altair.StrokeWidth

class altair.StrokeWidth(shorthand=Undefined, aggregate=Undefined, bin=Undefined, condition=Undefined, field=Undefined, legend=Undefined, scale=Undefined, sort=Undefined, timeUnit=Undefined, title=Undefined, type=Undefined, **kwds)

StrokeWidth schema wrapper

Mapping(required=[shorthand]) A FieldDef with Condition

Attributes
shorthandstring

shorthand for field, aggregate, and type

aggregateAggregate

Aggregation function for the field (e.g., "mean", "sum", "median", "min", "max", "count" ).

Default value: undefined (None)

See also: aggregate documentation.

binanyOf(boolean, BinParams, None)

A flag for binning a quantitative field, an object defining binning parameters, or indicating that the data for x or y channel are binned before they are imported into Vega-Lite ( "binned" ).

If true, default binning parameters will be applied.

If "binned", this indicates that the data for the x (or y ) channel are already binned. You can map the bin-start field to x (or y ) and the bin-end field to x2 (or y2 ). The scale and axis will be formatted similar to binning in Vega-Lite. To adjust the axis ticks based on the bin step, you can also set the axis’s tickMinStep property.

Default value: false

See also: bin documentation.

conditionValueConditionnumber

One or more value definition(s) with a selection or a test predicate.

Note: A field definition’s condition property can only contain conditional value definitions since Vega-Lite only allows at most one encoded field per encoding channel.

fieldField

Required. A string defining the name of the field from which to pull a data value or an object defining iterated values from the repeat operator.

See also: field documentation.

Notes: 1) Dots ( . ) and brackets ( [ and ] ) can be used to access nested objects (e.g., "field": "foo.bar" and "field": "foo['bar']" ). If field names contain dots or brackets but are not nested, you can use \ to escape dots and brackets (e.g., "a\.b" and "a\[0\]" ). See more details about escaping in the field documentation. 2) field is not required if aggregate is count.

legendanyOf(Legend, None)

An object defining properties of the legend. If null, the legend for the encoding channel will be removed.

Default value: If undefined, default legend properties are applied.

See also: legend documentation.

scaleanyOf(Scale, None)

An object defining properties of the channel’s scale, which is the function that transforms values in the data domain (numbers, dates, strings, etc) to visual values (pixels, colors, sizes) of the encoding channels.

If null, the scale will be disabled and the data value will be directly encoded.

Default value: If undefined, default scale properties are applied.

See also: scale documentation.

sortSort

Sort order for the encoded field.

For continuous fields (quantitative or temporal), sort can be either "ascending" or "descending".

For discrete fields, sort can be one of the following:

Default value: "ascending"

Note: null and sorting by another channel is not supported for row and column.

See also: sort documentation.

timeUnitanyOf(TimeUnit, TimeUnitParams)

Time unit (e.g., year, yearmonth, month, hours ) for a temporal field. or a temporal field that gets casted as ordinal.

Default value: undefined (None)

See also: timeUnit documentation.

titleanyOf(Text, None)

A title for the field. If null, the title will be removed.

Default value: derived from the field’s name and transformation function ( aggregate, bin and timeUnit ). If the field has an aggregate function, the function is displayed as part of the title (e.g., "Sum of Profit" ). If the field is binned or has a time unit applied, the applied function is shown in parentheses (e.g., "Profit (binned)", "Transaction Date (year-month)" ). Otherwise, the title is simply the field name.

Notes :

1) You can customize the default field title format by providing the fieldTitle property in the config or fieldTitle function via the compile function’s options.

2) If both field definition’s title and axis, header, or legend title are defined, axis/header/legend title will be used.

typeStandardType

The encoded field’s type of measurement ( "quantitative", "temporal", "ordinal", or "nominal" ). It can also be a "geojson" type for encoding ‘geoshape’.

Note:

  • Data values for a temporal field can be either a date-time string (e.g., "2015-03-07 12:32:17", "17:01", "2015-03-16". "2015" ) or a timestamp number (e.g., 1552199579097 ).

