SkillTable

SkillTable(data)

Visualize skill metrics.

SkillTable object for visualization and analysis returned by the comparer’s skill method. The object wraps the pd.DataFrame class which can be accessed from the attribute data.

The columns are assumed to be metrics and data for a single metric can be accessed by e.g. s.rmse or s["rmse"]. The resulting object can be used for plotting.

Examples

>>> sk = cc.skill()
>>> sk.mod_names
['SW_1', 'SW_2']
>>> sk.style()
>>> sk.sel(model='SW_1').style()
>>> sk.rmse.plot.bar()

Attributes

Name	Description
metrics	List of metrics (columns) in the SkillTable
mod_names	List of model names (in index)
obs_names	List of observation names (in index)
quantity_names	List of quantity names (in index)

Methods

Name	Description
query	Select a subset of the SkillTable by a query string
round	Round all values in SkillTable
sel	Filter (select) specific models or observations.
sort_index	Sort by index (level) e.g. sorting by observation
sort_values	Sort by values e.g. sorting by rmse values
style	Style SkillTable with colors using pandas style
swaplevel	Swap the levels of the MultiIndex e.g. swapping ‘model’ and ‘observation’
to_dataframe	Convert SkillTable to pd.DataFrame
to_geodataframe	Convert SkillTable to geopandas.GeoDataFrame

query

SkillTable.query(query)

Select a subset of the SkillTable by a query string

wrapping pd.DataFrame.query()

Parameters

Name	Type	Description	Default
query	str	string supported by pd.DataFrame.query()	required

Returns

Name	Type	Description
	SkillTable	A subset of the original SkillTable

Examples

>>> sk = cc.skill()
>>> sk_above_0p3 = sk.query("rmse>0.3")

round

SkillTable.round(decimals=3)

Round all values in SkillTable

Parameters

Name	Type	Description	Default
decimals	int	Number of decimal places to round to (default: 3). If decimals is negative, it specifies the number of positions to the left of the decimal point.	3

Returns

Name	Type	Description
	SkillTable	A new SkillTable with rounded values

sel

SkillTable.sel(query=None, reduce_index=True, **kwargs)

Filter (select) specific models or observations.

Select a subset of the SkillTable by a query, (part of) the index, or specific columns

Parameters

Name	Type	Description	Default
reduce_index	bool	Should unnecessary levels of the index be removed after subsetting? Removed levels will stay as columns. By default True	True
**kwargs	Any	Concrete keys depend on the index names of the SkillTable (from the “by” argument in cc.skill() method) “model”=… to select specific models, “observation”=… to select specific observations	{}

Returns

Name	Type	Description
	SkillTable	A subset of the original SkillTable

Examples

>>> sk = cc.skill()
>>> sk_SW1 = sk.sel(model = "SW_1")
>>> sk2 = sk.sel(observation = ["EPL", "HKNA"])

sort_index

SkillTable.sort_index(*args, **kwargs)

Sort by index (level) e.g. sorting by observation

Wrapping pd.DataFrame.sort_index()

Returns

Name	Type	Description
	SkillTable	A new SkillTable with sorted index

Examples

>>> sk = cc.skill()
>>> sk.sort_index()
>>> sk.sort_index(level="observation")

sort_values

SkillTable.sort_values(*args, **kwargs)

Sort by values e.g. sorting by rmse values

Wrapping pd.DataFrame.sort_values()

Returns

Name	Type	Description
	SkillTable	A new SkillTable with sorted values

Examples

>>> sk = cc.skill()
>>> sk.sort_values("rmse")
>>> sk.sort_values("rmse", ascending=False)
>>> sk.sort_values(["n", "rmse"])

style

SkillTable.style(
    decimals=3,
    metrics=None,
    cmap='OrRd',
    show_best=True,
    **kwargs,
)

Style SkillTable with colors using pandas style

Parameters

Name	Type	Description	Default
decimals	int	Number of decimal places to round to (default: 3).	3
metrics	str or List[str]	apply background gradient color to these columns, by default all; if columns is [] then no background gradient will be applied.	None
cmap	str	colormap of background gradient, by default “OrRd”, except “bias” column which will always be “coolwarm”	'OrRd'
show_best	bool	indicate best of each column by underline, by default True	True

Returns

Name	Type	Description
	pd.Styler	Returns a pandas Styler object.

Examples

>>> sk = cc.skill()
>>> sk.style()
>>> sk.style(precision=1, metrics="rmse")
>>> sk.style(cmap="Blues", show_best=False)

swaplevel

SkillTable.swaplevel(*args, **kwargs)

Swap the levels of the MultiIndex e.g. swapping ‘model’ and ‘observation’

Wrapping pd.DataFrame.swaplevel()

Returns

Name	Type	Description
	SkillTable	A new SkillTable with swapped levels

Examples

>>> sk = cc.skill()
>>> sk.swaplevel().sort_index(level="observation")
>>> sk.swaplevel("model", "observation")
>>> sk.swaplevel(0, 1)

to_dataframe

SkillTable.to_dataframe(drop_xy=True)

Convert SkillTable to pd.DataFrame

Parameters

Name	Type	Description	Default
drop_xy	bool	Drop the x, y coordinates?, by default True	True

Returns

Name	Type	Description
	pd.DataFrame	Skill data as pd.DataFrame

to_geodataframe

SkillTable.to_geodataframe(crs='EPSG:4326')

Convert SkillTable to geopandas.GeoDataFrame

Note: requires geopandas to be installed

Note: requires x and y columns to be present

Parameters

Name	Type	Description	Default
crs	str	Coordinate reference system identifier passed to the GeoDataFrame constructor, by default “EPSG:4326”	'EPSG:4326'

Returns

Name	Type	Description
	gpd.GeoDataFrame	Skill data as GeoDataFrame