Streamlit API

StreamlitRenderer

from pygwalker.api.streamlit import StreamlitRenderer
 
renderer = StreamlitRenderer(df, spec="./gw_config.json")

Parameters

Parameter	Type	Default	Description
dataset	Union[DataFrame, Connector]	-	The dataframe or connector to be used. refer Dataset Of Walker.
gid	Union[int, str]	None	ID for the GraphicWalker container div, formatted as 'gwalker-{gid}'. If gid is None, it will be automatically generated.
field_specs	Optional[Dict[str, FieldSpec]]	None	Specifications of fields. Will be automatically inferred from `dataset` if not specified.
theme_key	Literal['vega', 'g2']	'g2'	Theme type for the GraphicWalker.
dark	Literal['media', 'light', 'dark']	'media'	Theme setting. 'media' will auto-detect the OS theme.
spec	str	""	Chart configuration data. Can be a configuration ID, JSON, or remote file URL.
spec_io_mode	Literal["r", "rw"]	"r"	spec io mode, Default to "r", "r" for read, "rw" for read and write.
use_kernel_calc	bool	None	If True, uses kernel computation for data, it can support high performance in larger dataset. Default to None, automatically determine whether to use kernel calculation.
kanaries_api_key	str	""	kanaries api key, Default to "".
default_tab	Literal["data", "vis"]	"vis"	default tab to show. Default to "vis".
**kwargs	Any	-	Additional keyword arguments.

StreamlitRenderer.explorer

renderer.explorer()

Parameters

Parameter	Type	Default	Description
width	int	None	ui width, By default, it will adapt to the width of the page.
height	int	1000	ui height
scrolling	bool	False	scrolling
default_tab	Literal["data", "vis"]	"vis"	default tab to show. Default to "vis".

StreamlitRenderer.chart

renderer.chart(0)

Parameters

Parameter	Type	Default	Description
index	int	None	index of charts
width	int	None	ui width, By default, it will adapt to the width of the page.
height	int	1000	ui height
scrolling	bool	False	scrolling

StreamlitRenderer.explorer

renderer.render_filter_renderer()

Parameters

Parameter	Type	Default	Description
width	int	None	ui width, By default, it will adapt to the width of the page.
height	int	1000	ui height
scrolling	bool	False	scrolling

Example of use_kernel_calc enabled(Recommend)

Online demo: pygwalker demo (opens in a new tab)

from pygwalker.api.streamlit import StreamlitRenderer
import pandas as pd
import streamlit as st
 
# Adjust the width of the Streamlit page
st.set_page_config(
    page_title="Use Pygwalker In Streamlit",
    layout="wide"
)
 
# Add Title
st.title("Use Pygwalker In Streamlit")
 
# You should cache your pygwalker renderer, if you don't want your memory to explode
@st.cache_resource
def get_pyg_renderer() -> "StreamlitRenderer":
    df = pd.read_csv("data.csv")
    # If you want to use feature of saving chart config, set `spec_io_mode="rw"`
    return StreamlitRenderer(df, spec="./gw_config.json")
 
 
renderer = get_pyg_renderer()
 
st.subheader("Display Explore UI")
 
tab1, tab2, tab3, tab4 = st.tabs(
    ["graphic walker", "data profiling", "graphic renderer", "pure chart"]
)
 
with tab1:
    renderer.explorer()
 
with tab2:
    renderer.explorer(default_tab="data")
 
with tab3:
    renderer.render_filter_renderer()
 
with tab4:
    st.markdown("### registered per weekday")
    renderer.chart(0)
    st.markdown("### registered per day")
    renderer.chart(1)

Example of use_kernel_calc disabled

import pygwalker as pyg
import pandas as pd
import streamlit.components.v1 as components
import streamlit as st
 
# Adjust the width of the Streamlit page
st.set_page_config(
    page_title="Use Pygwalker In Streamlit",
    layout="wide"
)
 
# Add Title
st.title("Use Pygwalker In Streamlit")
 
# Import your data
df = pd.read_csv("/bike_sharing_dc.csv")
# Paste the copied Pygwalker chart code here
vis_spec = """[{"visId":"gw_rZy5","name":"Chart 1","encodings":{"dimensions":[{"dragId":"gw_BUE2","fid":"ZGF0ZV8x","name":"date","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_x1ug","fid":"bW9udGhfMg==","name":"month","semanticType":"ordinal","analyticType":"dimension"},{"dragId":"gw_zRAa","fid":"c2Vhc29uXzM=","name":"season","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_ZeVh","fid":"eWVhcl81","name":"year","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_JqXv","fid":"aG9saWRheV82","name":"holiday","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_OD2F","fid":"d29yayB5ZXMgb3Igbm90XzE0","name":"work yes or not","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_KgQu","fid":"YW0gb3IgcG1fMTU=","name":"am or pm","semanticType":"nominal","analyticType":"dimension"},{"dragId":"gw_PqvI","fid":"RGF5IG9mIHRoZSB3ZWVrXzE2","name":"Day of the week","semanticType":"ordinal","analyticType":"dimension"}],"measures":[{"dragId":"gw_7JNg","fid":"aW5kZXhfMA==","name":"index","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_IYM_","fid":"aG91cl80","name":"hour","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_ofd8","fid":"dGVtcGVyYXR1cmVfNw==","name":"temperature","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_sGlX","fid":"ZmVlbGluZ190ZW1wXzg=","name":"feeling_temp","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_674M","fid":"aHVtaWRpdHlfOQ==","name":"humidity","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_AxQD","fid":"d2luc3BlZWRfMTA=","name":"winspeed","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_iy94","fid":"Y2FzdWFsXzEx","name":"casual","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_9J2u","fid":"cmVnaXN0ZXJlZF8xMg==","name":"registered","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_WzEF","fid":"Y291bnRfMTM=","name":"count","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"gw_count_fid","fid":"gw_count_fid","name":"Row count","analyticType":"measure","semanticType":"quantitative","aggName":"sum","computed":true,"expression":{"op":"one","params":[],"as":"gw_count_fid"}}],"rows":[{"dragId":"gw_gJqj","fid":"cmVnaXN0ZXJlZF8xMg==","name":"registered","analyticType":"measure","semanticType":"quantitative","aggName":"sum"}],"columns":[{"dragId":"gw_uZ9C","fid":"RGF5IG9mIHRoZSB3ZWVrXzE2","name":"Day of the week","semanticType":"ordinal","analyticType":"dimension"}],"color":[{"dragId":"gw_04s5","fid":"c2Vhc29uXzM=","name":"season","semanticType":"nominal","analyticType":"dimension"}],"opacity":[],"size":[],"shape":[],"radius":[],"theta":[],"details":[],"filters":[],"text":[]},"config":{"defaultAggregated":true,"geoms":["auto"],"stack":"stack","showActions":false,"interactiveScale":false,"sorted":"none","zeroScale":true,"size":{"mode":"auto","width":320,"height":200},"format":{}}}]"""
 
# Generate the HTML using Pygwalker
pyg_html = pyg.to_html(df, spec=vis_spec)
 
# Embed the HTML into the Streamlit app
components.html(pyg_html, height=1000, scrolling=True)

Related Q&A

How to build a online open source alternative to Tableau/PowerBI which can handle large amount of data?

Answer: You can use pygwalker + streamlit + snowflake to build a online open source alternative to Tableau/PowerBI which can handle large amount of data.

Jupyter Walker APIs Html APIs