Slicing rows and columns in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of slicing specific rows and columns in a NumPy array

What is the output of the following code snippet that slices rows and columns from a NumPy array?

NumPy

import numpy as np
arr = np.array([[10, 20, 30, 40],
                [50, 60, 70, 80],
                [90, 100, 110, 120],
                [130, 140, 150, 160]])
result = arr[1:3, 2:4]
print(result)

A

[[60 70]
 [100 110]]

B

[[ 70  80]
 [110 120]]

C

[[50 60 70 80]
 [90 100 110 120]]

D

[[70 80 90]
 [110 120 130]]

💡 Hint

Remember that slicing arr[a:b, c:d] selects rows from a to b-1 and columns from c to d-1.

The slice arr[1:3, 2:4] selects rows 1 and 2, and columns 2 and 3. So it picks elements [[70, 80], [110, 120]].

❓ data_output

intermediate

Number of elements after slicing rows and columns

Given the NumPy array below, how many elements are in the sliced array `arr[::2, 1:4:2]`?

NumPy

import numpy as np
arr = np.arange(24).reshape(4,6)
sliced = arr[::2, 1:4:2]
print(sliced)

A 4

B 6

C 3

D 8

💡 Hint

Count how many rows and columns are selected by the slice.

arr[::2, 1:4:2] selects every 2nd row starting at 0 (rows 0 and 2) and columns 1 and 3 (since 1:4:2 means columns 1 and 3). So 2 rows * 2 columns = 4 elements.

🔧 Debug

advanced

Identify the error in slicing a 2D NumPy array

What error will the following code raise when slicing the array?

NumPy

import numpy as np
arr = np.array([[1,2,3],[4,5,6],[7,8,9]])
result = arr[1, 2:]
print(result[1])

A ValueError: too many indices for array

B TypeError: only integer scalar arrays can be converted to a scalar index

C No error, output is 9

D IndexError: index 1 is out of bounds for axis 0 with size 1

💡 Hint

Check the shape of the sliced array before accessing index 1.

arr[1, 2:] returns a 1D array [6,] of length 1 (row 1, columns from 2 to end). Accessing result[1] raises IndexError: index 1 is out of bounds for axis 0 with size 1.

❓ visualization

advanced

Visualize the sliced subarray from a heatmap

Given the heatmap of a 5x5 array, which sliced subarray corresponds to `arr[1:4, 0:3]`?

NumPy

import numpy as np
import matplotlib.pyplot as plt
arr = np.arange(25).reshape(5,5)
plt.imshow(arr, cmap='viridis')
plt.colorbar()
plt.title('Original 5x5 Array Heatmap')
plt.show()

A

[[ 5  6  7]
 [10 11 12]
 [15 16 17]]

B

[[ 5  6  7]
 [10 11 12]
 [15 16 17]
 [20 21 22]]

C

[[ 1  2  3]
 [ 6  7  8]
 [11 12 13]]

D

[[ 0  1  2]
 [ 5  6  7]
 [10 11 12]]

💡 Hint

Remember slicing rows 1 to 3 and columns 0 to 2 means rows 1,2,3 and columns 0,1,2.

arr[1:4, 0:3] selects rows 1,2,3 and columns 0,1,2. The values are [[5,6,7],[10,11,12],[15,16,17]].

🚀 Application

expert

Extract diagonal blocks using slicing in a large NumPy array

You have a 6x6 NumPy array representing a matrix divided into four 3x3 blocks. Which slice extracts the bottom-right 3x3 block?

NumPy

import numpy as np
matrix = np.arange(36).reshape(6,6)
# Extract bottom-right block here
block = matrix[3:6, 3:6]
print(block)

A matrix[0:3, 3:6]

B matrix[3:6, 0:3]

C matrix[3:6, 3:6]

D matrix[0:3, 0:3]

💡 Hint

The bottom-right block is the last 3 rows and last 3 columns.

The 6x6 matrix is split into four 3x3 blocks. The bottom-right block is rows 3 to 5 and columns 3 to 5, so matrix[3:6, 3:6].

Practice

1. What does the slicing notation array[1:3, 0:2] do on a 2D NumPy array?

easy

A. Selects rows 1 and 2, and columns 0 and 1

B. Selects rows 0 to 3 and columns 0 to 2

C. Selects rows 1 to 3 and columns 0 to 2

D. Selects rows 1 and 3, and columns 0 and 2

Solution

1. Step 1: Understand row slicing 1:3

   This means select rows starting at index 1 up to but not including index 3, so rows 1 and 2.

