Filter Excel rows using conditions across several numeric columns. Count rows across sheets, convert selected fields to numbers, remove duplicate rows and records with missing identifiers, then export the filtered results to a new workbook.
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根据多维数值条件筛选 Excel 数据并导出结果，支持大规模数据的自动性能优化处理。
安装前可以查看交给 AI 代理的指令内容。
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']
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 执行多维度条件筛选逻辑，提取符合特定数值特征的唯一记录。
# 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 将筛选后的结果保存为新的 Excel 文件，并生成下载链接。
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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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。