numpy.quantile() in Python

numpy.quantile(arr, q, axis = None) : Compute the q^th 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.

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:

# Python Program illustrating  
# numpy.quantile() method  
import numpy as np 
  
  
# 1D array  
arr = [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:

# Python Program illustrating  
# numpy.quantile() method  
import numpy as np 
   
# 2D array  
arr = [[14, 17, 12, 33, 44],   
       [15, 6, 27, 8, 19],  
       [23, 2, 54, 1, 4, ]]  
print("\narr : \n", arr)  
     
# quantile of the flattened array  
print("\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 = 0  
print("\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 = 1  
print("\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]]

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