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/master/doc/source/tutorials.
Make sure to place the data file in the same folder as this script/notebook.
<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)
Show relation of variables¶
In the first example, we show the relation between two variables, biomass and fossil energy use.
Show a scatter plot of meta indicators¶
In the second example, we show the relation between two meta indicators computed from the timeseries data.
Cumulative CO2 emissions¶
The first indicator computes the cumulative CO2 emissions from 2020 until the end of the century.
Temperature at the end of the century¶
The second indicator takes the value of the temperature variable in the latest year and assigns it as a meta indicator.
Draw the scatter plot!¶
df.plot.scatter(x="cumulative_co2", y="temperature", color="scenario")
Total running time of the script: ( 0 minutes 0.996 seconds)