Package: quality-lac-data-validator
Quality LAC data beta: Python validatorWe want to build a tool that improves the quality of data on Looked After Children so that Children’s Services Departments have all the information needed to enhance their services.We believe that a tool that highlights and helps fixing data errors would be valuable for:Reducing the time analysts, business support and social workers spend cleaning data.Enabling leadership to better use evidence in supporting Looked After Children.About this projectThe aim of this project is to deliver a tool to relieve some of the pain-points
ofreporting and qualityin children's services data. This project
focuses, in particular, on data on looked after children (LAC) and theSSDA903return.The project consists of a number of related pieces of work:Hosted ToolReact & Pyodie Front-EndPython Validator Engine & Rules[this repo]Local Authority Reference DataPostcode Reference DataThe core parts consist of aPythonvalidator engine and rules usingPandaswithPoetryfor dependency management. The tool is targeted
to run either standalone, or inpyodidein the browser for a zero-install
deployment with offline capabilities.It provides methods of finding the validation errors defined by the DfE in 903 data.
The validator needs to be provided with a set of input files for the current year and,
optionally, the previous year. These files are coerced into a common format and sent to
each of the validator rules in turn. The validators report on rows not meeting the rules
and a report is provided highlight errors for each row and which fields were included in
the checks.Data pipelineLoading of filesIdentification of tables - currently matched onexactfilenameConversion of CSV to tabular format -no type checkingEnrichment of provided data with Postcode distancesEvaluation of rulesReportProject StructureThese are the key filesproject
├─── pyproject.toml           - Project details and dependencies
├─── validator903
│    ├─── config.py           - High-level configuration
│    ├─── ingress.py          - Data ingress (handling CSV and XML files)
│    ├─── types.py            - Classes used across the work
│    ├─── validator.py        - The core validator process
│    └─── validators.py       - All individual validator codes
└─── tests                    - Unit testsMost of the work from contributors will be invalidators.pyand the associated testing files under
tests. Please do not submit a pull-request without a comprehensive test.DevelopmentTo install the code and dependencies, from the main project directory run:poetry installIf this does not work, it might be because you're running the wrong version of Python, the version of Numpy used by the 903 validator is locked at 3.9. The devcontainer and dockerfile should ensure you are running 3.9 and you may simply require a rebuild. If not, ensure you are working in an environment or venv with Python 3.9 as your interpreter.Adding validatorsValidators are simple functions, usually calledvalidate_XXX()which take no arguments and
return a tuple of anErrorDefinitionand a test function. The test function itself takes
a single argument, thedatastore, which is aMapping(a dict-like) following the structure below.The following is the expected structure for the input data that is given to each validator (thedfsobject).
You should assume that not all of these keys are present and handle that appropriately.Any XML uploads are converted into CSV form to give the same inputs.{
    # This years data
    'Header':   # header dataframe
    'Episodes': # episodes dataframe
    'Reviews':  # reviews dataframe
    'UASC':     # UASC dataframe
    'OC2':      # OC2 dataframe
    'OC3':      # OC3 dataframe
    'AD1':      # AD1 dataframe
    'PlacedAdoption':  # Placed for adoption dataframe
    'PrevPerm': # Previous permanence dataframe
    'Missing':  # Missing dataframe
    # Last years data
    'Header_last':   # header dataframe
    'Episodes_last': # episodes dataframe
    'Reviews_last':  # reviews dataframe
    'UASC_last':     # UASC dataframe
    'OC2_last':      # OC2 dataframe
    'OC3_last':      # OC3 dataframe
    'AD1_last':      # AD1 dataframe
    'PlacedAdoption_last':  # Placed for adoption dataframe
    'PrevPerm_last': # Previous permanence dataframe
    'Missing_last':  # Missing dataframe
    # Metadata
    'metadata': {
        'collection_start': # A datetime with the collection start date (year/4/1)
        'collection_end':   # A datetime with the collection end date (year + 1/4/1)
        'postcodes':        # Postcodes dataframe, columns laua, oseast1m, osnrth1m, pcd
        'localAuthority:    # The local authority code entered (long form, e.g. E07000026)
        'collectionYear':   # The raw collection year string - unlikely to need this (e.g. '2019/20')
    }
}ReleasesTo build and release a new version, make sure all your unit tests pass.We usesemantic versioning, so update the project version inpyproject.tomlaccordingly
and commit, creating a PR. Once the release version is on GitHub, create a GitHub release naming the release with the
current release name, e.g. 1.0 and the tag with the release name prefixed with a v, i.e. v1.0. Alpha and beta releases
can be flagged by appending-alpha.<number>and-beta.<number>.