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Learning pandas

You're reading from   Learning pandas High performance data manipulation and analysis using Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787123137
Length 446 pages
Edition 2nd Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Table of Contents (16) Chapters Close

Preface 1. pandas and Data Analysis FREE CHAPTER 2. Up and Running with pandas 3. Representing Univariate Data with the Series 4. Representing Tabular and Multivariate Data with the DataFrame 5. Manipulating DataFrame Structure 6. Indexing Data 7. Categorical Data 8. Numerical and Statistical Methods 9. Accessing Data 10. Tidying Up Your Data 11. Combining, Relating, and Reshaping Data 12. Data Aggregation 13. Time-Series Modelling 14. Visualization 15. Historical Stock Price Analysis

Numerical and Statistical Methods

pandas is extremely powerful for modeling and manipulating data, but it also provides many powerful tools for numerical and statistical analysis. These capabilities are tightly integrated with pandas data structures and thereby make complex calculations very simple once the data is modeled.

This chapter will examine many of these capabilities. It starts with common numerical methods such as arithmetic with alignment across multiple objects, as well as finding specific values such as minimums and maximums. We then will look at many of the statistical capabilities of pandas, such as working with quantiles, ranking of values, variance, correlation, and many others.

Last but not least, we will examine a very powerful capability provided in pandas known as the rolling window. Rolling windows provide a means of applying various methods such mean calculation...

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