Update quickstart code and copy to match

docs/quickstart.md

@@ -22,11 +22,10 @@ python -m pip install quixstreams
 
 ### Step 2. Producing data to Kafka
 
-In order to process events with Quix Streams, they first need to be in Kafka.  
-Let's create the file `producer.py` to generate some test data into the Kafka topic:
+In order to process events with Quix Streams, they first need to be in Kafka.
+Let's create the file `producer.py` to write some test data into a Kafka topic.
 
 ```python
-
 from quixstreams import Application
 
 # Create an Application - the main configuration entry point
@@ -65,8 +64,8 @@ if __name__ == "__main__":
 
 ### Step 3. Consuming data from Kafka
 
-Let's create the file `consumer.py` with streaming processing code.  
-It will start consuming messages from Kafka and applying transformations to them.
+Let's create the file `consumer.py` to process the data in the topic.
+It will start consuming messages from Kafka and apply transformations to them.
 
 ```python
 from quixstreams import Application
@@ -86,7 +85,7 @@ messages_topic = app.topic(name="messages", value_deserializer="json")
 sdf = app.dataframe(topic=messages_topic)
 
 # Print the input data
-sdf = sdf.update(lambda message: print("Input: ", message))
+sdf = sdf.update(lambda message: print(f"Input:  {message}"))
 
 # Define a transformation to split incoming sentences
 # into words using a lambda function
@@ -99,17 +98,16 @@ sdf = sdf.apply(
 sdf["length"] = sdf["text"].apply(lambda word: len(word))
 
 # Print the output result
-sdf = sdf.update(lambda word: print(word))
+sdf = sdf.update(lambda word: print(f"Output: {word}"))
 
 # Run the streaming application
 if __name__ == "__main__":
     app.run(sdf)
 ```
 
-
 ### Step 4. Running the Producer
 
-Let's run the `producer.py` to fill the topic with data.  
+Let's run the `producer.py` in a terminal to fill the topic with data.  
 If the topic does not exist yet, Quix Streams will create it with the default number of partitions.
 
 ```commandline
@@ -127,10 +125,11 @@ Produce event with key="id3" value="b'{"chat_id":"id3","text":"Mollis nunc sed i
 ### Step 5. Running the Consumer
 
 Now that you have a topic with data, you may start consuming events and process them.  
-Let's run the `consumer.py` to see the results:
+Let's run the `consumer.py` to see the results.
 
 ```commandline
 python consumer.py
+
 [2024-02-21 19:57:38,669] [INFO] : Initializing processing of StreamingDataFrame
 [2024-02-21 19:57:38,669] [INFO] : Topics required for this application: "messages", "words"
 [2024-02-21 19:57:38,699] [INFO] : Validating Kafka topics exist and are configured correctly...
@@ -147,7 +146,6 @@ Input:  {'chat_id': 'id2', 'text': 'Consectetur adipiscing elit sed'}
 ...
 ```
 
-
 ## Next steps
 
 Now that you have a simple Quix Streams application working, you can dive into more advanced features:
@@ -164,4 +162,4 @@ Or check out the tutorials for more in-depth examples:
 
 ## Getting help
 
-If you run into any problems, please create an [issue](https://github.com/quixio/quix-streams/issues) or ask in `#quix-help` in our **[Quix Community on Slack](https://quix.io/slack-invite)**.
+If you run into any problems, please create an [issue](https://github.com/quixio/quix-streams/issues) or ask in `#quix-help` in **[Quix Community on Slack](https://quix.io/slack-invite)**.