Manipulating Data with Pandas

Pandas is the central tool for working with tabular data in Python. It provides the DataFrame — a two-dimensional structure with labeled rows and columns — and a comprehensive API for loading, inspecting, transforming, and saving data.

This module covers the core Pandas operations you will use in every data project: reading data from files, selecting subsets, applying transformations, and aggregating by group. By the end, you will be able to take a raw CSV file and perform the kind of data manipulation that most real analysis requires.

Lessons

  1. Introduction to Pandas — What Pandas is, the Series and DataFrame data structures, and your first look at a real dataset.
  2. Creating, Reading, and Writing Data — Loading data from CSV and Excel files, creating DataFrames from scratch, and writing results back to disk.
  3. Selecting and Assigning Data — Indexing with loc and iloc, boolean filtering, and adding new columns.
  4. Mapping Data — Applying functions across rows and columns with map(), apply(), and applymap().
  5. Grouping and Sorting — Aggregating data with groupby() and sorting results with sort_values().
  6. Module Assessment — A hands-on assessment requiring you to load, transform, and summarize a dataset using Pandas.