Zingg
  • Welcome To Zingg
  • Step-By-Step Guide
    • Installation
      • Docker
        • Sharing Custom Data And Config Files
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      • Configuring Through Environment Variables
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        • Input Data
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      • Field Definitions
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    • Working With Training Data
      • Finding Records For Training Set Creation
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    • Verification of Blocking Model
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  • Working With Python
    • Python API
  • Running Zingg On Cloud
    • Running On AWS
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    • Running On Databricks
    • Running on Fabric
  • Zingg Models
    • Pre-Trained Models
  • Improving Accuracy
    • Ignoring Commonly Occuring Words While Matching
    • Defining Domain Specific Blocking And Similarity Functions
  • Documenting The Model
  • Interpreting Output Scores
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    • Setting Up Zingg Development Environment
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Working With Python

A whole new way to work with Zingg!

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Last updated 6 months ago

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Instead of configuring Zingg using JSON, we can now use Python to build and run Zingg entity and identity resolution programs. This is handy when you want to run Zingg on an existing Spark cluster. To run on a local machine, please install from the release before running Zingg Python programs.

The Zingg Python package can be installed by invoking:

python -m pip install zingg

Detailed documentation of the Python API is available at:

Example programs for Python exist under . Please check to get started.

Please refer to the for running Python programs. Please note that Zingg Python programs are PySpark programs and hence need the Zingg CLI to execute.

https://readthedocs.org/projects/zingg/
examples
examples/febrl/FebrlExample.py
command line guide