# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022-2025)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Collection of chart commands that are rendered via our vega-lite chart component."""
from __future__ import annotations
import json
import re
from contextlib import nullcontext
from dataclasses import dataclass
from typing import (
TYPE_CHECKING,
Any,
Final,
Literal,
TypedDict,
Union,
cast,
overload,
)
from typing_extensions import Required, TypeAlias
from streamlit import dataframe_util, type_util
from streamlit.elements.lib import dicttools
from streamlit.elements.lib.built_in_chart_utils import (
AddRowsMetadata,
ChartStackType,
ChartType,
generate_chart,
maybe_raise_stack_warning,
)
from streamlit.elements.lib.form_utils import current_form_id
from streamlit.elements.lib.policies import check_widget_policies
from streamlit.elements.lib.utils import Key, compute_and_register_element_id, to_key
from streamlit.errors import StreamlitAPIException
from streamlit.proto.ArrowVegaLiteChart_pb2 import (
ArrowVegaLiteChart as ArrowVegaLiteChartProto,
)
from streamlit.runtime.metrics_util import gather_metrics
from streamlit.runtime.scriptrunner_utils.script_run_context import get_script_run_ctx
from streamlit.runtime.state import WidgetCallback, register_widget
from streamlit.util import AttributeDictionary, calc_md5
if TYPE_CHECKING:
from collections.abc import Iterable, Sequence
import altair as alt
from streamlit.dataframe_util import Data
from streamlit.delta_generator import DeltaGenerator
from streamlit.elements.lib.color_util import Color
# See https://vega.github.io/vega-lite/docs/encoding.html
_CHANNELS: Final = {
"x",
"y",
"x2",
"y2",
"xError",
"xError2",
"yError",
"yError2",
"longitude",
"latitude",
"color",
"opacity",
"fillOpacity",
"strokeOpacity",
"strokeWidth",
"size",
"shape",
"text",
"tooltip",
"href",
"key",
"order",
"detail",
"facet",
"row",
"column",
}
VegaLiteSpec: TypeAlias = "dict[str, Any]"
AltairChart: TypeAlias = Union[
"alt.Chart",
"alt.ConcatChart",
"alt.FacetChart",
"alt.HConcatChart",
"alt.LayerChart",
"alt.RepeatChart",
"alt.VConcatChart",
]
class VegaLiteState(TypedDict, total=False):
"""
The schema for the Vega-Lite event state.
The event state is stored in a dictionary-like object that supports both
key and attribute notation. Event states cannot be programmatically
changed or set through Session State.
Only selection events are supported at this time.
Attributes
----------
selection : dict
The state of the ``on_select`` event. This attribute returns a
dictionary-like object that supports both key and attribute notation.
The name of each Vega-Lite selection parameter becomes an attribute in
the ``selection`` dictionary. The format of the data within each
attribute is determined by the selection parameter definition within
Vega-Lite.
Examples
--------
The following two examples have equivalent definitions. Each one has a
point and interval selection parameter include in the chart definition.
The point selection parameter is named ``"point_selection"``. The interval
or box selection parameter is named ``"interval_selection"``.
The follow example uses ``st.altair_chart``:
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>> import altair as alt
>>>
>>> if "data" not in st.session_state:
>>> st.session_state.data = pd.DataFrame(
... np.random.randn(20, 3), columns=["a", "b", "c"]
... )
>>> df = st.session_state.data
>>>
>>> point_selector = alt.selection_point("point_selection")
>>> interval_selector = alt.selection_interval("interval_selection")
>>> chart = (
... alt.Chart(df)
... .mark_circle()
... .encode(
... x="a",
... y="b",
... size="c",
... color="c",
... tooltip=["a", "b", "c"],
... fillOpacity=alt.condition(point_selector, alt.value(1), alt.value(0.3)),
... )
... .add_params(point_selector, interval_selector)
... )
>>>
>>> event = st.altair_chart(chart, key="alt_chart", on_select="rerun")
>>>
>>> event
The following example uses ``st.vega_lite_chart``:
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> if "data" not in st.session_state:
>>> st.session_state.data = pd.DataFrame(
... np.random.randn(20, 3), columns=["a", "b", "c"]
... )
>>>
>>> spec = {
... "mark": {"type": "circle", "tooltip": True},
... "params": [
... {"name": "interval_selection", "select": "interval"},
... {"name": "point_selection", "select": "point"},
... ],
... "encoding": {
... "x": {"field": "a", "type": "quantitative"},
... "y": {"field": "b", "type": "quantitative"},
... "size": {"field": "c", "type": "quantitative"},
... "color": {"field": "c", "type": "quantitative"},
... "fillOpacity": {
... "condition": {"param": "point_selection", "value": 1},
... "value": 0.3,
... },
... },
... }
>>>
>>> event = st.vega_lite_chart(
... st.session_state.data, spec, key="vega_chart", on_select="rerun"
... )
>>>
>>> event
Try selecting points in this interactive example. When you click a point,
the selection will appear under the attribute, ``"point_selection"``, which
is the name given to the point selection parameter. Similarly, when you
make an interval selection, it will appear under the attribute
``"interval_selection"``. You can give your selection parameters other
names if desired.
If you hold ``Shift`` while selecting points, existing point selections
will be preserved. Interval selections are not preserved when making
additional selections.
.. output::
https://doc-chart-events-vega-lite-state.streamlit.app
height: 600px
"""
selection: Required[AttributeDictionary]
@dataclass
class VegaLiteStateSerde:
"""VegaLiteStateSerde is used to serialize and deserialize the VegaLite Chart state."""
selection_parameters: Sequence[str]
def deserialize(self, ui_value: str | None) -> VegaLiteState:
empty_selection_state: VegaLiteState = {
"selection": AttributeDictionary(
# Initialize the select state with empty dictionaries for each selection parameter.
{param: {} for param in self.selection_parameters}
),
}
selection_state = (
empty_selection_state
if ui_value is None
else cast("VegaLiteState", AttributeDictionary(json.loads(ui_value)))
)
if "selection" not in selection_state:
selection_state = empty_selection_state # type: ignore[unreachable]
return cast("VegaLiteState", AttributeDictionary(selection_state))
def serialize(self, selection_state: VegaLiteState) -> str:
return json.dumps(selection_state, default=str)
def _patch_null_legend_titles(spec: VegaLiteSpec) -> None:
"""Patches null legend titles in the 'color' channel of the spec.
This is a fix for the Vega-Lite bug where null legend titles
cause a wrong formatting of the chart as shown on the issue #9339.
