[MINOR][DOCS] Fix minor typos in python example code

## What changes were proposed in this pull request?

Fix minor typos python example code in streaming programming guide

## How was this patch tested?

N/A

Author: Dmitriy Sokolov <silentsokolov@gmail.com>

Closes #14805 from silentsokolov/fix-typos.

docs/programming-guide.md

@@ -460,8 +460,10 @@ Elasticsearch ESInputFormat:
 {% highlight python %}
 $ SPARK_CLASSPATH=/path/to/elasticsearch-hadoop.jar ./bin/pyspark
 >>> conf = {"es.resource" : "index/type"}  # assume Elasticsearch is running on localhost defaults
->>> rdd = sc.newAPIHadoopRDD("org.elasticsearch.hadoop.mr.EsInputFormat",\
-    "org.apache.hadoop.io.NullWritable", "org.elasticsearch.hadoop.mr.LinkedMapWritable", conf=conf)
+>>> rdd = sc.newAPIHadoopRDD("org.elasticsearch.hadoop.mr.EsInputFormat",
+                             "org.apache.hadoop.io.NullWritable",
+                             "org.elasticsearch.hadoop.mr.LinkedMapWritable",
+                             conf=conf)
 >>> rdd.first()  # the result is a MapWritable that is converted to a Python dict
 (u'Elasticsearch ID',
  {u'field1': True,
@@ -797,7 +799,6 @@ def increment_counter(x):
 rdd.foreach(increment_counter)
 
 print("Counter value: ", counter)
-
 {% endhighlight %}
 </div>
 
@@ -1455,13 +1456,14 @@ The code below shows an accumulator being used to add up the elements of an arra
 
 {% highlight python %}
 >>> accum = sc.accumulator(0)
+>>> accum
 Accumulator<id=0, value=0>
 
 >>> sc.parallelize([1, 2, 3, 4]).foreach(lambda x: accum.add(x))
 ...
 10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s
 
-scala> accum.value
+>>> accum.value
 10
 {% endhighlight %}
 

docs/streaming-programming-guide.md

@@ -1495,16 +1495,15 @@ See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_
 </div>
 <div data-lang="python" markdown="1">
 {% highlight python %}
-
 def getWordBlacklist(sparkContext):
-    if ('wordBlacklist' not in globals()):
-        globals()['wordBlacklist'] = sparkContext.broadcast(["a", "b", "c"])
-    return globals()['wordBlacklist']
+    if ("wordBlacklist" not in globals()):
+        globals()["wordBlacklist"] = sparkContext.broadcast(["a", "b", "c"])
+    return globals()["wordBlacklist"]
 
 def getDroppedWordsCounter(sparkContext):
-    if ('droppedWordsCounter' not in globals()):
-        globals()['droppedWordsCounter'] = sparkContext.accumulator(0)
-    return globals()['droppedWordsCounter']
+    if ("droppedWordsCounter" not in globals()):
+        globals()["droppedWordsCounter"] = sparkContext.accumulator(0)
+    return globals()["droppedWordsCounter"]
 
 def echo(time, rdd):
     # Get or register the blacklist Broadcast
@@ -1626,12 +1625,12 @@ See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_
 
 # Lazily instantiated global instance of SparkSession
 def getSparkSessionInstance(sparkConf):
-    if ('sparkSessionSingletonInstance' not in globals()):
-        globals()['sparkSessionSingletonInstance'] = SparkSession\
+    if ("sparkSessionSingletonInstance" not in globals()):
+        globals()["sparkSessionSingletonInstance"] = SparkSession \
             .builder \
             .config(conf=sparkConf) \
             .getOrCreate()
-    return globals()['sparkSessionSingletonInstance']
+    return globals()["sparkSessionSingletonInstance"]
 
 ...
 
@@ -1830,7 +1829,7 @@ This behavior is made simple by using `StreamingContext.getOrCreate`. This is us
 # Function to create and setup a new StreamingContext
 def functionToCreateContext():
     sc = SparkContext(...)  # new context
-    ssc = new StreamingContext(...)
+    ssc = StreamingContext(...)
     lines = ssc.socketTextStream(...)  # create DStreams
     ...
     ssc.checkpoint(checkpointDirectory)  # set checkpoint directory

docs/structured-streaming-programming-guide.md

@@ -126,20 +126,20 @@ This `lines` DataFrame represents an unbounded table containing the streaming te
 # Create DataFrame representing the stream of input lines from connection to localhost:9999
 lines = spark \
     .readStream \
-    .format('socket')\
-    .option('host', 'localhost')\
-    .option('port', 9999)\
+    .format("socket") \
+    .option("host", "localhost") \
+    .option("port", 9999) \
     .load()
 
 # Split the lines into words
 words = lines.select(
    explode(
-       split(lines.value, ' ')
-   ).alias('word')
+       split(lines.value, " ")
+   ).alias("word")
 )
 
 # Generate running word count
-wordCounts = words.groupBy('word').count()
+wordCounts = words.groupBy("word").count()
 {% endhighlight %}
 
 This `lines` DataFrame represents an unbounded table containing the streaming text data. This table contains one column of strings named "value", and each line in the streaming text data becomes a row in the table. Note, that this is not currently receiving any data as we are just setting up the transformation, and have not yet started it. Next, we have used two built-in SQL functions - split and explode, to split each line into multiple rows with a word each. In addition, we use the function `alias` to name the new column as "word". Finally, we have defined the `wordCounts` DataFrame by grouping by the unique values in the Dataset and counting them. Note that this is a streaming DataFrame which represents the running word counts of the stream.
@@ -182,8 +182,8 @@ query.awaitTermination();
  # Start running the query that prints the running counts to the console
 query = wordCounts \
     .writeStream \
-    .outputMode('complete')\
-    .format('console')\
+    .outputMode("complete") \
+    .format("console") \
     .start()
 
 query.awaitTermination()
@@ -596,7 +596,6 @@ ds.groupByKey(new MapFunction<DeviceData, String>() { // using typed API
 <div data-lang="python"  markdown="1">
 
 {% highlight python %}
-
 df = ...  # streaming DataFrame with IOT device data with schema { device: string, type: string, signal: double, time: DateType }
 
 # Select the devices which have signal more than 10
@@ -657,7 +656,7 @@ words = ... # streaming DataFrame of schema { timestamp: Timestamp, word: String
 
 # Group the data by window and word and compute the count of each group
 windowedCounts = words.groupBy(
-    window(words.timestamp, '10 minutes', '5 minutes'),
+    window(words.timestamp, "10 minutes", "5 minutes"),
     words.word
 ).count()
 {% endhighlight %}