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numpy.quantile () in Python

The quantile plays a very important role in statistics when it comes to normal distribution.

In the above picture, Q2 — it is median of normally distributed data.  Q3 - Q2 represents the inter-quantum range of this 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 < code class = "plain"> ( "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 ( "arr: " , arr) 

  
# flattened array quantile

print ( "50th quantile of arr, axis = None:" , np.quantile (arr,. 50 )) 

print ( "0th quantile of arr, axis = None:" , np.quantile (arr, 0 )) 

 
# quantile by axes = 0

print ( "50th quantile of arr, axis = 0: " , np.quantile (arr,. 25 , axis = 0 )) 

print ( "0th quantile of arr, axis = 0:" , np.quantile (arr, 0 , axis = 0 )) 

 
# axis quantile = 1

print ( "50th quantile of arr, axis = 1:" , np.quantile (arr,. 50 , axis = 1 )) 

print < code class = "plain"> ( "0th quantile of arr, axis = 1:" , np.quantile (arr, 0 , axis = 1 )) 

  

print ( "0th quantile of arr, axis = 1:"

np.quantile (arr,. 50 , axis = 1 , keepdims = True ))

print ( "0th quantile of arr, axis = 1: "

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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