Update Chapter 4 README.md

Author: bastihaase

chapter4/README.md

@@ -25,7 +25,7 @@ services:
       - nw
 ```
 This service uses the same image that we used in Chapter 3. The declaration of the first volume is also analogous to what we declared in Chapter 3. The second volume mounts the script `init-tables.sh` in the `chapter4` folder to the folder `docker-entrypoint-initdb.d`. This is a feature of the `postgres` image: Any script in that folder will be used to setup the database when first started. We are using this feature to setup a table and insert two entries just like we did by hand in Chapter 3. Feel free to look into `init-tables.sh` to see that the commands are basically the same.
-`docker-compose` allows us to declare a bunch of enviromental variables via a file - we have named this file env_file and it is similar to what we defined back in Chapter 3.
+`docker-compose` allows us to declare a bunch of environmental variables via a file - we have named this file env_file and it is similar to what we defined back in Chapter 3.
 Lastly, we attach the `db` service to the network `nw`. More on this later! Let us first move on to the jupyter service.
 ```
   jupyter-server:
@@ -63,11 +63,15 @@ volumes:
 While there was a lot of new material in Chapter 4, building and running looks surprisingly familiar:
 ```
 docker-compose build
-docker-compose up 
+docker-compose up
 ```
 
-Those two commands setup both our services and establish the network `nw`. Note that we added an example notebook in our `jupyter-server` service. Enter the server as usual and open it. You find some commands that load the content of the database into a pandas dataframe. We have a working platform!
+Those two commands setup both our services and establish the network `nw`. Note that we added an example notebook in our `jupyter-server` service. Enter the server as usual and open it. You find some commands that load the content of the database into a pandas dataframe.
+
+![dataframe](./img/ipython.png)
+
+ We have a working platform!
 
 Note that we can connect to the database using `user@db:5432`, this is because in our network `nw`, the database server can be found via its name `db`.
 
-This completes our tutorial, we hope you enjoyed it! If you are interested in containerization technologies and concepts such as Infrastructure as Code, be sure to check out the [DevOps Engineering program](https://www.insightdevops.com) at Insight Data Science!
+This completes our tutorial, we hope you enjoyed it! If you are interested in containerization technologies and concepts such as Infrastructure as Code, continuous integration and deployment and scalable infrastructures, be sure to check out the [DevOps Engineering program](https://www.insightdevops.com) at Insight Data Science!