Python for Data Visualization

Python for Data Visualization

1h 21mIntermediate2024-01-16

Authors

Michael Galarnyk

Michael Galarnyk

Python Instructor and Blogger

Madecraft

Madecraft

Full-Service Learning Content Company

Course details

Data visualization is incredibly important for data scientists, as it helps them communicate their insights to nontechnical peers. But you don’t need to be a design pro. Python is a popular, easy-to-use programming language that offers a number of libraries specifically built for data visualization. In this course from the experts at Madecraft, you can learn how to build accurate, engaging, and easy-to-generate charts and graphs using Python. Explore the pandas and Matplotlib libraries, and then discover how to load and clean data sets and create simple and advanced plots, including heatmaps, histograms, and subplots. Instructor Michael Galarnyk provides all the instruction you need to create professional data visualizations through programming.

Skills covered

Data VisualizationPythonProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceSoftware DevelopmentOne-Off

Concepts

Introduction

  • Effectively present data with Python
  • Before you start
  • Using the exercise files

Data Visualization Overview

  • Value of data visualization
  • Leverage programming languages
  • Overview of Jupyter Notebooks

Leverage pandas for Analysis

  • Introduction to pandas
  • Create sample data
  • Load sample data
  • Basic operations
  • Simplify with slicing
  • Filter and clean data
  • Rename and delete columns
  • Aggregate functions
  • Identify missing data
  • Remove or fill in missing data
  • Convert pandas DataFrames
  • Export pandas DataFrames

Simplify Visualization with Matplotlib

  • Basics of Matplotlib
  • Set marker type and colors
  • MATLAB-style vs. object syntax
  • Set titles, labels, and limits
  • Add grids
  • Create legends
  • Save plots to files
  • Create plots with Matplotlib wrappers

Customize Visualizations with Matplotlib

  • Create heatmaps
  • Create histograms
  • Create subplots

Conclusion

  • Next steps
40,000 Toman