Add user guide for groupby apply

Author: icexelloss

docs/sql-programming-guide.md

@@ -1661,7 +1661,7 @@ installed and available on all cluster node Python environments. The current sup
 
 ## How to Enable for Conversion to/from Pandas
 
-Arrow is available as an optimization when converting a Spark DataFrame to Pandas using the call 
+Arrow is available as an optimization when converting a Spark DataFrame to Pandas using the call
 `toPandas()` and when creating a Spark DataFrame from Pandas with `createDataFrame(pandas_df)`.
 To use Arrow when executing these calls, it first must be enabled by setting the Spark conf
 'spark.sql.execution.arrow.enabled' to 'true', this is disabled by default.
@@ -1743,7 +1743,104 @@ df.select(multiply(col("x"), col("x"))).show()
 </div>
 </div>
 
-## GroupBy-Apply UDFs
+## GroupBy-Apply
+GroupBy-Apply implements the "split-apply-combine" pattern. Split-apply-combine consists of three steps:
+
+* Split the data into groups by using `DataFrame.groupBy`.
+* Apply a function on each group. The input and output of the function are both `pandas.DataFrame`. The
+  input data contains all the rows and columns for each group.
+* Combine the results into a new `DataFrame`.
+
+To use GroupBy-Apply, user needs to define:
+* A python function that defines the computation for each group
+* A `StructType` object or a string that defines the output schema of the output `DataFrame`
+
+Examples:
+
+The first example shows a simple use case: subtracting mean from each value in the group.
+
+<div class="codetabs">
+<div data-lang="python"  markdown="1">
+{% highlight python %}
+
+from pyspark.sql.functions import pandas_udf, PandasUDFType
+
+df = spark.createDataFrame(
+    [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
+    ("id", "v"))
+
+@pandas_udf("id long, v double", PandasUDFType.GROUP_MAP)
+def substract_mean(pdf):
+    v = pdf.v
+    return pdf.assign(v=v - v.mean())
+
+df.groupby("id").apply(substract_mean).show()
++---+----+
+| id|   v|
++---+----+
+|  1|-0.5|
+|  1| 0.5|
+|  2|-3.0|
+|  2|-1.0|
+|  2| 4.0|
++---+----+
+
+{% endhighlight %}
+</div>
+</div>
+
+The second example is a more complicated example. It shows how to run a OLS linear regression
+for each group using statsmodels. For each group, we calculate beta b = (b1, b2) for X = (x1, x2)
+according to statistical model Y = bX + c.
+
+<div class="codetabs">
+<div data-lang="python"  markdown="1">
+{% highlight python %}
+import pandas as pd
+import statsmodels.api as sm
+
+df = spark.createDataFrame(
+    [(1, 1.0, 1.0, -3.0),
+     (1, 2.0, 2.0, -6.0),
+     (2, 3.0, 6.0, -6.0),
+     (2, 5.0, 10.0, -10.0),
+     (2, 10.0, 20.0, -20.0)],
+    ("id", "y", 'x1', 'x2'))
+
+group_column = 'id'
+y_column = 'y'
+x_columns = ['x1', 'x2']
+schema = df.select(group_column, *x_columns).schema
+
+@pandas_udf(schema, PandasUDFType.GROUP_MAP)
+# Input/output are both a pandas.DataFrame
+def ols(pdf):
+    group_key = pdf[group_column].iloc[0]
+    Y = pdf[y_column]
+    X = pdf[x_columns]
+    X = sm.add_constant(X)
+    model = sm.OLS(Y, X).fit()
+
+    print(model.params)
+    print(model.summary())
+    return pd.DataFrame([[group_key] + [model.params[i] for i in x_columns]])
+
+beta = df.groupby(group_column).apply(ols)
+
+beta.show()
++---+-------------------+--------------------+
+| id|                 x1|                  x2|
++---+-------------------+--------------------+
+|  1|0.10000000000000003| -0.3000000000000001|
+|  2|0.24999999999999997|-0.24999999999999997|
++---+-------------------+--------------------+
+
+{% endhighlight %}
+</div>
+</div>
+
+For detailed usage, please see `pyspark.sql.functions.pandas_udf` and
+`pyspark.sql.GroupedData.apply`.
 
 ## Usage Notes
 
@@ -1786,7 +1883,7 @@ values will be truncated.
 Note that a standard UDF (non-Pandas) will load timestamp data as Python datetime objects, which is
 different than a Pandas timestamp. It is recommended to use Pandas time series functionality when
 working with timestamps in `pandas_udf`s to get the best performance, see
-[here](https://pandas.pydata.org/pandas-docs/stable/timeseries.html) for details. 
+[here](https://pandas.pydata.org/pandas-docs/stable/timeseries.html) for details.
 
 # Migration Guide
 
@@ -1936,7 +2033,7 @@ working with timestamps in `pandas_udf`s to get the best performance, see
     Note that, for <b>DecimalType(38,0)*</b>, the table above intentionally does not cover all other combinations of scales and precisions because currently we only infer decimal type like `BigInteger`/`BigInt`. For example, 1.1 is inferred as double type.
   - In PySpark, now we need Pandas 0.19.2 or upper if you want to use Pandas related functionalities, such as `toPandas`, `createDataFrame` from Pandas DataFrame, etc.
   - In PySpark, the behavior of timestamp values for Pandas related functionalities was changed to respect session timezone. If you want to use the old behavior, you need to set a configuration `spark.sql.execution.pandas.respectSessionTimeZone` to `False`. See [SPARK-22395](https://issues.apache.org/jira/browse/SPARK-22395) for details.
-  - In PySpark, `na.fill()` or `fillna` also accepts boolean and replaces nulls with booleans. In prior Spark versions, PySpark just ignores it and returns the original Dataset/DataFrame.  
+  - In PySpark, `na.fill()` or `fillna` also accepts boolean and replaces nulls with booleans. In prior Spark versions, PySpark just ignores it and returns the original Dataset/DataFrame.
   - Since Spark 2.3, when either broadcast hash join or broadcast nested loop join is applicable, we prefer to broadcasting the table that is explicitly specified in a broadcast hint. For details, see the section [Broadcast Hint](#broadcast-hint-for-sql-queries) and [SPARK-22489](https://issues.apache.org/jira/browse/SPARK-22489).
   - Since Spark 2.3, when all inputs are binary, `functions.concat()` returns an output as binary. Otherwise, it returns as a string. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set `spark.sql.function.concatBinaryAsString` to `true`.
   - Since Spark 2.3, when all inputs are binary, SQL `elt()` returns an output as binary. Otherwise, it returns as a string. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set `spark.sql.function.eltOutputAsString` to `true`.