![]() This is the converted date used that can be used and this gives up the idea of how this to_date function can be used using the Spark.sql function. We just need to pass this function and the conversion is done. This to date function can also be used with PySpark SQL function using the to_Date function in the PySpark. This converts the given format into To_Date and collected as result. df.select(to_date(df.date, 'yyyy-MM-dd HH:mm:ss').alias('date')).collect() This is used for creation of Date frame that has a column value as a date which we will use for conversion in which we can pass the format that can be used for conversion purposes. Let us try to check this with one more example giving the format of the date before conversion. which can be further used for data analysis. This will convert the column value to date function and the result is stored in the new data frame. We will try to store the converted data frame into a new data frame and will analyze the result out of it. We will try to collect the data frame and check the converted date column. This will return a new data frame with the alias value used.ĭf1.select(to_date(df1.timestamp).alias('to_Date')).collect() Here the df1.timestamp function will be used for conversion. We will start by selecting the column value that needs to be converted into date column value. df1.select(to_date(df1.timestamp).alias('to_Date')) ![]() This will import the necessary function out of it that will be used for conversion. We will start by importing the required functions from it. Now we will try to convert the timestamp column using the to_date function in the data frame. Let’s start by creating a simple data frame in PySpark. Let’s check the creation and working of PySpark To_Date with some coding examples. Df2:- The new data frame selected after conversion.To_date:- The to date function taking the column value as the input parameter with alias value as the new column name.Df1:- The data frame to be used for conversion.The import function in PySpark is used to import the function needed for conversion. Df2 = df1.select(to_date(df1.timestamp).alias('to_Date'))
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