Standard deviation (SD) is measured as the spread of the distribution of the data in a given dataset.
x = 1 1 1 1 1 Standard Deviation = 0. y = 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4 Step 1: strong> 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. Step 4: Standard Deviation = sqrt (Variance) = sqrt (8.9) = 2.983 ..
arr: [array_like] input array.
axis: [int or tuples of int] axis along which we want to calculate the standard deviation. Otherwise, it will consider arr to be flattened (works on all the axis). axis = 0 means SD along the column and axis = 1 means SD 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 SD.
Results: Standard Deviation of the array (a scalar value if axis is none) or array with standard deviation values along specified axis.
Code # 1:
arr: [20, 2, 7, 1, 34] std of arr: 12.576167937809991 More precision with float32 std of arr: 12.576168 More accuracy with float64 std of arr: 12.576167937809991
Code # 2:
< / p>
std of arr, axis = None: 15.3668474320532 std of arr, axis = 0: [7.56224173 17.68473918 18.59267329 3.04138127 0.] std of arr, axis = 1: [ 0.8.7772433 20.53874388 16.40243884]
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