Phase 5: Capstone · ~45 minutes · Python · pandas · matplotlib

Analyze and Visualize

French Quarter isn't even the most expensive New Orleans neighborhood in this real data. Lower Garden District is — and you'd only know that by actually running the numbers, not by guessing from reputation.

Hiring signal: Answers a self-generated real question with the specific chart type its data shape calls for, backed by real numbers rather than intuition

What you will learn

Answer a genuine question using real grouped aggregation and the correct chart type for its data shape
Choose and justify a chart type using Phase 03's data-shape framework
Generate and answer a second genuine question independently
Support each finding with real, correctly computed numbers, not intuition

Introduction

Type: Project Languages: Python, pandas, matplotlib, seaborn Prerequisites: Lesson 02 (Clean) Time: ~45 minutes

Objective

Learning objectives

Answer a genuine question using real grouped aggregation and the correct chart type for its data shape
Choose and justify a chart type using Phase 03's data-shape framework
Generate and answer a second genuine question independently
Support each finding with real, correctly computed numbers, not intuition

What you're building

Reproduce the neighborhood-price analysis above, confirming the real ranking and sample sizes
Generate one genuine question of your own about this dataset — not assigned here, your own real curiosity about something you noticed in Lessons 01-02
Classify your question's data shape (Phase 03), choose the matching chart type, and answer it with real numbers and a properly labeled chart
For both analyses, state the real sample size behind every reported number — Phase 04's discipline doesn't stop at the EDA phase

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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