"""
encoding = spec.get("encoding")
if not isinstance(encoding, dict):
return
color_spec = encoding.get("color")
if not isinstance(color_spec, dict):
return
if "title" in color_spec and color_spec.get("title") is None:
# Patch legend title given null value directly in the encoding
color_spec["title"] = " "
legend = color_spec.get("legend")
if isinstance(legend, dict) and "title" in legend and legend.get("title") is None:
# Patch legend title given null value in the legend
legend["title"] = " "
def _prepare_vega_lite_spec(
spec: VegaLiteSpec,
use_container_width: bool,
**kwargs: Any,
) -> VegaLiteSpec:
if kwargs:
# Support passing in kwargs.
# > marshall(proto, {foo: 'bar'}, baz='boz')
# Merge spec with unflattened kwargs, where kwargs take precedence.
# This only works for string keys, but kwarg keys are strings anyways.
spec = dict(spec, **dicttools.unflatten(kwargs, _CHANNELS))
else:
# Clone the spec dict, since we may be mutating it.
spec = dict(spec)
if len(spec) == 0:
raise StreamlitAPIException("Vega-Lite charts require a non-empty spec dict.")
if "autosize" not in spec:
# type fit does not work for many chart types. This change focuses
# on vconcat with use_container_width=True as there are unintended
# consequences of changing the default autosize for all charts.
# fit-x fits the width and height can be adjusted.
is_facet_chart = "facet" in spec or (
"encoding" in spec
and (any(x in spec["encoding"] for x in ["row", "column", "facet"]))
)
if "vconcat" in spec and use_container_width:
spec["autosize"] = {"type": "fit-x", "contains": "padding"}
elif is_facet_chart:
spec["autosize"] = {"type": "pad", "contains": "padding"}
else:
spec["autosize"] = {"type": "fit", "contains": "padding"}
_patch_null_legend_titles(spec)
return spec
def _marshall_chart_data(
proto: ArrowVegaLiteChartProto,
spec: VegaLiteSpec,
data: Data = None,
) -> None:
"""Adds the data to the proto and removes it from the spec dict.
These operations will happen in-place.
"""
# Pull data out of spec dict when it's in a 'datasets' key:
# datasets: {foo: df1_bytes, bar: df2_bytes}, ...}
if "datasets" in spec:
for dataset_name, dataset_data in spec["datasets"].items():
dataset = proto.datasets.add()
dataset.name = str(dataset_name)
dataset.has_name = True
# The ID transformer (id_transform function registered before conversion to dict)
# already serializes the data into Arrow IPC format (bytes) when the Altair object
# gets converted into the vega-lite spec dict.
# If its already in bytes, we don't need to serialize it here again.
# We just need to pass the data information into the correct proto fields.
# TODO(lukasmasuch): Are there any other cases where we need to serialize the data
# or can we remove the convert_anything_to_arrow_bytes here?
dataset.data.data = (
dataset_data
if isinstance(dataset_data, bytes)
else dataframe_util.convert_anything_to_arrow_bytes(dataset_data)
)
del spec["datasets"]
# Pull data out of spec dict when it's in a top-level 'data' key:
# > {data: df}
# > {data: {values: df, ...}}
# > {data: {url: 'url'}}
# > {data: {name: 'foo'}}
if "data" in spec:
data_spec = spec["data"]
if isinstance(data_spec, dict):
if "values" in data_spec:
data = data_spec["values"]
del spec["data"]
else:
data = data_spec
del spec["data"]
if data is not None:
proto.data.data = dataframe_util.convert_anything_to_arrow_bytes(data)
def _convert_altair_to_vega_lite_spec(
altair_chart: AltairChart,
) -> VegaLiteSpec:
"""Convert an Altair chart object to a Vega-Lite chart spec."""
import altair as alt
# Normally altair_chart.to_dict() would transform the dataframe used by the
# chart into an array of dictionaries. To avoid that, we install a
# transformer that replaces datasets with a reference by the object id of
# the dataframe. We then fill in the dataset manually later on.
datasets = {}
def id_transform(data: Any) -> dict[str, str]:
"""Altair data transformer that serializes the data,
creates a stable name based on the hash of the data,
stores the bytes into the datasets mapping and
returns this name to have it be used in Altair.
"""
# Already serialize the data to be able to create a stable
# dataset name:
data_bytes = dataframe_util.convert_anything_to_arrow_bytes(data)
# Use the md5 hash of the data as the name:
name = calc_md5(str(data_bytes))
datasets[name] = data_bytes
return {"name": name}
alt.data_transformers.register("id", id_transform) # type: ignore[arg-type,attr-defined,unused-ignore]
# The default altair theme has some width/height defaults defined
# which are not useful for Streamlit. Therefore, we change the theme to
# "none" to avoid those defaults.
with alt.themes.enable("none") if alt.themes.active == "default" else nullcontext(): # type: ignore[attr-defined,unused-ignore]
with alt.data_transformers.enable("id"): # type: ignore[attr-defined,unused-ignore]
chart_dict = altair_chart.to_dict()
# Put datasets back into the chart dict:
chart_dict["datasets"] = datasets
return chart_dict
def _disallow_multi_view_charts(spec: VegaLiteSpec) -> None:
"""Raise an exception if the spec contains a multi-view chart (view composition).
This is intended to be used as a temporary solution to prevent selections on
multi-view charts. There are too many edge cases to handle selections on these
charts correctly, so we're disallowing them for now.
More information about view compositions: https://vega.github.io/vega-lite/docs/composition.html
"""
if (
any(key in spec for key in ["layer", "hconcat", "vconcat", "concat", "spec"])
or "encoding" not in spec
):
raise StreamlitAPIException(
"Selections are not yet supported for multi-view charts (chart compositions). "
"If you would like to use selections on multi-view charts, please upvote "
"this [Github issue](https://github.com/streamlit/streamlit/issues/8643)."
)
def _extract_selection_parameters(spec: VegaLiteSpec) -> set[str]:
"""Extract the names of all valid selection parameters from the spec."""
if not spec or "params" not in spec:
return set()
param_names = set()
for param in spec["params"]:
# Check if it looks like a valid selection parameter:
# https://vega.github.io/vega-lite/docs/selection.html
if param.get("name") and param.get("select"):
# Selection found, just return here to not show the exception.
param_names.add(param["name"])
return param_names
def _parse_selection_mode(
spec: VegaLiteSpec,
selection_mode: str | Iterable[str] | None,
) -> list[str]:
"""Parse and check the user provided selection modes.
This will raise an exception if no valid selection parameters are found in the spec
or if the user provided selection modes are not defined in the spec.
Parameters
----------
spec : VegaLiteSpec
The Vega-Lite chart specification.
selection_mode : str, Iterable[str], or None
The user provided selection mode(s).
Returns
-------
list[str]
The parsed selection mode(s) that should be activated.
"""
# Extract all selection parameters from the spec:
all_selection_params = _extract_selection_parameters(spec)
if not all_selection_params:
raise StreamlitAPIException(
"Selections are activated, but the provided chart spec does not "
"have any selections defined. To add selections to `st.altair_chart`, check out the documentation "
"[here](https://altair-viz.github.io/user_guide/interactions.html#selections-capturing-chart-interactions)."
" For adding selections to `st.vega_lite_chart`, take a look "
"at the specification [here](https://vega.github.io/vega-lite/docs/selection.html)."
)
if selection_mode is None:
# Activate all selection parameters:
return sorted(all_selection_params)
if isinstance(selection_mode, str):
# Convert single string to list:
selection_mode = [selection_mode]
# Check that all provided selection parameters are defined in the spec:
for selection_name in selection_mode:
if selection_name not in all_selection_params:
raise StreamlitAPIException(
f"Selection parameter '{selection_name}' is not defined in the chart "
f"spec. Available selection parameters are: {all_selection_params}."
)
return sorted(selection_mode)
def _reset_counter_pattern(prefix: str, vega_spec: str) -> str:
"""Altair uses a global counter for unnamed parameters and views.
We need to reset these counters on a spec-level to make the
spec stable across reruns and avoid changes to the element ID.