  • Data type describes the semantics of the data rather than the primitive data types (number, string, etc.). The same primitive data type can have different types of measurement. For example, numeric data can represent quantitative, ordinal, or nominal data.

  • When using with bin, the type property can be either "quantitative" (for using a linear bin scale) or “ordinal” (for using an ordinal bin scale).

  • When using with timeUnit, the type property can be either "temporal" (for using a temporal scale) or “ordinal” (for using an ordinal scale).

  • When using with aggregate, the type property refers to the post-aggregation data type. For example, we can calculate count distinct of a categorical field "cat" using {"aggregate": "distinct", "field": "cat", "type": "quantitative"}. The "type" of the aggregate output is "quantitative".

  • Secondary channels (e.g., x2, y2, xError, yError ) do not have type as they have exactly the same type as their primary channels (e.g., x, y ).

See also: type documentation.

__init__(self, shorthand=Undefined, aggregate=Undefined, bin=Undefined, condition=Undefined, field=Undefined, legend=Undefined, scale=Undefined, sort=Undefined, timeUnit=Undefined, title=Undefined, type=Undefined, **kwds)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self[, shorthand, aggregate, bin, …])

Initialize self.

copy(self[, deep, ignore])

Return a copy of the object

from_dict(dct[, validate, _wrapper_classes])

Construct class from a dictionary representation

from_json(json_string[, validate])

Instantiate the object from a valid JSON string

resolve_references([schema])

Resolve references in the context of this object’s schema or root schema.

to_dict(self[, validate, ignore, context])

to_json(self[, validate, ignore, context, …])

Emit the JSON representation for this object as a string.

validate(instance[, schema])

Validate the instance against the class schema in the context of the rootschema.

validate_property(name, value[, schema])

Validate a property against property schema in the context of the rootschema
copy(self, deep=True, ignore=())

Return a copy of the object

Parameters
deepboolean or list, optional

If True (default) then return a deep copy of all dict, list, and SchemaBase objects within the object structure. If False, then only copy the top object. If a list or iterable, then only copy the listed attributes.

ignorelist, optional

A list of keys for which the contents should not be copied, but only stored by reference.

classmethod from_dict(dct, validate=True, _wrapper_classes=None)

Construct class from a dictionary representation

Parameters
dctdictionary

The dict from which to construct the class

validateboolean

If True (default), then validate the input against the schema.

_wrapper_classeslist (optional)

The set of SchemaBase classes to use when constructing wrappers of the dict inputs. If not specified, the result of cls._default_wrapper_classes will be used.

Returns
objSchema object

The wrapped schema

Raises
jsonschema.ValidationError :

if validate=True and dct does not conform to the schema

classmethod from_json(json_string, validate=True, **kwargs)

Instantiate the object from a valid JSON string

Parameters
json_stringstring

The string containing a valid JSON chart specification.

validateboolean

If True (default), then validate the input against the schema.

**kwargs :

Additional keyword arguments are passed to json.loads

Returns
chartChart object

The altair Chart object built from the specification.

classmethod resolve_references(schema=None)

Resolve references in the context of this object’s schema or root schema.

to_json(self, validate=True, ignore=[], context={}, indent=2, sort_keys=True, **kwargs)

Emit the JSON representation for this object as a string.

Parameters
validateboolean or string

If True (default), then validate the output dictionary against the schema. If “deep” then recursively validate all objects in the spec. This takes much more time, but it results in friendlier tracebacks for large objects.

ignorelist

A list of keys to ignore. This will not passed to child to_dict function calls.

contextdict (optional)

A context dictionary that will be passed to all child to_dict function calls

indentinteger, default 2

the number of spaces of indentation to use

sort_keysboolean, default True

if True, sort keys in the output

**kwargs

Additional keyword arguments are passed to json.dumps()

Returns
specstring

The JSON specification of the chart object.

classmethod validate(instance, schema=None)

Validate the instance against the class schema in the context of the rootschema.

classmethod validate_property(name, value, schema=None)

Validate a property against property schema in the context of the rootschema