General functions¶
- pyam.concat(objs, ignore_meta_conflict=False, **kwargs)[source]¶
Concatenate a series of IamDataFrame-like objects
- Parameters:
- objsiterable of IamDataFrames
A list of objects castable to
IamDataFrame
- ignore_meta_conflictbool, optional
If False, raise an error if any meta columns present in dfs are not identical. If True, values in earlier elements of dfs take precedence.
- 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.
Notes
The meta attributes are merged only for those objects of objs that are passed as
IamDataFrame
instances.The
dimensions
andindex
names of all elements of dfs must be identical. The returned IamDataFrame inherits the dimensions and index names.
- 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
IamDataFrame
instances 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()