TL;DR: We need DataFrame as a data structure in Ruby. There are several promising candidates but no one with good usability. Some considerations on requirements to good DataFrame library are proposed, alongside with some rants on using Ruby for science.

Intro: what is DataFrame and why we should has itz?

As programming languages evolve, our notion of “basic necessary” data types for high-level language also evolves. So, nowadays in modern languages we have different kinds of numbers for different tasks; we have arrays (which are not “just pointers to memory area”); we have strings (which are not “just arrays of characters”), hashes/dictionaries (which are not “some specialized algorithmic concept, available through separate library”), regexps, ranges and so on.

Having complex types in a language core and having literals for them has a great value: programmers don’t need to reinvent something already considered as a wheel. (If you tried to use together several C++ libraries, each having different classes for strings and arrays, you’ll understand.)

Now, I’m asking you to consider this thought:

We need new “standard” type in Ruby: DataFrame.

(“Standard” here does not mean adding to core language or even standard library—for now—but just well-designed, highly usable and widely aknowledged gem.)

What is DataFrame? It is currently widespread datatype you can find in many data processing languages and libraries, like R, python+pandas, Wolfram Mathematica, Julia and so on, for storing tabular data. It can be described this way:

like 2-dimensional array (columns × rows), but…

…columns are named,

…and rows are “indexed”: each row has corresponding label, which can be just sequential number (simplest and default case), or timestamp, or something else;

each column has data of only one type inside it (different columns can have different data types); though, any column can have empty places ( nil values) instead of data;

typical easy/cheap operations on DataFrame: change data values (without changing row count); including: create a “view” on some part of DataFrame and modify data inside it in one clean line of code (like “replace all nil values in column Salary with 0.0”); add/remove/switch columns; calculate new column on base of existing ones; select some rows/columns to another DataFrame;

typically, DataFrame provides methods, or supplied with libraries, for performing stats, summaries, and groupings on data;

often, DataFrame supports complex columns and complex indexes, like results of “pivotal table” operation (monthly income, grouped by department AND by manager inside department).

Why we need DataFrame? Because, it’s, like, 2016? Because data processing is everywhere: small data, big data, open data, internal data, frightening amounts of data. And “tabular data” is a common humus for experimenting, prototyping, developing new approaches and understanding the world—tasks Ruby once was prominent for. You know, before it fell into “the thing under Rails” tar-pit.

Ruby-ish DataFrame, its qualities and responsibilities

What does Ruby DataFrame need to have from the beginning?

Good Ruby DataFrame class should, of course, correspond to all expectations of modern data processing, coming from other languages and tools (look above for the list of expectations);

initialization of DataFrame should look as close to “literal”, as possible (for futher integration in libraries and practices);

public interface should be clear, terse and unambiguous: for ex., access to rows should be clearly distinguished from access to columns;

public interface should be as close as possible to Ruby’s best practices: see Array , and Hash , and Enumerable ; it should not resemble DataFrames of other languages and tools;

, and , and ; it should not resemble DataFrames of other languages and tools; column is an object, row is not (it is rather slice across all the columns)—column object even have a name in other DataFrame-y solutions: either Series or Vector—and this difference should be clearly visible from interface;

an object, row (it is rather slice across all the columns)—column object even have a name in other DataFrame-y solutions: either Series or Vector—and this difference should be clearly visible from interface; complex columns and complex indexes should ideally be supported;

as DataFrame is frequently used for experiments and prototypes, it should be pretty-printed by design (and this pretty-printing should by design consider “very large” data sets).

What Ruby DataFrame need not be at the beginning?

Import and export from tons of file formats and datasources: whether we’ll have good usable data structure, it will not be hard to add this functionality;

Plotting: the same as above;

All stats methods and algorithms somebody may ever need: they may be in mixins, some (or most) of them in different gems; main DataFrame responsibility is to hold data, you see? and make it easy to process;

You’ll laugh, but even performance topics are NOT as important as good API and universal aknowledgement of new DataFrame datatype; of course, performance does matter, but fast-and-ugly library is destined to have very limited usage, and pretty-yet-slow one can at least become popular for moderate-sized, “toy” tasks. After that, it becames widespread and somebody optimizes it, and—voila!—it is performant AND pretty.

