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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

Indexing Data

An index is a tool for optimized look up of values from a series or DataFrame. They are a lot like a key in a relational database, but more powerful. They provide the means of alignment for multiple sets of data and also carry semantics for how to handle various tasks with data such as resampling to different frequencies.

Much of the modeling that you will perform with pandas depends critically on how you set up your indexes. A properly implemented index will optimize performance and be an invaluable tool in driving your analysis.

We have previously used indexes briefly, and in this chapter, we will dive quite a bit deeper. During this deep dive, we will learn more about:

  • The importance of indexes
  • The types of pandas indexes, including RangeIndex, Int64Index, CategoricalIndex, Float64Index, Datetimeindex, and PeriodIndex
  • Setting and resetting an index
  • Creating hierarchical...
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