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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 2. NumPy Arrays FREE CHAPTER 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

Chapter 4. pandas Primer

pandas is named after panel data (an econometric term) and Python data analysis, and is a popular open source Python project. This chapter is a tutorial on basic pandas functionalities, where we will learn about pandas data structures and operations.

Note

The official pandas documentation insists on naming the project pandas in all lowercase letters. The other convention they insist on is this import statement: import pandas as pd. We will try to follow these conventions as much as possible.

In this chapter, we will install and explore pandas. Then, we will acquaint ourselves with the two central pandas data structures: DataFrame and Series. After this, you will learn how to perform SQL-like operations on the data contained in these data structures. pandas has statistical utilities including time-series routines, some of which will be demonstrated. The topics we will pursue are as follows:

  • Installing and exploring pandas
  • DataFrame and Series data structures...
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