numpy.nanmin() in Python
Last Updated :
29 Nov, 2018
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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]
