Lesson 2 - Introduction to Matplotlib

Creating Your First Plot#

You understand how graphs work. Now you will learn Matplotlib—Python’s foundational visualization library for creating professional charts and graphs.

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

• Import Matplotlib using the standard convention
• Create basic line plots with plt.plot()
• Display plots in Jupyter with plt.show()
• Understand how data lists become visual coordinates
• Read scientific notation on axes (1e6 = 1,000,000)
• Create line plots from lists, arrays, and Pandas Series

This lesson transforms your data into visual stories with just a few lines of code.

What is Matplotlib?#

The Standard Plotting Library#

Matplotlib is Python’s foundational visualization library, created in 2003.

Think of it as:

• The “Excel charts” of Python
• A digital canvas for creating graphs
• The tool underlying many other plotting libraries

Why Matplotlib?#

Industry Standard:

• Used by millions of data scientists worldwide
• Powers other libraries (Seaborn, Pandas plotting)
• Employers expect you to know it

Powerful and Flexible:

• Creates publication-quality figures
• Full control over every element
• Can create any type of plot

Well-Documented:

• Extensive documentation and examples
• Large community support
• Abundant tutorials and Stack Overflow answers

The pyplot Module#

We use matplotlib.pyplot - a collection of functions that make Matplotlib work like MATLAB:

• Simple function calls create plots
• Stateful interface (remembers current figure)
• Easy to get started, powerful when you need it

You will use pyplot for 95% of your visualization work.

Importing Matplotlib#

Standard Import Convention#

ALWAYS import Matplotlib this way:

import matplotlib.pyplot as plt

Breaking It Down#

1. import matplotlib.pyplot

   • Import the pyplot submodule from matplotlib package
   • Not the entire matplotlib library (too large)

2. as plt

   • Create alias plt (shorter, easier to type)
   • Universal convention - everyone uses plt
   • Makes your code readable to other data scientists

Why This Convention Matters#

Good (Standard):

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])

Bad (Non-standard):

import matplotlib.pyplot as p  # Don't use 'p'
import matplotlib.pyplot       # Too long to type
from matplotlib.pyplot import plot  # Pollutes namespace

Following conventions makes your code professional and collaborative.

Import Libraries#

# Import Matplotlib (standard way)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Check versions
print(f"Matplotlib version: {plt.matplotlib.__version__}")
print(f"NumPy version: {np.__version__}")
print(f"Pandas version: {pd.__version__}")

Your First Line Plot#

The Simplest Plot: Two Lists#

Let’s visualize COVID-19 new cases by month (January to July 2020, globally).

Step 1: Prepare the Data#

# COVID-19 new cases by month (January to July 2020)
months = [1, 2, 3, 4, 5, 6, 7]  # Month numbers
new_cases = [9926, 76246, 681488, 2336640, 2835147, 4226655, 6942042]  # New cases

print("Month Numbers:", months)
print("New Cases:", new_cases)
print(f"\nData points: {len(months)}")

Step 2: Create the Plot#

The Magic Command: plt.plot(x, y)

# Create line plot
plt.plot(months, new_cases)
plt.show()

# That's it! Just two lines of code for a graph!

Understanding What Happened#

Behind the scenes:

1. plt.plot(months, new_cases) creates the plot

   • First argument = x-coordinates (months)
   • Second argument = y-coordinates (new_cases)
   • Matplotlib connects the points with lines

2. plt.show() displays the plot

   • Shows the figure in Jupyter notebook
   • Necessary in some environments (scripts, terminals)
   • Not always needed in Jupyter, but good practice

The Data-to-Visual Translation#

Each pair of values becomes a coordinate:

months[0]=1, new_cases[0]=9926     → Point (1, 9926)
months[1]=2, new_cases[1]=76246    → Point (2, 76246)
months[2]=3, new_cases[2]=681488   → Point (3, 681488)
...and so on

Matplotlib automatically:

• Creates axes with appropriate scales
• Connects points with lines
• Adds tick marks
• Chooses reasonable limits

Key Insight: Two lists + two lines of code = A complete graph!

Understanding Scientific Notation#

Reading the Y-Axis#

Notice the y-axis label: 1e6, 2e6, 4e6, 6e6

What does this mean?

Scientific Notation Explained#

Format: 1e6 = 1 × 10^6 = 1,000,000

Breaking it down:

• e means “times 10 to the power of”
• Number after e is the exponent
• e6 = × 1,000,000 (six zeros)

Examples:

1e6 = 1 × 10^6 = 1,000,000 (1 million)
2e6 = 2 × 10^6 = 2,000,000 (2 million)
4e6 = 4 × 10^6 = 4,000,000 (4 million)
7e6 = 7 × 10^6 = 7,000,000 (7 million)

1e3 = 1 × 10^3 = 1,000 (1 thousand)
1e9 = 1 × 10^9 = 1,000,000,000 (1 billion)

Why Use Scientific Notation?#

Readability: Compare these:

Bad:  4000000 (hard to count zeros)
Good: 4e6     (immediately clear: 4 million)

Space: Large numbers do not fit well on axes

0, 1000000, 2000000, 3000000  ← Crowded!
0,  1e6,    2e6,     3e6      ← Clean!

