Data Generator - WIP
Overview
During every data project I came across a very basic common problem where we have to wait for the test data. For fewer columns it's quite easy to generate the data using online utilities but those have certain limitations on the number of columns and rows. To solve this, I’ve built a utility to generate the mock data based on the supplied json schema. This utility is using Python Faker module to randomly generate the test data. Step by step guide on medium.
How to use
Follow below steps to run the utility. I am open to your suggestions, please add comments or mail me your suggestions.
Note: Data Types are case insensitive
Inputs
It accept valid json schema files only with supported data types: "STRING","INT","INTEGER","NUMBER","FLOAT","DATE","BOOLEAN","BOOL","TIMESTAMP","ADDRESS","CITY","COUNTRY","COUNTRY_CODE","POSTCODE","LICENSE_PLATE","SWIFT","COMPANY","COMPANY_SUFFIX","CREDIT_CARD","CREDIT_CARD_PROVIDER","CREDIT_CARD_NUMBER","CURRENCY","DAY_NUM","DAY_NAME","MONTH_NUM","MONTH_NAME","YEAR","COORDINATE","LATITUDE","LONGITUDE","EMAIL","HOSTNAME","IPV4","IPV6","URI","URL","JOB","TEXT","PASSWORD","SHA1","SHA256","UUID","PASSPORT_NUMBER","NAME","LANGUAGE_NAME","LAST_NAME","FIRST_NAME","PHONE_NUMBER","SSN"
Supported Input Parameters
- --input_json_schema_path: Provide absolute path of the json schema file/folder. It accepts folders(that contains valid json schema files) or absolute path of a json schema file.
Json schema file format.
{
"type": "<object/record,etc>",
"number_of_rows": "<positive number>",
"properties": {
"<column_name>": { "type": "<data_type>" },
"<column_name>": { "type": "<data_type>" }
}
}
The sample json schema file would look like below.
{
"type": "object",
"number_of_rows": "<positive number>",
"properties": {
"price": { "type": "number" },
"name": { "type": "name" },
"location": { "type": "COORDINATE" },
"flt": { "type": "float" },
"email_id": { "type": "EMAIL" },
"dt": { "type": "date" },
"ts": { "type": "timestamp" },
"is_valid": { "type": "boolean" }
}
}
The generator will skip the current json schema file if an error occurred. Mock data would get generated for rest of the valid schema files.
-
--output_file_format: The output file format should be one of the
"CSV","JSON","XML","EXCEL","PARQUET","ORC" -
--output_path: Absolute path to store the generated mock dataset. If an output path does not exists, it will create it and store the data inside the directory into data. file.
Pre-requisites
- Python ^3.10
Steps to execute the utility
- pip install mock-data-generator
- specify the parameters mentioned above
- Sample command:
generate --input_json_schema_path=resources/schema.json --output_file_format=csv --output_path=output_data --number_of_rows=10:
Licensing
Distributed under the MIT license. See LICENSE for more information.
Metadata
Release files for mock-data-generator 1.2.9
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 | Actions |
|---|---|---|---|---|
| mock_data_generator-1.2.9.tar.gz | 8.7 kB | Without provenance | Details for mock_data_generator-1.2.9.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| mock_data_generator-1.2.9-py3-none-any.whl | Python 3 | none | any | Details for mock_data_generator-1.2.9-py3-none-any.whl |
Total release size: 20.1 kB
Release files / mock_data_generator-1.2.9.tar.gz
| Download URL | mock_data_generator-1.2.9.tar.gz |
|---|---|
| Size | 8.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
ebbdf60b9ed5abf39f1f0ef9f6d6a3dccc279d482888ef42fe1dbb1e48dbe665
|
|
BLAKE2b-256 checksum How to use checksums |
3b495d00d2dfd86023b6552171086fbea207d789a6c4be5eca51e414a3fdc24c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.4.2 CPython/3.7.7 Darwin/22.2.0
|
Release files / mock_data_generator-1.2.9-py3-none-any.whl
| Download URL | mock_data_generator-1.2.9-py3-none-any.whl |
|---|---|
| Size | 11.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a47afa50fed911e83d73aeef1732688f4aeea8b08ae4669b62fb371381cc07e8
|
|
BLAKE2b-256 checksum How to use checksums |
2e12f1789ccb2a2925ebdfd575e02f52879f6718657bf00a5cf5a628d32f487a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.4.2 CPython/3.7.7 Darwin/22.2.0
|
Security for mock-data-generator 1.2.9
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.
To learn more about protecting yourself from malicious actors, visit our package security documentation.