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Analyzes and prints timing pattern statistics for flock data by categorizing issues that occur before start time and after end time, grouped by flock type.

Purpose

This function performs data analysis on timing anomalies in flock management data. It combines two datasets (before_start and after_end issues), aggregates them by flock type, and displays the top 10 flock types with the most timing issues. The output helps identify which flock types have the most scheduling problems, breaking down issues into 'Before Start' and 'After End' categories.

Source Code

def analyze_flock_type_patterns(before_start, after_end):
    """Analyze timing patterns by flock type."""
    print("\nTIMING PATTERNS BY FLOCK TYPE")
    print("-" * 40)
    
    # Combine data
    all_issues = pd.concat([
        before_start.assign(IssueType='Before Start'),
        after_end.assign(IssueType='After End')
    ])
    
    # Analysis by type
    type_analysis = all_issues.groupby(['Type', 'IssueType']).size().unstack(fill_value=0)
    type_totals = type_analysis.sum(axis=1).sort_values(ascending=False)
    
    print("Top flock types with timing issues:")
    for ftype in type_totals.head(10).index:
        total = type_totals[ftype]
        before = type_analysis.loc[ftype, 'Before Start'] if 'Before Start' in type_analysis.columns else 0
        after = type_analysis.loc[ftype, 'After End'] if 'After End' in type_analysis.columns else 0
        print(f"  {ftype}: {total} total ({before} before start, {after} after end)")

Parameters

Parameter Details

before_start: A pandas DataFrame containing records of timing issues that occurred before the scheduled start time. Must have a 'Type' column representing flock type. Expected to be a filtered subset of flock data with timing violations.

after_end: A pandas DataFrame containing records of timing issues that occurred after the scheduled end time. Must have a 'Type' column representing flock type. Expected to be a filtered subset of flock data with timing violations.

Return Value

This function does not return any value (returns None implicitly). It produces console output showing timing pattern analysis, including a formatted table of the top 10 flock types with timing issues and their breakdown by issue type.

Dependencies

• pandas

Required Imports

import pandas as pd

Usage Example

import pandas as pd

# Create sample data
before_start_df = pd.DataFrame({
    'Type': ['Chicken', 'Duck', 'Chicken', 'Turkey'],
    'timestamp': ['2024-01-01 08:00', '2024-01-01 08:15', '2024-01-01 08:30', '2024-01-01 08:45']
})

after_end_df = pd.DataFrame({
    'Type': ['Chicken', 'Duck', 'Duck', 'Goose'],
    'timestamp': ['2024-01-01 18:00', '2024-01-01 18:15', '2024-01-01 18:30', '2024-01-01 18:45']
})

# Analyze timing patterns
analyze_flock_type_patterns(before_start_df, after_end_df)

# Output will display:
# TIMING PATTERNS BY FLOCK TYPE
# ----------------------------------------
# Top flock types with timing issues:
#   Chicken: 3 total (2 before start, 1 after end)
#   Duck: 3 total (1 before start, 2 after end)
#   Turkey: 1 total (1 before start, 0 after end)
#   Goose: 1 total (0 before start, 1 after end)

Best Practices

• Ensure both input DataFrames have a 'Type' column before calling this function
• Input DataFrames should be pre-filtered to contain only timing violations for accurate analysis
• The function prints directly to console, so redirect stdout if you need to capture output programmatically
• Consider the size of input DataFrames as the function creates a combined dataset in memory
• The function displays only the top 10 flock types; modify the head(10) parameter if you need more or fewer results
• Handle empty DataFrames gracefully by checking before calling this function to avoid errors in groupby operations

Tags

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