vamseeachanta Ydata Profiling
When to Use This Skill
USE YData Profiling when:
- Data quality assessment - Evaluating dataset health and completeness
- Initial data exploration - Understanding a new dataset quickly
- Missing value analysis - Detecting patterns in missing data
- Variable analysis - Understanding distributions and characteristics
- Data documentation - Creating shareable data quality reports
- Dataset comparison - Comparing training vs test data, or before/after
- Stakeholder reporting - Generating professional HTML reports
- Data validation - Checking data before ML model training
DON'T USE YData Profiling when:
- Real-time analysis - Need streaming data profiling
- Custom visualizations - Specific chart requirements
- Interactive dashboards - Use Streamlit or Dash instead
- Very large datasets - Over 10M rows (use sampling or minimal mode)
- Production pipelines - Need lightweight validation (use Great Expectations)
Prerequisites
# Basic installation
pip install ydata-profiling
# With all optional dependencies
pip install 'ydata-profiling[all]'
# Using uv (recommended)
uv pip install ydata-profiling pandas numpy
# Jupyter notebook support
pip install ydata-profiling ipywidgets notebook
# Verify installation
python -c "from ydata_profiling import ProfileReport; print('YData Profiling ready!')"
Complete Examples
Example 1: Data Quality Pipeline
from ydata_profiling import ProfileReport
import pandas as pd
import numpy as np
from datetime import datetime
import os
import json
def data_quality_pipeline(
df: pd.DataFrame,
*See sub-skills for full details.*
### Example 2: ML Dataset Profiling
```python
from ydata_profiling import ProfileReport, compare
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import os
def ml_dataset_profiling(
X: pd.DataFrame,
y: pd.Series,
*See sub-skills for full details.*
### Example 3: Time Series Data Profiling
```python
from ydata_profiling import ProfileReport
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import os
def profile_time_series(
df: pd.DataFrame,
date_column: str,
*See sub-skills for full details.*
## Version History
- **1.0.0** (2026-01-17): Initial release
- Basic profile report generation
- Variable analysis and type detection
- Missing value analysis patterns
- Correlation methods (Pearson, Spearman, Kendall, Phi-K)
- Large dataset handling (minimal mode, sampling)
- Comparison reports for datasets
- HTML report customization
- Time series profiling
- Complete pipeline examples
- Integration with Streamlit and Polars
- Best practices and troubleshooting
## Resources
- **Official Documentation**: https://docs.profiling.ydata.ai/
- **GitHub**: https://github.com/ydataai/ydata-profiling
- **PyPI**: https://pypi.org/project/ydata-profiling/
- **Migration from pandas-profiling**: https://docs.profiling.ydata.ai/latest/migration/
---
**Generate comprehensive data quality reports automatically with YData Profiling!**
## Sub-Skills
- [1. Basic Profile Report Generation (+1)](1-basic-profile-report-generation/SKILL.md)
- [3. Missing Value Analysis (+1)](3-missing-value-analysis/SKILL.md)
- [5. Large Dataset Handling](5-large-dataset-handling/SKILL.md)
- [6. Comparison Reports](6-comparison-reports/SKILL.md)
- [7. HTML Report Customization](7-html-report-customization/SKILL.md)
- [YData Profiling with Streamlit (+1)](ydata-profiling-with-streamlit/SKILL.md)
- [1. Use Minimal Mode for Large Datasets (+3)](1-use-minimal-mode-for-large-datasets/SKILL.md)
- [Common Issues](common-issues/SKILL.md)