dashboard-skills

Dashboard Skills

Task Objectives

• This Skill is used for: quickly building data dashboards and visualization applications with Python
• Capabilities include: Streamlit interface development, Pandas data processing, ECharts chart visualization, project architecture design
• Trigger conditions: when you need to quickly create data analysis and display applications

Skill Map

Basic Skills

• dashboard-core - Core architecture: project initialization, multi-page, caching strategy
• dashboard-streamlit - Streamlit basics: text, tables, charts, input components
• dashboard-pandas - Pandas data processing: data reading, cleaning, transformation, aggregation

Advanced Skills

• dashboard-echarts - ECharts visualization: PyECharts, Streamlit-ECharts, interactive charts

Engineering Skills

• dashboard-best-practices - Best practices: performance optimization, secure deployment, testing

Learning Path

Quick Start

1. dashboard-core - Understand the project structure
2. dashboard-streamlit - Master basic components
3. dashboard-pandas - Data processing basics

Chart Advancement

1. dashboard-echarts - ECharts advanced charts

Production Deployment

1. dashboard-best-practices - Performance and deployment

Quick Start

Install Dependencies

uv pip install streamlit streamlit-echarts pandas pyecharts

Create Your First Dashboard

import streamlit as st
import pandas as pd
from streamlit_echarts import st_echarts

st.title("我的数据看板")

# 读取数据
df = pd.read_csv("sales.csv")

# 显示数据
st.dataframe(df)

# 绑定筛选
with st.sidebar:
    category = st.selectbox("选择类别", df['category'].unique())

# 图表
options = {
    "xAxis": {"type": "category", "data": df['date'].tolist()},
    "yAxis": {"type": "value"},
    "series": [{"data": df['sales'].tolist(), "type": "line"}]
}
st_echarts(options=options)

Resource Index

• Streamlit documentation:https://docs.streamlit.io/
• PyECharts：https://pyecharts.readthedocs.io/
• Streamlit-ECharts：https://github.com/andfanilo/streamlit-echarts
• Pandas documentation:https://pandas.pydata.org/

Notes

• Use uv to manage Python dependencies
• Use @st.cache_data caching appropriately
• Session State manages state across reruns
• Use secrets to manage sensitive configuration in production environments