Data Science NumPy & Pandas - Practice Questions 2026

Master NumPy and Pandas: Data Science Practice Questions 2026Welcome to the most comprehensive practice exams designed to help you master the foundational pillars of Python Data Science: NumPy and Pandas. In 2026, the demand for data professionals who can manipulate, clean, and analyze data with precision is higher than ever. These practice tests are engineered to bridge the gap between theoretical knowledge and practical execution.Why Serious Learners Choose These Practice ExamsSerious learners understand that watching tutorials is only half the battle. True mastery comes from being tested on edge cases, syntax nuances, and performance optimization. This course provides:Retakeability: You can retake the exams as many times as you want to ensure total mastery.Original Question Bank: Access a huge, unique set of questions designed to mimic real-world technical interviews and certification environments.Expert Support: You get direct support from instructors if you have questions regarding any concept or specific problem.Comprehensive Explanations: Each question includes a detailed explanation to ensure you understand the "why" behind every answer.Learning on the Go: Fully mobile-compatible with the Udemy app for studying anytime, anywhere.Risk-Free Learning: A 30-day money-back guarantee if you are not satisfied with the course content.Course StructureThe exams are strategically organized to guide you through a logical learning progression:Basics / Foundations: Focuses on the absolute essentials, such as creating arrays in NumPy and Series/DataFrames in Pandas. You will be tested on data types, shapes, and basic indexing.Core Concepts: Covers fundamental operations including vectorization, broadcasting, and essential Pandas methods like filtering and sorting. This section ensures your "muscle memory" for daily coding tasks is sharp.Intermediate Concepts: Moves into data transformation. Expect questions on GroupBy operations, pivot tables, merging/joining multiple datasets, and handling multi-index structures.Advanced Concepts: Challenges your ability to optimize code. Topics include performance tuning, memory management for large datasets, window functions, and complex NumPy ufuncs.Real-world Scenarios: Situational questions where you must clean "dirty" data, handle missing values (NaNs), and prepare datasets for machine learning models.Mixed Revision / Final Test: A comprehensive simulation of a professional environment, mixing all difficulty levels and topics to test your retention and speed.Sample Practice QuestionsQuestion 1You have a NumPy array $A$ defined as A = np.array([[1, 2], [3, 4]]). What is the result of np.sum(A, axis=1)?Option 1: [4, 6]Option 2: [3, 7]Option 3: [10]Option 4: [[1, 2], [3, 4]]Option 5: ErrorCorrect Answer: Option 2Correct Answer Explanation: In NumPy, axis=1 refers to the horizontal axis (rows). When you sum across axis=1, you are summing the elements within each row. Row 1 ($1 + 2$) equals 3, and Row 2 ($3 + 4$) equals 7. This results in the array [3, 7].Wrong Answers Explanation:Option 1: This would be the result of np.sum(A, axis=0), which sums vertically down the columns.Option 3: This is the result of np.sum(A) without an axis specified, which provides the global sum of all elements.Option 4: This is just the original array; no summation has occurred.Option 5: The syntax is perfectly valid in NumPy, so no error is triggered.Question 2In Pandas, which method is most efficient for filling missing values in a DataFrame with a specific constant?Option 1: df.replace(np.nan, value)Option 2: df.dropna()Option 3: df.fillna(value)Option 4: df.apply(lambda x: value if pd.isna(x) else x)Option 5: df.isna() = valueCorrect Answer: Option 3Correct Answer Explanation: The .fillna() method is the specialized, built-in Pandas function designed specifically for handling missing data. It is highly optimized for performance across large DataFrames compared to generic replacement or iteration methods.Wrong Answers Explanation:Option 1: While .replace() works, it is more computationally expensive as it searches for specific values rather than focusing solely on the internal null bitmask.Option 2: This method removes rows or columns containing missing values entirely rather than filling them.Option 3: Using .apply() with a lambda function is significantly slower because it forces Python-level iteration over each element, losing the benefits of vectorization.Option 5: This is invalid Python syntax. You cannot assign a value directly to a function call.We hope that by now you are convinced! There are hundreds more questions waiting for you inside the course to ensure you become a proficient Data Science professional.