libraries, e.g., perfplot, to benchmark various methods, especially if you're working on bigger datasets.

Snapshot: Comparison Table

Method	Best For	Initialization Style	Performance
np.full()	Any size	Immediate	High
np.empty().fill()	Larger size	Two-Step	Very High
Slice notation [:]	Small-Medium	Immediate	High
Loops/np.repeat()	Just Don't	Iterative	Low

Yes, everything else is slower than np.empty().fill(). No, we don't sell turtles here!

Quick note: Data Types and Values

Beware of data types (dtype). For object arrays or complex data structures, specifying dtype prevents "I-didn't-see-that-coming" moments.

Practical tips and tricks

We love NumPy's array initialization— useful everywhere, from tuning parameters in simulation to creating masks for image processing

Behold: Matching dimensions:

other_array = np.random.rand(3, 4)
matching_array = np.full(other_array.shape, 3.14)
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Creating arrays with exactly the same dimensions as others. Pi for everyone!

Speed matters: Performance Tuning

When speed is the name of the game, trial different initialization methods for the best memory footprint vs computation time balance.