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Mastering pandas

You're reading from   Mastering pandas A complete guide to pandas, from installation to advanced data analysis techniques

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Product type Paperback
Published in Oct 2019
Publisher
ISBN-13 9781789343236
Length 674 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Ashish Kumar Ashish Kumar
Author Profile Icon Ashish Kumar
Ashish Kumar
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Toc

Table of Contents (21) Chapters Close

Preface 1. Section 1: Overview of Data Analysis and pandas FREE CHAPTER
2. Introduction to pandas and Data Analysis 3. Installation of pandas and Supporting Software 4. Section 2: Data Structures and I/O in pandas
5. Using NumPy and Data Structures with pandas 6. I/Os of Different Data Formats with pandas 7. Section 3: Mastering Different Data Operations in pandas
8. Indexing and Selecting in pandas 9. Grouping, Merging, and Reshaping Data in pandas 10. Special Data Operations in pandas 11. Time Series and Plotting Using Matplotlib 12. Section 4: Going a Step Beyond with pandas
13. Making Powerful Reports In Jupyter Using pandas 14. A Tour of Statistics with pandas and NumPy 15. A Brief Tour of Bayesian Statistics and Maximum Likelihood Estimates 16. Data Case Studies Using pandas 17. The pandas Library Architecture 18. pandas Compared with Other Tools 19. A Brief Tour of Machine Learning 20. Other Books You May Enjoy

pandas styling

pandas allow for a wide variety of operations to be performed on DataFrames, making it easier to handle structured data. Another intriguing property of DataFrames is that they allow us to format and style regular rows and columns in tabular data. These styling properties help enhance the readability of tabular data. The Dataframe.style method returns a Styler object. Any formatting to be applied before displaying a DataFrame can be applied over this Styler object. Styling can be done either with in-built functions that have predefined rules for formatting or with user-defined rules.

Let's consider the following DataFrames so that we can take a look at pandas' styling properties:

  df = pd.read_csv("titanic.csv")
  df

The following screenshot shows the preceding DataFrame loaded into Jupyter Notebook:

DataFrame loaded into Jupyter Notebook...
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