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Generates a comprehensive corrective action report for data quality issues in treatment records, categorizing actions by urgency and providing impact assessment.

Purpose

This function analyzes data quality issues in veterinary treatment records and produces a formatted console report. It identifies immediate, short-term, and long-term corrective actions needed to address timing issues, invalid dates (particularly 1900-01-01 errors), and treatments recorded outside flock lifespans. The report includes specific recommendations, affected flock counts, error rates, and business impact assessments to guide data quality improvement efforts.

Source Code

def generate_action_report(before_start, after_end, severe_cases, flocks_issues):
    """Generate a corrective action report."""
    print("\nCORRECTIVE ACTION REPORT")
    print("=" * 40)
    
    # Immediate actions
    print("IMMEDIATE ACTIONS (Within 1 week):")
    
    # 1900 date fixes
    errors_1900 = before_start[before_start['AdministeredDate'].dt.year == 1900]
    if len(errors_1900) > 0:
        print(f"1. Fix {len(errors_1900)} treatments with 1900-01-01 dates")
        print("   Action: Update AdministeredDate to correct values")
        print("   Affected flocks:")
        for flock in errors_1900['FlockCD'].unique():
            print(f"     - {flock}")
    
    # Perfect timing issue flocks
    perfect_issues = flocks_issues[flocks_issues['TimingIssueRate'] == 1.0]
    print(f"\n2. Review {len(perfect_issues)} flocks with 100% timing issues")
    print("   Action: Check if flock dates or treatment dates are incorrect")
    
    # Short-term actions
    print(f"\nSHORT-TERM ACTIONS (Within 1 month):")
    print("1. Implement data validation rules")
    print("   - Treatment dates must be within flock lifespan ± 7 days")
    print("   - Flag dates before 2000 or after current date + 1 year")
    print("   - Require confirmation for treatments outside normal range")
    
    extreme_future = after_end[after_end['DaysAfterEnd'] > 365]
    if len(extreme_future) > 0:
        print(f"\n2. Review {len(extreme_future)} treatments >1 year after flock end")
        print("   Action: Verify if these are data entry errors")
    
    # Long-term actions
    print(f"\nLONG-TERM ACTIONS (Within 3 months):")
    print("1. Implement automated data quality monitoring")
    print("2. Create monthly data quality reports")
    print("3. Train staff on proper date entry procedures")
    print("4. Review and update data entry interfaces")
    
    # Cost/benefit analysis
    total_issues = len(before_start) + len(after_end)
    total_treatments = 247640  # From previous analysis
    error_rate = (total_issues / total_treatments) * 100
    
    print(f"\nIMPACT ASSESSMENT:")
    print(f"- Current error rate: {error_rate:.2f}% of all treatments")
    print(f"- Data quality improvement potential: High")
    print(f"- Estimated effort: Medium (primarily data validation setup)")
    print(f"- Business impact: Improved analysis accuracy and regulatory compliance")

Parameters

Parameter Details

before_start: A pandas DataFrame containing treatment records that occurred before the flock start date. Must have columns 'AdministeredDate' (datetime) and 'FlockCD' (flock identifier). Used to identify and report on pre-start timing issues.

after_end: A pandas DataFrame containing treatment records that occurred after the flock end date. Must have a 'DaysAfterEnd' column (numeric) indicating how many days after flock end the treatment occurred. Used to identify post-end timing issues and extreme future dates.

severe_cases: A pandas DataFrame containing severe data quality cases. This parameter is accepted but not currently used in the function implementation.

flocks_issues: A pandas DataFrame containing flock-level summary statistics. Must have columns 'TimingIssueRate' (float between 0-1) representing the proportion of timing issues per flock. Used to identify flocks with 100% timing issue rates.

Return Value

This function returns None. It produces output by printing a formatted report directly to the console (stdout). The report includes sections for immediate actions, short-term actions, long-term actions, and impact assessment.

Dependencies

• pandas

Required Imports

import pandas as pd

Usage Example

import pandas as pd
from datetime import datetime

# Create sample data
before_start = pd.DataFrame({
    'AdministeredDate': pd.to_datetime(['1900-01-01', '2020-01-15', '1900-01-01']),
    'FlockCD': ['FLOCK001', 'FLOCK002', 'FLOCK003']
})

after_end = pd.DataFrame({
    'DaysAfterEnd': [10, 400, 500],
    'FlockCD': ['FLOCK004', 'FLOCK005', 'FLOCK006']
})

severe_cases = pd.DataFrame()  # Not used but required

flocks_issues = pd.DataFrame({
    'FlockCD': ['FLOCK001', 'FLOCK002', 'FLOCK003'],
    'TimingIssueRate': [1.0, 0.5, 1.0]
})

# Generate the report
generate_action_report(before_start, after_end, severe_cases, flocks_issues)

# Output will be printed to console with formatted sections

Best Practices

• Ensure all input DataFrames have the required columns with correct data types before calling this function
• The 'AdministeredDate' column in before_start must be datetime type for year extraction to work
• The hardcoded total_treatments value (247,640) should be parameterized for reusability across different datasets
• Consider redirecting output to a file or returning a string instead of printing directly for better testability
• The severe_cases parameter is unused and could be removed or implemented in future versions
• Validate that DaysAfterEnd values are numeric to avoid errors in the extreme_future calculation
• This function is designed for console output; consider creating a version that returns structured data for programmatic use

Tags

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