eda-engineering
EDA engineering practices
Datos de origen
- Repositorio
- ffsshhttiikk/opencode-agents-skills
- Última actividad en el origen
- 28 de febrero de 2026 a las 22:51
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 3
- Forks
- 2
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura- name
- eda/engineering
- description
- EDA engineering practices
- license
- MIT
- compatibility
- opencode
- metadata
- {"audience":"data-engineers","category":"data-science"}
## What I do
- Build scalable EDA pipelines
- Automate data profiling
- Create reproducible data analysis workflows
- Implement data quality monitoring
- Design data exploration tools
- Build self-service analytics infrastructure
## When to use me
Use me when:
- Building data infrastructure
- Automating EDA for large datasets
- Creating data quality dashboards
- Implementing data governance
- Building analytics platforms
## Key Concepts
### Data Profiling Pipeline
```python
from great_expectations import GreatExpectations
import pandas as pd
# Define expectations
expectations = [
{"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {"column": "id"}},
{"expectation_type": "expect_column_values_to_be_unique",
"kwargs": {"column": "id"}},
{"expectation_type": "expect_column_values_to_be_between",
"kwargs": {"column": "age", "min_value": 0, "max_value": 120}},
{"expectation_type": "expect_column_distinct_values_to_be_in_set",
"kwargs": {"column": "status", "value_set": ["active", "inactive"]}}
]
# Validate data
ge = GreatExpectations()
batch = ge.get_batch("data.csv", "default")
results = ge.validate(batch, expectations=expectations)
```
### Automated Profiling
```python
from pandas_profiling import ProfileReport
# Generate comprehensive EDA report
profile = ProfileReport(
df,
title="Data Profiling Report",
explorative=True,
missing_diagrams=True,
correlations={"high_cardinality": "include"}
)
profile.to_file("eda_report.html")
```
### Data Quality Metrics
- **Completeness**: % non-null values
- **Uniqueness**: % unique values
- **Validity**: % values in valid range
- **Consistency**: Cross-field validation
- **Timeliness**: Data freshness
### Monitoring Setup
- Great Expectations for validation
- Datafold for diff analysis
- Great Expectations Cloud
- Amundsen for data catalog
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