You will see scientific notation in many plots with large values.

Verify Understanding#

# Let's verify our understanding
print("Y-axis values in scientific notation:")
print("1e6 =", 1e6, "= 1,000,000")
print("2e6 =", 2e6, "= 2,000,000")
print("4e6 =", 4e6, "= 4,000,000")
print("6e6 =", 6e6, "= 6,000,000")

print("\nSo our peak (month 7) is about:")
print(f"{new_cases[-1]:,} new cases")
print(f"Or approximately {new_cases[-1]/1e6:.1f} million cases")

Different Data Sources#

Matplotlib Works with Multiple Data Types#

You can plot from:

1. Python lists (what we just did)
2. NumPy arrays
3. Pandas Series

All work the same way!

Example: NumPy Arrays#

# Using NumPy arrays
months_array = np.array([1, 2, 3, 4, 5, 6, 7])
cases_array = np.array([9926, 76246, 681488, 2336640, 2835147, 4226655, 6942042])

plt.plot(months_array, cases_array)
plt.show()

# Same result! Matplotlib handles NumPy arrays natively

Example: Pandas Series#

# Using Pandas Series
months_series = pd.Series([1, 2, 3, 4, 5, 6, 7])
cases_series = pd.Series([9926, 76246, 681488, 2336640, 2835147, 4226655, 6942042])

plt.plot(months_series, cases_series)
plt.show()

# Also works perfectly!

Working with DataFrames#

When working with Pandas DataFrames (most common in real work):

# Create a DataFrame
covid_data = pd.DataFrame({
    'month': [1, 2, 3, 4, 5, 6, 7],
    'new_cases': [9926, 76246, 681488, 2336640, 2835147, 4226655, 6942042]
})

print(covid_data)
print("\nNow we can plot columns:")

# Plot DataFrame columns
plt.plot(covid_data['month'], covid_data['new_cases'])
plt.show()

# Access columns, then plot - very common pattern!

Key Insight: Whether your data is in lists, arrays, or Pandas columns, plt.plot() handles it all!

Practice Exercises#

Apply your new skills with these exercises.

Exercise 1: COVID Deaths#

Plot COVID-19 deaths by month (same time period):

# Deaths data (January-July 2020)
months = [1, 2, 3, 4, 5, 6, 7]
deaths = [213, 2465, 34822, 175893, 292801, 416086, 550164]

# YOUR CODE HERE:
# Create a line plot of months vs deaths

Exercise 2: Temperature Trend#

Create a line plot showing daily high temperature for a week:

# Temperature data
days = [1, 2, 3, 4, 5, 6, 7]  # Monday to Sunday
temperature_high = [22, 24, 26, 25, 23, 21, 20]  # Celsius

# YOUR CODE HERE:
# 1. Import matplotlib if not already done
# 2. Create line plot
# 3. Display with plt.show()

Exercise 3: Sales Data#

Plot monthly sales for a small business:

# Create a DataFrame with sales data
sales_data = pd.DataFrame({
    'month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'revenue': [45000, 52000, 49000, 61000, 58000, 67000]
})

# For now, we'll use month numbers instead of names
month_numbers = [1, 2, 3, 4, 5, 6]

# YOUR CODE HERE:
# Plot month_numbers vs sales_data['revenue']

# Note: We'll learn how to use month names on x-axis in next lesson!

Summary#

You now create line plots with Matplotlib. Let’s review the key concepts.

Key Concepts#

Importing Matplotlib

• Always use: import matplotlib.pyplot as plt
• plt is the universal convention
• Import pyplot submodule, not entire matplotlib

Creating Line Plots

• plt.plot(x, y) creates the plot
• plt.show() displays it
• First argument = x-coordinates
• Second argument = y-coordinates

Scientific Notation

• 1e6 = 1,000,000 (1 million)
• e means “× 10 to the power of”
• Common for large numbers on axes

Data Sources

• Works with lists, NumPy arrays, Pandas Series
• Access DataFrame columns with df['column']
• Same plot function for all data types

The Essential Pattern#

import matplotlib.pyplot as plt

# Prepare data
x = [1, 2, 3, 4, 5]
y = [10, 20, 25, 30, 35]

# Create and display plot
plt.plot(x, y)
plt.show()

This is the foundation of all Matplotlib visualizations!

Common Questions#

Q: Do I always need plt.show()? A: In Jupyter notebooks, often not needed (plots appear automatically). But it is good practice and required in Python scripts.

Q: Can I plot without connecting the lines? A: Yes! We will learn about markers and scatter plots in Module 2.

Q: What if my x and y lists are different lengths? A: Matplotlib will throw an error. Lists must have equal length!

Q: Can I plot multiple lines on one graph? A: Yes! Coming up in Lesson 4.

Next Steps#

Your plots work, but they are plain and hard to understand. In the next lesson, you will learn to add titles, labels, colors, and styles to create professional-looking graphs.

Create Visualizations from Any Data#

You can now create line plots from any numeric data. Next, we make them beautiful and informative.

Transform your data into visual insights with Matplotlib!