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        "author": "Agustin Benassi",
        "author_email": "agusbenassi@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Topic :: Office/Business"
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        "description": "xlseries\n========\n\nA python package to scrape `time\nseries <https://en.wikipedia.org/wiki/Time_series>`__ from *any* excel\nfile and return them turned into `pandas <http://pandas.pydata.org/pandas-docs/dev/index.html>`__ `data frames <http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.html>`__.\n\nInstallation\n------------\n\nIf you want to install in developer mode, `clone the repository <https://github.com/abenassi/xlseries.git>`__ and follow these instructions:\n\n**If you are using Anaconda as your python distribution**\n\n1. ``conda create -n xlseries python=2`` *Create new environment*\n2. ``cd project_directory``\n3. ``source activate xlseries`` *Activate the environment*\n4. ``pip install -e .`` *Install the package in developer mode*\n5. ``pip install -r requirements.txt`` *Install dependencies*\n6. ``deactivate`` *Deactivate when you are done*\n\n**If you are using a standard python installation**\n\n1. ``cd project_directory``\n2. ``virtualenv venv`` *Create new environment*\n3. ``source venv/bin/activate`` *Activate the environment*\n4. ``pip install -r requirements.txt`` *Install dependencies*\n5. ``deactivate`` *Deactivate when you are done*\n\nIf you just want to use it:\n\n``pip install xlseries`` in your environment, instead of cloning and pip\ninstalling in developer mode.\n\nQuick start\n-----------\n\n.. code:: python\n\n    from xlseries import XlSeries\n    xl = XlSeries(\"path_to_excel_file\" or openpyxl.Workbook instance)\n    dfs = xl.get_data_frames(\"path_to_json_parameters\" or parameters_dictionary)\n\nWith the test case number 1:\n\n.. code:: python\n\n    from xlseries import XlSeries\n    from xlseries.utils.path_finders import get_orig_cases_path, get_param_cases_path\n\n    # this will only work if you clone the repo with all the test files\n    path_to_excel_file = get_orig_cases_path(1)\n    path_to_json_parameters = get_param_cases_path(1)\n\n    xl = XlSeries(path_to_excel_file)\n    dfs = series.get_data_frames(path_to_json_parameters)\n\nor passing only the critical parameters as a dictionary:\n\n.. code:: python\n\n    parameters_dictionary = {\n        \"headers_coord\": [\"B1\",\"C1\"],\n        \"data_starts\": 2,\n        \"frequency\": \"M\",\n        \"time_header_coord\": \"A1\"\n    }\n    dfs = xl.get_data_frames(parameters_dictionary)\n\nyou can specify what worksheet you want to scrape (otherwise the first\none will be used):\n\n.. code:: python\n\n    dfs = xl.get_data_frames(parameters_dictionary, ws_name=\"my_worksheet\")\n\nyou can ask an XlSeries object for a template dictionary of the critical\nparameters you need to fill:\n\n.. code:: python\n\n    >>> params = xl.critical_params_template()\n    >>> params\n    {'data_starts': 2,\n     'frequency': 'M',\n     'headers_coord': ['B1', 'C1', 'E1-G1'],\n     'time_header_coord': 'A1'}\n    >>> params[\"headers_coord\"] = [\"B1\",\"C1\"]\n    >>> dfs = xl.get_data_frames(params, ws_name=\"my_worksheet\")\n\nif this doesn't work and you want to see exactly where the scraping is\nfailing, you may want to fill out all the parameters and try again to\nsee where the exception is raised:\n\n.. code:: python\n\n    >>> params = xl.complete_params_template()\n    >>> params\n    {'alignment': u'vertical',\n     'blank_rows': False,\n     'continuity': True,\n     'data_ends': None,\n     'data_starts': 2,\n     'frequency': 'M',\n     'headers_coord': ['B1', 'C1', 'E1-G1'],\n     'missing_value': [None, '-', '...', '.', ''],\n     'missings': False,\n     'series_names': None,\n     'time_alignment': 0,\n     'time_composed': False,\n     'time_header_coord': 'A1',\n     'time_multicolumn': False}\n    >>> params[\"headers_coord\"] = [\"B1\",\"C1\"]\n    >>> params[\"data_ends\"] = 256\n    >>> params[\"missings\"] = True\n    >>> dfs = xl.get_data_frames(params, ws_name=\"my_worksheet\")\n\n-  **Excel file**: Up to this development point the excel file should\n   not be more *complicated* than the `7 test cases <https://github.com/abenassi/xlseries#test-cases>`__:\n\n-  **Parameters**: Together with the excel file, some parameters about\n   the series must be provided. These could be passed to\n   get\\_data\\_frames() as a path to a *JSON file* or as a *python\n   dictionary*. ``xlseries`` use about 14 parameters to characterize the\n   time series of a spreadsheet, but only 4 of them are *critical* most\n   of the time: the rest can be guessed by the package. The only\n   difference between specifying more or less parameters than the 4\n   critical is the total time that ``xlseries`` will need to complete\n   the task (more parameters, less time). Go to the `parameters <https://github.com/abenassi/xlseries#parameters>`__ section for a more detailed\n   explanation about how to use them, and when you need to specify more\n   than the basic 4 (``headers_coord``, ``data_starts``, ``frequency``\n   and ``time_header_coord``).\n\nTake a look to this `ipython notebook\ntemplate <https://github.com/abenassi/xlseries/blob/master/docs/notebooks/Example%20use%20case.ipynb>`__ to get started!.\n\nIf you want to dig inside the test cases and get an idea of how far is\ngoing ``xlseries`` at the moment, check out this `ipython notebook with\nthe 7 test cases <https://github.com/abenassi/xlseries/blob/master/docs/notebooks/Test%20cases.ipynb>`__.\n\nFor more details go to the official repository on github:\nhttps://github.com/abenassi/xlseries\n",
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