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]	-	El dataframe o conector a utilizar.
gid	Union[int, str]	None	ID para el contenedor div de GraphicWalker, formateado como 'gwalker-{gid}'. Si gid es None, se generará automáticamente.
field_specs	Optional[Dict[str, FieldSpec]]	None	Especificaciones de campos. Se inferirá automáticamente de `dataset` si no se especifica.
theme_key	Literal['vega', 'g2']	'g2'	Tipo de tema para GraphicWalker.
dark	Literal['media', 'light', 'dark']	'media'	Configuración del tema. 'Media' detectará automáticamente el tema del sistema operativo.
spec	str	""	Datos de configuración del gráfico. Puede ser un ID de configuración, JSON o URL de archivo remoto.
spec_io_mode	Literal["r", "rw"]	"r"	Modo io de spec, por defecto es "r", "r" para leer, "rw" para leer y escribir.
use_kernel_calc	bool	None	Si es Verdadero, utiliza el cálculo de núcleo para los datos, puede admitir un rendimiento alto en conjuntos de datos más grandes. Por defecto, es None, determina automáticamente si se debe usar el cálculo de núcleo.
kanaries_api_key	str	""	Clave de API de kanaries, por defecto es "".
default_tab	Literal["data", "vis"]	"vis"	pestaña predeterminada a mostrar. Por defecto es "vis".
**kwargs	Any	-	Argumentos de palabras clave adicionales.

StreamlitRenderer.explorer

renderer.explorer()

Parameters

Parameter	Type	Default	Description
width	int	None	ancho de la interfaz de usuario, por defecto se adaptará al ancho de la página.
height	int	1000	altura de la interfaz de usuario
scrolling	bool	False	desplazamiento
default_tab	Literal["data", "vis"]	"vis"	pestaña predeterminada a mostrar. Por defecto es "vis".

StreamlitRenderer.chart

renderer.chart(0)

Parameters

Parameter	Type	Default	Description
index	int	None	índice de los gráficos
width	int	None	ancho de la interfaz de usuario, por defecto se adaptará al ancho de la página.
height	int	1000	altura de la interfaz de usuario
scrolling	bool	False	desplazamiento

StreamlitRenderer.viewer

renderer.viewer()

Parameters

Parameter	Type	Default	Description
width	int	None	ancho de la interfaz de usuario, por defecto se adaptará al ancho de la página.
height	int	1000	altura de la interfaz de usuario
scrolling	bool	False	desplazamiento

Ejemplo de uso con use_kernel_calc habilitado (Recomendado)

Demo en línea: demo de pygwalker (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.viewer()
 
with tab4:
    st.markdown("### registered per weekday")
    renderer.chart(0)
    st.markdown("### registered per day")
    renderer.chart(1)

Ejemplo de use_kernel_calc deshabilitado

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)

Preguntas y respuestas relacionadas

¿Cómo construir una alternativa gratuita en línea a Tableau/PowerBI que pueda manejar una gran cantidad de datos?

Respuesta: Puedes utilizar pygwalker + streamlit + snowflake para construir una alternativa gratuita en línea a Tableau/PowerBI que pueda manejar una gran cantidad de datos.

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