Aggregation along specific axes 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_sum = arr.[1]()

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

Amax

Bmean

Csum

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of sum()

Using max() or min() which find extremes, not totals

The sum() function adds all elements in the array.

2fill in blank

medium

Complete the code to calculate the mean of the array elements along axis 0 (columns).

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4]])
col_mean = arr.[1](axis=0)

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

Asum

Bmean

Cmax

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=1 which averages rows instead of columns

Using sum() instead of mean()

The mean(axis=0) calculates the average of each column.

3fill in blank

hard

Fix the error in the code to find the maximum value along axis 1 (rows).

NumPy

import numpy as np
arr = np.array([[5, 7, 2], [3, 8, 1]])
max_per_row = arr.[1](axis=1)

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

Amax

Bmin

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using min() which finds smallest values

Using mean() or sum() which calculate averages or totals

The max(axis=1) function finds the largest value in each row.

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 characters.

NumPy

words = ['cat', 'elephant', 'dog', 'giraffe']
lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > in the condition

Using word instead of len(word) for the value

We use len(word) to get the length and filter words with length greater than 3 using >.

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 4 characters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = { [1]: [2] for w in words if len(w) [3] 4 }

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

Aw.upper()

Blen(w)

C>

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using w instead of w.upper() for keys

Using < instead of > in the condition

Using w instead of len(w) for values

We convert words to uppercase for keys using w.upper(), get lengths with len(w), and filter words longer than 4 with >.

Practice

1. What does setting axis=0 do when aggregating a 2D NumPy array?

easy

A. Aggregates values down each column (per column)

B. Aggregates values across each row (per row)

C. Flattens the array before aggregation

D. Aggregates only the diagonal elements

Solution

1. Step 1: Understand axis parameter meaning

   In NumPy, axis=0 means aggregation happens down the rows, combining values in each column.

2. Step 2: Apply to 2D array

   Aggregating with axis=0 summarizes each column separately.

3. Final Answer:

   Aggregates values down each column (per column) -> Option A

4. Quick Check:

   axis=0 means down rows = per column aggregation [OK]

Hint: axis=0 aggregates columns, axis=1 aggregates rows [OK]

Common Mistakes:

• Confusing axis=0 with axis=1
• Thinking axis=0 aggregates rows instead of columns
• Assuming axis=0 flattens the array

2. Which of the following is the correct syntax to sum a 2D NumPy array arr along rows (per row)?

easy

A. np.sum(arr, axis=1)

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

C. arr.sum(axis=2)

D. np.sum(arr)

Solution

1. Step 1: Identify axis for row-wise sum

   Summing across columns (per row) requires axis=1.

2. Step 2: Check syntax correctness

   np.sum(arr, axis=1) correctly sums each row's elements.

3. Final Answer:

   np.sum(arr, axis=1) -> Option A

4. Quick Check:

   Sum per row uses axis=1 [OK]

Hint: Use axis=1 to sum across columns (per row) [OK]

Common Mistakes:

• Using axis=0 to sum rows
• Using invalid axis like 2 for 2D array
• Forgetting to specify 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. [3 7 11]

B. [5 7 9]

C. [6 15]

D. [1 2 3 4 5 6]

Solution

1. Step 1: Understand axis=0 sum

   Sum down rows means add elements in each column: (1+4), (2+5), (3+6).

2. Step 2: Calculate sums

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

3. Final Answer:

   [5 7 9] -> Option B

4. Quick Check:

   Sum axis=0 adds columns down rows [OK]

Hint: Sum axis=0 adds columns down rows [OK]

Common Mistakes:

• Adding rows instead of columns
• Confusing axis=0 with axis=1
• Returning flattened array instead of summed

4. What is wrong with this code snippet?

arr = np.array([[1, 2], [3, 4]])
result = np.mean(arr, axis=2)

medium

A. No error, code runs fine

B. np.mean cannot be used on arrays

C. Missing parentheses in np.array

D. axis=2 is invalid for 2D array

Solution

1. Step 1: Check array dimensions

   The array arr is 2D with shape (2,2), so valid axes are 0 and 1.

2. Step 2: Validate axis parameter

   Using axis=2 is invalid and causes an error because axis 2 does not exist.

3. Final Answer:

   axis=2 is invalid for 2D array -> Option D

4. Quick Check:

   Axis must be less than array dimensions [OK]

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

Common Mistakes:

• Using axis number too large for array
• Thinking np.mean can't be used on arrays
• Ignoring error messages

5. You have a 3D NumPy array data with shape (4, 3, 2). You want to compute the sum of each 2D slice along the first axis (axis=0), resulting in a 2D array. Which code correctly does this?

hard

A. np.sum(data)

B. np.sum(data, axis=1)

C. np.sum(data, axis=0)

D. np.sum(data, axis=2)

Solution

1. Step 1: Understand array shape and axis

   Array shape is (4,3,2). Summing along axis=0 collapses the first dimension (4), resulting in shape (3,2).

2. Step 2: Apply sum along axis=0

   Using np.sum(data, axis=0) sums all 4 slices element-wise, producing a 2D array.

3. Final Answer:

   np.sum(data, axis=0) -> Option C

4. Quick Check:

   Sum axis=0 collapses first dimension to 2D [OK]

Hint: Sum axis=0 collapses first dimension in 3D arrays [OK]

Common Mistakes:

• Using wrong axis and getting wrong shape
• Summing all elements without axis
• Confusing axis numbering in 3D arrays