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].