Question 1

What are the differences between Python and R?

Ariane Rasseck · Accepted Answer

In addition to the above similarities, there are also differences. It is important to be aware of these differences before choosing one of the two languages.
• R is much more difficult to learn if you have no programming knowledge yet. Python code is intuitive so that even as a layman in the field of programming, you quickly understand what is in a Python script.
• R is used by academics and within R&D departments, Python by developers / programmers
• Python is better with big data applications than R
• Python is a leader in machine learning and artificial intelligence
• The vast majority of data analysis can be done in Python with just a few packages (Numpy, Pandas, Scikit-learn, and Seaborn)
• Python integrates better in applications or websites
• R is really for statistical analysis, Python more widely applicable. This makes R especially suitable for very specific analytical activities for which specialist packages have been developed.
Python code is more robust and easier to maintain than R code.
• R has standard nice options for communicating the output of analyzes, in Python this is less. Python has made a big catch up here, so that differences have become smaller.

Question 2

What are the differences between Python and R?

Ariane Rasseck · Accepted Answer

In addition to the above similarities, there are also differences. It is important to be aware of these differences before choosing one of the two languages.
• R is much more difficult to learn if you have no programming knowledge yet. Python code is intuitive so that even as a layman in the field of programming, you quickly understand what is in a Python script.
• R is used by academics and within R&D departments, Python by developers / programmers
• Python is better with big data applications than R
• Python is a leader in machine learning and artificial intelligence
• The vast majority of data analysis can be done in Python with just a few packages (Numpy, Pandas, Scikit-learn, and Seaborn)
• Python integrates better in applications or websites
• R is really for statistical analysis, Python more widely applicable. This makes R especially suitable for very specific analytical activities for which specialist packages have been developed.
Python code is more robust and easier to maintain than R code.
• R has standard nice options for communicating the output of analyzes, in Python this is less. Python has made a big catch up here, so that differences have become smaller.

Question 3

What are the differences between Python and R?

Ariane Rasseck · Accepted Answer

In addition to the above similarities, there are also differences. It is important to be aware of these differences before choosing one of the two languages.
• R is much more difficult to learn if you have no programming knowledge yet. Python code is intuitive so that even as a layman in the field of programming, you quickly understand what is in a Python script.
• R is used by academics and within R&D departments, Python by developers / programmers
• Python is better with big data applications than R
• Python is a leader in machine learning and artificial intelligence
• The vast majority of data analysis can be done in Python with just a few packages (Numpy, Pandas, Scikit-learn, and Seaborn)
• Python integrates better in applications or websites
• R is really for statistical analysis, Python more widely applicable. This makes R especially suitable for very specific analytical activities for which specialist packages have been developed.
Python code is more robust and easier to maintain than R code.
• R has standard nice options for communicating the output of analyzes, in Python this is less. Python has made a big catch up here, so that differences have become smaller.

Question 4

What are the differences between Python and R?

Ariane Rasseck · Accepted Answer

In addition to the above similarities, there are also differences. It is important to be aware of these differences before choosing one of the two languages.
• R is much more difficult to learn if you have no programming knowledge yet. Python code is intuitive so that even as a layman in the field of programming, you quickly understand what is in a Python script.
• R is used by academics and within R&D departments, Python by developers / programmers
• Python is better with big data applications than R
• Python is a leader in machine learning and artificial intelligence
• The vast majority of data analysis can be done in Python with just a few packages (Numpy, Pandas, Scikit-learn, and Seaborn)
• Python integrates better in applications or websites
• R is really for statistical analysis, Python more widely applicable. This makes R especially suitable for very specific analytical activities for which specialist packages have been developed.
Python code is more robust and easier to maintain than R code.
• R has standard nice options for communicating the output of analyzes, in Python this is less. Python has made a big catch up here, so that differences have become smaller.

Question 5

What are the differences between Python and R?

Ariane Rasseck · Accepted Answer

In addition to the above similarities, there are also differences. It is important to be aware of these differences before choosing one of the two languages.
• R is much more difficult to learn if you have no programming knowledge yet. Python code is intuitive so that even as a layman in the field of programming, you quickly understand what is in a Python script.
• R is used by academics and within R&D departments, Python by developers / programmers
• Python is better with big data applications than R
• Python is a leader in machine learning and artificial intelligence
• The vast majority of data analysis can be done in Python with just a few packages (Numpy, Pandas, Scikit-learn, and Seaborn)
• Python integrates better in applications or websites
• R is really for statistical analysis, Python more widely applicable. This makes R especially suitable for very specific analytical activities for which specialist packages have been developed.
Python code is more robust and easier to maintain than R code.
• R has standard nice options for communicating the output of analyzes, in Python this is less. Python has made a big catch up here, so that differences have become smaller.