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Convert Csv To Vcf Python (Ultimate)

Reviews by Yael Waknin

convert csv to vcf python
  • Author: Sara Cate
  • Genre: Romance
  • Kindle pages: 358

Synopsis

I’m a scoundrel

Playboy. Man whore.

Basically, I get around, and I’m not afraid to admit it.

So when my best friend opens up Salacious Players’ Club and asks me to head the construction, how could I say no?

Now we’re on a cross-country road trip touring other kink clubs, and I couldn’t be happier.

Life is good.

Then Hunter suddenly asks me to sleep with his wife…while he watches.

I’ll do anything for my best friend, but this is the one request I should say no to.

Isabel is the woman of my dreams, but she’s his.

And the exact reason I should say no is the one reason I say yes.

Because it’s not only Isabel I want.

 

These are the two most important people in my life, and if we go down this path, how will I ever be able to walk away?

I’m not sure my best friend understands just how much I’m willing to do for him—and why

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with open(csv_file, 'r', encoding=encoding) as infile: # Auto-detect delimiter if not specified if delimiter == ',': sample = infile.read(1024) infile.seek(0) sniffer = csv.Sniffer() if sniffer.has_header(sample): delimiter = sniffer.sniff(sample).delimiter reader = csv.DictReader(infile, delimiter=delimiter) with open(vcf_file, 'w', encoding='utf-8') as outfile: for row_num, row in enumerate(reader, 1): try: # Start vCard outfile.write('BEGIN:VCARD\n') outfile.write('VERSION:3.0\n') # Get name information full_name = find_column(row, column_mapping['full_name']) first_name = find_column(row, column_mapping['first_name']) last_name = find_column(row, column_mapping['last_name']) # Set full name if not directly provided if not full_name and (first_name or last_name): full_name = f"{first_name or ''} {last_name or ''}".strip() if full_name: outfile.write(f'FN:{sanitize_text(full_name)}\n') # Structured name (N: last;first;middle;prefix;suffix) if last_name or first_name: outfile.write(f'N:{sanitize_text(last_name or "")};{sanitize_text(first_name or "")};;;\n') # Phone numbers phone = find_column(row, column_mapping['phone']) if phone: outfile.write(f'TEL;TYPE=CELL:{sanitize_text(phone)}\n') phone_home = find_column(row, column_mapping['phone_home']) if phone_home: outfile.write(f'TEL;TYPE=HOME:{sanitize_text(phone_home)}\n') phone_work = find_column(row, column_mapping['phone_work']) if phone_work: outfile.write(f'TEL;TYPE=WORK:{sanitize_text(phone_work)}\n') # Email addresses email = find_column(row, column_mapping['email']) if email: outfile.write(f'EMAIL:{sanitize_text(email)}\n') email_home = find_column(row, column_mapping['email_home']) if email_home: outfile.write(f'EMAIL;TYPE=HOME:{sanitize_text(email_home)}\n') email_work = find_column(row, column_mapping['email_work']) if email_work: outfile.write(f'EMAIL;TYPE=WORK:{sanitize_text(email_work)}\n') # Address (simple version) address = find_column(row, column_mapping['address']) if address: outfile.write(f'ADR;TYPE=HOME:;;{sanitize_text(address)};;{sanitize_text(city or "")};{sanitize_text(state or "")};{sanitize_text(zip or "")};{sanitize_text(country or "")}\n') # Company and title company = find_column(row, column_mapping['company']) if company: outfile.write(f'ORG:{sanitize_text(company)}\n') title = find_column(row, column_mapping['title']) if title: outfile.write(f'TITLE:{sanitize_text(title)}\n') # Website website = find_column(row, column_mapping['website']) if website: outfile.write(f'URL:{sanitize_text(website)}\n') # Birthday birthday = find_column(row, column_mapping['birthday']) if birthday: # Try to format as YYYYMMDD if possible bday_clean = re.sub(r'[^0-9]', '', str(birthday)) if len(bday_clean) == 8: outfile.write(f'BDAY:{bday_clean}\n') else: outfile.write(f'BDAY:{birthday}\n') # Notes notes = find_column(row, column_mapping['notes']) if notes: outfile.write(f'NOTE:{sanitize_text(notes)}\n') # End vCard outfile.write('END:VCARD\n') outfile.write('\n') contacts_count += 1 except Exception as e: print(f"Error processing row {row_num}: {e}") continue

# Column mapping (customize based on your CSV structure) column_mapping = { 'full_name': ['Name', 'Full Name', 'FN', 'Fullname'], 'first_name': ['First Name', 'FirstName', 'Given Name'], 'last_name': ['Last Name', 'LastName', 'Family Name'], 'phone': ['Phone', 'Mobile', 'Phone Number', 'Tel'], 'phone_home': ['Home Phone', 'Phone (Home)'], 'phone_work': ['Work Phone', 'Phone (Work)'], 'email': ['Email', 'E-mail', 'Email Address'], 'email_home': ['Home Email'], 'email_work': ['Work Email'], 'address': ['Address', 'Street', 'Address (Home)'], 'address_work': ['Work Address', 'Business Address'], 'city': ['City', 'Town'], 'state': ['State', 'Province'], 'zip': ['ZIP', 'Postal Code', 'Zip Code'], 'country': ['Country'], 'company': ['Company', 'Organization', 'Org'], 'title': ['Title', 'Job Title', 'Position'], 'website': ['Website', 'URL', 'Web'], 'birthday': ['Birthday', 'Bday', 'Date of Birth'], 'notes': ['Notes', 'Comments', 'Description'] }

return contacts_count if name == " main ": # Simple usage csv_to_vcf_advanced('contacts.csv', 'output.vcf')

