pyspark.sql.functions.startswith¶

pyspark.sql.functions.startswith(str: ColumnOrName, prefix: ColumnOrName) → pyspark.sql.column.Column[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.

New in version 3.5.0.

Parameters

strColumn or str: A column of string.
prefixColumn or str: A column of string, the prefix.

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|
+----------------+----------------+

pyspark.sql.functions.split_part

pyspark.sql.functions.substr