Hello and welcome to this tutorial on Numpy fmax. In this tutorial, we will be learning about the NumPy fmax() method and also seeing a lot of examples regarding the same. So let us begin!
Also read: NumPy ones_like – A Complete Guide
What is NumPy fmax?
fmax() is a function in NumPy that compares two arrays and returns an array that contains the element-wise maximum of these two arrays.
Syntax of NumPy fmax
Let us have a look at the syntax of the fmax() function.
numpy.fmax(x1, x2, out=None)
| Parameter | Description | Required/Optional |
| x1 | Input array 1. | Required |
| x2 | Input array 2. | Required |
| out | An alternative output array in which to place the result. It must have the same shape as the expected output. | Optional |
Returns:
A new array containing the element-wise maximum of x1 and x2.
- If x1 and x2 are both scalars, then the output is also a scalar.
- If any of x1 or x2 contains a NaN value, then the output for that element-wise comparison is the non-NaN value.
- If both elements in the comparison are NaNs, then the first NaN is returned.
Examples of using NumPy fmax
Let’s now look at a few examples to understand the fmax() function better.
Using NumPy fmax when both inputs are scalars
import numpy as np
a = 15
b = 8
# using fmax function to calculate the element-wise maximum
ans = np.fmax(a, b)
print("a =", a, "\nb =", b)
print("Result =", ans)
Output:
a = 15
b = 8
Result = 15
Since 15>8, the answer is 15.
Element-wise maximum of 1-d arrays
import numpy as np
a = [2, 36, 1, 5, 10]
b = [6, 3 ,48, 2, 18]
# using fmax function to calculate the element-wise maximum
ans = np.fmax(a, b)
print("a =", a, "\nb =", b)
print("Result =", ans)
Output:
a = [2, 36, 1, 5, 10]
b = [6, 3, 48, 2, 18]
Result = [ 6 36 48 5 18]
The resulting array is calculated as:
ans[0] = max(a[0], b[0]) = max(2, 6) = 6
ans[1] = max(a[1], b[1]) = max(36, 3) = 36
ans[2] = max(a[2], b[2]) = max(1, 48) = 48
ans[3] = max(a[3], b[3]) = max(5, 2) = 5
ans[4] = max(a[4], b[4]) = max(10, 18) = 18
Element-wise maximum of 2-d arrays
import numpy as np
a = [[6, -8, 4], [2, 21, 16]]
b = [[-5, -12, 1], [0, 10, 27]]
# using fmax function to calculate the element-wise maximum
ans = np.fmax(a, b)
print("a =", a, "\nb =", b)
print("Result =\n", ans)
Output:
a = [[6, -8, 4], [2, 21, 16]]
b = [[-5, -12, 1], [0, 10, 27]]
Result =
[[ 6 -8 4]
[ 2 21 27]]
Here, both the input arrays are 2×3 arrays, hence the resulting array is also a 2×3 computed as:
ans[0][0] = max(a[0][0], b[0][0]) = max(6, -5) = 6
ans[0][1] = max(a[0][1], b[0][1]) = max(-8, -12) = -8
ans[0][2] = max(a[0][2], b[0][2]) = max(4, 1) = 4
ans[1][0] = max(a[1][0], b[1][0]) = max(2, 0) = 2
ans[1][1] = max(a[1][1], b[1][1]) = max(21, 10) = 21
ans[1][2] = max(a[1][2], b[1][2]) = max(16, 27) = 27
Element-wise maximum of arrays containing NaNs
import numpy as np
a = [8, np.nan, 5, 3]
b = [0, np.nan, np.nan, -6]
# using fmax function to calculate the element-wise maximum
ans = np.fmax(a, b)
print("a =", a, "\nb =", b)
print("Result =", ans)
Output:
a = [8, nan, 5, 3]
b = [0, nan, nan, -6]
Result = [ 8. nan 5. 3.]
Here,
ans[0] = max(a[0], b[0]) = max(8, 0) = 8
Now, a[1] and b[1] are both NaNs, so the maximum of these is also returned as NaN.
ans[1] = NaN
a[2] = 5 and b[2] = NaN, so the maximum is the non-NaN value i.e. 5.
ans[2] = 5
ans[3] = max(a[3], b[3]) = max(3, -6) = 3
Conclusion
That’s all! In this tutorial, we learned about the Numpy fmax method and practiced different types of examples using the same.
If you want to learn more about NumPy, feel free to go through our NumPy tutorials.