# numpy.quantile() in Python

`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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