diff --git a/src/main/java/com/highperformancespark/examples/dataframe/JavaHappyPandas.java b/src/main/java/com/highperformancespark/examples/dataframe/JavaHappyPandas.java
index 73636de..09b5e2f 100644
--- a/src/main/java/com/highperformancespark/examples/dataframe/JavaHappyPandas.java
+++ b/src/main/java/com/highperformancespark/examples/dataframe/JavaHappyPandas.java
@@ -19,7 +19,7 @@ public class JavaHappyPandas {
   /**
    * Creates SQLContext with an existing SparkContext.
    */
-  public SQLContext sqlContext(JavaSparkContext jsc) {
+  public static SQLContext sqlContext(JavaSparkContext jsc) {
     SQLContext sqlContext = new SQLContext(jsc);
     return sqlContext;
   }
@@ -27,7 +27,7 @@ public SQLContext sqlContext(JavaSparkContext jsc) {
   /**
    * Creates HiveContext with an existing SparkContext.
    */
-  public HiveContext hiveContext(JavaSparkContext jsc) {
+  public static HiveContext hiveContext(JavaSparkContext jsc) {
     HiveContext hiveContext = new HiveContext(jsc);
     return hiveContext;
   }
@@ -35,7 +35,7 @@ public HiveContext hiveContext(JavaSparkContext jsc) {
   /**
    * Illustrate loading some JSON data.
    */
-  public DataFrame loadDataSimple(JavaSparkContext jsc, SQLContext sqlContext, String path) {
+  public static DataFrame loadDataSimple(JavaSparkContext jsc, SQLContext sqlContext, String path) {
     DataFrame df1 = sqlContext.read().json(path);
 
     DataFrame df2 = sqlContext.read().format("json").option("samplingRatio", "1.0").load(path);
@@ -46,7 +46,7 @@ public DataFrame loadDataSimple(JavaSparkContext jsc, SQLContext sqlContext, Str
     return df1;
   }
 
-  public DataFrame jsonLoadFromRDD(SQLContext sqlContext, JavaRDD<String> input) {
+  public static DataFrame jsonLoadFromRDD(SQLContext sqlContext, JavaRDD<String> input) {
     JavaRDD<String> rdd = input.filter(e -> e.contains("panda"));
     DataFrame df = sqlContext.read().json(rdd);
     return df;
@@ -60,7 +60,7 @@ public DataFrame jsonLoadFromRDD(SQLContext sqlContext, JavaRDD<String> input) {
    * @param pandaInfo the input DataFrame
    * @return Returns DataFrame of (place, percentage of happy pandas)
    */
-  public DataFrame happyPandasPercentage(DataFrame pandaInfo) {
+  public static DataFrame happyPandasPercentage(DataFrame pandaInfo) {
     DataFrame happyPercentage = pandaInfo.select(pandaInfo.col("place"),
       pandaInfo.col("happyPandas").divide(pandaInfo.col("totalPandas")).as("percentHappy"));
     return happyPercentage;
@@ -72,7 +72,7 @@ public DataFrame happyPandasPercentage(DataFrame pandaInfo) {
    * @param pandaInfo the input DataFrame
    * @return Returns a DataFrame of pandaId and integer value for pandaType.
    */
-  public DataFrame encodePandaType(DataFrame pandaInfo) {
+  public static DataFrame encodePandaType(DataFrame pandaInfo) {
     DataFrame encodedDF = pandaInfo.select(pandaInfo.col("id"),
         when(pandaInfo.col("pt").equalTo("giant"), 0).
         when(pandaInfo.col("pt").equalTo("red"), 1).
@@ -84,21 +84,21 @@ public DataFrame encodePandaType(DataFrame pandaInfo) {
   /**
    * Gets places with happy pandas more than minHappinessBound.
    */
-  public DataFrame minHappyPandas(DataFrame pandaInfo, int minHappyPandas) {
+  public static DataFrame minHappyPandas(DataFrame pandaInfo, int minHappyPandas) {
     return pandaInfo.filter(pandaInfo.col("happyPandas").geq(minHappyPandas));
   }
 
   /**
    * Find pandas that are sad.
    */
-  public DataFrame sadPandas(DataFrame pandaInfo) {
+  public static DataFrame sadPandas(DataFrame pandaInfo) {
     return pandaInfo.filter(pandaInfo.col("happy").notEqual(true));
   }
 
