5 Best Ways to Convert Python CSV to VCF

💡 Problem Formulation: Converting contacts from a CSV file to a VCF (vCard) format is often necessary when transferring data between different platforms or address books. For example, a user may want to export contacts from a spreadsheet (CSV) and import them into a smartphone’s contacts (VCF). This article explores various Python methods for accomplishing this task effectively.

Method 1: Using Python’s Standard Library

This method involves utilizing the built-in csv module to read CSV files and format the data into vCard format manually. It is a straightforward approach that relies on Python’s standard library without the need for any external packages.

Here’s an example:

import csv

def csv_to_vcf(filename):
    with open(filename, newline='') as csvfile, open('contacts.vcf', 'w') as vcf:
        reader = csv.reader(csvfile)
        for row in reader:
            vcf.write(f"BEGIN:VCARD\nVERSION:3.0\nFN:{row[0]}\nTEL:{row[1]}\nEND:VCARD\n\n")

csv_to_vcf('contacts.csv')

Output: This creates a ‘contacts.vcf’ file with vCard entries for each contact in the ‘contacts.csv’ file.

This code snippet opens and reads a CSV file containing contact information, then writes it to a VCF file in vCard format. It loops through the rows of the CSV, writing each contact’s full name and telephone number to the VCF file, with the appropriate vCard syntax.

Method 2: Using vobject Library

The vobject library is a third-party Python module that handles vCard creation more elegantly and handles various vCard properties and parameters. This method simplifies the process and ensures standard compliance.

Here’s an example:

import csv
import vobject

def csv_to_vcf_vobject(filename):
    with open(filename, newline='') as csvfile, open('contacts.vcf', 'w') as vcf:
        reader = csv.reader(csvfile)
        for row in reader:
            card = vobject.vCard()
            card.add('fn').value = row[0]
            card.add('tel').value = row[1]
            vcf.write(card.serialize())

csv_to_vcf_vobject('contacts.csv')

Output: This generates a ‘contacts.vcf’ file with vCard entries for each contact in ‘contacts.csv’, formatted according to vCard standards.

This snippet utilizes the vobject library to create vCard objects for each contact. After reading from the CSV, it adds full name and telephone number properties to each vCard and writes the serialized output to a VCF file, ensuring proper formatting and encoding.

Method 3: Using pandas and vobject Libraries

For larger datasets, pandas can be paired with vobject for enhanced data manipulation and batch processing of contacts. This combination is powerful for dealing with complex CSV files and varied vCard properties.

Here’s an example:

import pandas as pd
import vobject

def csv_to_vcf_pandas(filename):
    df = pd.read_csv(filename)
    with open('contacts.vcf', 'w') as vcf:
        for index, contact in df.iterrows():
            card = vobject.vCard()
            card.add('fn').value = contact['Name']
            card.add('tel').value = contact['Phone']
            vcf.write(card.serialize())

csv_to_vcf_pandas('contacts.csv')

Output: A ‘contacts.vcf’ file is produced with vCard entries for each contact listed in the ‘contacts.csv’ DataFrame.

This method uses pandas to read the CSV file into a DataFrame, which allows for easy access and manipulation of contact data. It then iterates through each contact row, creating vCard objects with vobject and serializing them to a VCF file.

Method 4: Using EzVcard Library

EzVcard is another external library designed specifically for parsing and generating vCards. It offers a high-level API for vCard manipulation, supporting various versions of the vCard specification.

Here’s an example:

import csv
from ezvcard import Ezvcard

def csv_to_vcf_ezvcard(filename):
    with open(filename, newline='') as csvfile, open('contacts.vcf', 'w') as vcf:
        reader = csv.reader(csvfile)
        for row in reader:
            vcard = Ezvcard.create()
            vcard.setFormattedName(row[0])
            vcard.addTelephoneNumber(row[1])
            vcf.write(vcard.write())

csv_to_vcf_ezvcard('contacts.csv')

Output: This results in a ‘contacts.vcf’ file populated with vCard entries from the ‘contacts.csv’ file.

In this code, Ezvcard is used to create vCard instances for each contact in the CSV file. The full name and phone number are set for each vCard, and then the cards are written to a VCF file with the correct formatting and encoding applied by the library.

Bonus One-Liner Method 5: pandas and PyVCF

For a quick, one-liner solution, pandas can be used in conjunction with PyVCF. This method might not provide as much control over the output but is incredibly concise for simple tasks.

Here’s an example:

import pandas as pd
import pyvcf

contacts = pd.read_csv('contacts.csv')
contacts.apply(lambda x: pyvcf.Card(x['Name'], x['Phone']).write('contacts.vcf'), axis=1)

Output: A ‘contacts.vcf’ file is quickly created containing vCard entries from the ‘contacts.csv’ DataFrame.

This succinct line reads the CSV file using pandas and then applies a lambda function that creates and writes a PyVCF Card object for each row, bundling the contacts into a VCF file.

Summary/Discussion

• Method 1: Using Python’s Standard Library. Strengths: No additional installation required, quick setup. Weaknesses: Manual formatting can be error-prone, not suitable for complex vCard properties.
• Method 2: Using vobject Library. Strengths: Simplifies vCard creation, complies with standards. Weaknesses: Requires third-party library installation, might be overkill for very simple tasks.
• Method 3: Using pandas and vobject Libraries. Strengths: Great for large or complex datasets, powerful data manipulation capabilities. Weaknesses: Involves two third-party libraries, higher complexity.
• Method 4: Using EzVcard Library. Strengths: High-level API, supports different vCard formats. Weaknesses: Requires knowledge of the EzVcard library, third-party installation.
• Bonus Method 5: pandas and PyVCF One-Liner. Strengths: Extremely concise. Weaknesses: Less control over output, assumes a specific library structure.