🔹 Step 1: Importing Seaborn

import seaborn as sns
import matplotlib.pyplot as plt

📊 Bar Plot

tips = sns.load_dataset("tips")
sns.barplot(x="day", y="total_bill", data=tips)
plt.title("Average Bill Amount by Day")
plt.show()

Barplots help compare averages across categories.

📈 Line Plot

flights = sns.load_dataset("flights")
flights_pivot = flights.pivot("month", "year", "passengers")
sns.heatmap(flights_pivot, annot=True, fmt="d")
plt.title("Monthly Flights Heatmap")
plt.show()

This heatmap is a creative way to visualize changes over time!

📊 Histogram (Distplot)

sns.histplot(tips["total_bill"], bins=20, kde=True)
plt.title("Distribution of Total Bills")
plt.show()

Use histograms to observe value distributions and skewness.

🔧 Practice Task

• Visualize average tip amount by gender using barplot.
• Try sns.scatterplot() for numeric column relationships.
• Use sns.countplot() to show frequency of categorical values.