Parsing a csv file in Pyspark using Spark inbuilt functions or methods

Question

I am using spark version 2.3 and working on some poc wherein, I have to load some bunch of csv files to spark dataframe.

Considering below csv as a sample which I need t

Answer 1

As requested by the OP, I am jotting down the answer here in PySpark -

First of all, just load the data without any prespecified schema, also as done by @AndrzejS

df = spark.read.option("header", "true").csv("data/yourdata.csv")
df.show()
+---+----+---+-----------+----+
| id|name|age|loaded_date| sex|
+---+----+---+-----------+----+
|  1| ABC| 32| 2019-09-11|   M|
|  2|null| 33| 2019-09-11|   M|
|  3| XYZ| 35| 2019-08-11|   M|
|  4| PQR| 32| 2019-30-10|   M|
|  5| EFG| 32|       null|null|
|  6| DEF| 32| 2019/09/11|   M|
|  7| XYZ| 32| 2017-01-01|   9|
|  8| KLM| XX| 2017-01-01|   F|
|  9| ABC|3.2| 2019-10-10|   M|
| 10| ABC| 32| 2019-02-29|   M|
+---+----+---+-----------+----+

Then, we need to determine the which of the values do not fit into the scheme of columns. For eg; XX or 32 cannot be an age, so these values need to be marked as Null. We do a test if this value is an Integer or else. Similarly, we do the test if loaded_date is indeed a date or not and fianlly we check if the sex is either F/M. Please refer to my previous post on these tests.

df = df.select('id','name',
               'age', (col('age').cast('int').isNotNull() & (col('age').cast('int') - col('age') == 0)).alias('ageInt'),
               'loaded_date',(col('loaded_date').cast('date').isNotNull()).alias('loaded_dateDate'),
               'sex'
              )
df.show()
+---+----+---+------+-----------+---------------+----+
| id|name|age|ageInt|loaded_date|loaded_dateDate| sex|
+---+----+---+------+-----------+---------------+----+
|  1| ABC| 32|  true| 2019-09-11|           true|   M|
|  2|null| 33|  true| 2019-09-11|           true|   M|
|  3| XYZ| 35|  true| 2019-08-11|           true|   M|
|  4| PQR| 32|  true| 2019-30-10|          false|   M|
|  5| EFG| 32|  true|       null|          false|null|
|  6| DEF| 32|  true| 2019/09/11|          false|   M|
|  7| XYZ| 32|  true| 2017-01-01|           true|   9|
|  8| KLM| XX| false| 2017-01-01|           true|   F|
|  9| ABC|3.2| false| 2019-10-10|           true|   M|
| 10| ABC| 32|  true| 2019-02-29|          false|   M|
+---+----+---+------+-----------+---------------+----+

Finally, using if/else, which is pyspark is when/otherwise to mark irrelevant values as Null.

df = df.withColumn('age',when(col('ageInt')==True,col('age')).otherwise(None))\
       .withColumn('loaded_date',when(col('loaded_dateDate')==True,col('loaded_date')).otherwise(None))\
       .withColumn('sex',when(col('sex').isin('M','F'),col('sex')).otherwise(None))\
       .drop('ageInt','loaded_dateDate')
df.show()
+---+----+----+-----------+----+
| id|name| age|loaded_date| sex|
+---+----+----+-----------+----+
|  1| ABC|  32| 2019-09-11|   M|
|  2|null|  33| 2019-09-11|   M|
|  3| XYZ|  35| 2019-08-11|   M|
|  4| PQR|  32|       null|   M|
|  5| EFG|  32|       null|null|
|  6| DEF|  32|       null|   M|
|  7| XYZ|  32| 2017-01-01|null|
|  8| KLM|null| 2017-01-01|   F|
|  9| ABC|null| 2019-10-10|   M|
| 10| ABC|  32|       null|   M|
+---+----+----+-----------+----+