Daniel Huppmann

We had the same issue in the scenario explorer development. The solution there was to get the list of all years with at least one data point and then interpolate...

Thanks @znicholls - see related issue #266 - `stack_plot()` completely removes timeseries that start with zero values. Will see if I can fix that and will update the illustrative dataset...

For completeness, I currently implement this "manually" as ``` def year_of_peak_warming(x): return int(x[x == x.max()].index[0]) df_mean_temperature.set_meta( df_mean_temperature.timeseries().apply(year_of_peak_warming, raw=False, axis=1), 'year of peak warming' ) ```

@gidden @znicholls @Rlamboll, what do you think?

FWIW, the `pyam.IIASA.Connection` class has a [`properties()`](https://pyam-iamc.readthedocs.io/en/stable/api/iiasa.html#pyam.iiasa.Connection.properties) method that allows to check when each scenario was last updated - this could be helpful to check if a local copy of...

Two consideration that make me quite skeptical of option 1: - How would this work if various meta-indicators have different dimensions? Some sort of dictionary or nested pandas.DataFrame objects? -...

I don't see why adding such attributes to the `meta` table would be problematic. In particular if you think about reading data from multiple sources (files) using `append()`, it would...

One option to implement this would be the following ```python xl = pd.ExcelWriter() for m in df.model: df.filter(model=m).to_excel(xl, sheet_name=f"data_{m}", include_meta=False) df.export_meta(xl) xl.close() ``` Problem is that this would still fail...

@gidden @znicholls @byersiiasa @khaeru @Rlamboll - any comments or preferences?

Well, so at least we identified one potential misunderstanding between the different tools! One immediate reaction, though - the current `filter()` in pyam only works per column, so it is...