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Cleaning your messy data.

Project description

Cleaning your messy data.

Getting started

Consider you need to clean up some messy data. Here is a deep nested dictionary containing unnecessary nesting, tuples and so.

some_messy_data = {
    "body": {
        "articles": [
            {"title": "Title",
             "published": {"date": ("2014-11-05", "23:00:00")}},
    "meta": {
        "meta1": {
            "meta3": ('19911105', {"author": "Author name"})
        "meta4": {"assetType": 1}

Necessary values are 'articles', 'author' and 'assetType'.

Now let the hack begin with the dripper. Defile ‘declaration’ dictionary to drip essential data.

declaration = {
    "articles": {
        "__type__": "list",  # 'articles' is list of dictionary
        "__source_root__": ['body', 'articles'],  # The root position of 'articles'

        "title": ["title"],  # each dictionary of 'articles' will contain 'title'
        "published": ["published", "date", 0],  # and 'published'. you can pass the path to the value
    "meta": {
        "__source_root__": ["meta", "meta1", "meta3", 1],

        "author": ["author"],
        "author_lower": lambda d: d["author"].lower(),  # Pass a callable accepting one argument ('meta' dictionary)
    "asset_type": ["meta", "meta4", "assetType"],

And just use like this.

from dripper import dripper_factory
d = dripper_factory(declaration)
d(some_messy_data) == {
    "articles": [
        {'title': "Title",
         'published': "2014-11-05"},
    "meta": {
        "author": "Author name",
        "author_lower": "author name",
    "asset_type": 1,


  • Initial version

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