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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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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

Cross tooling – combining pandas awesomeness with R, Julia, H20.ai, and Azure ML Studio

Pandas can be regarded as a "wonder tool" when it comes to applications like data manipulation, data cleaning, or handling time series data. It is extremely fast and efficient, and it is powerful enough to handle small to intermediate datasets. The best part is that the use of pandas is not restricted just to Python. There are methods enabling the supremacy of pandas to be utilized in other frameworks, like R, Julia, Azure ML Studio and H20.ai. These methods of using the benefits of a superior framework from another tool is called cross-tooling and is frequently applied. One of the main reasons for this to exist is that it is almost impossible for one tool to have all the functionalities. Suppose one task has two sub-tasks: sub-task 1 can be done well in R while the sub-task...

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