threshold-cell-coloring

Skill Steps

Note: This sub-skill covers one step of the Excel analysis workflow. For the full pipeline (file reading, row counting, large-file optimization, export), see the parent workflow SKILL.md.

Step1 读取文件并统计所有 sheet 的行数，汇总后打印总行数，用于判断是否需要大文件加速。

import pandas as pd
import openpyxl

file_path = "input_data.xlsx"

# 获取所有sheet名称
wb = openpyxl.load_workbook(file_path, read_only=True)
sheet_names = wb.sheetnames
print("Sheet列表:", sheet_names)
print("Sheet数量:", len(sheet_names))

# 统计每个sheet的行数
total_rows = 0
for name in sheet_names:
    df_temp = pd.read_excel(file_path, sheet_name=name, header=None)
    rows = len(df_temp)
    total_rows += rows
    print(f"Sheet '{name}': {rows} 行")

print(f"\n总行数 = {total_rows}")

Step2 提取目标实体的时间序列数据，计算平均值，并构建包含比较结果的结构化 DataFrame。

target_entity = 'Target_Entity' # 占位示例，如 'US'

# 提取目标行数据 (假设第0列为实体名称)
target_row = df[df[0] == target_entity]

# 提取时间标签和对应数值 (假设第6行为表头，1:10列为数据)
time_labels = df.iloc[6, 1:10].tolist()
target_values = target_row.iloc[0, 1:10].tolist()
target_values_numeric = [float(v) for v in target_values]

# 计算平均值
avg_value = sum(target_values_numeric) / len(target_values_numeric)