Pie chart visualizations

# sphinx_gallery_thumbnail_number = 3

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.

import matplotlib.pyplot as plt

import pyam

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

/home/docs/checkouts/readthedocs.org/user_builds/pyam-iamc/checkouts/stable/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)

A pie chart of the energy supply

We generate a pie plot of all components of primary energy supply for one scenario.

data = df.filter(
    model="AIM/CGE 2.1",
    scenario="CD-LINKS_NPi",
    variable="Primary Energy|*",
    year=2050,
    region="World",
)

data.plot.pie()
plt.tight_layout()
plt.show()

A pie chart with a legend

Sometimes a legend is preferable to labels, so we can use that instead.

data.plot.pie(labels=None, legend=True)
plt.tight_layout()
plt.show()

A pie chart of regional contributions

We don’t just have to plot subcategories of variables, any data or meta indicators from the IamDataFrame can be used. Here, we show the contribution by region to CO2 emissions.

data = df.filter(
    model="AIM/CGE 2.1", scenario="CD-LINKS_NPi", variable="Emissions|CO2", year=2050
).filter(region="World", keep=False)
data.plot.pie(category="region", cmap="tab20")
plt.tight_layout()
plt.show()