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]]
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course