### Basic HTML Template for Embedding
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
An example of a basic HTML template that can be used to embed an Altair/Vega-Lite chart.
```html
```
--------------------------------
### Shorthand Syntax for Quantitative Data
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
Illustrates the shorthand syntax for specifying a quantitative data type in an encoding channel.
```python
y = alt.Y('average(b):Q')
print(y.to_json())
```
--------------------------------
### Saving a Chart to an HTML File
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
Provides an example of how to save an Altair chart to a stand-alone HTML file.
```python
chart = alt.Chart(data).mark_bar().encode(
x='a',
y='average(b)',
)
chart.save('chart.html')
```
--------------------------------
### Geo Line Example Setup
Source: https://github.com/vega/altair/blob/main/doc/user_guide/marks/line.rst
Setup for drawing lines through geographic points by mapping coordinate data to longitude and latitude channels with a projection.
```python
import altair as alt
from altair.datasets import data
import pandas as pd
airports = data.airports.url
flights_airport = data.flights_airport.url
```
--------------------------------
### Inspecting JSON Output
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
Shows how to use the `to_json` method to inspect the Vega-Lite JSON specification generated by Altair.
```python
chart = alt.Chart(data).mark_bar().encode(
x='a',
y='average(b)',
)
print(chart.to_json())
```
--------------------------------
### Install Altair with all optional dependencies using pip
Source: https://github.com/vega/altair/blob/main/doc/getting_started/installation.rst
Installs Altair along with all its optional dependencies.
```bash
pip install "altair[all]"
```
--------------------------------
### Basic Point Mark
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
This example shows how to create a basic point mark for the chart object without specifying any encodings.
```python
alt.Chart(data).mark_point()
```
--------------------------------
### Installing altair_data_server
Source: https://github.com/vega/altair/blob/main/doc/user_guide/large_datasets.rst
Install the altair_data_server package using pip.
```none
pip install altair_data_server
```
--------------------------------
### Explicit Channel Specification in Chart
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
Shows how to use the verbose channel specification directly within an Altair chart.
```python
alt.Chart(data).mark_bar().encode(
alt.Y('a', type='nominal'),
alt.X('b', type='quantitative', aggregate='average')
)
```
--------------------------------
### Install vega via pip
Source: https://github.com/vega/altair/blob/main/doc/user_guide/display_frontends.rst
Installation command for the vega package using pip.
```bash
$ pip install vega
```
--------------------------------
### Default Theme Configuration
Source: https://github.com/vega/altair/blob/main/doc/user_guide/customization.rst
Example showing how to get and inspect the default theme configuration in Altair.
```python
import altair as alt
default = alt.theme.get()
default()
```
--------------------------------
### Verbose Channel Specification
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
Demonstrates the more verbose way of specifying channel parameters by name, which is useful for advanced configurations.
```python
y = alt.Y(field='b', type='quantitative', aggregate='average')
print(y.to_json())
```
--------------------------------
### Install VegaFusion
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/index.rst
Instructions to install VegaFusion with embed extras enabled.
```none
pip install "vegafusion[embed]"
```
--------------------------------
### Install Altair in Pyodide environments using micropip
Source: https://github.com/vega/altair/blob/main/doc/getting_started/installation.rst
Installs Altair in a Pyodide-based environment using the micropip package manager.
```python
import micropip
await micropip.install("altair")
```
--------------------------------
### Install Python with uv
Source: https://github.com/vega/altair/blob/main/CONTRIBUTING.md
Command to install a specific Python version using uv.
```cmd
uv python install 3.12
```
--------------------------------
### Horizontal Bar Chart
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
Demonstrates how to create a horizontal bar chart by swapping the x and y encoding channels.
```python
alt.Chart(data).mark_bar().encode(
y='a',
x='average(b)'
)
```
--------------------------------
### Header Configuration Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/configuration.rst
This example demonstrates how to configure the appearance of chart headers, including title and label colors and font sizes.
```python
import altair as alt
from altair.datasets import data
source = data.cars.url
chart = alt.Chart(source).mark_point().encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q',
color='Origin:N',
column='Origin:N'
).properties(
width=180,
height=180
)
chart.configure_header(
titleColor='green',
titleFontSize=14,
labelColor='red',
labelFontSize=14
)
```
--------------------------------
### Bar Mark with Aggregation
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
This example demonstrates using bar marks to visualize the aggregated average of the 'b' column for each category in 'a'.