2. Step 2: Understand column slicing 0:2

   This means select columns starting at index 0 up to but not including index 2, so columns 0 and 1.

3. Final Answer:

   Selects rows 1 and 2, and columns 0 and 1 -> Option A

4. Quick Check:

   Slice end is excluded = A [OK]

Hint: Remember end index is excluded in slicing [OK]

Common Mistakes:

• Including the end index in the slice
• Confusing rows and columns order
• Using commas incorrectly in slicing

2. Which of the following is the correct syntax to select all rows and columns 2 to 4 (inclusive start, exclusive end) from a NumPy array named arr?

easy

A. arr[2:4, :]

B. arr[:, 2:5]

C. arr[2:5, :]

D. arr[:, 2:4]

Solution

1. Step 1: Identify rows and columns in slicing

   The first index before comma is rows, second is columns.

2. Step 2: Select all rows and columns 2 to 4

   All rows means :, columns 2 to 4 means 2:5 because end is excluded.

3. Final Answer:

   arr[:, 2:5] -> Option B

4. Quick Check:

   All rows = :, columns 2 to 4 = 2:5 [OK]

Hint: Use ':' for all rows or columns [OK]

Common Mistakes:

• Swapping rows and columns in slicing
• Using wrong end index in slice
• Missing colon for all rows or columns

3. Given the NumPy array:

import numpy as np
arr = np.array([[10, 20, 30, 40],
                [50, 60, 70, 80],
                [90, 100, 110, 120]])
print(arr[0:2, 1:3])

What is the output?

medium

A. [[50 60]
[90 100]]

B. [[10 20 30]
[50 60 70]]

C. [[20 30 40]
[60 70 80]]

D. [[20 30]
[60 70]]

Solution

1. Step 1: Slice rows 0 to 1

   Rows 0 and 1 are selected (indices 0 and 1), so first two rows.

2. Step 2: Slice columns 1 to 2

   Columns 1 and 2 are selected (indices 1 and 2), so second and third columns.

3. Final Answer:

   [[20 30] [60 70]] -> Option D

4. Quick Check:

   Rows 0-1 and columns 1-2 slice = A [OK]

Hint: Remember slicing excludes the end index [OK]

Common Mistakes:

• Including the end index in slice
• Mixing up rows and columns
• Printing the whole array instead of slice

4. What is wrong with this code snippet to select rows 1 to 3 and columns 0 to 2 from a NumPy array data?

slice = data[1:3, 0:2]

medium

A. It selects rows 1 and 2 only, not row 3

B. It causes a syntax error due to variable name 'slice'

C. It selects columns 0, 1, and 2 instead of 0 and 1

D. It selects rows 0 to 3 instead of 1 to 3

Solution

1. Step 1: Examine row slicing 1:3

   In Python/NumPy slicing, the end index is exclusive, so 1:3 selects rows 1 and 2 only, not row 3.

2. Step 2: Column slicing 0:2

   Selects columns 0 and 1 (excludes 2).

3. Final Answer:

   It selects rows 1 and 2 only, not row 3 -> Option A

4. Quick Check:

   End index excluded in slicing = A [OK]

Hint: Slicing excludes the end index [OK]

Common Mistakes:

• Assuming slice includes end index
• Ignoring variable naming conflicts
• Confusing rows and columns in slicing

5. You have a 5x5 NumPy array arr. How do you extract the middle 3 rows and middle 3 columns as a new array?

hard

A. arr[0:3, 0:3]

B. arr[2:5, 2:5]

C. arr[1:4, 1:4]

D. arr[1:3, 1:3]

Solution

1. Step 1: Identify middle rows in 5x5 array

   Rows are indexed 0 to 4. Middle 3 rows are indices 1, 2, 3, so slice 1:4.

2. Step 2: Identify middle columns similarly

   Columns 1, 2, 3 are middle 3 columns, so slice 1:4.

3. Final Answer:

   arr[1:4, 1:4] -> Option C

4. Quick Check:

   Middle 3 rows and columns = 1:4 slice [OK]

Hint: Count indices carefully; middle 3 means 1 to 3 inclusive [OK]

Common Mistakes:

• Using wrong slice end index
• Selecting too few or too many rows/columns
• Confusing zero-based indexing