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 Interquartile Range of the given dataset.
Parameters : 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.
Code #1:
Python3
# Python Program illustrating# numpy.quantile() methodimport numpy as np# 1D arrayarr = [20, 2, 7, 1, 34]print("arr : ", arr)print("Q2 quantile of arr : ", np.quantile(arr, .50))print("Q1 quantile of arr : ", np.quantile(arr, .25))print("Q3 quantile of arr : ", np.quantile(arr, .75))print("100th quantile of arr : ", np.quantile(arr, .1)) |
Output :
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)
Code #2:
Python3
# Python Program illustrating# numpy.quantile() methodimport numpy as np # 2D arrayarr = [[14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4, ]]print("\narr : \n", arr) # quantile of the flattened arrayprint("\n50th quantile of arr, axis = None : ", np.quantile(arr, .50))print("0th quantile of arr, axis = None : ", np.quantile(arr, 0)) # quantile along the axis = 0print("\n50th quantile of arr, axis = 0 : ", np.quantile(arr, .25, axis = 0))print("0th quantile of arr, axis = 0 : ", np.quantile(arr, 0, axis = 0)) # quantile along the axis = 1print("\n50th quantile of arr, axis = 1 : ", np.quantile(arr, .50, axis = 1))print("0th quantile of arr, axis = 1 : ", np.quantile(arr, 0, axis = 1)) print("\n0th quantile of arr, axis = 1 : \n", np.quantile(arr, .50, axis = 1, keepdims = True))print("\n0th quantile of arr, axis = 1 : \n", np.quantile(arr, 0, axis = 1, keepdims = True)) |
Output :
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 : [[12] [ 6] [ 1]]
