Exploratory Data Analysis with Pandas, NumPy & Matplotlib

Start an EDA: Business Questions and Data Grain

Audit Data Before Trusting It

Clean Missing Values, Labels and Numeric Types

Understand One Variable at a Time

Compare Segments with the Right Denominator

Investigate Relationships and Unusual Values

Join Tables Without Inflating Results

Case Study: What Explains Retail Performance Differences?

Parse Dates and Build Calendar Features

Handle Time Zones, Durations and Date Boundaries

Build a Regular Time Series from Event Data

Explore Trends, Seasonality and Period Changes

Case Study: Are Support Response Times Getting Worse?

Case Study: Explore Energy Demand and Collection Gaps

Capstone: Explain a Retail Revenue Dip with Evidence