Encodings

The key to creating meaningful visualizations is to map properties of the data to visual properties in order to effectively communicate information. In Alair, this mapping of visual properties to data columns is referred to as an encoding, and is most often expressed through the Chart.encode() method.

For example, here we will visualize the cars dataset using four of the available encodings: x (the x-axis value), y (the y-axis value), color (the color of the marker), and shape (the shape of the point marker):

import altair as alt
from vega_datasets import data
cars = data.cars()

alt.Chart(cars).mark_point().encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color='Origin',
    shape='Origin'
)

For data specified as a DataFrame, Altair can automatically determine the correct data type for each encoding, and creates appropriate scales and legends to represent the data.

Encoding Channels

Altair provides a number of encoding channels that can be useful in different circumstances; the following table summarizes them:

Position Channels:

Channel	Altair Class	Description	Example
x	X	The x-axis value	Simple Scatter Plot
y	Y	The y-axis value	Simple Scatter Plot
x2	X2	Second x value for ranges	Error Bars showing Confidence Interval
y2	Y2	Second y value for ranges	Line chart with Confidence Interval Band
longitude	Longitude	Longitude for geo charts	Locations of US Airports
latitude	Latitude	Latitude for geo charts	Locations of US Airports
longitude2	Longitude2	Second longitude value for ranges	N/A
latitude2	Latitude2	Second latitude value for ranges	N/A

Mark Property Channels:

Channel	Altair Class	Description	Example
color	Color	The color of the mark	Simple Heatmap
fill	Fill	The fill for the mark	N/A
opacity	Opacity	The opacity of the mark	Horizon Graph
shape	Shape	The shape of the mark	N/A
size	Size	The size of the mark	Table Bubble Plot (Github Punch Card)
stroke	Stroke	The stroke of the mark	N/A

Text and Tooltip Channels:

Channel	Altair Class	Description	Example
text	Text	Text to use for the mark	Simple Scatter Plot with Labels
key	Key	–	N/A
tooltip	Tooltip	The tooltip value	Scatter Plot with Tooltips

Hyperlink Channel:

Channel	Altair Class	Description	Example
href	Href	Hyperlink for points	N/A

Level of Detail Channel:

Channel	Altair Class	Description	Example
detail	Detail	Additional property to group by	Selection Detail Example

Order Channel:

Channel	Altair Class	Description	Example
order	Order	Sets the order of the marks	Connected Scatterplot (Lines with Custom Paths)

Facet Channels:

Channel	Altair Class	Description	Example
column	Column	The column of a faceted plot	Trellis Scatter Plot
row	Row	The row of a faceted plot	Becker’s Barley Trellis Plot

Data Types

The details of any mapping depend on the type of the data. Altair recognizes four main data types:

Data Type	Shorthand Code	Description
quantitative	Q	a continuous real-valued quantity
ordinal	O	a discrete ordered quantity
nominal	N	a discrete unordered category
temporal	T	a time or date value

If types are not specified for data input as a DataFrame, Altair defaults to quantitative for any numeric data, temporal for date/time data, and nominal for string data, but be aware that these defaults are by no means always the correct choice!

The types can either be expressed in a long-form using the channel encoding classes such as X and Y, or in short-form using the Shorthand Syntax discussed below. For example, the following two methods of specifying the type will lead to identical plots:

alt.Chart(cars).mark_point().encode(
    x='Acceleration:Q',
    y='Miles_per_Gallon:Q',
    color='Origin:N'
)

alt.Chart(cars).mark_point().encode(
    alt.X('Acceleration', type='quantitative'),
    alt.Y('Miles_per_Gallon', type='quantitative'),
    alt.Color('Origin', type='nominal')
)

The shorthand form, x="name:Q", is useful for its lack of boilerplate when doing quick data explorations. The long-form, alt.X('name', type='quantitative'), is useful when doing more fine-tuned adjustments to the encoding, such as binning, axis and scale properties, or more.

Specifying the correct type for your data is important, as it affects the way Altair represents your encoding in the resulting plot.

Effect of Data Type on Color Scales

As an example of this, here we will represent the same data three different ways, with the color encoded as a quantitative, ordinal, and nominal type, using three vertically-concatenated charts (see Vertical Concatenation):

base = alt.Chart(cars).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
).properties(
    width=150,
    height=150
)

alt.vconcat(
   base.encode(color='Cylinders:Q').properties(title='quantitative'),
   base.encode(color='Cylinders:O').properties(title='ordinal'),
   base.encode(color='Cylinders:N').properties(title='nominal'),
)

The type specification influences the way Altair, via Vega-Lite, decides on the color scale to represent the value, and influences whether a discrete or continuous legend is used.

