Python Data Analysis & Visualization Libraries

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Libraries Covered

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Overview

This repository contains revision notebooks for core Python data libraries used in data analysis, visualization, and scientific computing:

• NumPy for numerical operations and array processing
• Pandas for structured data manipulation
• Matplotlib for foundational plotting
• Seaborn for high-level statistical visualizations

The notebooks are designed as a clear and structured refresher for learners preparing for Data Science, Machine Learning, AI, or Analytics.

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What You Will Learn

NumPy

• Array creation and properties
• Vectorized operations
• Indexing, slicing, and reshaping
• Broadcasting and utilities

Pandas

• Series and DataFrames
• Data inspection and cleaning
• Filtering, grouping, and aggregation
• Basic exploratory analysis

Matplotlib

• Line, scatter, bar, and histogram plots
• Plot customization and styling
• Visualizing NumPy and Pandas data

Seaborn

• Categorical and distribution plots
• Relationship visualizations
• Heatmaps and pair plots
• Styling and color palettes

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Repository Structure

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├── numpy.ipynb
├── pandas.ipynb
├── matplotlib.ipynb
├── seaborn.ipynb
└── README.md

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Intended Audience

• Python learners entering data-driven fields

• Students revising core data libraries

• Beginners preparing for:

  • Data Science
  • Machine Learning
  • Artificial Intelligence
  • Data Analytics

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Author

M. Haris Yar AI Engineer | Computer Vision