2D array indexing (row, col) in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the expression array[2, 3] mean in a 2D NumPy array?

It means accessing the element at row 2 and column 3 of the 2D array. Rows and columns start counting from 0.

beginner

How do you select the entire 3rd row from a 2D NumPy array named arr?

Use arr[2, :]. The colon : means all columns in row 2.

beginner

How do you select the entire 2nd column from a 2D NumPy array named arr?

Use arr[:, 1]. The colon : means all rows in column 1.

intermediate

What happens if you use negative indices like arr[-1, -2] in a 2D NumPy array?

Negative indices count from the end. -1 is the last row, -2 is the second last column.

intermediate

How can you select a submatrix from rows 1 to 3 and columns 2 to 4 in a 2D NumPy array arr?

Use slicing: arr[1:4, 2:5]. The end index is exclusive, so it selects rows 1,2,3 and columns 2,3,4.

In a 2D NumPy array, what does arr[0, 0] access?

A First column, last row element

B Last row, last column element

C First row, first column element

D Last column, first row element

Indexing starts at 0, so arr[0, 0] is the element in the first row and first column.

How do you select all rows but only the 2nd column from a 2D array arr?

A arr[2, :]

B arr[1, :]

C arr[:, 2]

D arr[:, 1]

The colon : means all rows, and 1 selects the second column.

What does arr[-1, :] select in a 2D NumPy array?

A Last row

B First row

C Last column

D First column

Negative index -1 means the last row, and : means all columns.

Which slice selects rows 0 to 2 and columns 1 to 3 in arr?

A arr[0:3, 1:4]

B arr[1:3, 0:4]

C arr[0:2, 1:3]

D arr[1:4, 0:3]

Slicing is exclusive at the end, so 0:3 means rows 0,1,2 and 1:4 means columns 1,2,3.

What is the shape of arr[:, 2] if arr is 5x5?

A (5, 5)

B (5,)

C (1, 5)

D (5, 1)

Selecting a single column returns a 1D array with length equal to the number of rows.

Explain how to access a single element, a full row, and a full column in a 2D NumPy array.

Think about how rows and columns are indexed and how slicing works.

Describe how negative indices work in 2D array indexing and give an example.

Negative numbers start counting backwards from the last element.

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