Choosing the right programming language when taking on a new project is perhaps one of the most daunting decisions programmers often make.

在進行新項目時選擇正確的編程語言可能是程序員經常做出的最艱巨的決定之一。

Python and R are no doubt among the top options while picking a programming language for a Data Science project. Over the years, both R and Python have garnered a lot of positive feedback from developers and users for a variety of modern tasks. It might seem hard at first to decide which one is better among the two but let me tell you something, even though they are similar in certain areas, such as being free and open-source, they both can offer some unique and game-changing features.

在為數據科學項目選擇編程語言時，毫無疑問， Python 和 R是首選。 多年來，R和Python都已從開發人員和用戶那里獲得了許多針對各種現代任務的積極反饋 。 乍看起來似乎很難決定哪一個更好，但是讓我告訴您一些事情，即使它們在某些領域是相似的，例如免費和開源 ，它們都可以提供一些獨特且改變游戲規則的東西。特點 。

Some examples of sub-communities using Python/R:

使用Python / R的子社區的一些示例：

• Deep Learning
  深度學習
• Machine Learning
  機器學習
• Advanced Analytics
  進階分析
• Predictive Analytics
  預測分析
• Statistics
  統計
• Exploration and Data Analysis
  探索與數據分析
• Academic Scientific Research
  學術科研

With the help of this article, we would like to shed some light on the features separating Python from R.

在本文的幫助下，我們希望闡明一些將Python與R分開的功能。

Python和R的介紹 (Introduction of Python and R)

●Python (● Python)

Python is an experiment in how much freedom programmers need. Too much freedom and nobody can read another’s code; too little and expressiveness is endangered.

Python是程序員需要多少自由度的實驗。 太多的自由，沒人能讀懂別人的密碼。 太少，表現力受到威脅。

- Guido van Rossum

-Guido van Rossum

Python has been around since 1989 as a high-level general-purpose programming language, which was built to emphasize code readability. Python encourages developers to write clear and logical code for projects of all scales. Built to be extremely extensible, Python comes with hundreds of libraries that extend its core functionality while its open-source nature allows developers to freely build and share custom libraries.

自1989年以來， Python就已經成為一種高級通用編程語言 ，其目的是強調代碼的可讀性。 Python鼓勵開發人員為各種規模的項目編寫清晰而邏輯的代碼 。 Python的構建具有極強的可擴展性，帶有數百個庫 擴展了其核心功能，同時其開源特性允許開發人員自由構建和共享自定義庫。

Python also serves as an exceptional tool for Data Science, Machine Learning, and Deep Learning due to the availability of several packages and libraries, such as TensorFlow, Pandas, Keras, NumPy, PyTorch, and more.

Python也作為一個特殊的數據科學工具 ， 機器學習和深度學習由于幾個包和庫，如可用性TensorFlow ， 熊貓 ， Keras ， NumPy的 ， PyTorch ，等等。

優點 (Advantages)

● Hugely popular among developers due to its easy to use nature.

●由于其易用性，在開發人員中非常受歡迎。

● Supports multiple programming paradigms, such as object-oriented and procedural.

●支持多種編程范例，例如面向對象和過程。

● Takes comparatively less execution time than others.

●比其他方法花費更少的執行時間。

● Has a vast collection of third-party libraries.

●擁有大量的第三方庫。

缺點 (Disadvantages)

● Python may lack alternatives to some of the popular libraries in R.

●Python可能缺少R中某些流行庫的替代方法。

● Dynamic typing can sometimes make it difficult to track faults properly.

●動態類型化有時會導致難以正確跟蹤故障。

●R (●R)

First launched in 1993 by Ross Ihaka and Robert Gentleman, R was built to put unmatched statistical computing and graphical capabilities in the hands of the developers, statisticians, analysts, and data miners. It comes with a command-line interface.

由Ross Ihaka于1993年首次推出 羅伯特·金特爾曼(Robert Gentleman， R)的創建是為了將無與倫比的統計計算和圖形功能提供給開發人員，統計學家，分析師和數據挖掘者。 它帶有命令行界面。

When it comes to Data Science, many researchers still prefer R over Python due to its powerful statistics-oriented nature and interactive visualization capabilities. Also, using R’s frameworks, you can create dashboards and interactive visualizations for actionable insights.

在數據科學方面，由于其強大的面向統計的特性和交互式可視化功能 ，許多研究人員仍然更喜歡R而不是Python 。 另外，使用R的框架，您可以創建儀表板和交互式可視化效果，以獲取可行的見解。

R being a procedural language allows the developers to break complex portions of the problem into smaller chunks to make problem-solving easier.

R是一種過程語言，使開發人員可以將問題的復雜部分分解為較小的塊，從而更輕松地解決問題。

優點 (Advantages)

● Comes equipped with a robust set of analysis tools.

