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"author": "Tim Fischer",
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"description": "# wrangle_pypes\n_Easy, model based data wrangling._\n\n
\n
\n\n## Origin\nThis project came about thanks to having implemented this concept in a prior project ([dfb_predict](https://github.com/tim-fi/dfb_predict)) and wanting to reuse it. In said previous project it came about, because we wanted to play around with data-oriented design and figured that if we break up the transformations required during data-wrangling/munging into a simple \"_AST_\" where each node represents a single transformation. And to further the idea we to some inspiration from one of the [Unix philosophy](https://en.wikipedia.org/wiki/Unix_philosophy) core tenants, i.e. a single transformation should do one simple thing only, and allow for composition to implement more complex behavior.\n\n## Example\n```python\nfrom dataclasses import dataclass\nimport json\n\nfrom wrangle_pypes import Pipeline\nfrom wrangle_pypes.transformations import Get, Create, Cast\n\n@dataclass\nclass Point:\n x: int\n y: int\n\n\n@dataclass\nclass Square:\n A: Point\n B: Point\n\n\npipeline = Pipline({\n Square: {\n \"A\": Get(\"A\") | Create(Point),\n \"A\": Get(\"B\") | Create(Point),\n },\n Point: {\n \"x\": Get(\"x\") | Cast(int),\n \"y\": Get(\"y\") | Cast(int),\n }\n})\n\ndata = \"\"\"\n[\n {\n \"A\": {\"x\": 0, \"y\", 0},\n \"B\": {\"x\": 1, \"y\", 1},\n }, {\n \"A\": {\"x\": 10, \"y\", 10},\n \"B\": {\"x\": 11, \"y\", 11},\n }\n]\n\"\"\"\n\nsquares = list(pipeline.create_multiple(json.loads(data)))\n```\n\n\n",
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