Pandas is a Python package for working with tabular data—as in a spreadsheet or database table. It provides similar functionality to R’s data frames.
As pandas is rich in features it can be difficult to remember all its operations and syntax, so Enthought have produced a visual guide to the package in 8 handy pages.
Andy Kirk at Visualising Data ran a Twitter poll about the relative accessibility of R and Python to non-developers.
59% said that R was more accessible.
Obviously, the poll is far from scientific, but the comments he received reflect my own experiences of teaching both languages—such as the significance of the RStudio IDE and the
tidyverse packages in getting people off the ground.
However, there are some downsides to notebooks—mostly to do with software engineering best practices.
Both authors make a good case and have interesting points. As ever, the truth is that notebooks are good in some situations and not so good in others.
The sources used to compiled the list cover
contexts that include social chatter, open-source code production, and job postings.
However, interest in Python clearly remains high. As it does in R—#7 is impressive for a domain-specific language.