[dan_yall]

I've found Python + Pandas much better in this regard than R. Maybe it's just me, but for grouping, indexing, and manipulating tabular data, Python syntax just makes more sense.

That said, R is better for stats and matrix operations.

[hadley]

Are you using Pandas? If so, your comment would be ironic because pandas borrows heavily from R ;)

[RaSoJo]

They might have borrowed from R. Wes McKinney admits to being influenced by R especialy data frames...but it makes data analysis all the more easier when i can do everything i want within the the Python environment. pandas is proving to be a bit of a longer learning curve i must admit, but then the python environment and native Matplotlib support made life oh so much simpler.

That's just me though.

[Myrmornis]

What has pandas borrowed from R, other than a 2D data structure with heterogeneously-typed columns?

I guess the data frame merge invocations are similar.

(I know patsy/statsmodels are introducing R's formula syntax to python, but that's not pandas.)

[hadley]

The split-apply-combine framework dealing with group by tasks (http://www.jstatsoft.org/v40/i01/paper, not that there aren't other precedents) for one. But generally, Wes has used R to figure out what people want to do, and then ported an elegant interface to python.

[crayola]

I would agree with you if it wasn't for the data.table package in R. It is a game changer. Really.

[tanyaM]

Can you elaborate on data.table being 'a game changer'. I am inclined to agree, but I'm am just starting to get a handle on it. I am still hesitant and switching between sqldf, reshape2, base::merge and data.table more than I would like. Do you think it could become a dominant method for data preparation?

[dagw]

Python has PyTables which complements Pandas nicely and seems to offer the same sort of features as data.table (note, I've not actually used data.table)

[tanyaM]

I am using R to analyse and document (knitr and latex) epidemiologic data which does not involve parsing a lot of text to extract my analysis data set. Data preparation for this type of research involves more combining data from different source tables, restructuring repeated measures, etc. I only know how to do that using R. Can Python be incorporated into the knitr literate programming framework and is it worth learning another language?

[yihui]

Python will be better supported in knitr in future; for now it only has preliminary support: http://yihui.name/knitr/demo/engines/