time-series-and-categorical-analysis
对时间序列或分类数据进行多维度趋势分析、百分比清洗、绩效分级建模与预测，并生成高分辨率的可视化综合报告，适用于业务指标监控与预测场景。
npx skills add OpenSenseNova/SenseNova-Skills --skill time-series-analysis --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 加载并检查原始数据，配置中文字体以确保图表正常显示。 ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore') # 设置中文字体，兼容不同操作系统 plt.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Zen Hei', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False # 加载Excel文件 file_path = 'data.xlsx' df = pd.read_excel(file_path) print(f"数据形状: {df.shape}") print(f"列名: {list(df.columns)}") ``` Step2 提取时间序列或分类维度数据，处理百分比格式，并计算变化趋势。 ```python def convert_percentage(pct_str): """将百分比字符串转换为数值，处理空值和非字符串类型""" if pd.isna(pct_str): return None if isinstance(pct_str, str) and '%' in pct_str: try: return float(pct_str.replace('%', '')) except ValueError: return None return pct_str time_col = '时间列' # 占位示例 target_cols = ['指标1占比', '指标2占比', '指标3占比'] # 占位示例 # 转换百分比字符串为数值并提取数据 ts_df = df[[time_col] + target_cols].copy() if time_col in df.columns else df.copy() for col in target_cols: if col in ts_df.columns: ts_df[col] = ts_df[col].apply(convert_percentage) # 计算变化趋势并识别状态 diff_col = f'{col}_变化' trend_col = f'{col}_趋势' ts_df[diff_col] = ts_df[col].diff() ts_df[trend_col] = ['上升' if x > 0 else '下降' if x < 0 else '稳定' for x in
- Skill Steps
What does the time-series-and-categorical-analysis skill do?
对时间序列或分类数据进行多维度趋势分析、百分比清洗、绩效分级建模与预测，并生成高分辨率的可视化综合报告，适用于业务指标监控与预测场景。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill time-series-analysis --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.