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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

Lightweight access with sqlite3


SQLite is a very popular relational database. It's very lightweight and used by many applications, for instance, web browsers such as Mozilla Firefox. The sqlite3 module in the standard Python distribution can be used to work with a SQLite database. With sqlite3, we can either store the database in a file or keep it in RAM. For this example, we will do the latter. Import sqlite3 as follows:

import sqlite3

A connection to the database is needed to proceed. If we wanted to store the database in a file, we would provide a filename. Instead, do the following:

with sqlite3.connect(":memory:") as con:

The with statement is standard Python and relies on the presence of a __exit__() method in a special context manager class. With this statement, we don't need to explicitly close the connection. The closing of the connection is done automatically by the context manager. After connecting to a database, we need a cursor. That's generally how it works with databases by the...

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