✅ Python for Data Science: Part-4

Data Visualization with Matplotlib, Seaborn Plotly 📊📈

1️⃣ Matplotlib – Basic Plotting
Great for simple line, bar, and scatter plots.

Import and Line Plot

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
plt.plot(x, y)
plt.title("Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Bar Plot

names = ["A", "B", "C"]
scores = [80, 90, 70]
plt.bar(names, scores)
plt.title("Scores by Name")
plt.show()

2️⃣ Seaborn – Statistical Visualization
Built on Matplotlib with better styling.

Import and Plot

import seaborn as sns
import pandas as pd

df = pd.DataFrame({
    "Name": ["Riya", "Aman", "John", "Sara"],
    "Score": [85, 92, 78, 88]
})

sns.barplot(x="Name", y="Score", data=df)

Other Seaborn Plots

sns.histplot(df["Score"])          # Histogram  
sns.boxplot(x=df["Score"])         # Box plot  

3️⃣ Plotly – Interactive Graphs
Great for dashboards and interactivity.

Basic Line Plot

import plotly.express as px

df = pd.DataFrame({
    "x": [1, 2, 3],
    "y": [10, 20, 15]
})

fig = px.line(df, x="x", y="y", title="Interactive Line Plot")
fig.show()

🎯 Why Visualization Matters
• Helps spot patterns in data
• Makes insights clear and shareable
• Supports better decision-making

Practice Task:
• Create a line plot using matplotlib
• Use seaborn to plot a boxplot for scores
• Try any interactive chart using plotly

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