Exploratory Data Analysis (EDA) — A Real Methodology

ds1-3 through ds1-8 built a toolkit — reading data, cleaning it, combining it, describing it, plotting it — one technique at a time, on one running coffee-shop dataset. This chapter formally names the method those tools were all building toward, and deliberately applies it to a fresh dataset the coffee-shop material never touched: a small used-car listings table. The method, not a memorized sequence of steps on one specific dataset, is what this chapter needs to transfer — which is exactly why ds1-10's own capstone will apply this same seven-step structure to a third, still-different dataset.

The Seven-Step Methodology

Steps 3–5, Worked

df["price"].plot(kind="hist")                          # univariate — ds1-7's histogram
df["fuel_type"].value_counts()                          # univariate — categorical breakdown

df.plot(kind="scatter", x="mileage", y="price")          # bivariate — ds1-7's scatter plot
sns.boxplot(data=df, x="fuel_type", y="price")           # bivariate — ds1-8's box plot, grouped

sns.heatmap(df[["year","mileage","price"]].corr(), annot=True)   # multivariate — ds1-8's heatmap
sns.pairplot(df[["year","mileage","price"]])                     # multivariate — ds1-8's pair plot

The mileage-vs-price scatter plot here shows something the coffee-shop dataset's own quantity-vs-revenue scatter (ds1-7) never did: a clear negative correlation — higher mileage, lower price. Same tool, same underlying Pearson mechanism (ds1-6), a genuinely different real-world relationship.

Step 6: Spotting Anomalies, Honestly

Running ds1-4's own IQR rule on price flags the 1968 Jaguar E-Type immediately — its price sits far above every other listing. Exactly the honest distinction ds1-4 and ds1-6 already insisted on: this isn't a data-entry error to delete. A genuine vintage collector's car legitimately commands a price with no relationship at all to the mileage/age/price pattern the rest of the dataset follows — flagging it is correct; discarding it would be a real mistake, and would also quietly remove the row most worth asking a follow-up question about.

Step 7: Forming Hypotheses — The Actual Point of EDA

Everything above describes what the data is. This step turns that description into specific, testable questions:

• "Does mileage predict price more strongly than the car's year does?" — a question the heatmap's own two separate correlation values already give a first, rough answer to.
• "Is the Jaguar a genuinely different category of listing (collectible) that should be modeled separately from ordinary used cars?"
• "Does fuel type meaningfully shift price once mileage and year are accounted for?"

Hands-On Exercises