Skip to content

intro_matplotlib.md

Latest commit

 

History

History
171 lines (115 loc) · 4.28 KB

intro_matplotlib.md

File metadata and controls

171 lines (115 loc) · 4.28 KB
Raw

Introduction to Matplotlib

Matplotlib is the fundamental plotting library in Python for creating static, animated, and interactive visualizations. It's the foundation upon which many other Python visualization libraries (like Seaborn, Pandas plotting, and Plotly) are built.

It is used for:

  • Analysing trends
  • Comparing values
  • Monitoring systems
  • Presenting findings
  • Debugging performance issues

Installing Matplotlib

If you have deployed the Anaconda/Jupyter environment in Docker you may skip this step, numpy, pandas, and matplotlib are installed automatically via the requirements.txt file.

Matplotlib is not included in the Standard Python Library, but it can be installed using pip.

Optional: Create a virtual environment with python -m venv ./venv then activate it with .venv\Scripts\activate.ps1

Install Matplotlib by running pip install Matplotlib in your terminal, then import it into your workspace just like any other module, however by convention we alias it as pd:

import Matplotlib.pyplot as plt

Why Matplotlib?

  • Versatility: Create almost any type of plot imaginable
  • Fine-grained control: Customize every aspect of your plots
  • Integration: Works seamlessly with NumPy, Pandas, and SciPy
  • Cross-platform: Works on Windows, macOS, Linux, and in web backends
  • Publication-quality: Produce high-resolution figures for papers and presentations

Architecture

Matplotlib has a three-layer architecture:

  • Backend Layer: Handles rendering to various outputs (screen, files, etc.)
  • Artist Layer: Everything you see (lines, text, ticks, etc.) is an Artist object
  • Scripting Layer (pyplot): The interface most users interact with

Anatomy of a Matplotlib Figure

Matplotlib graphs your data on Figures which are made of several parts:

  • Figure: Think of the Figure as a blank canvas or a piece of paper. It's the top-level container that holds everything.
  • Axes: Where the actual plotting happens. A Figure can contain multiple Axes objects.
  • Axis: Handles the x-axis and y-axis, including limits, ticks, and labels.
  • Ticks: the marks on the axes
  • Labels: axis labels
  • Title: the plot title
  • Legend: labels for multiple lines or categories

Think of the Figure as a blank canvas or a piece of paper. It's the top-level container that holds everything.

Creating Plots

Line Plots

Line plots show trends over time.

import matplotlib.pyplot as plt

cpu_usage = [40, 55, 70, 65, 80]

plt.plot(cpu_usage)
plt.show()
  • plot(): draws the graph
  • show(): renders it
  • The default x-axis is the index position

Adding Labels and Title

A chart without labels is almost useless.

plt.plot(cpu_usage)
plt.title("CPU Usage Over Time")
plt.xlabel("Time")
plt.ylabel("CPU %")
plt.show()

Customising Style

Basic styling makes plots readable, markers show data points, and line styles improve clarity. Common customisations include marker, linestyle, linewidth, and color.

plt.plot(cpu_usage, marker="o", linestyle="--", linewidth=2)

Multiple Lines on One Plot

You can define multiple plots as part of the same figure; ensure you include a Legend to identify datasets.

cpu = [40, 55, 70, 65]
memory = [60, 65, 75, 85]

plt.plot(cpu, label="CPU")
plt.plot(memory, label="Memory")

plt.legend()
plt.show()

Bar Charts

Bar charts compare categories.

servers = ["A", "B", "C"]
cpu = [65, 80, 55]

plt.bar(servers, cpu)
plt.show()

Histograms

Histogram shows frequency distribution

response_times = [0.5, 0.7, 0.6, 1.2, 1.5, 0.9]

plt.hist(response_times)
plt.show()

Scatter Plots

Shows correlation between variables.

cpu = [40, 50, 60, 70]
response = [0.5, 0.6, 0.9, 1.4]

plt.scatter(cpu, response)
plt.show()

Plot Types Summary

Goal Best Plot
Show a trend Line plot
Compare categories Bar chart
Show distribution Histogram
Show relationship Scatter plot
Compare multiple trends Multi‑line plot
Compare distributions Box plot

Saving Plots

Simple, but important

plt.savefig("cpu_plot.png")

Matplotlib Exercises

Practice your knowledge by trying the tasks linked below.