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This seems very poor - the comparison is between pandas and DataFrames.jl, not Julia; syntax comparison is very surface-level; cheatsheats are low resolution; t

by jstx1 3y ago

This seems very poor - the comparison is between pandas and DataFrames.jl, not Julia; syntax comparison is very surface-level; cheatsheats are low resolution; the learning curve section says nothing about the learning curve; and the conclusion is "do whatever you like".

xgdgsc 3y ago
But there's CSV.jl so I didn't change title. I don't see any low resolution, maybe a font choice issue? I'd say the conclusion is the right thing to say for such a short comparison.
- jstx1 3y ago
  You have many errors in the tables, for example the pandas indexing is obviously wrong.
  - xgdgsc 3y ago
    It' s not my website.
    - jstx1 3y ago
      Sorry, thought you were the author.
misja111 3y ago
Well Pandas is a framework, not a language, so it only makes sense to compare it to DataFrames.jl and not to Julia as a lanugage. But I agree this should have been reflected in the title of the article.
- ChrisRackauckas 3y ago
  I both agree and disagree. It does look weird as library vs language. However at the same time, in Pandas everything tends to be in the Pandas library, whereas when using DataFrames.jl you tend to mix it with a lot of features that are external. Most of the calls just use overloaded functions from Julia's Base library (mean, first, last, findall). The Pandas model is to look at the Pandas docs and find the Pandas dataframe function that does your job. The DataFrames.jl model is to do whatever you would have done normally in Julia, like use the sort function, but now just use it on a DataFrame. The idea of DataFrames.jl is that you know the language and so it extends/adds as few functions as possible (joins, groupby, split-apply-combine I think are it?). This plus many other calls use functions from the more general Julia data science ecosystem (CSV.jl, JSON.jl, ...). So the title ends up being a bit apples and oranges, but the usage is also quite apples and oranges and the cheat sheet does accurately reflect that.

by jstx1 3y ago

xgdgsc 3y ago

But there's CSV.jl so I didn't change title. I don't see any low resolution, maybe a font choice issue? I'd say the conclusion is the right thing to say for such a short comparison.

jstx1 3y ago
You have many errors in the tables, for example the pandas indexing is obviously wrong.
- xgdgsc 3y ago
  It' s not my website.
  - jstx1 3y ago
    Sorry, thought you were the author.

misja111 3y ago

Well Pandas is a framework, not a language, so it only makes sense to compare it to DataFrames.jl and not to Julia as a lanugage. But I agree this should have been reflected in the title of the article.

ChrisRackauckas 3y ago
I both agree and disagree. It does look weird as library vs language. However at the same time, in Pandas everything tends to be in the Pandas library, whereas when using DataFrames.jl you tend to mix it with a lot of features that are external. Most of the calls just use overloaded functions from Julia's Base library (mean, first, last, findall). The Pandas model is to look at the Pandas docs and find the Pandas dataframe function that does your job. The DataFrames.jl model is to do whatever you would have done normally in Julia, like use the sort function, but now just use it on a DataFrame. The idea of DataFrames.jl is that you know the language and so it extends/adds as few functions as possible (joins, groupby, split-apply-combine I think are it?). This plus many other calls use functions from the more general Julia data science ecosystem (CSV.jl, JSON.jl, ...). So the title ends up being a bit apples and oranges, but the usage is also quite apples and oranges and the cheat sheet does accurately reflect that.