np.cumsum() for cumulative sum in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.cumsum() do in numpy?

np.cumsum() calculates the cumulative sum of array elements along a given axis. It adds each element to the sum of all previous elements.

beginner

How does np.cumsum() behave on a 1D array like [1, 2, 3]?

It returns [1, 3, 6] because:

• 1 (first element)
• 1 + 2 = 3 (sum of first two)
• 1 + 2 + 3 = 6 (sum of all three)

intermediate

Can np.cumsum() work on multi-dimensional arrays?

Yes, it can compute cumulative sums along a specified axis (rows or columns). If no axis is given, it treats the array as flattened.

intermediate

What is the output of np.cumsum(np.array([[1,2],[3,4]]), axis=0)?

The cumulative sum along rows (axis=0) is:

[[1, 2],
 [4, 6]]

Explanation: sums down each column: 1, then 1+3=4; 2, then 2+4=6.

beginner

Why is np.cumsum() useful in real life?

It helps track running totals, like daily sales, distances traveled, or cumulative scores, making it easier to see progress over time.

What does np.cumsum([1, 2, 3, 4]) return?

A [1, 2, 3, 4]

B [1, 3, 6, 10]

C [10, 9, 7, 4]

D [4, 7, 9, 10]

It returns the running total: 1, 1+2=3, 3+3=6, 6+4=10.

If arr = np.array([[1,2],[3,4]]), what does np.cumsum(arr, axis=1) compute?

A Cumulative sum down each column

B Difference between elements

C Sum of all elements

D Cumulative sum across each row

Axis=1 means sum across rows (left to right).

What happens if you call np.cumsum() without specifying an axis on a 2D array?

A It sums elements row-wise

B It sums elements column-wise

C It treats the array as flat and sums cumulatively

D It throws an error

Without axis, the array is flattened and cumulative sum is computed on the flat array.

Which of these is a real-life example where np.cumsum() is useful?

A Calculating daily total sales over a month

B Finding the maximum value in a dataset

C Sorting a list of names alphabetically

D Counting the number of unique items

Running totals like daily sales are perfect for cumulative sums.

What is the shape of the output of np.cumsum() compared to the input array?

A Same shape

B Always larger

C Always smaller

D Depends on the axis

The output array has the same shape as the input.

Explain how np.cumsum() works on a simple list of numbers.

Think about adding numbers one by one and keeping track of the total.

Describe how you would use np.cumsum() on a 2D array to get cumulative sums along rows.

Remember axis=1 means left to right across each row.