It’s true that even though the context of each company and client demands differ from each other on each project, almost every time we talk about data analysis, the same programming language comes up: Python.
Over the years, Python has emerged as the main programming resource for the development of tools that allow the analysis, treatment and processing of data. And that’s why it’s no surprise that in a world where Big Data is becoming increasingly important, learning Python becomes a higher priority for those looking to enter the world of data analytics.
Although there are other programming languages that have also gained traction in the sector, it’s undeniable that there are many arguments for learning Python if the data fields are calling your name:
One of the main advantages is how simple it is to learn; anyone with minimal programming knowledge can learn the principles of this language with no problem at all.
As you learn, you’ll see some more of its advantages, such as its versatility and reproducibility.
Not only does it allow you to perform a multitude of tasks but a piece of code written in Python can be played on any platform.
It also has a wide development community, which allows it to advance very quickly in the development of new functionalities and scripts.
It’s open source and free and programmers are encouraged to investigate different solutions, incorporate various improvements, and develop new functions.
Now that you have the basics down pat, let’s dive into some of the most burning questions surrounding Python and its use in data.
Should I Learn Python or R for Data Analysis?
One of Python’s main competitors that seemed to indicate a possible paradigm shift in the Big Data industry, was R, a programming language that’s also quite versatile, but didn’t quite manage to win the battle against its main opponent:
One of R's strengths was its data visualization, an area Python wasn't quite as advanced in.
R had a wide variety of graph libraries that allowed users to show the data that has been analyzed in a clear and simple way.
However, thanks to the combined efforts of committed Python developers, it has been updated to offer packages and libraries such as Seaborn or Plotly.
Which Python Libraries Should I Learn?
Learning Python is just the first step towards landing a job in tech; as some experienced developers who are already working in the field will say, although it is helpful to learn the principles of this language, the best scenario is to carefully select the resources used in order to steer learning towards data analysis. If you don’t choose correctly, you could end up leaning towards other branches such as programming, web development, software engineering, or any other application that Python has (and there’s a lot!).
So if you’re truly set on using Python for your data career, the Python libraries most used for data analysis are:
Pandas
Don't be fooled by the name–in addition to sharing its name with an adorable animal, the Pandas library is one of the most versatile and robust and, therefore, the favorite of many data analysts.
This open source library has a peculiar way of operating, where it takes a series of data (CSV format, TSV or SQL database) and creates a Python object with rows and columns called a “dataframe.” The result of this transformation is a table with a structure very similar to that of a statistical software, such as Excel. That is why Pandas is one of the most used libraries, since it is extremely easy to work with.
Do you want to practice and learn the basic knowledge of Pandas? Try these beginning exercises.
Are you already familiar with the library and want to make the qualitative leap in data analysis? Download this "cheat sheet" to remember the most important formulas and functions.
NumPy
NumPy is a Python package that comes from the term "Numerical Python.” It is by far the best library for applying scientific computing, providing powerful data structures that you can implement multidimensional arrays and perform more complex calculations with arrays.
Matplotlib
When it comes to creating high-quality, ready-to-publish graphics, the Matplotlib package is usually the right choice. It also supports a wide range of raster and vector graphics, such as PNG, EPS, PDF and SVG.
Matplotlib different functions will help you present the information contained in your analyzes in a more understandable way. The key is to adapt the display format to the audience type; presenting your findings to the management team is not the same as presenting to your colleagues in the analytics department.
Want to learn how to make this chart with Matplotlib along with 49 other types of visualizations? Check out this article.
Learning Python for Data Analytics
So as we’ve already mentioned, it’s not just about learning Python, but about guiding it towards the tasks that interest you. You need to be clear about the world you’re dedicating yourself to (in this case, data analytics). If this is the case, as with any other programming language or technology, you can do your research on your own or you can opt for tech bootcamps where you will not only have more resources, but also more support to find work in the data fields.
One alternative is Ironhack's Data Analytics or Data Science and Machine Learning Bootcamp, where you will learn to work with Python as well as with libraries such as Pandas or NumPy that help you obtain the necessary skills to enter the tech workforce as a data analyst or scientist.
