Project github address: bitcarmanlee easy-algorithm-interview-and-practice
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1. Calculate the mean
import numpy as np
a = [5, 6, 16, 9]
print(np.mean(a))
final result
9.0
np.mean method can find the mean
2. Calculate the variance
var = np.var(a)
print(var)
Output result
18.5
If we simulate the process of calculating variance
var2 = [math.pow(x-np.mean(a), 2) for x in a]
print(np.mean(var2))
Output result
18.5
np.var calculates the overall variance, if you want to calculate the sample variance, that is, the denominator of the divisor is N-1, you can specify the ddof parameter
sample_var = np.var(a, ddof=1)
print(sample_var)
The output result is
24.666666666666668
3. Calculate the standard deviation
std = np.std(a)
std2 = np.std(a, ddof=1)
print(std)
print(std2)
The std function calculates the overall standard deviation. Like the var function, if ddof=1 is specified, the sample standard deviation is calculated.