"""
pattern = re.compile(rf'"{prefix}\d+"')
# Get all matches without duplicates in order of appearance.
# Using a set here would not guarantee the order of appearance,
# which might lead to different replacements on each run.
# The order of the spec from Altair is expected to stay stable
# within the same session / Altair version.
# The order might change with Altair updates, but that's not really
# a case that is relevant for us since we mainly care about having
# this stable within a session.
if matches := list(dict.fromkeys(pattern.findall(vega_spec))):
# Add a prefix to the replacement to avoid
# replacing instances that already have been replaced before.
# The prefix here is arbitrarily chosen with the main goal
# that its extremely unlikely to already be part of the spec:
replacement_prefix = "__replace_prefix_o9hd101n22e1__"
# Replace all matches with a counter starting from 1
# We start from 1 to imitate the altair behavior.
for counter, match in enumerate(matches, start=1):
vega_spec = vega_spec.replace(
match, f'"{replacement_prefix}{prefix}{counter}"'
)
# Remove the prefix again from all replacements:
vega_spec = vega_spec.replace(replacement_prefix, "")
return vega_spec
def _stabilize_vega_json_spec(vega_spec: str) -> str:
"""Makes the chart spec stay stable across reruns and sessions.
Altair auto creates names for unnamed parameters & views. It uses a global counter
for the naming which will result in a different spec on every rerun.
In Streamlit, we need the spec to be stable across reruns and sessions to prevent the chart
from getting a new identity. So we need to replace the names with counter with a stable name.
Having a stable chart spec is also important for features like forward message cache,
where we don't want to have changing messages on every rerun.
Parameter counter:
https://github.com/vega/altair/blob/f345cd9368ae2bbc98628e9245c93fa9fb582621/altair/vegalite/v5/api.py#L196
View counter:
https://github.com/vega/altair/blob/f345cd9368ae2bbc98628e9245c93fa9fb582621/altair/vegalite/v5/api.py#L2885
This is temporary solution waiting for a fix for this issue:
https://github.com/vega/altair/issues/3416
Other solutions we considered:
- working on the dict object: this would require to iterate through the object and do the
same kind of replacement; though we would need to know the structure and since we need
the spec in String-format anyways, we deemed that executing the replacement on the
String is the better alternative
- resetting the counter: the counter is incremented already when the chart object is created
(see this GitHub issue comment https://github.com/vega/altair/issues/3416#issuecomment-2098530464),
so it would be too late here to reset the counter with a thread-lock to prevent interference
between sessions
"""
# We only want to apply these replacements if it is really necessary
# since there is a risk that we replace names that where chosen by the user
# and thereby introduce unwanted side effects.
# We only need to apply the param_ fix if there are actually parameters defined
# somewhere in the spec. We can check for this by looking for the '"params"' key.
# This isn't a perfect check, but good enough to prevent unnecessary executions
# for the majority of charts.
if '"params"' in vega_spec:
vega_spec = _reset_counter_pattern("param_", vega_spec)
# Simple check if the spec contains a composite chart:
# https://vega.github.io/vega-lite/docs/composition.html
# Other charts will not contain the `view_` name,
# so its better to not replace this pattern.
if re.search(r'"(vconcat|hconcat|facet|layer|concat|repeat)"', vega_spec):
vega_spec = _reset_counter_pattern("view_", vega_spec)
return vega_spec
class VegaChartsMixin:
"""Mix-in class for all vega-related chart commands.
Altair is a python wrapper on top of the vega-lite spec. And our
built-in chart commands are just another layer on-top of Altair.
All of these chart commands will be eventually converted to a vega-lite
spec and rendered using the same vega-lite chart component.
"""
@gather_metrics("line_chart")
def line_chart(
self,
data: Data = None,
*,
x: str | None = None,
y: str | Sequence[str] | None = None,
x_label: str | None = None,
y_label: str | None = None,
color: str | Color | list[Color] | None = None,
width: int | None = None,
height: int | None = None,
use_container_width: bool = True,
) -> DeltaGenerator:
"""Display a line chart.
This is syntax-sugar around ``st.altair_chart``. The main difference
is this command uses the data's own column and indices to figure out
the chart's Altair spec. As a result this is easier to use for many
"just plot this" scenarios, while being less customizable.
If ``st.line_chart`` does not guess the data specification
correctly, try specifying your desired chart using ``st.altair_chart``.
Parameters
----------
data : Anything supported by st.dataframe
Data to be plotted.
x : str or None
Column name or key associated to the x-axis data. If ``x`` is
``None`` (default), Streamlit uses the data index for the x-axis
values.
y : str, Sequence of str, or None
Column name(s) or key(s) associated to the y-axis data. If this is
``None`` (default), Streamlit draws the data of all remaining
columns as data series. If this is a ``Sequence`` of strings,
Streamlit draws several series on the same chart by melting your
wide-format table into a long-format table behind the scenes.
x_label : str or None
The label for the x-axis. If this is ``None`` (default), Streamlit
will use the column name specified in ``x`` if available, or else
no label will be displayed.
y_label : str or None
The label for the y-axis. If this is ``None`` (default), Streamlit
will use the column name(s) specified in ``y`` if available, or
else no label will be displayed.
color : str, tuple, Sequence of str, Sequence of tuple, or None
The color to use for different lines in this chart.
For a line chart with just one line, this can be:
- None, to use the default color.
- A hex string like "#ffaa00" or "#ffaa0088".
- An RGB or RGBA tuple with the red, green, blue, and alpha
components specified as ints from 0 to 255 or floats from 0.0 to
1.0.
For a line chart with multiple lines, where the dataframe is in
long format (that is, y is None or just one column), this can be:
- None, to use the default colors.
- The name of a column in the dataset. Data points will be grouped
into lines of the same color based on the value of this column.
In addition, if the values in this column match one of the color
formats above (hex string or color tuple), then that color will
be used.
For example: if the dataset has 1000 rows, but this column only
contains the values "adult", "child", and "baby", then those 1000
datapoints will be grouped into three lines whose colors will be
automatically selected from the default palette.
But, if for the same 1000-row dataset, this column contained
the values "#ffaa00", "#f0f", "#0000ff", then then those 1000
datapoints would still be grouped into three lines, but their
colors would be "#ffaa00", "#f0f", "#0000ff" this time around.
For a line chart with multiple lines, where the dataframe is in
wide format (that is, y is a Sequence of columns), this can be:
- None, to use the default colors.
- A list of string colors or color tuples to be used for each of
the lines in the chart. This list should have the same length
as the number of y values (e.g. ``color=["#fd0", "#f0f", "#04f"]``
for three lines).
width : int or None
Desired width of the chart expressed in pixels. If ``width`` is
``None`` (default), Streamlit sets the width of the chart to fit
its contents according to the plotting library, up to the width of
the parent container. If ``width`` is greater than the width of the
parent container, Streamlit sets the chart width to match the width
of the parent container.
To use ``width``, you must set ``use_container_width=False``.
height : int or None
Desired height of the chart expressed in pixels. If ``height`` is
``None`` (default), Streamlit sets the height of the chart to fit
its contents according to the plotting library.
use_container_width : bool
Whether to override ``width`` with the width of the parent
container. If ``use_container_width`` is ``True`` (default),
Streamlit sets the width of the chart to match the width of the
parent container. If ``use_container_width`` is ``False``,
Streamlit sets the chart's width according to ``width``.
