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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 FREE CHAPTER 2. NumPy Arrays 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

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 an SQLite database connection or an 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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