In this recipe, we will use a small dataset that is located in the data folder, namely, fourier_signal.csv:

signal_df = spark.read.csv(
    '../data/fourier_signal.csv'
    , header=True
    , inferSchema=True
)

steps = feat.QuantileDiscretizer(
       numBuckets=10,
       inputCol='signal',
       outputCol='discretized')

transformed = (
    steps
    .fit(signal_df)
    .transform(signal_df)
)