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.
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 ))
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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:
# 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 ))
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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]]