line-chart-visualization
提取结构化数据并进行特征清洗与聚类分析，生成包含趋势对比、分布特征与参数敏感性的多维度综合可视化图表，适用于各类趋势预测与多维对比场景。
npx skills add OpenSenseNova/SenseNova-Skills --skill line-chart-visualization --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.
Step1 数据加载与预处理（支持大文件Parquet转换与动态表头识别）。 ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler import os import re # 设置中英文字体与图表美化 plt.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Zen Hei', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False file_path = 'input_data.xlsx' # 处理大型Excel文件：统计总行数，若≥1万则转换为Parquet格式提升效率 xls = pd.ExcelFile(file_path) total_rows = sum(pd.read_excel(xls, sheet_name=s, header=None).shape[0] for s in xls.sheet_names) if total_rows >= 10000: parquet_path = "temp_converted_file.parquet" with pd.ExcelWriter(parquet_path, engine='pyarrow') as writer: for sheet in xls.sheet_names: df_sheet = pd.read_excel(xls, sheet_name=sheet, header=None) df_sheet.to_excel(writer, sheet_name=sheet, index=False, header=False) df = pd.read_excel(parquet_path, sheet_name='Sheet1', header=None) else: df = pd.read_excel(file_path, sheet_name='Sheet1', header=None) # 动态识别表头并提取数据 header_row_idx = None target_cols = ['group_col', 'value_col1', 'value_col2'] # 占位示例列名 for idx, row in df.iterrows(): row_vals = row.astype(str).tolist() if all(col in ro
What does the line-chart-visualization skill do?
提取结构化数据并进行特征清洗与聚类分析，生成包含趋势对比、分布特征与参数敏感性的多维度综合可视化图表，适用于各类趋势预测与多维对比场景。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill line-chart-visualization --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.