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Introduction to Data Visualization with Matplotlib

4.5+
101 reviews
Beginner

Learn how to create, customize, and share data visualizations using Matplotlib.

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4 Hours14 Videos44 Exercises
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Course Description

Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you communicate your data to others, and are useful to data analysts and other consumers of the data. In this course, you will learn how to use Matplotlib, a powerful Python data visualization library. Matplotlib provides the building blocks to create rich visualizations of many different kinds of datasets. You will learn how to create visualizations for different kinds of data and how to customize, automate, and share these visualizations.
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In the following Tracks

Certification Available

Associate Data Scientist in Python

Go To Track

Data Visualization with Python

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  1. 1

    Introduction to Matplotlib

    Free

    This chapter introduces the Matplotlib visualization library and demonstrates how to use it with data.

    Play Chapter Now
    Introduction to data visualization with Matplotlib
    50 xp
    Using the matplotlib.pyplot interface
    100 xp
    Adding data to an Axes object
    100 xp
    Customizing your plots
    50 xp
    Customizing data appearance
    100 xp
    Customizing axis labels and adding titles
    100 xp
    Small multiples
    50 xp
    Creating a grid of subplots
    50 xp
    Creating small multiples with plt.subplots
    100 xp
    Small multiples with shared y axis
    100 xp
  2. 2

    Plotting time-series

    Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data.

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    Plotting time-series data
    50 xp
    Read data with a time index
    100 xp
    Plot time-series data
    100 xp
    Using a time index to zoom in
    100 xp
    Plotting time-series with different variables
    50 xp
    Plotting two variables
    100 xp
    Defining a function that plots time-series data
    100 xp
    Using a plotting function
    100 xp
    Annotating time-series data
    50 xp
    Annotating a plot of time-series data
    100 xp
    Plotting time-series: putting it all together
    100 xp
  3. 3

    Quantitative comparisons and statistical visualizations

    Visualizations can be used to compare data in a quantitative manner. This chapter explains several methods for quantitative visualizations.

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    Quantitative comparisons: bar-charts
    50 xp
    Bar chart
    100 xp
    Stacked bar chart
    100 xp
    Quantitative comparisons: histograms
    50 xp
    Creating histograms
    100 xp
    "Step" histogram
    100 xp
    Statistical plotting
    50 xp
    Adding error-bars to a bar chart
    100 xp
    Adding error-bars to a plot
    100 xp
    Creating boxplots
    100 xp
    Quantitative comparisons: scatter plots
    50 xp
    Simple scatter plot
    100 xp
    Encoding time by color
    100 xp
  4. 4

    Sharing visualizations with others

    This chapter shows you how to share your visualizations with others: how to save your figures as files, how to adjust their look and feel, and how to automate their creation based on input data.

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    Preparing your figures to share with others
    50 xp
    Selecting a style for printing
    50 xp
    Switching between styles
    100 xp
    Saving your visualizations
    50 xp
    Saving a file several times
    100 xp
    Save a figure with different sizes
    100 xp
    Automating figures from data
    50 xp
    Unique values of a column
    100 xp
    Automate your visualization
    100 xp
    Where to go next
    50 xp
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

In the following Tracks

Certification Available

Associate Data Scientist in Python

Go To Track

Data Visualization with Python

Go To Track

Datasets

Seattle weatherAustin weatherClimate dataMedals by countryMedalist weights

Collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Amy Peterson

Prerequisites

Introduction to Python
Ariel Rokem HeadshotAriel Rokem

Senior Data Scientist, University of Washington

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Don’t just take our word for it

*4.5
from 101 reviews
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  • Roman Z.
    about 2 months

    I appreciate a lot the way Ariel explains things. It's very concise, clear and engaging.

  • James S.
    about 2 months

    Plotting skills are essential! This course was awesome.

  • PASCAL P.
    2 months

    Excellent teaching , very clear explanations, gradual progression, interesting datasets. Perfect.

  • Sait O.
    3 months

    I benefited a lot from this course..

  • Jessica R.
    4 months

    This was my first coding course I've ever completed. It was incredibly helpful to start with something that gave context to what I was learning. "Hello, World" tutorials never really taught me anything, and I've likened them to teaching someone to speak not through talking to them, but saying "Ah" while pointing at an 'A'. Intro to Matplotlib illustrated the practical applications of the basics without handholding me through the coding equivalent of "Ah" = 'A'. I specifically want to call out how slow this guy spoke: I really appreciated it. It seems like the type of thing maybe others would complain about, so I wanted to note that I found it incredibly helpful to my retention.

"I appreciate a lot the way Ariel explains things. It's very concise, clear and engaging."

Roman Z.

"Plotting skills are essential! This course was awesome."

James S.

"Excellent teaching , very clear explanations, gradual progression, interesting datasets. Perfect."

PASCAL P.

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