Data Visualization

:dart: Learning Objectives

By the end of this lesson, you will be able to:

• Understand what data visualization is and why it's important
• Create basic graphs using Matplotlib in Python
• Add titles, labels, and legends to your graphs
• Customize your graphs with different colors and styles

:information_source: Data visualization is the process of turning data into pictures, graphs, and charts to make it easier to understand.

:bar_chart: Why Use Visualization?

Data visualization helps us because:

• Our eyes love patterns: We can spot trends in pictures faster than in numbers
• Big data becomes simple: Large amounts of data become easy to understand
• Many ways to show data: We can use graphs, charts, and other visual tools

Let's look at an example of a bar chart:

Who Uses Data Visualization?

Many professionals use data visualization:

• Teachers: Show student test scores and class progress
• Computer scientists: Track AI improvements and research results
• Business owners: Display sales trends and customer data
• Scientists: Present research findings and experiments

:bulb: Key Benefits:

1. Quick understanding: See patterns in seconds instead of minutes
2. Better decisions: Clear visuals help you make smart choices
3. Keep attention: Pictures are more interesting than numbers!

:emoji: What is Matplotlib?

:information_source: Matplotlib is a Python tool that helps you create graphs and charts from your data.

Matplotlib can do amazing things:

• Works with NumPy: Uses NumPy arrays to create visualizations
• Many graph types: Create line graphs, bar charts, scatter plots, histograms, and more
• Customizable: Change colors, sizes, and styles to make your graphs look great

Every graph has different parts. Look at this diagram:

Key Parts of a Graph

We'll learn about these important parts:

• Title: The name at the top of your graph
• X-axis: The horizontal line (goes left to right)
• Y-axis: The vertical line (goes up and down)
• Labels: Names that tell what each axis shows
• Graph: The actual line, bars, or dots showing your data
• Legend: A box that explains what each line or color means

:memo: Want to learn more? Check out the official Matplotlib tutorials: https://matplotlib.org/tutorials/index.html

:memo: Steps to Create a Graph

Follow these simple steps:

1. Import the package: Tell Python you want to use Matplotlib
2. Plot your data: Create the basic graph
3. Show the graph: Display it on screen
4. Add title and labels: Make it clear what your graph shows
5. Add a legend: Explain what each line means
6. Customize: Change colors and styles
7. Try different graphs: Explore bar charts, pie charts, and more

:computer: Let's Code with Matplotlib!

Step One: Import the Package

First, we need to import two packages. NumPy helps with numbers, and Matplotlib creates the graphs:

python

import numpy as np
import matplotlib.pyplot as plt

tip Remember: We use plt as a short name for matplotlib.pyplot to save typing!

Step 2: Plot Your First Graph

Let's use real data about people not following safety rules in Malaysia during 2020:

month = ["Aug", "Sep", "Oct", "Nov"]
social_dict = [1973, 1530, 3787,3424]
wear_mask = [413, 617, 5051, 3155]

This data shows "Statistics of not following SOP in Malaysia 2020"

To create a graph, use plt.plot(x-axis, y-axis). Here's how to show social distancing violations by month:

plt.plot(month, social_dict)

You can add multiple lines to the same graph:

plt.plot(month, social_dict)
plt.plot(month, wear_mask)

:memo: Pro tip: Each plt.plot() adds a new line to your graph!

Step 3: Show Your Graph

To see your graph on screen, use plt.show():

python

plt.show()

Step 4: Add Title and Labels

Your graph needs a title and labels so people understand what it shows:

python

plt.title("Statistics of not following SOP in Malaysia 2020")
plt.xlabel("Month")
plt.ylabel("Number of cases")

Here's what each command does:

• plt.title() - Adds a title at the top
• plt.xlabel() - Names the x-axis (horizontal)
• plt.ylabel() - Names the y-axis (vertical)

Step 5: Add a Legend

When you have multiple lines, you need a legend to tell them apart. A legend is like a key that explains what each line means:

python

plt.legend(["Social Distance", "Wear Mask"])

The legend appears in a box on your graph

tip Alternative method: You can add labels directly when plotting:

plt.plot(month, social_dict, label="Social Distance")
plt.plot(month, wear_mask, label="Wear Mask")
plt.legend()

This method is cleaner and easier to read!

Step 6: Make Your Graph Look Cool! :art:

Change how your lines look by adding style codes. For example:

• "r--" = red dashed line
• "k^" = black triangles
• "g-" = green solid line
• "b:" = blue dotted line

plt.plot(month, social_dict, "r--")
plt.plot(month, wear_mask, "k^")

Here's how to use different styles:

:memo: Learn more styles:

• Pyplot Tutorial
• Line graph styles and colors

:books: Summary

Great job! You've learned how to:

• Import Matplotlib and create graphs
• Plot data using x and y coordinates
• Add titles, labels, and legends
• Customize your graphs with colors and styles

Data visualization helps us understand complex information quickly. With Matplotlib, you can turn boring numbers into exciting visual stories!

:movie_camera: Video Tutorial

:emoji: AI Practice Prompts

Code with AI: Try using AI to create and customize charts.

Beginner Prompts:

• "Create a bar chart in Matplotlib using this data: [provide data]."
• "Help me add a title and labels to my Matplotlib graph."
• "Show me how to change the color of my line graph to blue."

Advanced Prompts:

• "Create a multi-line graph comparing temperature data across seasons."
• "Customize the chart with labels, title, and different colors for each line."
• "Help me create a subplot with 4 different graphs in one figure."

:dart: Practice Exercises

1. Create Your First Graph: Plot your daily study hours for a week
2. Add Details: Include a title, axis labels, and make the line green
3. Compare Data: Plot both study hours and sleep hours on the same graph with a legend
4. Experiment: Try creating a bar chart instead of a line graph

:bulb: Tip Remember: The best way to learn is by doing. Start with simple graphs and gradually add more features!