Python

Supercharge your data science skills using Python's most popular and robust data visualization libraries. Learn how to use Matplotlib, Seaborn, Bokeh, and others to create beautiful static and interactive visualizations of categorical, aggregated, and geospatial data.

Supercharge your data science skills using Python's most popular and robust data visualization libraries.

with Python

Data Visualization

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

Introduction to Matplotlib

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

Plotting time-series

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

Quantitative comparisons and statistical visualizations

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.

Sharing visualizations with others

Seattle weather

Austin weather

Climate data

Medals by country

Medalist weights

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.

Introduction to Python

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

Introduction to Data Visualization with Matplotlib

What is Seaborn, and when should you use it? In this chapter, you will find out! Plus, you will learn how to create scatter plots and count plots with both lists of data and pandas DataFrames. You will also be introduced to one of the big advantages of using Seaborn - the ability to easily add a third variable to your plots by using color to represent different subgroups.

Introduction to Seaborn

In this chapter, you will create and customize plots that visualize the relationship between two quantitative variables. To do this, you will use scatter plots and line plots to explore how the level of air pollution in a city changes over the course of a day and how horsepower relates to fuel efficiency in cars. You will also see another big advantage of using Seaborn - the ability to easily create subplots in a single figure!

Visualizing Two Quantitative Variables

Categorical variables are present in nearly every dataset, but they are especially prominent in survey data. In this chapter, you will learn how to create and customize categorical plots such as box plots, bar plots, count plots, and point plots. Along the way, you will explore survey data from young people about their interests, students about their study habits, and adult men about their feelings about masculinity.

Visualizing a Categorical and a Quantitative Variable

In this final chapter, you will learn how to add informative plot titles and axis labels, which are one of the most important parts of any data visualization! You will also learn how to customize the style of your visualizations in order to more quickly orient your audience to the key takeaways. Then, you will put everything you have learned together for the final exercises of the course!

Customizing Seaborn Plots

Countries

Mileage per gallon

Students

Survey responses

Seaborn is a powerful Python library that makes it easy to create informative and attractive visualizations. This course provides an introduction to Seaborn and teaches you how to visualize your data using plots such as scatter plots, box plots, and bar plots. You’ll do this while exploring survey responses about student hobbies and the factors that are associated with academic success. You’ll also learn about some of Seaborn’s advantages as a statistical visualization tool, such as how it automatically calculates confidence intervals. By the end of the course, you will be able to use Seaborn in a variety of situations to explore your data and effectively communicate the results of your data analyses to others.

Introduction to Data Science in Python

Learn how to create informative and attractive visualizations in Python using the Seaborn library.

Introduction to Data Visualization with Seaborn

How do you show all of your data while making sure that viewers don't miss an important point or points? Here we discuss how to guide your viewer through the data with color-based highlights and text. We also introduce a dataset on common pollutant values across the United States.

Highlighting your data

Color is a powerful tool for encoded values in data visualization. However, with this power comes danger. In this chapter, we talk about how to choose an appropriate color palette for your visualization based upon the type of data it is showing.

Using color in your visualizations

Uncertainty occurs everywhere in data science, but it's frequently left out of visualizations where it should be included. Here, we review what a confidence interval is and how to visualize them for both single estimates and continuous functions. Additionally, we discuss the bootstrap resampling technique for assessing uncertainty and how to visualize it properly. 

Showing uncertainty

Often visualization is taught in isolation, with best practices only discussed in a general way. In reality, you will need to bend the rules for different scenarios.  From messy exploratory visualizations to polishing the font sizes of your final product; in this chapter, we dive into how to optimize your visualizations at each step of a data science workflow.

Visualization in the data science workflow

State populations dataset

U.S. farmer's markets dataset

Pollution dataset

Great data visualization is the cornerstone of impactful data science. Visualization helps you to both find insight in your data and share those insights with your audience. Everyone learns how to make a basic scatter plot or bar chart on their journey to becoming a data scientist, but the true potential of data visualization is realized when you take a step back and think about what, why, and how you are visualizing your data. In this course you will learn how to construct compelling and attractive visualizations that help you communicate the results of your analyses efficiently and effectively. We will cover comparing data, the ins and outs of color, showing uncertainty, and how to build the right visualization for your given audience through the investigation of a datasets on air pollution around the US and farmer's markets. We will finish the course by examining open-access farmers market data to build a polished and impactful visual report.

Python Data Science Toolbox (Part 2)

Introduction to Data Visualization in Python

Intermediate Data Visualization with Seaborn

Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.

Improving Your Data Visualizations in Python

This chapter provides an introduction to basic plotting with Bokeh. You will create your first plots, learn about different data formats Bokeh understands, and make visual customizations for selections and mouse hovering.

Basic plotting with Bokeh

Learn how to combine multiple Bokeh plots into different kinds of layouts on a page, how to easily link different plots together, and how to add annotations such as legends and hover tooltips.

Layouts,  Interactions, and Annotations

Bokeh server applications allow you to connect all of the powerful Python libraries for data science and analytics, such as NumPy and pandas to create rich, interactive Bokeh visualizations. Learn about Bokeh's built-in widgets, how to add them to Bokeh documents alongside plots, and how to connect everything to real Python code using the Bokeh server.

Building interactive apps with Bokeh

In this final chapter, you'll build a more sophisticated Bokeh data exploration application from the ground up based on the famous Gapminder dataset.

Putting It All Together! A Case Study

AAPL stock

Automobile miles per gallon

Gapminder

Blood glucose levels

Female literacy and birth rate

Olympic medals (100m sprint)

Bokeh is an interactive data visualization library for Python—and other languages—that targets modern web browsers for presentation. It can create versatile, data-driven graphics and connect the full power of the entire Python data science stack to create rich, interactive visualizations.

pandas Foundations

Learn to create versatile, data-driven graphics with Bokeh, an interactive data visualization library that targets modern web browsers for presentation. 

Bokeh Data Visualization

Learn how to create versatile and interactive data visualizations using Bokeh.

Interactive Data Visualization with Bokeh

In this chapter, you will learn how to create a two-layer map by first plotting regions from a shapefile and then plotting location points as a scatterplot.

Building 2-layer maps : combining polygons and scatterplots

You'll work with GeoJSON to create polygonal plots, learn about projections and coordinate reference systems, and get practice spatially joining data in this chapter.

Creating and joining GeoDataFrames

First you will learn to get information about the geometries in your data with three different GeoSeries attributes and methods. Then you will learn to create a street map layer using folium.

GeoSeries and  folium

In this chapter, you will learn about a special map called a choropleth. Then you will learn and practice building choropleths using two different packages: geopandas and folium.

Creating a choropleth building permit density in Nashville

Building permits issued in Nashville in 2017

Council district GIS data

Nashville neighborhoods GIS data

Public artworks in Nashville

School district GIS data

Schools in Nashville

One of the most important tasks of a data scientist is to understand the relationships between their data's physical location and their geographical context. In this course you'll be learning to make attractive visualizations of geospatial data with the GeoPandas package. You will learn to spatially join datasets, linking data to context. Finally you will learn to overlay geospatial data to maps to add even more spatial cues to your work. You will use several datasets from the City of Nashville's open data portal to find out where the chickens are in Nashville, which neighborhood has the most public art, and more!

Manipulating DataFrames with pandas

Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.

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Join over 7,490,000 learners and start Data Visualization with Python today!