What is the method for generating a PySpark DataFrame within a loop?

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The method for generating a PySpark DataFrame within a loop is as follows:

1. Import the necessary classes from PySpark:

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType
from pyspark.sql import Row

1. Create a SparkSession object:

spark = SparkSession.builder \
    .appName("DataFrame in Loop") \
    .getOrCreate()

1. Define the schema of the DataFrame:

schema = StructType([
    StructField('id', IntegerType(), True),
    StructField('name', StringType(), True),
    StructField('age', IntegerType(), True),
])

1. Create an empty list to store the rows of the DataFrame:

data = []

1. Define a loop to generate the rows of the DataFrame:

for i in range(1, 11):
    row = Row(id=i, name='name_' + str(i), age=i*10)
    data.append(row)

1. Convert the list of rows to a DataFrame:

df = spark.createDataFrame(data, schema)

1. Show the DataFrame:

df.show()

The resulting DataFrame would look like this:

+---+------+---+
| id|  name|age|
+---+------+---+
|  1|name_1| 10|
|  2|name_2| 20|
|  3|name_3| 30|
|  4|name_4| 40|
|  5|name_5| 50|
|  6|name_6| 60|
|  7|name_7| 70|
|  8|name_8| 80|
|  9|name_9| 90|
| 10|name_10|100|
+---+------+---+