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 and summarize the row counts of all worksheets in the Excel file to assess the data scale.
import pandas as pd
file_path = 'input_file.xlsx'
# 读取所有 sheet 名称并统计总行数
xls = pd.ExcelFile(file_path)
sheet_names = xls.sheet_names
total_rows = 0
for sheet in sheet_names: # header=None is used to quickly count the total number of rows including the header df_tmp = pd.read_excel(file_path, sheet_name=sheet, header=None) rows = len(df_tmp) total_rows += rows print(f"Sheet '{sheet}': {rows} rows")
print(f"\nTotal rows summary: {total_rows}")
Step2 对目标数据表进行清洗，将指定列的非数值内容转换为缺失值并剔除，确保数据类型为数值型。
```python
target_sheet = 'Sheet1'
target_col = '数量' # 待处理的目标列名
header_idx = 1 # 表头所在行索引（0开始计数）
df = pd.read_excel(file_path, sheet_name=target_sheet, header=header_idx)
# 强制转换数值类型，无法转换的内容变为 NaN 并删除
df[target_col] = pd.to_numeric(df[target_col], errors='coerce')
df_cleaned = df.dropna(subset=[target_col])
print(f"清洗完成，有效数据行数: {len(df_cleaned)}")
Step3 Filter records that meet specific numeric conditions and perform statistics.
filter_threshold = 10
df_filtered = df_cleaned[df_cleaned[target_col] > filter_threshold]
print(f"{target_col} 大于 {filter_threshold} 的记录共有 {len(df_filtered)} 条")
Step4 Use openpyxl to process the original file