如何使用Spark更改所有数据集中的非数值？

Question

I'm using a data set with a lot of columns, this data set has ? in all the data set. I would like to Spark (Java) to change the ? to 0. By far I can only do this with one column but I would like to do everywhere:

    Dataset<Row> csvData = spark.read()
            .option("header", false)
            .option("inferSchema", true)
            .option("maxColumns", 50000)
            .csv("src/main/resources/K9.data");

    csvData = csvData.withColumn("_c5409", when(col("_c5409").isNull(),0).otherwise(col("_c5409")) )
        .withColumn("_c0", when(col("_c0").equalTo("?"),0).otherwise(col("_c0")) );

Maybe this has an easy solution, I'm new with Java and Spark :)

Answer 1

You can create the list of columns using when, and use that in select if it has to deal with complex if and else cases

List<org.apache.spark.sql.Column> list = new ArrayList<org.apache.spark.sql.Column>();
for( String col : csvData.columns()){
    list.add(when(csvData.col(col).isNull(),0).otherwise(csvData.col(col)).alias(col));
}
csvData = csvData.select(list.toArray(new org.apache.spark.sql.Column[0]));

If it is simply to replace nulls, this is good enough

csvData = csvData.na().fill(0, df.columns());