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        "author": "Rafael Alves Ribeiro",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": ".. image:: https://img.shields.io/pypi/v/sgs.svg\n    :target: https://pypi.org/project/sgs/\n\n.. image:: https://img.shields.io/pypi/l/sgs.svg\n    :target: https://pypi.org/project/sgs/\n\n.. image:: https://img.shields.io/pypi/pyversions/sgs.svg\n    :target: https://pypi.org/project/sgs/\n\n.. image:: https://img.shields.io/pypi/dm/sgs.svg\n    :target: https://pypi.org/project/sgs/\n\n.. image:: https://img.shields.io/travis/rafpyprog/pysgs.svg\n    :target: https://travis-ci.org/rafpyprog/pySGS/\n\n.. image:: https://img.shields.io/codecov/c/github/rafpyprog/pysgs.svg\n    :target: https://codecov.io/github/rafpyprog/pysgs\n    :alt: codecov.io\n\n.. image:: https://img.shields.io/readthedocs/pysgs.svg\n    :target: https://pysgs.readthedocs.io/en/stable/\n    :alt: Read the docs!\n\n|pic 1| **SGS**\n=================\n\n.. |pic 1| image:: https://raw.githubusercontent.com/rafpyprog/sgs/master/icon.png\n\nIntroduction\n------------\nThis library provides a pure Python interface for the Brazilian Central Bank's\n`Time Series Management System (SGS) <https://www.bcb.gov.br/?sgs>`_  api.\nIt works with Python 3.5 and above.\n\nSGS is a service with more than 18,000 time series with economical and financial information.\nThis library is intended to make it easier for Python programmers to use this data in projects of\nany kind, providing mechanisms to search for, extract and join series.\n\n\nQuickstart\n----------\nAccess time series data with **sgs** is very simple\n\nBegin by importing the ``sgs`` module:\n\n\n.. code-block:: python\n\n    import sgs\n\n\nNow, let's try to get a time serie. For this example, let's get the\n\"Interest rate - CDI\" time serie in 2018, wich has the code 12.\n\n\n.. code-block:: python\n\n    CDI_CODE = 12\n    ts = sgs.time_serie(CDI_CODE, start='02/01/2018', end='31/12/2018')\n\n\nNow, we have a Pandas Series object called ``ts``, with all the data and\nthe index representing the dates.\n\n.. code-block:: python\n\n    ts.head()\n\n+------------+----------+\n| 2018-01-02 | 0.026444 |\n+------------+----------+\n| 2018-01-03 | 0.026444 |\n+------------+----------+\n| 2018-01-04 | 0.026444 |\n+------------+----------+\n| 2018-01-05 | 0.026444 |\n+------------+----------+\n| 2018-01-08 | 0.026444 |\n+------------+----------+\n\nFeature Suport\n--------------\n\n* Get time serie data with an one-liner using ``sgs.time_serie``\n* Create a dataframe from a list of time series codes with ``sgs.dataframe``\n* Search time series by text or code with ``sgs.search_ts``\n* Get metadata from all the series in a dataframe using ``sgs.metadata``\n* Support to search and metadata in English and Portuguese\n* Automatic retry\n* Automatic cached requests\n\n\nInstallation\n------------\nTo install, simply use pip:\n\n.. code-block:: bash\n\n    $ pip install sgs\n\nDocumentation\n-------------\n\nComplete documentation is available at https://pysgs.readthedocs.io/en/stable/.\n\n\n",
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