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"""
Kit Composition Data Cleaner
This script converts the Kit_Composition_and_relation.csv file into a cleaned format
with line types according to the following rules:
1. Master Kits:
- If appears only once (standalone master): line_type = "long line"
- If appears multiple times: line_type = "" (empty/theoretical)
2. Sub Kits:
- All sub kits get line_type = "long line"
3. Prepacks:
- All prepacks get line_type = "miniload"
The output includes columns: kit_name, kit_description, kit_type, line_type
"""
import pandas as pd
import os
from typing import Tuple
class KitCompositionCleaner:
"""
Cleans and processes kit composition data with line type assignments.
This class maintains state across processing steps, allowing for:
- Single data load
- Step-by-step processing
- Intermediate result storage
"""
def __init__(self, input_file: str, output_file: str = None):
"""
Initialize the cleaner with file paths.
Args:
input_file: Path to input CSV file (Kit_Composition_and_relation.csv)
output_file: Path to output CSV file (optional, can be set later)
"""
self.input_file = input_file
self.output_file = output_file
# State variables for processing pipeline
self.df = None
self.master_df = None
self.subkit_df = None
self.prepack_df = None
self.final_df = None
def load_data(self) -> pd.DataFrame:
"""Load the Kit Composition and relation CSV file."""
if not os.path.exists(self.input_file):
raise FileNotFoundError(f"File not found: {self.input_file}")
self.df = pd.read_csv(self.input_file)
print(f"Loaded {len(self.df)} rows from {self.input_file}")
return self.df
def process_master_kits(self) -> pd.DataFrame:
"""
Process Master Kits according to business rules:
- Standalone masters (no subkits/prepacks, only components): line_type = "long line"
- Non-standalone masters (have subkits/prepacks): line_type = "" (empty - no production needed)
"""
if self.df is None:
raise ValueError("Data not loaded. Call load_data() first.")
print("Processing Master Kits...")
# Identify masters with hierarchy (subkits or prepacks)
masters_with_subkits = set(self.df[self.df['Sub kit'].notna()]['Master Kit'].unique())
masters_with_prepacks = set(self.df[self.df['Prepack'].notna()]['Master Kit'].unique())
masters_with_hierarchy = masters_with_subkits.union(masters_with_prepacks)
# All masters
all_masters = set(self.df['Master Kit'].unique())
# Standalone masters are those WITHOUT subkits/prepacks (only have components)
standalone_masters = all_masters - masters_with_hierarchy
print(f"Total unique Master Kits: {len(all_masters)}")
print(f"Masters with subkits/prepacks: {len(masters_with_hierarchy)}")
print(f"Standalone masters (only components): {len(standalone_masters)}")
# Create master kit records
master_data = []
# Get unique master kits with descriptions
unique_masters = self.df[['Master Kit', 'Master Kit Description']].drop_duplicates()
for _, row in unique_masters.iterrows():
master_kit = row['Master Kit']
master_desc = row['Master Kit Description']
# Determine line_type based on standalone status
if master_kit in standalone_masters:
line_type = "long line"
else:
line_type = "" # Empty for non-standalone (theoretical)
master_data.append({
'kit_name': master_kit,
'kit_description': master_desc,
'kit_type': 'master',
'line_type': line_type
})
self.master_df = pd.DataFrame(master_data)
return self.master_df
def process_sub_kits(self) -> pd.DataFrame:
"""
Process Sub Kits according to business rules:
- All sub kits get line_type = "long line"
- Remove duplicates
"""
if self.df is None:
raise ValueError("Data not loaded. Call load_data() first.")
print("Processing Sub Kits...")
