解读dashboard：提升数据分析效率的关键工具

import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.express as px
import pandas as pd

# 构造一个简单的数据集
df = pd.DataFrame({
    "城市": ["北京", "上海", "广州", "深圳"],
    "人口": [2153, 2424, 1500, 1303],
    "GDP": [30698, 35506, 19612, 21323],
    "面积": [16410, 6340, 7434, 1997]
})

app = dash.Dash(__name__)

app.layout = html.Div([
    html.H1("中国主要城市数据分析"),
    dcc.Graph(
        id="graph-bar",
        figure={
            "data": [
                {
                    "x": df["城市"],
                    "y": df["人口"],
                    "type": "bar",
                    "name": "人口"
                },
                {
                    "x": df["城市"],
                    "y": df["GDP"],
                    "type": "bar",
                    "name": "GDP"
                }
            ],
            "layout": {
                "title": "人口和GDP对比",
                "xaxis_title": "城市",
                "yaxis_title": "人口和GDP"
            }
        }
    ),
    dcc.Graph(
        id="graph-scatter",
        figure={
            "data": [
                {
                    "x": df["人口"],
                    "y": df["GDP"],
                    "mode": "markers",
                    "text": df["城市"]
                }
            ],
            "layout": {
                "title": "人口和GDP的关系",
                "xaxis_title": "人口",
                "yaxis_title": "GDP"
            }
        }
    ),
    dcc.Graph(
        id="graph-pie",
        figure={
            "data": [
                {
                    "values": df["面积"],
                    "labels": df["城市"],
                    "type": "pie"
                }
            ],
            "layout": {
                "title": "城市面积占比"
            }
        }
    )
])

if __name__ == '__main__':
    app.run_server(debug=True)