当Excel文件总行数超过1万行时,通过转换为Parquet格式提升读取性能,提取目标指标并计算最大值,最后将结果输出为Excel并对特定行进行高亮标注。
npx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill large-file-parquet-analysis-and-highlight
Step1 读取文件并统计所有 sheet 的行数,汇总后打印总行数,用于判断数据规模是否需要启用大文件处理。
import pandas as pd
file_path = "input_data.xlsx"
# 读取所有sheet并统计总行数
xls = pd.ExcelFile(file_path)
sheet_names = xls.sheet_names
print(f"Sheet列表: {sheet_names}")
total_rows = 0
for sheet in sheet_names:
# 仅读取一列以加快行数统计速度
df_temp = pd.read_excel(file_path, sheet_name=sheet, usecols=[0], header=None)
rows = len(df_temp)
total_rows += rows
print(f"Sheet '{sheet}': {rows} 行")
print(f"\n总行数 = {total_rows}")
Step2 当总行数 ≥ 1万时,读取已转换为 Parquet 格式的数据文件,通过行列匹配提取目标指标数据,并找出最大值及其对应分类。
import pandas as pd
# 假设已通过大文件处理技能将Excel转换为Parquet
parquet_path = "converted_data.parquet"
df = pd.read_parquet(parquet_path)
# 假设第2行(索引1)是分类表头(如:控股类型、区域等)
header_row = df.iloc[1].tolist()
print("分类表头:", header_row)
# 找到目标指标所在的行(占位示例:'目标指标名称')
target_metric = '目标指标名称'
target_rows = df[df[0] == target_metric]
if not target_rows.empty:
# 提取数值
values = target_rows.iloc[0, 1:].tolist()
# 清洗数据并找出最大值及其对应的分类
numeric_values = []
for val in values:
try:
numeric_values.append(float(val))
except:
numeric_values.append(0)
max_val = max(numeric_values)
max_idx = numeric_values.index(max_val)
max_type = header_row[1:][max_idx]
print(f"\n指标最高的分类: {max_type} ({max_val})")
# 准备写入Excel的数据结构
result_data = list(zip(header_row[1:], numeric_values))
Step3 将提取的分析结果保存为新的 Excel 文件,并使用 openpyxl 对最大值所在行进行背景色高亮标注,最后验证输出。
from openpyxl import Workbook
from openpyxl.styles import PatternFill
from openpyxl import load_workbook
output_path = "analysis_result.xlsx"
wb = Workbook()
ws = wb.active
ws.title = "数据分析结果"
# 写入表头
headers = ["分类类型", "指标数值"]
ws.append(headers)
# 写入数据 (使用Step2提取的 result_data,此处为防空值做备用示例)
if 'result_data' not in locals():
result_data = [("分类A", 100), ("分类B", 500), ("分类C", 200)]
max_type = "分类B"
for row in result_data:
ws.append(row)
# 找到最大值所在行并标绿
green_fill = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid")
for row in ws.iter_rows(min_row=2, max_row=ws.max_row):
if row[0].value == max_type:
for cell in row:
cell.fill = green_fill
# 保存文件
wb.save(output_path)
print(f"文件已保存到: {output_path}")
# 验证输出文件内容及格式
wb_check = load_workbook(output_path)
ws_check = wb_check.active
print("\n文件内容验证:")
for row in ws_check.iter_rows(values_only=True):
print(row)
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Take opensensenova/large-file-parquet-analysis-and-highlight from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.