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Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. The Pandas Primer 4. Statistics and Linear Algebra 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources

Reading and writing to Excel with Pandas


Excel files contain a lot of important data. Of course, we can export that data in other more portable formats, such as CSV. However, it is more convenient to read and write Excel files with Python. As is common in the Python world, there is currently more than one project working towards the goal of providing Excel I/O capabilities. The modules that we will need to install to get Excel I/O to work with pandas are somewhat obscurely documented. The reason is that the projects that pandas depends on are independent and rapidly developing. The pandas package is picky about the files it accepts as Excel files. These files must have the .xls or .xlsx suffix, otherwise, we get the following error:

ValueError: No engine for filetype: ''

This is easy to fix. For instance, if we create a temporary file, we just give it the proper suffix. If you don't install anything, you will get the following error message:

ImportError: No module named openpyxl.workbook...
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