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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
def load_kit_composition_data(file_path: str) -> pd.DataFrame:
"""Load the Kit Composition and relation CSV file."""
if not os.path.exists(file_path):
raise FileNotFoundError(f"File not found: {file_path}")
df = pd.read_csv(file_path)
print(f"Loaded {len(df)} rows from {file_path}")
return df
def process_master_kits(df: pd.DataFrame) -> 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)
"""
print("Processing Master Kits...")
# Identify masters with hierarchy (subkits or prepacks)
masters_with_subkits = set(df[df['Sub kit'].notna()]['Master Kit'].unique())
masters_with_prepacks = set(df[df['Prepack'].notna()]['Master Kit'].unique())
masters_with_hierarchy = masters_with_subkits.union(masters_with_prepacks)
# All masters
all_masters = set(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 = 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
})
master_df = pd.DataFrame(master_data)
print(f"Created {len(master_df)} master kit records")
print(f"Standalone masters with 'long line': {sum(master_df['line_type'] == 'long line')}")
return master_df
def process_sub_kits(df: pd.DataFrame) -> pd.DataFrame:
"""
Process Sub Kits according to business rules:
- All sub kits get line_type = "long line"
- Remove duplicates
"""
print("Processing Sub Kits...")
# Filter rows that have sub kits
subkit_df = df[df['Sub kit'].notna()].copy()
if len(subkit_df) == 0:
print("No sub kits found")
return pd.DataFrame(columns=['kit_name', 'kit_description', 'kit_type', 'line_type'])
# 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'
})
subkit_result = pd.DataFrame(subkit_data)
print(f"Created {len(subkit_result)} sub kit records")
return subkit_result
def process_prepacks(df: pd.DataFrame) -> pd.DataFrame:
"""
Process Prepacks according to business rules:
- All prepacks get line_type = "miniload"
- Remove duplicates
"""
print("Processing Prepacks...")
# Filter rows that have prepacks
prepack_df = df[df['Prepack'].notna()].copy()
if len(prepack_df) == 0:
print("No prepacks found")
return pd.DataFrame(columns=['kit_name', 'kit_description', 'kit_type', 'line_type'])
# 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'
})
prepack_result = pd.DataFrame(prepack_data)
print(f"Created {len(prepack_result)} prepack records")
return prepack_result
def concatenate_and_save(master_df: pd.DataFrame, subkit_df: pd.DataFrame,
prepack_df: pd.DataFrame, output_path: str) -> pd.DataFrame:
"""
Concatenate all processed dataframes and save to output file.
"""
print("Concatenating results...")
# Concatenate all dataframes
final_df = pd.concat([master_df, subkit_df, prepack_df], ignore_index=True)
# Ensure empty strings instead of NaN for line_type
final_df['line_type'] = final_df['line_type'].fillna('')
# Sort by kit_type for better organization
final_df = final_df.sort_values(['kit_type', 'kit_name']).reset_index(drop=True)
print(f"Final dataset contains {len(final_df)} records:")
print(f" - Masters: {len(master_df)}")
print(f" - Subkits: {len(subkit_df)}")
print(f" - Prepacks: {len(prepack_df)}")
# Save to file (keep empty strings as empty, not NaN)
final_df.to_csv(output_path, index=False, na_rep='')
print(f"Saved cleaned data to: {output_path}")
return 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:
# Load the original data
df = load_kit_composition_data(input_file)
# Process each type of kit
master_df = process_master_kits(df)
subkit_df = process_sub_kits(df)
prepack_df = process_prepacks(df)
# Concatenate and save
final_df = concatenate_and_save(master_df, subkit_df, prepack_df, output_file)
# Display summary statistics
print("\n=== SUMMARY ===")
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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