Python Libraries For Data Science

Python is one of the most popular programming languages ​​because it is complete and easy to learn. One consequence of this is a vast community, which makes Python libraries available to various areas of technology.

The main area is data science. Data science has gained prominence, as it is a powerful tool for organizations, assisting in the collection of information and in making strategic decisions.

Let’s take a look at the 10 most used Python libraries for data science by the community:

Top 10 Python Libraries for Data Science

1. Arrow

Arrow is one of the most basic, useful and interesting Python libraries. Even a novice programmer can make very interesting code with his knowledge. It mainly deals with timing.

This means that you can generate, change, remove and convert dates and times . In addition, Arrow has an intelligent API that allows you to integrate it with several standard frameworks from other applications

This has a very significant impact on data science, as it is possible to organize and analyze the information for periods of time, in addition to creating scenarios to observe changes in the data according to the temporal variation.

2. Numpy

This is a library that does the processing of matrices and vectors (objects of the type “array”). It provides a set of frameworks to manipulate and manage this information quickly and efficiently.

Numpy still allows integration not only with several Python libraries, but also with other programming languages like C and C ++.

The idea is to simplify projects that deal with a large amount of information, through the use of modern mathematical concepts.

For this reason, it is a valuable tool for data science, helping to organize the huge flow of data, its manipulation and the compilation of statistics.

3. Pandas

The Pandas are one of Python libraries more important for data science. It stands out because, in addition to being complete, it is easy to use. People with little programming experience can easily work with its features.

Among them, we can organize, search, represent and manipulate information with simplicity. That’s because Pandas is a platform that offers intuitive data structures in an easy, didactic and adjustable way.

You can work with any type of information, from structured data to time series. It supports several different formats (like JSON and Excel ), being possible to work with more than one database at the same time.

4. Bokeh

The Bokeh is a library that allows data visualization and charting. It works in a different way, because although it is for Python, it displays graphics using HTML and JavaScript.

This makes it particularly interesting for dashboards and applications that are based on web programming. In addition to generating graphics, you can use some Bokeh commands to create and simulate statistical scenarios.

5. NLTK (Natural Language Toolkit)

This is one of the most used Python libraries due to its importance. She is open source, and works with NLP (Natural Language Processing). In other words, it helps computers understand natural human language.

This is extremely important for data science, because that way we can transform information from databases into a language that humans understand and vice versa. It is a powerful tool that greatly increases the amount of information available.

The NLTK allows to classify, select, under the filter base, the syntax analysis and the semantic meaning of words. Thus, we can quantify information such as online surveys, survey responses, among other valuable inputs .

6. Pytil

Pytil is a very complete library. It has a very wide variety of applications: automation, advanced image and video processing, among many other features.

And it is very interesting for data science because of its simple solutions for data mining ( Data Mining) and knowledge extraction (KDD – Knowledge Discovery in Data).

Basically, KDD seeks to find meaning among a large volume of information in a database, establishing relationships. Data Mining, on the other hand, is a KDD step, and it is a refined search looking for consistent data patterns.

7. Poetry

When working with data science, a series of libraries are used for different functionalities, and it is always important to keep everything in order. Therefore, the programmer can use this library to organize the project.

The Poetry is a simple tool that lets you manage your other Python libraries systematically. It seeks to offer all the tools your project may need, from start to finish.

It has compatibility with several versions of Python and operating systems.

8. Theano

Theano is a library used in applications with large amounts of data. It makes computing information about 140 times faster. It is able to analyze, describe, optimize and manipulate several mathematical expressions at the same time.

It does these using multidimensional matrices, correcting the imperfections of the projects. It also offers several tools for identifying and analyzing errors and serious problems in the code. Because of this, it is called an optimizer compiler.

9. Scikit Learn

This is a simple library that addresses an increasingly relevant subject: machine learning . It is also written in other languages, such as C and C ++, but most of the standard library is programmed in the Python language.

Machine learning and artificial intelligence are very important for data science because they implement mathematical and statistical models to answer questions and take action based on the information collected.

With Scikit Learn , we can still use several ways to represent data, such as tables and matrices. It is free, and can work alongside other Python libraries, such as Numpy.

10. Flow tensioner

TensorFlow is one of the most famous Python libraries. It is easy to learn, free, and open source and brings several tools for machine learning programming. It presents itself as a solution to several problems in this area.

But not only that, it can also be used to control data flow and data science, mainly for creation and testing. This is because it brings a very valuable element to this field: Deep Learning.

Deep Learning consists of using multiple layers in a neural network, which allows the program to have more autonomy when deciding which types of data should be considered for each situation, based on pre-established parameters.

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