vaquero 0.0.5

A library for iterative and interactive data wrangling

What is Vaquero?

TL;DR

It’s a library for iterative and interactive data wrangling at laptop-scale. If you spend a lot of time in a Jupyter notebook, trying to clean dirty, raw data, it’s probably useful.

It would be nice if it were possible to write data cleaning code correctly. But, the people who pay you to do data analysis don’t do data analysis and don’t understand how dangerous dirty data are, so you rarely get the luxury of feeling secure with what you extract. Vaquero tries to find a balance between “business” demands and good hygiene. Borrowing from Larry Wall, it tries “to make the easy things easy, and the hard things possible.” In this context, “hard things” refers to those wonderfully fun situations where, you write some code that you know will break in the future but you have no time to fix it; then, three months later, it breaks and you have no idea what your code does.

See also: On Disappearing Code

An Example

It’s easier to get a sense of “why” by looking at a notebook.

• Notebook for the common usage pattern

• Notebook for inline pipelines

Expecting Exceptions

Vaquero expects exceptions, making them pretty unexceptional. But, Python’s exception handling is cheap, so that’s fine (i.e. EAFP – Easier to ask for forgiveness than permission). Plus, with dirty data, you know it will probably fail for some records. During development, rather than halting each time, vaquero continues on its merry way, up to some failure limit. For each failure, the library logs the exception, including the name of the file and the arguments which resulted in a failure.

After you have processed all the documents, you can then inspect the errors. This helps you scan for error patterns, rather than programming by the coincidence of the first error raised. Moreover, since you the offending function and its arguments, it is easy to update the new function, ensuring it passes with the prior bad example. Vaquero reloads the pipeline for you. (Or, at least tries to, because reloading is tricky.)

Modules as Pipelines

Namespaces are one honking great idea – let’s do more of those!

Programmers use namespaces everywhere to organize their code. Yet, when writing data cleaning code, everything ends up in a big file with lots of poorly-named functions. Think: from hellishlib import *. The perfectionist in me says, “this is awful, and I should write it properly, as a full library with lots of unit tests!” But, for “perfectionists with deadlines,” that’s not possible.

Furthermore, the single-file-of-functions pattern emerges not only because of time constraints; it’s a reflection of the problem! ELT code is inherently tightly-coupled. Code that extracts this variable probably depends on that one which in turn also depends on some other one. This leads to a tree of transformations, encapsulated by function calls.

Recognizing this, vaquero doesn’t try to move you away from collecting all your ELT code in a single file. It’s going to happen anyway. Instead, it makes it safer with some conventions.

1. A module represents a single encapsulated pipeline. It should process a well-defined document.

2. The function definition order is meaningful. Functions at the top of the file execute before those above them. Again, it’s a pipeline.

3. As per pythonic convention, functions prefixed with _ are private. Here, that means, the pipeline constructor ignores it when compiling the pipeline. This gives you nice helper functions.

4. You’re probably not going to use unit tests – you don’t have time. But, since it’s a module, pepper it with assertions. And, using the _-prefix, you can actually write namespaced tests (e.g. _my_test()), and immediately call them in the module. (I actually write a lot of my code with unittest in the pipeline module and it gets called right before the module fully imports.) Then, when you break something, you can’t even start pipeline processing. It fails fast. (You can deviate from this pattern – but, in general, don’t.)

Installation

pip install vaquero

Tips

The f(src, dst) Pattern

For most of my pipelines, I tend to write functions that look like,

def f(src_d, dst_d):
    dst_d['age'] = int(src_d['AGE1'])

Coming from functional languages, I’d prefer immutable objects. But, in Python, that tends to be painfully slow. This pattern represents a compromise that usually works well. On the one side (dst_d) you have already processed elements; on the other, the raw data.

Hidden field pattern

• Assume you are processing a pipeline with a dict destination document. Use ‘_key_name’ fields for intermediary results in a document. You can delete them at the end of the pipeline (easily, via vaquero.transformations.remove_private_keys), but in the interim, you’ll see these fields on failure.

Disclaimer

I have this big monstrous library called vaquero on my computer. It’s a collection of lots of functions I’ve written over (entirely too) many data munging projects. I use it often, and keep telling myself “once I find the time, I’ll release it!” And, that never happens. It’s too big to clean up in a way that makes me comfortable. Instead, I’ll be releasing little bits of code in a ad-hoc, just-in-time fashion. When I absolutely need some feature of the big library going forward, I’ll extract it and put it here.

That makes me wildly uncomfortable, but…I’m starving for time.

In any case, library-user beware. Things will break.