Metadata-Version: 2.1
Name: brazil-monthly-deaths
Version: 1.0.0
Summary: Brazil deaths by city as pandas dataframe or csv file
Home-page: https://github.com/pedrobern/brazil-monthly-deaths-by-city
Author: pedrobern
Author-email: pedrobermoreira@gmail.com
Maintainer: pedrobern
License: MIT
Project-URL: Documentation, https://github.com/pedrobern/brazil-monthly-deaths-by-city
Project-URL: Issues, https://github.com/pedrobern/brazil-monthly-deaths-by-city/issues
Description: Brazil Monthly Deaths by City
        =============================
        
        Web Scraping Package of Brazil Deaths.
        
        Installation
        ------------
        
        First install the package:
        
        .. code:: bash
        
            pip install brazil_monthly_deaths
        
        Then install the chrome driver in order to use selenium, you can see
        more information in the `selenium
        documentation <https://selenium-python.readthedocs.io/installation.html#drivers>`__
        and the `chrome driver download
        page <https://sites.google.com/a/chromium.org/chromedriver/downloads>`__.
        
        Usage
        -----
        
            Assuming you have installed the chrome driver
        
        .. code:: python
        
            from brazil_monthly_deaths import brazil_deaths, data, update_df
        
            # data is the data from 2015 to 2020
            print(data)
        
            # Everyday there are new records,
            # so you should get the most recent data.
            # Depending on your internet connection
            # it may take up to 6 minutes for each month
            # if you run for all states. Consider selecting
            # only the states you want to work on.
            new_data = brazil_deaths(years=[2020], months=[5])
        
            # update the lagging data provided by this package
            current_data = update_df(data, new_data)
            print(current_data)
        
        Data example
        ------------
        
        +------------+--------+---------+-------------+------------------+---------------+----------+
        | city\_id   | year   | month   | region      | state            | city          | deaths   |
        +============+========+=========+=============+==================+===============+==========+
        | 3516805    | 2020   | 1       | Southeast   | Rio de Janeiro   | Tracunhaém    | 8        |
        +------------+--------+---------+-------------+------------------+---------------+----------+
        | 21835289   | 2020   | 1       | Southeast   | Rio de Janeiro   | Trindade      | 13       |
        +------------+--------+---------+-------------+------------------+---------------+----------+
        | 10791950   | 2020   | 1       | Southeast   | Rio de Janeiro   | Triunfo       | 16       |
        +------------+--------+---------+-------------+------------------+---------------+----------+
        | 81875827   | 2020   | 1       | Southeast   | Rio de Janeiro   | Tupanatinga   | 18       |
        +------------+--------+---------+-------------+------------------+---------------+----------+
        | 99521011   | 2020   | 1       | Southeast   | Rio de Janeiro   | Tuparetama    | 4        |
        +------------+--------+---------+-------------+------------------+---------------+----------+
        
        --------------
        
        API
        ---
        
        Dataframes
        ~~~~~~~~~~
        
        This package exports some
        `pandas <https://github.com/pandas-dev/pandas>`__ dataframe with the
        following columns:
        
        -  city\_id : unique integer from state and city,
        -  year : from 2015 to 2020,
        -  month : from 1 to 12,
        -  region : [North, Northeast, South, Southeast, Center\_West],
        -  state : one of the 27 states of Brazil, including country capital,
        -  city : city name
        -  deaths : number os deaths
        
        .. code:: python
        
            from brazil_monthly_deaths import (
              data, # full data
              data_2015,
              data_2016,
              data_2017,
              data_2018,
              data_2019,
              data_2020 # always out of date, you need to update it
            )
        
        brazil\_deaths
        ~~~~~~~~~~~~~~
        
        You can use this function to scrap new data directly from the `Civil
        Registry Offices
        website <https://transparencia.registrocivil.org.br/registros>`__. Just
        make sure you have installed the chrome driver, as pointed above.
        
        Oficial note about the legal deadlines:
        
            The family has up to 24 hours after the death to register the death
            in the Registry, which, in turn, has up to five days to perform the
            death registration, and then up to eight days to send the act done
            to the National Information Center of the Civil Registry ( CRC
            Nacional), which updates this platform.
        
        It means: **The last 13 days are always changing.**
        
        .. code:: python
        
            from brazil_monthly_deaths import brazil_deaths
        
        Since it will access an external website, it will depend on your
        internet connection and world location. Consider selecting only the
        ``states`` you want to work on. For each month, for all states it may
        take up to 6 min to run for a single year.
        
        .. code:: python
        
            df = brazil_deaths(
                years=[2015, 2016, 2017, 2018, 2019, 2020],
                months=range(1, 13, 1),
                regions=_regions_names,
                states=_states,
                filename="data",
                return_df=True,
                save_csv=True,
                verbose=True,
                *args,
                **kwargs
            )  
        
        The ``_regions_names`` is:
        
        .. code:: python
        
            ["North", "Northeast", "South", "Southeast", "Center_West"]
        
        The ``_states`` is:
        
        .. code:: python
        
            [
              "Acre", "Amazonas", "Amapá", "Pará", 
              "Rondônia", "Roraima", "Tocantins", "Paraná",
              "Rio Grande do Sul", "Santa Catarina", "Espírito Santo",
              "Minas Gerais", "Rio de Janeiro", "São Paulo",
              "Distrito Federal", "Goiás", "Mato Grosso do Sul",
              "Mato Grosso", "Alagoas", "Bahia", "Ceará",
              "Maranhão", "Paraíba", "Pernambuco",
              "Piauí", "Rio Grande do Norte", "Sergipe"
            ]
        
        The ``*args`` and ``**kwargs`` are passed down to
        ``df.to_csv(..., *args, **kwargs)``
        
        update\_df
        ~~~~~~~~~~
        
        Use this function after you have scraped recent data from the Civil
        Registry Offices website to update the data provided in this package.
        
        .. code:: python
        
            from brazil_monthly_deaths import brazil_deaths, data, update_df
        
            new_data = brazil_deaths(years=[2020], months=[5])
            current_data = update_df(data, new_data)
        
        It basically put the new data below the old data in the dataframe, then
        remove the duplicates (excluding deaths) keeping the most recent
        entries.
        
Keywords: brazil death rate data science
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/x-rst
Provides-Extra: test
Provides-Extra: dev
