np.sum() and axis parameter in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the np.sum() function do in NumPy?

np.sum() adds up all the numbers in an array. It can add all elements or add along a specific direction (axis).

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What is the purpose of the axis parameter in np.sum()?

The axis parameter tells np.sum() which direction to add numbers along. For example, axis=0 adds down columns, axis=1 adds across rows.

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If you have a 2D array, what does np.sum(array, axis=0) do?

It adds the numbers down each column and returns a 1D array with sums for each column.

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What happens if you call np.sum() without specifying the axis?

It adds all the numbers in the whole array and returns a single number.

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Explain axis=1 in np.sum() for a 2D array.

axis=1 means add numbers across each row. The result is a 1D array with the sum of each row.

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

A Adds numbers across each row

B Adds numbers down each column

C Adds all numbers in the array

D Subtracts numbers in each column

axis=0 sums down columns in a 2D array.

If you want to add all elements in a NumPy array, what should you do?

A Use <code>np.sum(array, axis=0)</code>

B Use <code>np.sum(array, axis=1)</code>

C Use <code>np.mean(array)</code>

D Use <code>np.sum(array)</code> without axis

Without specifying axis, np.sum() adds all elements.

What does axis=1 mean in np.sum() for a 2D array?

A Add across rows

B Add down columns

C Add all elements

D Multiply elements

axis=1 sums across each row.

What type of result does np.sum() return when you specify an axis?

A A list of original numbers

B A single number

C An array with sums along that axis

D A boolean value

Specifying axis returns sums along that direction as an array.

Which axis sums down the columns in a 2D NumPy array?

A axis=0

B axis=1

C axis=2

D axis=-1

axis=0 sums down columns in 2D arrays.

Describe how the axis parameter changes the behavior of np.sum() when working with 2D arrays.

Think about adding numbers in rows vs columns.

Explain what happens when you use np.sum() on a 3D array with axis=1.

Imagine stacking multiple 2D arrays and summing across the middle dimension.