Four focused Jupyter notebooks covering NumPy from fundamentals to advanced array operations — creation, indexing, broadcasting, math, views vs copies, and practice exercises.
| Notebook | Topic | What's Covered |
|---|---|---|
basic.ipynb |
Fundamentals | np.array(), dtype, shape, ndim, size, zeros, ones, arange |
Array-operations.ipynb |
Operations | Element-wise math, broadcasting rules, universal functions (ufuncs), manipulation |
practise.ipynb |
Practice | Real-world problems applying everything from basics and operations |
last_part.ipynb |
Advanced | Advanced indexing (boolean, fancy), views vs copies, memory management, stats |
Sample .npy arrays are included (array1.npy, array2.npy, array3.npy) — loaded directly in the notebooks so you can run exercises without sourcing external data.
git clone https://github.com/Wcoder547/Numpy.git
cd Numpy
pip install numpy jupyter matplotlib
jupyter notebookWork through the notebooks in order: basic → Array-operations → practise → last_part.
- Array creation —
np.zeros,np.ones,np.arange,np.linspace,np.random - Array attributes —
shape,dtype,ndim,size - Indexing & slicing — basic, boolean, fancy indexing
- Broadcasting — rules and real examples
- Math & stats — element-wise ops,
mean,std,min,max, matrix operations - Manipulation —
reshape,transpose,concatenate,stack - Views vs copies — understanding when NumPy shares memory vs copies it
- Performance — why NumPy is fast and how to write vectorized code
- Python 3.11+
- NumPy 2.0+
- Jupyter Notebooks
- Matplotlib (optional, used in some visualizations)
NumPy is the foundation everything else in the Python data stack is built on — pandas, scikit-learn, and PyTorch all use NumPy arrays under the hood. Understanding broadcasting, views vs copies, and vectorized operations makes you a faster and more efficient data engineer.
Built by Waseem Akram — Full-Stack Developer and DevOps Engineer based in Pakistan, working across the MERN stack, Generative AI integrations, and cloud automation.
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