Build practical NumPy fluency by working through 100 focused exercises in a single Jupyter Notebook. The first 50 exercises establish the essentials of array creation, computation, indexing, reshaping, sorting, and statistics; the remaining 50 optional exercises combine these techniques in more demanding problems.
Each exercise includes a worked code example followed by a blank cell where you can reproduce or adapt the solution yourself. The progression moves from everyday ndarray operations to topics such as missing values, normalization, correlation, eigenvalues, and image-to-array conversion, making this a practice-driven way to strengthen numerical Python skills.
What You Will Learn
- Create one-, two-, and three-dimensional arrays with NumPy constructors
- Apply element-wise arithmetic, matrix operations, and mathematical functions
- Select and transform data with indexing, slicing, reshaping, stacking, and splitting
- Summarize arrays with extrema, averages, variance, standard deviation, and percentiles
- Sort arrays, locate values, handle missing data, and compare array elements
- Normalize numerical data and calculate distances, correlations, and eigenvalues
- Combine multiple NumPy operations to solve compact array-manipulation problems
- Convert image data to an
ndarrayand display the reconstructed result
Who This Course Is For
This course is for Python learners who want substantial hands-on NumPy practice, as well as data science learners who need stronger array-manipulation foundations. Beginners can follow the worked examples in the first half, while learners with prior NumPy experience can use the optional second half as a skills check.
Prerequisites: Basic Python syntax, including variables, functions, and lists, is recommended. Previous NumPy experience is helpful but not required.
Learning environment: A browser-based Jupyter Notebook on Ubuntu 22.04 with Python 3.10 and NumPy 2.0.0 is provided. The notebook contains all 100 exercises, worked examples, and blank practice cells, so no local installation is needed.
Frequently Asked Questions
Is this a guided course or an exercise collection?
It is one extensive practice notebook rather than a sequence of separate guided labs. Short explanations introduce the main concepts, but most of your time is spent reading, running, and rewriting NumPy code.
Are solutions provided for the exercises?
Yes. Each exercise shows a worked implementation and provides a blank code cell for manual practice. For the strongest learning experience, try to reproduce the result without copying the example directly.
Do I need to complete all 100 exercises in order?
The first 50 exercises form the basic section and provide the clearest progression. Exercises 51–100 are explicitly optional and can be attempted selectively or used to review areas where you need more practice.
Does the course teach machine learning or data science projects?
No. It develops NumPy techniques that support later work in those fields, but it does not build predictive models or complete end-to-end projects. Its focus is array operations and numerical problem solving.
