• Python is a general-purpose, object-oriented programming language.
  • It emphasises code readability by using white space.
  • It is easy to learn.
  • It is a favourite of programmers and developers.
  • Python is very well suited for use in machine learning at a large scale.
  • Its suite of specialised deep learning and machine learning libraries includes tools like scikit-learn, Keras and TensorFlow. It enables data scientists to develop sophisticated data models that plug directly into a production system.

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Python vs. R: What’s the Difference?

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  • R is also is an open-source programming language.
  • It's optimised for statistical analysis and data visualisation.
  • R has a rich ecosystem with complex data models and practical tools for data reporting.
  • R is popular among data science scholars and researchers.
  • R provides various libraries and tools for cleansing and prepping data, creating visualisations, and training and evaluating machine learning and deep learning algorithms.
  • R is used within RStudio.
  • R applications can be used directly on the web via Shiny.

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They approach data science differently:

  • R is used for statistical analysis.
  • Python provides a more general approach to data.
  • R is built by statisticians and leans heavily into statistical models and specialised analytics.
  • Python is a multipurpose language that can provide data analysis or machine learning in scalable production environments.
  • You might use R for customer behaviour analysis or genomics research.
  • You might use Python for developing a machine learning application. 

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Data collection

  • Python supports all kinds of data formats.
  • R is designed for data analysts to import data from Excel, CSV and text files.

Data exploration

  • In Python, you explore data with Pandas, the data analysis library for Python.
  • With R, you can build probability distributions, apply different tests, use standard machine learning and data mining techniques.

Data modelling

  • Python has standard libraries.
  • With R, you'll sometimes have to rely on packages outside of R's core functionality.

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The language to choose depends on your situation.

Points to consider:

  • Do you have programming experience? Python has a learning curve that's linear and smooth. With R, novices can Run data analysis tasks in minutes, but it takes longer to develop expertise.
  • What do your colleagues use? Academics, engineers and scientists use R. Python is used in a wide range of industries.
  • What problems are you trying to solve? R is used for statistical learning, and Python for machine learning and large-scale applications.

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