.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/plot_stack.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_plot_stack.py: =================== Stacked line charts =================== .. GENERATED FROM PYTHON SOURCE LINES 7-10 .. code-block:: Python # sphinx_gallery_thumbnail_number = 2 .. GENERATED FROM PYTHON SOURCE LINES 11-21 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 21-29 .. code-block:: Python import matplotlib.pyplot as plt import pyam df = pyam.IamDataFrame("tutorial_data.csv") df .. rst-class:: sphx-glr-script-out .. code-block:: none 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 30-32 First, we generate a simple stacked line chart of all components of primary energy supply for one scenario. .. GENERATED FROM PYTHON SOURCE LINES 32-44 .. code-block:: Python model, scenario = "IMAGE 3.0.1", "CD-LINKS_NPi2020_400" data = df.filter( model=model, scenario=scenario, variable="Primary Energy|*", region="World" ) data.plot.stack(title=scenario) plt.legend(loc=1) plt.tight_layout() plt.show() .. image-sg:: /gallery/images/sphx_glr_plot_stack_001.png :alt: CD-LINKS_NPi2020_400 :srcset: /gallery/images/sphx_glr_plot_stack_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/pyam-iamc/checkouts/latest/pyam/plotting.py:466: FutureWarning: The behavior of array concatenation with empty entries is deprecated. In a future version, this will no longer exclude empty items when determining the result dtype. To retain the old behavior, exclude the empty entries before the concat operation. .. GENERATED FROM PYTHON SOURCE LINES 45-48 We don't just have to plot subcategories of variables, any data dimension or meta indicators from the IamDataFrame can be used. Here, we show the contribution by region to total CO2 emissions. .. GENERATED FROM PYTHON SOURCE LINES 48-57 .. code-block:: Python data = df.filter(model=model, scenario=scenario, variable="Emissions|CO2").filter( region="World", keep=False ) data.plot.stack(stack="region", cmap="tab20", title=scenario, total=True) plt.legend(loc=1) plt.tight_layout() plt.show() .. image-sg:: /gallery/images/sphx_glr_plot_stack_002.png :alt: CD-LINKS_NPi2020_400 :srcset: /gallery/images/sphx_glr_plot_stack_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.517 seconds) .. _sphx_glr_download_gallery_plot_stack.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_stack.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_stack.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_