Excel Statistical Viz Large File Agent Skill
对 Excel 数据进行多维度统计分析与可视化。
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill excel-statistical-viz-large-fileThe instruction itself
1 sections, as written by the authorexcel_statistical_visualization
> This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.
Step1 数据清洗与标准化。提取目标分析列，处理合并单元格（ffill），并利用正则表达式或类型转换清理数值字段，确保分析数据集的准确性。
import re
# 假设 target_col_x 和 target_col_y 是分析目标
# 处理合并单元格导致的缺失值
df['group_col'] = df['group_col'].fillna(method='ffill')
def clean_numeric_string(value):
if pd.isna(value): return None
# 保留数字、小数点和负号，移除空格及非法字符
cleaned = re.sub(r'[^\d\.\-]', '', str(value))
try:
return float(cleaned)
except ValueError:
return None
df['x_val'] = df['target_col_x'].apply(clean_numeric_string)
df['y_val'] = df['target_col_y'].apply(clean_numeric_string)
# 过滤无效数据
df_clean = df.dropna(subset=['x_val', 'y_val']).copy()
Step2 执行多维度统计分析。计算分类占比、均值、标准差，并构建交叉分析表（crosstab/pivot），为可视化提供数据支撑。
# 分类统计与占比
stats_summary = df_clean.groupby('group_col')['y_val'].agg(['count', 'mean', 'std', 'min', 'max'])
stats_summary['percentage'] = (stats_summary['count'] / stats_summary['count'].sum()) * 100
# 添加总计行
total_row = pd.DataFrame(df_clean[['y_val']].agg(['count', 'mean']).T)
total_row.index = ['Total']
# 交叉分析示例
pivot_table = pd.pivot_table(df_clean, values='y_val', index='group_col', columns='category_col', aggfunc='count', fill_value=0)
Step3 生成高分辨率可视化图表。包含散点图、线性趋势线（R²、p值）、箱线图或柱状图组合，并配置中文字体与美化参数。
import matplotlib.pyplot as plt
import matplotlib
from scipy import stats
import numpy as np
# 字体配置：优先使用 SimHei 或 DejaVu Sans 确保中文显示
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Zen Hei', 'DejaVu Sans']
matplotlib.rcParams['axes.unicode_minus'] = False
x = df_clean['x_val'].values
y = df_clean['y_val'].values
# 线性回归计算
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
line = slope * x + intercept
plt.figure(figsize=(12, 8), dpi=300)
# 散点图：添加随机抖动 (jitter) 避免点重叠
jitter_x = x + np.random.normal(0, 0.01, size=len(x))
plt.scatter(jitter_x, y, alpha=0.6, edgecolors='w', label='Data Points')
# 趋势线
plt.plot(x, line, color='red', linestyle='--', linewidth=2,
label=f'Trend: y={slope:.4f}x+{intercept:.4f}\n$R^2$={r_value**2:.4f}, p={p_value:.4e}')
# 数据点标注 (实战技巧：仅标注极值或特定点)
for i, (xi, yi) in enumerate(zip(x, y)):
if i % (len(x)//5 or 1) == 0: # 抽样标注避免拥挤
plt.annotate(f'({xi:.2f}, {yi:.2f})', (xi, yi), textcoords="offset points", xytext=(5,5), fontsize=8)
plt.xlabel('Dimension X')
plt.ylabel('Dimension Y')
plt.title('Statistical Distribution & Trend Analysis')
plt.grid(True, linestyle=':', alpha=0.6)
plt.legend()
output_img = 'analysis_plot.png'
plt.savefig(output_img, bbox_inches='tight')
plt.show()
Step4 导出分析结果并生成下载链接。将清洗后的数据及统计摘要保存为 CSV 或 Excel 文件。
output_csv = 'cleaned_analysis_data.csv'
# 使用 utf-8-sig 确保 Excel 打开中文不乱码
df_clean.to_csv(output_csv, index=False, encoding='utf-8-sig')
print(f"Visualization saved to: {output_img}")
print(f"Data exported to: {output_csv}")
# 打印回归关键指标供快速参考
print(f"R-squared: {r_value**2:.6f}, P-value: {p_value:.6f}")
Other skills for the same job
different authors, same section of the catalogue
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
How to use it
Take opensensenova/excel-statistical-viz-large-file from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.