Jupyter Notebook

🔹 1. Introduction to Jupyter Notebook

• What is Jupyter Notebook?

• Use cases and benefits

• Difference between Jupyter Notebook and traditional IDEs

• Installing Jupyter via Anaconda or pip

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🔹 2. Interface Overview

• Navigating the Jupyter dashboard

• Creating, renaming, and deleting notebooks

• Saving and checkpointing

• Keyboard shortcuts for productivity

• Code cell vs Markdown cell

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🔹 3. Working with Cells

• Executing code cells (Shift + Enter)

• Inserting and deleting cells

• Cell types: Code, Markdown, Raw

• Cell output: text, plots, errors, images

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🔹 4. Writing Markdown in Jupyter

• Headers, bold, italics, and lists

• Adding links and images

• Writing equations using LaTeX

• Embedding code snippets

• Creating tables

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🔹 5. Python Coding in Jupyter

• Running Python code interactively

• Variable declaration, loops, functions

• Displaying outputs and print formatting

• Inline error handling

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🔹 6. Data Analysis with Jupyter

• Importing libraries: pandas, numpy, matplotlib

• Reading CSV/Excel files with pandas

• Displaying and manipulating DataFrames

• Plotting data inline

• Cleaning and analyzing datasets

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🔹 7. Visualizations in Jupyter

• Inline plotting with %matplotlib inline

• Basic and advanced plots using matplotlib, seaborn

• Interactive charts with Plotly

• Embedding images or graphs in output cells

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🔹 8. Using Magic Commands

• What are magics (% and %%)

• %time, %timeit, %pwd, %run, %lsmagic

• %%writefile, %%capture

• %load and %store for memory management

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🔹 9. Extensions and Customization

• Installing Jupyter extensions (e.g. nbextensions)

• Useful extensions: Table of Contents, Variable Inspector, Codefolding

• Theming and customizing appearance

• Exporting notebooks to PDF, HTML, Markdown

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🔹 10. Version Control and Sharing

• Using Git with Jupyter Notebooks

• Saving to GitHub

• Exporting to different formats (PDF, HTML, .py)

• Collaborating with Google Colab

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🔹 11. JupyterLab (Advanced UI)

• Difference between Jupyter Notebook and JupyterLab

• Tabbed interface, file browser, and terminals

• Opening multiple notebooks and consoles

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🔹 12. Integrating with Other Languages

• Installing and using different kernels (e.g., R, Julia)

• Using ! to run terminal/command-line code

• Magic for bash commands (!ls, !pip install, etc.)

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🔹 13. Debugging and Error Handling

• Displaying tracebacks and debugging output

• %debug magic command

• Using assertions for testing

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🔹 14. Projects You Can Do

• Data analysis and visualization projects

• Exploratory Data Analysis (EDA)

• Python automation scripts

• Machine Learning model training

• Documentation and reports for class/study/work