```python
alt.Chart(data).mark_bar().encode(
x='a',
y='average(b)'
)
```
--------------------------------
### Install Altair with all optional dependencies using conda
Source: https://github.com/vega/altair/blob/main/doc/getting_started/installation.rst
Installs Altair along with all its optional dependencies using the conda package manager.
```bash
conda install -c conda-forge altair-all
```
--------------------------------
### Install vega via conda
Source: https://github.com/vega/altair/blob/main/doc/user_guide/display_frontends.rst
Installation command for the vega package using conda.
```bash
$ conda install vega --channel conda-forge
```
--------------------------------
### Install a specific version of Altair in Pyodide environments
Source: https://github.com/vega/altair/blob/main/doc/getting_started/installation.rst
Installs a specific version of Altair in a Pyodide-based environment.
```python
await micropip.install("altair==6.1.0")
```
--------------------------------
### Install VegaFusion with pip
Source: https://github.com/vega/altair/blob/main/doc/user_guide/large_datasets.rst
Command to install VegaFusion using pip.
```none
pip install vegafusion
```
--------------------------------
### Customizing Mark Color and Axis Titles
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
Demonstrates how to customize the mark color and axis titles using Altair's API.
```python
alt.Chart(data).mark_bar(color='firebrick').encode(
alt.Y('a').title('category'),
alt.X('average(b)').title('avg(b) by category')
)
```
--------------------------------
### View Configuration Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/configuration.rst
This example shows how to configure the view of the chart, controlling properties like continuous height and width, stroke width, fill color, and stroke color.
```python
import altair as alt
from altair.datasets import data
source = data.cars.url
chart = alt.Chart(source).mark_point().encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q',
)
chart.configure_view(
continuousHeight=200,
continuousWidth=200,
strokeWidth=4,
fill='#FFEEDD',
stroke='red',
)
```
--------------------------------
### Example Data Loading
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/lookup.rst
Loads the example people and groups data for demonstration.
```python
from altair.datasets import data
people = data.lookup_people()
groups = data.lookup_groups()
```
--------------------------------
### Index selection example setup
Source: https://github.com/vega/altair/blob/main/doc/user_guide/interactions/jupyter_chart.rst
This code sets up a point selection named 'point' without specific fields or encodings, which is suitable for index-based selections where the selection value will be a list of row indices.
```python
import altair as alt
from altair.datasets import data
source = data.cars()
brush = alt.selection_point(name="point")
```
--------------------------------
### Install Altair with save dependencies using pip
Source: https://github.com/vega/altair/blob/main/doc/getting_started/installation.rst
Installs Altair with only the dependencies necessary for saving charts to offline HTML files or PNG/SVG/PDF formats.
```bash
pip install "altair[save]"
```
--------------------------------
### Title Configuration Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/configuration.rst
This example demonstrates how to configure the chart title with specific font size, font family, anchor, and color.
```python
import altair as alt
from altair.datasets import data
source = data.cars.url
chart = alt.Chart(source).mark_point().encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q',
).properties(
title='Cars Data'
)
chart.configure_title(
fontSize=20,
font='Courier',
anchor='start',
color='gray'
)
```
--------------------------------
### Create example image arrays
Source: https://github.com/vega/altair/blob/main/doc/case_studies/numpy-tooltip-images.rst
This code snippet generates example image arrays with blobs of different shapes and sizes, and measures their areas.
```python
import numpy as np
import pandas as pd
from scipy import ndimage as ndi
rng = np.random.default_rng([ord(c) for c in 'altair'])
n_rows = 200
def create_blobs(blob_shape, img_width=96, n_dim=2, sizes=[0.05, 0.1, 0.15]):
"""Helper function to create blobs in the images"""
shape = tuple([img_width] * n_dim)
mask = np.zeros(shape)
points = (img_width * rng.random(n_dim)).astype(int)
mask[tuple(indices for indices in points)] = 1
if blob_shape == 'circle':
im = ndi.gaussian_filter(mask, sigma=rng.choice(sizes) * img_width)
elif blob_shape == 'square':
im = ndi.uniform_filter(mask, size=rng.choice(sizes) * img_width, mode='constant') * rng.normal(4, size=(img_width, img_width))
return im / im.max()
df = pd.DataFrame({
'image1': [create_blobs('circle') for _ in range(n_rows)],
'image2': [create_blobs('square', sizes=[0.3, 0.4, 0.5]) for _ in range(n_rows)],
'group': rng.choice(['a', 'b', 'c'], size=n_rows)
})
# Compute the area as the proportion of pixels above a threshold
df[['image1_area', 'image2_area']] = df[['image1', 'image2']].map(lambda x: (x > 0.4).mean())
df
```
--------------------------------
### Create a Pandas DataFrame
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
This snippet shows how to create a simple pandas DataFrame with categorical and numerical columns, which is the fundamental data structure for Altair visualizations.
