Jupyter Notebook Lab — Module 09#

Pandas & DataFrames

Practice loading, exploring, filtering, transforming, and aggregating datasets using the core Pandas operations that form the backbone of business analytics work.

Download#

📓 Student Notebook .ipynb 🔑 Answer Key coming soon

Open the .ipynb file in Google Colab by selecting File → Upload notebook.

What You’ll Practice#

  1. Creating DataFrames from dictionaries, CSV files, and JSON
  2. Exploring data with .head(), .info(), .describe(), and .dtypes
  3. Filtering rows with boolean indexing and selecting specific columns
  4. Adding derived columns using vectorized operations and .apply()
  5. Aggregating with groupby().agg() — totals, means, and counts by category
  6. Merging two DataFrames with pd.merge() on a common key column

Learning Objectives#

By completing this notebook you will be able to:

  • Create, load, and inspect Pandas DataFrames
  • Select specific rows and columns using filtering and column selection
  • Add calculated columns using vectorized arithmetic and .apply()
  • Use groupby().agg() to produce summary statistics by category
  • Merge two DataFrames with pd.merge() on a common key column

Next Module: Module 10 — Data Visualization →