General functions¶
- pyam.concat(dfs, ignore_meta_conflict=False, **kwargs)[source]¶
Concatenate a series of IamDataFrame-like objects
- Parameters
- dfsiterable of IamDataFrames
A list of objects castable to
IamDataFrame- ignore_meta_conflictbool, default False
If False, raise an error if any meta columns present in dfs are not identical. If True, values in earlier elements of dfs take precendence.
- kwargs
Passed to
IamDataFrame(other, **kwargs)for any item of dfs which isn’t already an IamDataFrame.
- Returns
- IamDataFrame
- Raises
- TypeError
If dfs is not a list.
- ValueError
If time domain or other timeseries data index dimension don’t match.
- pyam.compare(left, right, left_label='left', right_label='right', drop_close=True, **kwargs)[source]¶
Compare the data in two IamDataFrames and return a pandas.DataFrame
- Parameters
- left, rightIamDataFrames
two
IamDataFrameinstances to be compared- left_label, right_labelstr, default left, right
column names of the returned
pandas.DataFrame- drop_closebool, optional
remove all data where left and right are close
- kwargsarguments for comparison of values
passed to
numpy.isclose()
- pyam.require_variable(df, variable, unit=None, year=None, exclude_on_fail=False, **kwargs)[source]¶
Check whether all scenarios have a required variable
- Parameters
- dfIamDataFrame
- argspassed to
IamDataFrame.require_variable() - kwargsused for downselecting IamDataFrame
passed to
IamDataFrame.filter()
- pyam.validate(df, criteria={}, exclude_on_fail=False, **kwargs)[source]¶
Validate scenarios using criteria on timeseries values
Returns all scenarios which do not match the criteria and prints a log message or returns None if all scenarios match the criteria.
When called with exclude_on_fail=True, scenarios in df not satisfying the criteria will be marked as exclude=True (object modified in place).
- Parameters
- dfIamDataFrame
- argspassed to
IamDataFrame.validate() - kwargsused for downselecting IamDataFrame
passed to
IamDataFrame.filter()
- pyam.categorize(df, name, value, criteria, color=None, marker=None, linestyle=None, **kwargs)[source]¶
Assign scenarios to a category according to specific criteria or display the category assignment
- Parameters
- dfIamDataFrame
- argspassed to
IamDataFrame.categorize() - kwargsused for downselecting IamDataFrame
passed to
IamDataFrame.filter()
- pyam.check_aggregate(df, variable, components=None, exclude_on_fail=False, multiplier=1, **kwargs)[source]¶
Check whether the timeseries values match the aggregation of sub-categories
- Parameters
- dfIamDataFrame
- argspassed to
IamDataFrame.check_aggregate() - kwargsused for downselecting IamDataFrame
passed to
IamDataFrame.filter()