```python
import pandas as pd
data = pd.DataFrame({'a': list('CCCDDDEEE'),
'b': [2, 7, 4, 1, 2, 6, 8, 4, 7]})
```
--------------------------------
### Example Layered Chart
Source: https://github.com/vega/altair/blob/main/doc/user_guide/data_transformers.rst
A simple layered chart example using Altair and pandas.
```python
import altair as alt
import pandas as pd
df = pd.DataFrame({'x': range(5),
'y': [1, 3, 4, 3, 5]})
line = alt.Chart(df).mark_line().encode(x='x', y='y')
points = alt.Chart(df).mark_point().encode(x='x', y='y')
chart = line + points
```
--------------------------------
### Install JupyterLab
Source: https://github.com/vega/altair/wiki/Display-Troubleshooting
Instructions for installing or updating JupyterLab using conda.
```bash
$ conda install jupyterlab # if you don't have it installed
$ conda update jupyterlab
```
--------------------------------
### Install Vega3 JupyterLab Extension
Source: https://github.com/vega/altair/wiki/Display-Troubleshooting
Command to install the necessary JupyterLab extension for Vega-3 compatibility.
```bash
jupyter labextension install @jupyterlab/vega3-extension
```
--------------------------------
### Install JupyterLab Vega Extension
Source: https://github.com/vega/altair/blob/main/doc/user_guide/display_frontends.rst
Command to install the vega labextension for offline rendering in JupyterLab.
```bash
jupyter labextension install @jupyterlab/vega5-extension
```
--------------------------------
### Shorthand Encoding Examples
Source: https://github.com/vega/altair/blob/main/doc/user_guide/encodings/index.rst
Examples demonstrating the shorthand syntax for various encodings, including quantitative types and aggregations.
```python
x='name:Q'
alt.X('name', type='quantitative')
```
```python
x='sum(name)'
alt.X('name', aggregate='sum')
```
```python
x='sum(name):Q'
alt.X('name', aggregate='sum', type='quantitative')
```
```python
x='count():Q'
alt.X(aggregate='count', type='quantitative')
```
--------------------------------
### Install VegaFusion with conda
Source: https://github.com/vega/altair/blob/main/doc/user_guide/large_datasets.rst
Command to install VegaFusion using conda.
```none
conda install -c conda-forge vegafusion vl-convert-python
```
--------------------------------
### Interactive Scatter Plot Example
Source: https://github.com/vega/altair/blob/main/doc/getting_started/overview.rst
An example demonstrating how to use the Vega-Altair API to create an interactive scatter plot with linked data columns and visual encoding channels.
```python
import altair as alt
from altair.datasets import data
cars = data.cars()
alt.Chart(cars).mark_point().encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin',
).interactive()
```
--------------------------------
### Example of using the sample transform
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/sample.rst
This example demonstrates how to use the transform_sample method to create a chart using a random subset of 100 rows from the cars dataset, displayed alongside the full dataset.
```python
import altair as alt
from altair.datasets import data
source = data.cars.url
chart = alt.Chart(source).mark_point().encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q',
color='Origin:N'
).properties(
width=200,
height=200
)
chart | chart.transform_sample(100)
```
--------------------------------
### Legend Configuration Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/configuration.rst
This example shows how to customize the appearance of chart legends, including stroke color, fill color, padding, corner radius, and orientation.
```python
import altair as alt
from altair.datasets import data
source = data.cars.url
chart = alt.Chart(source).mark_point().encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q',
color='Origin:N'
)
chart.configure_legend(
strokeColor='gray',
fillColor='#EEEEEE',
padding=10,
cornerRadius=10,
orient='top-right'
)
```
--------------------------------
### Build and Serve Documentation (Faster Build)
Source: https://github.com/vega/altair/blob/main/CONTRIBUTING.md
Commands to build and serve documentation faster by excluding autosummary and gallery generation.
