News
Careers
Contact
QlikQlik
QlikQlik Cloud
powerbiPower BI
timextenderTimextender
alteryxalteryx
snowflakeSnowflake
Data Management
Data Connectivity
Data Hosting
Data Storage
Data Science
Data Analytics
Open the menu
close the menu

Python or R?

2 programming languages ​​- those are the differences
When it comes to data science there are two programming languages ​​that are widely used namely Python and R. Of course there are many more languages ​​such as SQL, MatLab, Julia etc. but these two languages ​​stand out among the typical user group being data scientists ( data scientist). In recent years, we have also seen a growth in data analysts who are increasingly working with these tools. At first glance, the capabilities of Python and R are very similar, but for the connoisseur there are clear differences. Below is a brief explanation of the differences, similarities and recommendations from Victa.
a

What is Python?

Python is a broader language than R and is also a leader in the data science world. There are less analysis options than in R, but on the other hand Python allows you to perform better analyzes on a large scale (big data) or within broader applications or websites. Python is making an impressive advance in the field of artificial intelligence / machine learning. From a BI perspective, Python is very suitable for retrieving, cleaning and writing data. Pandas (python library) is very suitable for data analysis and manipulation. Matplotlib is used for visualizing pandas data frames. Here too, for production purposes (end users) it is more likely to be chosen Qlik or Power BI. Jupyter notebook is for making the analysis clear and transparent.

a

What is R?

R is a language in which you perform specialized and general statistical analyzes. The enormous database with packages (more than ten thousand) ensures that you can use R for almost any analysis perfectly. The language was developed by academics and statisticians and has a very solid basis. In addition, there are good possibilities within R for reporting and visualizing outcomes. However, this is not nearly as complete and friendly as in Qlik or Power BI. You often see that you use this function when you are exploring exploratively and you want to share the end result with the business users on other platforms such as Qlik and Power BI.

a

What are the differences between Python and R?

In addition to the above similarities, there are also differences. It is important to be aware of these differences before choosing one of the two languages.
• R is much more difficult to learn if you have no programming knowledge yet. Python code is intuitive so that even as a layman in the field of programming, you quickly understand what is in a Python script.
• R is used by academics and within R&D departments, Python by developers / programmers
• Python is better with big data applications than R
• Python is a leader in machine learning and artificial intelligence
• The vast majority of data analysis can be done in Python with just a few packages (Numpy, Pandas, Scikit-learn, and Seaborn)
• Python integrates better in applications or websites
• R is really for statistical analysis, Python more widely applicable. This makes R especially suitable for very specific analytical activities for which specialist packages have been developed.
Python code is more robust and easier to maintain than R code.
• R has standard nice options for communicating the output of analyzes, in Python this is less. Python has made a big catch up here, so that differences have become smaller.

How do I make a choice?
Do you want to become a good data scientist? Then you learn both languages ​​in the ideal world. Because every language has unique qualities, you must distinguish yourself as a data scientist if you can work with both languages. Unfortunately, this is not possible for everyone, given the time investment involved in learning both languages.

To help you make the right choice in your situation, you can ask yourself the following questions:
  • What do my colleagues use? By working in the same language as much as possible, you bring enormous benefits for yourself and for the organization. You can think together when you get stuck, work faster by working together on other aspects of a problem, or build on what is already in your organization.
  • What do competitors use? In certain sectors, one of the two languages ​​may be leading. For example, R is the logical choice in bioinformatics. You can find out by engaging in conversations with con colleagues, by viewing available packages, or by asking questions in online communities.
  • Do you already know another programming language? If not, Python is a lot more accessible. Also read: How long does it take to learn Python?
  • Are you in a software development environment? Then choose Python because of possible integrations.
  • Do you want to bring your analyzes into production within operational processes? Then Python learning is much smarter.
  • Do you want to do specialist statistical analyzes or research? Find out in advance which language has the most suitable packages, the possibilities within R will be wider.
  • Do you want to maximize your chances of a job as a data scientist? In vacancies, Python is more often asked than R.
Conclusion and recommendations
Conclusion and recommendations
From Victa we can offer both solutions, but there is a clear preference for Python at the moment. Python is much more widely applicable for BI applications and offers more possibilities than R. R is really focused on statistics and with BI we often want more than just statistics, we often want to integrate directly with other applications and we also want to make more use of other functions and applications mainly focused on ETL, Big Data, AI and Machine Learning.

What we also see in practice is a hybrid solution, which means that you use a combination of both. Suppose a particularly good R model has been developed, you can also call it from a python script or even directly from a visual analytics tool such as Qlik, Power BI or Alteryx.

What we often do at Victa is use Python with Predictive Analytics. We use the “Circle of Predictive Life” for this. Data is often already in a data warehouse or data lake and is visualized and brought together in Qlik Sense. We filter relevant data from this and offer it by means of a CSV export in a Python script that reads, adjusts and enriches the data (ETL). When the data is ready the way we want it to be, we call special packages that work with this data. We then export this output to a CSV (or blob storage, database / warehouse / lake) and use it again in a visual analytics tool such as Qlik. In Qlik we try to convert the often complex output back into actionable insights for the end user and we try to get more predictive (predictive) value from the data for making better data-driven decisions.
agreements
Disclaimer
Privacy