numpy.nanmin () in Python

`numpy.nanmin()` is used when it returns the minimum value of an array or along any particular 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: Work

 ` # 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.nanm in () method   import numpy as np   # 2D array arr = [[np.nan, 17 , 12 , 33 , 44 ],    [ 15 , 6 , 27 , 8 , 19 ]]  print ( " arr: " , arr)    # Minimum smoothed array print ( "Min 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 the 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]

```