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Python and R Are the two most popular programming languages for data analysis , If you are a beginner , There is bound to be a problem ： It's a choice Python still R？
The rapid spread of data in our lives has led to the rise of tools for analyzing and extracting valuable insights from this information . Python and R Are the two most popular programming languages for parsing data . If you are trying a new data science project , Choosing between them can be challenging .
Python and R Each has its own advantages and disadvantages , And in the field of Data Science , They have many overlapping features , To help you choose the right programming language , I will elaborate on the following points ：
The advantages and disadvantages of both
Python and R The future of
R from Ross Ihaka and Robert Gentleman Developed more than 20 years ago , Is an open source programming language and free software , It has a rich ecosystem in statistical analysis and data visualization .
R It has a wide range of statistical and graphical methods , Including linear regression 、 The time series 、 Machine learning algorithm 、 Statistical inference, etc . Besides , It also provides complex data models and tools for data reporting .
R It is very popular among data scientists and researchers , There is a library for all the analytics you might want to deal with .
in fact , A large number of libraries make R Become the first choice for statistical analysis , Especially the professional analysis work . Many well-known companies are using R programing language , for example Facebook、Uber、Airbnb、Google etc. .
Use R Data analysis can be done in a few short steps - Programming 、 transformation 、 Find out 、 modeling , Then output the result .
When exchanging research results , Let's go R What stands out . R Have an excellent set of tools , Allow results to be shared as presentations or documents , Make the report very elegant .
Usually ,R stay RStudio Use in ,RStudio It's an integrated development environment (IDE), Statistical analysis can be simplified 、 Visualization and reporting .
But this is not running R The only way , for example ,R Applications can be Shiny stay Web Use directly and interactively .
Python Is an object-oriented general high-level programming language , On 1989 First released in 2004 .
It emphasizes the readability of the code by using a lot of white space . To make a long story short , It is written and understood in a relatively intuitive way , send Python Become the ideal coding language for those seeking rapid development .
There are many large companies or organizations in the world —— from NASA To Netflix、Spotify、 Google and so on —— To make use of in some form Python To support their services . according to TIOBE Index ,Python Is the third most popular programming language in the world , Second only to Java and C.
There are many reasons for this achievement , Include Python Ease of use 、 Simple grammar 、 A thriving community , And, most importantly, versatility .
Python It can be used in various projects , From data analysis and visualization to artificial intelligence 、 Language development 、 Design and Web Development .
Python Especially suitable for large-scale deployment of machine learning , Because it contains TensorFlow、scikit-learn and Keras And other tools , These tools can create complex data models that can be directly inserted into production systems .
Besides , many Python The library supports data science tasks in some professional fields , for example ：
Biopython—— Non business Python A collection of tools , Used to represent biological sequences and sequence annotations
Bokeh—— One Python Interactive visualization Library , Helps you quickly create interactive drawings 、 Dashboards and data applications
DEAP—— A very suitable computing framework for rapid prototyping and idea testing
If you are facing Python And R The choice between is difficult , So it is very important to understand the differences between the two languages , So that you can make a wise decision . Here are R and Python The main difference between .
Generally speaking , The difficulty of learning depends mainly on your background .
Due to non standardized code ,R Language is quite difficult for beginners to master . Even for some experienced programmers , This language also looks very cumbersome and awkward . On the other hand ,Python More easily , And the learning curve is smoother , Although statisticians often feel that this language focuses on seemingly unimportant things .
therefore , The programming language for your data science project will be a language that looks closer to the way you are used to thinking about data .
for example , If you like ease and time efficiency above all else , that Python It may seem more attractive to you . This language requires less coding time , This is due to its similar grammar with English .
There is a joke that , Pseudocode is going to be a Python Program , The only thing you need is to keep it in a .py In file .
This allows you to complete tasks quickly , This in turn gives you more time to deal with Python. Besides ,R Coding for requires a lengthy learning period .
Python and R Are very popular .
However , And R comparison ,Python Used by more people .R And Python comparison , Considered a niche programming language . As mentioned earlier , Many organizations will Python For its production system .
On the other hand ,R Generally used in academia and research industry .
Although the current industry users prefer Python, But because of R Advantages in data processing , They also began to think about R.
R and Python Have provided thousands of open source packages , You can use it anytime in your project .
R Came up with a CRAN And hundreds of alternative packages to perform a task , But they are less standardized . therefore ,API It is quite different from its usage , Make it difficult to learn and combine .
Besides ,R The authors of highly specialized software packages in languages are often scientists and statisticians , Not programmers . This means that the result is just a set of special tools designed for a specific purpose , Such as DNA Sequencing data analysis , Even generalized statistical analysis .
However ,R The package for does not Python So mixed and matched . at present , Some attempts are being made , To coordinate the toolkit , Such as tidyverse, It aggregates a series of toolkits according to coding standards .
Speaking of Python, Its software package is more customizable , More efficient , But they are usually not the same in terms of data analysis tasks R So specialized .
For all that ,Python Do have some solid data science tools , Such as scikit-learn、Keras（ML）、TensorFlow、pandas、NumPy（ Data manipulation ）、matplotlib、seaborn and plotly（ visualization ）. On the other hand ,R Yes caret（ML）、tidyverse（ Data manipulation ） and ggplot2（ Excellent visualization ）.
Besides ,R Yes Shiny For rapid application deployment , And for Python, You will have to pay more .