```cmd
uv run task doc-build -- --clean --no-autosummary --no-gallery
uv run task doc-serve
```
--------------------------------
### Cumulative Frequency Distribution Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/window.rst
This example demonstrates how to create a cumulative frequency distribution using Altair's transform_aggregate and transform_window. It first counts movies by IMDB Rating and then calculates the cumulative count over a sorted window.
```python
import altair as alt
from altair.datasets import data
alt.Chart(data.movies.url).transform_aggregate(
count='count(*)',
groupby=['IMDB Rating']
).transform_window(
sort=[{'field': 'IMDB Rating'}],
frame=[None, 0],
cumulative_count='sum(count)',
).mark_area().encode(
x='IMDB Rating:Q',
y='cumulative_count:Q',
)
```
--------------------------------
### X and Y Axis Encoding
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
This example demonstrates encoding both 'a' to the x-axis and 'b' to the y-axis, allowing for a scatter plot visualization.
```python
alt.Chart(data).mark_point().encode(
x='a',
y='b'
)
```
--------------------------------
### Example Dataset and Head
Source: https://github.com/vega/altair/blob/main/doc/user_guide/times_and_dates.rst
Loads the Seattle weather hourly normals dataset and displays the first few rows.
```python
import altair as alt
from altair.datasets import data
temps = data.seattle_weather_hourly_normals()
temps.head()
```
--------------------------------
### Selection Predicate Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/filter.rst
This example demonstrates using a multi-selection predicate to filter data. The top chart shows data selected by clicking or shift-clicking bars in the bottom chart.
```python
import altair as alt
from altair.datasets import data
pop = data.population.url
selection = alt.selection_point(fields=['year'])
top = alt.Chart(width=600, height=200).mark_line().encode(
x="age:O",
y="sum(people):Q",
color="year:O"
).transform_filter(
selection
)
color = alt.when(selection).then(alt.value("steelblue")).otherwise(alt.value("lightgray"))
bottom = alt.Chart(width=600, height=100).mark_bar().encode(
x="year:O",
y="sum(people):Q",
color=color
).add_params(
selection
)
alt.vconcat(top, bottom, data=pop)
```
--------------------------------
### Build and Serve Documentation
Source: https://github.com/vega/altair/blob/main/CONTRIBUTING.md
Commands to clean, build, and serve the documentation locally. The `--clean` flag removes previously generated files.
```cmd
uv run task doc-build -- --clean
uv run task doc-serve
```
--------------------------------
### X-axis Encoding
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
This snippet illustrates encoding the 'a' column to the x-axis channel, which visually separates points by category.
```python
alt.Chart(data).mark_point().encode(
x='a',
)
```
--------------------------------
### Initialize virtual environment with uv
Source: https://github.com/vega/altair/blob/main/CONTRIBUTING.md
Command to initialize a new virtual environment using uv.
```cmd
cd altair/
uv venv -p 3.12
```
--------------------------------
### Aggregation with Average
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
This snippet shows how to use aggregation within an encoding channel to display the average of the 'b' column for each category in 'a'.
```python
alt.Chart(data).mark_point().encode(
x='a',
y='average(b)'
)
```
--------------------------------
### Initialize a Chart Object
Source: https://github.com/vega/altair/blob/main/doc/getting_started/starting.rst
This snippet demonstrates how to create the fundamental Altair Chart object, which takes a pandas DataFrame as its primary argument.
```python
import altair as alt
chart = alt.Chart(data)
```
--------------------------------
### Tutorial Outline
Source: https://github.com/vega/altair/wiki/PyCon-Tutorial-Planning
A basic outline of the topics to be covered in the tutorial.
```text
1. Install Altair
2. Create a bar chart
3. Marks
4. Encodings
5. Transformations
6. ...
```
--------------------------------
### Build and Serve Documentation with Watch Mode
Source: https://github.com/vega/altair/blob/main/CONTRIBUTING.md
Command to build documentation with `--watch` flag for automatic rebuilding and refreshing upon file changes, optimized for local development.
```cmd
uv run task doc-build -- --clean --no-autosummary --watch
```
--------------------------------
### Initial Chart Setup
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/filter.rst
Sets up the base Altair chart using population data.
