numpy.var (arr, axis = None) : calculate the variance of the data (array elements) along the specified axis (if any).
Example: strong >
x = 1 1 1 1 1
Standard Deviation = 0. Variance = 0
y = 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4
Step 1: Mean of distribution 4 = 7
Step 2: Summation of (x - x.mean ()) * * 2 = 178
Step 3: Finding Mean = 178/20 = 8.9
This Result is Variance.
arr: [array_like] input array.
axis: [int or tuples of int] axis along which we want to calculate the variance. Otherwise, it will consider
arrto be flattened (works on all the axis). axis = 0 means variance along the column and axis = 1 means variance along the row.
out: [ndarray, optional] Different array in which we want to place the result. The array must have the same dimensions as expected output.
dtype: [data-type, optional] Type we desire while computing variance.
Results: Variance of the array (a scalar value if axis is none) or array with variance values along specified axis.
Code # 1: p>
arr: [20, 2, 7, 1, 34] var of arr: 158.16 var of arr: 158.16 var of arr: 158.16
Code # 2:
var of arr, axis = None: 236.14000000000004 var of arr, axis = 0: [57.1875 312.75 345.6875 9.25 0.] var of arr, axis = 1: [0. 77.04 421.84 269.04]
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