# Filter rows that have sub kits
subkit_df = self.df[self.df['Sub kit'].notna()].copy()
if len(subkit_df) == 0:
print("No sub kits found")
self.subkit_df = pd.DataFrame(columns=['kit_name', 'kit_description', 'kit_type', 'line_type'])
return self.subkit_df
# Get unique sub kits with descriptions
unique_subkits = subkit_df[['Sub kit', 'Sub kit description']].drop_duplicates()
subkit_data = []
for _, row in unique_subkits.iterrows():
subkit_data.append({
'kit_name': row['Sub kit'],
'kit_description': row['Sub kit description'],
'kit_type': 'subkit',
'line_type': 'long line'
})
self.subkit_df = pd.DataFrame(subkit_data)
print(f"Created {len(self.subkit_df)} sub kit records")
return self.subkit_df
def process_prepacks(self) -> pd.DataFrame:
"""
Process Prepacks according to business rules:
- All prepacks get line_type = "miniload"
- Remove duplicates
"""
if self.df is None:
raise ValueError("Data not loaded. Call load_data() first.")
print("Processing Prepacks...")
# Filter rows that have prepacks
prepack_df = self.df[self.df['Prepack'].notna()].copy()
if len(prepack_df) == 0:
print("No prepacks found")
self.prepack_df = pd.DataFrame(columns=['kit_name', 'kit_description', 'kit_type', 'line_type'])
return self.prepack_df
# Get unique prepacks with descriptions
unique_prepacks = prepack_df[['Prepack', 'Prepack Description']].drop_duplicates()
prepack_data = []
for _, row in unique_prepacks.iterrows():
prepack_data.append({
'kit_name': row['Prepack'],
'kit_description': row['Prepack Description'],
'kit_type': 'prepack',
'line_type': 'miniload'
})
self.prepack_df = pd.DataFrame(prepack_data)
print(f"Created {len(self.prepack_df)} prepack records")
return self.prepack_df
def concatenate_and_save(self, output_path: str = None) -> pd.DataFrame:
"""
Concatenate all processed dataframes and save to output file.
Args:
output_path: Path to save the output file (uses self.output_file if not provided)
"""
if self.master_df is None or self.subkit_df is None or self.prepack_df is None:
raise ValueError("Processing not complete. Run process_master_kits(), process_sub_kits(), and process_prepacks() first.")
print("Concatenating results...")
# Concatenate all dataframes
self.final_df = pd.concat([self.master_df, self.subkit_df, self.prepack_df], ignore_index=True)
# Ensure empty strings instead of NaN for line_type
self.final_df['line_type'] = self.final_df['line_type'].fillna('')
# Sort by kit_type for better organization
self.final_df = self.final_df.sort_values(['kit_type', 'kit_name']).reset_index(drop=True)
print(f"Final dataset contains {len(self.final_df)} records:")
print(f" - Masters: {len(self.master_df)}")
print(f" - Subkits: {len(self.subkit_df)}")
print(f" - Prepacks: {len(self.prepack_df)}")
# Determine output path
save_path = output_path or self.output_file
if save_path is None:
raise ValueError("No output path provided. Specify output_path parameter or set self.output_file")
# Save to file (keep empty strings as empty, not NaN)
self.final_df.to_csv(save_path, index=False, na_rep='')
print(f"Saved cleaned data to: {save_path}")
return self.final_df
def main():
"""Main function to execute the kit composition cleaning process."""
# Define file paths
base_dir = "/Users/halimjun/Coding_local/SD_roster_real"
input_file = os.path.join(base_dir, "data/real_data_excel/converted_csv/Kit_Composition_and_relation.csv")
output_file = os.path.join(base_dir, "data/real_data_excel/converted_csv/Kit_Composition_and_relation_cleaned_with_line_type.csv")
try:
# Initialize cleaner with class
cleaner = KitCompositionCleaner(input_file, output_file)
# Execute pipeline step by step
cleaner.load_data()
cleaner.process_master_kits()
cleaner.process_sub_kits()
cleaner.process_prepacks()
final_df = cleaner.concatenate_and_save()
# Display summary statistics
print("Line type distribution:")
print(final_df['line_type'].value_counts(dropna=False))
print("\nKit type distribution:")
print(final_df['kit_type'].value_counts())
print("\nSample of final data:")
print(final_df.head(10))
except Exception as e:
print(f"❌ Error processing kit composition data: {e}")
raise
if __name__ == "__main__":
main()
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