🔬 Learning, Experimenting, and Visualizing Data — The NumPy Way 🧩

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

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🧠 Tech Stack Badges

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🧩 Mission Statement

This repository serves as my 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 mastering array operations, reshaping, broadcasting, and data manipulation, forming a strong base for my future journey in AI, ML, and Data Science.

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📂 Folder Structure

💡 Each notebook inside the NumPy 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 

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🧮 Topics Covered

Notebook	Description
Creating_Numpy_Arrays	Different ways to create NumPy arrays
NumPy_Array_Operations	Performing mathematical and logical operations
NumPy_Properties_&_Attributes	Understanding shape, size, dtype, and dimensions
NumPy_Functions	Common functions and their practical uses
Reshaping_NumPy_Array	Reshaping, flattening, and stacking arrays
PythonList_Vs_NumpyArray	Comparing performance and structure
Array_Modification	Updating, inserting, and deleting elements
Indexing_Slicing_Iteration	Accessing and looping through arrays
Indexing_with_boolean_arrays	Conditional selections using Boolean indexing
Handling_Missing_&_Infinite_Values	Managing NaN and inf values gracefully
Broadcasting	Efficient operations between arrays of different shapes
Plotting_Graphs_Using_NumPy	Visualizing data trends using NumPy and Matplotlib

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📚 Learning Resources

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

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🧰 Tools & Environment

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

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✨ Author

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

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🔖 Tags for SEO

numpy python data-analysis data-science machine-learning arrays matrix numerical-computing scientific-computing jupyter-notebooks learning-lab

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“Mastering arrays means mastering the language of data.”