apply OneHotEncoder for several categorical columns in SparkMlib

I think the above code will not give the same results as required. In the encoders section, there is required a little modification. Because, again the StringIndexer is applied on Indexers.So, that will results in the same results.

#In the following section:
encoders = [
    StringIndexer(
        inputCol=indexer.getOutputCol(),
        outputCol="{0}_encoded".format(indexer.getOutputCol())) 
    for indexer in indexers
]

#Replace the StringIndexer with OneHotEncoder as follows:
encoders = [OneHotEncoder(dropLast=False,inputCol=indexer.getOutputCol(),
            outputCol="{0}_encoded".format(indexer.getOutputCol())) 
            for indexer in indexers
]

Now, the complete code look like the following:

from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler

categorical_columns= ['Gender', 'Age', 'Occupation', 'City_Category','Marital_Status']

# The index of string vlaues multiple columns
indexers = [
    StringIndexer(inputCol=c, outputCol="{0}_indexed".format(c))
    for c in categorical_columns
]

# The encode of indexed vlaues multiple columns
encoders = [OneHotEncoder(dropLast=False,inputCol=indexer.getOutputCol(),
            outputCol="{0}_encoded".format(indexer.getOutputCol())) 
    for indexer in indexers
]

# Vectorizing encoded values
assembler = VectorAssembler(inputCols=[encoder.getOutputCol() for encoder in encoders],outputCol="features")

pipeline = Pipeline(stages=indexers + encoders+[assembler])
model=pipeline.fit(data_df)
transformed = model.transform(data_df)
transformed.show(5)

For more details,please refer: visit:[1] https://spark.apache.org/docs/2.0.2/api/python/pyspark.ml.html#pyspark.ml.feature.StringIndexer visit:[2] https://spark.apache.org/docs/2.0.2/api/python/pyspark.ml.html#pyspark.ml.feature.OneHotEncoder.

Spark >= 3.0:

In Spark 3.0 OneHotEncoderEstimator has been renamed to OneHotEncoder:

from pyspark.ml.feature import OneHotEncoderEstimator, OneHotEncoderModel

encoder = OneHotEncoderEstimator(...)

with

from pyspark.ml.feature import OneHotEncoder, OneHotEncoderModel

encoder = OneHotEncoder(...)

Spark >= 2.3

You can use newly added OneHotEncoderEstimator:

from pyspark.ml.feature import OneHotEncoderEstimator, OneHotEncoderModel

encoder = OneHotEncoderEstimator(
    inputCols=[indexer.getOutputCol() for indexer in indexers],
    outputCols=[
        "{0}_encoded".format(indexer.getOutputCol()) for indexer in indexers]
)

assembler = VectorAssembler(
    inputCols=encoder.getOutputCols(),
    outputCol="features"
)

pipeline = Pipeline(stages=indexers + [encoder, assembler])
pipeline.fit(df).transform(df)

Spark < 2.3

It is not possible. StringIndexer transformer operates only on a single column at the time so you'll need a single indexer and a single encoder for each column you want to transform.

from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler

cols = ['a', 'b', 'c', 'd']

indexers = [
    StringIndexer(inputCol=c, outputCol="{0}_indexed".format(c))
    for c in cols
]

encoders = [
    OneHotEncoder(
        inputCol=indexer.getOutputCol(),
        outputCol="{0}_encoded".format(indexer.getOutputCol())) 
    for indexer in indexers
]

assembler = VectorAssembler(
    inputCols=[encoder.getOutputCol() for encoder in encoders],
    outputCol="features"
)


pipeline = Pipeline(stages=indexers + encoders + [assembler])
pipeline.fit(df).transform(df).show()