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