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# Compute the mean, standard deviation, and variance of a given NumPy array

In NumPy, we can compute the mean, standard deviation, and variance of a given array along the second axis by two approaches first is by using inbuilt functions and second is by the formulas of the mean, standard deviation, and variance.

Method 1: Using numpy.mean(), numpy.std(), numpy.var()

## Python

 `import` `numpy as np`` ` ` ` `# Original array``array ``=` `np.arange(``10``)``print``(array)`` ` `r1 ``=` `np.mean(array)``print``(``"\nMean: "``, r1)`` ` `r2 ``=` `np.std(array)``print``(``"\nstd: "``, r2)`` ` `r3 ``=` `np.var(array)``print``(``"\nvariance: "``, r3)`

Output:

```[0 1 2 3 4 5 6 7 8 9]

Mean:  4.5

std:  2.8722813232690143

variance:  8.25
```

Method 2: Using the formulas

## Python3

 `import` `numpy as np`` ` `# Original array``array ``=` `np.arange(``10``)``print``(array)`` ` `r1 ``=` `np.average(array)``print``(``"\nMean: "``, r1)`` ` `r2 ``=` `np.sqrt(np.mean((array ``-` `np.mean(array)) ``*``*` `2``))``print``(``"\nstd: "``, r2)`` ` `r3 ``=` `np.mean((array ``-` `np.mean(array)) ``*``*` `2``)``print``(``"\nvariance: "``, r3)`

Output:

```[0 1 2 3 4 5 6 7 8 9]

Mean:  4.5

std:  2.8722813232690143

variance:  8.25
```

Example: Comparing both inbuilt methods and formulas

## Python

 `import` `numpy as np`` ` `# Original array``x ``=` `np.arange(``5``)``print``(x)`` ` `r11 ``=` `np.mean(x)``r12 ``=` `np.average(x)``print``(``"\nMean: "``, r11, r12)`` ` `r21 ``=` `np.std(x)``r22 ``=` `np.sqrt(np.mean((x ``-` `np.mean(x)) ``*``*` `2``))``print``(``"\nstd: "``, r21, r22)`` ` `r31 ``=` `np.var(x)``r32 ``=` `np.mean((x ``-` `np.mean(x)) ``*``*` `2``)``print``(``"\nvariance: "``, r31, r32)`

Output:

```[0 1 2 3 4]

Mean:  2.0 2.0

std:  1.4142135623730951 1.4142135623730951

variance:  2.0 2.0
```