The Python programming language , created in the early 1990s, quickly became one of the most widely used languages in the world.
Python ‘s popularity is even present when we talk about the universe that involves big data . This is a language widely used when it comes to carrying out activities such as data analysis , data mining and data visualization , among other related tasks.
Its considerable ease of use, especially when compared to other languages, combined with the fact that it is an open source language, has led to the emergence of a large volume of libraries capable of assisting the work of programmers and data scientists in a very significant way.
Discover, below, 3 essential Python libraries for anyone who works or is thinking about working with big data .
Pandas
The Pandas library , whose name is derived from the term panel data , began success is embracing continual in 2008 and the first version was published in 2012.
Its creator, American software developer and entrepreneur Wes McKinney , is also the author of the book Python for Data Analysis .
Some of the standout features of the Pandas library are its focus on collective feedback , high performance and speed for data merging , and a wide variety of tools aimed at structuring and manipulating data.
TensorFlow
Initially created by Google for the purpose of training deep neural networks, this library sales page copy – 5 things to do before you write it in 2015 has also proven to be a great ally for developers and data scientists.
With TensorFlow , for example, it is possible for a programmer to develop several machine learning applications with the help of the various resources and tools made available by this library.
Other advantages of the TensorFlow library for those who need to deal with big data are the elimination of the possibility of errors by 60%, easy implementation and high scalability.
Matplotlib
Aimed at plotting 2D graphs in the Python language. The Matplotlib library was initially in 2003 and by American mobile lead John D. Hunter .
Hunter’s profession, in fact, has a direct relationship with the origins of the library. He created it with the aim of visualizing electrocorticography data from patients with epilepsy during his postdoctoral research.
Matplotlib is very useful for better understanding data visualization, data analysis, and other insights . Another advantage is that this library supports a wide range of backends and output types . In practice, this means that your outputs do not necessarily need to be based on the operating system used.