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    "info": {
        "author": "Jens Finn\u00e4s",
        "author_email": "jens.finnas@gmail.com",
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        "description": "\nThis is a scraper for statistical data from the (https://www.viltolycka.se/statistik/viltolyckor-for-respektive-viltslag/)[viltolycka.se]  built on top of the `Statscraper package <https://github.com/jplusplus/statscraper>`.\n\nThe scraper is limited to the data avialble through https://www.viltolycka.se/statistik/viltolyckor-for-respektive-viltslag/\n\nInstall\n-------\n\n.. code:: bash\n\n  $ pip install viltolyckor_scraper\n\n\nExample usage\n-------------\n\n.. code:: python\n\n  from viltolyckor import ViltolyckorScraper\n\n  # Init scraper\n  scraper = ViltolyckorScraper()\n\n  # List all available datasets\n  datasets = scraper.items\n  # [<ViltolyckorDataset: viltolyckor per viltslag>]\n\n  # Select a dataset\n  dataset = scraper.items[\"viltolyckor per viltslag\"]\n\n  # List all available dimensions\n  datasets = verksamhetsform.dimensions\n  # [<Dimension: region>, <Dimension: year>, <Dimension: month>, <Dimension: viltslag>]\n\n  # Make a query\n  res = dataset.fetch()  # Get latest available data for whole country by default\n\n  # Analyze the results with Pandas\n  df = res.pandas\n\n  # Make a more specific query\n  # Get data for a given period\n  res = dataset.fetch({\"period\": \"2015\"})\n\n  # Or from a specific region (all years)\n  res = ds.fetch({\"region\": u\"Stockholms l\u00e4n\", \"year\": \"*\"})\n\n\nDevelop\n------\n\nSet up:\n\n.. code:: bash\n\n  $ pip install -r requirements.txt\n\nRun tests:\n\n.. code:: bash\n\n  $ make tests\n",
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        "license": "MIT",
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        "name": "viltolyckor_scraper",
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        "summary": "A scraper of statistical data from viltolyckor.se, built on top of Statscraper.",
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