por You’ve probably heard that Python and Big Data It is one of the most valuable combinations today. Because? Because data and information serve to be more efficient, make good decisions and know your customers. In this article We will tell you in more depth why it is so important. But the amount of data that companies generate today is impressive. Plus, it doesn’t stop growing.
Thanks to IDC report, we know that by 2025 there will be more than 175 zettabytes of data circulating in the cloud. However, today we are here to also talk to you about Python. In fact, The Python programming language and big data are closely related. It is not for nothing that it is considered the best programming language for data analysis. So read on to find out the reasons. Let’s go there!
Why is the combination of Python and Big Data so good?
The truth is that choosing a programming language for big data It depends on the project you have in hand. However, whatever your goal, Python will always be a very suitable option. This is because, in addition to being a language in constant development, its simple code and its immense libraries (SciPy, Pandas, Numpy or Scikit-Learn…) make it the preferred option for most programmers. Something logical, since compared to other languages, Python has simple code and syntax that makes it very easy to learn. With a few lines of code, you can run programs without further complications.
Furthermore, and this is a great advantage, It is open source. Therefore, anyone has access to its resources for free. And, thanks to the large community of Python users, you can easily find the answers to your questions. Likewise, another great advantage of the language is its great processing speed. Think about the information we gave you at the beginning and the amount of information that 175 zettabytes represent. To analyze and process that information, you better look for a fast language.
Finally, you have to keep in mind that it is an object-oriented language that allows you to use data structures such as dictionaries, lists and much more. It also supports scientific computing operations such as arrays. So, with it, you can simplify operations and make them much faster. Finally, although there are many more reasons why Python and big data They are the perfect combination, it is important to mention that has support for data processing. That is, it has an integrated function that allows you to process structured, unstructured and semi-structured data. What more could you want?
How is it used for data analysis?
Now that you know why Python is so recommended for big data, you should know some of its uses. For example, The most common way to use Python for data analysis is to quickly create and manage various data structures. For example, the Panda library offers a wealth of tools to analyze, manipulate, and even represent data structures and complex data sets.
On the other hand, Scikit-Learn is perfect for analysis of data from social networks or marketing campaigns. With it, you will have advanced tools at your disposal to perform analytics and make sense of that data. For example, to make decisions based on the success of campaigns or to see the type of users that interact with your networks.
Finally, with Python, you yourself can write your own algorithms for data analysis and integrate them into your tools. Would you like to know how to handle Python and the big data and much more? Well discover our bootcamps and look at everything we put at your disposal. We will wait for you!
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