数据分析师 (Data Analyst)

AI-powered data analysis workflow. Cover the full pipeline from data ingestion to interactive HTML report generation.

When to Use

Trigger when the user asks to:

• Analyze a dataset (CSV / Excel / JSON / SQLite)
• Generate a data analysis report
• Do exploratory data analysis (EDA)
• Clean or preprocess data
• Create data visualizations
• Understand data distributions and relationships

Workflow Overview

The skill follows a 7-phase CRISP-DM pipeline, executed automatically:

1. Data Loading — Auto-detect format, load into DataFrame
2. Data Audit — 4-layer defense: health check, structure, business rules, model readiness
3. Data Cleaning — Missing values, outliers, type conversion, dedup
4. EDA — Distribution analysis, correlation, group aggregation
5. Statistical Analysis — Descriptive stats, hypothesis tests, trend detection
6. Visualization — Charts for distributions, correlations, category breakdowns
7. Report Generation — Interactive HTML report with scorecards, charts, and insights

Usage

Quick Start

To analyze a data file:

python {baseDir}/scripts/run_analysis.py <data_file> [--output report.html]

The script auto-detects the file format and runs the full pipeline.

Module-Level Usage

Each module can be used independently:

# Load data
from data_loader import load_data
df = load_data("sales.csv")

# Audit data quality
from data_auditor import audit_data
report = audit_data(df)

# Clean data
from data_cleaner import clean_data
df_clean = clean_data(df)

# Run EDA
from eda_runner import run_eda
eda_results = run_eda(df_clean)

# Generate report
from report_builder import build_report
build_report(df_clean, eda_results, "report.html")

Scripts Reference

| Script | Purpose | Input | Output | |--------|---------|-------|--------| | scripts/run_analysis.py | Main entry — orchestrates full pipeline | data file path | HTML report | | scripts/data_loader.py | Multi-format data loading | file path | pandas DataFrame | | scripts/data_auditor.py | 4-layer quality defense | DataFrame | audit dict | | scripts/data_cleaner.py | Data cleaning & preprocessing | DataFrame | cleaned DataFrame | | scripts/eda_runner.py | Exploratory data analysis | DataFrame | EDA results dict | | scripts/visualizer.py | Chart generation | DataFrame + config | saved .png charts | | scripts/report_builder.py | HTML report generation | Data + results | HTML report |

Templates

• templates/report.html — Jinja2 template for the final HTML report

Config

• config/business_rules.yaml — Optional business validation rules

Dependencies

Install before first use:

pip install pandas numpy matplotlib seaborn scipy jinja2 pyyaml missingno

Notes

• For files > 100MB, the audit module uses sampling (n=50000) to stay performant
• Business rules in config/business_rules.yaml are optional; skip if no domain-specific rules exist
• All charts are saved to a charts/ subdirectory in the output folder before embedding in HTML