np.cumsum() for cumulative sum in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.cumsum() on a 1D array

What is the output of the following code using np.cumsum()?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
cum_sum = np.cumsum(arr)
print(cum_sum)

A [1 3 6 10]

B [1 2 3 4]

C [10 9 7 4]

D [1 4 9 16]

💡 Hint

Think about adding each element to the sum of all previous elements.

The np.cumsum() function returns the cumulative sum of the elements along a given axis. For the array [1, 2, 3, 4], the cumulative sums are:

• 1
• 1 + 2 = 3
• 3 + 3 = 6
• 6 + 4 = 10

❓ data_output

intermediate

Cumulative sum along axis in 2D array

What is the output of np.cumsum() when applied along axis 0 on this 2D array?

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6]])
cum_sum = np.cumsum(arr, axis=0)
print(cum_sum)

A

[[1 2]
 [3 4]
 [5 6]]

B

[[1 2]
 [4 6]
 [9 12]]

C

[[1 5 10]
 [2 6 12]]

D

[[1 3]
 [2 4]
 [5 6]]

💡 Hint

Cumulative sum along axis 0 adds values down each column.

Applying np.cumsum() along axis 0 sums elements down each column:

• First row stays the same: [1, 2]
• Second row: [1+3=4, 2+4=6]
• Third row: [4+5=9, 6+6=12]

🔧 Debug

advanced

Identify the error in np.cumsum usage

What error will this code raise?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
cum_sum = np.cumsum(arr, axis=1)
print(cum_sum)

A No error, outputs [1 3 6]

B ValueError: operands could not be broadcast together

C AxisError: axis 1 is out of bounds for array of dimension 1

D TypeError: cumsum() got an unexpected keyword argument 'axis'

💡 Hint

Check the shape and dimensions of the array and the axis parameter.

The array is 1-dimensional, so axis 1 does not exist. Using axis=1 causes an AxisError.

🚀 Application

advanced

Using np.cumsum() to calculate running totals

You have daily sales data: [100, 200, 150, 300]. Which option shows the running total of sales using np.cumsum()?

NumPy

import numpy as np
sales = np.array([100, 200, 150, 300])
running_total = np.cumsum(sales)
print(running_total)

A [100 300 350 650]

B [100 200 150 300]

C [100 250 400 700]

D [100 300 450 750]

💡 Hint

Add each day's sales to the sum of all previous days.

The running total sums sales day by day:

• Day 1: 100
• Day 2: 100 + 200 = 300
• Day 3: 300 + 150 = 450
• Day 4: 450 + 300 = 750

🧠 Conceptual

expert

Effect of np.cumsum() with different data types

Given arr = np.array([1, 2, 3], dtype='int8'), what is the output of np.cumsum(arr)?

NumPy

import numpy as np
arr = np.array([1, 2, 3], dtype='int8')
cum_sum = np.cumsum(arr)
print(cum_sum)

A [1 3 6]

B [1 3 -127]

C [1 3 5]

D [1 2 3]

💡 Hint

Consider how cumulative sum works and the range of int8.

The cumulative sum adds values normally. Since the sum 6 fits in int8 range (-128 to 127), the output is [1, 3, 6].

Practice

1. What does the np.cumsum() function do when applied to a 1D numpy array?

easy

A. Calculates the cumulative sum of the elements step by step

B. Calculates the product of all elements

C. Sorts the array in ascending order

D. Finds the maximum value in the array

Solution

1. Step 1: Understand the function purpose

   np.cumsum() adds elements cumulatively, meaning each element is the sum of all previous elements plus itself.

2. Step 2: Compare with other options

   Options B, C, and D describe different operations: product, sorting, and max value, which are not what np.cumsum() does.