●配備了一組強大的分析工具。

● Has a wide range of packages for enhancing its core behavior and capabilities.

●具有廣泛的軟件包，可增強其核心行為和功能。

● GUIs like RStudio IDE and Jupyter can add a graphical interface to an already powerful tool while adding more features such as integrated help, code debugger, code completion.

●RStudio IDE和Jupyter之類的GUI可以在已經強大的工具中添加圖形界面，同時添加更多功能，例如集成的幫助，代碼調試器，代碼完成。

● Allows for powerful data import options, including files, such as Microsoft Excel.

●提供強大的數據導入選項，包括Microsoft Excel等文件。

● Supports various third-party packages for extensibility.

●支持各種第三方程序包以進行擴展。

缺點 (Disadvantages)

● R is difficult to learn and can make things go down if not used carefully.

●R很難學習，如果使用不當，可能會使事情惡化。

● Lack of proper documentation for some libraries can waste the developer’s efforts.

●對于某些庫而言，缺少適當的文檔會浪費開發人員的精力。

● Relatively slower performer than Python.

●與Python相比，性能相對較慢。

Python vs R-詳細比較 (Python vs R— Detailed Comparison)

Choosing one language over another for your next Data Science project can be challenging, especially when both the languages can carry out the same tasks. Now that the introduction is out of the way, we will cover the comparison between both the languages in the upcoming section, keeping in mind a set of notable features that most developers will find extremely helpful.

為您的下一個數據科學項目選擇一種語言而不是另一種語言可能具有挑戰性，尤其是當兩種語言都可以執行相同的任務時。 既然介紹已經結束，我們將在下一部分中介紹這兩種語言之間的比較，同時牢記大多數開發人員會發現非常有用的一系列顯著功能。

1.數據收集的差異 (1. Differences in Data Collection)

To facilitate data collection, Python can support a variety of commonly used data formats, such as CSVs, JSON files, and even SQL files. Another widely used source of data in Python among Data Scientists is the datasets. Python can also allow you to extract data directly from the internet with the help of suitable libraries.

為了促進數據收集，Python可以支持各種常用的數據格式，例如CSV，JSON文件甚至SQL文件 。 在數據科學家中，Python中另一個廣泛使用的數據源是數據集 。 Python還可以讓您借助合適的庫直接從Internet提取數據。

Although not as versatile as Python, R allows you to import data via Excel, CSV, and text files. Files built using packages such as Minitab or SPSS can also be turned into data frames for use in R. Packages such as Rvest and magrittr can help you scrape and clean the data from the web.

盡管R不如Python通用，但R允許您通過Excel，CSV和文本文件導入數據。 使用Minitab或SPSS等程序包構建的文件也可以轉換為數據幀，以用于R。Rvest等程序包 magrittr可以幫助您從網絡上抓取和清理數據。

2. 數據探索的差異 (2. Differences in Data Exploration)

Python’s various libraries can help you analyze structured and unstructured data very easily. Libraries such as pandas, NumPy, PyPI are undoubtedly among the best for data exploration. Pandas, for example, allows you to organize the data into data frames and makes cleaning simpler. Moreover, pandas can even hold a huge amount of data while offering additional benefits.

Python的各種庫可以幫助您非常輕松地分析結構化和非結構化數據 。 諸如pandas，NumPy， PyPI之類的圖書館無疑是最適合數據探索的圖書館。 熊貓 ，例如，允許你來組織數據到數據幀 ，使清潔更簡單。 此外，大熊貓甚至可以容納大量數據，同時還能帶來更多好處。

Built specifically for Data Exploration, R delivers exceptional results, as it was built specifically for statisticians and data miners. With R, you can apply a range of tests, and techniques, such as probability distributions, data mining on your data. R can perform data optimization, random number generation, signal processing, and even offers support for third-party libraries.

R是專為數據探索而構建的，它為統計人員和數據挖掘者特別構建，因此可提供出色的結果。 使用R，您可以在數據上應用一系列測試和技術，例如概率分布，數據挖掘 。 R可以執行數據優化，隨機數生成，信號處理 ，甚至提供對第三方庫的支持。

3. 數據可視化的差異 (3. Differences in Data Visualization)

With Python, you can create effective and customizable visualizations in the form of graphs and charts. Libraries like IPython and matplotlib exist to help developers and researchers create powerful and interactive visualizations. While the Python ecosystem does consist of more libraries, the most commonly used is matplotlib.

使用Python，您可以以圖形和圖表的形式創建有效且可自定義的可視化 。 像IPython和Matplotlib這樣的庫 可以幫助開發人員和研究人員創建強大的交互式可視化效果。 盡管Python生態系統確實包含更多的庫，但最常用的是matplotlib。

On the other hand, R can offer advanced visualizations as it is among the core functions provided by the programming language. R comes with built-in support for many standard graphs, for even more complex visualizations, you can use libraries, such as ggplot2, Plotly, and Lattice.

