Zingg0.4.0
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Python API

Zingg Entity Resolution Python Package

Zingg Python APIs for entity resolution, identity resolution, record linkage, data mastering and deduplication using ML (https://www.zingg.ai)
NOTE
Requires python 3.6+; spark 3.5.0 Otherwise, zingg.client.Zingg() cannot be executed

API Reference

Example API Usage

from zingg.client import *
from zingg.pipes import *
#build the arguments for zingg
args = Arguments()
#set field definitions
fname = FieldDefinition("fname", "string", MatchType.FUZZY)
lname = FieldDefinition("lname", "string", MatchType.FUZZY)
stNo = FieldDefinition("stNo", "string", MatchType.FUZZY)
add1 = FieldDefinition("add1","string", MatchType.FUZZY)
add2 = FieldDefinition("add2", "string", MatchType.FUZZY)
city = FieldDefinition("city", "string", MatchType.FUZZY)
areacode = FieldDefinition("areacode", "string", MatchType.FUZZY)
state = FieldDefinition("state", "string", MatchType.FUZZY)
dob = FieldDefinition("dob", "string", MatchType.FUZZY)
ssn = FieldDefinition("ssn", "string", MatchType.FUZZY)
fieldDefs = [fname, lname, stNo, add1, add2, city, areacode, state, dob, ssn]
args.setFieldDefinition(fieldDefs)
#set the modelid and the zingg dir
args.setModelId("100")
args.setZinggDir("models")
args.setNumPartitions(4)
args.setLabelDataSampleSize(0.5)
#reading dataset into inputPipe and settint it up in 'args'
#below line should not be required if you are reading from in memory dataset
#in that case, replace df with input df
schema = "id string, fname string, lname string, stNo string, add1 string, add2 string, city string, areacode string, state string, dob string, ssn string"
inputPipe = CsvPipe("testFebrl", "examples/febrl/test.csv", schema)
args.setData(inputPipe)
outputPipe = CsvPipe("resultFebrl", "/tmp/febrlOutput")
args.setOutput(outputPipe)
options = ClientOptions([ClientOptions.PHASE,"match"])
#Zingg execution for the given phase
zingg = Zingg(args, options)
zingg.initAndExecute()