Eda/engineering

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

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

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