Assignment 4: https://classroom.github.com/a/-8Ns6P8q

See DLC: Advanced Data Cleaning for advanced topics:

Alignment and broadcasting tricks
Function application patterns and method chaining notes
Ranking strategies and working with duplicate index labels
Extended I/O and performance tips (Excel, JSON, chunked reads)

Jupyter Notebooks: Interactive Data Analysis

In Lectures 1-3, you wrote Python scripts (.py files) that run top-to-bottom. Jupyter notebooks (.ipynb files) let you run code in any order, see results immediately, and mix code with documentation - perfect for data exploration and analysis. Think of .py files for production code and automation, and .ipynb files for interactive analysis and storytelling with data.

Jupyter notebooks provide an interactive environment for data analysis, combining code execution with rich output display. They're essential for exploratory data analysis, prototyping, and sharing results with stakeholders.

Jupyter Notebook Interface

Jupyter notebooks organize work into cells that can contain code or markdown. This structure enables iterative analysis and clear documentation of the analytical process.

I started off with countless problems. But now I know, thanks to COUNT(), that I have "#REF! ERROR: Circular dependency detected" problems.

Reference:

Code cells: Execute Python code and display output
Markdown cells: Write documentation and explanations
Cell execution: Shift+Enter (run and advance), Ctrl+Enter (run and stay)