Data Visualization

Plotting with Seaborn you own this product

This project is part of the liveProject series Data Visualization with Matplotlib and Seaborn
basic to intermediate Python 3 programming skills • familiarity with Anaconda Python • familiarity with Jupyter Notebook • familiarity with Matplotlib
skills learned
visualize relations in categorical data • visualize relations in numerical data • visualize pairwise relationships
Nimrita Koul
1 week · 6-8 hours per week · BEGINNER
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liveProject This project is part of the liveProject series Data Visualization with Matplotlib and Seaborn liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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You’re a data analyst at Global Consensus Bureau, and your manager has asked you to determine how factors like education, hours worked per week, gender, and age relate to citizens’ incomes. Using pandas, you’ll pre-process data from a survey conducted by the U.S. Census Bureau. Then, you’ll create different charts to identify statistical relationships in data, using Python’s seaborn library, which provides custom functions for visual elements. When you’re done, you’ll know how to use seaborn to create visually appealing charts that reveal accurate and useful insights.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

project author

Nimrita Koul

Nimrita Koul is an assistant professor of Computer Science and Machine Learning from Bangalore, India. She holds a Phd in Computer Science with 16 years of experience in teaching Computer Science courses to University undergraduates. Nimrita is a principal investigator for three funded research projects in machine learning. She has designed and demonstrated about 20 data science and machine learning projects to more than 650 students. Nimrita has spoken at multiple international conferences and events.


This liveProject is for data analysts, research scholars, and software engineers who would like to learn how to enhance their plotting skillset with Python’s seaborn library. To begin these liveProjects you’ll need to be familiar with the following:

  • Intermediate Python programming skills (variables, functions, loops)
  • Familiarity with Anaconda Python Distribution and Jupyter Notebook
  • Familiarity with Matplotlib (create and customize plots using object-oriented Matplotlib interface)
  • Familiarity with pandas
  • Use Jupyter Notebook for Python
  • Download and install Anaconda Distribution of Python and Jupyter Notebook
  • Install Python packages using conda or pip package managers

you will learn

In this liveProject, you’ll learn to use seaborn to create visually appealing charts that reveal accurate and useful insights.

  • Use pandas to read a CSV file into a pandas dataframe
  • Data wrangling with pandas: extract a subset of rows and columns, input special values in data, change categorical values, save a dataframe to CSV file
  • Set color palette with seaborn
  • Set runtime configuration for seaborn plots and FacetGrid
  • Create category plots: barplot, catplot, regression plot (lmplot), distribution plot (rug plot, kde plot), PairGrid, jointplot, and scatterplot
  • Customize plots’ axis labels, titles, font sizes, and color palettes
  • Use data columns as hue to differentiate based on categories
  • Adjust spacing in subplots
  • Save your plots as image files with the desired resolution


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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.