excel-conditional-filtering-optimization

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 Read the data of all worksheets in the Excel file, count the number of rows in each sheet and summarize them, to assess the data scale.

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。
```python
# 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']
for col in numeric_cols:
    if col in df.columns:
        df[col] = pd.to_numeric(df[col], errors='coerce')

# 处理合并单元格（如有）
# df = df.ffill()

Step3 Execute the multi-dimensional condition filtering logic to extract unique records that meet specific numerical characteristics.

# 筛选逻辑：例如 val_a, val_b, val_c 同时满足特定阈值（如均为 0）
mask = (df['val_a'] == 0) & (df['val_b'] == 0) & (df['val_c'] == 0)
filtered_df = df[mask][['target_id', 'val_a', 'val_b', 'val_c']]

# 提取唯一编号并去除空值
result = filtered_df.drop_duplicates().dropna(subset=['target_id']).reset_index(drop=True)

Step4 Save the filtered results as a new Excel file and generate a download link.

output_path = "filtered_analysis_result.xlsx"

# 格式化输出列名
result.columns = ['Target_Index', 'Value_A', 'Value_B', 'Value_C']

# 导出文件
result.to_excel(output_path, index=False)

# 打印结果摘要与下载路径
print(f"Filtered records count: {len(result)}")
print(f"Result saved to: {output_path}")

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