Examples
--------
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
>>>
>>> st.line_chart(chart_data)
.. output::
https://doc-line-chart.streamlit.app/
height: 440px
You can also choose different columns to use for x and y, as well as set
the color dynamically based on a 3rd column (assuming your dataframe is in
long format):
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(
... {
... "col1": np.random.randn(20),
... "col2": np.random.randn(20),
... "col3": np.random.choice(["A", "B", "C"], 20),
... }
... )
>>>
>>> st.line_chart(chart_data, x="col1", y="col2", color="col3")
.. output::
https://doc-line-chart1.streamlit.app/
height: 440px
Finally, if your dataframe is in wide format, you can group multiple
columns under the y argument to show multiple lines with different
colors:
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(
... np.random.randn(20, 3), columns=["col1", "col2", "col3"]
... )
>>>
>>> st.line_chart(
... chart_data,
... x="col1",
... y=["col2", "col3"],
... color=["#FF0000", "#0000FF"], # Optional
... )
.. output::
https://doc-line-chart2.streamlit.app/
height: 440px
"""
chart, add_rows_metadata = generate_chart(
chart_type=ChartType.LINE,
data=data,
x_from_user=x,
y_from_user=y,
x_axis_label=x_label,
y_axis_label=y_label,
color_from_user=color,
size_from_user=None,
width=width,
height=height,
use_container_width=use_container_width,
)
return cast(
"DeltaGenerator",
self._altair_chart(
chart,
use_container_width=use_container_width,
theme="streamlit",
add_rows_metadata=add_rows_metadata,
),
)
@gather_metrics("area_chart")
def area_chart(
self,
data: Data = None,
*,
x: str | None = None,
y: str | Sequence[str] | None = None,
x_label: str | None = None,
y_label: str | None = None,
color: str | Color | list[Color] | None = None,
stack: bool | ChartStackType | None = None,
width: int | None = None,
height: int | None = None,
use_container_width: bool = True,
) -> DeltaGenerator:
"""Display an area chart.
This is syntax-sugar around ``st.altair_chart``. The main difference
is this command uses the data's own column and indices to figure out
the chart's Altair spec. As a result this is easier to use for many
"just plot this" scenarios, while being less customizable.
If ``st.area_chart`` does not guess the data specification
correctly, try specifying your desired chart using ``st.altair_chart``.
Parameters
----------
data : Anything supported by st.dataframe
Data to be plotted.
x : str or None
Column name or key associated to the x-axis data. If ``x`` is
``None`` (default), Streamlit uses the data index for the x-axis
values.
y : str, Sequence of str, or None
Column name(s) or key(s) associated to the y-axis data. If this is
``None`` (default), Streamlit draws the data of all remaining
columns as data series. If this is a ``Sequence`` of strings,
Streamlit draws several series on the same chart by melting your
wide-format table into a long-format table behind the scenes.
x_label : str or None
The label for the x-axis. If this is ``None`` (default), Streamlit
will use the column name specified in ``x`` if available, or else
no label will be displayed.
y_label : str or None
The label for the y-axis. If this is ``None`` (default), Streamlit
will use the column name(s) specified in ``y`` if available, or
else no label will be displayed.
color : str, tuple, Sequence of str, Sequence of tuple, or None
The color to use for different series in this chart.
For an area chart with just 1 series, this can be:
- None, to use the default color.
- A hex string like "#ffaa00" or "#ffaa0088".
- An RGB or RGBA tuple with the red, green, blue, and alpha
components specified as ints from 0 to 255 or floats from 0.0 to
1.0.
For an area chart with multiple series, where the dataframe is in
long format (that is, y is None or just one column), this can be:
- None, to use the default colors.
- The name of a column in the dataset. Data points will be grouped
into series of the same color based on the value of this column.
In addition, if the values in this column match one of the color
formats above (hex string or color tuple), then that color will
be used.
For example: if the dataset has 1000 rows, but this column only
contains the values "adult", "child", and "baby", then those 1000
datapoints will be grouped into three series whose colors will be
automatically selected from the default palette.
But, if for the same 1000-row dataset, this column contained
the values "#ffaa00", "#f0f", "#0000ff", then then those 1000
datapoints would still be grouped into 3 series, but their
colors would be "#ffaa00", "#f0f", "#0000ff" this time around.
For an area chart with multiple series, where the dataframe is in
wide format (that is, y is a Sequence of columns), this can be:
- None, to use the default colors.
- A list of string colors or color tuples to be used for each of
the series in the chart. This list should have the same length
as the number of y values (e.g. ``color=["#fd0", "#f0f", "#04f"]``
for three lines).
stack : bool, "normalize", "center", or None
Whether to stack the areas. If this is ``None`` (default),
Streamlit uses Vega's default. Other values can be as follows:
- ``True``: The areas form a non-overlapping, additive stack within
the chart.
- ``False``: The areas overlap each other without stacking.
- ``"normalize"``: The areas are stacked and the total height is
normalized to 100% of the height of the chart.
- ``"center"``: The areas are stacked and shifted to center their
baseline, which creates a steamgraph.
width : int or None
Desired width of the chart expressed in pixels. If ``width`` is
``None`` (default), Streamlit sets the width of the chart to fit
its contents according to the plotting library, up to the width of
the parent container. If ``width`` is greater than the width of the
parent container, Streamlit sets the chart width to match the width
of the parent container.
To use ``width``, you must set ``use_container_width=False``.
height : int or None
Desired height of the chart expressed in pixels. If ``height`` is
``None`` (default), Streamlit sets the height of the chart to fit
its contents according to the plotting library.
use_container_width : bool
Whether to override ``width`` with the width of the parent
container. If ``use_container_width`` is ``True`` (default),
Streamlit sets the width of the chart to match the width of the
parent container. If ``use_container_width`` is ``False``,
Streamlit sets the chart's width according to ``width``.
Examples
--------
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
>>>
>>> st.area_chart(chart_data)
.. output::
https://doc-area-chart.streamlit.app/
height: 440px
You can also choose different columns to use for x and y, as well as set
the color dynamically based on a 3rd column (assuming your dataframe is in
long format):
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(
... {
... "col1": np.random.randn(20),
... "col2": np.random.randn(20),
... "col3": np.random.choice(["A", "B", "C"], 20),
... }
... )
>>>
>>> st.area_chart(chart_data, x="col1", y="col2", color="col3")
.. output::
https://doc-area-chart1.streamlit.app/
height: 440px
If your dataframe is in wide format, you can group multiple
columns under the y argument to show multiple series with different
colors:
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(
... np.random.randn(20, 3), columns=["col1", "col2", "col3"]
... )
>>>
>>> st.area_chart(
... chart_data,
... x="col1",
... y=["col2", "col3"],
... color=["#FF0000", "#0000FF"], # Optional
... )
.. output::
https://doc-area-chart2.streamlit.app/
height: 440px
You can adjust the stacking behavior by setting ``stack``. Create a
steamgraph:
>>> import streamlit as st
>>> from vega_datasets import data
>>>
>>> source = data.unemployment_across_industries()
>>>
>>> st.area_chart(source, x="date", y="count", color="series", stack="center")
.. output::
https://doc-area-chart-steamgraph.streamlit.app/
height: 440px
"""
# Check that the stack parameter is valid, raise more informative error message if not
maybe_raise_stack_warning(
stack,
"st.area_chart",
"https://docs.streamlit.io/develop/api-reference/charts/st.area_chart",
)
# st.area_chart's stack=False option translates to a "layered" area chart for
# vega. We reserve stack=False for
# grouped/non-stacked bar charts, so we need to translate False to "layered"
# here. The default stack type was changed in vega-lite 5.14.1:
# https://github.com/vega/vega-lite/issues/9337
# To get the old behavior, we also need to set stack to layered as the
# default (if stack is None)
if stack is False or stack is None:
stack = "layered"
chart, add_rows_metadata = generate_chart(
chart_type=ChartType.AREA,
data=data,
x_from_user=x,
y_from_user=y,
x_axis_label=x_label,
y_axis_label=y_label,
color_from_user=color,
size_from_user=None,
width=width,
height=height,
stack=stack,
use_container_width=use_container_width,
)
return cast(
"DeltaGenerator",
self._altair_chart(
chart,
use_container_width=use_container_width,
theme="streamlit",
add_rows_metadata=add_rows_metadata,
),
)
@gather_metrics("bar_chart")
def bar_chart(
self,
data: Data = None,
*,
x: str | None = None,
y: str | Sequence[str] | None = None,
x_label: str | None = None,
y_label: str | None = None,
color: str | Color | list[Color] | None = None,
horizontal: bool = False,
stack: bool | ChartStackType | None = None,
width: int | None = None,
height: int | None = None,
use_container_width: bool = True,
) -> DeltaGenerator:
"""Display a bar chart.
