There were some discussions about when is the right time to release a version 1.0 of any package.
Releasing 1.0 of DataFrames resulted not in a article on the german number one IT news site:
Header says: DataFrames.jl is the answer to Pythons pandas
Subheader: The package DataFrames.jl, now released in version 1.0, is used like the Python library pandas for processing and evaluating tabular data.
(DeepL translated for my convinience )
Possibly (all publicity is good, except an obituary notice), but I think the article misses the point; dataframes for tabular data were introduced with S/R (early 1990s), so I am not sure that the Julia implementation of the concept is “the answer to Pandas” (late 2000s).
In the above context (versioning) quote from the articel:
Der Sprung auf Version 1.0 markiert eine ausreichende Reife fĂĽr den produktiven Einsatz und stabilisiert die API.
The jump to version 1.0 marks sufficient maturity for productive use and stabilizes the API.
(again DeepL)
That’s the point.
Of course you are right. DataFames aren’t invented by pandas.
All in all it’s a nice (but short) article about Julia, the package eco system and it’s growing maturity
I don’t think the author is a julia expert and the pandas thing he got from the DataFrames docs: Introduction · DataFrames.jl?
DataFrames.jl provides a set of tools for working with tabular data in Julia. Its design and functionality are similar to those of pandas (in Python) and data.frame , data.table and dplyr (in R), making it a great general purpose data science tool, especially for those coming to Julia from R or Python.
Hmm, this is one of my biggest gripes I have with Julia: that Julia is nearly always compared to Python. But I experience this as mainly driven from us (this community, actually not me, but them ). I see that Julia has its strongest arguments in the data analysis world (where I am too) but on the other side I see this as the biggest obstacle for Julia to be more widely adopted. There are still some features missing (slim standalone executables, modern GUI,…) for this to become real and the focus on data analysis puts these features into the low priority queue.
The result is: Julia is only good for data analysis, thats what it is made for, so it is the natural thing to compare it to Python, when ever it comes to some articles or blogs.
I want to see Julia to overcome this burden and free itself into more than just data analysis.
By the way: overall, the article isn’t “pitching” against python, it’s more a very neutral comparison and even the title doesn’t sound (in german) very “pitching”. It is more that it tries to attract the python data analyst who doesn’t know too much about Julia with some trigger words. Not so much clickbaiting, the Heise site is all in all very good and classical journalism.
)
). I see that Julia has its strongest arguments in the data analysis world (where I am too) but on the other side I see this as the biggest obstacle for Julia to be more widely adopted. There are still some features missing (slim standalone executables, modern GUI,…) for this to become real and the focus on data analysis puts these features into the low priority queue.