在Dash AgGrid中实现基于数据动态行背景渐变

# 在回调函数内部对筛选后的数据进行处理
# filtered_df 是经过产品和桶筛选后的DataFrame
filtered_df['Combined Value'] = filtered_df['Raised to Date'] + filtered_df['Years in Operation']
max_combined_value = filtered_df['Combined Value'].max()

getRowStyle = {
    'styleConditions': [
        {
            # JavaScript表达式，判断当前行的'Combined Value'是否等于某个特定值
            # params.data 用于在AgGrid客户端访问当前行的数据
            'condition': f'params.data["Combined Value"] == {value}',
            # 定义样式，backgroundColor使用rgba()，通过alpha通道实现渐变
            'style': {'backgroundColor': f'rgba(0, 123, 255, {value / max_combined_value})'},
        }
        # 遍历所有唯一的Combined Value，为每个值生成一个条件
        for value in filtered_df['Combined Value'].unique()
    ]
}

from dash import Input, Output
import dash_ag_grid as dag
import dash
import pandas as pd
import numpy as np
from dash import dcc, html

# 示例数据（n_rows需要定义）
n_rows = 50
data = {
    "Company name": ["Company {}".format(i) for i in range(1, n_rows + 1)],
    "Years in Operation": np.random.randint(1, 20, n_rows),
    "Raised to Date": np.random.uniform(1, 100, n_rows).round(2),
    "Product": np.random.choice(["Product A", "Product B", "Product C"], n_rows),
    "Bucket": np.random.choice(["Bucket 1", "Bucket 2", "Bucket 3"], n_rows),
}
df = pd.DataFrame(data)

app = dash.Dash(__name__)

app.layout = html.Div([
    html.H1("Valuation Table"),
    dcc.Dropdown(id='product-dropdown', options=[{'label': 'Product A', 'value': 'Product A'}, {'label': 'Product B', 'value': 'Product B'}, {'label': 'Product C', 'value': 'Product C'}], value='Product A'),
    dcc.Dropdown(id='bucket-dropdown', options=[{'label': 'All', 'value': 'All'}, {'label': 'Bucket 1', 'value': 'Bucket 1'}, {'label': 'Bucket 2', 'value': 'Bucket 2'}, {'label': 'Bucket 3', 'value': 'Bucket 3'}], value='All'),
    dag.AgGrid(
        id='valuation-table',
        columnDefs=[
            {"headerName": "Company Name", "field": "Company name"},
            {"headerName": "Years in Operation (yr)", "field": "Years in Operation"},
            {"headerName": "Raised to Date ($m)", "field": "Raised to Date"},
            {"headerName": "Product", "field": "Product"},
            {"headerName": "Bucket", "field": "Bucket"},
        ],
        style={'height': '400px', 'width': '100%'},
    ),
    html.Div(id='table-heading')
])

@app.callback(
    [Output('valuation-table', 'rowData'),
     Output('valuation-table', 'getRowStyle'), # 新增的Output
     Output('table-heading', 'children')],
    [Input('product-dropdown', 'value'),
     Input('bucket-dropdown', 'value')]
)
def update_table(selected_product, selected_bucket):
    if selected_bucket == 'All':
        filtered_df = df[df['Product'] == selected_product]
    else:
        filtered_df = df[(df['Bucket'] == selected_bucket) & (df['Product'] == selected_product)]

    # 避免空数据导致max()错误
    if filtered_df.empty:
        return [], {}, html.Div(f"No data for Product: {selected_product}, Bucket: {selected_bucket}")

    # 计算组合值
    filtered_df['Combined Value'] = filtered_df['Raised to Date'] + filtered_df['Years in Operation']
    max_combined_value = filtered_df['Combined Value'].max()

    # 如果max_combined_value为0，则避免除零错误
    if max_combined_value == 0:
        # 可以选择不应用颜色，或者应用一个默认颜色
        getRowStyle = {}
    else:
        # 应用条件行样式
        getRowStyle = {
            'styleConditions': [
                {
                    'condition': f'params.data["Combined Value"] == {value}',
                    'style': {'backgroundColor': f'rgba(0, 123, 255, {value / max_combined_value})'},
                }
                for value in filtered_df['Combined Value'].unique()
            ]
        }

    # 创建rowData
    row_data = filtered_df.to_dict('records')

    return row_data, getRowStyle, html.Div(f"Filtered by Product: {selected_product}, Bucket: {selected_bucket}")

if __name__ == '__main__':
    app.run_server(debug=True, port=8573)