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Building Data Science Solutions with Anaconda

You're reading from  Building Data Science Solutions with Anaconda

Product type Book
Published in May 2022
Publisher Packt
ISBN-13 9781800568785
Pages 330 pages
Edition 1st Edition
Languages
Author (1):
Dan Meador Dan Meador
Profile icon Dan Meador

Table of Contents (16) Chapters

Preface 1. Part 1: The Data Science Landscape – Open Source to the Rescue
2. Chapter 1: Understanding the AI/ML landscape 3. Chapter 2: Analyzing Open Source Software 4. Chapter 3: Using the Anaconda Distribution to Manage Packages 5. Chapter 4: Working with Jupyter Notebooks and NumPy 6. Part 2: Data Is the New Oil, Models Are the New Refineries
7. Chapter 5: Cleaning and Visualizing Data 8. Chapter 6: Overcoming Bias in AI/ML 9. Chapter 7: Choosing the Best AI Algorithm 10. Chapter 8: Dealing with Common Data Problems 11. Part 3: Practical Examples and Applications
12. Chapter 9: Building a Regression Model with scikit-learn 13. Chapter 10: Explainable AI - Using LIME and SHAP 14. Chapter 11: Tuning Hyperparameters and Versioning Your Model 15. Other Books You May Enjoy

Visualization with Matplotlib

Like many other things discussed in this book, there are many packages that can tackle any particular area. For the job of visualizing, Matplotlib is easily one of the most widely used. Not only is it quite easy to show simple graphs, but there are also many advanced options that you can use as well. It also works very well with panda DataFrames and has carved out its place as one of the most widely used packages in data science.

Let's start with a straightforward example of how to display a plot.

There are a few basic steps that you should take almost every time you want to show a plot:

  1. Preparing the data
  2. Plotting the data
  3. Customizing the plot
  4. Showing the plot

We'll walk through all of these, but we've already done much of step one in the Cleaning data with pandas section. Let's take that data and group it to focus on the categories of majors.

Preparing data for plotting

Let's take the existing...

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