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This article mainly explains "is it better to use Python or R language?" the content in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "is it better to use Python or R language"?

I. data Visualization

An important part of data science is communication. The results of the analysis need to be presented in an effective and easy-to-understand way. Therefore, any language or software package used in data science should have good data visualization tools. Good data visualization is clear. No matter how complex the model is, the results will be explained in a simple and straightforward way that even laymen can understand.

Python

Python is known for its large library. There are many libraries available for drawing and visualization. The most popular libraries are matplotlib and seaborn. The matplotlib library is adapted from MATLAB and has similar features and styles. The matplotlib library is a very powerful visualization tool with a variety of built-in functions, especially when working well with other Python data science libraries pandas and numpy, it can be easily used to draw simple plot functions.

Although matplotlib can generate a large number of graphs and charts, it lacks simplicity. The most troublesome aspect is resizing the plot function: if there are many variables, you may spend a lot of effort trying to put them neatly in a plot function. Another big problem is creating subplot functions, and again, it can be complicated to adjust them all to one diagram.

Now, matplotlib-based seaborn includes more aesthetic graphics and plot functions. This library is undoubtedly an improvement on the stale style of matplotlib, but it still has the same basic problem: creating graphics can be very complex. This is also the improvement direction of Python in the future.

R

There are many libraries that can be used for R data visualization, including ggplot2 in terms of usage and breadth. The library uses graphic philosophy syntax and uses layers to draw objects on the drawing. Layers are usually connected to each other and can share many common characteristics. These layers allow you to create very complex diagrams with very little code. The library allows you to draw summary functions. To sum up, ggplot2 is more flexible and concise than matplotlib, so R advantage is more obvious in this area.

It is worth noting, however, that Python includes a ggplot library that is similar to the original ggplot2 in R.

2. Modeling base

Data science requires the use of many algorithms. These complex mathematical methods require robust calculations. Rewriting algorithm code is time-consuming for data scientists, who need a language with built-in modeling support. Python and R just meet this point.

Python

Python has a large number of machine learning libraries, including scikit-learn, XGboost, TensorFlow, Keras and PyTorch. Python also has pandas, which is compatible with tabular data. The pandas library makes it very easy to process csv or excel-based data. In addition, Python has excellent scientific software packages, such as numpy. Numpy can help you complete complex mathematical calculations, such as matrix operations, in an instant. The combination of all these packages makes Python a powerful tool for hard-core modeling.

R

Like Python, R has a large number of libraries-about 10000. Among them, mice,rpart, party and caret are the most widely used. These packages will help you from the pre-modeling phase to the post-model / optimization phase.

These libraries can solve almost all data problems. But in contrast, Python lacks statistical nonlinear regression (except for simple curve fitting) and mixed effect model. On the other hand, R lacks the speed provided by Python, especially when dealing with large amounts of data.

III. Ease of learning

The market has a high thirst for data analysis and processing personnel. Many people want to join the tide of data science, many of whom have little programming experience. Therefore, when comparing the two languages, we need to consider whether they are easy to learn and easy to use.

Python

Python was designed in 1989 with the idea of emphasizing the readability of the code and making programming simple or concise, and the designers of Python obviously did it because the language was very easy to learn. Although the syntax of Python is inspired by C, unlike C, it is not complex. Therefore, as a beginner's language learning, Python can be learned by anyone in a relatively short time.

R

The R language is not difficult to learn. It is simpler than many languages such as C++ or JavaScript. Like Python, most of R's syntax is based on C, but unlike Python, R was originally designed specifically for statisticians and scientists, so the bar is high.

IV. Community support

As a data scientist, we often need to solve some data problems. In cases where it is difficult to find relevant libraries or packages to solve the problem, you can search in the official documentation of the language or online community forums to get good community support.

Both languages have active Stackoverflow members and both have an active mailing list (where you can easily ask experts for solutions). R has an online R document where you can find information about certain functions and function inputs. Most Python libraries, such as pandas and scikit-learn, have their own official online documentation to explain each library.

Both languages have a large user base and both have very active support communities. It is not difficult to see that the two seem to be equal in this respect.

Thank you for your reading, the above is the content of "using Python or R language". After the study of this article, I believe you have a deeper understanding of whether it is better to use Python or R language, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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