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numpy.quantile() in Python

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  • Last Updated : 09 Aug, 2022
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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() 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: 

Python3




# 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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