Tuesday 19 March 2024

The statistics module in Python

 

Calculating Mean:

import statistics


data = [1, 2, 3, 4, 5]

mean = statistics.mean(data)

print("Mean:", mean)


#clcoding.com

Mean: 3

Calculating Median:

import statistics


data = [1, 2, 3, 4, 5]

median = statistics.median(data)

print("Median:", median)


#clcoding.com

Median: 3

Calculating Mode:

import statistics


data = [1, 2, 2, 3, 4, 4, 4, 5]

mode = statistics.mode(data)

print("Mode:", mode)


#clcoding.com

Mode: 4

Calculating Variance:

import statistics


data = [1, 2, 3, 4, 5]

variance = statistics.variance(data)

print("Variance:", variance)


#clcoding.com

Variance: 2.5

Calculating Standard Deviation:

import statistics


data = [1, 2, 3, 4, 5]

std_dev = statistics.stdev(data)

print("Standard Deviation:", std_dev)


#clcoding.com

Standard Deviation: 1.5811388300841898

Calculating Quartiles:

import statistics


data = [1, 2, 3, 4, 5]

q1 = statistics.quantiles(data, n=4)[0]

q3 = statistics.quantiles(data, n=4)[-1]

print("First Quartile (Q1):", q1)

print("Third Quartile (Q3):", q3)


#clcoding.com

First Quartile (Q1): 1.5

Third Quartile (Q3): 4.5

Calculating Correlation Coefficient:

import statistics


data1 = [1, 2, 3, 4, 5]

data2 = [2, 4, 6, 8, 10]

corr_coeff = statistics.correlation(data1, data2)

print("Correlation Coefficient:", corr_coeff)


#clcoding.com

Correlation Coefficient: 1.0


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