This is syntax-sugar around ``st.altair_chart``. The main difference
is this command uses the data's own column and indices to figure out
the chart's Altair spec. As a result this is easier to use for many
"just plot this" scenarios, while being less customizable.
If ``st.bar_chart`` does not guess the data specification
correctly, try specifying your desired chart using ``st.altair_chart``.
Parameters
----------
data : Anything supported by st.dataframe
Data to be plotted.
x : str or None
Column name or key associated to the x-axis data. If ``x`` is
``None`` (default), Streamlit uses the data index for the x-axis
values.
y : str, Sequence of str, or None
Column name(s) or key(s) associated to the y-axis data. If this is
``None`` (default), Streamlit draws the data of all remaining
columns as data series. If this is a ``Sequence`` of strings,
Streamlit draws several series on the same chart by melting your
wide-format table into a long-format table behind the scenes.
x_label : str or None
The label for the x-axis. If this is ``None`` (default), Streamlit
will use the column name specified in ``x`` if available, or else
no label will be displayed.
y_label : str or None
The label for the y-axis. If this is ``None`` (default), Streamlit
will use the column name(s) specified in ``y`` if available, or
else no label will be displayed.
color : str, tuple, Sequence of str, Sequence of tuple, or None
The color to use for different series in this chart.
For a bar chart with just one series, this can be:
- None, to use the default color.
- A hex string like "#ffaa00" or "#ffaa0088".
- An RGB or RGBA tuple with the red, green, blue, and alpha
components specified as ints from 0 to 255 or floats from 0.0 to
1.0.
For a bar chart with multiple series, where the dataframe is in
long format (that is, y is None or just one column), this can be:
- None, to use the default colors.
- The name of a column in the dataset. Data points will be grouped
into series of the same color based on the value of this column.
In addition, if the values in this column match one of the color
formats above (hex string or color tuple), then that color will
be used.
For example: if the dataset has 1000 rows, but this column only
contains the values "adult", "child", and "baby", then those 1000
datapoints will be grouped into three series whose colors will be
automatically selected from the default palette.
But, if for the same 1000-row dataset, this column contained
the values "#ffaa00", "#f0f", "#0000ff", then then those 1000
datapoints would still be grouped into 3 series, but their
colors would be "#ffaa00", "#f0f", "#0000ff" this time around.
For a bar chart with multiple series, where the dataframe is in
wide format (that is, y is a Sequence of columns), this can be:
- None, to use the default colors.
- A list of string colors or color tuples to be used for each of
the series in the chart. This list should have the same length
as the number of y values (e.g. ``color=["#fd0", "#f0f", "#04f"]``
for three lines).
horizontal : bool
Whether to make the bars horizontal. If this is ``False``
(default), the bars display vertically. If this is ``True``,
Streamlit swaps the x-axis and y-axis and the bars display
horizontally.
stack : bool, "normalize", "center", "layered", or None
Whether to stack the bars. If this is ``None`` (default),
Streamlit uses Vega's default. Other values can be as follows:
- ``True``: The bars form a non-overlapping, additive stack within
the chart.
- ``False``: The bars display side by side.
- ``"layered"``: The bars overlap each other without stacking.
- ``"normalize"``: The bars are stacked and the total height is
normalized to 100% of the height of the chart.
- ``"center"``: The bars are stacked and shifted to center the
total height around an axis.
width : int or None
Desired width of the chart expressed in pixels. If ``width`` is
``None`` (default), Streamlit sets the width of the chart to fit
its contents according to the plotting library, up to the width of
the parent container. If ``width`` is greater than the width of the
parent container, Streamlit sets the chart width to match the width
of the parent container.
To use ``width``, you must set ``use_container_width=False``.
height : int or None
Desired height of the chart expressed in pixels. If ``height`` is
``None`` (default), Streamlit sets the height of the chart to fit
its contents according to the plotting library.
use_container_width : bool
Whether to override ``width`` with the width of the parent
container. If ``use_container_width`` is ``True`` (default),
Streamlit sets the width of the chart to match the width of the
parent container. If ``use_container_width`` is ``False``,
Streamlit sets the chart's width according to ``width``.
Examples
--------
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
>>>
>>> st.bar_chart(chart_data)
.. output::
https://doc-bar-chart.streamlit.app/
height: 440px
You can also choose different columns to use for x and y, as well as set
the color dynamically based on a 3rd column (assuming your dataframe is in
long format):
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(
... {
... "col1": list(range(20)) * 3,
... "col2": np.random.randn(60),
... "col3": ["A"] * 20 + ["B"] * 20 + ["C"] * 20,
... }
... )
>>>
>>> st.bar_chart(chart_data, x="col1", y="col2", color="col3")
.. output::
https://doc-bar-chart1.streamlit.app/
height: 440px
If your dataframe is in wide format, you can group multiple
columns under the y argument to show multiple series with different
colors:
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(
... {
... "col1": list(range(20)),
... "col2": np.random.randn(20),
... "col3": np.random.randn(20),
... }
... )
>>>
>>> st.bar_chart(
... chart_data,
... x="col1",
... y=["col2", "col3"],
... color=["#FF0000", "#0000FF"], # Optional
... )
.. output::
https://doc-bar-chart2.streamlit.app/
height: 440px
You can rotate your bar charts to display horizontally.
>>> import streamlit as st
>>> from vega_datasets import data
>>>
>>> source = data.barley()
>>>
>>> st.bar_chart(source, x="variety", y="yield", color="site", horizontal=True)
.. output::
https://doc-bar-chart-horizontal.streamlit.app/
height: 440px
You can unstack your bar charts.
>>> import streamlit as st
>>> from vega_datasets import data
>>>
>>> source = data.barley()
>>>
>>> st.bar_chart(source, x="year", y="yield", color="site", stack=False)
.. output::
https://doc-bar-chart-unstacked.streamlit.app/
height: 440px
"""
# Check that the stack parameter is valid, raise more informative error message if not
maybe_raise_stack_warning(
stack,
"st.bar_chart",
"https://docs.streamlit.io/develop/api-reference/charts/st.bar_chart",
)
# Offset encodings (used for non-stacked/grouped bar charts) are not supported in Altair < 5.0.0
if type_util.is_altair_version_less_than("5.0.0") and stack is False:
raise StreamlitAPIException(
"Streamlit does not support non-stacked (grouped) bar charts with "
"Altair 4.x. Please upgrade to Version 5."
