code: def sql_dataframe_reader_api(spark):
      print("Start running dataframe reader API")
  
      sc = spark.sparkContext
      sqlContext = SQLContext(sc)
  
      # csv
      df = spark.read.csv(
          "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/ages.csv"
      )
      print(df.dtypes)
      rdd = sc.textFile(
          "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/ages.csv"
      )
      df2 = spark.read.option("header", "true").csv(rdd)
      print(df2.dtypes)
      print("csv and option API finished")
  
      # format
      df = spark.read.format("json").load(
          "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/people.json"
      )
      print(df.dtypes)
      print("format API finished")
  
      # jdbc
  
      # json
      df1 = spark.read.json(
          "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/people.json"
      )
      print(df1.dtypes)
      rdd = sc.textFile(
          "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/people.json"
      )
      df2 = spark.read.json(rdd)
      print(df2.dtypes)
      print("json API finished")
  
      # load
      df = spark.read.format("json").load(
          [
              "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/people.json",
              "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/people1.json",
          ]
      )
      print(df.dtypes)
  
      # orc
      df = spark.read.orc(
          "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/orc_partitioned"
      )
      print(df.dtypes)
      print("orc API finished")
  
      # parquet
      df = spark.read.parquet(
          "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/parquet_partitioned"
      )
      print(df.dtypes)
      print("parquet API finished")
  
      # schema
      s = spark.read.schema("col0 INT, col1 DOUBLE")
      print(s)
      print("schema API finished")
  
      # table
      df = spark.read.parquet(
          "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/parquet_partitioned"
      )
      df.createOrReplaceTempView("tmpTable")
      res = spark.read.table("tmpTable").dtypes
      print(res)
      print("table API finished")
  
      # text
      df = spark.read.text(
          "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/text-test.txt"
      )
      df.show()
      df = spark.read.text(
          "/ppml/trusted-big-data-ml/work/spark-2.4.6/python/test_support/sql/text-test.txt",
          wholetext=True,
      )
      df.show()
      print("text API finished")
  
      print("Finish running dataframe reader API")
  
imports: from pyspark.sql.functions import *
  from pyspark.sql import Row, Window, SparkSession, SQLContext
  from pyspark.sql.types import IntegerType
  from pyspark.sql import functions
  from pyspark.sql.functions import rank, min, col, mean
  import random
label_desc: Use image node_id 1 for calling a global function with example usage: sql_dataframe_reader_api(spark) without return types