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numpy.quantile() in Python
• Last Updated : 29 Nov, 2018

`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 Interquantile 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:

 `# 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:

 `# 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 :
[
[ 6]
[ 1]]
```

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