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Aggregations & Axis Operations: sum, mean, std, argmax
Mastering the axis parameter: axis=0 (down columns) vs axis=1 (across rows)
Aggregation functions collapse dimensions along specified axes to compute summary statistics such as sum, mean, variance, and extreme values.
Understanding the Axis Parameter
No axis specified (arr.sum()): Collapses the entire array into a single scalar value.
axis=0: Collapses along the first dimension (collapses rows, computing column-wise totals).
axis=1: Collapses along the second dimension (collapses columns, computing row-wise totals).
np.argmax(arr, axis=...): Returns the integer indices of the maximum values along an axis, crucial for reading classification model outputs.
import numpy as np
# 3 students (rows), 4 exam scores (columns)
grades = np.array([
[85, 90, 78, 92],
[70, 75, 80, 85],
[95, 98, 92, 100]
])
print("Overall class mean: ", round(grades.mean(), 2))
print("Exam averages (axis=0): ", grades.mean(axis=0))
print("Student averages (axis=1):", grades.mean(axis=1))
print("Top scorer per exam: ", grades.argmax(axis=0))Remember: the axis specified is the dimension that gets collapsed (eliminated) in the output shape.
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Test Yourself With Exercise
When computing the mean of a 2D matrix of shape (100, 5) with `arr.mean(axis=0)`, what is the shape of the result?