Preface
The world generates data at an increasing pace. Consumers, sensors, or scientific experiments emit data points every day. In finance, business, administration and the natural or social sciences, working with data can make up a significant part of the job. Being able to efficiently work with small or large datasets has become a valuable skill.
There are a variety of applications to work with data, from spreadsheet applications, which are widely deployed and used, to more specialized statistical packages for experienced users, which often support domain-specific extensions for experts.
Python started as a general purpose language. It has been used in industry for a long time, but it has been popular among researchers as well. Around ten years ago, in 2006, the first version of NumPy was released, which made Python a first class language for numerical computing and laid the foundation for a prospering development, which led to what we today call the PyData ecosystem: A growing set of high-performance libraries to be used in the sciences, finance, business or anywhere else you want to work efficiently with datasets.
In contrast to more specialized applications and environments, Python is not only about data analysis. The list of industrial-strength libraries for many general computing tasks is long, which makes working with data in Python even more compelling. Whether your data lives inside SQL or NoSQL databases or is out there on the Web and must be crawled or scraped first, the Python community has already developed packages for many of those tasks.
And the outlook seems bright. Working with bigger datasets is getting simpler and sharing research findings and creating interactive programming notebooks has never been easier. It is the perfect moment to learn about data analysis in Python. This book lets you get started with a few core libraries of the PyData ecosystem: Numpy, Pandas, and matplotlib. In addition, two NoSQL databases are introduced. The final chapter will take a quick tour through one of the most popular machine learning libraries in Python.
We hope you find Python a valuable tool for your everyday data work and that we can give you enough material to get productive in the data analysis space quickly.
