pyspark.sql.functions.to_varchar��

pyspark.sql.functions.to_varchar(col: ColumnOrName, format: ColumnOrName) → pyspark.sql.column.Column[source]��

Convert col to a string based on the format. Throws an exception if the conversion fails. The format can consist of the following characters, case insensitive: ��0�� or ��9��: Specifies an expected digit between 0 and 9. A sequence of 0 or 9 in the format string matches a sequence of digits in the input value, generating a result string of the same length as the corresponding sequence in the format string. The result string is left-padded with zeros if the 0/9 sequence comprises more digits than the matching part of the decimal value, starts with 0, and is before the decimal point. Otherwise, it is padded with spaces. ��.�� or ��D��: Specifies the position of the decimal point (optional, only allowed once). ��,�� or ��G��: Specifies the position of the grouping (thousands) separator (,). There must be a 0 or 9 to the left and right of each grouping separator. �� $��: Specifies the location of the$ currency sign. This character may only be specified once. ��S�� or ��MI��: Specifies the position of a ��-�� or ��+�� sign (optional, only allowed once at the beginning or end of the format string). Note that ��S�� prints ��+�� for positive values but ��MI�� prints a space. ��PR��: Only allowed at the end of the format string; specifies that the result string will be wrapped by angle brackets if the input value is negative.

New in version 3.5.0.

Parameters

colColumn or str: Input column or strings.
formatColumn or str, optional: format to use to convert char values.

Examples

>>> df = spark.createDataFrame([(78.12,)], ["e"])
>>> df.select(to_varchar(df.e, lit("$99.99")).alias('r')).collect()
[Row(r='$78.12')]

pyspark.sql.functions.to_number

pyspark.sql.functions.translate