plotnine.scales.scale.scale_continuous

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

Base class for all continuous 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.

trans : str | function

Name of a trans function or a trans function. See mizani.transforms for possible options.

oob : function

Function to deal with out of bounds (limits) data points. Default is to turn them into np.nan, which then get dropped.

minor_breaks : list-like or int or callable or None

If a list-like, it is the minor breaks points. If an integer, it is the number of minor breaks between any set of major breaks. If a function, it should have the signature func(limits) and return a list-like of consisting of the minor break points. If None, no minor breaks are calculated. The default is to automatically calculate them.

rescaler : function, optional

Function to rescale data points so that they can be handled by the palette. Default is to rescale them onto the [0, 1] range. Scales that inherit from this class may have another default.

Notes

If using the class directly all arguments must be keyword arguments.

static rescaler(x, to=(0, 1), _from=None)

Rescale numeric vector to have specified minimum and maximum.

Parameters:
x : array_like | numeric

1D vector of values to manipulate.

to : tuple

output range (numeric vector of length two)

_from : tuple

input range (numeric vector of length two). If not given, is calculated from the range of x

Returns:
out : array_like

Rescaled values

Examples

>>> x = [0, 2, 4, 6, 8, 10]
>>> rescale(x)
array([0. , 0.2, 0.4, 0.6, 0.8, 1. ])
>>> rescale(x, to=(0, 2))
array([0. , 0.4, 0.8, 1.2, 1.6, 2. ])
>>> rescale(x, to=(0, 2), _from=(0, 20))
array([0. , 0.2, 0.4, 0.6, 0.8, 1. ])
static oob(x, range=(0, 1), only_finite=True)

Convert any values outside of range to a NULL type object.

Parameters:
x : array_like

Values to manipulate

range : tuple

(min, max) giving desired output range

only_finite : bool

If True (the default), will only modify finite values.

Returns:
x : array_like

Censored array

Notes

All values in x should be of the same type. only_finite parameter is not considered for Datetime and Timedelta types.

The NULL type object depends on the type of values in x.

Examples

>>> a = [1, 2, np.inf, 3, 4, -np.inf, 5]
>>> censor(a, (0, 10))
[1, 2, inf, 3, 4, -inf, 5]
>>> censor(a, (0, 10), False)
[1, 2, nan, 3, 4, nan, 5]
>>> censor(a, (2, 4))
[nan, 2, inf, 3, 4, -inf, nan]
train(x)[source]

Train scale

Parameters:
x: pd.series | np.array

a column of data to train over

transform_df(df)[source]

Transform dataframe

transform(x)[source]

Transform array|series x

inverse(x)[source]

Inverse transform array|series x

dimension(expand=(0, 0, 0, 0))[source]

The phyical size of the scale, if a position scale Unlike limits, this always returns a numeric vector of length 2

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.

break_info(range=None)[source]

Return break information for the axis

The range, major breaks & minor_breaks are in transformed space. The labels for the major breaks depict data space values.

get_breaks(limits=None, strict=False)[source]

Generate breaks for the axis or legend

Parameters:
limits : list-like | None

If None the self.limits are used They are expected to be in transformed space.

strict : bool

If True then the breaks gauranteed to fall within the limits. e.g. when the legend uses this method.

Returns:
out : array-like

Notes

Breaks are calculated in data space and returned in transformed space since all data is plotted in transformed space.

get_minor_breaks(major, limits=None)[source]

Return minor breaks

get_labels(breaks=None)[source]

Generate labels for the axis or legend