| """Data Analysis Skill - Phân tích dữ liệu.""" |
| from __future__ import annotations |
| from typing import List |
| from .base import Skill, SkillResult, SkillContext, SkillCategory, SkillPriority |
|
|
|
|
| class DataAnalysisSkill(Skill): |
| """Phân tích dữ liệu: EDA, statistics, visualization, insights.""" |
| |
| category = SkillCategory.DATA |
| priority = SkillPriority.MEDIUM |
| keywords: List[str] = [ |
| "analyze", "phân tích", "data", "dữ liệu", "dataset", |
| "statistics", "thống kê", "eda", "exploratory", |
| "pandas", "numpy", "visualization", "biểu đồ", |
| "insights", "pattern", "mẫu", "trend", "xu hướng", |
| ] |
| |
| @property |
| def name(self) -> str: |
| return "data_analysis" |
| |
| @property |
| def description(self) -> str: |
| return ( |
| "Phân tích dữ liệu: EDA, descriptive/inferential statistics, " |
| "data cleaning, visualization, pattern detection, insight extraction." |
| ) |
| |
| def execute(self, context: SkillContext) -> SkillResult: |
| analysis_steps = [ |
| "1. Data loading & schema inspection", |
| "2. Missing value analysis & imputation", |
| "3. Descriptive statistics (mean, median, std, quartiles)", |
| "4. Distribution analysis (histograms, KDE)", |
| "5. Correlation analysis (Pearson, Spearman)", |
| "6. Outlier detection (IQR, Z-score, Isolation Forest)", |
| "7. Group-by analysis & aggregations", |
| "8. Time series decomposition (trend, seasonality, residual)", |
| "9. Visualization (matplotlib, seaborn, plotly)", |
| "10. Insight extraction & recommendations", |
| ] |
| return SkillResult( |
| success=True, |
| output=f"[DataAnalysis] {len(analysis_steps)}-step analysis pipeline.", |
| metadata={ |
| "skill": self.name, |
| "analysis_steps": analysis_steps, |
| "libraries": ["pandas", "numpy", "scipy", "matplotlib", "seaborn", "plotly"], |
| }, |
| suggestions=[ |
| "Always check data quality first", |
| "Visualize before modeling", |
| "Document assumptions", |
| ], |
| ) |
|
|
