Rama, a package to clean, process and analyse Companies House data

This package helps clean, process and analyse the data from the Companies House API. The package uses a simple cleaning process to create a network of ownership/control between Persons with Significant Control (PSCs) and companies. The aim of the analysis is to understand the legal and fiscal reasons that give rise to the structure of the observed networks.

This package is part of the Effects of the legal and fiscal framework in UK ownership networks project of the Computational Social Science group of the Alan Turing Institute.

co-authors of the project: Dr. Leonardo Castro-Gonzalez, Dr. Alejandro Beltran, Dr. Omar Guerrero.

Index

• 1. Installation
• 2. User guide
• 3. Contact

Installation

The package is written in Python (minimal version: 3.10). We recommend that the installation is made inside a virtual environment trough conda.

Using conda

The tool conda, which comes bundled with Anaconda has the advantage that it lets us specify the version of Python that we want to use. Python>=3.10 is required.

A new environment can be created with

$ conda create -f environment.yaml

Like before, the environment's name can be anything else instead of rama (simply change the name below). We activate it using

$ conda activate rama

Local installation

Once we are working inside an active virtual environment, we install (the dependencies and) the package by running

$ pip install -e .

User guide

The cleaning and pre-processing has been wrapped in the function initialise().

from rama import initialise

path = "local_path_to_files"
companies_filename = "companies.csv"
psc_filename = "psc.csv"

string_nature = "ownership-of-shares"

graph, connected_components, dict_cluster = initialise(path, psc_filename, companies_filename, string_nature)

There is also a wrapper to initialise for graphs with only humans as root

from rama import initialise_humans

graph, connected_components, graphs_with_humans = initialise_humans(path, psc_filename, companies_filename, string_nature)

** Coming soon ** A collection of notebooks can be found in notebooks/ with a series of quick and simple tutorial on how to analyse the processed data.

Contact

• Leonardo Castro-Gonzalez: [email protected]
• Alejandro Beltran: [email protected]
• Omar Guerrero: [email protected]