{
    "info": {
        "author": "Sergiy Kuzmenko",
        "author_email": "",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: Public Domain",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Software Development :: Interpreters"
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        "description": "[![Build Status](https://semaphoreci.com/api/v1/shelldweller-39/etlite/branches/master/badge.svg)](https://semaphoreci.com/shelldweller-39/etlite)\n\n# ETlite\n\nExtract/Transform Light - a simple library for reading delimited files.\n\n## Example\n\nGiven CSV file:\n\n```\nArea id,Male,Female,Area\nA12345,34,45,0.25\nA12346,108,99,0.32\n```\n\nDefine a list of transformation:\n\n```python\ntransformations = [\n    # Map existing fields into dictionary.\n    # For nested dictionaries use dot.delimited.keys.\n    # Optional \"via\" parameter takes a callable returning transformed value.\n    { \"from\": \"Area id\", \"to\": \"id\" },\n    { \"from\": \"Male\", \"to\": \"population.male\", \"via\": int },\n    { \"from\": \"Female\", \"to\": \"population.female\", \"via\": int },\n    { \"from\": \"Area\", \"to\": \"area\", \"via\": float },\n\n    # You can also add computed values, not present in the original data source.\n    # Computer values take transformed dictionary as argument\n    # and they do not require \"from\" parameter:\n    {\n        \"to\": \"population.total\",\n        \"via\": lambda x: x['population']['male'] + x['population']['female']\n    },\n    # Note that transformations are executed in the order they were defined.\n    # This transformation uses population.total value computed in the previous step:\n    {\n        \"to\": 'population.density',\n        \"via\": lambda x: round(x['population']['total'] / x['area']),\n    }\n]\n```\n\nRead the file:\n\n```python\nfrom etlite import delim_reader\n\nwith open(\"mydatafile.csv\") as csvfile:\n  reader = delim_reader(csvfile, transformations)\n  data = [row for row in reader]\n```\n\nThis produces a list of dictionaries:\n\n```python\n[\n    {\n        'id': 'A12345',\n        'area': 0.25,\n        'population': {\n            'male': 34,\n            'female': 45,\n            'total': 79,\n            'density': 316\n        }\n    },\n    {\n        'id': 'A12346',\n        'area': 0.32,\n        'population': {\n            'male': 108,\n            'female': 99,\n            'total': 207,\n            'density': 647\n        }\n    }\n]\n```\n\n## `delim_reader` options\n\nETlite is just a thin wrapper on top of Python built-in [CSV module](https://docs.python.org/3/library/csv.html). Thus you can pass to `delim_reader` same options as you would pass to `csv.reader`. For example:\n\n```python\nreader = delim_reader(csvfile, transformations, delimiter=\"\\t\")\n```\n\n## Exception handling\n\nIf desired transtormation cannot be performed, ETLite will raise `TransformationError`. If you do not want to abort data loading, you can pass an error handler to `delim_reader`.\n\nError handler must be a function. It will be passed an instance of `TransformationError`. *Note: `on_error` must be pased as keywod argument.*\n\n```python\nfrom etlite import delim_reader\n\ntransformations = [\n    # ...\n]\n\ndef error_handler(err):\n    # err is an instance of TransformationError\n    print(err) # prints error message\n    print(err.record) # prints raw record, prior to transformation\n\n\nwith open('my-data.csv') as stream:\n    reader = delim_reader(stream, transformations, on_error=error_handler)\n    for row in reader:\n        do_something(row)\n```\n\n\n",
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        "license": "Public Domain",
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        "name": "etlite",
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