Description

Transform data type from Columns to Kv.

Parameters

Name Description Type Required？ Default Value
handleInvalid Strategy to handle unseen token String “ERROR”
reservedCols Names of the columns to be retained in the output table String[] null
kvCol Name of the KV column String ✓
kvColDelimiter Delimiter used between key-value pairs when data in the input table is in sparse format String “,”
kvValDelimiter Delimiter used between keys and values when data in the input table is in sparse format String “:”
selectedCols Names of the columns used for processing String[] null
lazyPrintTransformDataEnabled Enable lazyPrint of ModelInfo Boolean false
lazyPrintTransformDataTitle Title of ModelInfo in lazyPrint String null
lazyPrintTransformDataNum Title of ModelInfo in lazyPrint Integer -1
lazyPrintTransformStatEnabled Enable lazyPrint of ModelInfo Boolean false
lazyPrintTransformStatTitle Title of ModelInfo in lazyPrint String null

Script Example

Code

  1. import numpy as np
  2. import pandas as pd
  5. data = np.array([['1', '{"f1":"1.0","f2":"2.0"}', '$3$1:1.0 2:2.0', '1:1.0,2:2.0', '1.0,2.0', 1.0, 2.0],
  6. ['2', '{"f2":"4.0","f4":"8.0"}', '$3$1:4.0 2:8.0', '1:4.0,2:8.0', '4.0,8.0', 4.0, 8.0]])
  8. df = pd.DataFrame({"row":data[:,0], "json":data[:,1], "vec":data[:,2], "kv":data[:,3], "csv":data[:,4], "f0":data[:,5], "f1":data[:,6]})
  9. data = dataframeToOperator(df, schemaStr="row string, json string, vec string, kv string, csv string, f0 double, f1 double",op_type="batch")
  12. op = ColumnsToKv()\
  13.     .setSelectedCols(["f0", "f1"])\
  14.     .setReservedCols(["row"]).setKvCol("kv")\
  15.     .transform(data)
  16. op.print()

Results

  1. |row|kv|
  2. |--|--------------|
  3. |1|f0:1.0,f1:2.0|
  4. |2|f0:4.0,f1:8.0|