numpy.quantile() in Python

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.

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:

filter_none

edit
close

play_arrow

link
brightness_4
code

# 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)) 
    

chevron_right






                        filter_none










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:













                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# 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)) 



















                        chevron_right







                        filter_none










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]]




          
          
          
          

            

    

        My Personal Notes
        arrow_drop_up
    

    

        
        

            
            
        

    


Recommended Posts:
Mohit Gupta_OMG 🙂
Aspire to Inspire before I expire
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on  the "Improve Article" button below.