Streamlit API
StreamlitRenderer
from pygwalker.api.streamlit import StreamlitRenderer
renderer = StreamlitRenderer(df, spec="./gw_config.json")
Parameter
Parameter | Typ | Standard | Beschreibung |
---|---|---|---|
Datensatz | Union[DataFrame, Connector] | - | Der DataFrame oder Connector, der verwendet werden soll. Siehe Dataset of Walker. |
GID | Union[int, str] | None | ID für das GraphicWalker-Container-Div, formatiert als 'gwalker-{gid}'. Wenn GID None ist, wird es automatisch generiert. |
Feldspezifikationen | Optional[Dict[str, FieldSpec]] | None | Spezifikationen der Felder. Wird automatisch aus dem Datensatz abgeleitet, wenn nicht angegeben. |
Theme-Schlüssel | Literal['vega', 'g2'] | 'g2' | Thementyp für den GraphicWalker. |
Dunkel | Literal['media', 'licht', 'dunkel'] | 'medien' | Themaeinstellung. 'media' erkennt automatisch das Betriebssystemdesign. |
Spez | str | "" | Diagramm-Konfigurationsdaten. Kann eine Konfigurations-ID, JSON oder eine Remote-Datei-URL sein. |
Spec_io_mode | Literal["r", "rw"] | "r" | Spez-IO-Modus, Standardmäßig "r", "r" für Lesen, "rw" für Lesen und Schreiben. |
use_kernel_calc | bool | None | Wenn True, wird die Kernelberechnung für Daten verwendet, um eine hohe Leistung bei größeren Datensätzen zu ermöglichen. Standardmäßig None, bestimmt automatisch, ob die Kernelberechnung verwendet werden soll. |
kanaries_api_key | str | "" | Kanaries-API-Schlüssel, Standardmäßig "". |
Standardtab | Literal["data", "vis"] | "vis" | Standardtab zum Anzeigen. Standardmäßig "vis". |
**kwargs | Any | - | Zusätzliche Schlüsselwortargumente. |
StreamlitRenderer.explorer
renderer.explorer()
Parameter
Parameter | Typ | Standard | Beschreibung |
---|---|---|---|
Breite | int | None | UI-Breite, passt standardmäßig an die Breite der Seite an. |
Höhe | int | 1000 | UI-Höhe |
Scrollen | bool | False | Scrollen |
Standardtab | Literal["data", "vis"] | "vis" | Standardtab zum Anzeigen. Standardmäßig "vis". |
StreamlitRenderer.chart
renderer.chart(0)
Parameter
Parameter | Typ | Standard | Beschreibung |
---|---|---|---|
Index | int | None | Index der Diagramme |
Breite | int | None | UI-Breite, passt standardmäßig an die Breite der Seite an. |
Höhe | int | 1000 | UI-Höhe |
Scrollen | bool | False | Scrollen |
StreamlitRenderer.viewer
renderer.viewer()
Parameter
Parameter | Typ | Standard | Beschreibung |
---|---|---|---|
Breite | int | None | UI-Breite, passt standardmäßig an die Breite der Seite an. |
Höhe | int | 1000 | UI-Höhe |
Scrollen | bool | False | Scrollen |
Verwendungsbeispiel von aktiviertem use_kernel_calc (Empfohlen)
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.viewer()
with tab4:
st.markdown("### registered per weekday")
renderer.chart(0)
st.markdown("### registered per day")
renderer.chart(1)
Beispiel für die Deaktivierung von use_kernel_calc
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)
Verwandte Fragen und Antworten
Wie erstelle ich eine Online-Open-Source-Alternative zu Tableau/PowerBI, die große Datenmengen verarbeiten kann?
Antwort: Sie können pygwalker + streamlit + snowflake verwenden, um eine Online-Open-Source-Alternative zu Tableau/PowerBI zu erstellen, die große Datenmengen verarbeiten kann.