2D array indexing (row, col) in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to access the element in the 2D array at row 1, column 2.

NumPy

import numpy as np
arr = np.array([[10, 20, 30], [40, 50, 60]])
element = arr[[1]]
print(element)

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

A1, 2

Attempts:

3 left

💡 Hint

Common Mistakes

Using arr[1][2] instead of arr[1, 2]

Using one index instead of two

Mixing up row and column indices

To access an element in a 2D numpy array, use arr[row, column]. Here, row 1 and column 2 gives the element 60.

2fill in blank

medium

Complete the code to extract the entire second row from the 2D array.

NumPy

import numpy as np
arr = np.array([[5, 10, 15], [20, 25, 30], [35, 40, 45]])
row = arr[[1]]
print(row)

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

A0

B2

C1

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using index 2 to get the second row

Using arr[:, 1] which gets the second column

Using negative indices incorrectly

Rows are indexed starting at 0. The second row is at index 1.

3fill in blank

hard

Fix the error in the code to correctly access the element at row 0, column 1.

NumPy

import numpy as np
arr = np.array([[7, 8, 9], [10, 11, 12]])
element = arr[[1]]
print(element)

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

A[0, 1]

B0, 1

C0][1

D0;1

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiple square brackets like arr[0][1]

Using a list inside brackets like arr[[0,1]]

Using semicolon instead of comma

Use comma-separated indices inside square brackets to access elements in numpy arrays: arr[0, 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 characters.

NumPy

words = ['cat', 'house', 'dog', 'elephant']
lengths = {word: [1] for word in words if [2]
print(lengths)

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Alen(word)

Blen(word) > 3

Cword > 3

Dword.length > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing the word string directly to a number

Using incorrect attribute like word.length

Not filtering words at all

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

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words longer than 4 characters.

NumPy

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

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

Aw.upper()

Blen(w)

Clen(w) > 4

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys

Not filtering words by length

Mixing up keys and values in the dictionary comprehension

Use w.upper() for keys, len(w) for values, and filter words with length greater than 4 using len(w) > 4.

Practice

1. What does arr[2, 1] mean when arr is a 2D numpy array?

easy

A. Access the element in the 3rd row and 2nd column

B. Access the element in the 2nd row and 3rd column

C. Access the entire 2nd row

D. Access the entire 1st column

Solution

1. Step 1: Understand zero-based indexing

   In numpy, counting starts at 0, so index 2 means the 3rd row, and index 1 means the 2nd column.

2. Step 2: Interpret the indexing

   arr[2, 1] accesses the element at row 3, column 2.

3. Final Answer:

   Access the element in the 3rd row and 2nd column -> Option A

4. Quick Check:

   Row 2, Col 1 means 3rd row, 2nd column [OK]

Hint: Remember: rows and columns start counting at zero [OK]

Common Mistakes:

• Confusing row and column order
• Starting count from 1 instead of 0
• Thinking arr[2,1] returns a row or column

2. Which of the following is the correct syntax to access the element in the 1st row and 3rd column of a numpy 2D array arr?

easy

A. arr[1, 3]

B. arr[0, 2]

C. arr[3, 1]

D. arr[2, 0]

Solution

1. Step 1: Recall zero-based indexing

   The 1st row is index 0, and the 3rd column is index 2.

2. Step 2: Match indexes to syntax

   So, arr[0, 2] accesses the element in the 1st row and 3rd column.

3. Final Answer:

   arr[0, 2] -> Option B

4. Quick Check:

   Row 0, Col 2 = 1st row, 3rd column [OK]

Hint: Count rows and columns from zero for correct indexing [OK]

Common Mistakes:

• Using 1-based indexes like arr[1,3]
• Swapping row and column indexes
• Using parentheses instead of brackets

3. Given the numpy array:

import numpy as np
arr = np.array([[5, 10, 15], [20, 25, 30], [35, 40, 45]])
print(arr[1, 2])

What is the output?

medium

A. 30

B. 25

C. 20

D. 45

Solution

1. Step 1: Identify the element at row 1, column 2

   Row 1 is the second row: [20, 25, 30]. Column 2 is the third element in that row, which is 30.

2. Step 2: Confirm the printed value

   The code prints arr[1, 2], so the output is 30.

3. Final Answer:

   30 -> Option A

4. Quick Check:

   arr[1,2] = 30 [OK]

Hint: Remember row then column to find the element [OK]

Common Mistakes:

• Choosing element from wrong row or column
• Confusing zero-based indexing
• Reading the array incorrectly

4. What is wrong with this code snippet?

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

medium

A. Prints 3 correctly

B. SyntaxError due to wrong brackets

C. IndexError because row 2 does not exist

D. Prints 1 correctly

Solution

1. Step 1: Check array shape and indexes

   The array has 2 rows (index 0 and 1). Index 2 for row is out of range.

2. Step 2: Understand error type

   Accessing arr[2, 0] causes an IndexError because row 2 does not exist.

3. Final Answer:

   IndexError because row 2 does not exist -> Option C

4. Quick Check:

   Row index 2 is invalid for 2-row array [OK]

Hint: Check array shape before indexing to avoid errors [OK]

Common Mistakes:

• Assuming indexing starts at 1
• Ignoring array shape
• Confusing error types

5. You have a 4x4 numpy array arr filled with numbers 1 to 16 in row-major order. Which code correctly changes the element in the last row and second column to 100?

hard

A. arr[3, -2] = 100

B. arr[4, 2] = 100

C. arr[-1, 2] = 100

D. arr[3, 1] = 100

Solution

1. Step 1: Identify last row and second column indexes

   For a 4x4 array, rows and columns are indexed 0 to 3. Last row is index 3, second column is index 1.

2. Step 2: Check each option

   arr[3, 1] = 100 uses arr[3, 1] which is correct. arr[4, 2] = 100 uses invalid index 4. arr[-1, 2] = 100 uses column index 2 (third column), not second. arr[3, -2] = 100 uses negative index -2 for column which is the third column, not second.

3. Final Answer:

   arr[3, 1] = 100 -> Option D

4. Quick Check:

   Last row = 3, second column = 1 [OK]

Hint: Use zero-based indexes: last row is size-1, second column is 1 [OK]

Common Mistakes:

• Using out-of-range indexes like 4
• Confusing negative indexes for columns
• Mixing row and column positions