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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 2. Up and Running with pandas FREE CHAPTER 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

Working with CSV and text/tabular format data

CSV formatted data is likely to be one of the most common forms of data you may use in pandas. Many web-based services provide data in a CSV format, as well as many information systems within an enterprise. It is an easy format to use and is commonly used as an export format for spreadsheet applications such as Excel.

A CSV is a file consisting of multiple lines of text-based data, with values separated by commas. It can be thought of as a table of data similar to a single sheet in a spreadsheet program. Each row of the data is in its own line in the file, and each column for each row is stored in text format, with a comma separating the data in each column.

For more details on the specifics of CSV files, feel free to visit http://en.wikipedia.org/wiki/Comma-separated_values.

As CSV is so common and easily understood, we will spend...

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