Streamlit API

StreamlitRenderer

StreamlitRenderer是一个用于可视化呈现数据的类。

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

参数

参数	类型	默认值	描述
dataset	Union[DataFrame, Connector]	-	要使用的数据帧或连接器。参考图形漫步数据集。
gid	Union[int, str]	None	GraphicWalker容器div的ID，格式为'gwalker-{gid}'。如果gid为None，则将自动生成。
field_specs	Optional[Dict[str, FieldSpec]]	None	字段规格。如果未指定，将从`datase`t自动推断。
theme_key	Literal['vega', 'g2']	'g2'	GraphicWalker的主题类型。
appearance	Literal['media', 'light', 'dark']	'media'	主题设置。'media'将自动检测OS主题。
spec	str	""	图表配置数据。可以是配置ID、JSON或远程文件URL。
spec_io_mode	Literal["r", "rw"]	"r"	spec io模式，默认为"r"，"r"表示只读，"rw"表示读写。
kernel_computation	bool	None	如果为True，将使用核计算数据，支持在较大的数据集中高性能。默认为None，自动确定是否使用核计算。
kanaries_api_key	str	""	kanaries api密钥，默认为""。
default_tab	Literal["data", "vis"]	"vis"	要显示的默认选项卡。默认为"vis"。
**kwargs	Any	-	其他关键字参数。

StreamlitRenderer.explorer

StreamlitRenderer.explorer是一个用于浏览数据的方法。

renderer.explorer()

参数

参数	类型	默认值	描述
width	int	None	UI宽度，默认将适应页面宽度。
height	int	1000	UI高度。
scrolling	bool	False	滚动设置。
default_tab	Literal["data", "vis"]	"vis"	要显示的默认选项卡。默认为"vis"。

StreamlitRenderer.chart

StreamlitRenderer.chart是一个用于显示图表的方法。

renderer.chart(0)

参数

参数	类型	默认值	描述
index	int	None	图表的索引。
width	int	None	UI宽度，默认将适应页面宽度。
height	int	1000	UI高度。
scrolling	bool	False	滚动设置。

StreamlitRenderer.viewer

StreamlitRenderer.viewer是一个预览全部图表的方法。

renderer.viewer()

参数

参数	类型	默认值	描述
width	int	None	UI宽度，默认将适应页面宽度。
height	int	1000	UI高度。
scrolling	bool	False	滚动设置。

kernel_computation启用的示例(推荐)

在线演示：pygwalker演示 (opens in a new tab)

这是一个在Streamlit应用中使用StreamlitRenderer的示例。

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.viewer()
 
with tab4:
    st.markdown("### registered per weekday")
    renderer.chart(0)
    st.markdown("### registered per day")
    renderer.chart(1)

禁用kernel_computation的示例

这是一个在Streamlit应用中使用Pygwalker的示例。

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)