categorical-comparison-analysis SKILL.md skillA
categorical-comparison-analysis from opensensenova/sensenova-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add opensensenova/sensenova-skills/categorical-comparison-analysis@git:20260426.f0ab496Pins git:20260426.f0ab496 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- opensensenova/sensenova-skills
- Path
skills/sn-da-excel-workflow/capability/excel-data-analysis/comparison-analysis/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260426.f0ab496, committed Apr 26, 2026
- Size
- 3,666 bytes
- Repository stars
- 6,031
- License
- MIT
对两类分类数据进行对比分析，统计数量差异与比例关系并生成可视化图表。.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# categorical-comparison-analysis
> This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.
Step1 读取文件并统计所有 sheet 的总行数，评估是否需要进行大文件优化处理。
```python
import pandas as pd
from pandas import read_excel
from pathlib import Path
# 统计所有 sheet 的行数以决定处理策略
file_path = "input_data.xlsx"
sheet_names = pd.ExcelFile(file_path).sheet_names
total_rows = 0
for sheet in sheet_names:
# 仅读取行索引以快速计数
df_tmp = read_excel(file_path, sheet_name=sheet, usecols=[0])
total_rows += len(df_tmp)
print(f"Total rows across all sheets: {total_rows}")
```
Step2 提取对比维度的分类信息，执行数据清洗，包括去除空值、处理合并单元格填充以及排除非数据行。
```python
# 定义目标列名
target_col_a = "category_a_column"
target_col_b = "category_b_column"
# 处理合并单元格（ffill）并清洗数据
df[target_col_a] = df[target_col_a].ffill()
df[target_col_b] = df[target_col_b].ffill()
# 排除标题行占位符（如 '代码'、'名称'）及空值
exclude_val = "代码"
data_a = df[target_col_a].dropna()
data_a = data_a[data_a != exclude_val]
data_b = df[target_col_b].dropna()
data_b = data_b[data_b != exclude_val]
```
Step3 统计分类数量，计算差异值与占比，生成多维度对比统计表。
```python
count_a = len(data_a)
count_b = len(data_b)
total_count = count_a + count_b
…Read the whole file at its exact version.
How to install
mdr add opensensenova/sensenova-skills/categorical-comparison-analysis@sha256:7ad779d213935f50The line at the top of this page pins the label git:20260426.f0ab496, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_ickgfi7gzzggtszd)
4 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 1 version of this file, committed on Apr 26, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (3666 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
opensensenova/sensenova-skills · 6,031 stars · license MIT · pushed 2026-11-03 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ickgfi7gzzggtszd GET https://markdownregistry.com/api/v1/resolve?ref=opensensenova/sensenova-skills/categorical-comparison-analysis GET https://markdownregistry.com/api/v1/blob/7ad779d213935f5087e0c998fa183be439d82fffdbac325b8bd88c99f9a945ce
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.