另一方面，R可以提供高級可視化效果，因為它是編程語言提供的核心功能之一。 R內置了對許多標準圖形的支持，對于更復雜的可視化，您可以使用庫，例如 ggplot2 ， Plotly 和 Lattice 。

4. 數據建模的差異 (4. Differences in Data Modeling)

For data modeling, Python provides several libraries that will cater to the desired modeling type. Say, for numerical modeling, Python provides its NumPy library, similarly, for scientific computing, we have SciPy. Various other libraries and techniques allow for more data modeling options in Python.

對于數據建模，Python提供了一些庫，可以滿足所需的建模類型。 假設，對于數值建模 ，Python提供了其NumPy庫，同樣， 對于科學計算 ，我們還有SciPy 。 其他各種庫和技術也允許在Python中使用更多數據建模選項。

In R, you can do statistical modeling efficiently due to the robust statistical capabilities offered by the programming language. It comes with plenty of support packages to help you in statistical modeling, even for specific analyses, such as Poisson Distribution, Linear & Logistic Regression.

在R中，由于編程語言提供了強大的統計功能，因此可以有效地進行統計建模 。 它帶有大量支持包，可幫助您進行統計建模，甚至用于特定分析，例如泊松分布，線性和邏輯回歸。

5.表現 (5. Performance)

Performance is a critical aspect of any programming language, and it often becomes the prime reason for picking one language over the other. One of the key reasons why most programmers and even data scientists are beginning to prefer Python over R is due to its ability to rapidly perform most data science tasks with relative ease. Another area where Python outshines R is that it can perform comparatively faster. Other factors against R can include a lack of features, such as unit testing and insufficient code readability.

性能是任何編程語言的關鍵方面，并且通常成為選擇一種語言而不是另一種語言的主要原因。 為什么大多數程序員甚至數據科學家開始偏愛Python而不是R的關鍵原因之一是由于它能夠相對輕松地快速執行大多數數據科學任務。 Python勝過R的另一個方面是它可以相對更快地執行。 反對R的其他因素可能包括缺乏功能，例如單元測試和代碼可讀性不足。

Python Performance Tips —

Python性能提示-

https://wiki.python.org/moin/PythonSpeed/PerformanceTips

https://wiki.python.org/moin/PythonSpeed/PerformanceTips

https://stackify.com/20-simple-python-performance-tuning-tips/

https://stackify.com/20-simple-python-performance-tuning-tips/

6.圖書館 (6. Libraries)

When it comes to the packages and libraries provided by these programming languages, they both offer thousands of useful packages for almost every situation.

當談到這些編程語言提供的軟件包和庫時，它們都為幾乎每種情況提供了數千個有用的軟件包。

PyPI hosts and manages Python’s packages, whereas R’s side of things are handled by CRAN. If you’re more interested in the numbers, Python has over 257 thousand packages, while CRAN has a little over 16 thousand. That’s a lot!

PyPI托管和管理Python的軟件包，而R方面的事務由CRAN處理。 如果您對數字更感興趣，Python擁有超過25.7萬個軟件包 ，而CRAN則有超過 1.6 萬個 。 好多啊！

Although Python does offer more than 10 times the packages available for R, not all of them are useful for Data Science. One shouldn’t forget while reading those numbers that Python is a general-purpose programming language, whereas R isn’t.

盡管Python提供的R軟件包的確超過10倍，但并不是所有軟件包對Data Science都有用。 在閱讀這些數字時，請不要忘記Python是一種通用編程語言，而R不是。

7.人氣 (7. Popularity)

Both of the programming languages are fairly popular among developers and data scientists and are good options to add under their command. Python seems to be taking the lead here due to its general-purpose nature and the availability of several libraries focused around Data Science, but R is not far behind.

兩種編程語言在開發人員和數據科學家中都相當流行，并且是在其命令下添加的不錯的選擇。 由于Python的通用性和幾個專注于Data Science的庫的可用性，Python似乎在這里處于領先地位，但是R緊隨其后。

According to StackOverflow, Python is the fastest-growing major programming language.

根據StackOverflow的介紹，Python是增長最快的主要編程語言。

Several statisticians and data miners still prefer R for its powerful number-crunching and visualization capabilities. Moreover, R provides better control over data analysis due to its inclination towards statistical and numerical computing and its collection of libraries, providing more advanced and in-depth results to substantiate the claim.

一些統計人員和數據挖掘者仍然喜歡R，因為它具有強大的數字處理和可視化功能。 此外，由于R傾向于統計和數值計算及其庫的收集，因此R對數據分析提供了更好的控制，從而提供了更高級和更深入的結果來證實該主張。

The programming language R continues to rise and is on schedule to become TIOBE’s programming language of the year 2020.

