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Getting Started with Python Data Analysis

You're reading from  Getting Started with Python Data Analysis

Product type Book
Published in Nov 2015
Publisher
ISBN-13 9781785285110
Pages 188 pages
Edition 1st Edition
Languages

Table of Contents (15) Chapters

Getting Started with Python Data Analysis
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Introducing Data Analysis and Libraries 2. NumPy Arrays and Vectorized Computation 3. Data Analysis with Pandas 4. Data Visualization 5. Time Series 6. Interacting with Databases 7. Data Analysis Application Examples 8. Machine Learning Models with scikit-learn Index

Summary


We finished covering the basics of interacting with data in different commonly used storage mechanisms from the simple ones, such as text files, over more structured ones, such as HDF5, to more sophisticated data storage systems, such as MongoDB and Redis. The most suitable type of storage will depend on your use case. The choice of the data storage layer technology plays an important role in the overall design of data processing systems. Sometimes, we need to combine various database systems to store our data, such as complexity of the data, performance of the system or computation requirements.

Practice exercise

  • Take a data set of your choice and design storage options for it. Consider text files, HDF5, a document database, and a data structure store as possible persistent options. Also evaluate how difficult (by some metric, for examples, how many lines of code) it would be to update or delete a specific item. Which storage type is the easiest to set up? Which storage type supports...

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