range-filtering

Excel_Conditional_Filtering_Optimization

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 Excel 文件中所有工作表的数据，统计各表行数并汇总，用于评估数据规模。

import pandas as pd

file_path = "input_data.xlsx"

# 读取所有 sheet，统计行数
xls = pd.ExcelFile(file_path)
print("Sheet names:", xls.sheet_names)

total_rows = 0
sheet_details = []
for sheet in xls.sheet_names:
    df_temp = pd.read_excel(file_path, sheet_name=sheet)
    row_count = len(df_temp)
    sheet_details.append({"sheet": sheet, "rows": row_count})
    total_rows += row_count

print(f"Sheet details: {sheet_details}")
print(f"Total rows across all sheets: {total_rows}")

Step2 对目标数据进行清洗，处理表头偏移，并将关键列转换为数值类型以确保计算准确。

# 读取目标数据表
target_sheet = 'Sheet1'
df = pd.read_excel(file_path, sheet_name=target_sheet, header=0)

# 处理可能的子表头或空行偏移（示例：跳过第一行）
# df = df.iloc[1:].reset_index(drop=True)

# 统一设置列名（根据实际业务逻辑调整占位符）
# df.columns = ['col_1', 'col_2', 'col_3', 'target_id', 'val_a', 'val_b', 'val_c']

# 强制转换数值列，处理非数值数据为 NaN
numeric_cols = ['val_a', 'val_b', 'val_c', 'target_id']