numpy.quantile() in Python
Last Updated :
09 Aug, 2022
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
import numpy as np
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 ))
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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
import numpy as np
arr = [[ 14 , 17 , 12 , 33 , 44 ],
[ 15 , 6 , 27 , 8 , 19 ],
[ 23 , 2 , 54 , 1 , 4 , ]]
print ("\narr : \n", arr)
print ("\n50th quantile of arr, axis = None : ", np.quantile(arr, . 50 ))
print (" 0th quantile of arr, axis = None : ", np.quantile(arr, 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 ))
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 ))
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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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