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Even though with every project, the context of each company and the demands of the clients differ from each other, the truth is that almost every time we talk about data analysis, the same programming language comes up: Python.
Over the years, this has emerged as the main programming resource for the development of tools that allow the analysis, treatment and processing of data. And it’s no surprise that in a world where Big Data has more and more weight for companies, 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 the arguments for Python imposing itself on the data analysis industry are high in number. 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. And as you learn you’ll see some more of its advantages, such as its versatility and reproducibility. So, not only does it allow you to perform a multitude of tasks but a piece of code, a script written in Python, can be played on any platform.
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 also has multiple advantages but didn’t quite manage to win the battle against its main opponent. One of R's strengths was their 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, their language was updated to offer packages and libraries such as Seaborn or Plotly.
Another of the debates that Python and R have been embroiled in was the speed of execution, since experts claimed that the times were reduced when using the first compared to the second which was considered somewhat slower. However, there were those that argued this was down to the libraries they were working with, and therefore, it was not a factor to be taken into account.
What every programmer who wants to enter this market should be clear about is that it is not enough to learn Python but you must also put it into practice in Big Data. 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 learning other branches such as programming, the development of websites, or derivatives towards any of the other applications that this language has.
To this end, the Python libraries most used for data analysis are:
This open source library has a peculiar way of operating, whereby 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 it.
Manipulating dataframes with Pandas
Do you want to practice and learn the basic knowledge of Pandas? Have a go at these initiation 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.
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NumPy is a Python package that comes from the term "Numerical Python". It is by far the best library for applying scientific computing. In short, it provides powerful data structures, you can implement multidimensional arrays and perform more complex calculations with arrays.
Multiplying matrices with NumPy
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.
Stacked bar chart of brand cast by car type
Want to learn how to make this chart with Matplotlib along with 49 other types of visualizations? Check out this article.
So as we’ve already told you, it’s not just about learning Python, but about guiding it towards the tasks that interest you. You need to be clear that 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 code schools where you will not only have more resources, but also more support for your learning and more options to find work in the Big Data market.
One alternatives is Ironhack's Data Analytics 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 work as a data analyst in the field.
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