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

# 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"

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

Output:

✅ 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"

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

Example 3: Generate Full Report

"生成完整的数据报告"

{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

# 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

# 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

# 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"

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