300+ Numpy Interview Questions for Data Science

300+ Numpy Interview Questions for Data Science udemy course free download

Ace NumPy Interview Questions with Confidence Using Real-World MCQs and Hands-on Practice

NumPy Interview Preparation Course

This course is a focused collection of multiple-choice questions designed to prepare you for real-world NumPy interview scenarios. You'll cover core concepts like arrays, indexing, broadcasting, reshaping, vectorization, and performance optimization — all through question-and-explanation format.

Unlike basic tutorials, this course helps you think like an interviewer, focusing on why certain answers are correct and how to avoid common pitfalls.

NumPy Interview Topics for Data Science

This guide focuses exclusively on NumPy, covering fundamental to advanced concepts crucial for data science roles.

I. NumPy Fundamentals

1. Introduction to NumPy and ndarray

• Topics:

  • What is NumPy and why is it essential for data science? (Benefits over Python lists: speed, memory efficiency, mathematical operations, integration with other libraries)

  • Understanding the ndarray object: homogeneous, fixed-size at creation, n-dimensional

  • Key attributes of ndarray: ndim, shape, size, dtype, itemsize, nbytes

• Difficulty Level: Easy

• MCQ Count: 15

2. Array Creation

• Topics:

  • Creating arrays from Python lists/tuples using np array()

  • Creating arrays with initial placeholders: np zeros(), np ones(), np full(), np empty()

  • Creating sequences: np arange(), np linspace(), np logspace()

  • Creating identity matrices: np eye()

  • Understanding dtype and type casting (astype())

• Difficulty Level: Easy

• MCQ Count: 20

3. Array Indexing and Slicing

• Topics:

  • Basic indexing (integer indexing for single elements, negative indexing)

  • Slicing 1D, 2D, and multi-dimensional arrays ([start:stop:step])

  • Differences between copy() and view (np view())

  • Boolean indexing/Masking: filtering elements based on conditions

  • Fancy indexing: using integer arrays for indexing

• Difficulty Level: Medium

• MCQ Count: 25

II. Array Manipulation and Operations

4. Reshaping and Transposing

• Topics:

  • reshape(): changing the shape of an array (total elements must remain constant)

  • ravel() and flatten(): flattening multi-dimensional arrays (differences in copy vs. view)

  • transpose() and .T attribute: swapping axes

  • Adding/removing dimensions: np newaxis, np expand_dims(), np squeeze()

• Difficulty Level: Medium

• MCQ Count: 20

5. Concatenation and Splitting

• Topics:

  • Joining arrays: np concatenate(), np vstack(), np hstack(), np dstack()

  • Splitting arrays: np split(), np vsplit(), np hsplit(), np array_split()

  • Understanding the axis parameter in concatenation and splitting

• Difficulty Level: Medium

• MCQ Count: 20

6. Universal Functions (Ufuncs)

• Topics:

  • Concept of Ufuncs: element-wise operations, speed benefits

  • Common Ufuncs: arithmetic operations (add, subtract, multiply, divide), trigonometric functions (sin, cos, tan), exponential and logarithmic functions (exp, log), comparison operators

  • Broadcasting rules: how NumPy handles operations on arrays of different shapes

  • np vectorize(): applying non-vectorized Python functions element-wise (and its limitations compared to true ufuncs)

• Difficulty Level: Medium to Hard

• MCQ Count: 25

III. Mathematical and Statistical Operations

7. Basic Mathematical Operations

• Topics:

  • Element-wise arithmetic operations

  • Dot product (np dot(), @ operator for matrix multiplication), cross product (np cross())

  • Matrix multiplication vs. element-wise multiplication

  • Inner and outer products

• Difficulty Level: Medium

• MCQ Count: 20

8. Aggregation Functions

• Topics:

  • Calculating sum, mean, median, standard deviation, variance (np sum(), np mean(), np median(), np std(), np var())

  • Min/Max values and their indices (np min(), np max(), np argmin(), np argmax())

  • Cumulative sum and product (np cumsum(), np cumprod())

  • Understanding the axis parameter for aggregations

  • Differences between np mean() and np average()

• Difficulty Level: Medium

• MCQ Count: 20

9. Linear Algebra (np linalg)

• Topics:

  • Determinant (np linalg det())

  • Inverse of a matrix (np linalg inv())

  • Eigenvalues and eigenvectors (np linalg eig(), np linalg eigh() for Hermitian matrices)

  • Solving linear equations (np linalg solve())

  • Singular Value Decomposition (SVD) (np linalg svd())

  • Norms (np linalg norm())

• Difficulty Level: Hard

• MCQ Count: 25

IV. Advanced NumPy Concepts

10. Broadcasting in Depth

• Topics:

  • Detailed rules of broadcasting (dimension matching, size 1 dimensions)

  • Practical examples of broadcasting in different scenarios (scalar with array, 1D with 2D)

  • Common broadcasting errors and how to resolve them

• Difficulty Level: Hard

• MCQ Count: 20

11. Memory Management and Performance

• Topics:

  • Contiguous memory layout (C-order vs. Fortran-order)

  • Views vs. copies and their implications on memory and performance

  • Strategies for optimizing NumPy code (vectorization, choosing appropriate dtypes, in-place operations)

  • np memmap() for large datasets that don't fit in memory

• Difficulty Level: Hard

• MCQ Count: 20

12. Structured Arrays and Record Arrays

• Topics:

  • Creating structured arrays: arrays with different data types for different fields

  • Accessing data in structured arrays by field names

  • Use cases for structured arrays in data science (e.g., representing tabular data before Pandas)

• Difficulty Level: Medium

• MCQ Count: 15

13. Missing Values and Masked Arrays (np ma)

• Topics:

  • Representing missing data with np nan and np inf

  • Handling NaN values in calculations (np nanmean(), np nansum(), etc.)

  • Introduction to masked arrays (np ma MaskedArray): concept and basic operations

  • When to use masked arrays vs. simply handling NaNs

• Difficulty Level: Medium

• MCQ Count: 15

14. Random Number Generation (np random)

• Topics:

  • Generating random numbers from various distributions: uniform (rand(), randint(), random()), normal (randn(), normal()), binomial, Poisson

  • np random seed() for reproducibility

  • Shuffling arrays (np random shuffle())

  • Random choices (np random choice())

• Difficulty Level: Medium

• MCQ Count: 20

V. Integration and Advanced Applications

15. Advanced Array Operations/Techniques

• Topics:

  • np where(): conditional element selection

  • np unique(): finding unique elements

  • Sorting arrays

  • Set operations (np union1d(), np intersect1d(), np setdiff1d(), np setxor1d())

  • np meshgrid(): creating coordinate matrices for plotting/evaluating functions

  • Fourier Transform basics (np fft)

• Difficulty Level: Hard

• MCQ Count: 20

And Much More !!!