   /**
    * Find pandas that are happy and fuzzier than squishy.
    */
-  public DataFrame happyFuzzyPandas(DataFrame pandaInfo) {
+  public static DataFrame happyFuzzyPandas(DataFrame pandaInfo) {
     DataFrame df = pandaInfo.filter(
       pandaInfo.col("happy").and(pandaInfo.col("attributes").apply(0)).gt(pandaInfo.col("attributes").apply(1))
     );
@@ -109,31 +109,31 @@ public DataFrame happyFuzzyPandas(DataFrame pandaInfo) {
   /**
    * Gets places that contains happy pandas more than unhappy pandas.
    */
-  public DataFrame happyPandasPlaces(DataFrame pandaInfo) {
+  public static DataFrame happyPandasPlaces(DataFrame pandaInfo) {
     return pandaInfo.filter(pandaInfo.col("happyPandas").geq(pandaInfo.col("totalPandas").divide(2)));
   }
 
   /**
    * Remove duplicate pandas by id.
    */
-  public DataFrame removeDuplicates(DataFrame pandas) {
+  public static DataFrame removeDuplicates(DataFrame pandas) {
     DataFrame df = pandas.dropDuplicates(new String[]{"id"});
     return df;
   }
 
-  public DataFrame describePandas(DataFrame pandas) {
+  public static DataFrame describePandas(DataFrame pandas) {
     return pandas.describe();
   }
 
-  public DataFrame maxPandaSizePerZip(DataFrame pandas) {
+  public static DataFrame maxPandaSizePerZip(DataFrame pandas) {
     return pandas.groupBy(pandas.col("zip")).max("pandaSize");
   }
 
-  public DataFrame minMaxPandaSizePerZip(DataFrame pandas) {
+  public static DataFrame minMaxPandaSizePerZip(DataFrame pandas) {
     return pandas.groupBy(pandas.col("zip")).agg(min("pandaSize"), max("pandaSize"));
   }
 
-  public DataFrame minPandaSizeMaxAgePerZip(DataFrame pandas) {
+  public static DataFrame minPandaSizeMaxAgePerZip(DataFrame pandas) {
     Map<String, String> map = new HashMap<>();
     map.put("pandaSize", "min");
     map.put("age", "max");
@@ -142,11 +142,11 @@ public DataFrame minPandaSizeMaxAgePerZip(DataFrame pandas) {
     return df;
   }
 
-  public DataFrame minMeanSizePerZip(DataFrame pandas) {
+  public static DataFrame minMeanSizePerZip(DataFrame pandas) {
     return pandas.groupBy(pandas.col("zip")).agg(min(pandas.col("pandaSize")), mean(pandas.col("pandaSize")));
   }
 
-  public DataFrame simpleSqlExample(DataFrame pandas) {
+  public static DataFrame simpleSqlExample(DataFrame pandas) {
     SQLContext sqlContext = pandas.sqlContext();
     pandas.registerTempTable("pandas");
 
@@ -159,11 +159,11 @@ public DataFrame simpleSqlExample(DataFrame pandas) {
    * Pandas will be sorted by "size" first and if two pandas
    * have the same "size"  will be sorted by "age".
    */
-  public DataFrame orderPandas(DataFrame pandas) {
+  public static DataFrame orderPandas(DataFrame pandas) {
     return pandas.orderBy(pandas.col("pandaSize").asc(), pandas.col("age").desc());
   }
 
-  public DataFrame computeRelativePandaSizes(DataFrame pandas) {
+  public static DataFrame computeRelativePandaSizes(DataFrame pandas) {
     //tag::relativePandaSizesWindow[]
     WindowSpec windowSpec = Window
       .orderBy(pandas.col("age"))
@@ -179,7 +179,7 @@ public DataFrame computeRelativePandaSizes(DataFrame pandas) {
     //end::relativePandaSizesQuery[]
   }
 
-  public void joins(DataFrame df1, DataFrame df2) {
+  public static void joins(DataFrame df1, DataFrame df2) {
     //tag::innerJoin[]
     // Inner join implicit
     df1.join(df2, df1.col("name").equalTo(df2.col("name")));
@@ -203,7 +203,7 @@ public void joins(DataFrame df1, DataFrame df2) {
     //end::leftsemiJoin[]
   }
 
-  public DataFrame selfJoin(DataFrame df) {
+  public static DataFrame selfJoin(DataFrame df) {
     return df.as("a").join(df.as("b")).where(df.col("name").equalTo(df.col("name")));
   }