Repository files navigation

A hands-on journey throughNumPy, exploring array creation, manipulation, broadcasting, indexing, and data visualization — the foundation of scientific computing with Python.

This repository serves asmy personal NumPy Lab 🧪 — a place where I experiment, learn, and practice the building blocks of numerical computing in Python.

Each notebook is a step forward in masteringarray operations,reshaping,broadcasting, anddata manipulation, forming a strong base for my future journey inAI, ML, and Data Science.

💡 Each notebook inside theNumPy folder covers a unique concept of NumPy — from the fundamentals to more advanced operations.

 numpy-lab/ │             └── NumPy/             ├── Creating_Numpy_Arrays.ipynb             ├── NumPy_Array_Operations.ipynb             ├── NumPy_Properties_&Attributes.ipynb             ├── NumPy_Functions.ipynb             ├── Reshaping_NumPy_Array.ipynb             ├── PythonList_Vs_NumpyArray.ipynb             ├── Array_Modification.ipynb             ├── Indexing_Slicing_Iteration.ipynb             ├── Indexing_with_boolean_arrays.ipynb             ├── Handling_Missing&_Infinite_Values.ipynb             ├── Broadcasting.ipynb             └── Plotting_Graphs_Using_NumPy.ipynb

• 🔹NumPy Official Docs
• 🔹W3Schools NumPy Tutorial
• 🔹Numpy for Data Science by Sagar Chouksey (YouTube)
• 🔹NumPy Playlist by CampusX)

• Python 3.x
• NumPy
• Jupyter Notebook
• Matplotlib (for plotting)

Shafaq Aslam
📍 Passionate learner exploring AI, ML, and Data Science through continuous hands-on practice.

numpypythondata-analysisdata-sciencemachine-learningarraysmatrixnumerical-computingscientific-computingjupyter-notebookslearning-lab

“Mastering arrays means mastering the language of data.”