```python
import altair as alt
from altair.datasets import data
source = data.population.url
chart = alt.Chart(source).mark_line().encode(
x="age:O",
y="sum(people):Q",
color="year:O"
).properties(
width=600, height=200
)
between_1950_60 = alt.FieldRangePredicate(field="year", range=[1950, 1960])
```
--------------------------------
### Setting Localization Options
Source: https://github.com/vega/altair/blob/main/doc/user_guide/customization.rst
Example of setting format and time format locales for chart rendering.
```python
alt.renderers.set_embed_options(format_locale="it-IT", time_format_locale="it-IT")
source = data.stocks.url
chart = alt.Chart(source).mark_area().transform_filter('year(datum.date) == 2009').encode(
x='date:T',
y=alt.Y('price:Q', axis=alt.Axis(format="$.0f")),
color='symbol:N'
)
chart
```
--------------------------------
### TOML Configuration for Vega Versions
Source: https://github.com/vega/altair/blob/main/NOTES_FOR_MAINTAINERS.md
Example TOML configuration for specifying Vega and related library versions.
```toml
[tool.altair.vega]
vega-datasets = "..." # https://github.com/vega/vega-datasets
vega-embed = "..." # https://github.com/vega/vega-embed
vega-lite = "..." # https://github.com/vega/vega-lite
```
--------------------------------
### Build and publish documentation
Source: https://github.com/vega/altair/blob/main/RELEASING.md
Commands to build and publish the project's documentation. Requires write access to the altair-viz/altair-viz.github.io repository.
```bash
uv run task doc-build -- --clean
uv run task doc-publish
```
--------------------------------
### Example DataFrame
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/joinaggregate.rst
This code snippet demonstrates the creation of a sample pandas DataFrame used in the explanation.
```python
import pandas as pd
import numpy as np
rand = np.random.RandomState(0)
df = pd.DataFrame({
'label': rand.choice(['A', 'B', 'C'], 10),
'value': rand.randn(10),
})
```
--------------------------------
### Histogram Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/encodings/index.rst
Example of creating a histogram by binning data and counting occurrences.
```python
alt.Chart(cars).mark_bar().encode(
alt.X('Horsepower').bin(),
y='count()'
# could also use alt.Y(aggregate='count', type='quantitative')
)
```
--------------------------------
### Vertical Concatenation Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/compound_charts.rst
An example demonstrating vertical concatenation of charts with interactive brushing.
```python
import altair as alt
from altair.datasets import data
source = data.sp500.url
brush = alt.selection_interval(encodings=['x'])
base = alt.Chart(source).mark_area().encode(
x = 'date:T',
y = 'price:Q'
).properties(
width=600,
height=200
)
upper = base.encode(alt.X('date:T').scale(domain=brush))
lower = base.properties(
height=60
).add_params(brush)
alt.vconcat(upper, lower)
```
--------------------------------
### MaxRowsError Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/large_datasets.rst
An example that triggers the MaxRowsError due to a dataset exceeding the default row limit.
```python
import altair as alt
import pandas as pd
data = pd.DataFrame({"x": range(10000)})
alt.Chart(data).mark_point()
```
--------------------------------
### Using Named Color Schemes
Source: https://github.com/vega/altair/blob/main/doc/user_guide/customization.rst
Demonstrates how to apply a named color scheme to a scale, using 'lightgreyred' as an example.
```python
import altair as alt
from altair.datasets import data
cars = data.cars()
alt.Chart(cars).mark_point().encode(
x='Horsepower',
y='Miles_per_Gallon',
color=alt.Color('Acceleration').scale(scheme="lightgreyred")
)
```
--------------------------------
### Publish to PyPI
Source: https://github.com/vega/altair/blob/main/RELEASING.md
Command to build a source distribution and wheel, and publish to PyPI. Requires appropriate permissions and UV_PUBLISH_TOKEN environment variable.
```bash
uv run task publish
```
--------------------------------
### JSONPATH Expression Tester Examples
Source: https://github.com/vega/altair/wiki/Hackathon-Helper-Links
Examples of JSONPATH expressions to query JSON schema definitions.
```jsonpath
$.definitions[*].properties[*].description
```
```jsonpath
$.definitions[*].properties[*].['$ref']
```
```jsonpath
$.definitions[*].properties.[?(@.anyOf)]
```
--------------------------------
### FieldEqualPredicate Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/filter.rst
This example uses FieldEqualPredicate to filter data, selecting only records where the 'year' field is equal to 2000.
