Comment by vegabook
3 hours ago
"revolutionary"? It just copied and pasted the decades-old R (previous "S") dataframe into Python, including all the paradigms (with worse ergonomics since it's not baked into the language).
3 hours ago
"revolutionary"? It just copied and pasted the decades-old R (previous "S") dataframe into Python, including all the paradigms (with worse ergonomics since it's not baked into the language).
This is an interesting question.
Dataframes first appeared in S-PLUS in 1991-1992. Then R copied S, and from 1995-1996-1997 onwards R started to grow in popularity in statistics. As free and open source software, R started to take over the market among statisticians and other people who were using other statistical software, mainly SAS, SPSS and Stata.
Given that S and R existed, why were they mostly not picked up by data analysts and programmers in 1995-2008, and only Python and Pandas made dataframes popular from 2008 onwards?
No other modern language will compete with R on ergonomics because of how it allows functions to read the context they’re called in, and S expressions are incredibly flexibly. The R manual is great.
To say pandas just copied it but worse is overly dismissive. The core of pandas has always been indexing/reindexing, split-apply-combine, and slicing views.
It’s a different approach than R’s data tables or frames.
Exactly. I was programming in R in 2004 and Pandas didnt exist. I remember trying Pandas once and it felt unergonomic for fata analysis and it lacked the vast library of statistical analysis library.
It was revolutionary to Python. Without NumPy and Pandas, ML in Python would never have been a thing.
(Yes, yes - I know some people wish that were the case!)