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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

Pivoting data to and from value and indexes

Data is often stored in a stacked format, which is also referred to as record format. This is common in databases, .csv files, and Excel spreadsheets. In a stacked format, the data is often not normalized and has repeated values in many columns, or values that should logically exist in other tables (violating another concept of tidy data).

Take the following data which represents a stream of data from an accelerometer on a

An issue with this data as it is organized is: how does one go about determining the readings for a specific axis? This can be naively done with Boolean selections:

An issue here is, what if you want to know the values for all axes at a given time and not just the x axis. To do this, you can perform a selection for each value of the axis, but that is repetitive code and does not handle the scenario of new axis values...

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