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].