Exploratory Analysis of the Tips Dataset

This post summarises my first proper notebook. I used the Tips dataset — 244 restaurant bills with tip amounts and details like gender, smoking section, and day of the week.

1. Load the data

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_csv(
    "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv"
)

2. First look at the table

df.head()
df.columns

head() shows the first 5 rows. You see columns like total_bill, tip, sex, smoker, day, time, and size. columns lists every column name in the table.

df.shape

shape returns (rows, columns) — here 244 rows, 7 columns.

df.info()
df.isnull().sum()

3. Summary statistics

df.describe()

describe() gives quick stats for numeric columns — mean, min, max, and quartiles. Useful before plotting: average bill is around $19.78, average tip around $2.99.

4. Visual exploration

Distribution of bills

import matplotlib.pyplot as plt

df["total_bill"].hist(bins=20)

plt.title("Distribution of Total Bills")
plt.xlabel("Bill Amount")
plt.ylabel("Frequency")

plt.show()

A histogram groups values into bins and counts how many fall in each. kde=True adds a smooth curve over the bars. Most bills sit between $10–$25.

Bill vs tip

plt.figure(figsize=(8,5))

plt.scatter(df["total_bill"], df["tip"])

plt.title("Bill Amount vs Tip")
plt.xlabel("Total Bill")
plt.ylabel("Tip")

plt.show()

A scatter plot puts one dot per row. Higher bills tend to have higher tips — a positive relationship.

5. Filtering rows

df[df["total_bill"] > 40]

This keeps only rows where the bill was over $40 — a quick way to find high-spending tables.

6. Grouping and aggregation

df.groupby("sex")["tip"].mean()

groupby("sex") splits the table by gender. ["tip"].mean() calculates the average tip per group.

df.groupby("day")["total_bill"].sum()

Same idea — total revenue per day of the week.

df.groupby("smoker")["tip"].mean()

Compares average tips: non-smokers $2.99, smokers $3.00 — nearly the same.

What I took away

This notebook is small, but it covers the core loop of data analysis: load → inspect → visualise → summarise. Everything after this builds on the same pattern.