編程語言R持續增長，并有望成為IOBE的2020年編程語言。

8. 工作機會 (8. Job Opportunities)

Job opportunities in Data Science are on the rise, and statistics show that more jobs demand Python than R. Both the programming languages are much more needed now than ever due to the pace at which Data Science is growing.

數據科學領域的工作機會正在增加，統計數據表明， 與R相比，Python需要更多的工作 。 由于數據科學的發展速度，現在比以往任何時候都更需要這兩種編程語言。

Python, being an all-rounder programming language, can be a solid overall choice since it can allow you to do software engineering, and provide a reputable entry point into Data Science. Whereas R will be a much better option if you are to focus on extracting valuable statistics within a short period, make beautiful visualizations that speak for the numbers, and create graphical interfaces for web applications.

Python是一種全面的編程語言，可以作為一個可靠的整體選擇，因為它可以幫助您進行軟件工程設計，并為您提供著名的數據科學切入點。 如果您要專注于在短時間內提取有價值的統計信息，進行漂亮的可視化表示數字，并為Web應用程序創建圖形界面，則R是一個更好的選擇。

9.社區 (9. Community)

A community offers support and guidance to the developers and one can say that it is the second most visited place by a developer, after the project code. It holds a significant value in quickly finding the root cause and solution to the problems at hand while offering dozens of useful tips.

社區向開發人員提供支持和指導 ，可以說它是開發人員訪問量第二高的地方 ，僅次于項目代碼。 它在快速找到問題的根本原因和解決方案的同時， 提供了許多有用的技巧 ，具有重要的價值。

When we talk about a programming language’s community, the first thing that comes to mind is its target users. Usually, it will include developers, but our case includes statisticians and data miners as well. Python is used by a diverse audience that includes applications of all sorts. R, on the other hand, is primarily used by enterprises and researchers chasing primarily statistics.

當我們談論編程語言的社區時，首先想到的是它的目標用戶。 通常，它將包括開發人員，但我們的案例還包括統計人員和數據挖掘人員。 Python被各種各樣的讀者所使用，其中包括各種應用程序。 另一方面，R主要由追求統計數據的企業和研究人員使用。

Needless to say, both the programming languages provide an active community of developers and contributors, regularly providing invaluable insight to others and the language.

不用說，這兩種編程語言都為開發人員和貢獻者提供了一個活躍的社區，它們定期為其他人和語言提供寶貴的見解。

Python Community —

Python社區—

RStudio Community —

RStudio社區—

結論 (Conclusion)

The competing nature of the two languages might help us produce the simplest and the most efficient code for our purposes.

兩種語言的競爭性質可能有助于我們為我們的目的生成最簡單，最有效的代碼。

Throughout this article, we discussed a handful of deciding factors among Python and R playing a leading role in picking one programming language over the other. We can conclude that even though both the languages are a respectable choice for Data Science, they still have their pros and cons. Learning Python gives you the versatility to work with a majority of Data Science-centric projects while learning R gives you a stronger hold on the statistics in Data Science. Learning both will undoubtedly give you an upper hand in your upcoming Data Science projects, but we’d like to leave the final decision-making up to you.

在整個本文中，我們討論了Python和R中的一些決定性因素，這些因素在選擇一種編程語言而不是另一種編程語言中起著主導作用。 我們可以得出結論，盡管這兩種語言都是數據科學的不錯選擇，但它們仍然各有利弊。 學習Python使您可以處理大多數以數據科學為中心的項目，而學習R則可以使您更牢固地掌握數據科學中的統計信息。 兩者的學習無疑將使您在即將到來的Data Science項目中占上風，但是我們希望最終的決定權由您決定。

Note: To eliminate problems of different kinds, I want to alert you to the fact this article represent just my personal opinion I want to share, and you possess every right to disagree with it.

注意： 為消除各種問題，我謹在此提醒您，本文僅代表我要分享的個人觀點，您擁有反對該觀點的一切權利。

更有趣的讀物— (More Interesting Readings —)

I hope you’ve found this article useful! Below are some interesting readings hope you like them too —

希望本文對您有所幫助！ 以下是一些有趣的讀物，希望您也喜歡它們-

About Author

關于作者

Claire D. is a Content Crafter and Marketer at Digitalogy — a tech sourcing and custom matchmaking marketplace that connects people with pre-screened & top-notch developers and designers based on their specific needs across the globe. Connect with Digitalogy on Linkedin, Twitter, Instagram.

克萊爾·D 。 是 Digitalogy 的Content Crafter and Marketinger ，這 是一個技術采購和自定義配對市場，可根據人們在全球的特定需求，將他們與預先篩選和一流的開發商和設計師聯系起來。 在 Linkedin ， Twitter ， Instagram 上 與 Digitalogy聯系 。