Lernen Casting and Converting Data Types

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inferSchema=True does a good job for most columns, but it sometimes gets types wrong – or you may need to convert columns for downstream processing. PySpark provides explicit casting and conversion functions for this.

Checking and Casting Types


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import urllib.request
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.types import IntegerType, FloatType, StringType

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

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

flights_df = spark.read.csv("flights.csv", header=True, inferSchema=True)

# Checking current types
flights_df.printSchema()

# Casting DEPARTURE_DELAY from float to integer
flights_df = flights_df.withColumn(
    "DEPARTURE_DELAY",
    col("DEPARTURE_DELAY").cast(IntegerType())
)

# Casting FLIGHT_NUMBER to string
flights_df = flights_df.withColumn(
    "FLIGHT_NUMBER",
    col("FLIGHT_NUMBER").cast(StringType())
)

flights_df.select("DEPARTURE_DELAY", "FLIGHT_NUMBER").printSchema()

If a value cannot be cast – for example, casting "ABC" to IntegerType – PySpark replaces it with null instead of raising an error.

Building Datetime Columns

The flights dataset stores date components as separate integers. You can combine them into a proper date column:


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from pyspark.sql.functions import lpad, concat_ws, to_date

# Constructing a date string and converting to DateType
flights_df = flights_df.withColumn(
    "FLIGHT_DATE",
    to_date(
        concat_ws("-",
            col("YEAR").cast(StringType()),
            lpad(col("MONTH").cast(StringType()), 2, "0"),
            lpad(col("DAY").cast(StringType()), 2, "0")
        ),
        "yyyy-MM-dd"
    )
)

flights_df.select("YEAR", "MONTH", "DAY", "FLIGHT_DATE").show(5)

lpad left-pads single-digit months and days with "0" so the format matches yyyy-MM-dd.

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