Data Analyst

Enterprise-grade data analysis assistant. Clean, analyze, and visualize data automatically. **Triggers when user mentions:** - Data cleaning: "数据清洗", "整理数据", "清理数据", "数据预处理" - Data analysis: "分析数据", "数据分析", "数据报表", "生成报告" - Visualization: "画图", "图表", "可视化", "生成图表" - Excel/CSV: "处理Excel", "分析CSV", "读取表格" - Insights: "数据洞察", "发现规律", "趋势分析" Supports Excel (.xlsx), CSV, JSON formats. Generates reports, charts, and insights.

Data Analyst Skill

Automatically clean, analyze, and visualize enterprise data.

Features

Feature	Description	Reference
Data Cleaning	Remove duplicates, handle missing values, standardize formats	references/data_cleaning.md
Data Analysis	Statistics, trends, correlations	references/data_analysis.md
Visualization	Charts, graphs, dashboards	references/visualization.md
Report Generation	Automated insights and recommendations	references/report_generation.md

Quick Start

Step 1: Prepare Your Data

Place your data file (Excel/CSV/JSON) in a known location.

Step 2: Analyze Data

bash

# Basic analysis
{baseDir}/tools/analyze.py data.csv

# With specific options
{baseDir}/tools/analyze.py data.xlsx --clean --visualize --report

Step 3: Get Results

Output includes:

• Cleaned data file
• Analysis summary
• Visualization charts
• Insights report

Available Tools

Tool	Function	Input	Output
analyze.py	Main analysis entry point	Data file	Summary + options
clean.py	Data cleaning	Raw data	Clean data
visualize.py	Generate charts	Data	PNG/PDF charts
report.py	Generate reports	Analysis results	Markdown report

Usage Examples

Example 1: Quick Analysis

"帮我分析这个销售数据"

bash

# Place your file as sales_data.csv
{baseDir}/tools/analyze.py sales_data.csv

Output:

text

✅ Data loaded: 1,234 rows, 8 columns
📊 Summary statistics generated
📈 Visualization: sales_trend.png
💡 3 key insights found

Example 2: Data Cleaning + Analysis

"清洗并分析客户数据"

bash

{baseDir}/tools/analyze.py customer_data.xlsx --clean --visualize

Example 3: Generate Full Report

"生成完整的数据报告"

bash

{baseDir}/tools/analyze.py data.csv --report --output report.md

Supported Formats

Format	Read	Write	Notes
CSV	✅	✅	Universal format
Excel (.xlsx)	✅	✅	Requires openpyxl
JSON	✅	✅	Structured data
TSV	✅	✅	Tab-separated

Output Files

File	Description
*_cleaned.csv	Cleaned data
*_summary.txt	Statistical summary
*_chart_*.png	Visualizations
*_report.md	Full analysis report

Common Use Cases

Business Analytics

• Sales trend analysis
• Customer segmentation
• Revenue forecasting
• Performance dashboards

Data Quality

• Duplicate detection
• Missing value handling
• Format standardization
• Anomaly detection

Reporting

• Executive summaries
• Department reports
• Trend analysis
• KPI tracking

Advanced Features

Custom Analysis

bash

# Specific columns only
{baseDir}/tools/analyze.py data.csv --columns "sales,date,region"

# Time series analysis
{baseDir}/tools/analyze.py data.csv --timeseries --date-column "date"

# Group by category
{baseDir}/tools/analyze.py data.csv --group-by "region" --aggregate "sum,mean"

Visualization Options

bash

# Chart types
{baseDir}/tools/visualize.py data.csv --type bar
{baseDir}/tools/visualize.py data.csv --type line
{baseDir}/tools/visualize.py data.csv --type scatter
{baseDir}/tools/visualize.py data.csv --type pie

# Styling
{baseDir}/tools/visualize.py data.csv --style professional
{baseDir}/tools/visualize.py data.csv --colors "blue,green,red"

Setup

bash

# Install dependencies
pip3 install pandas openpyxl matplotlib seaborn

# Verify installation
python3 -c "import pandas, matplotlib; print('Dependencies OK')"

Notes

• ⚠️ Large files (>100MB) may take time to process
• ⚠️ Excel files require openpyxl
• ⚠️ Charts saved as PNG by default
• ⚠️ All processing is local (no data sent externally)

Troubleshooting

"Module not found"

bash

pip3 install pandas openpyxl matplotlib

"File encoding error"

• Try converting to UTF-8 first
• Or specify encoding: --encoding gbk

"Memory error with large files"

• Process in chunks: --chunk-size 10000
• Or sample data: --sample 0.1