Encodings¶
The key to creating meaningful visualizations is to map properties of the data
to visual properties in order to effectively communicate information.
Altair abstracts this mapping through the idea of channel encodings.
For example, here we will plot the cars dataset using four of the available
channel 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.
Channels¶
Altair provides a number of encoding channels that can be useful in different circumstances; the following table summarizes them:
TODO: link to examples of each
Position Channels:
| Channel | Altair Class | Description | Example |
|---|---|---|---|
| x | X |
The x-axis value | |
| y | Y |
The y-axis value | |
| x2 | X2 |
Second x value for ranges | |
| y2 | Y2 |
Second y value for ranges | |
| longitude | Longitude |
Longitude for geo charts | |
| latitude | Latitude |
Latitude for geo charts | |
| longitude2 | Longitude2 |
Second longitude value for ranges | |
| latitude2 | Latitude2 |
Second latitude value for ranges |
Mark Property Channels:
| Channel | Altair Class | Description | Example |
|---|---|---|---|
| color | Color |
The color of the mark | |
| fill | Fill |
The fill for the mark | |
| opacity | Opacity |
The opacity of the mark | |
| shape | Shape |
The shape of the mark | |
| size | Size |
The size of the mark | |
| stroke | Stroke |
The stroke of the mark |
Text and Tooltip Channels:
| Channel | Altair Class | Description | Example |
|---|---|---|---|
| text | Text |
Text to use for the mark | |
| key | Key |
– | |
| tooltip | Tooltip |
The tooltip value |
Hyperlink Channel:
| Channel | Altair Class | Description | Example |
|---|---|---|---|
| href | Href |
Hyperlink for points |
Level of Detail Channel:
| Channel | Altair Class | Description | Example |
|---|---|---|---|
| detail | Detail |
Additional property to group by |
Order Channels:
| Channel | Altair Class | Description | Example |
|---|---|---|---|
| order | Order |
Sets the order of the marks |
Facet Channels:
| Channel | Altair Class | Description | Example |
|---|---|---|---|
| column | Column |
The column of a faceted plot | |
| row | Row |
The row of a faceted 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 vconcat):
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.
Binning and Aggregation¶
Beyond simple channel encodings, Altair’s visualizations are built on the concept of the database-style grouping and aggregation; that is, the split-apply-combine abstraction that underpins many data analysis approaches.
For example, building a histogram from a one-dimensional dataset involves splitting data based on the bin it falls in, aggregating the results within each bin using a count of the data, and then combining the results into a final figure.
In Altair, such an operation looks like this:
alt.Chart(cars).mark_bar().encode(
alt.X('Horsepower', bin=True),
y='count()'
# could also use alt.Y(aggregate='count', type='quantitative')
)