In short , If you plan to build a mature application ,Python Would be the ideal choice .R There is a special statistical package , and Python The ability in this particular field is not as good as R. Besides ,R Excellent at handling data from most popular data stores .
Another aspect worth mentioning here is maintainability .Python Allow you to create 、 Use 、 Destroy and replicate a separate environment , Each environment has a different package installed . about R Come on , This happens to be a challenge , And this challenge is further exacerbated by package incompatibility .
R Is specifically created for data analysis and visualization .
therefore , Its visualization is better than Python A large number of visual libraries are easier to understand , Because the latter makes visualization complex . stay R in ,ggplot2 Make the custom graphics better than Python Medium Matplotlib Much simpler , It's also much more intuitive .
However , You can use... That provides standard solutions Seaborn Library to overcome Python The question of .Seaborn It can help you to realize and... With relatively few lines of code ggplot2 Similar drawings .
in general , For which programming language is more suitable for high efficiency 、 Clear 、 Create drawings visually , People have different views . The ideal software for you will depend on your personal programming language preferences and experience .
Last , You can use Python and R To clearly show the data , but Python It is more suitable for deep learning , Not data visualization .
Python It's a high-level programming language , This means that if you plan to build critical applications quickly , It is a perfect choice . On the other hand ,R It usually takes longer code , Even a simple process , This greatly increases development time .
Speaking of execution speed ,Python and R The difference is small .
Even though R or Python Not as fast as some compiled programming languages , But they are compatible C/C++ Interface to avoid this problem .
Python and R Both have advantages and disadvantages . A few of them are obvious , Others are easily overlooked .
R The advantages of
use R It only takes a few lines of code to check the statistical assumptions , Because many functions required for data analysis are built-in language functions .
RStudio（IDE） And other basic data processing packages are easy to install .
R There are many data structures 、 Parameters and operators , It involves many things – From array to matrix 、 Recursion and loops , And with other programming languages such as Fortran、C and C++ Integration of
R It is mainly used for statistical calculation . One of its main highlights is that it provides a set of algorithms for machine learning engineers . Besides , It is also used to classify 、 Linear modeling 、 Time series analysis 、 Clustering, etc
R It provides an efficient toolkit and a large number of ready-made test cases for almost all types of data science and machine learning
Data visualization for various tasks , There are many kinds of high quality bags
Basic statistical methods are performed as standard functions , Improved development speed R The shortcomings of
Usually ,R The performance of the programming language is low , Although you can still find packages in the system that allow developers to speed up .
Compared with other programming languages ,R It is highly specialized , This means that its skills cannot be easily applied to other fields
because R Most of the code is written by people who are not familiar with programming , Therefore, the readability of quite a few programs is questionable . After all , Not every user adheres to the correct code design guidelines
R There are a lot of Libraries , But the documentation of some niche libraries is incomplete Python The advantages of
Python Is a versatile programming language
Its interactivity is important for data analysis 、 Ad hoc testing is very useful
Every new version , Its performance and syntax are improving
Well known , The application scenarios are rich Python The shortcomings of
When it comes to choosing software for data analysis , Visualization is an important capability that you should consider . However , although Python There are a number of libraries for visualization , however Python Visualization in is often better than R More complicated in , The result is not as good as R intuitive
Python Lack of most R Alternatives to the library , This makes the field of statistical data analysis Python and R There is still a certain gap
In terms of programming languages , There is no denying that ,Python Very popular .
Although it was created as a general scripting language , but Python It soon became the most popular language in data science . Some people even began to put forward R Destined to be Python Complete substitution .
However , although Python May seem to be replacing R, but R Language is far from dead . No matter what the dissenters say ,R Language is making a mad comeback in the field of data science . The popularity index continues to show the resurgence of this programming language , And prove that it is still a strong candidate in the data science project .
since R Since its appearance , Its popularity in the field of data science has been rising . from 2008 year 12 Month's Day 73 position ,R stay 2021 year 8 Month be TIOBE Th in the index 14 The most popular language . On the other hand ,Python This year from Java He took the second place in his hands , Reached 11.86% Popularity of . meanwhile ,R The popularity of is 1.05%, It's down from the year before 1.75%.
Many data also show that ,Python Years of success have been achieved at sacrifice R At a cost . For all that , Measuring the popularity of a language is an extremely difficult task . Almost every language has a natural life , There is no foolproof way to determine when their life cycle might end , Again , There is no way to predict the exact future of any particular language .
Python and R Are high-level open source programming languages , It is one of the most popular languages in data science and statistics . For all that ,R It is often suitable for traditional statistical analysis , and Python It is an ideal choice for traditional data science applications .
Python It's a simple 、 Well designed 、 Powerful language , It is created for the purpose of network development . and , It is still efficient in data science projects .
Python Relatively easy to learn , Because it focuses on simplicity . therefore , As long as you get the right tools and Libraries , This language can easily bring you from statistics to data science , To a mature production application . in fact , This is the use of Python One of the most important advantages of .
On the other hand ,R The biggest advantage of is the existence of highly specialized software packages , Can take you effortlessly to achieve less customizable data operations . Besides ,R It is created for statistical calculation , Inexperienced people first find it difficult to use the language .
even so , In some cases , You can use a combination of two languages . for example , You can go through r2py stay Python The code uses R. When you want to use R When implementing core computing tasks , This is particularly beneficial .
author ： Seven step programming
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