# .var()

Published Apr 23, 2024
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The `.var()` function computes the variance of the elements in an array, either across all elements or along a specified axis (if provided). Variance is a statistical measurement that shows how far each number in the array is spread out from the mean. In other words, it measures the dispersion or spread in the data.

A high variance indicates that the numbers in the array are far from the mean, while a low variance indicates that they are close to the mean.

## Syntax

``````numpy.var(array, axis=None)
``````
• `array`: The array for which the variance is to be calculated.
• `axis`: An optional parameter that specifies the axis along which the variance is to be calculated.

If `axis` is not specified, the variance is calculated for the entire (flattened) array. If `axis` is specified, then the variance is calculated along that axis. For a 2D array, if `axis=0`, the variance is calculated column-wise, and if `axis=1`, the variance is calculated row-wise.

## Example

The following example creates a 2D array and calculates the overall variance (variance of the flattened array), column-wise variance, and row-wise variance of the array using the `.var()` function:

```import numpy as np
# Creating a 2D arrayarray_2d = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])
# Calculating the overall varianceoverall_variance = np.var(array_2d)print(overall_variance)
# Calculating the column-wise variance (axis=0)column_variance = np.var(array_2d, axis=0)print(column_variance)
# Calculating the row-wise variance (axis=1)row_variance = np.var(array_2d, axis=1)print(row_variance)
```

This produces the following output:

```6.666666666666667[6. 6. 6.][0.66666667 0.66666667 0.66666667]
```