I presume then that the "datetime" string you provided is a string column?
If so, try the following code. This will not perform any transformation to timestamps:

from pyspark.sql.functions import lit, split, concat

data = [{"date_string": "2002-01-01T00:00:00.000"}]

data_df = spark.createDataFrame(data) \
    .withColumn(
        "date_string_reformatted", concat(
            lit(split("date_string", "-")[1]), 
            lit("-"),
            lit(split(split("date_string", "-")[2], "T")[0]),
            lit("-"),
            lit(split("date_string", "-")[0]), 
            lit(" "),
            lit(split(split(split("date_string", "-")[2], "T")[1], "\.")[0]),
        )
    )

data_df.display()	

The result:

Hi @Mister-Dinky ,

I think what @richakamat130 would like to obtain is to format datetime according to his needs, but to keep orginal data format (in this case datetime). Unfortunately, this is not possible in spark. If you want to have different display format then you need to have this conversion. 

Ah, the "without changing datatype/ having it in datetime data type" can be interpreted in that way as well. Then I agree with your statement.

Hi @Mister-Dinky ,

As @szymon_dybczak if you have a datetime, then you have a datetime.

What you see is just a format of defined in the Databricks UI. Other applications may display it differently depending on the defaults, regional formats etc.

If you want to see datetime displayed differently in Databricks UI, then just navigate to your user settings and change it accordingly to your preferences: