Performance Task - Project 3

📊 Python Charts & Graphs Project Guide

🧠 Project Overview
In this project, teams of 2–3 students will explore real-world data and create Python visualizations using libraries like Matplotlib, Pandas, and Seaborn. You’ll pick a topic, analyze a dataset, and create charts to communicate insights visually.

Project Tasks:

  • Select a topic and explain why it interests you

  • Load, clean, and analyze your dataset

  • Create at least 3 types of charts

  • Write a summary of your insights and how the visuals helped you understand the data

✅ Project Requirements

Your final Notebook (Jupyter, CoLab, other) should include:

  • Clear explanation of your topic and purpose

  • Clean, labeled data (no missing values)

  • Three+ chart types (bar, line, scatter, pie, etc.)

  • Labeled axes, meaningful titles, and legible visuals

  • A summary of your findings

📚 Why Use Charts and Graphs?

Charts turn complex numbers into simple visuals. They help you:

  1. Understand Data Faster – See trends or comparisons instantly

  2. Spot Patterns – Detect increases, decreases, or outliers

  3. Compare Categories – Visually contrast different values

  4. Communicate Clearly – Make reports and presentations more engaging

🔍 Introduction to Python Charts

Python is ideal for data visualization. Libraries you’ll use:

  • Matplotlib – Basic charting

  • Pandas – Built-in plotting support

  • Seaborn – Advanced, stylish visuals

Just a few lines of code can turn a spreadsheet into an insightful chart.

📊 Types of Charts & When to Use Them

Chart Type

Use Case

Python Example

Bar Chart

Compare quantities across categories

data['col'].value_counts().plot(kind='bar')

Pie Chart

Show proportions or percentages

data['col'].value_counts().plot(kind='pie')

Line Chart

Show trends over time

data.plot(x='Date', y='Value', kind='line')

Histogram

Show distribution of one variable

data['col'].plot(kind='hist')

Scatter Plot

Show relationship between two variables

data.plot(kind='scatter', x='X', y='Y')

Box Plot

Show data spread and detect outliers

data.boxplot(column='Value', by='Group')

Heatmap (Seaborn)

Show correlation or intensity

sns.heatmap(data.corr(), annot=True)

📚 Suggested Dataset Topics

Your dataset should have:

  • 200+ rows

  • At least 4 columns (e.g., category, value, date, region)

🎓 Education

🌍 Demographics & Income

🏀 Sports

🍟 Health & Nutrition

🎬 Pop Culture

📘 Jupyter Notebook Starter Template

Step 1: Import Libraries

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

Step 2: Load Your Dataset

data = pd.read_csv('your_dataset.csv')
data.head()

Step 3: Explore the Data

print(data.columns)
print(data.describe())
print(data.isnull().sum())

Step 4: Create Charts

Bar Chart

data['Category'].value_counts().plot(kind='bar')
plt.title('Category Distribution')
plt.xlabel('Category')
plt.ylabel('Count')
plt.xticks(rotation=45)
plt.show()

Line Chart

data['Date'] = pd.to_datetime(data['Date'])
data.groupby('Date')['Value'].mean().plot(kind='line')
plt.title('Time Trend')
plt.xlabel('Date')
plt.ylabel('Value')
plt.show()

Scatter Plot

data.plot(kind='scatter', x='X_Column', y='Y_Column', alpha=0.6)
plt.title('X vs Y Relationship')
plt.show()

Heatmap

# Seaborn
plt.figure(figsize=(8,6))
sns.heatmap(data.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.show()

💡 Project Ideas

  • Trend Analysis – Use line charts to show changes over time

  • Category Comparison – Use bar/pie charts to compare groups

  • Correlation Study – Use scatter plots for relationships

  • Distribution Analysis – Use histograms/box plots to explore a variable

  • Geographic Comparison – Use heatmaps if you have location data

💻 Jupyter vs Google Colab

Feature

Jupyter Notebook

Google Colab

Setup

Installed locally

Runs in browser

Sharing

Manual (file-based)

Google Drive integration

Installation

Required

None

Speed

Depends on your computer

Uses Google’s servers

Best For

Offline work, full control

Beginners, Chromebooks, quick setup

Recommendation: Start with Google Colab—no install, easy to use, and perfect for teams!

🎯 Grading Rubric (20 Points)

Category

Points

Description

Topic & Purpose

4

Clear, thoughtful topic explanation

Data Use & Cleaning

4

Relevant dataset, cleaned and prepared

Chart Variety & Accuracy

6

At least 3 clear, well-labeled chart types

Insight & Interpretation

4

Analysis clearly explained and interpreted

Collaboration & Presentation

2

Teamwork, clear formatting, easy to follow