pyspark.sql.functions.startswith#

pyspark.sql.functions.startswith(str, prefix)[source]#

Returns a boolean. The value is True if str starts with prefix. Returns NULL if either input expression is NULL. Otherwise, returns False. Both str or prefix must be of STRING or BINARY type.

Added in version 3.5.0.

Parameters:

strColumn or str: The input value to test. A column that evaluates to a string or binary.
prefixColumn or str: The prefix to test for. A column that evaluates to a string or binary.

Examples

>>> df = spark.createDataFrame([("Spark SQL", "Spark",)], ["a", "b"])
>>> df.select(startswith(df.a, df.b).alias('r')).collect()
[Row(r=True)]

>>> df = spark.createDataFrame([("414243", "4142",)], ["e", "f"])
>>> df = df.select(to_binary("e").alias("e"), to_binary("f").alias("f"))
>>> df.printSchema()
root
 |-- e: binary (nullable = true)
 |-- f: binary (nullable = true)
>>> df.select(startswith("e", "f"), startswith("f", "e")).show()
+----------------+----------------+
|startswith(e, f)|startswith(f, e)|
+----------------+----------------+
|            true|           false|
+----------------+----------------+