data = [
    (1, "Sagar", 23, "Male", 68.0),
    (2, "Kim", 35, "Female", 90.2),
    (3, "Alex", 40, "Male", 79.1),
]
schema = "Id int,Name string,Age int,Gender string,Marks float"
df = spark.createDataFrame(data, schema)

df.show()
df.printSchema()

+---+-----+---+------+-----+
| Id| Name|Age|Gender|Marks|
+---+-----+---+------+-----+
|  1|Sagar| 23|  Male| 68.0|
|  2|  Kim| 35|Female| 90.2|
|  3| Alex| 40|  Male| 79.1|
+---+-----+---+------+-----+

root
 |-- Id: integer (nullable = true)
 |-- Name: string (nullable = true)
 |-- Age: integer (nullable = true)
 |-- Gender: string (nullable = true)
 |-- Marks: float (nullable = true)

for i in df.dtypes:
    print(i)

('Id', 'int')
('Name', 'string')
('Age', 'int')
('Gender', 'string')
('Marks', 'float')

set_dtypes = set(i[1] for i in df.dtypes)

print(set_dtypes)

{'string', 'float', 'int'}

for i in set_dtypes:
    cols = []
    for j in df.dtypes:
        if(i==j[1]):
            cols.append(j[0])
    df.select(cols).write.mode("overwrite").save(f"Files/Output/{i}")