Boxplot charts

# sphinx_gallery_thumbnail_number = 2

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

<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)
Meta indicators:
   exclude (bool) False (1)

A boxplot of CO emissions

We generate a simple boxplot of CO2 emissions across one scenario implemented by a range of models.

data = df.filter(
    scenario="CD-LINKS_NPi2020_1000", variable="Emissions|CO2", region="World"
)

data.plot.box(x="year")
plt.tight_layout()
plt.show()

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

A grouped boxplot

We can add sub-groupings of the data using the keyword argument by.

data = df.filter(
    scenario="CD-LINKS_NPi2020_1000",
    variable="Emissions|CO2",
    year=[2010, 2020, 2030, 2050, 2100],
).filter(region="World", keep=False)

data.plot.box(x="year", by="region", legend=True)

# We can use matplotlib arguments to make the figure more appealing.
plt.legend(loc=1)
plt.tight_layout()
plt.show()