![]() for example, header to output the DataFrame column names as header record and delimiter to specify the delimiter on the CSV output file. While writing a CSV file you can use several options. For detailed example refer to Writing Spark DataFrame to CSV File using Options. Use the write() method of the Spark DataFrameWriter object to write Spark DataFrame to a CSV file. Please refer to the link for more details. ![]() Once you have created DataFrame from the CSV file, you can apply all transformation and actions DataFrame support. add("EstimatedPopulation",IntegerType,true) If you know the schema of the file ahead and do not want to use the inferSchema option for column names and types, use user-defined custom column names and type using schema option. Reading CSV files with a user-specified custom schema ![]() Note: Besides the above options, Spark CSV dataset also supports many other options, please refer to this article for details. 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 CSV to consider as null. but using this option you can set any character. When you have a column with a delimiter that used to split the columns, use quotes option to specify the quote character, by default it is ” and delimiters inside quotes are ignored. By default the value of this option is false , and all column types are assumed to be a string. This option is used to read the first line of the CSV file as column names. Note that, it requires reading the data one more time to infer the schema. The default value set to this option is false when setting to true it automatically infers column types based on the data. By default, it is comma (,) character, but can be set to pipe (|), tab, space, or any character using this option. Delimiter option is used to specify the column delimiter of the CSV file.
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