# Does the concept of type-stability apply to DataFrames or Tables?

**URL:** https://discourse.julialang.org/t/does-the-concept-of-type-stability-apply-to-dataframes-or-tables/6434
**Category:** Data
**Tags:** dataframes, tables, type-stability
**Created:** [October 14, 2017, 12:12pm UTC](https://discourse.julialang.org/t/does-the-concept-of-type-stability-apply-to-dataframes-or-tables/6434 "2017-10-14T12:12:58Z")
**Posts on this page:** 1
**Showing post:** 10

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### Author: ![piever](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/piever/32/1815_2.png) [@piever](https://discourse.julialang.org/u/piever)
#### Post date: [October 14, 2017, 4:10pm UTC](https://discourse.julialang.org/t/does-the-concept-of-type-stability-apply-to-dataframes-or-tables/6434/10 "2017-10-14T16:10:52Z")

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I fully agree with @mkborregaard : the data developers are doing an insane amount of work!

Concerning “typed-ness”, I believe that one issue with the current DataFrame implementation (unless of course I’m missing something) is that it’s difficult to do `map` and `filter` on a DataFrame performantly, even though I believe those are basic manipulations. By `map` I mean something that takes a DataFrame and a function from named tuples to named tuples and outputs a DataFrame, and `filter` would take a a DataFrame and a predicate on named tuples. One can of course resort to external packages (such as Query), but even there the `@select` statement, which would be what here I call `map` has some limitations as it relies on type inference on NamedTuples to work.

Still, there has been a lot of discussion and interesting ideas on these topics and I’m curious to see what the outcome will be.

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