)
bar_chart_type = (
ChartType.HORIZONTAL_BAR if horizontal else ChartType.VERTICAL_BAR
)
chart, add_rows_metadata = generate_chart(
chart_type=bar_chart_type,
data=data,
x_from_user=x,
y_from_user=y,
x_axis_label=x_label,
y_axis_label=y_label,
color_from_user=color,
size_from_user=None,
width=width,
height=height,
use_container_width=use_container_width,
stack=stack,
horizontal=horizontal,
)
return cast(
"DeltaGenerator",
self._altair_chart(
chart,
use_container_width=use_container_width,
theme="streamlit",
add_rows_metadata=add_rows_metadata,
),
)
@gather_metrics("scatter_chart")
def scatter_chart(
self,
data: Data = None,
*,
x: str | None = None,
y: str | Sequence[str] | None = None,
x_label: str | None = None,
y_label: str | None = None,
color: str | Color | list[Color] | None = None,
size: str | float | int | None = None,
width: int | None = None,
height: int | None = None,
use_container_width: bool = True,
) -> DeltaGenerator:
"""Display a scatterplot chart.
This is syntax-sugar around ``st.altair_chart``. The main difference
is this command uses the data's own column and indices to figure out
the chart's Altair spec. As a result this is easier to use for many
"just plot this" scenarios, while being less customizable.
If ``st.scatter_chart`` does not guess the data specification correctly,
try specifying your desired chart using ``st.altair_chart``.
Parameters
----------
data : Anything supported by st.dataframe
Data to be plotted.
x : str or None
Column name or key associated to the x-axis data. If ``x`` is
``None`` (default), Streamlit uses the data index for the x-axis
values.
y : str, Sequence of str, or None
Column name(s) or key(s) associated to the y-axis data. If this is
``None`` (default), Streamlit draws the data of all remaining
columns as data series. If this is a ``Sequence`` of strings,
Streamlit draws several series on the same chart by melting your
wide-format table into a long-format table behind the scenes.
x_label : str or None
The label for the x-axis. If this is ``None`` (default), Streamlit
will use the column name specified in ``x`` if available, or else
no label will be displayed.
y_label : str or None
The label for the y-axis. If this is ``None`` (default), Streamlit
will use the column name(s) specified in ``y`` if available, or
else no label will be displayed.
color : str, tuple, Sequence of str, Sequence of tuple, or None
The color of the circles representing each datapoint.
This can be:
- None, to use the default color.
- A hex string like "#ffaa00" or "#ffaa0088".
- An RGB or RGBA tuple with the red, green, blue, and alpha
components specified as ints from 0 to 255 or floats from 0.0 to
1.0.
- The name of a column in the dataset where the color of that
datapoint will come from.
If the values in this column are in one of the color formats
above (hex string or color tuple), then that color will be used.
Otherwise, the color will be automatically picked from the
default palette.
For example: if the dataset has 1000 rows, but this column only
contains the values "adult", "child", and "baby", then those 1000
datapoints be shown using three colors from the default palette.
But if this column only contains floats or ints, then those
1000 datapoints will be shown using a colors from a continuous
color gradient.
Finally, if this column only contains the values "#ffaa00",
"#f0f", "#0000ff", then then each of those 1000 datapoints will
be assigned "#ffaa00", "#f0f", or "#0000ff" as appropriate.
If the dataframe is in wide format (that is, y is a Sequence of
columns), this can also be:
- A list of string colors or color tuples to be used for each of
the series in the chart. This list should have the same length
as the number of y values (e.g. ``color=["#fd0", "#f0f", "#04f"]``
for three series).
size : str, float, int, or None
The size of the circles representing each point.
This can be:
- A number like 100, to specify a single size to use for all
datapoints.
- The name of the column to use for the size. This allows each
datapoint to be represented by a circle of a different size.
width : int or None
Desired width of the chart expressed in pixels. If ``width`` is
``None`` (default), Streamlit sets the width of the chart to fit
its contents according to the plotting library, up to the width of
the parent container. If ``width`` is greater than the width of the
parent container, Streamlit sets the chart width to match the width
of the parent container.
To use ``width``, you must set ``use_container_width=False``.
height : int or None
Desired height of the chart expressed in pixels. If ``height`` is
``None`` (default), Streamlit sets the height of the chart to fit
its contents according to the plotting library.
use_container_width : bool
Whether to override ``width`` with the width of the parent
container. If ``use_container_width`` is ``True`` (default),
Streamlit sets the width of the chart to match the width of the
parent container. If ``use_container_width`` is ``False``,
Streamlit sets the chart's width according to ``width``.
Examples
--------
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
>>>
>>> st.scatter_chart(chart_data)
.. output::
https://doc-scatter-chart.streamlit.app/
height: 440px
You can also choose different columns to use for x and y, as well as set
the color dynamically based on a 3rd column (assuming your dataframe is in
long format):
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(
... np.random.randn(20, 3), columns=["col1", "col2", "col3"]
... )
>>> chart_data["col4"] = np.random.choice(["A", "B", "C"], 20)
>>>
>>> st.scatter_chart(
... chart_data,
... x="col1",
... y="col2",
... color="col4",
... size="col3",
... )
.. output::
https://doc-scatter-chart1.streamlit.app/
height: 440px
Finally, if your dataframe is in wide format, you can group multiple
columns under the y argument to show multiple series with different
colors:
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(
... np.random.randn(20, 4), columns=["col1", "col2", "col3", "col4"]
... )
>>>
>>> st.scatter_chart(
... chart_data,
... x="col1",
... y=["col2", "col3"],
... size="col4",
... color=["#FF0000", "#0000FF"], # Optional
... )
.. output::
https://doc-scatter-chart2.streamlit.app/
height: 440px
"""
chart, add_rows_metadata = generate_chart(
chart_type=ChartType.SCATTER,
data=data,
x_from_user=x,
y_from_user=y,
x_axis_label=x_label,
y_axis_label=y_label,
color_from_user=color,
size_from_user=size,
width=width,
height=height,
use_container_width=use_container_width,
)
return cast(
"DeltaGenerator",
self._altair_chart(
chart,
use_container_width=use_container_width,
theme="streamlit",
add_rows_metadata=add_rows_metadata,
),
)
# When on_select=Ignore, return DeltaGenerator.
@overload
def altair_chart(
self,
altair_chart: AltairChart,
*,
use_container_width: bool | None = None,
theme: Literal["streamlit"] | None = "streamlit",
key: Key | None = None,
on_select: Literal["ignore"] = "ignore",
selection_mode: str | Iterable[str] | None = None,
) -> DeltaGenerator: ...
# When on_select=rerun, return VegaLiteState.
@overload
def altair_chart(
self,
altair_chart: AltairChart,
*,
use_container_width: bool | None = None,
theme: Literal["streamlit"] | None = "streamlit",
key: Key | None = None,
on_select: Literal["rerun"] | WidgetCallback,
selection_mode: str | Iterable[str] | None = None,
) -> VegaLiteState: ...
@gather_metrics("altair_chart")
def altair_chart(
self,
altair_chart: AltairChart,
*,
use_container_width: bool | None = None,
theme: Literal["streamlit"] | None = "streamlit",
key: Key | None = None,
on_select: Literal["rerun", "ignore"] | WidgetCallback = "ignore",
selection_mode: str | Iterable[str] | None = None,
) -> DeltaGenerator | VegaLiteState:
"""Display a chart using the Vega-Altair library.
`Vega-Altair `_ is a declarative
statistical visualization library for Python, based on Vega and
Vega-Lite.
Parameters
----------
altair_chart : altair.Chart
The Altair chart object to display. See
https://altair-viz.github.io/gallery/ for examples of graph
descriptions.
use_container_width : bool or None
Whether to override the chart's native width with the width of
the parent container. This can be one of the following:
- ``None`` (default): Streamlit will use the parent container's
width for all charts except those with known incompatibility
(``altair.Facet``, ``altair.HConcatChart``, and
``altair.RepeatChart``).
