np.sum/min/max
Collapse an array into a single number, or collapse each row or column using the axis argument.
In this page:
Syntax
np.sum(arr)
np.min(arr, axis=0)
np.max(arr, axis=1)np.sum/min/max
sum, min and max reduce an array to one value by default. With axis=0 they work down columns, and axis=1 across rows. argmin and argmax return positions instead of values.
Note:
keepdims=True keeps the reduced axis as length 1, which helps broadcasting later.
Example: np.sum/min/max
import numpy as np
m = np.array([[3, 1, 4], [1, 5, 9]])
print(m.sum(), m.min(), m.max())
print("column sums:", m.sum(axis=0))
print("row max:", m.max(axis=1))
print("argmax:", m.argmax())
# Output:
# 23 1 9
# column sums: [ 4 6 13]
# row max: [4 9]
# argmax: 5Related Topics
Common Mistakes
- Mixing up axis 0 and axis 1
- Using Python's sum on a large array, which is slow
- Forgetting argmax returns an index
Chapter Summary
- Reductions return one value by default
- axis=0 goes down columns
- axis=1 goes across rows
- argmin and argmax return indices
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