# numpy.nanquantile() in Python

`numpy.nanquantile(arr, q, axis = None)` : Compute the qth quantile of the given data (array elements) along the specified axis, ignoring the nan values.

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, ignoring nan values.

Code #1 :

 `# Python Program illustrating  ` `# numpy.nanquantile() method   ` `import` `numpy as np  ` `     `  `# 1D array  ` `arr ``=` `[``20``, ``2``, ``7``, np.nan, ``34``]  ` `print``(``"arr : "``, arr)  ` ` `  `print``(``"\n-Q1 quantile of arr : "``, np.quantile(arr, .``50``))  ` `print``(``"Q2 - quantile of arr : "``, np.quantile(arr, .``25``))  ` `print``(``"Q3 - quantile of arr : "``, np.quantile(arr, .``75``))  ` ` `  `print``(``"\nQ1 - nanquantile of arr : "``, np.nanquantile(arr, .``50``))  ` `print``(``"Q2 - nanquantile of arr : "``, np.nanquantile(arr, .``25``))  ` `print``(``"Q3 - nanquantile of arr : "``, np.nanquantile(arr, .``75``))  `

Output :

```arr : [20, 2, 7, nan, 34]

Q1 - quantile of arr : nan
Q2 - quantile of arr : nan
Q3 - quantile of arr : nan

Q1 - nanquantile of arr : 13.5
Q2 - nanquantile of arr : 5.75
Q3 - nanquantile of arr : 23.5
```

Code #2:

 `# Python Program illustrating  ` `# numpy.nanquantile() method  ` ` `  `import` `numpy as np  ` ` `  `# 2D array  ` `arr ``=` `[[``14``, np.nan, ``12``, ``33``, ``44``],  ` `       ``[``15``, np.nan, ``27``, ``8``, ``19``],  ` `       ``[``23``, ``2``, np.nan, ``1``, ``4``, ]]  ` `print``(``"\narr : \n"``, arr)  ` `     `  `# quantile of the flattened array  ` `print``(``"\nQ2 quantile of arr, axis = None : "``, np.quantile(arr, .``50``))  ` `print``(``"\nQ2 quantile of arr, axis = None : "``, np.nanquantile(arr, .``50``))  ` `print``(``"0th quantile of arr, axis = None : "``, np.nanquantile(arr, ``0``))  `

Output:

```arr :
[[14, nan, 12, 33, 44], [15, nan, 27, 8, 19], [23, 2, nan, 1, 4]]

Q2 quantile of arr, axis = None : nan
Q2 quantile of arr, axis = None : 14.5
0th quantile of arr, axis = None : 1.0
```

Code #3:

 `# Python Program illustrating  ` `# numpy.nanquantile() method  ` `import` `numpy as np  ` ` `  `# 2D array  ` `arr ``=` `[[``14``, np.nan, ``12``, ``33``, ``44``],  ` `    ``[``15``, np.nan, ``27``, ``8``, ``19``],  ` `    ``[``23``, ``2``, np.nan, ``1``, ``4``, ]]  ` `print``(``"\narr : \n"``, arr)  ` `         `  `# quantile along the axis = 0  ` `print``(``"\nQ2 quantile of arr, axis = 0 : "``, np.nanquantile(arr, .``50``, axis ``=` `0``))  ` `print``(``"0th quantile of arr, axis = 0 : "``, np.nanquantile(arr, ``0``, axis ``=` `0``))  ` ` `  `# quantile along the axis = 1  ` `print``(``"\nQ2 quantile of arr, axis = 1 : "``, np.nanquantile(arr, .``50``, axis ``=` `1``))  ` `print``(``"0th quantile of arr, axis = 1 : "``, np.nanquantile(arr, ``0``, axis ``=` `1``))  ` ` `  `print``(``"\nQ2 quantile of arr, axis = 1 : \n"``, ` `  ``np.nanquantile(arr, .``50``, axis ``=` `1``, keepdims ``=` `True``))  ` `print``(``"\n0th quantile of arr, axis = 1 : \n"``, ` `    ``np.nanquantile(arr, ``0``, axis ``=` `1``, keepdims ``=` `True``))  `

Output:

```arr :
[[14, nan, 12, 33, 44], [15, nan, 27, 8, 19], [23, 2, nan, 1, 4]]

Q2 quantile of arr, axis = 0 : [15.  2. 19.5  8.  19. ]
0th quantile of arr, axis = 0 : [14. 2. 12.  1.  4.]

Q2 quantile of arr, axis = 1 : [23.5 17.   3. ]
0th quantile of arr, axis = 1 : [12.  8.  1.]

Q2 quantile of arr, axis = 1 :
[[23.5]
[17. ]
[ 3. ]]

0th quantile of arr, axis = 1 :
[[12.]
[ 8.]
[ 1.]]```

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