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    "info": {
        "author": "Daniel Francis",
        "author_email": "daniel.francis@infusionsoft.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "# Popelines\n\nThis is a simple ETL tool for BigQuery, named for the author's surname. \n\nPopelines provides some basic functionality, such as writing to line-delimited JSON, writing to BigQuery, chunking dates, and other tools that are often needed when writing an ETL. It's sparse now, but I plan to expand it to include other Google Cloud functionalities.\n\nInstall\n-------\nTo install popelines:\n```bash\n$ pip install popelines\n```\n\nUsage\n-----\n\nTo get started:\n```python\nimport popelines\n\npope = popelines.popeline(dataset_id='', service_key_file_loc=None, directory='.', verbose=False)\n```\n\nProviding a `dataset_id` is required. Everything else is optional - a service key will be inferred from your GOOGLE_ACCOUNT_CREDENTIALS env variable if not provided in `service_key_file_loc`, and `directory` defaults to the current directory if not provided. \n\nPopelines does some big handy things like you might expect:\n```python\n# write a dict to line-delimited JSON, perfect for uploading to BQ\npope.write_to_json(file_name=file_name, jayson=your_dict, mode='w')\n\n# then you can turn around and upload that line-delimtited JSON...\npope.write_to_bq(table_name=table_name, file_name=file_name, append=True, \n    ignore_unknown_values=False, bq_schema_autodetect=False)\n\n# or you can write it to GCS! leave bucket_name=None and popelines\n# will try to upload to a bucket with the dataset_id you gave when you\n# first initialized your pope object!\npope.write_to_gcs(gcs_path='folder/file.py', file_name='file.py', bucket_name=None)\n\n# you can even call your API endpoints! This method returns a dict of data.\ndata = pope.call_api(url=url, method='GET', headers=None, params=None, data=None)\n```\n\nPopelines also does small handy things:\n```python\n# get a logger at your chosen verbosity and use it to log things\nlog = pope.log\nlog.info('Does the code get to this point?')\n\n# chunk a date range into chunks n-days large\nstart_datetime = datetime.datetime(2018, 3, 1)\nend_datetime = datetime.datetime(2018, 9, 1)\nfor day in pope.chunk_date_range(start_datetime=start, end_datetime=end, chunk_size=1):\n    print(f\"I think I may have been drunk on {day}, can you name another date?\")\n\n# find the last entry in a table - basically, query for the MAX() of a column\nlatest_day = pope.find_last_entry(table_name='my_table', date_column='day')\n```\n\nFinally, Popelines even does weird experimental things:\n```python\n# messed up JSON keys? fix_json_keys takes your dict obj and a callback\n# function and applies the callback to each key recursively!\nmy_good_json = pope.fix_json_keys(obj=my_bad_json, callback=key_fixing_function)\n\n# if your JSON values are messed up, have no fear! There is a similar \n# function for that!\nmy_good_json = pope.fix_json_values(obj=my_bad_json, callback=value_fixing_function)\n```\n*Note that `key_fixing_function` should take one argument (the key) while `value_fixing_function` must handle both a value and a key as arguments.*\n\n",
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        "name": "popelines",
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