pyspark.sql.functions.to_varchar#
- pyspark.sql.functions.to_varchar(col, format)[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. If col is a datetime, format shall be a valid datetime pattern, see <a href=”https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html”>Patterns</a>. If col is a binary, it is converted to a string in one of the formats: ‘base64’: a base 64 string. ‘hex’: a string in the hexadecimal format. ‘utf-8’: the input binary is decoded to UTF-8 string.
New in version 3.5.0.
- Parameters
Examples
>>> df = spark.createDataFrame([(78.12,)], ["e"]) >>> df.select(to_varchar(df.e, lit("$99.99")).alias('r')).collect() [Row(r='$78.12')]