Aggregation along specific axes in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does aggregation along a specific axis mean in numpy?

It means combining values in an array along a chosen direction (axis), like summing all rows or columns separately.

beginner

How do you sum all elements in each column of a 2D numpy array?

Use np.sum(array, axis=0). Axis 0 means moving down rows, summing column-wise.

beginner

What is the difference between axis=0 and axis=1 in numpy aggregation?

axis=0 aggregates down the rows (column-wise), axis=1 aggregates across columns (row-wise).

beginner

How to find the maximum value in each row of a 2D numpy array?

Use np.max(array, axis=1). This finds the max value moving across columns for each row.

beginner

Why is specifying the axis important in numpy aggregation functions?

Because it tells numpy which direction to combine values, affecting the shape and meaning of the result.

What does np.sum(array, axis=0) do on a 2D array?

A Returns the original array

B Sums each row separately

C Sums all elements into one number

D Sums each column separately

Axis 0 means summing down rows, so each column's values are added together.

If you want to find the minimum value in each row, which axis should you use?

A axis=1

B axis=2

C axis=0

D No axis needed

Axis 1 moves across columns, so it finds the min value in each row.

What shape will the result have after np.mean(array, axis=1) on a 2D array with shape (4,5)?

A (4,5)

B (5,)

C (4,)

D (1,5)

Aggregating along axis 1 reduces columns, so result shape is (4,), one mean per row.

Which numpy function can you use to get the sum of all elements regardless of axis?

A np.sum(array)

B np.sum(array, axis=0)

C np.sum(array, axis=1)

D np.sum(array, axis=2)

Without axis, np.sum adds all elements into a single number.

What happens if you use an invalid axis number in numpy aggregation?

A Returns zeros

B Raises an error

C Ignores the axis and sums all

D Returns the original array

Numpy raises an error because the axis does not exist in the array shape.

Explain how aggregation along axis 0 and axis 1 differ in a 2D numpy array.

Think about summing columns vs summing rows.

Describe why specifying the axis is important when using numpy aggregation functions.

Consider what happens if you don't specify axis.