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Generate synthetic datasets for Pandas using their definitions

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data-faker

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Getting Started

just config schema to genrate file

length: 1000000
columns:
  - name: 'I_MSISDN'
    type: int64
    params:
      min: 9999999990
      max: 9999999999
  - name: 'I_RECHARGE_AMOUNT'
    type: int64
    params:
      min: 120
      max: 150
  - name: 'SEQUNCE'
    type: int64
    params:
      min: 0
      max: 10

run via

python generator.py examples/financial.yaml -o /root/dummydata/result2.csv

build as a debian app

cd data_faker (the folder contains setup.py)

pip install .

then

datafaker examples/financial.yaml -o /root/dummydata/result2.csv

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License

This project is licensed under the MIT License - see the LICENSE.md file for details SUBIN x

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Generate synthetic datasets for Pandas using their definitions

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