Array indexing and slicing in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of slicing a 2D NumPy array

What is the output of the following code snippet?

Data Analysis Python

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

A

[[50 60]
 [80 90]]

B

[[40 50]
 [70 80]]

C

[[20 30]
 [50 60]]

D

[[40 60]
 [70 90]]

💡 Hint

Remember that arr[1:, :2] means rows from index 1 to end, columns from index 0 up to 2 (not including 2).

The slice arr[1:, :2] selects rows starting from index 1 (second row) to the end, and columns from index 0 up to but not including 2 (first two columns). So it picks rows [[40,50,60],[70,80,90]] and columns [[40,50],[70,80]].

❓ data_output

intermediate

Number of elements after slicing a 1D array

Given the array arr = [5, 10, 15, 20, 25, 30], how many elements are in arr[2:5]?

Data Analysis Python

arr = [5, 10, 15, 20, 25, 30]
sliced = arr[2:5]
print(len(sliced))

A 3

B 4

C 2

D 5

💡 Hint

Remember slicing includes the start index but excludes the end index.

arr[2:5] includes elements at indices 2, 3, and 4, which are 15, 20, and 25. So the length is 3.

🔧 Debug

advanced

Identify the error in slicing a NumPy array

What error does the following code raise?

Data Analysis Python

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
sliced = arr[::2, 1]
print(sliced)

A SyntaxError: invalid syntax

B TypeError: unhashable type: 'slice'

C IndexError: too many indices for array

D No error, outputs [1 3 5]

💡 Hint

Check how many dimensions the array has and how many indices are used.

The array arr is 1D, but the code tries to index it with two indices (arr[::2, 1]), which causes an IndexError for too many indices.

🧠 Conceptual

advanced

Effect of negative step in slicing

What is the output of this code? import numpy as np arr = np.array([10, 20, 30, 40, 50]) sliced = arr[4:1:-1] print(sliced)

A [50 40 30 20]

B [40 30 20]

C [10 20 30]

D [50 40 30]

💡 Hint

Negative step means slicing backwards, start index is included, end index is excluded.

arr[4:1:-1] starts at index 4 (value 50), goes backwards up to but not including index 1, so it includes indices 4,3,2 which are 50,40,30.

🚀 Application

expert

Extracting a diagonal from a 2D NumPy array using slicing

Given a 3x3 NumPy array, which slicing expression extracts the main diagonal elements as a 1D array?

Data Analysis Python

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

A arr[range(3), range(3)]

B arr[:, 0]

C arr[0:3, 0:3]

D arr[::2, ::2]

💡 Hint

Think about how to select elements where row and column indices are the same.

Using arr[range(3), range(3)] selects elements where row and column indices match: (0,0), (1,1), (2,2), which is the main diagonal.