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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

Summary

We stored annual sunspots cycles data in different relational and NoSQL databases.

The term relational here does not just pertain to relationships between tables; firstly, it has to do with the relationship between columns inside a table; secondly, it relates to connections between tables.

The sqlite3 module in the standard Python distribution can be used to work with a SQLite database. We can give pandas a SQLite database connection or a SQLAlchemy connection.

SQLAlchemy is renowned for its ORM, based on a design pattern, where Python classes are mapped to database tables. The ORM pattern is a general architectural pattern applicable to other object-oriented programming languages. SQLAlchemy abstracts away the technical details of working with databases including writing SQL.

MongoDB is a document-based store, which can hold a huge amount of data.

In the in-memory mode, Redis is extremely fast, with writing and reading being almost equally fast. Redis is a key-value store that functions...

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