Data Analyst Skill#

Automatically clean, analyze, and visualize enterprise data.

Features#

FeatureDescriptionReferenceData CleaningRemove duplicates, handle missing values, standardize formatsreferences/data_cleaning.mdData AnalysisStatistics, trends, correlationsreferences/data_analysis.mdVisualizationCharts, graphs, dashboardsreferences/visualization.mdReport GenerationAutomated insights and recommendationsreferences/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#

ToolFunctionInputOutputanalyze.pyMain analysis entry pointData fileSummary + optionsclean.pyData cleaningRaw dataClean datavisualize.pyGenerate chartsDataPNG/PDF chartsreport.pyGenerate reportsAnalysis resultsMarkdown 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#

FormatReadWriteNotesCSV✅✅Universal formatExcel (.xlsx)✅✅Requires openpyxlJSON✅✅Structured dataTSV✅✅Tab-separated

Output Files#

FileDescription*_cleaned.csvCleaned data*_summary.txtStatistical summary*_chart_*.pngVisualizations*_report.mdFull 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