.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/plot_dec_oper_sist.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_plot_dec_oper_sist.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_plot_dec_oper_sist.py:


=============================================
Processamento de dados do dec_oper_sist.csv
=============================================

.. GENERATED FROM PYTHON SOURCE LINES 8-11

O primeiro passo para realizar o processamento do arquivo, assim como os
demais arquivos .csv, é a leitura. Tendo lido o arquivo, é possível recuperar
a versão em que o modelo foi executado.

.. GENERATED FROM PYTHON SOURCE LINES 11-17

.. code-block:: Python

    from idecomp.decomp import DecOperSist

    arq = DecOperSist.read("./decomp/dec_oper_sist.csv")
    print(arq.versao)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    31.14




.. GENERATED FROM PYTHON SOURCE LINES 18-20

A tabela de informações é fornecida como um dataframe, onde os nomes das colunas geralmente
remetem aos nomes originais das colunas do arquivo .csv, porém em camelCase.

.. GENERATED FROM PYTHON SOURCE LINES 20-24

.. code-block:: Python

    df = arq.tabela
    print(df.columns)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Index(['estagio', 'no', 'cenario', 'patamar', 'duracao', 'codigo_submercado',
           'nome_submercado', 'demanda_MW', 'geracao_pequenas_usinas_MW',
           'geracao_termica_MW', 'geracao_termica_antecipada_MW',
           'geracao_hidroeletrica_MW', 'geracao_eolica_MW',
           'energia_bombeamento_MW', 'energia_importada_MW',
           'energia_exportada_MW', 'intercambio_liquido_MW', 'itaipu_50MW',
           'itaipu_60MW', 'deficit_MW', 'ena_MWmes', 'earm_inicial_MWmes',
           'earm_inicial_percentual', 'earm_final_MWmes', 'earm_final_percentual',
           'cmo'],
          dtype='object')




.. GENERATED FROM PYTHON SOURCE LINES 25-28

A partir deste dataframe é possível realizar análises e produzir visualizações. Por exemplo,
utilizando o módulo plotly. Deste ponto em diante, não é mais necessário o conhecimento
específico do arquivo ou da idecomp.

.. GENERATED FROM PYTHON SOURCE LINES 28-38

.. code-block:: Python

    import plotly.express as px

    estagio_mensal = df["estagio"].max()
    # sphinx_gallery_thumbnail_number = 1
    fig = px.box(
        df.loc[df["estagio"] == estagio_mensal],
        x="nome_submercado",
        y="geracao_termica_MW",
    )
    fig





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.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.099 seconds)


.. _sphx_glr_download_examples_plot_dec_oper_sist.py:

.. only:: html

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    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_dec_oper_sist.ipynb <plot_dec_oper_sist.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_dec_oper_sist.py <plot_dec_oper_sist.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_dec_oper_sist.zip <plot_dec_oper_sist.zip>`


.. only:: html

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