datafaker | scale test data and flow test data generation tool

by gangly Python Version: 0.7.6 License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | datafaker Summary

datafaker is a Python library typically used in Big Data, Kafka, Spark, Oracle applications. datafaker has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can install using 'pip install datafaker' or download it from GitHub, PyPI.

Datafaker - Tool for faking data.

Support

Quality

Security

License

Reuse

Support

datafaker has a low active ecosystem.

It has 573 star(s) with 158 fork(s). There are 19 watchers for this library.

It had no major release in the last 12 months.

There are 86 open issues and 19 have been closed. On average issues are closed in 27 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of datafaker is 0.7.6

Quality

datafaker has 0 bugs and 29 code smells.

Security

datafaker has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

datafaker code analysis shows 0 unresolved vulnerabilities.

There are 4 security hotspots that need review.

License

datafaker does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

datafaker releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

datafaker saves you 686 person hours of effort in developing the same functionality from scratch.

It has 1589 lines of code, 195 functions and 35 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed datafaker and discovered the below as its top functions. This is intended to give you an instant insight into datafaker implemented functionality, and help decide if they suit your requirements.

Generate fake data
Run fake data
Format the data
Fake fake column
Print data from queue
Safely decode an item
Format column data
Save data into a table
Load SQLAlchemy session
Save data to Elasticsearch
Format the given columns
Saves data to the table
Return a list of arguments
Parse command line arguments
Return the value of the first integer
Find the version string
Construct the self rows
Parse the schema
Format data
Return an integer value
Load database class
Put fake data in queue
Safely quote a string
Saves data to the database
Save data to queue
Construct the rows from self table

Get all kandi verified functions for this library.

datafaker Key Features

No Key Features are available at this moment for datafaker.

datafaker Examples and Code Snippets

No Code Snippets are available at this moment for datafaker.

Community Discussions

Trending Discussions on datafaker

Error in creating seed file for sequalize involving DataTypes.JSON

QUESTION

Error in creating seed file for sequalize involving DataTypes.JSON

Asked 2019-Mar-13 at 20:52

I am using Sequalize for my ORM for my Node.Js/PSQL project. I am working on creating a seed file to prepopulate my database; however, when I run the seed file I get an error related to one of the fields with the relevant information below. There appears to be an issue with using a JSON object with

Error

...

ANSWER

Answered 2019-Mar-13 at 19:36

Because queryInterface is a little bit low level you have to specify that you're inserting json like:

Source https://stackoverflow.com/questions/55149411

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install datafaker

You can install using 'pip install datafaker' or download it from GitHub, PyPI.
You can use datafaker like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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