datamaker-faker
Table of Contents
Installation
pip install datamaker-faker
Features
This package:
- Aims to be a replacement for the
fakerpackage in Python but NOT a drop-in replacement. - Fast: Generate data quickly at large scales.
- Reproducible: Set a seed to generate the same data over and over.
- Relational: Generate data that is related to each other.
- Extensible: Swop out the data for your own, or bring your own generators.
Usage
import json
from datamaker_faker import Faker
# define your data model
model = {
"first": "first_name",
"last": "last_name",
"sex": "sex",
"city": "city",
"country": "country",
}
# create a new faker instance
faker = Faker(model, seed=9)
# generate some fake data
df = faker.generate(10)
# write the data to a csv file
df.to_csv("datafaker.csv")
# or leverage pandas to convert the data to json
data = df.to_dict("records")
print(json.dumps(data, indent=2))
See generated JSON data
[
{
"first": "sophia",
"last": "weber",
"sex": "female",
"city": "munich",
"country": "germany"
},
{
"first": "aiden",
"last": "brown",
"sex": "male",
"city": "toronto",
"country": "canada"
},
{
"first": "sophia",
"last": "weber",
"sex": "female",
"city": "munich",
"country": "germany"
},
{
"first": "aaradhya",
"last": "singh",
"sex": "female",
"city": "delhi",
"country": "india"
},
{
"first": "liam",
"last": "brown",
"sex": "male",
"city": "chicago",
"country": "united states"
},
{
"first": "daniel",
"last": "kamau",
"sex": "male",
"city": "nairobi",
"country": "kenya"
},
{
"first": "alexander",
"last": "smirnov",
"sex": "male",
"city": "nizhny novgorod",
"country": "russia"
},
{
"first": "johann",
"last": "weber",
"sex": "male",
"city": "munich",
"country": "germany"
},
{
"first": "arjun",
"last": "singh",
"sex": "male",
"city": "delhi",
"country": "india"
},
{
"first": "leonardo",
"last": "ferrari",
"sex": "male",
"city": "naples",
"country": "italy"
}
]
Disclaimer
This package is a work in progress and is not yet ready for production use.
License
datamaker-faker is distributed under the terms of the MIT license.
1 maintainer
Metadata
1 maintainer
Release files for datamaker-faker 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| datamaker_faker-0.0.1.tar.gz | 12.6 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| datamaker_faker-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 25.2 kB
Release files / datamaker_faker-0.0.1.tar.gz
| Download URL | datamaker_faker-0.0.1.tar.gz |
|---|---|
| Size | 12.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
1cb41471afa47694848dda77af3ba3abe3dedc7da96ca87f0e3f40a72f5d8297
|
|
BLAKE2b-256 checksum How to use checksums |
aa84bc2b78bb633f908010100b27116fe0e3f313fd67a247cc078bee43af1ad9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.9.19
|
Release files / datamaker_faker-0.0.1-py3-none-any.whl
| Download URL | datamaker_faker-0.0.1-py3-none-any.whl |
|---|---|
| Size | 12.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
f4adfd4685db4afa7dae1c847939a80670320742d8a8e549979f3635b4579127
|
|
BLAKE2b-256 checksum How to use checksums |
6abe2dfff588c4ec54a2c9e4f84b85c68eaf0e71176c7ebd0b71e546dfc9bb79
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.9.19
|
Security for datamaker-faker 0.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
