(animal), 'image': image, 'title': title, 'lazy_index': lazy_index, 'loyalty_index': loyalty_index, 'survival_index': survival_index, 'state': state, 'edit': edit, 'details': details, 'delete': delete, } data = pd.DataFrame(data=data) data.index = data['id'] return data #...
selectbox("请选择一种主题", theme_list) draw_table(df, theme=theme, table_height=300) st.sidebar.header("Streamlit自带的表格样式") st.sidebar.info("st.write") st.sidebar.write(df) st.sidebar.info("st.table") st.sidebar.table(df) st.sidebar.info("st.dataframe") st.sidebar.dataframe...
conn = sqlite3.connect("data.db", check_same_thread=False) conn.row_factory = sqlite3.Row return conn @handle_db_errors defquery_db(query, params=None, _conn=None): """执行查询并返回DataFrame""" conn = _conn or get_connection() return pd.read_sql(query, conn, params=params) @han...
df=pd.DataFrame([{"command":"st.selectbox","rating":4,"is_widget":True},{"command":"st.balloons","rating":5,"is_widget":False},{"command":"st.time_input","rating":3,"is_widget":True},])edited_df=st.data_editor(df)favorite_command=edited_df.loc[edited_df["rating"].idxmax()...
Checklist I have searched the existing issues for similar feature requests. I added a descriptive title and summary to this issue. Summary I applaud the Streamlit team for adding st.dataframe select options and responding to users wantin...
import streamlit as stimport pandas as pdimport timedef expensive_process(option, add):with st.spinner('Processing...'):time.sleep(5)df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C':[7, 8, 9]}) + addreturn (df, add) ...
Problem In the official tutorial about st.dataframe, it provides a well interactive UI, but it lacks one important feature: indicate the selected (row, column). For example: import streamlit as st import pandas as pd import numpy as np d...
• select() — 这将从提供的表达式创建一个新的数据帧(类似于 SQL SELECT) • collect() — 此方法执行整个数据帧并将结果具体化 我们首先从之前引入记录的 S3 存储桶中读取 Hudi 表。接下来,我们使用该 select() 方法来选择分析所需的字段。由于 Daft DataFrame是惰性的,这意味着它们在明确指示之前不会...
importstreamlitasstimportpandasaspdimporttimedefexpensive_process(option,add):withst.spinner('Processing...'):time.sleep(5)df=pd.DataFrame({'A':[1,2,3],'B':[4,5,6],'C':[7,8,9]})+addreturn(df,add) 1. 2. 3. 4. 5.
wrapper@st.cache_resource(show_spinner=False)defget_connection():"""获取数据库连接""" conn = sqlite3.connect("data.db", check_same_thread=False) conn.row_factory = sqlite3.Rowreturn conn@handle_db_errorsdefquery_db(query, params=None, _conn=None):"""执行查询并返回DataFrame""" ...