numeric-extraction-and-distribution-analysis
从带单位的字符串列中提取数值并清洗，生成包含直方图、饼图、条形图和累积分布图的多维度综合分布可视化图表，用于展示数据的集中趋势与分布特征。
npx skills add OpenSenseNova/SenseNova-Skills --skill data-bar-formatting --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.
# Numeric_Extraction_and_Distribution_Analysis ## Skill Steps Step1 从原始数据中提取目标列，清理无效和空值数据，并安全地将带单位的字符串转换为数值类型 ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt # 配置中英文字体，避免图表乱码 plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False item_col = '项目名称' # 占位示例：分类或名称列 value_col = '带单位的数值' # 占位示例：需要提取数值的原始列 numeric_col = '提取数值' unit_str = 'g' # 占位示例：需要移除的单位字符串 def extract_numeric_value(val_str): """从带单位的字符串中提取数值""" if pd.isna(val_str): return None try: # 移除单位并转换为浮点数 return float(str(val_str).replace(unit_str, '').strip()) except ValueError: return None # 清理缺失值与异常占位符 df_clean = df.dropna(subset=[item_col, value_col]).copy() df_clean = df_clean[df_clean[item_col] != '...'] # 应用提取函数并过滤转换失败的行 df_clean[numeric_col] = df_clean[value_col].apply(extract_numeric_value) df_clean = df_clean.dropna(subset=[numeric_col]) ``` Step2 创建基础分布直方图，并添加平均值和中位数的参考线以展示数据的集中趋势 ```python plt.figure(figsize=(12, 8)) # 绘制直方图 plt.hist(df_clean[numeric_col], bins=10, alpha=0.7, color='skyblue', edgecolor='black') # 计算并添加平均值和中位数参考线 mean_val = df_clean[numeric_col].mean() median_val = df_clean[numeric_col].median() plt.axvline(mean_val, colo
- Skill Steps
What does the numeric-extraction-and-distribution-analysis skill do?
从带单位的字符串列中提取数值并清洗，生成包含直方图、饼图、条形图和累积分布图的多维度综合分布可视化图表，用于展示数据的集中趋势与分布特征。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill data-bar-formatting --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 4,911 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.