Lectture Note for Data Analytics 1

Essential Python Libraries for Data Analysis

Python offers powerful libraries for data analysis and visualization. Here are some key libraries you’ll often use:

Pandas

• Industry-standard library for data manipulation
• Provides DataFrame structures for efficient data handling
• Excellent for reading/writing various data formats
• Powerful tools for data cleaning and analysis

Matplotlib

• Foundational plotting library in Python
• Creates publication-quality figures
• Supports various plot types (line, scatter, bar, etc.)
• Highly customizable for complex visualizations

Seaborn

• Built on top of Matplotlib
• Specializes in statistical data visualization
• Offers attractive default styles
• Simplified interface for common statistical plots

These libraries work seamlessly with Jupyter Notebook, making data analysis and visualization interactive and efficient.

Get started with Jupyter Notebook

Run your jupyter lab book using in your terminal.

jupyter lab

This will open a new tab in the using your browser at https://localhost:8888

You can start creating new block, this block can be a markdown block to write text, code block to run code,…

Start by writing some Python code in your new code block

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