How NumPy Arrays are faster than Python List?

Numpy is the core library for scientific computing in Python. A NumPy array is a grid of values, all of the same type, and is indexed by a tuple of non-negative integers. The Python core library provided Lists. A list is the Python equivalent of an array, but is resizeable and can contain elements of different types.

How is Numpy so fast? In this video we find out how Numpy’s ndarray is stored and how it is usually manipulated by Numpy functions.

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[aakashjana]

I had a simple code in which a list and a numpy array are created of same size and a function adds up all the values and returns a sum . i found numpy code speed at around 0.0003636 and my list took 8.9e-05 seconds which is way lesser . How is this possible

[mattralston]

What about how numpy arrays and array.arrays are fixed data structures. The append performance is bad because arrays must be preallocated and sequential.

[maheshmedam]

Good topic that you took. But, how Vectorization decreases the time taken is explained vaguely.

[the_curious_tunafish]

What about functionality like inbuilt linear algebra thing comparison of numpy with list?

[freejobawares]

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[DontTakeCrack]

thanks, this helps give me a good intro. my only thought was it looks like the memory doesn't have that much of an improvement.

[rameshpawar]

at 1Cr entries numpy is 27 times faster then normal lists

[mnih]

The title is misleading; it does not talk about WHY but HOW.

[Crimau]

Really bad pronunciation, though good explanantion