Merge pull request #168 from LaunchCodeEducation/audit-data-viz

Audit for data viz

Author: jwoolbright23

content/data-visualization/_index.md

@@ -33,7 +33,7 @@ Here is a list of key terms for this chapter, broken down by the page the term f
 1. composition chart
 
 
-### Matplotlib and Seaborn
+### Matplotlib
 
 1. bins
 

content/data-visualization/next-steps.md

@@ -9,3 +9,5 @@ hidden = false
 Next we will be learning how to use pandas with a sqlite3 database. Before continuing on, here are some of our favorite additional resources on data visualization in Python:
 
 1. [Johns Hopkins' Data Visualization Guides](https://guides.library.jhu.edu/datavisualization/design)
+1. [Matplotlib Official Tutorial](https://matplotlib.org/stable/tutorials/index.html)
+1. [Python Graph Gallery](https://python-graph-gallery.com/) - Examples of different chart types with code

content/data-visualization/reading/chart-styles/_index.md

@@ -63,13 +63,13 @@ Pie charts are some of the most commonly-used composition charts. Pie charts sho
 
 {{% notice green Question %}}
 
-Match each chart style to its purpose.
+The following table shows incorrect matches between chart styles and their purposes. Provide the correct purpose for each chart style.
 
-| Chart Style | Purpose |
-|-------------|---------|
-| Pie chart | Comparison |
-| Scatterplot | Composition |
-| Bar chart | Relationship |
+| Chart Style | Incorrect Purpose | Correct Purpose |
+|-------------|-------------------|-----------------|
+| Pie chart | Comparison | ? |
+| Scatterplot | Composition | ? |
+| Bar chart | Relationship | ? |
 
 {{% /notice %}}
 

content/data-visualization/reading/vizzes-with-python/_index.md

@@ -25,8 +25,6 @@ However, if you are running your code in an environment like a Jupyter notebook,
 
 ## Bar Charts and Column Charts
 
-### Matplotlib
-
 For column charts, you will need to use `bar()` method and for bar charts, you will need to use the `barh()` method. Both methods have similar parameters so once you master one, it will be simpler to master the other one. 
 
 Let's revisit the plant nursery.
@@ -42,8 +40,6 @@ The above code would create a column chart that has three different citrus trees
 
 ## Scatterplots
 
-### Matplotlib
-
 We can use the `scatter()` method to put together a scatterplot in Matplotlib and finally see if there is a relationship between the butterfly population and the number of participants in the pollinator planting program.
 
 ```python {linenos=table}
@@ -55,8 +51,6 @@ plt.scatter(pollinator_participants, butterflies)
 
 ## Histograms
 
-### Matplotlib
-
 Let's use histograms to better understand the butterfly population at the nursery. When we chart a histogram, we need to decide on the bin size. **Bins** are the columns in a histogram. If someone counted the number of butterflies every week for 8 weeks, then it makes sense for us to have 8 bins.
 
 ```python {linenos=table}
@@ -67,21 +61,15 @@ plt.hist(butterflies, bins=8)
 
 ## Pie Charts
 
-### MatplotLib
-
-Finally, let's explore how we can use these Python libraries to make pie charts. If we needed to create a visualization breaking down the nursery sales by plant category, we could use the `pie()` method in Matplotlib.
+Finally, let's explore how we can use Matplotlib to make pie charts. If we needed to create a visualization breaking down the nursery sales by plant category, we could use the `pie()` method in Matplotlib.
 
 ```python {linenos=table}
-categories = ["annuals", "perenials", "trees", "houseplants"]
+categories = ["annuals", "perennials", "trees", "houseplants"]
 sales = [678, 1894, 1234, 768]
 
 plt.pie(sales,labels=categories)
 ```
 
 The `labels` parameter specifies what each slice of the pie chart should be called.
 
-### Seaborn
-
-As we mentioned on the previous page, not everyone likes pie charts and Seaborn is no exception. Seaborn does not come with a pie chart method. 
-
-As you explore making visualizations in Python, we suggest bookmarking both the [Matplotlib documentation](https://matplotlib.org/stable/index.html) This documentation comes with lots of examples of how you can properly use the multitude of parameters that come with each of the above methods.
+As you explore making visualizations in Python, we suggest bookmarking the [Matplotlib documentation](https://matplotlib.org/stable/index.html). This documentation comes with lots of examples of how you can properly use the multitude of parameters that come with each of the above methods.

content/data-visualization/studio/_index.md

@@ -44,11 +44,9 @@ Some publishers' names are in non-Latin scripts, such as Japanese and Russian. M
 
 ## Create Your Visualizations
 
-Once you have your group, everyone in the group needs to make **two** visualizations:
+Once you have your group, everyone in the group needs to make **two** visualizations using Matplotlib:
 
-1. One made using Matplotlib.
-1. One made using Seaborn.
-1. These visualizations can be of any chart type.
+1. Each visualization should use a different chart type.
 1. Make sure that everyone is doing something different! 
 
 ## Share with Your Group