3. Final Answer:

   Calculates the cumulative sum of the elements step by step -> Option A

4. Quick Check:

   np.cumsum() = cumulative sum [OK]

Hint: Remember: cumsum means running total sum [OK]

Common Mistakes:

• Confusing cumulative sum with product
• Thinking it sorts the array
• Assuming it finds max or min values

2. Which of the following is the correct syntax to compute the cumulative sum of a numpy array arr?

easy

A. np.cumsum(arr)

B. All of the above

C. np.cumsum(arr, axis=0)

D. arr.cumsum()

Solution

1. Step 1: Check numpy function usage

   np.cumsum(arr) is the basic syntax to get cumulative sum of all elements in the array.

2. Step 2: Check array method and axis parameter

   arr.cumsum() is a valid method call on numpy arrays. Also, np.cumsum(arr, axis=0) computes cumulative sum along the first axis, valid for multi-dimensional arrays.

3. Final Answer:

   np.cumsum(arr) -> Option A

4. Quick Check:

   Basic syntax is np.cumsum(arr) [OK]

Hint: np and array method both work; axis optional [OK]

Common Mistakes:

• Thinking only np.cumsum(arr) works
• Ignoring axis parameter for multi-dimensional arrays
• Assuming cumsum is not an array method

3. What is the output of the following code?

import numpy as np
arr = np.array([2, 4, 6, 8])
cum_sum = np.cumsum(arr)
print(cum_sum)

medium

A. [2 8 14 22]

B. [2 4 6 8]

C. [2 6 12 20]

D. [20 18 14 8]

Solution

1. Step 1: Calculate cumulative sums stepwise

   Start with 2, then 2+4=6, then 6+6=12, then 12+8=20.

2. Step 2: Form the cumulative sum array

   The resulting array is [2, 6, 12, 20].

3. Final Answer:

   [2 6 12 20] -> Option C

4. Quick Check:

   Running total sums = [2, 6, 12, 20] [OK]

Hint: Add each element to sum of all before it [OK]

Common Mistakes:

• Confusing cumulative sum with original array
• Adding elements incorrectly
• Reversing the order of sums

4. The following code throws an error. What is the problem?

import numpy as np
arr = np.array([[1, 2], [3, 4]])
cum_sum = np.cumsum(arr, axis=2)
print(cum_sum)

medium

A. Syntax error in np.cumsum call

B. np.cumsum does not accept axis parameter

C. Array must be 1D for cumsum

D. Axis 2 does not exist for a 2D array

Solution

1. Step 1: Check array dimensions

   The array has shape (2, 2), so it has axes 0 and 1 only.

2. Step 2: Validate axis parameter

   Using axis=2 is invalid because axis 2 does not exist for this 2D array, causing an error.

3. Final Answer:

   Axis 2 does not exist for a 2D array -> Option D

4. Quick Check:

   Axis must be within array dimensions [OK]

Hint: Axis must be less than array dimensions [OK]

Common Mistakes:

• Assuming axis parameter is optional but invalid
• Thinking cumsum only works on 1D arrays
• Confusing syntax error with axis error

5. Given a 2D numpy array representing daily sales for 3 products over 4 days:

sales = np.array([[5, 3, 2],
                  [4, 6, 1],
                  [7, 2, 3],
                  [3, 5, 4]])

Which code correctly computes the cumulative sales per product over the days?

hard

A. np.cumsum(sales)

B. np.cumsum(sales, axis=0)

C. np.cumsum(sales, axis=1)

D. np.cumsum(sales, axis=2)

Solution

1. Step 1: Understand the array shape and axis meaning

   The array shape is (4, 3): 4 days (rows), 3 products (columns). Axis 0 moves down rows (days), axis 1 moves across products.

2. Step 2: Choose axis for cumulative sum per product over days

   To get cumulative sales per product over days, sum down each column (axis=0).

3. Final Answer:

   np.cumsum(sales, axis=0) -> Option B

4. Quick Check:

   Cumulative sum down rows = axis 0 [OK]

Hint: Sum down rows (axis=0) for cumulative per product [OK]

Common Mistakes:

• Using axis=1 sums across products, not over days
• Ignoring axis parameter
• Using invalid axis=2 for 2D array