Existing solution(s)?

There were several attempts to create DataFrame gem for Ruby (the latter even had been sponsored with Ruby Association’s grant, but there is no commits since grant program finish). Most of those attempts are just silently dying, used by no one except original author (if anyone at all).

And it’s just a standalone gems, not to mention Table-y and DataFrame-y solutions inside bigger libraries, using them for plotting or console output.

But lately, there is one candidate, receiving significant amount of attention inside SciRuby community: it is called DaRu, which stands for Data Analysis in RUby. You can look at pretty detailed showcase of it here.

First and really important: I should say DaRu is definitely a great effort and it definitely should be highly appreciated.

But the fact it became (relatively) popular recently, makes me rather sad than happy.

Why? For two (connected) reasons:

despite being useful, having many features and incorporating large amount of work, DaRu fails to be good Ruby library;

which, for me, is one of signs of bad communication between general Ruby community and scientific Ruby community.

About first part of that rant (“fails to be good Ruby library”): just look at DaRu README and examples, and try to use it by yourself. It contains many useful statistical features, integrates with IRuby scientific notebook, but…

it provides no examples in README, except for links to those notebooks (like, it from the beginning says “if you don’t know our staff, you’ll not need our libraries!”);

examples in notebooks use old wordy syntax for hashes, like DataFrame.new({:column => values}) instead of DataFrame.new(column: values) —not a sin on its own, but tends to show an attitude;

instead of —not a sin on its own, but tends to show an attitude; it has no reasonable methods like columns (they are vectors , very scientific, wow) or rows ;

(they are , very scientific, wow) or ; it doesn’t respond to first and last like Ruby collections (they are head and tail !);

and like Ruby collections (they are and !); it has no select and reject , but has filter and where ;

and , but has and ; it has no slice , but has overloaded [] for all kinds of data slicing (just like Pythonists accustomed to do!), just try yourself to guess what data_frame[1] should do or “how to extract first two rows”.

You can say “OK, it is not shiniest API possible, but heck with that! DaRu works and it is only alive and working Ruby’s DataFrame!”

And here the big problem of Ruby+Science comes.

Ruby has brevity, flexibility, consistency and deserves to be language for data processing and scientific experiments. It is very clear and readable for writing reference implementations for state-of-the-art algorithms. Yet, we all know, it is not the case in today’s world.

There’s only one way to fix this: have a great tools for scientists. And “port Pythons tools” is not the way to achieve this goal. (Those using Python for science would not migrate to Ruby “because it has almost the same tools”).

Great tools can be made by positive feedback loop:

provide something good for existing rubyists (even if they are not scientists);

receive their feedback and support, enchance the tools, make them integrated into Ruby ecosystem and widely accepted inside it;

…make users outside the Ruby community envious.

(Or, if you feel really brave, there’s also another way, the way Rails were made: invent new, ingenious approach to known problems, approach that only Ruby makes possible.)

Currently, SciRuby (part of which DaRu is), being solid and honorable initiative, seems to go through negative feedback loop:

not much of generic Ruby community uses those libraries: they are “not for us” (all examples are in IRuby, for artificial scientific tasks);

not much of scientists use Ruby/SciRuby—it is still far from maturity, and, faithfully, SciRuby does not feel like “Ruby will open new horizons for you!”, it is rather like “Ruby also can do something (almost) like Python/pandas”;

almost nobody uses libraries → almost no support for further development → things don’t become better → ….

I think, we should at least start to try to go out of this “scientific ghetto” by clean, easy-to-use, Ruby-ish DataFrame concept.

Conclusions

We need DataFrame in Ruby for many kind of tasks;

It requires solid community effort for planning, testing and inventing use cases; natural API is the most important thing to achieve (even before good performance for large datasets!);

good performance for large datasets!); DaRu may be, or may not be, a good starting point for Good DataFrame, but its current state is very far from what we need;

Would be great if scientific-oriented projects in Ruby come out from their ghetto and try to play with other guys.

Unnecessary addition: here is some rough draft of “how Good DataFrame could work in Ruby”. Really rough and underthougt, though.