```python
import altair as alt
from altair.datasets import data
pop = data.population.url
alt.Chart(pop).mark_line().encode(
x='age:O',
y='sum(people):Q',
color='year:O'
).transform_filter(
alt.FieldEqualPredicate(field='year', equal=2000)
)
```
--------------------------------
### Example: Plotting Stock Prices
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/window.rst
This code snippet demonstrates a basic Altair chart to plot stock prices over time.
```python
import altair as alt
from altair.datasets import data
alt.Chart(data.stocks.url).mark_line().encode(
x='date:T',
y='price:Q',
color='symbol:N',
)
```
--------------------------------
### Fold Transform Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/fold.rst
An example demonstrating the use of the fold transform to convert wide-form data to long-form data.
```python
import numpy as np
import pandas as pd
import altair as alt
rand = np.random.RandomState(0)
data = pd.DataFrame({
'date': pd.date_range('2019-01-01', freq='D', periods=30),
'A': rand.randn(30).cumsum(),
'B': rand.randn(30).cumsum(),
'C': rand.randn(30).cumsum(),
})
alt.Chart(data).transform_fold(
['A', 'B', 'C'],
).mark_line().encode(
x='date:T',
y='value:Q',
color='key:N'
)
```
--------------------------------
### Run JupyterLab
Source: https://github.com/vega/altair/wiki/Display-Troubleshooting
Command to launch JupyterLab after installation.
```bash
$ jupyter lab
```
--------------------------------
### Heatmap Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/marks/rect.rst
An example of creating a heatmap using rect marks with discrete fields on x and y channels.
```python
import altair as alt
from altair.datasets import data
source = data.seattle_weather()
alt.Chart(source).mark_rect().encode(
alt.X("date(date):O").axis(labelAngle=0, format="%e").title("Day"),
alt.Y("month(date):O").title("Month"),
alt.Color("max(temp_max):Q").title("Max Temp"),
)
```
--------------------------------
### Defining and Registering a Custom Theme
Source: https://github.com/vega/altair/blob/main/doc/user_guide/customization.rst
Example of defining a custom theme ('black_marks') that sets all marks to black and registers/enables it.
```python
import altair as alt
from altair.datasets import data
# define, register and enable theme
@alt.theme.register("black_marks", enable=True)
def black_marks() -> alt.theme.ThemeConfig:
return {
"config": {
"view": {"continuousWidth": 300, "continuousHeight": 300},
"mark": {"color": "black", "fill": "black"},
}
}
# draw the chart
cars = data.cars.url
alt.Chart(cars).mark_point().encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q'
)
```
--------------------------------
### Error Bar Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/marks/errorband.rst
This example shows the equivalent visualization using `mark_errorbar` for comparison with the error band.
```python
import altair as alt
from altair.datasets import data
source = data.cars.url
alt.Chart(source).mark_errorbar(extent="ci", ticks=True).encode(
x="year(Year)",
y=alt.Y(
"Miles_per_Gallon:Q",
scale=alt.Scale(zero=False),
title="Miles per Gallon (95% CIs)",
),
)
```
--------------------------------
### Basic Area Chart Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/marks/area.rst
An example of a basic area chart showing unemployment data over time.
```python
import altair as alt
from altair.datasets import data
source = data.unemployment_across_industries.url
alt.Chart(source).mark_area().encode(
x="yearmonth(date):T",
y="sum(count):Q",
).properties(width=300, height=200)
```
--------------------------------
### Pivot Transform Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/pivot.rst
An example demonstrating the pivot transform to convert long-form Olympic medals data to wide-form.
```python
import altair as alt
import pandas as pd
df = pd.DataFrame.from_records([
{"country": "Norway", "type": "gold", "count": 14},
{"country": "Norway", "type": "silver", "count": 14},
{"country": "Norway", "type": "bronze", "count": 11},
{"country": "Germany", "type": "gold", "count": 14},
{"country": "Germany", "type": "silver", "count": 10},
{"country": "Germany", "type": "bronze", "count": 7},
{"country": "Canada", "type": "gold", "count": 11},
{"country": "Canada", "type": "silver", "count": 8},
{"country": "Canada", "type": "bronze", "count": 10}
])
alt.Chart(df).transform_pivot(
'type',
groupby=['country'],
value='count'
).mark_bar().encode(
x='gold:Q',
y='country:N',
)
```
--------------------------------
### Quantile Plot Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/quantile.rst
An example of creating a quantile plot of normally-distributed data using Altair's transform_quantile method.
