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摘要: In recent years I’ve seen the evolution of the use of both of these languages in the world of Data Analytics. Here are my thoughts.

My thoughts about Python and R

In recent years I have been able to observe the evolution of the use of both of these languages in the world of Data Analytics. So, I have come to the following personal conclusions:

✔ The R language is much more straightforward for interactive data analysis and data exploration, especially for analysts or those who come from the world of business intelligence, where SQL holds sway. Transforming the data with R is very reminiscent of the mental process done by those using SQL, with the advantage of being able to use specific functions that simplify complex transformations (such as data pivoting, for example), or that apply statistical operations useful for analysis. The approach taken by Python to transform data is more related to a programmer’s experience. Having to necessarily resort to lambda expressions, for example, for rather basic data manipulation tasks, disorients any analyst used to a more set-based approach (which is the right way to think when working with data!) and makes us realize that those who developed the Python packages needed for data wrangling were primarily developers rather than analysts.

✔ R is the language par excellence used in academia (Statistics, Mathematics, Data Science, and so on). It is therefore very likely to find new data science algorithms implemented directly in R, even before finding them implemented in Python. Therefore, if you need to use these new algorithms for a project, you must necessarily use R.


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