single-sheet-reading-and-analysis
读取并解析单个Excel工作表数据，支持合并单元格处理、数据清洗、交叉分析及多维度可视化，适用于需要从单表中提取关键指标并进行趋势模拟与图表生成的场景。
npx skills add OpenSenseNova/SenseNova-Skills --skill single-sheet-reading --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Skill Steps Step1 导入依赖并配置中英文字体，防止图表乱码 ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import re import base64 from IPython.display import HTML # 设置中英文字体 plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'WenQuanYi Zen Hei'] plt.rcParams['axes.unicode_minus'] = False ``` Step2 加载数据与基础清洗，包含合并单元格处理与正则提取 ```python def load_and_clean_data(file_path, sheet_name=0): # 读取数据 df = pd.read_excel(file_path, sheet_name=sheet_name) # 处理合并单元格：向前填充并还原 # df['group_col'] = df['group_col'].ffill() # 标准化列名：去除首尾空格及换行符 df.columns = [str(col).strip().replace('\n', '') for col in df.columns] # 数据清洗正则表达式示例：提取数值 if 'target_col' in df.columns: df['target_col'] = df['target_col'].astype(str).apply(lambda x: re.sub(r'[^\d.]', '', x)) df['target_col'] = pd.to_numeric(df['target_col'], errors='coerce') # 处理全空行缺失值 df = df.dropna(how='all') return df ``` Step3 数据分类映射与多维度评分/分级算法 ```python def categorize_and_score(df, target_col): # 分类映射函数骨架 def map_category(val): if pd.isna(val): return '未知' elif val > 100: # 占位示例：高阈值 return 'A类' elif val > 50: # 占位示例：中阈值 return 'B类' else: return 'C类' if target_col in df.columns: df['category'] = df[target_col].app
- Skill Steps
What does the single-sheet-reading-and-analysis skill do?
读取并解析单个Excel工作表数据，支持合并单元格处理、数据清洗、交叉分析及多维度可视化，适用于需要从单表中提取关键指标并进行趋势模拟与图表生成的场景。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill single-sheet-reading --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenSenseNova/SenseNova-Skills, a repository with 4,855 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.