to_char

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.

If col is a binary, it is converted to a string in one of the formats:

For the corresponding Databricks SQL function, see to_char function.

Syntax

Python
from pyspark.databricks.sql import functions as dbf

dbf.to_char(col=<col>, format=<format>)

Parameter	Type	Description
`col`	`pyspark.sql.Column` or `str`	Input column or strings.
`format`	`pyspark.sql.Column` or `str, optional`	format to use to convert char values.

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