numpy.quantile(arr, q, axis = None) : Compute the qth quantile of the given data (array elements) along the specified axis.
Quantile plays a very important role in Statistics when one deals with the Normal Distribution.
In the figure given above,
Q2 is the
median of the normally distributed data.
Q3 - Q2 represents the Interquantile Range of the given dataset.
arr : [array_like]input array.
q : quantile value.
axis : [int or tuples of int]axis along which we want to calculate the quantile value. Otherwise, it will consider arr to be flattened(works on all the axis). axis = 0 means along the column and axis = 1 means working along the row.
out : [ndarray, optional]Different array in which we want to place the result. The array must have same dimensions as expected output.
Results : qth quantile of the array (a scalar value if axis is none) or array with quantile values along specified axis.
arr : [20, 2, 7, 1, 34] Q2 quantile of arr : 7.0) Q1 quantile of arr : 2.0) Q3 quantile of arr : 20.0) 100th quantile of arr : 1.4)
arr : [[14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4]] 50th quantile of arr, axis = None : 15.0 0th quantile of arr, axis = None : 1) 50th quantile of arr, axis = 0 : [14.5 4. 19.5 4.5 11.5] 0th quantile of arr, axis = 0 : [14 2 12 1 4] 50th quantile of arr, axis = 1 : [17. 15. 4.] 0th quantile of arr, axis = 1 : [12 6 1] 0th quantile of arr, axis = 1 : [[17.] [15.] [ 4.]] 0th quantile of arr, axis = 1 : [ [ 6] [ 1]]
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