np.sum() and axis parameter in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the sum of all elements in the array.

NumPy

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

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

Asum

Bnp

Carr

D[1, 2, 3, 4]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the module name 'np' instead of the array.

Passing the function name 'sum' instead of the array.

Passing a list literal instead of the array variable.

We use np.sum(arr) to add all elements in the array arr.

2fill in blank

medium

Complete the code to calculate the sum of each column in the 2D array.

NumPy

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

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

A-1

B1

C2

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=1 to sum columns instead of rows.

Using an invalid axis number.

Omitting the axis parameter.

Using axis=1 sums across columns for each row, giving the sum of each row's elements.

But since we want the sum of each column, we use axis=0.

However, the question asks for sum of each column, so the correct axis is 0.

3fill in blank

hard

Fix the error in the code to sum each row in the 2D array.

NumPy

import numpy as np
arr = np.array([[5, 6, 7], [8, 9, 10]])
sums = np.sum(arr, axis=[1])
print(sums)

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

A1

B2

C0

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 to sum rows instead of columns.

Using an axis value that is out of range.

To sum each row, we sum across columns, which is axis=1.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(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 instead of its length as the value.

Using '<' instead of '>' in the condition.

The dictionary comprehension uses len(word) to get the length, and the condition len(word) > 3 to filter words longer than 3 letters.

5fill in blank

hard

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

NumPy

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

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

Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word instead of uppercase for keys.

Using '<' instead of '>' in the condition.

Using word instead of len(word) for values.

The dictionary comprehension uses word.upper() as keys, len(word) as values, and filters words with length greater than 3 using >.

Practice

1. What does the axis parameter in np.sum() control?

easy

A. The data type of the output array.

B. The direction along which the sum is calculated (rows or columns).

C. The size of the array to sum.

D. The number of times the sum is repeated.

Solution

1. Step 1: Understand the role of axis in np.sum()

   The axis parameter tells numpy whether to sum across rows (axis=1) or columns (axis=0).

2. Step 2: Clarify what axis does not affect

   It does not change data type, size, or repetition of sums.

3. Final Answer:

   The direction along which the sum is calculated (rows or columns). -> Option B

4. Quick Check:

   axis controls sum direction [OK]

Hint: Axis decides row or column sum direction [OK]

Common Mistakes:

• Thinking axis changes data type
• Confusing axis with array size
• Assuming axis repeats sums

2. Which of the following is the correct syntax to sum all elements of a 2D numpy array arr?

easy

A. np.sum(arr)

B. np.sum(arr, axis=1, 0)

C. np.sum(arr, axis=2)

D. np.sum(arr, axis='all')

Solution

1. Step 1: Recall default behavior of np.sum()

   Without axis, np.sum(arr) sums all elements in the array.

2. Step 2: Check invalid axis values

   axis=2 is invalid for 2D arrays, axis=1,0 is incorrect syntax, axis='all' is invalid.

3. Final Answer:

   np.sum(arr) -> Option A

4. Quick Check:

   Default np.sum sums all elements [OK]

Hint: No axis sums all elements [OK]

Common Mistakes:

• Using invalid axis values
• Passing multiple axis values incorrectly
• Using string instead of integer for axis

3. Given the array arr = np.array([[1, 2, 3], [4, 5, 6]]), what is the output of np.sum(arr, axis=0)?

medium

A. [5 7 9]

B. [3 7]

C. [6 15]

D. [1 2 3 4 5 6]

Solution

1. Step 1: Understand axis=0 sums columns

   Summing along axis=0 adds elements vertically: (1+4), (2+5), (3+6).

2. Step 2: Calculate sums for each column

   1+4=5, 2+5=7, 3+6=9, so result is [5 7 9].

3. Final Answer:

   [5 7 9] -> Option A

4. Quick Check:

   Sum columns = [5 7 9] [OK]

Hint: axis=0 sums columns vertically [OK]

Common Mistakes:

• Confusing axis=0 with axis=1
• Adding rows instead of columns
• Expecting a scalar output

4. What is wrong with this code snippet?

import numpy as np
arr = np.array([[1, 2], [3, 4]])
result = np.sum(arr, axis=2)

medium

A. The array is not defined correctly.

B. np.sum() cannot be used on 2D arrays.

C. The axis value 2 is invalid for a 2D array.

D. The import statement is missing.

Solution

1. Step 1: Check array dimensions

   The array has shape (2, 2), so valid axis values are 0 or 1.

2. Step 2: Validate axis parameter

   axis=2 is out of bounds for this 2D array, causing an error.

3. Final Answer:

   The axis value 2 is invalid for a 2D array. -> Option C

4. Quick Check:

   Axis must be less than array dimensions [OK]

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

Common Mistakes:

• Using axis larger than array dimensions
• Thinking np.sum can't handle 2D arrays
• Ignoring error messages

5. You have a 3D numpy array arr = np.array([[[1,2],[3,4]], [[5,6],[7,8]]]). What is the shape of the result when you run np.sum(arr, axis=1)?

hard

A. (1, 2)

B. (3, 2)

C. (2, 4)

D. (2, 2)

Solution

1. Step 1: Understand the shape of arr

   arr has shape (2, 2, 2): 2 blocks, each with 2 rows and 2 columns.

2. Step 2: Summing along axis=1

   axis=1 sums over the second dimension (rows), reducing it from 2 to 1, so shape changes from (2, 2, 2) to (2, 2).

3. Final Answer:

   (2, 2) -> Option D

4. Quick Check:

   Sum axis=1 reduces dimension 2 to 1 [OK]

Hint: Sum along axis removes that dimension [OK]

Common Mistakes:

• Confusing which axis is summed
• Expecting shape to stay same
• Mixing up dimensions in 3D arrays