Extract keyword-matched entries from an Excel sheet, clean whitespace and empty values, and count distinct entries by category. Export separate Excel files containing summary counts and detailed lists for review.
中文简介正在准备中。当前显示英语简介。
统计多Sheet Excel总行数并根据规模选择处理策略，提取特定维度信息进行去重统计，并生成摘要与明细报表。
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 加载目标数据表，并进行初步的数据预览与结构检查。
import pandas as pd
file_path = 'input_file.xlsx'
target_sheet = 'Sheet1' # 根据实际情况指定 sheet 名称
# 读取数据，header=None 用于处理无表头或非标准表头文件
df = pd.read_excel(file_path, sheet_name=target_sheet, header=None)
print(f"数据形状: {df.shape}")
print("前 5 行预览：")
print(df.head())
Step2 遍历数据行，基于关键词提取目标信息，并执行数据清洗（去除空格、空值过滤）。
import pandas as pd
# 设定目标列索引及过滤关键词
target_col_idx = 1
keywords = ["关键词A", "关键词B"] # 示例：如"综合楼"、"控制中心"
extracted_data = []
for idx, row in df.iterrows():
cell_val = str(row[target_col_idx]) if pd.notna(row[target_col_idx]) else ""
# 数据清洗：去除首尾空格并匹配关键词
clean_val = cell_val.strip()
if any(k in clean_val for k in keywords):
if clean_val and clean_val.lower() not in ["nan", "null", ""]:
extracted_data.append(clean_val)
print(f"提取到相关记录共 {len(extracted_data)} 条")
Step3 对提取的信息进行分类去重，统计各维度的唯一项数量。
# 使用 set 进行高效去重
category_a_items = set()
category_b_items = set()
for item in extracted_data:
if "关键词A" in item:
category_a_items.add(item)
elif "关键词B" in item:
category_b_items.add(item)
# 转换为排序后的列表
list_a = sorted(list(category_a_items))
list_b = sorted(list(category_b_items))
print(f"类别A 唯一项数量: {len(list_a)}")
print(f"类别B 唯一项数量: {len(list_b)}")
Step4 将统计摘要与详细清单整理为 DataFrame，并导出为 Excel 文件提供下载。
import pandas as pd
# 1. 生成统计摘要
summary_df = pd.DataFrame({
'分类名称': ['类别A', '类别B'],
'唯一项总数': [len(list_a), len(list_b)]
})
# 2. 生成详细清单
detail_list = []
for val in list_a:
detail_list.append({'分类': '类别A', '详细名称': val})
for val in list_b:
detail_list.append({'分类': '类别B', '详细名称': val})
detail_df = pd.DataFrame(detail_list)
# 导出结果
output_summary_path = 'summary_report.xlsx'
output_detail_path = 'detail_list.xlsx'
summary_df.to_excel(output_summary_path, index=False)
detail_df.to_excel(output_detail_path, index=False)
print(f"统计摘要已保存: {output_summary_path}")
print(f"详细清单已保存: {output_detail_path}")
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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。