If you are curious and fascinated by data analysis problems, you must have come across a market tool that did not solve the problem completely or effectively, right? In the world of technology, it is almost “mandatory” to leave your comfort zone and look for other tools that are more useful for your team, according to your business. Fortunately, Python is an excellent solution for almost any team in the field, including data analysis beginners.
In this text, we’ll talk about why this tool is a good choice and which are the best Python libraries for those who are taking their first steps in data analysis.
Python is a high-level programming language, released in 1991. It is very popular among information technology professionals because it has a large number of applications, has open code (open source), and is developed in a community way. The tool is managed by the Python Software Foundation, a non-profit organization.
In addition to being a high-level, open-source language, the tool is easy to learn, scriptable, functional, and dynamically typed. The art of Python programming is like writing a letter to the computer – in English.
Photo credits by CNJ.
You might be wondering why choose Python over another programming language. Here are some very relevant advantages:
This tool is not only used in small and medium-sized companies, but also multinationals and leading companies such as Google, Spotify, Instagram, and Dropbox. Organizations such as NASA, Electronic Arts (EA), and Disney are among the top non-IT giants that have migrated to the Python environment.
There are over 137,000 libraries and 198,826 Python packages available to simplify programming. Libraries and packages are reusable collections and sets of accessible script modules and useful functions. They are created to facilitate the use of code in programming, standardizing the most used commands and preventing their repetition.
For beginners on the data analysis journey with Python, it is recommended to start with the following libraries:
NumPy: contains all the essential linear algebra functions, the ability to integrate with other tools, and functions for image manipulation, being mainly used for array calculations.
Pandas: this library works with two main structures: Series and Dataframes.
Scikit Learn: focused on the use of Machine Learning, being developed from the NumPy, SciPy, and MatplotLib libraries, widely used in artificial intelligence and statistical modeling.
MatplotLib: used for data visualization.
Seaborn: works on top of matplotlib improving the appearance of the graphics, making the look more pleasant.
Photo credits by Remessa Online.
It just got easier to start data analysis with Python, didn’t it? Using the available libraries, it is much simpler to resolve certain functions while programming your code. It is possible to apply the language in several stages of data analysis since it is quite complete. With your analytical questions ready, just start working and put what you’ve learned into practice!
Do you want to know more about how Python can be used in your business to help your results? Contact us! Let’s look for the best way to use data to leverage your results!