plotnine.scales.scale.scale

class plotnine.scales.scale.scale(**kwargs)[source]

Base class for all scales

Parameters:
breaks : array_like or callable, optional

Major break points. Alternatively, a callable that takes a tuple of limits and returns a list of breaks. Default is to automatically calculate the breaks.

expand : array_like, optional

Multiplicative and additive expansion constants that determine how the scale is expanded. If specified must of of length 2 or 4. Specifically the the values are of this order:

(mul, add)
(mul_low, add_low, mul_high, add_high)

If not specified, suitable defaults are chosen.

name : str, optional

Name used as the label of the scale. This is what shows up as the axis label or legend title. Suitable defaults are chosen depending on the type of scale.

labels : list or callable, optional

List of str. Labels at the breaks. Alternatively, a callable that takes an array_like of break points as input and returns a list of strings.

limits : array_like, optional

Limits of the scale. Most commonly, these are the min & max values for the scales. For scales that deal with categoricals, these may be a subset or superset of the categories.

na_value : scalar

What value to assign to missing values. Default is to assign np.nan.

palette : callable, optional

Function to map data points onto the scale. Most scales define their own palettes.

aesthetics : list, optional

list of str. Aesthetics covered by the scale. These are defined by each scale and the user should probably not change them. Have fun.

expand = waiver()

multiplicative and additive expansion constants

static palette(x)[source]

Aesthetic mapping function

map(x, limits=None)[source]

Map every element of x

The palette should do the real work, this should make sure that sensible values are sent and return from the palette.

train(x)[source]

Train scale

Parameters:
x: pd.series | np.array

a column of data to train over

dimension(expand=None)[source]

The phyical size of the scale.

transform_df(df)[source]

Transform dataframe

transform(x)[source]

Transform array|series x

inverse(x)[source]

Inverse transform array|series x

reset()[source]

Set the range of the scale to None.

i.e Forget all the training

train_df(df)[source]

Train scale from a dataframe

map_df(df)[source]

Map df