import pandas as pd

country_mapping = {
    'NL': 'Netherlands'
    }   

# Read the input CSV file, assuming the second row is the header
read_df = pd.read_csv('../1/SCInstalledBaseLocation__c.csv', header=0, keep_default_na=False)
for row in read_df.to_dict('records'):
    try:
        # Your processing logic here
        pass
    except KeyError as e:
        print(f'KeyError: {e}')

# Columns for reindexing
reindex_columns = ['City__c','Country__c','Extension__c','FlatNo__c','Floor__c','GeoX__c','GeoY__c','HouseNo__c','Id','PostalCode__c','Street__c']

# Reindex the columns to match the desired format
df = read_df.reindex(reindex_columns, axis=1)

df['Street'] = (
    df['Street__c'].astype(str) + ' ' +
    df['HouseNo__c'].astype(str) + ' ' +
    df['Extension__c'].astype(str)
)

# Remove any trailing spaces that may result from missing values
df['Street'] = df['Street'].str.rstrip()

df['PKey__c'] = (
    df['Street'].astype(str) + ', ' +
    df['PostalCode__c'].astype(str) + ' ' +
    df['City__c'].astype(str) + ', ' +
    df['Country__c'].astype(str)
)

## 1. Address.csv
# Columns needed for Address table based on the input CSV structure
address_columns = ['City__c', 'Country__c', 
                   'PostalCode__c', 'Street', 
                   'GeoY__c', 'GeoX__c', 'PKey__c']

# Extract data for Address
address_df = df[address_columns].copy()

address_df['CountryCode'] = address_df['Country__c'].map(country_mapping)

# Now create the 'Name' column without any warnings
address_df['Parent.Name'] = address_df['PKey__c']

# Rename columns to match the desired format
address_df.columns = ['City', 'CountryCode', 'PostalCode', 'Street', 'Latitude', 'Longitude', 'PKey__c', 'Country', 'Parent.Name']

# Check for duplicates in Address table based on OldId, City, CountryCode, PostalCode, and Street
address_df = address_df.drop_duplicates(subset=['City', 'Country', 'PostalCode', 'Street'], keep='first')

## 2. Parent_Location.csv
parent_columns = ['City__c', 'Country__c', 
                   'PostalCode__c', 'Street', 'PKey__c']
parent_df = df[parent_columns]
# Rename columns to match the desired format
parent_df.columns = ['City', 'Country',
                     'PostalCode', 'Street', 'Name']

parent_df['CountryCode'] = parent_df['Country'].map(country_mapping)

# Check for duplicates in Parent Location based on OldId and ConstructionEndDate
parent_df = parent_df.drop_duplicates(subset=['Name'], keep='first')

parent_df = parent_df.drop('Street', axis=1)
parent_df = parent_df.drop('PostalCode', axis=1)
parent_df = parent_df.drop('City', axis=1)
parent_df = parent_df.drop('CountryCode', axis=1)
parent_df = parent_df.drop('Country', axis=1)

parent_df['DuplicateCheck__c'] = 'false'
parent_df['IsInventoryLocation'] = 'false'
parent_df['IsMobile'] = 'false'
parent_df['LocationType'] = 'Site'

## 3. Child_Location.csv
child_columns = ['Extension__c', 'FlatNo__c', 'Floor__c', 'City__c', 'Country__c', 
                   'PostalCode__c', 'Street', 'PKey__c']
# Modify child_df by explicitly creating a new DataFrame
child_df = df[child_columns].copy()  # Add .copy() to create an explicit copy

# Now create the 'Name' column without any warnings
child_df['Name'] = (
    child_df['Floor__c'].astype(str) + '-' +
    child_df['FlatNo__c'].astype(str) + '-' +
    child_df['Extension__c'].astype(str)
)

# Rename columns to match the desired format
child_df.columns = ['Extension', 'Flat', 'Floor', 'City', 'Country',
                     'PostalCode', 'Street', 'PKey__c', 'Name']

child_df = child_df.drop_duplicates(subset=['Extension', 'Flat', 'Floor','City', 'Country', 'PostalCode', 'Street'], keep='first')

child_df = child_df.drop('Country', axis=1)
child_df = child_df.drop('PostalCode', axis=1)
child_df = child_df.drop('City', axis=1)
child_df = child_df.drop('Street', axis=1)

child_df['DuplicateCheck__c'] = 'false'
child_df['IsInventoryLocation'] = 'false'
child_df['IsMobile'] = 'false'
child_df['LocationType'] = 'Site'


# Write each DataFrame to a separate CSV file
address_df.to_csv('../3/Address.csv', index=False)
parent_df.to_csv('../3/Location.csv', index=False)
child_df.to_csv('../5/Location.csv', index=False)

print('Data has been successfully split into Address.csv, Parent_Location.csv, and Child_Location.csv files with duplicate checks applied.')