🚀 NumPy - Complete Learning Repository

A comprehensive NumPy learning repository covering everything from the fundamentals of arrays to advanced data analysis, CSV handling, statistics, sorting, searching, array manipulation, and a real-world retail analytics project.

This repository documents my complete NumPy learning journey through theory, practical examples, coding exercises, and projects.

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📚 Topics Covered

📌 NumPy Basics

• Introduction to NumPy
• Creating Arrays
• Array Attributes
• Data Types
• Dimensions
• Shape
• Size
• Itemsize
• Memory Usage

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🔢 Indexing & Slicing

• 0-D Arrays
• 1-D Arrays
• 2-D Arrays
• 3-D Arrays
• Integer Indexing
• Negative Indexing
• Fancy Indexing
• Boolean Indexing
• Array Slicing
• Multi-dimensional Slicing

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➕ Mathematical Operations

• Arithmetic Operations
• Element-wise Operations
• Broadcasting
• Dot Product
• Matrix Multiplication
• Power
• Square Root
• Exponential Functions
• Trigonometric Functions

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📊 Statistical Functions

• Mean
• Median
• Mode
• Standard Deviation
• Variance
• Sum
• Minimum
• Maximum
• Percentile
• Quantile

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🔍 Searching Functions

Implemented using:

• np.where()
• np.nonzero()
• np.argwhere()
• np.argmax()
• np.argmin()
• np.count_nonzero()

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🔃 Sorting Functions

Implemented:

• np.sort()
• np.argsort()
• Quick Sort
• Merge Sort
• Heap Sort

Sorting based on:

• Revenue
• Quantity
• Rating
• Store
• Product
• Category

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📈 Universal Functions (ufuncs)

• abs()
• sqrt()
• square()
• exp()
• log()
• log10()
• log2()
• sign()
• ceil()
• floor()
• rint()
• modf()
• isnan()

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🔧 Array Manipulation

• reshape()
• flatten()
• ravel()
• transpose()
• swapaxes()
• concatenate()
• stack()
• hstack()
• vstack()
• split()

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🎲 Random Module

• randint()
• rand()
• randn()
• choice()
• shuffle()
• seed()

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📂 CSV Handling

Reading CSV files using

• loadtxt()
• genfromtxt()

Saving CSV files using

• savetxt()

Topics Covered

• Missing Values
• Invalid Values
• Mixed Data Types
• UTF-8 Encoding
• Structured Arrays

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📊 Data Analysis Projects

🛒 MegaMart Retail Analytics

A complete retail analytics project developed using only NumPy.

Features

• Generate Retail Dataset
• Revenue Analysis
• Discount Analysis
• Customer Rating Analysis
• Store Analysis
• Category Analysis
• City Analysis
• Payment Analysis
• Boolean Indexing
• Searching
• Sorting
• Statistical Analysis
• Array Manipulation
• Automatic CSV Report Generation

Generated Reports

• Top10Stores.csv
• HighDiscountProducts.csv
• BestRatedProducts.csv
• DelhiSales.csv
• ElectronicsSales.csv
• StoreRevenue.csv

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📂 Repository Structure

Numpy/
│
├── 01_Basics/
├── 02_Mathematical_Functions/
├── 03_Searching_Sorting/
├── 04_Statistics/
├── 05_Array_Manipulation/
├── 06_CSV_Handling/
├── 07_Practice_Questions/
├── 08_MegaMart_Project/
│
├── README.md
├── requirements.txt
└── LICENSE

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💻 Technologies Used

• Python
• NumPy

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▶️ Installation

Clone the repository

git clone https://github.com/TITAN247/Numpy.git

Move into the repository

cd Numpy

Install dependencies

pip install -r requirements.txt

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🎯 Learning Outcomes

After completing this repository, I gained practical experience in:

• Numerical Computing
• Vectorized Programming
• Scientific Computing
• Statistical Analysis
• CSV Data Processing
• Business Analytics
• Data Manipulation
• Array Operations
• Real-world NumPy Applications

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📸 Projects Included

✅ NumPy Practice Programs

✅ CSV Handling

✅ Statistics

✅ Searching & Sorting

✅ Array Manipulation

✅ Business Analytics

✅ MegaMart Retail Analytics

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⭐ Future Improvements

• Data Visualization using Matplotlib
• Data Analysis using Pandas
• Machine Learning using Scikit-Learn
• Deep Learning Data Preprocessing

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🤝 Contributions

Contributions, suggestions, and improvements are always welcome.

Feel free to fork this repository and submit a pull request.

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📄 License

This project is licensed under the MIT License.

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👨‍💻 Author

Shivansh Chaurasiya

• GitHub: https://github.com/TITAN247
• LinkedIn: https://www.linkedin.com/in/shivansh-chaurasiya-2345722a9/

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