data-analysis

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Data Analysis

数据分析

Part of Agent Skills™ by googleadsagent.ai™

由 googleadsagent.ai™ 提供的 Agent Skills™ 的一部分

Description

描述

Data Analysis provides a structured framework for statistical analysis using pandas, numpy, and scipy, with visualization through matplotlib, seaborn, and plotly. The agent follows a rigorous pipeline from data ingestion and cleaning through exploratory analysis, statistical testing, and publication-quality visualization, ensuring reproducibility at every step.

Scientific data analysis is not exploratory coding—it is a disciplined process where every transformation is justified, every statistical test has verified assumptions, and every visualization accurately represents the underlying data. This skill enforces that discipline by requiring the agent to document data provenance, validate distributions before applying parametric tests, and report effect sizes alongside p-values.

The visualization layer produces figures suitable for journal submission: proper axis labels with units, colorblind-safe palettes, appropriate figure sizes for single or double-column layouts, and vector output formats (SVG, PDF). Interactive plotly visualizations are generated for exploratory work; static matplotlib/seaborn figures for publication.

数据分析使用 pandas、numpy 和 scipy 提供结构化的统计分析框架，并通过 matplotlib、seaborn 和 plotly 实现可视化。Agent 遵循一套严谨的流水线，涵盖数据摄取与清理、探索性分析、统计检验和出版级可视化，确保每个步骤均可复现。

科学数据分析并非简单的探索性编码，而是一个严谨 disciplined 的过程：每次转换都有充分依据，每项统计检验都会验证相关假设，每张可视化图表都能准确呈现底层数据。该 skill 通过强制要求 Agent 记录数据来源、在应用参数检验前验证分布，并在报告 p 值的同时报告效应量，来确保分析过程的严谨性。

可视化层可生成适合期刊投稿的图表：包含单位和恰当的坐标轴标签、采用色盲友好型配色、针对单栏或双栏排版设置适当的图形尺寸，并支持矢量输出格式（SVG、PDF）。探索性工作会生成交互式 plotly 可视化，出版用途则采用静态 matplotlib/seaborn 图形。

Use When

使用场景

Performing statistical analysis on experimental or observational data
Cleaning and transforming datasets for downstream analysis
Creating publication-quality figures and plots
Running hypothesis tests with proper assumption checking
Exploratory data analysis on new datasets
Building reproducible analysis pipelines

对实验数据或观察性数据进行统计分析
清理和转换数据集，以供下游分析使用
创建出版级图表
在正确检查假设条件的前提下运行假设检验
对新数据集执行探索性数据分析
构建可复现的分析流水线

How It Works

工作原理

mermaid

graph TD
    A[Raw Data] --> B[Ingest + Validate Schema]
    B --> C[Clean: Missing Values, Outliers, Types]
    C --> D[Exploratory Data Analysis]
    D --> E[Distribution Assessment]
    E --> F{Parametric Assumptions Met?}
    F -->|Yes| G[Parametric Tests]
    F -->|No| H[Non-Parametric Tests]
    G --> I[Effect Size + Confidence Intervals]
    H --> I
    I --> J[Publication Visualization]
    J --> K[Reproducible Report]

The pipeline enforces assumption checking before test selection. Parametric tests (t-test, ANOVA) require normality and homoscedasticity; when assumptions fail, the agent automatically selects non-parametric alternatives (Mann-Whitney, Kruskal-Wallis).

mermaid

graph TD
    A[Raw Data] --> B[Ingest + Validate Schema]
    B --> C[Clean: Missing Values, Outliers, Types]
    C --> D[Exploratory Data Analysis]
    D --> E[Distribution Assessment]
    E --> F{Parametric Assumptions Met?}
    F -->|Yes| G[Parametric Tests]
    F -->|No| H[Non-Parametric Tests]
    G --> I[Effect Size + Confidence Intervals]
    H --> I
    I --> J[Publication Visualization]
    J --> K[Reproducible Report]

该流水线会在选择检验方法之前强制检查假设条件。参数检验（t-test、ANOVA）要求满足正态性和方差齐性；当假设条件不成立时，Agent 会自动选择非参数替代方法（Mann-Whitney、Kruskal-Wallis）。

Implementation

实现

python

import pandas as pd
import numpy as np
from scipy import stats
import seaborn as sns
import matplotlib.pyplot as plt

def analysis_pipeline(filepath: str) -> dict:
    df = pd.read_csv(filepath)

    report = {
        "shape": df.shape,
        "missing": df.isnull().sum().to_dict(),
        "dtypes": df.dtypes.astype(str).to_dict(),
    }

    numeric_cols = df.select_dtypes(include=[np.number]).columns
    for col in numeric_cols:
        stat, p = stats.shapiro(df[col].dropna()[:5000])
        report[f"{col}_normality"] = {"statistic": stat, "p_value": p, "normal": p > 0.05}

    return report

def compare_groups(df: pd.DataFrame, value_col: str, group_col: str) -> dict:
    groups = [g[value_col].dropna() for _, g in df.groupby(group_col)]

    normality_ok = all(stats.shapiro(g[:5000]).pvalue > 0.05 for g in groups)
    _, levene_p = stats.levene(*groups)
    homoscedastic = levene_p > 0.05

    if normality_ok and homoscedastic:
        stat, p = stats.f_oneway(*groups) if len(groups) > 2 else stats.ttest_ind(*groups)
        test_name = "ANOVA" if len(groups) > 2 else "t-test"
    else:
        stat, p = stats.kruskal(*groups)
        test_name = "Kruskal-Wallis"

    effect = compute_cohens_d(groups[0], groups[1]) if len(groups) == 2 else compute_eta_squared(groups)

    return {"test": test_name, "statistic": stat, "p_value": p, "effect_size": effect}

def publication_figure(df: pd.DataFrame, x: str, y: str, output: str):
    fig, ax = plt.subplots(figsize=(3.5, 3))  # Single-column journal width
    sns.boxplot(data=df, x=x, y=y, palette="colorblind", ax=ax)
    ax.set_xlabel(x.replace("_", " ").title())
    ax.set_ylabel(y.replace("_", " ").title())
    sns.despine()
    fig.tight_layout()
    fig.savefig(output, dpi=300, bbox_inches="tight")
    plt.close(fig)