large-file-parquet-analysis-and-highlight
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 OpenSenseNova/SenseNova-Skills --skill category-coloring --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Skill Steps 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。 ```python 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。 ```python 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
What does the large-file-parquet-analysis-and-highlight skill do?
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。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill category-coloring --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenSenseNova/SenseNova-Skills, a repository with 4,855 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.