![]() ![]() While writing a JSON file you can use several options. Use the PySpark DataFrameWriter object “write” method on DataFrame to write a JSON file.ĭf2.write.json("/tmp/spark_output/zipcodes.json") Please refer to the link for more details. Once you have create PySpark DataFrame from the JSON file, you can apply all transformation and actions DataFrame support. Note: Besides the above options, PySpark JSON dataset also supports many other options. dateFormatĭateFormat option to used to set the format of the input DateType and TimestampType columns. For example, if you want to consider a date column with a value “” set null on DataFrame. Using nullValues option you can specify the string in a JSON to consider as null. Options while reading JSON file nullValues Spark.sql("select * from zipcode").show() Spark.sql("CREATE OR REPLACE TEMPORARY VIEW zipcode USING json OPTIONS" ![]() PySpark SQL also provides a way to read a JSON file by creating a temporary view directly from the reading file using (“load JSON to temporary view”) StructField("TotalWages",IntegerType(),True),ĭf_with_schema = (schema) \ StructField("EstimatedPopulation",IntegerType(),True), StructField("TaxReturnsFiled",StringType(),True), StructField("Decommisioned",BooleanType(),True), StructField("Location",StringType(),True), StructField("LocationText",StringType(),True), StructField("Country",StringType(),True), StructField("WorldRegion",StringType(),True), StructField("LocationType",StringType(),True), StructField("ZipCodeType",StringType(),True), StructField("Zipcode",IntegerType(),True), StructField("RecordNumber",IntegerType(),True), Use the PySpark StructType class to create a custom schema, below we initiate this class and use add a method to add columns to it by providing the column name, data type and nullable option. If you know the schema of the file ahead and do not want to use the default inferSchema option, use schema option to specify user-defined custom column names and data types. PySpark SQL provides StructType
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