# Frustrated using DataFrames

**URL:** <https://discourse.julialang.org/t/frustrated-using-dataframes/67833>\
**Category:** New to Julia\
**Tags:** dataframes, data\_structures\
**Created:** [September 7, 2021, 9:38pm UTC](https://discourse.julialang.org/t/frustrated-using-dataframes/67833 "2021-09-07T21:38:10Z")\
**Posts on this page:** 1\
**Showing post:** 33

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**Author:** ![aplavin](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/aplavin/32/222056_2.png) [@aplavin](https://discourse.julialang.org/u/aplavin)\
**Post date:** [September 8, 2021, 3:32pm UTC](https://discourse.julialang.org/t/frustrated-using-dataframes/67833/33 "2021-09-08T15:32:25Z")

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I was long frustrated by dataframes - not DataFrames.jl specifically, just dataframes in general, in any language. It’s often much more natural and convenient to represent data as something other than a flat 2d table of plain values.

Languages like Python are slow and working with arbitrary objects quickly becomes infeasible - that’s one reason dataframes are required there. But not in Julia! Built-in data structures like vectors, tuples, dicts, and custom problem-specific structs work together without any issues and slowdowns. They smoothly operate with the Tables.jl ecosystem, and with other packages such as SplitApplyCombine.jl.

Depending on your usecases, built-in structures can outperform DataFrames.jl. Syntax for common operations is reasonably convenient as-is, and can be made even smoother with a thin macro package - see [[ANN] DataPipes.jl - #41 by aplavin](https://discourse.julialang.org/t/ann-datapipes-jl/60734/41) for an example.

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