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
import numpy as np
arr = [ 1 , 2 , 7 , 0 , np.nan]
print ( "arr : " , arr)
print ( "Min of arr : " , np.amin(arr))
print ( "nanMin of arr : " , np.nanmin(arr))
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Output :
arr : [1, 2, 7, 0, nan]
Min of arr : nan
nanMin of arr : 0.0
Code #2 :
import numpy as np
arr = [[np.nan, 17 , 12 , 33 , 44 ],
[ 15 , 6 , 27 , 8 , 19 ]]
print ( "\narr : \n" , arr)
print ( "\nMin of arr, axis = None : " , np.nanmin(arr))
print ( "Min of arr, axis = 0 : " , np.nanmin(arr, axis = 0 ))
print ( "Min of arr, axis = 1 : " , np.nanmin(arr, axis = 1 ))
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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 :
import numpy as np
arr1 = np.arange( 5 )
print ( "Initial arr1 : " , arr1)
np.nanmin(arr, axis = 0 , out = arr1)
print ( "Changed arr1(having results) : " , arr1)
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Output :
Initial arr1 : [0 1 2 3 4]
Changed arr1(having results) : [14 6 12 8 19]
Last Updated :
29 Nov, 2018
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