🎨Section 5 of 5

🎨 Part 4 — Seaborn

🎬 Why Seaborn?

Seaborn sits on top of Matplotlib and adds two superpowers: beautiful defaults and statistical thinking baked into every chart. One line gives you what would take ten in raw Matplotlib.

Theme & convention

python

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="whitegrid", palette="mako")
plt.figure(figsize=(5, 0.5))
plt.title("Theme set ✓")
plt.axis("off")

Distribution — histplot & kdeplot

python

import seaborn as sns, numpy as np, matplotlib.pyplot as plt
sns.set_theme(style="whitegrid")

x = np.random.default_rng(0).normal(75, 12, 500)
plt.figure(figsize=(6, 3.5))
sns.histplot(x, bins=20, kde=True, color="#0f766e")
plt.title("Score distribution with KDE")
plt.tight_layout()

Categorical — boxplot

python

import seaborn as sns, pandas as pd, matplotlib.pyplot as plt
sns.set_theme(style="whitegrid")

df = pd.read_csv("sales.csv")
plt.figure(figsize=(6, 3.5))
sns.boxplot(data=df, x="City", y="Revenue", hue="City", palette="mako", legend=False)
plt.title("Revenue spread by city")
plt.tight_layout()

Categorical — barplot with hue

python

import seaborn as sns, pandas as pd, matplotlib.pyplot as plt
sns.set_theme(style="whitegrid")

df = pd.read_csv("sales.csv")
plt.figure(figsize=(6, 3.5))
sns.barplot(data=df, x="City", y="Revenue", hue="Product", palette="mako")
plt.title("Revenue by City and Product")
plt.tight_layout()

Relationship — scatter + regression

python

import seaborn as sns, pandas as pd, matplotlib.pyplot as plt
sns.set_theme(style="whitegrid")

df = pd.read_csv("sales.csv")
plt.figure(figsize=(6, 3.5))
sns.regplot(data=df, x="Units_Sold", y="Revenue", color="#0f766e",
            scatter_kws={"alpha": 0.7})
plt.title("Units vs Revenue (with regression line)")
plt.tight_layout()

Heatmap — correlation matrix

python

import seaborn as sns, pandas as pd, matplotlib.pyplot as plt
sns.set_theme()

df = pd.read_csv("sales.csv")
corr = df[["Units_Sold", "Revenue"]].corr()

plt.figure(figsize=(4, 3))
sns.heatmap(corr, annot=True, cmap="mako", fmt=".2f")
plt.title("Correlation")
plt.tight_layout()

Pairplot — multi-variable overview

python

import seaborn as sns, pandas as pd, matplotlib.pyplot as plt
sns.set_theme(style="whitegrid")

df = pd.read_csv("sales.csv")
g = sns.pairplot(df, vars=["Units_Sold", "Revenue"], hue="City",
                 palette="mako", height=2.2)
g.fig.suptitle("Pairwise relationships", y=1.02)

🧠

Quick Check

Q1.Which Seaborn function adds a regression line through a scatter?

Q2.What does the `hue` parameter do?

Q3.Best chart to visualize a correlation matrix?

🎉

You finished Lesson 4

You've used NumPy, Pandas, Matplotlib and Seaborn — all running real Python in your browser. Time to build the board report.

Matplotlib