In the last few years, Python has become the go-to programming language for Data Scientists. In a way, this is kind of surprising. Python was not originally developed to do analytical tasks or data science, but it has evolved to become the ‘Swiss-army’ tool in the Data Scientist toolbox. The reason for this comes from a large number of third-party packages available for Data Scientists. For example, there is ‘Pandas’ for manipulation of heterogeneous and labeled data, ‘SciPy’ for common scientific computing tasks, ‘Matplotlib’ for visualizations, ‘NumPy’ for the manipulation of array-based data, and many, many others.
Why Data Scientists <3 Python
Nowadays Python is used for everything from data handling to visualization to web development. It has become one of the most important and most popular open source programming languages in use today. Many people think of it as a new language, but it is older than both Java and R. Python was created by Guido van Rossum of the Dutch CWI research institute in 1989. One of its main strengths is its easy ability to be extended as well as its support for multiple platforms. The ability of Python to be able to communicate with different file formats and libraries makes it very useful and is the main reason it is used by Data Scientist today.
For programmers, Python is not a difficult language to learn. In fact, most experienced programmers regard Python as an easy language to learn. Many now even recommend Python as the first language anyone should learn, which says a lot. The syntax of the language itself is very easy to pick up. Write a ‘Hello World’ program in any language. Java and C take no less than three lines of code, whereas Python takes just one. Now, its all that easy, learning how to use libraries for example takes time, but its an easy language to start and get coding with, easier than most.
Talend and Python
This year, Talend introduced a new, cloud-first app called Talend Data Streams. With Data Streams everything is a “stream”, like a flow. Even batch processing is a stream that is time bound. It means we have one architecture for both batch and real-time stream processing. Data Streams has a live preview so that developers will know their design is right every step along the way. When they drop the final target connector on the canvas, they can instantly see that their design is complete. Now, Data Quality relies on complex mathematics to solve the problem of data deduplication, matching, and standardization. Data Streams is designed to let anyone easily add snippets of Python using an embedded code editor that provides code auto-completion as well as intuitive syntax highlighting. We want to empower the user with the power of Python.
Now, sometimes it’s just easier to code, and we developers often go straight to it, depending upon the user and the task at hand. And this is where Python comes on board. Talend Data Streams has native support for Python built in. So we at Talend are investing in Python. We think it offers great functionality as well as ease of programming. We invite you to give Talend Data Streams a try and see how you can easily extend your data pipelines with embedded Python coding components.