Clean Excel columns by removing unwanted prefixes from numeric values and extracting Chinese characters from text. Add the cleaned results as new columns, then save an Excel file and provide a download link.
中文简介正在准备中。当前显示英语简介。
对Excel文件进行文本标准化清洗（如去除异常前缀、提取纯中文字符等），并，最终输出清洗后的Excel文件并提供下载链接。
安装前可以查看交给 AI 代理的指令内容。
This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.
Step1 识别并清洗包含前缀符号的异常数值字段，统一转换为整数类型；同时使用正则表达式清洗文本字段，仅保留 Unicode 范围内的中文字符。
import re
import numpy as np
target_numeric_col = '需要转数字的文本列' # 示例：'获赞'
target_text_col = '需要提取中文的列' # 示例：'收货人'
# 1. 清洗包含前缀符号的数值字段
prefix_patterns = ['.', 'I ', '■ ', '一 ', '_', '. ']
def clean_numeric_with_prefix(value):
val_str = str(value).strip()
if val_str in ['None', 'nan', '', 'nan']:
return np.nan
for prefix in prefix_patterns:
if val_str.startswith(prefix):
val_str = val_str[len(prefix):].strip()
break
if val_str == '':
return np.nan
try:
return int(val_str)
except ValueError:
return np.nan
# 2. 清洗文本字段，仅保留 Unicode 范围内的中文字符（\u4e00-\u9fff）
def clean_chinese_name(name):
if pd.isna(name):
return name
s = str(name)
chinese_chars = re.findall(r'[\u4e00-\u9fff]', s)
cleaned = ''.join(chinese_chars)
return cleaned if cleaned else ''
if target_numeric_col in df.columns:
df[f'{target_numeric_col}_清洗后'] = df[target_numeric_col].apply(clean_numeric_with_prefix)
if target_text_col in df.columns:
df[f'{target_text_col}_清洗后'] = df[target_text_col].apply(clean_chinese_name)
Step2 将清洗后的结果保存为 Excel 文件，在报告中提供下载链接，并执行内存清理以应对大文件处理时的内存压力。
output_path = '/mnt/data/标准化清洗结果.xlsx'
# 保存清洗结果
df.to_excel(output_path, index=False, engine='openpyxl')
print(f'清洗结果已保存到: {output_path}')
# 生成可下载链接
print(f'[下载清洗结果表](sandbox:{output_path})')
# 内存清理
if 'df' in locals():
del df
gc.collect()
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Compare counts and shares for two categories in Excel data. The skill cleans the selected columns, calculates differences and percentages, and produces a summary spreadsheet plus bar and pie charts.
中文简介正在准备中。当前显示英语简介。
Analyze Excel data using custom categories, count distributions, and compare groups with charts. Score text using its length, technical terms, numbers, and selected complex concepts, then visualize difficulty levels and the relationship between text length and scores.
中文简介正在准备中。当前显示英语简介。
Count categories in a chosen data column after removing missing values and placeholder labels. Calculate each category’s share and create high-resolution charts combining counts, percentages, pie charts, and horizontal bars for distribution reports.
中文简介正在准备中。当前显示英语简介。
Clean category and numeric fields in structured data, compare groups with counts and cross-tabulations, and produce charts, Excel results, and a Markdown report. Export visuals as high-resolution PNG and SVG files for use in reports.
中文简介正在准备中。当前显示英语简介。
Filter Excel data using candidate column names, RGB color values, text prefixes, and regular expressions, which match text patterns. Calculate totals and averages, then export results to Excel with highlighted rows and a download link.
中文简介正在准备中。当前显示英语简介。
Filter rows from Excel or previously converted Parquet files, a format designed for efficient data storage. Summarize matching records by group with counts, percentages, and totals, then export the summary to Excel.
中文简介正在准备中。当前显示英语简介。