🎯Projects on Pandas DataFrame with Matplotlib

 

🎯 Project 1: Student Marks Data Analysis + Bar Chart

Step 1: Create DataFrame

import pandas as pd
import matplotlib.pyplot as plt

data = {
    "Name": ["Amit", "Riya", "Sohan", "Neha", "Karan"],
    "Maths": [85, 92, 76, 88, 90],
    "Science": [78, 89, 84, 91, 87],
    "English": [80, 75, 88, 85, 90]
}

df = pd.DataFrame(data)
print(df)

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Step 2: Add Total & Average

df["Total"] = df["Maths"] + df["Science"] + df["English"]
df["Average"] = df["Total"] / 3
print(df)

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Step 3: Visualize Marks (Bar Plot)

plt.figure(figsize=(8,5))
plt.bar(df["Name"], df["Total"])
plt.title("Total Marks of Students")
plt.xlabel("Students")
plt.ylabel("Total Marks")
plt.show()

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✔ What You Learn

• Bar chart

• Column operations

• Student performance comparison

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🎯 Project 2: Store Sales Analysis + Line Chart

Step 1: Create DataFrame

data = {
    "Day": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
    "Sales": [1200, 1500, 1600, 1700, 2000, 2500, 2200]
}

df = pd.DataFrame(data)
print(df)

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Step 2: Insights

print("Total Weekly Sales:", df["Sales"].sum())
print("Average Daily Sales:", df["Sales"].mean())
print("Highest Sales Day:", df.loc[df["Sales"].idxmax()])

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Step 3: Visualize Sales (Line Chart)

plt.plot(df["Day"], df["Sales"], marker="o")
plt.title("Weekly Sales Trend")
plt.xlabel("Day")
plt.ylabel("Sales (₹)")
plt.grid(True)
plt.show()

---

✔ What You Learn

• Line graph

• Insights for business

• Day-wise performance

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🎯 Project 3: Employee Attendance Tracker + Horizontal Bar Chart

Step 1: Create DataFrame

data = {
    "Employee": ["Amit", "Riya", "Sohan", "Neha"],
    "Mon": [1, 1, 1, 0],
    "Tue": [1, 1, 0, 1],
    "Wed": [1, 1, 1, 1],
    "Thu": [0, 1, 1, 1],
    "Fri": [1, 1, 1, 1]
}

df = pd.DataFrame(data)
print(df)

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Step 2: Calculate Total Present Days

df["Total_Present"] = df[["Mon", "Tue", "Wed", "Thu", "Fri"]].sum(axis=1)
print(df)

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Step 3: Visualize Attendance (Horizontal Bar Plot)

plt.barh(df["Employee"], df["Total_Present"])
plt.title("Weekly Attendance Report")
plt.xlabel("Days Present")
plt.ylabel("Employee")
plt.show()

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✔ What You Learn

• Row-wise operations

• Horizontal bar graph

• HR-style attendance report

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🎯 Project 4: Movie Rating Analyzer + Pie Chart

Step 1: Create DataFrame

data = {
    "Movie": ["Movie A", "Movie B", "Movie C", "Movie D"],
    "Rating": [4.5, 3.8, 4.2, 4.9],
    "Votes": [200, 150, 180, 220]
}

df = pd.DataFrame(data)
print(df)

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Step 2: Weighted Score

df["Weighted_Score"] = df["Rating"] * df["Votes"]
print(df)

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Step 3: Visualize Ratings (Pie Chart)

plt.pie(df["Rating"], labels=df["Movie"], autopct="%1.1f%%")
plt.title("Movie Rating Distribution")
plt.show()

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✔ What You Learn

• Pie charts

• Weighted score calculation

• Comparing multiple movies

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🎉 BONUS PROJECT

Project 5: Daily Steps Tracker (Fitness) + Line Chart

Step 1: Create DataFrame

data = {
    "Day": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
    "Steps": [3500, 4200, 5000, 6000, 7500, 8200, 9000]
}

df = pd.DataFrame(data)
print(df)

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Step 2: Plot Steps Progress

plt.plot(df["Day"], df["Steps"], marker="o")
plt.title("Weekly Steps Progress")
plt.xlabel("Day")
plt.ylabel("Steps")
plt.grid(True)
plt.show()

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✔ What You Learn

• Health data visualization

• Understanding weekly progress

• Line chart usage