np.cumsum() for cumulative sum in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the cumulative sum of the array using numpy.

NumPy

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

Drag options to blanks, or click blank then click option'

Acumsum

Bsum

Ccumprod

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.sum() which returns the total sum, not cumulative sums.

Using np.cumprod() which calculates cumulative product instead.

The np.cumsum() function calculates the cumulative sum of the elements in the array.

2fill in blank

medium

Complete the code to calculate the cumulative sum along axis 0 of the 2D array.

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4]])
cum_sum_axis0 = np.cumsum(arr, axis=[1])
print(cum_sum_axis0)

Drag options to blanks, or click blank then click option'

A1

B0

C2

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=1 which sums along columns instead of rows.

Using axis=2 which is invalid for 2D arrays.

Axis 0 means cumulative sum down the rows (along columns).

3fill in blank

hard

Fix the error in the code to correctly compute the cumulative sum of the list.

NumPy

import numpy as np
lst = [5, 10, 15]
cum_sum = np.cumsum([1])
print(cum_sum)

Drag options to blanks, or click blank then click option'

Anp.array(lst)

Blst

Clist(lst)

Dnp.sum(lst)

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the list directly without conversion.

Using np.sum which returns total sum, not cumulative sums.

np.cumsum requires a numpy array or array-like. Converting list to np.array ensures correct operation.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their cumulative sum of lengths as values for words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
cum_lengths = {word: [1] for word in words if len(word) [2] 3}
print(cum_lengths)

Drag options to blanks, or click blank then click option'

Alen(word)

B>

C<=

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of length.

Using wrong comparison operator like '<='.

We want lengths of words longer than 3 letters, so use len(word) and > operator.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values for words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = { [1]: [2] for word in words if len(word) [3] 3 }
print(result)

Drag options to blanks, or click blank then click option'

Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using original word as key instead of uppercase.

Using wrong comparison operator.

Swapping keys and values.

The dictionary keys are uppercase words, values are their lengths, filtered by length > 3.

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