pandas-indexing is a helpers package to make interacting with pandas multi-indices less painful. It contains functions, that

• filter easily into multi indices: isin, ismatch
• add or update levels in a multiindex: assignlevel
• select one or multiple specific levels: projectlevel

Given you have a time-series like dataframe with several multi index levels, like model, scenario, variable: then you can select a subset with:

df.loc[isin(model="m1", scenario=["s1", "s2"])]

or with shell like glob-patterns:

df.loc[
    ismatch(
        model="REMIND*", variable="Emissions|**", unit=["Mt CO2/yr", "kt N2O/yr"]
    )
]

You can overwrite index levels:

assignlevel(df, selected=1)

or project your data to only a few desired index levels:

projectlevel(df, ["model", "scenario"])

All commands are described in detail in the API reference in documentation and are togehter with introductions for installing and using this package, but they are mostly bare-bones atm.

As usual for any GitHub-based project, raise an issue if you find any bug or want to suggest an improvement, or open a discussion if you want to discuss.