Time analysis of different library methods

While implementing the simple solutions, I wondered how to optimise the solutions using different library functions. Here I timed 3 approaches to calculating mean and medians of 1D arrays and how different library functions react to the different sizes of the array.

The three approaches are:

1. Naive calculation of mean and approach by implementing the algorithms for both.

2. using numpy mean and median functions.

3. using statistics mean and median functions.

def list_stats_1 (arr) :

size = len(arr)

arr.sort()

mid = math.floor(size/2)

mean = sum(arr)/size if size > 0 else 0

median = 0

if size > 0:

if size % 2 == 0:

median = (arr[mid-1]+arr[mid])/2

else:

median = arr[mid]

return np.round(median, 5), np.round(mean, 5)

def list_stats_2 (arr) :

mean = np.mean(arr)

median = np.median(arr)

return np.round(median, 5), np.round(mean, 5)

def list_stats_3 (arr) :

mean = statistics.mean(arr)

median = statistics.median(arr)

return np.round(median, 5), np.round(mean, 5)

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I ran the code on array of sizes from 10 to 10^8 having random numbers.

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