```python
import altair as alt
import pandas as pd
import numpy as np
np.random.seed(42)
df = pd.DataFrame({'x': np.random.randn(200)})
alt.Chart(df).transform_quantile(
'x', step=0.01
).mark_point().encode(
x='prob:Q',
y='value:Q'
)
```
--------------------------------
### Example Data
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/impute.rst
This code snippet demonstrates how to create a sample DataFrame with missing values, which will be used in subsequent examples.
```python
import numpy as np
import pandas as pd
data = pd.DataFrame({
't': range(5),
'x': [2, np.nan, 3, 1, 3],
'y': [5, 7, 5, np.nan, 4]
}).melt('t').dropna()
data
```
--------------------------------
### Streamgraph Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/marks/area.rst
An example of a streamgraph created by setting the stack baseline to 'center' and enabling interactivity for zooming and panning.
```python
import altair as alt
from altair.datasets import data
# The actual code for the streamgraph example is missing in the provided content.
# This is a placeholder based on the description.
alt.Chart(data.unemployment_across_industries.url).mark_area(stack='center').encode(
x='yearmonth(date):T',
y='sum(count):Q',
color='series:N'
).interactive()
```
--------------------------------
### Run Test Suite
Source: https://github.com/vega/altair/blob/main/NOTES_FOR_MAINTAINERS.md
Command to run the Altair test suite.
```bash
uv run task test
```
--------------------------------
### Single Bar Chart Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/marks/bar.rst
A basic example of a single bar chart by mapping a quantitative field to the x-axis.
```python
import altair as alt
from altair import datum
from altair.datasets import data
source = data.population.url
alt.Chart(source).mark_bar().encode(
alt.X("sum(people):Q").title("Population")
).transform_filter(
datum.year == 2000
)
```
--------------------------------
### Bar Mark Properties Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/marks/bar.rst
This example demonstrates the use of cornerRadius property for bar marks, controlled by a slider.
```python
import altair as alt
import pandas as pd
corner_slider = alt.binding_range(min=0, max=50, step=1)
corner_var = alt.param(bind=corner_slider, value=0, name="cornerRadius")
source = pd.DataFrame(
{
"a": ["A", "B", "C", "D", "E", "F", "G", "H", "I"],
"b": [28, 55, 43, 91, 81, 53, 19, 87, 52],
}
)
alt.Chart(source).mark_bar(cornerRadius=corner_var).encode(
x=alt.X("a:N").axis(labelAngle=0),
y="b:Q",
).add_params(corner_var)
```
--------------------------------
### Altair Join Aggregate Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/transform/joinaggregate.rst
An example of using the join aggregate transform in Altair to compare normalized movie ratings.
```python
import altair as alt
from altair.datasets import data
alt.Chart(data.movies.url).transform_filter(
'datum["IMDB Rating"] != null && datum["Rotten Tomatoes Rating"] != null'
).transform_joinaggregate(
IMDB_mean='mean(IMDB Rating)',
IMDB_std='stdev(IMDB Rating)',
RT_mean='mean(Rotten Tomatoes Rating)',
RT_std='stdev(Rotten Tomatoes Rating)'
).transform_calculate(
IMDB_Deviation='(datum["IMDB Rating"] - datum.IMDB_mean) / datum.IMDB_std',
Rotten_Tomatoes_Deviation='(datum["Rotten Tomatoes Rating"] - datum.RT_mean) / datum.RT_std'
).mark_point().encode(
x='IMDB_Deviation:Q',
y="Rotten_Tomatoes_Deviation:Q"
)
```
--------------------------------
### Attribute-Based Syntax Example
Source: https://github.com/vega/altair/blob/main/doc/user_guide/encodings/index.rst
Shows the equivalent of the method-based syntax using the traditional attribute-based approach.
```python
alt.Chart(cars).mark_point().encode(
alt.X('Horsepower', axis=alt.Axis(tickMinStep=50)),
alt.Y('Miles_per_Gallon', title="Miles per Gallon"),
color='Origin',
shape='Origin'
)
```