to_varchar
Converts col to a string based on the format. Throws an exception if the conversion fails. Supports Spark Connect.
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 Patterns.
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.
For the corresponding Databricks SQL function, see to_varchar function.
Syntax
Python
from pyspark.databricks.sql import functions as dbf
dbf.to_varchar(col=<col>, format=<format>)
Parameters
Parameter | Type | Description |
|---|---|---|
|
| Input column or strings. |
|
| Format to use to convert char values. |
Examples
Python
from pyspark.databricks.sql import functions as dbf
from pyspark.sql.functions import lit
df = spark.createDataFrame([(78.12,)], ['e'])
df.select(dbf.to_varchar(df.e, lit("$99.99")).alias('r')).collect()