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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
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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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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

Navigating Jupyter Notebook

Jupyter Notebook, known as IPython Notebook previously, is a wonderful tool for reporting. It allows us to integrate regular code with rich styling, formatting, markdown, and special text such as equation plots and live coding. This section will help you understand the essence of a Jupyter Notebook.

Jupyter Notebook can be launched through Anaconda Navigator or from the Terminal using the Jupyter Notebook command. It opens in the browser. The following window opens on startup:

Figure 9.20: Startup screen once a Jupyter Notebook loads up

A Jupyter Notebook can be created in any of the folders in the directory. The New option creates a new notebook, folder, or Terminal. The most interesting feature of this option is that it allows us to shift between multiple Conda environments with ease. For example, both Python 2 and Python 3 environments can be accessed...

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