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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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