Run the script:

def csv_to_vcf_advanced(csv_file, vcf_file, encoding='utf-8', delimiter=','): """ Advanced CSV to VCF converter with flexible column mapping

def find_column(row, possible_names): """Find the first matching column from possible names""" for name in possible_names: if name in row and row[name]: return row[name] return None

Args: csv_file: Input CSV file path vcf_file: Output VCF file path encoding: File encoding (default: utf-8) delimiter: CSV delimiter (default: ',') """

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Convert Csv To Vcf Python (Ultimate)

with open(csv_file, 'r', encoding=encoding) as infile: # Auto-detect delimiter if not specified if delimiter == ',': sample = infile.read(1024) infile.seek(0) sniffer = csv.Sniffer() if sniffer.has_header(sample): delimiter = sniffer.sniff(sample).delimiter reader = csv.DictReader(infile, delimiter=delimiter) with open(vcf_file, 'w', encoding='utf-8') as outfile: for row_num, row in enumerate(reader, 1): try: # Start vCard outfile.write('BEGIN:VCARD\n') outfile.write('VERSION:3.0\n') # Get name information full_name = find_column(row, column_mapping['full_name']) first_name = find_column(row, column_mapping['first_name']) last_name = find_column(row, column_mapping['last_name']) # Set full name if not directly provided if not full_name and (first_name or last_name): full_name = f"{first_name or ''} {last_name or ''}".strip() if full_name: outfile.write(f'FN:{sanitize_text(full_name)}\n') # Structured name (N: last;first;middle;prefix;suffix) if last_name or first_name: outfile.write(f'N:{sanitize_text(last_name or "")};{sanitize_text(first_name or "")};;;\n') # Phone numbers phone = find_column(row, column_mapping['phone']) if phone: outfile.write(f'TEL;TYPE=CELL:{sanitize_text(phone)}\n') phone_home = find_column(row, column_mapping['phone_home']) if phone_home: outfile.write(f'TEL;TYPE=HOME:{sanitize_text(phone_home)}\n') phone_work = find_column(row, column_mapping['phone_work']) if phone_work: outfile.write(f'TEL;TYPE=WORK:{sanitize_text(phone_work)}\n') # Email addresses email = find_column(row, column_mapping['email']) if email: outfile.write(f'EMAIL:{sanitize_text(email)}\n') email_home = find_column(row, column_mapping['email_home']) if email_home: outfile.write(f'EMAIL;TYPE=HOME:{sanitize_text(email_home)}\n') email_work = find_column(row, column_mapping['email_work']) if email_work: outfile.write(f'EMAIL;TYPE=WORK:{sanitize_text(email_work)}\n') # Address (simple version) address = find_column(row, column_mapping['address']) if address: outfile.write(f'ADR;TYPE=HOME:;;{sanitize_text(address)};;{sanitize_text(city or "")};{sanitize_text(state or "")};{sanitize_text(zip or "")};{sanitize_text(country or "")}\n') # Company and title company = find_column(row, column_mapping['company']) if company: outfile.write(f'ORG:{sanitize_text(company)}\n') title = find_column(row, column_mapping['title']) if title: outfile.write(f'TITLE:{sanitize_text(title)}\n') # Website website = find_column(row, column_mapping['website']) if website: outfile.write(f'URL:{sanitize_text(website)}\n') # Birthday birthday = find_column(row, column_mapping['birthday']) if birthday: # Try to format as YYYYMMDD if possible bday_clean = re.sub(r'[^0-9]', '', str(birthday)) if len(bday_clean) == 8: outfile.write(f'BDAY:{bday_clean}\n') else: outfile.write(f'BDAY:{birthday}\n') # Notes notes = find_column(row, column_mapping['notes']) if notes: outfile.write(f'NOTE:{sanitize_text(notes)}\n') # End vCard outfile.write('END:VCARD\n') outfile.write('\n') contacts_count += 1 except Exception as e: print(f"Error processing row {row_num}: {e}") continue

# Column mapping (customize based on your CSV structure) column_mapping = { 'full_name': ['Name', 'Full Name', 'FN', 'Fullname'], 'first_name': ['First Name', 'FirstName', 'Given Name'], 'last_name': ['Last Name', 'LastName', 'Family Name'], 'phone': ['Phone', 'Mobile', 'Phone Number', 'Tel'], 'phone_home': ['Home Phone', 'Phone (Home)'], 'phone_work': ['Work Phone', 'Phone (Work)'], 'email': ['Email', 'E-mail', 'Email Address'], 'email_home': ['Home Email'], 'email_work': ['Work Email'], 'address': ['Address', 'Street', 'Address (Home)'], 'address_work': ['Work Address', 'Business Address'], 'city': ['City', 'Town'], 'state': ['State', 'Province'], 'zip': ['ZIP', 'Postal Code', 'Zip Code'], 'country': ['Country'], 'company': ['Company', 'Organization', 'Org'], 'title': ['Title', 'Job Title', 'Position'], 'website': ['Website', 'URL', 'Web'], 'birthday': ['Birthday', 'Bday', 'Date of Birth'], 'notes': ['Notes', 'Comments', 'Description'] } convert csv to vcf python

return contacts_count if name == " main ": # Simple usage csv_to_vcf_advanced('contacts.csv', 'output.vcf') delimiter=delimiter) with open(vcf_file

Run the script:

def csv_to_vcf_advanced(csv_file, vcf_file, encoding='utf-8', delimiter=','): """ Advanced CSV to VCF converter with flexible column mapping encoding='utf-8') as outfile: for row_num

def find_column(row, possible_names): """Find the first matching column from possible names""" for name in possible_names: if name in row and row[name]: return row[name] return None

Args: csv_file: Input CSV file path vcf_file: Output VCF file path encoding: File encoding (default: utf-8) delimiter: CSV delimiter (default: ',') """

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