- ``True``: Streamlit sets the width of the chart to match the
width of the parent container.
- ``False``: Streamlit sets the width of the chart to fit its
contents according to the plotting library, up to the width of
the parent container.
theme : "streamlit" or None
The theme of the chart. If ``theme`` is ``"streamlit"`` (default),
Streamlit uses its own design default. If ``theme`` is ``None``,
Streamlit falls back to the default behavior of the library.
The ``"streamlit"`` theme can be partially customized through the
configuration options ``theme.chartCategoricalColors`` and
``theme.chartSequentialColors``. Font configuration options are
also applied.
key : str
An optional string to use for giving this element a stable
identity. If ``key`` is ``None`` (default), this element's identity
will be determined based on the values of the other parameters.
Additionally, if selections are activated and ``key`` is provided,
Streamlit will register the key in Session State to store the
selection state. The selection state is read-only.
on_select : "ignore", "rerun", or callable
How the figure should respond to user selection events. This
controls whether or not the figure behaves like an input widget.
``on_select`` can be one of the following:
- ``"ignore"`` (default): Streamlit will not react to any selection
events in the chart. The figure will not behave like an input
widget.
- ``"rerun"``: Streamlit will rerun the app when the user selects
data in the chart. In this case, ``st.altair_chart`` will return
the selection data as a dictionary.
- A ``callable``: Streamlit will rerun the app and execute the
``callable`` as a callback function before the rest of the app.
In this case, ``st.altair_chart`` will return the selection data
as a dictionary.
To use selection events, the object passed to ``altair_chart`` must
include selection parameters. To learn about defining interactions
in Altair and how to declare selection-type parameters, see
`Interactive Charts \
`_
in Altair's documentation.
selection_mode : str or Iterable of str
The selection parameters Streamlit should use. If
``selection_mode`` is ``None`` (default), Streamlit will use all
selection parameters defined in the chart's Altair spec.
When Streamlit uses a selection parameter, selections from that
parameter will trigger a rerun and be included in the selection
state. When Streamlit does not use a selection parameter,
selections from that parameter will not trigger a rerun and not be
included in the selection state.
Selection parameters are identified by their ``name`` property.
Returns
-------
element or dict
If ``on_select`` is ``"ignore"`` (default), this command returns an
internal placeholder for the chart element that can be used with
the ``.add_rows()`` method. Otherwise, this command returns a
dictionary-like object that supports both key and attribute
notation. The attributes are described by the ``VegaLiteState``
dictionary schema.
Example
-------
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>> import altair as alt
>>>
>>> chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
>>>
>>> c = (
... alt.Chart(chart_data)
... .mark_circle()
... .encode(x="a", y="b", size="c", color="c", tooltip=["a", "b", "c"])
... )
>>>
>>> st.altair_chart(c)
.. output::
https://doc-vega-lite-chart.streamlit.app/
height: 450px
"""
return self._altair_chart(
altair_chart=altair_chart,
use_container_width=use_container_width,
theme=theme,
key=key,
on_select=on_select,
selection_mode=selection_mode,
)
# When on_select=Ignore, return DeltaGenerator.
@overload
def vega_lite_chart(
self,
data: Data = None,
spec: VegaLiteSpec | None = None,
*,
use_container_width: bool | None = None,
theme: Literal["streamlit"] | None = "streamlit",
key: Key | None = None,
on_select: Literal["ignore"] = "ignore",
selection_mode: str | Iterable[str] | None = None,
**kwargs: Any,
) -> DeltaGenerator: ...
# When on_select=rerun, return VegaLiteState.
@overload
def vega_lite_chart(
self,
data: Data = None,
spec: VegaLiteSpec | None = None,
*,
use_container_width: bool | None = None,
theme: Literal["streamlit"] | None = "streamlit",
key: Key | None = None,
on_select: Literal["rerun"] | WidgetCallback,
selection_mode: str | Iterable[str] | None = None,
**kwargs: Any,
) -> VegaLiteState: ...
@gather_metrics("vega_lite_chart")
def vega_lite_chart(
self,
data: Data = None,
spec: VegaLiteSpec | None = None,
*,
use_container_width: bool | None = None,
theme: Literal["streamlit"] | None = "streamlit",
key: Key | None = None,
on_select: Literal["rerun", "ignore"] | WidgetCallback = "ignore",
selection_mode: str | Iterable[str] | None = None,
**kwargs: Any,
) -> DeltaGenerator | VegaLiteState:
"""Display a chart using the Vega-Lite library.
`Vega-Lite `_ is a high-level
grammar for defining interactive graphics.
Parameters
----------
data : Anything supported by st.dataframe
Either the data to be plotted or a Vega-Lite spec containing the
data (which more closely follows the Vega-Lite API).
spec : dict or None
The Vega-Lite spec for the chart. If ``spec`` is ``None`` (default),
Streamlit uses the spec passed in ``data``. You cannot pass a spec
to both ``data`` and ``spec``. See
https://vega.github.io/vega-lite/docs/ for more info.
use_container_width : bool or None
Whether to override the chart's native width with the width of
the parent container. This can be one of the following:
- ``None`` (default): Streamlit will use the parent container's
width for all charts except those with known incompatibility
(``altair.Facet``, ``altair.HConcatChart``, and
``altair.RepeatChart``).
- ``True``: Streamlit sets the width of the chart to match the
width of the parent container.
- ``False``: Streamlit sets the width of the chart to fit its
contents according to the plotting library, up to the width of
the parent container.
theme : "streamlit" or None
The theme of the chart. If ``theme`` is ``"streamlit"`` (default),
Streamlit uses its own design default. If ``theme`` is ``None``,
Streamlit falls back to the default behavior of the library.
The ``"streamlit"`` theme can be partially customized through the
configuration options ``theme.chartCategoricalColors`` and
``theme.chartSequentialColors``. Font configuration options are
also applied.
key : str
An optional string to use for giving this element a stable
identity. If ``key`` is ``None`` (default), this element's identity
will be determined based on the values of the other parameters.
Additionally, if selections are activated and ``key`` is provided,
Streamlit will register the key in Session State to store the
selection state. The selection state is read-only.
on_select : "ignore", "rerun", or callable
How the figure should respond to user selection events. This
controls whether or not the figure behaves like an input widget.
``on_select`` can be one of the following:
- ``"ignore"`` (default): Streamlit will not react to any selection
events in the chart. The figure will not behave like an input
widget.
- ``"rerun"``: Streamlit will rerun the app when the user selects
data in the chart. In this case, ``st.vega_lite_chart`` will
return the selection data as a dictionary.
- A ``callable``: Streamlit will rerun the app and execute the
``callable`` as a callback function before the rest of the app.
In this case, ``st.vega_lite_chart`` will return the selection data
as a dictionary.
To use selection events, the Vega-Lite spec defined in ``data`` or
``spec`` must include selection parameters from the charting
library. To learn about defining interactions in Vega-Lite, see
`Dynamic Behaviors with Parameters \
`_
in Vega-Lite's documentation.
selection_mode : str or Iterable of str
The selection parameters Streamlit should use. If
``selection_mode`` is ``None`` (default), Streamlit will use all
selection parameters defined in the chart's Vega-Lite spec.
When Streamlit uses a selection parameter, selections from that
parameter will trigger a rerun and be included in the selection
state. When Streamlit does not use a selection parameter,
selections from that parameter will not trigger a rerun and not be
included in the selection state.
