 # How to Highlight the Bottom Bar Chart Values in Tableau

I decided to make this video after someone left a comment on another video I made titled “How to Highlight the Top 3 Bar Chart Values in Tableau” asking how to find the last three values. In this video I will show you how to highlight the bottom three sales values on a bar chart. You’ll also learn how to use a parameter to dynamically change the number of lowest bars highlighted. We can accomplish the highlighting of the bottom N bar chart values via two ways. We can either create a set or create a calculated field to accomplish this task. The set method is cleaner but has its limitations when multiple dimensions are used in the visual. Therefore, the calculated field approach serves us well when we add multiple dimensions.

Watch the video to see how it all comes together but the calculation boils down to this:

`RANK(SUM(0-[Sales]))<=[Highlight Parameter]`

By adjusting the [Highlight Parameter] control, the user can determine how many bottom sales values are highlighted in the visual. This method also maintains its functionality when an additional dimension is added to the visual.

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those of my employer. # Create Multiple Bar Charts in Python using Matplotlib and Pandas

In this Python visualization tutorial you’ll learn how to create and save as a file multiple bar charts in Python using Matplotlib and Pandas. We’ll easily read in a .csv file to a Pandas dataframe and then let Matplotlib perform the visualization. As a bonus you’ll also learn how to save the plot as a file.

The key to making two plots work is the creation of two axes that will hold the respective bar chart subplots.

```# define the figure container and the two plot axes
fig = plt.figure(figsize=(20,5))

# add subplots to the figure (build a 1x2 grid and place chart in the first or second section)

Understanding the subplot nomenclature is essential. Adding axes to the figure as part of a subplot arrangement is simple with the fig.add_subplot() call. In this arrangement the first digit is the number of rows, the second represents the number of columns, and the third is the index of the subplot (where we want to place our visualization).

Of course you need to watch the video to see how all of the code comes together.

Also, keep this Matplotlib style sheet reference handy for changing up the style on your visual. As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those of my employer.

See the following links for additional background:

https://matplotlib.org/3.1.0/gallery/subplots_axes_and_figures/subplots_demo.html