diff --git a/website/_posts/DataViz/2013-01-03-Part-2-Graph.md b/website/_posts/DataViz/2013-01-03-Part-2-Graph.md
index 34a9f7a5..a0996eeb 100644
--- a/website/_posts/DataViz/2013-01-03-Part-2-Graph.md
+++ b/website/_posts/DataViz/2013-01-03-Part-2-Graph.md
@@ -157,7 +157,8 @@ If you are curious about the `plot()` function, open a `python` prompt in your t
 Just creating the variable `day_tuple` for our x-axis isn’t enough — we also have to assign it to our `plt` by using the method `xticks()`:
 
 ```python
-    # Assign labels to the plot
+    # create the amount of ticks needed for our x-axis, and assign
+    # the labels
     plt.xticks(range(len(day_tuple)), day_tuple)
 ```
 
@@ -281,7 +282,7 @@ def visualize_type():
     # by category
 
     # Set the labels which are based on the keys of our counter.
-    # Since order doesn't matter, we can just used counter.keys()
+    # Since order doesn't matter, we can just use counter.keys()
 
     # Set exactly where the labels hit the x-axis 
 
@@ -325,6 +326,16 @@ Next we finally use a bit of numpy magic (we had imported the numpy library as `
 
 We have a new variable, `xlocations`, which will be used to help place the `plt.xticks()`. We’re using the `numpy` (aka `np`) library to access the `arange` function. This creates a list similar to what `range()` would make, into an array that you can manipulate a bit differently. Here, we're adding `0.5`. If you were to `print xlocations`, you would see `[0.5, 1.5, 2.5, ... , 16.5, 17.5]` where `0.5` was added to each int of the list.  You’ll see why we need the `0.5` a bit later.
 
+We need to set the width variable and assign data to the bar plot:
+
+```python
+    # Width of each bar
+    width = 0.5
+
+    # Assign data to a bar plot
+    plt.bar(xlocations, counter.values(), width=width)
+```
+
 Now we assign our x- & y-ticks (should be familiar to `visualize_days()`):
 
 ```python
@@ -336,7 +347,7 @@ For the `plt.xticks()`, the first parameter should look similar to before, but h
 
 Notice how we can pass `xticks()` more parameters than we did before. If you read the [documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xticks) of that function, you can pass it `*args` and `**kwargs`, or arguments and keyword arguments. It mentions that you can pass matplotlib-defined [text properties](http://matplotlib.org/api/artist_api.html#matplotlib.text.Text) for the labels — so that would explain the `**kwargs` element there. If nothing is passed in for `rotation` then it’s set to a default defined in their text properties documentation.
 
-Next, we just add a little bit of spacing to the bottom of the graph so the labels (since some of them are long, like `Forgery/Counterfeiting`). We use the `.subplots_adjust()` function. In matplotlib, you have the ability to render multiple graphs on one window/function, called subplots. With one graph, subplots can be used to adjust the spacing around the graph itself.
+Next, we just add a little bit of spacing to the bottom of the graph so the labels (since some of them are long, like `Forgery/Counterfeiting`) aren't cut off in the graph. We use the `.subplots_adjust()` function. In matplotlib, you have the ability to render multiple graphs on one window/function, called subplots. With one graph, subplots can be used to adjust the spacing around the graph itself.
 
 ```python
     # Give some more room so the labels aren't cut off in the graph
@@ -357,7 +368,7 @@ Again — here I just played with the numbers until I got something I liked. I e
 Finally, our favorite — rendering and closing the graph!
 
 ```python
-    # Save the plot!
+    # Save the graph!
     plt.savefig("Type.png")
 
     # Close figure