Python Data Analysis - Practice Questions 2026

Mastering data manipulation and analysis is the cornerstone of any successful career in Data Science or Business Analytics. This comprehensive practice exam course is meticulously designed to bridge the gap between theoretical knowledge and practical proficiency in NumPy and Pandas. Welcome to the ultimate preparation resource for your Python Data Analysis journey.

Whether you are preparing for a technical interview, a certification, or simply looking to solidify your coding skills, these practice tests provide a rigorous environment to test your limits. Why Serious Learners Choose These Practice ExamsSerious learners understand that watching tutorials is not enough. Proficiency comes from solving problems and understanding the logic behind every line of code.

These exams are built to simulate real-world coding challenges and academic assessments. By enrolling, you gain access to a curated question bank that focuses on edge cases, performance optimization, and common pitfalls in data manipulation. Course StructureBasics / Foundations: This section focuses on the fundamental building blocks.

You will be tested on NumPy array creation, basic Pandas Series and DataFrame structures, and simple indexing techniques. Understanding these basics is crucial for ensuring data integrity from the start. Core Concepts: Here, we dive into essential operations.

You will handle arithmetic operations, universal functions (ufuncs), and the primary Pandas methods for data selection, filtering, and sorting. Intermediate Concepts: This level introduces data cleaning and transformation. You will solve problems involving missing data (NaN), merging and joining multiple datasets, and performing complex group-by operations to aggregate information.

Advanced Concepts: Challenge yourself with high-level data manipulation. This includes multi-indexing, pivot tables, time-series analysis, and vectorization techniques to ensure your code runs efficiently on large datasets. Real-world Scenarios: These questions are framed as business problems.

You are given a data-driven objective and must identify the correct sequence of NumPy or Pandas functions to reach the solution, mirroring the daily tasks of a Data Analyst. Mixed Revision / Final Test: The ultimate challenge. This comprehensive exam pulls questions from all previous levels at random, testing your ability to switch context and apply the right tools under pressure.

Sample Practice QuestionsQUESTION 1You have a Pandas DataFrame named df with a column 'Sales'. Which of the following commands will return a new DataFrame containing only the rows where 'Sales' is greater than 500 and not null? OPTION 1: df[df['Sales'] > 500 & df['Sales'].

notnull()]OPTION 2: df. query('Sales > 500')OPTION 3: df. loc[df['Sales'] > 500]OPTION 4: df[(df['Sales'] > 500) & (df['Sales'].

notna())]OPTION 5: df. filter(sales > 500)CORRECT ANSWER: OPTION 4CORRECT ANSWER EXPLANATION: In Pandas, when combining multiple boolean conditions, you must wrap each condition in parentheses due to operator precedence. The & operator requires this to correctly evaluate the bitwise comparison.

notna() is a standard method to ensure null values are excluded. WRONG ANSWERS EXPLANATION:OPTION 1: Fails because it lacks parentheses around the conditions, which leads to a TypeError or incorrect evaluation in Python. OPTION 2: While query is powerful, it does not explicitly handle the "not null" requirement as requested in the prompt logic, although query often handles NaNs implicitly depending on the engine.

OPTION 3: Similar to Option 2, this only checks the "greater than 500" condition but does not demonstrate the compound logic requested. OPTION 5: The filter method in Pandas is used for subsetting columns or rows by labels (names), not for boolean indexing based on values. QUESTION 2What is the result of the following NumPy operation?

import numpy as npa = np. array([1, 2, 3])b = np. array([[10], [20]])result = a + bOPTION 1: A ValueError due to incompatible shapes.

OPTION 2: A 1D array: [11, 22, 23]. OPTION 3: A 2D array of shape (2, 3) where each row of b is added to a. OPTION 4: A 2D array of shape (3, 2).

OPTION 5: A scalar value: 57. CORRECT ANSWER: OPTION 3CORRECT ANSWER EXPLANATION: This is a classic example of NumPy Broadcasting. Array a has shape (3,) and array b has shape (2, 1).

NumPy expands b to (2, 3) and a to (2, 3) by duplicating values, resulting in a 2x3 matrix where the first row is [11, 12, 13] and the second is [21, 22, 23]. WRONG ANSWERS EXPLANATION:OPTION 1: Incorrect because NumPy's broadcasting rules allow operations between arrays of different dimensions if one of the dimensions is 1. OPTION 2: Incorrect because the operation results in a 2D structure, not a flattened 1D array.

OPTION 4: Incorrect because the trailing dimension of a (3) and the leading dimension of b (2) dictate a (2, 3) output. OPTION 5: Addition of arrays in NumPy is element-wise, not a summation into a scalar. What You Get With This CourseYou can retake the exams as many times as you want to ensure mastery .

This is a huge original question bank designed to cover every corner of the libraries . You get support from instructors if you have questions regarding any logic or solution . Each question has a detailed explanation to help you learn from your mistakes .

Mobile-compatible with the Udemy app for learning on the go . 30-days money-back guarantee if you're not satisfied with the quality . We hope that by now you're convinced!

And there are a lot more questions inside the course.