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Pandas 1.x Cookbook

You're reading from   Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Length 626 pages
Edition 2nd Edition
Languages
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Authors (2):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Matthew Harrison Matthew Harrison
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Matthew Harrison
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Toc

Table of Contents (17) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Creating and Persisting DataFrames 4. Beginning Data Analysis 5. Exploratory Data Analysis 6. Selecting Subsets of Data 7. Filtering Rows 8. Index Alignment 9. Grouping for Aggregation, Filtration, and Transformation 10. Restructuring Data into a Tidy Form 11. Combining Pandas Objects 12. Time Series Analysis 13. Visualization with Matplotlib, Pandas, and Seaborn 14. Debugging and Testing Pandas 15. Other Books You May Enjoy
16. Index

Writing CSV

For better or worse, there are a lot of CSV files in the world. Like most technologies, there are good and bad parts to CSV files. On the plus side, they are human-readable, can be opened in any text editor, and most spreadsheet software can load them. On the downside, there is no standard for CSV files, so encoding may be weird, there is no way to enforce types, and they can be large because they are text-based (though they can be compressed).

In this recipe, we will show how to create a CSV file from a pandas DataFrame.

There are a few methods on the DataFrame that start with to_. These are methods that export DataFrames. We are going to use the .to_csv method. We will write out to a string buffer in the examples, but you will usually use a filename instead.

How to do it...

  1. Write the DataFrame to a CSV file:
    >>> beatles
         first       last  birth
    0     Paul  McCartney   1942
    1     John     Lennon   1940
    2  Richard    Starkey...
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