Phase 0: Working with Data in Python · ~30 minutes · Python · pandas
Meet the DataFrame
Everything from the last two lessons — parsing, filtering, aggregating — is real work a DataFrame already does correctly. It has to actually feel earned first.
Hiring signal: Treats a DataFrame's index as real, meaningful row identity — not just row position — and knows exactly when that distinction bites
What you will learn
- Load real tabular data into a pandas DataFrame
- Use .head()/.info()/.describe() as the standard first three checks on any new DataFrame
- Explain what a DataFrame's index is and why it isn't always the same as row position
- Diagnose behavior differences between .loc (label-based) and .iloc (position-based) after filtering
Introduction
Type: Learn Languages: Python, pandas Prerequisites: Lesson 02 (Why This Gets Painful Fast) Time: ~30 minutes
Objective
Learning objectives
- Load real tabular data into a pandas DataFrame
- Use
.head()/.info()/.describe() as the standard first three checks on any new DataFrame - Explain what a DataFrame's index is and why it isn't always the same as row position
- Diagnose behavior differences between
.loc (label-based) and .iloc (position-based) after filtering
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.
Browse all courses · View pricing · DeVenture Academy