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whyqd: simplicity, transparency, speed

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What is it?

whyqd provides an intuitive method for restructuring messy data to conform to a standardised metadata schema. It supports data managers and researchers looking to rapidly, and continuously, normalise any messy spreadsheets using a simple series of steps. Once complete, you can import wrangled data into more complex analytical systems or full-feature wrangling tools.

It aims to get you to the point where you can perform automated data munging prior to committing your data into a database, and no further. It is built on Pandas, and plays well with existing Python-based data-analytical tools. Each raw source file will produce a json schema and method file which defines the set of actions to be performed to produce refined data, and a destination file validated against that schema.

whyqd ensures complete audit transparency by saving all actions performed to restructure your input data to a separate json-defined methods file. This permits others to scrutinise your approach, validate your methodology, or even use your methods to import data in production.

Once complete, a method file can be shared, along with your input data, and anyone can import whyqd and validate your method to verify that your output data is the product of these inputs.

Read the docs and a full tutorial.

Why use it?

If all you want to do is test whether your source data are even useful, spending days or weeks slogging through data restructuring could kill a project. If you already have a workflow and established software which includes Python and pandas, having to change your code every time your source data changes is really, really frustrating.

There are two complex and time-consuming parts to preparing data for analysis: social, and technical.

The social part requires multi-stakeholder engagement with source data-publishers, and with destination database users, to agree structural metadata. Without any agreement on data publication formats or destination structure, you are left with the tedious frustration of manually wrangling each independent dataset into a single schema.

whyqd allows you to get to work without requiring you to achieve buy-in from anyone or change your existing code.

Wrangling process

  • Create, update or import a data schema which defines the destination data structure;
  • Create a new method and associate it with your schema and input data source/s;
  • Assign a foreign key column and (if required) merge input data sources;
  • Structure input data fields to conform to the requriements for each schema field;
  • Assign categorical data identified during structuring;
  • Transform and filter input data to produce a final destination data file;
  • Share your data and a citation;

Installation and dependencies

You'll need at least Python 3.6, then:

pip install whyqd

Code requirements have been tested on the following versions:

  • numpy>=1.18.1
  • openpyxl>=3.0.3
  • pandas>=1.0.0
  • tabulate>=0.8.3
  • xlrd>=1.2.0


The version history can be found in the changelog.


whyqd was created to serve a continuous data wrangling process, including collaboration on more complex messy sources, ensuring the integrity of the source data, and producing a complete audit trail from data imported to our database, back to source. You can see the product of that at

whyqd uses's table schema as a starting-point, but our objectives are different. It is intended as a containerised CSV validation schema, however, there is no guarantee that a restructured dataset emerging from a whyqd method will validate against table schema as this output is still an interim point prior to automated data munging. There is also no expectation that the final destination for these data would be a CSV, since it is more likely you are going to import into a database.

The 'backronym' for whyqd /wɪkɪd/ is Whythawk Quantitative Data, Whythawk is an open data science and open research technical consultancy.