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Python: Real-World Data Science

You're reading from   Python: Real-World Data Science Real-World Data Science

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Product type Course
Published in Jun 2016
Publisher
ISBN-13 9781786465160
Length 1255 pages
Edition 1st Edition
Languages
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Authors (5):
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Fabrizio Romano Fabrizio Romano
Author Profile Icon Fabrizio Romano
Fabrizio Romano
Phuong Vo.T.H Phuong Vo.T.H
Author Profile Icon Phuong Vo.T.H
Phuong Vo.T.H
Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
Martin Czygan Martin Czygan
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Martin Czygan
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Table of Contents (12) Chapters Close

Table of Contents FREE CHAPTER
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline
1. Course Module 1: Python Fundamentals 2. Course Module 2: Data Analysis 3. Course Module 3: Data Mining 4. Course Module 4: Machine Learning Index

Chapter 6. Interacting with Databases

Data analysis starts with data. It is therefore beneficial to work with data storage systems that are simple to set up, operate and where the data access does not become a problem in itself. In short, we would like to have database systems that are easy to embed into our data analysis processes and workflows. In this module, we focus mostly on the Python side of the database interaction, and we will learn how to get data into and out of pandas data structures.

There are numerous ways to store data. In this chapter, we are going to learn to interact with three main categories: text formats, binary formats and databases. We will focus on two storage solutions, MongoDB and Redis. MongoDB is a document-oriented database, which is easy to start with, since we can store JSON documents and do not need to define a schema upfront. Redis is a popular in-memory data structure store on top of which many applications can be built. It is possible to use...

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