Selection parameters are identified by their ``name`` property.
**kwargs : any
The Vega-Lite spec for the chart as keywords. This is an alternative
to ``spec``.
Returns
-------
element or dict
If ``on_select`` is ``"ignore"`` (default), this command returns an
internal placeholder for the chart element that can be used with
the ``.add_rows()`` method. Otherwise, this command returns a
dictionary-like object that supports both key and attribute
notation. The attributes are described by the ``VegaLiteState``
dictionary schema.
Example
-------
>>> import streamlit as st
>>> import pandas as pd
>>> import numpy as np
>>>
>>> chart_data = pd.DataFrame(np.random.randn(200, 3), columns=["a", "b", "c"])
>>>
>>> st.vega_lite_chart(
... chart_data,
... {
... "mark": {"type": "circle", "tooltip": True},
... "encoding": {
... "x": {"field": "a", "type": "quantitative"},
... "y": {"field": "b", "type": "quantitative"},
... "size": {"field": "c", "type": "quantitative"},
... "color": {"field": "c", "type": "quantitative"},
... },
... },
... )
.. output::
https://doc-vega-lite-chart.streamlit.app/
height: 450px
Examples of Vega-Lite usage without Streamlit can be found at
https://vega.github.io/vega-lite/examples/. Most of those can be easily
translated to the syntax shown above.
"""
return self._vega_lite_chart(
data=data,
spec=spec,
use_container_width=use_container_width,
theme=theme,
key=key,
on_select=on_select,
selection_mode=selection_mode,
**kwargs,
)
def _altair_chart(
self,
altair_chart: AltairChart,
use_container_width: bool | None = None,
theme: Literal["streamlit"] | None = "streamlit",
key: Key | None = None,
on_select: Literal["rerun", "ignore"] | WidgetCallback = "ignore",
selection_mode: str | Iterable[str] | None = None,
add_rows_metadata: AddRowsMetadata | None = None,
) -> DeltaGenerator | VegaLiteState:
"""Internal method to enqueue a vega-lite chart element based on an Altair chart.
See the `altair_chart` method docstring for more information.
"""
if type_util.is_altair_version_less_than("5.0.0") and on_select != "ignore":
raise StreamlitAPIException(
"Streamlit does not support selections with Altair 4.x. Please upgrade "
"to Version 5. "
"If you would like to use Altair 4.x with selections, please upvote "
"this [Github issue](https://github.com/streamlit/streamlit/issues/8516)."
)
vega_lite_spec = _convert_altair_to_vega_lite_spec(altair_chart)
return self._vega_lite_chart(
data=None, # The data is already part of the spec
spec=vega_lite_spec,
use_container_width=use_container_width,
theme=theme,
key=key,
on_select=on_select,
selection_mode=selection_mode,
add_rows_metadata=add_rows_metadata,
)
def _vega_lite_chart(
self,
data: Data = None,
spec: VegaLiteSpec | None = None,
use_container_width: bool | None = None,
theme: Literal["streamlit"] | None = "streamlit",
key: Key | None = None,
on_select: Literal["rerun", "ignore"] | WidgetCallback = "ignore",
selection_mode: str | Iterable[str] | None = None,
add_rows_metadata: AddRowsMetadata | None = None,
**kwargs: Any,
) -> DeltaGenerator | VegaLiteState:
"""Internal method to enqueue a vega-lite chart element based on a vega-lite spec.
See the `vega_lite_chart` method docstring for more information.
"""
if theme not in ["streamlit", None]:
raise StreamlitAPIException(
f'You set theme="{theme}" while Streamlit charts only support '
"theme=”streamlit” or theme=None to fallback to the default "
"library theme."
)
if on_select not in ["ignore", "rerun"] and not callable(on_select):
raise StreamlitAPIException(
f"You have passed {on_select} to `on_select`. But only 'ignore', "
"'rerun', or a callable is supported."
)
key = to_key(key)
is_selection_activated = on_select != "ignore"
if is_selection_activated:
# Run some checks that are only relevant when selections are activated
is_callback = callable(on_select)
check_widget_policies(
self.dg,
key,
on_change=cast("WidgetCallback", on_select) if is_callback else None,
default_value=None,
writes_allowed=False,
enable_check_callback_rules=is_callback,
)
# Support passing data inside spec['datasets'] and spec['data'].
# (The data gets pulled out of the spec dict later on.)
if isinstance(data, dict) and spec is None:
spec = data
data = None
if spec is None:
spec = {}
# Set the default value for `use_container_width`.
if use_container_width is None:
# Some multi-view charts (facet, horizontal concatenation, and repeat;
# see https://altair-viz.github.io/user_guide/compound_charts.html)
# don't work well with `use_container_width=True`, so we disable it for
# those charts (see https://github.com/streamlit/streamlit/issues/9091).
# All other charts (including vertical concatenation) default to
# `use_container_width=True`.
is_facet_chart = "facet" in spec or (
"encoding" in spec
and (any(x in spec["encoding"] for x in ["row", "column", "facet"]))
)
use_container_width = not (
is_facet_chart or "hconcat" in spec or "repeat" in spec
)
vega_lite_proto = ArrowVegaLiteChartProto()
spec = _prepare_vega_lite_spec(spec, use_container_width, **kwargs)
_marshall_chart_data(vega_lite_proto, spec, data)
# Prevent the spec from changing across reruns:
vega_lite_proto.spec = _stabilize_vega_json_spec(json.dumps(spec))
vega_lite_proto.use_container_width = use_container_width
vega_lite_proto.theme = theme or ""
if is_selection_activated:
# Load the stabilized spec again as a dict:
final_spec = json.loads(vega_lite_proto.spec)
# Temporary limitation to disallow multi-view charts (compositions) with selections.
_disallow_multi_view_charts(final_spec)
# Parse and check the specified selection modes
parsed_selection_modes = _parse_selection_mode(final_spec, selection_mode)
vega_lite_proto.selection_mode.extend(parsed_selection_modes)
vega_lite_proto.form_id = current_form_id(self.dg)
ctx = get_script_run_ctx()
vega_lite_proto.id = compute_and_register_element_id(
"arrow_vega_lite_chart",
user_key=key,
form_id=vega_lite_proto.form_id,
dg=self.dg,
vega_lite_spec=vega_lite_proto.spec,
# The data is either in vega_lite_proto.data.data
# or in a named dataset in vega_lite_proto.datasets
vega_lite_data=vega_lite_proto.data.data,
# Its enough to just use the names here since they are expected
# to contain hashes based on the dataset data.
named_datasets=[dataset.name for dataset in vega_lite_proto.datasets],
theme=theme,
use_container_width=use_container_width,
selection_mode=parsed_selection_modes,
)
serde = VegaLiteStateSerde(parsed_selection_modes)
widget_state = register_widget(
vega_lite_proto.id,
on_change_handler=on_select if callable(on_select) else None,
deserializer=serde.deserialize,
serializer=serde.serialize,
ctx=ctx,
value_type="string_value",
)
self.dg._enqueue(
"arrow_vega_lite_chart",
vega_lite_proto,
add_rows_metadata=add_rows_metadata,
)
return widget_state.value
# If its not used with selections activated, just return
# the delta generator related to this element.
return self.dg._enqueue(
"arrow_vega_lite_chart",
vega_lite_proto,
add_rows_metadata=add_rows_metadata,
)
@property
def dg(self) -> DeltaGenerator:
"""Get our DeltaGenerator."""
return cast("DeltaGenerator", self)