Effect of Data Type on Axis Scales

Similarly, for x and y axis encodings, the type used for the data will affect the scales used and the characteristics of the mark. For example, here is the difference between a quantitative and ordinal scale for an column that contains integers specifying a year:

pop = data.population.url

base = alt.Chart(pop).mark_bar().encode(
    alt.Y('mean(people):Q', axis=alt.Axis(title='total population'))
).properties(
    width=200,
    height=200
)

alt.hconcat(
    base.encode(x='year:Q').properties(title='year=quantitative'),
    base.encode(x='year:O').properties(title='year=ordinal')
)

In altair, quantitative scales always start at zero unless otherwise specified, while ordinal scales are limited to the values within the data.

Overriding the behavior of including zero in the axis, we see that even then the precise appearance of the marks representing the data are affected by the data type:

base.encode(
    alt.X('year:Q',
        scale=alt.Scale(zero=False)
    )
)

Because quantitative values do not have an inherent width, the bars do not fill the entire space between the values. This view also makes clear the missing year of data that was not immediately apparent when we treated the years as categories.

This kind of behavior is sometimes surprising to new users, but it emphasizes the importance of thinking carefully about your data types when visualizing data: a visual encoding that is suitable for categorical data may not be suitable for quantitative data, and vice versa.

Encoding Channel Options

Each encoding channel allows for a number of additional options to be expressed; these can control things like axis properties, scale properties, headers and titles, binning parameters, aggregation, sorting, and many more.

The particular options that are available vary by encoding type; the various options are listed below.

The X and Y encodings accept the following options:

Property	Type	Description
aggregate	Aggregate	Aggregation function for the field (e.g., mean, sum, median, min, max, count). Default value: undefined (None)
axis	anyOf(Axis, null)	An object defining properties of axis’s gridlines, ticks and labels. If null, the axis for the encoding channel will be removed. Default value: If undefined, default axis properties are applied.
bin	anyOf(boolean, BinParams)	A flag for binning a quantitative field, or an object defining binning parameters. If true, default binning parameters will be applied. Default value: false
field	anyOf(string, RepeatRef)	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` <https://vega.github.io/vega-lite/docs/repeat.html>`_ operator. Note: 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. Note: field is not required if aggregate is count.
scale	anyOf(Scale, null)	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.
sort	anyOf(array(string), SortOrder, SortField, null)	Sort order for the encoded field. Supported sort values include "ascending", "descending", null (no sorting), or an array specifying the preferred order of values. For fields with discrete domains, sort can also be a sort field definition object. For sort as an array specifying the preferred order of values, the sort order will obey the values in the array, followed by any unspecified values in their original order. Default value: "ascending"
stack	anyOf(StackOffset, null)
timeUnit	TimeUnit	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)
title	[string, null]	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 <compile.html#field-title>`_. 2) If both field definition’s title and axis, header, or legend title are defined, axis/header/legend title will be used.
type	Type	The encoded field’s type of measurement ("quantitative", "temporal", "ordinal", or "nominal"). It can also be a "geojson" type for encoding ‘geoshape’.

The Color, Fill, Opacity, Shape, Size, and Stroke encodings accept the following options:

Property	Type	Description
aggregate	Aggregate	Aggregation function for the field (e.g., mean, sum, median, min, max, count). Default value: undefined (None)
bin	anyOf(boolean, BinParams)	A flag for binning a quantitative field, or an object defining binning parameters. If true, default binning parameters will be applied. Default value: false
condition	anyOf(ConditionalValueDef, array(ConditionalValueDef))	One or more value definition(s) with a selection predicate. Note: A field definition’s condition property can only contain value definitions since Vega-Lite only allows at most one encoded field per encoding channel.
field	anyOf(string, RepeatRef)	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` <https://vega.github.io/vega-lite/docs/repeat.html>`_ operator. Note: 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. Note: field is not required if aggregate is count.
legend	anyOf(Legend, null)	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.
scale	anyOf(Scale, null)	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.
sort	anyOf(array(string), SortOrder, SortField, null)	Sort order for the encoded field. Supported sort values include "ascending", "descending", null (no sorting), or an array specifying the preferred order of values. For fields with discrete domains, sort can also be a sort field definition object. For sort as an array specifying the preferred order of values, the sort order will obey the values in the array, followed by any unspecified values in their original order. Default value: "ascending"
timeUnit	TimeUnit	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)
title	[string, null]	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 <compile.html#field-title>`_. 2) If both field definition’s title and axis, header, or legend title are defined, axis/header/legend title will be used.
type	Type	The encoded field’s type of measurement ("quantitative", "temporal", "ordinal", or "nominal"). It can also be a "geojson" type for encoding ‘geoshape’.

The Row and Column encodings accept the following options:

Property	Type	Description
aggregate	Aggregate	Aggregation function for the field (e.g., mean, sum, median, min, max, count). Default value: undefined (None)
bin	anyOf(boolean, BinParams)	A flag for binning a quantitative field, or an object defining binning parameters. If true, default binning parameters will be applied. Default value: false
field	anyOf(string, RepeatRef)	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` <https://vega.github.io/vega-lite/docs/repeat.html>`_ operator. Note: 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. Note: field is not required if aggregate is count.
header	Header	An object defining properties of a facet’s header.
sort	SortOrder	Sort order for a facet field. This can be "