fakedpy 1.5

A Python library for generating fake data with various output formats.

FakedPy

A Python library for generating fake data with various output formats.

Installation

pip install fakedpy

Features

• Generate various types of fake data
• Support multiple output formats (CSV, JSON, Excel, Parquet), but CSV as a default
• Support localization (including Indonesian)
• Generate related/dependent data
• Easy to use and flexible

Basic Usage

from fakedpy import FakedGenerator

# Initialize generator
generator = FakedGenerator(locale='id_ID')  # For Indonesian data
# orr
generator = FakedGenerator(locale=['id_ID', 'en_US'])  # For mixed data

# Generate simple data
df = generator.generate(
    rows=5,
    fields=["name", "email", "address"]
)

# Save to different formats
df = generator.generate(
    rows=5,
    fields=["name", "email", "job"],
    output_format="json",
    output_path="data/output"
)

Available Fields

Personal Information

• name: Full name
• first_name: First name
• last_name: Last name
• email: Email address
• phone_number: Phone number
• date: Date
• gender: Gender

Address Information

• address: Full address
• street_address: Street address
• city: City
• state: State/Province
• country: Country
• postcode: Postal code
• latitude: Latitude
• longitude: Longitude

Internet Information

• username: Username
• password: Password
• domain_name: Domain name
• url: URL address
• ipv4: IPv4 address
• ipv6: IPv6 address
• mac_address: MAC address

Company Information

• company: Company name
• job: Job title

Financial Information

• credit_card_number: Credit card number
• credit_card_provider: Credit card provider
• currency_code: Currency code

Technology

• file_name: File name
• file_extension: File extension
• mime_type: MIME type

Miscellaneous

• text: Text paragraph
• word: Single word
• sentence: Single sentence
• paragraph: Full paragraph
• color_name: Color name
• hex_color: HEX color code
• rgb_color: RGB color code

Advanced Usage

Related Data

# Generate data with ID and department
df = generator.generate(
    rows=5,
    fields=["user_id", "name", "department"],
    related_fields={
        "user_id": {"type": "counter", "start": 1000},
        "department": {
            "type": "choice",
            "values": ["IT", "HR", "Finance", "Marketing"]
        }
    }
)

Different Output Formats

# CSV
df = generator.generate(
    rows=10,
    fields=["name", "email"],
    output_format="csv",
    output_path="data/users"
)

# JSON
df = generator.generate(
    rows=10,
    fields=["name", "email"],
    output_format="json",
    output_path="data/users"
)

# Excel
df = generator.generate(
    rows=10,
    fields=["name", "email"],
    output_format="excel",
    output_path="data/users"
)

# Parquet
df = generator.generate(
    rows=10,
    fields=["name", "email"],
    output_format="parquet",
    output_path="data/users"
)

Contributing

Feel free to contribute by creating issues or pull requests in this repository.

License

MIT License