Two of the most popular programming languages available, Python and R share many similarities. While both are ideal for data science tasks such as data automation, manipulation, and exploration, each language has distinct characteristics that make it more suitable for specific tasks.
What is Python?
Released in 1991, Python is an open-source, object-oriented, general-purpose programming language. It is considered one of the most readable languages available due to its use of white space.
Common uses for Python include:
Data analysis and visualization
Software development and testing
Python features a large number of modules and libraries (such as TensorFlow, SciKit-Learn, and Keras) that are ideal for machine learning and artificial intelligence. Using these tools, you can create rich and detailed data models that can be shared across teams.
What is R?
Similar to Python, R is an open-source programming language specifically designed for tasks such as data visualization, statistical analysis, classification, and clustering. It includes a wide library of data tools such as data.table and Shiny that allow for easy data cleaning and prepping, modeling, and machine learning training.
A major benefit of R is its ease of use. It’s a highly flexible and powerful language that can be used to create detailed, high-quality data visualizations or perform data scraping or cleaning
Differences between R and Python
The key differences between these two programming languages are found in their purposes. As previously mentioned, R is mainly used for statistical analysis and visualization. It uses only a few lines of code to create these models which is why the language is popular among researchers, statisticians, and engineers who may have little to no computer programming skills.
Python is a general-purpose language and is used mainly for production and deployment tasks such as data analysis. While it does require some computer programming skills, it is incredibly easy to learn thanks to its readable syntax.
Some additional differences include:
Data collection: R allows you to import data from Excel, text files, or CSV and is designed for basic web scraping tasks. Python is more versatile and can collect data from a variety of formats.
Data exploration: Pandas is the most common data exploration tool in Python. It allows you to filter, sort, and display data in a matter of seconds. R features a number of tools for data exploration and mining and is optimized for larger datasets. Additionally, R relies heavily on formulas and statistical tests.
Data modeling: Python contains multiple options for data modeling, including NumPy, SciKit-Learn, and SciPy. R, on the other hand, features a few options but you may need to use external packages for more specific or detailed data analysis.
Data visualization: While Python was not specifically built for data visualization, it does feature a number of libraries that allow you to perform such tasks. R was specifically designed to demonstrate statistical analysis results through basic charts, graphs, or scatter plots.
Many of the tools utilized within data science are programming languages, with Python and R being two of the most common. While each of these languages is a powerful tool in its own right, when used together, you can leverage their strengths to do more with your data.
Rather than having these two languages compete with each other, take advantage of the statistical distributions native to R and the robust object-oriented capabilities of Python. By using both these languages for a single project, you’ll have access to both language’s libraries, including R’s tidyr and ggplot2 and Python’s SnakeCharmR and reticultate for further data manipulation and modeling.
While using both Python and R together does require more education and learning, the advantages are well worth the time. Some of the long-term benefits include:
Increased efficiency: Your data science team can more quickly work through the data science workflow, enabling you to stay ahead of the competition.
Increased productivity: With less time spent switching between platforms, your team can spend more time on tasks that increase revenue and accelerate business growth.
Increased capability: Utilizing both these languages enables your data science team to not only produce more but learn more, better equipping them for the future.
How industries are utilizing Python and R
Companies like Zopa and Robinhood rely on Python for quantitative finance, banking software, and cryptocurrency tasks as well as its flexibility and power. Additionally, the advanced statistical tools within R allow companies like Bank of America to perform risk measurements or predict the movement of the stock market.
The healthcare industry relies heavily on Python for predictive analytics for diseases. Using Python, doctors are able to predict the path of the disease and create an effective treatment plan around the results. Clinical laboratories rely on R to calculate and process large datasets in order to streamline manual, time-consuming tasks.
Retailers looking to gain insights on customer behavior or buying patterns rely on Python’s data collection, personalization, and machine learning capabilities which enable retailers to do this and more with little effort. Similarly, R’s machine learning capabilities help retailers to improve cross-selling techniques and related products at checkout.
The future of Python and R
Data has become deeply entwined with core business processes and organizations around the world are taking advantage of the powerful insights it provides. The future of data science is strong as businesses continue to prioritize technology and data in order to achieve company objectives and increase revenue.
Equally sure in their future are Python and R. Python’s flexibility and ease of use combined with R’s robust and detailed data visualizations will provide teams with the data needed to make smarter business decisions, predict future needs, and assess risk. Additionally, as more businesses implement machine learning and artificial intelligence into their business processes, Python and R will provide teams with the toolkit needed to get the most from this technology.
Together, Python and R will revolutionize processes from healthcare and transportation to fintech and marketing. The versatility, ease of use, and power of each will aid the push to provide advanced personalization, smarter search results, and quantum computing for years to come.
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