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

.. only:: html

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

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

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

.. _sphx_glr_gallery_plot_timeseries.py:


======================
Timeseries data charts
======================

.. GENERATED FROM PYTHON SOURCE LINES 9-19

Read in tutorial data and show a summary
****************************************

This gallery uses the scenario data from the first-steps tutorial.

If you haven't cloned the **pyam** GitHub repository to your machine,
you can download the file from
https://github.com/IAMconsortium/pyam/tree/main/docs/tutorials.

Make sure to place the data file in the same folder as this script/notebook.

.. GENERATED FROM PYTHON SOURCE LINES 19-25

.. code-block:: Python


    import pyam

    df = pyam.IamDataFrame("tutorial_data.csv")
    df





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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/pyam-iamc/checkouts/latest/pyam/utils.py:318: FutureWarning:

    The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.


    <class 'pyam.core.IamDataFrame'>
    Index:
     * model    : AIM/CGE 2.1, GENeSYS-MOD 1.0, ... WITCH-GLOBIOM 4.4 (8)
     * scenario : 1.0, CD-LINKS_INDCi, CD-LINKS_NPi, ... Faster Transition Scenario (8)
    Timeseries data coordinates:
       region   : R5ASIA, R5LAM, R5MAF, R5OECD90+EU, R5REF, R5ROWO, World (7)
       variable : ... (6)
       unit     : EJ/yr, Mt CO2/yr, °C (3)
       year     : 2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, ... 2100 (10)



.. GENERATED FROM PYTHON SOURCE LINES 26-33

A simple line chart
*******************

We show a simple line chart of the regional components
of CO2 emissions for one scenario.

Then, also show the data as a wide IAMC-style dataframe.

.. GENERATED FROM PYTHON SOURCE LINES 33-42

.. code-block:: Python


    model, scenario = "REMIND-MAgPIE 1.7-3.0", "CD-LINKS_INDCi"

    data = df.filter(model=model, scenario=scenario, variable="Emissions|CO2").filter(
        region="World", keep=False
    )

    data.plot(color="region", title="CO2 emissions by region")
    data.timeseries()



.. image-sg:: /gallery/images/sphx_glr_plot_timeseries_001.png
   :alt: CO2 emissions by region
   :srcset: /gallery/images/sphx_glr_plot_timeseries_001.png
   :class: sphx-glr-single-img



.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th>2010</th>
          <th>2020</th>
          <th>2030</th>
          <th>2040</th>
          <th>2050</th>
          <th>2060</th>
          <th>2070</th>
          <th>2080</th>
          <th>2090</th>
          <th>2100</th>
        </tr>
        <tr>
          <th>model</th>
          <th>scenario</th>
          <th>region</th>
          <th>variable</th>
          <th>unit</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="5" valign="top">REMIND-MAgPIE 1.7-3.0</th>
          <th rowspan="5" valign="top">CD-LINKS_INDCi</th>
          <th>R5ASIA</th>
          <th>Emissions|CO2</th>
          <th>Mt CO2/yr</th>
          <td>13319.0940</td>
          <td>17953.8288</td>
          <td>18860.5696</td>
          <td>20919.9611</td>
          <td>21346.1127</td>
          <td>20797.3295</td>
          <td>20655.9721</td>
          <td>20852.5609</td>
          <td>19611.0956</td>
          <td>18684.4507</td>
        </tr>
        <tr>
          <th>R5LAM</th>
          <th>Emissions|CO2</th>
          <th>Mt CO2/yr</th>
          <td>3841.7418</td>
          <td>4100.6996</td>
          <td>3293.7924</td>
          <td>2044.4228</td>
          <td>2348.7068</td>
          <td>2546.6130</td>
          <td>2466.7808</td>
          <td>2357.3265</td>
          <td>2160.2127</td>
          <td>1898.8835</td>
        </tr>
        <tr>
          <th>R5MAF</th>
          <th>Emissions|CO2</th>
          <th>Mt CO2/yr</th>
          <td>3338.4200</td>
          <td>5794.1257</td>
          <td>7223.7294</td>
          <td>8619.6929</td>
          <td>10859.7901</td>
          <td>12689.7547</td>
          <td>13635.7874</td>
          <td>14349.2884</td>
          <td>14149.6996</td>
          <td>13582.5528</td>
        </tr>
        <tr>
          <th>R5OECD90+EU</th>
          <th>Emissions|CO2</th>
          <th>Mt CO2/yr</th>
          <td>13425.7405</td>
          <td>11886.5214</td>
          <td>9499.4507</td>
          <td>8374.9951</td>
          <td>7484.1184</td>
          <td>6456.2150</td>
          <td>5624.2110</td>
          <td>5243.8898</td>
          <td>4656.6352</td>
          <td>4315.5721</td>
        </tr>
        <tr>
          <th>R5REF</th>
          <th>Emissions|CO2</th>
          <th>Mt CO2/yr</th>
          <td>1437.8754</td>
          <td>1692.1186</td>
          <td>1856.6008</td>
          <td>1959.4409</td>
          <td>2085.8475</td>
          <td>2021.2251</td>
          <td>1720.9301</td>
          <td>1374.2122</td>
          <td>1067.1381</td>
          <td>894.9409</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** (0 minutes 0.349 seconds)


.. _sphx_glr_download_gallery_plot_timeseries.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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

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

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

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


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_