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I am currently using python pandas and want to know if there is a way to output the data from pandas into julia Dataframes and vice versa. (I think you can call python from Julia with Pycall but I am not sure if it works with dataframes) Is there a way to call Julia from python and have it take in pandas dataframes? (without saving to another file format like csv)
When would it be advantageous to use Julia Dataframes than Pandas other than extremely large datasets and running things with many loops(like neural networks)?
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Pandas library is heavily used for Data Analytics, Machine learning, data science projects, and many more. Pandas can load the data by reading CSV, JSON, SQL, many other formats and creates a DataFrame which is a structured object containing rows and columns (similar to SQL table).
A Data frame is a two-dimensional data structure that resembles a table, where the columns represent variables and rows contain values for those variables. It is mutable and can hold various data types.
While the process takes 16.62 seconds for Pandas, Datatable is only at 6.55 seconds. Overall Datatable is 2 times faster than Pandas.
On joining two datasets task, Polars has done it in 43 seconds. Meanwhile, Pandas did it in 628 seconds. We can see that Polars is almost 15 times faster than Pandas.
So there is a library developed for this
PyJulia is a library used to interface with Julia using Python 2 and 3
https://github.com/JuliaLang/pyjulia
It is experimental but somewhat works
Secondly Julia also has a front end for pandas which is pandas.jl
https://github.com/malmaud/Pandas.jl
It looks to be just a wrapper for pandas but you might be able to execute multiple functions using julia's parallel features.
As for the which is better so far pandas has faster I/O according to this reading csv in Julia is slow compared to Python
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