Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
Online Resources

Storing data in Redis


Remote Dictionary Server (Redis) is an in-memory, key-value database, written in C. In the in-memory mode, Redis is extremely fast, with writing and reading being almost equally fast. Redis follows the publish/subscribe model and uses Lua scripts as stored procedures. Publish/subscribe makes use of channels to which a client can subscribe in order to receive messages. I had installed Redis version 3.2.6 at the time of writing the book. Redis can be downloaded from the Redis home page at http://redis.io/. After installing the Redis distribution, issue the following command to run the server:

$ src/redis-server

Now let's install a Python driver:

$ pip3 install redis

It's pretty easy to use Redis when you realize it's a giant dictionary. However, Redis does have its limitations. Sometimes, it's just convenient to store a complex object as a JSON string (or other format). That's what we are going to do with a Pandas DataFrame. Connect to Redis as follows:

r = redis.StrictRedis...