Introduction to Matplotlib

Reviewed & published by Brayan K

Matplotlib is a Python library for creating charts and visualizations — it turns raw lists of numbers into clear line plots, bar charts, histograms, and more that you can save, share, and explore.

Part of the free Matplotlib course at LearnCodingFast — hands-on lessons with worked examples and the output they print, plus practice exercises and a quick quiz.

In this first lesson you'll install Matplotlib, import it the standard way, and render your very first chart.

What You'll Learn in This Lesson

• What Matplotlib is and what it's used for
• How to install it with pip
• The standard pyplot import convention
• How to draw and display your first chart
• The difference between plt.show() and savefig()
• Where Matplotlib fits in the data ecosystem

1 What Is Matplotlib?

Matplotlib is the most widely used plotting library in Python. It lets you transform data — numbers in lists, arrays, or DataFrames — into visual charts that humans can actually understand at a glance.

📊 What can it draw?

• Line plots — trends over time
• Bar charts — comparing categories
• Histograms — the shape of a distribution
• Scatter plots — relationships between variables
• Pie charts, heatmaps, 3D surfaces, and more

Almost every Python data tool — pandas, seaborn, scikit-learn — builds on top of Matplotlib, so learning it gives you a foundation for the entire data-visualization world.

2 Installing and Importing Matplotlib

Matplotlib isn't built into Python, so install it once from your terminal:

pip install matplotlib

Then, in your Python file, import the pyplot module. Almost everyone uses the alias plt:

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

# Two simple lists of data
months = [1, 2, 3, 4, 5]
sales = [100, 140, 120, 200, 240]

# Draw a line connecting the points
plt.plot(months, sales)

# Display the chart in a window
plt.show()

What you'll see: a window opens with a blue line rising from 100 up to 240, with the five month numbers along the bottom (x-axis) and the sales values up the side (y-axis). That's a complete chart in just three lines of real code.

3 Showing vs. Saving a Chart

There are two ways to get your chart out of Python: plt.show() opens an interactive window, while plt.savefig() writes the chart to an image file on disk.

import matplotlib.pyplot as plt

x = [0, 1, 2, 3, 4]
y = [0, 1, 4, 9, 16]   # squares of x

plt.plot(x, y)

# Save the chart as a PNG image instead of (or as well as) showing it
plt.savefig("squares.png")

# Then display it on screen too
plt.show()

What you'll see: a smooth upward curve (a parabola, because y is x squared). A file named squares.png is also written to your working folder — open it and you'll see the exact same chart as an image.

🎯 Your Turn: Fill in the Blanks

Replace each ___ to plot a small dataset and display it.

import matplotlib.pyplot as ___   # the standard alias

days = [1, 2, 3, 4]
steps = [3000, 5500, 4200, 8000]

# Plot the data
plt.___(days, steps)

# Display the chart
plt.___()

📋 Common Errors & Quick Reference

❌ ModuleNotFoundError: No module named 'matplotlib'

You haven't installed it. Run pip install matplotlib in your terminal first.

❌ Nothing appears on screen

You forgot plt.show(). In a plain script the window only opens when you call it.

🏆 Mini Challenge: Temperature Trend

Plot a week of daily temperatures and save the chart to a file.

import matplotlib.pyplot as plt

days = [1, 2, 3, 4, 5, 6, 7]
temps_c = [14, 16, 15, 19, 21, 20, 18]

# 1. Plot the temperatures
plt.plot(days, temps_c)

# 2. Save it as 'week_temps.png'
plt.savefig("week_temps.png")

# 3. Show it on screen
plt.show()

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr"]
sales = [120, 145, 132, 178]

fig, ax = plt.subplots()
ax.plot(months, sales, marker="o")
ax.set_title("Sales by Month")

print("Figure size in inches:", fig.get_size_inches().tolist())
print("Figures open:", len(plt.get_fignums()))
print("Axes on the figure:", len(fig.axes))
print("Lines on the axes:", len(ax.lines))
print("Points plotted:", len(ax.lines[0].get_ydata()))
print("Title:", ax.get_title())

# ✅ Expected output:
# Figure size in inches: [6.4, 4.8]
# Figures open: 1
# Axes on the figure: 1
# Lines on the axes: 1
# Points plotted: 4
# Title: Sales by Month

❓ Frequently Asked Questions

Lesson complete — you've drawn your first chart!

You installed Matplotlib, imported pyplot the standard way, and rendered and saved a real chart with just a few lines of code.

Practice quiz

Continue this course

• Next: The Figure & Axes — Understand the Figure and Axes objects that hold every plot
• Quick reference: Matplotlib cheat sheet › Basics

Frequently asked questions

What is Matplotlib used for?

Matplotlib is a Python library for creating static, animated, and interactive charts and visualizations — line plots, bar charts, histograms, scatter plots, and much more — directly from your data.

Do I need to install Matplotlib separately?

Yes. Matplotlib is not part of the Python standard library. Install it with 'pip install matplotlib'. If you use Anaconda, it is already included.

What is pyplot?

pyplot is a module inside Matplotlib that provides a simple, MATLAB-style interface for building plots. It is conventionally imported as 'import matplotlib.pyplot as plt'.

How do I display a chart?

After building your plot, call plt.show() to open a window with the figure. In notebooks the figure usually appears inline automatically.