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Raw columns often contain more information than they appear to. Extracting that information into new columns – derived features – frequently improves model performance more than adding new data sources.

Extracting Departure Hour and Time of Day

SCHEDULED_DEPARTURE is stored as an integer in HHMM format. You can extract the hour and classify it into time-of-day buckets:


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import urllib.request
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, floor, when

urllib.request.urlretrieve(
    "https://content-media-cdn.codefinity.com/courses/aa80ac56-0d50-49e8-9231-2c2374cd3e9d/flights.csv",
    "flights.csv"
)

spark = SparkSession.builder \
    .appName("DerivedFeatures") \
    .master("local[*]") \
    .getOrCreate()

flights_df = spark.read.csv("flights.csv", header=True, inferSchema=True) \
    .fillna(0, subset=["DEPARTURE_DELAY", "ARRIVAL_DELAY"])

# Extracting departure hour
flights_df = flights_df.withColumn(
    "DEPARTURE_HOUR",
    floor(col("SCHEDULED_DEPARTURE") / 100).cast("integer")
)

# Classifying into time-of-day buckets
flights_df = flights_df.withColumn(
    "TIME_OF_DAY",
    when(col("DEPARTURE_HOUR") < 6, "night")
    .when(col("DEPARTURE_HOUR") < 12, "morning")
    .when(col("DEPARTURE_HOUR") < 18, "afternoon")
    .otherwise("evening")
)

flights_df.select("SCHEDULED_DEPARTURE", "DEPARTURE_HOUR", "TIME_OF_DAY").show(5)

Computing Total Delay and Delay Ratio


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# Total delay as a combination of departure and arrival delay
flights_df = flights_df.withColumn(
    "TOTAL_DELAY",
    col("DEPARTURE_DELAY") + col("ARRIVAL_DELAY")
)

# Delay as a fraction of scheduled flight time – how badly delayed relative to duration
flights_df = flights_df.withColumn(
    "DELAY_RATIO",
    (col("ARRIVAL_DELAY") / col("SCHEDULED_TIME")).cast("double")
)

flights_df.select("DEPARTURE_DELAY", "ARRIVAL_DELAY", "SCHEDULED_TIME", "TOTAL_DELAY", "DELAY_RATIO").show(5)

Is Weekend Flag


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# Adding a binary flag for weekend flights (DAY_OF_WEEK: 6=Saturday, 7=Sunday)
flights_df = flights_df.withColumn(
    "IS_WEEKEND",
    (col("DAY_OF_WEEK") >= 6).cast("integer")
)

flights_df.select("DAY_OF_WEEK", "IS_WEEKEND").distinct().orderBy("DAY_OF_WEEK").show()

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Creating New Features from Existing Columns