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

Chapter 8.  Working with Databases

This chapter introduces various databases (relational and NoSQL) and related APIs. A relational database is a database that has a collection of tables containing data organized by the relations between data items. A relationship can be set up between each row in the table and a row in another table. Relationships may also be between columns inside a table (obviously columns within a table have to be related, for instance, a name column and an address column in a customer table) as well as connections between columns in other tables.

Not Only SQL (NoSQL) databases are frequently used in big data and web applications. NoSQL systems may permit SQL-like query languages to be employed. NoSQL databases allow data to be stored in a more flexible manner than the relational model permits. This may mean not having a database schema or a flexible database schema. Of course, the flexibility and speed may come at a price, such as limited support for consistent transactions...