# numpy.nanmin() in Python

`numpy.nanmin()`function is used when to returns minimum value of an array or along any specific mentioned axis of the array, ignoring any Nan value.

Syntax : numpy.nanmin(arr, axis=None, out=None)
Parameters :
arr :Input array.
axis :Axis along which we want the min 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 :Different array in which we want to place the result. The array must have same dimensions as expected output.

Return :Minimum array value(a scalar value if axis is none) or array with minimum value along specified axis.

Code #1 : Working

 `# Python Program illustrating  ` `# numpy.nanmin() method  ` `   `  `import` `numpy as np ` `   `  `# 1D array  ` `arr ``=` `[``1``, ``2``, ``7``, ``0``, np.nan] ` `print``(``"arr : "``, arr)  ` `print``(``"Min of arr : "``, np.amin(arr)) ` ` `  `# nanmin ignores NaN values.  ` `print``(``"nanMin of arr : "``, np.nanmin(arr)) `

Output :

```arr :  [1, 2, 7, 0, nan]
Min of arr :  nan
nanMin of arr :  0.0
```

Code #2 :

 `# Python Program illustrating  ` `# numpy.nanmin() method  ` ` `  `import` `numpy as np ` ` `  `# 2D array  ` `arr ``=` `[[np.nan, ``17``, ``12``, ``33``, ``44``],   ` `       ``[``15``, ``6``, ``27``, ``8``, ``19``]]  ` `print``(``"\narr : \n"``, arr)  ` `    `  `# Minimum of the flattened array  ` `print``(``"\nMin of arr, axis = None : "``, np.nanmin(arr))  ` `    `  `# Minimum along the first axis  ` `# axis 0 means vertical  ` `print``(``"Min of arr, axis = 0 : "``, np.nanmin(arr, axis ``=` `0``))  ` `    `  `# Minimum along the second axis  ` `# axis 1 means horizontal  ` `print``(``"Min of arr, axis = 1 : "``, np.nanmin(arr, axis ``=` `1``))   `

Output :

```arr :
[[14, 17, 12, 33, 44], [15, 6, 27, 8, 19]]

Min of arr, axis = None :  6
Min of arr, axis = 0 :  [14  6 12  8 19]
Min of arr, axis = 1 :  [12  6]
```

Code #3 :

 `# Python Program illustrating  ` `# numpy.nanmin() method  ` ` `  `import` `numpy as np ` ` `  `arr1 ``=` `np.arange(``5``)  ` `print``(``"Initial arr1 : "``, arr1) ` `  `  `# using out parameter ` `np.nanmin(arr, axis ``=` `0``, out ``=` `arr1) ` `  `  `print``(``"Changed arr1(having results) : "``, arr1) `

Output :

```Initial arr1 :  [0 1 2 3 4]
Changed arr1(having results) :  [14  6 12  8 19]
```

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