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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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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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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 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
Index

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 more than one project with 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

The following command...

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