added vertical column show support

common/client/src/main/java/zingg/common/client/ZFrame.java

@@ -10,6 +10,11 @@ public interface ZFrame<D, R, C> {
 
 	public static final String COL_COUNT = "count";
 	public static final String COL_VALUE = "VALUE";
+
+    public static String PIVOT_COLUMN = "field";
+    public static String VALUE_1 = "value1";
+    public static String VALUE_2 = "value2";
+    public static String ORDER = "order";
 
     public ZFrame<D, R, C> cache();
     public ZFrame<D, R, C> as(String s);
@@ -179,4 +184,7 @@ public interface ZFrame<D, R, C> {
 
 	public C gt(C column1, C column2);
 
+    public ZFrame<D, R, C> transpose(String pivotColumn);
+
+    public void showVertical();
 }

common/core/src/main/java/zingg/common/core/executor/LabelDataViewHelper.java

@@ -87,7 +87,8 @@ public String getMsg2(double prediction, double score) {
 	public void displayRecords(ZFrame<D, R, C> records, String preMessage, String postMessage) {
 		//System.out.println();
 		System.out.println(preMessage);
-		records.show(false);
+//		records.show(false);
+		records.showVertical();
 		System.out.println(postMessage);
 		System.out.println("\tWhat do you think? Your choices are: ");
 		System.out.println();

common/core/src/main/java/zingg/common/core/util/ListConverter.java

@@ -0,0 +1,14 @@
+package zingg.common.core.util;
+
+import scala.collection.JavaConverters;
+import scala.collection.Seq;
+
+import java.util.List;
+
+public class ListConverter<C> {
+    public Seq<C> convertListToSeq(List<C> inputList) {
+        return JavaConverters.asScalaIteratorConverter(inputList.iterator())
+                .asScala()
+                .toSeq();
+    }
+}

spark/client/src/main/java/zingg/spark/client/SparkFrame.java

@@ -1,5 +1,7 @@
 package zingg.spark.client;
 
+import java.util.ArrayList;
+import java.util.Collections;
 import java.util.List;
 
 import org.apache.spark.internal.config.R;
@@ -14,6 +16,8 @@
 import zingg.common.client.FieldData;
 import zingg.common.client.ZFrame;
 import zingg.common.client.util.ColName;
+import zingg.common.core.util.ListConverter;
+import zingg.spark.client.util.ExtendedFunction;
 
 //Dataset, Row, column
 public class SparkFrame implements ZFrame<Dataset<Row>, Row, Column> {
@@ -470,6 +474,60 @@ public Column substr(Column col, int startPos, int len) {
     public Column gt(Column column1, Column column2) {
 		return column1.gt(column2);
 	}
-
+
+    @Override
+    public ZFrame<Dataset<Row>, Row, Column> transpose(String pivotColumn) {
+        ExtendedFunction extendedFunction = new ExtendedFunction();
+        List<String> columnsExceptPivot = new ArrayList<>(List.of(df.columns()));
+        columnsExceptPivot.remove(pivotColumn);
+        ListConverter<String> listConverter = new ListConverter<String>();
+        Dataset<Row> r = extendedFunction.TransposeDF(df, listConverter.convertListToSeq(columnsExceptPivot), pivotColumn);
+        return new SparkFrame(r);
+    }
+
+    @Override
+    public void showVertical() {
+        ZFrame<Dataset<Row>, Row, Column> headerIncludedFrame = getHeaderIncludedDataFrame(new SparkFrame(df));
+        ZFrame<Dataset<Row>, Row, Column> vertical = headerIncludedFrame.transpose(PIVOT_COLUMN);
+        vertical.sortAscending(ORDER).drop(ORDER).show(1000);
+    }
+
+    /***
+     * Add auto incremental row like {1, 2, 3, 4, 5} to the dataframe
+     * @return ZFrame
+     */
+
+    private ZFrame<Dataset<Row>, Row, Column> addAutoIncrementalRow() {
+        String[] columns = df.columns();
+        Dataset<Row> temporaryDF = df.limit(1);
+        List<String> monotonicIncreasing = new ArrayList<>();
+        for (int idx = 0; idx < columns.length; idx++) {
+            monotonicIncreasing.add(String.valueOf(idx));
+        }
+        Collections.sort(monotonicIncreasing);
+        for (int idx = 0; idx < columns.length; idx++) {
+            temporaryDF = temporaryDF.withColumn(columns[idx], functions.lit(monotonicIncreasing.get(idx)));
+        }
+        return new SparkFrame(df.union(temporaryDF));
+    }
+
+    /***
+     * return new ZFrame with new Column added as PIVOT used for transposing the matrix
+     * @param records
+     * @return header included zFrame
+     */
+    private ZFrame<Dataset<Row>, Row, Column> getHeaderIncludedDataFrame(ZFrame<Dataset<Row>, Row, Column> records) {
+        ZFrame<Dataset<Row>, Row, Column> orderedRowAdded = addAutoIncrementalRow();
+
+        ZFrame<Dataset<Row>, Row, Column> firstRecord = orderedRowAdded.limit(1);
+        ZFrame<Dataset<Row>, Row, Column> secondRecord = orderedRowAdded.except(firstRecord).limit(1);
+        ZFrame<Dataset<Row>, Row, Column> thirdRecord = orderedRowAdded.except(firstRecord.union(secondRecord));
+
+        //return new ZFrame with Field column added to be used as pivot
+        return firstRecord.withColumn(PIVOT_COLUMN, VALUE_1).
+                union(secondRecord.withColumn(PIVOT_COLUMN, VALUE_2)).
+                union(thirdRecord.withColumn(PIVOT_COLUMN, ORDER));
+    }
+
 }
 

spark/client/src/main/java/zingg/spark/client/util/ExtendedFunction.scala

@@ -0,0 +1,18 @@
+package zingg.spark.client.util
+
+import org.apache.spark.sql.functions.{col, collect_list, concat_ws}
+import org.apache.spark.sql.{Dataset, Row}
+
+class ExtendedFunction {
+  def TransposeDF(df: Dataset[Row], columns: Seq[String], pivotCol: String): Dataset[Row] = {
+    val columnsValue = columns.map(x => "'" + x + "', " + x)
+    val stackCols = columnsValue.mkString(",")
+    val df_1 = df.selectExpr(pivotCol, "stack(" + columns.size + "," + stackCols + ")")
+      .select(pivotCol, "col0", "col1")
+
+    val final_df = df_1.groupBy(col("col0")).pivot(pivotCol).agg(concat_ws("", collect_list(col("col1"))))
+      .withColumnRenamed("col0", pivotCol)
+    final_df
+  }
+
+}