Visualizing trends

1. Visualizing trends

You saw that PE ratios vary by sector in the S&P 100. In your case study, you made a scatter plot of these ratios for companies in the information technology sector. Let's take a closer look at this data.

2. Your mission - outlier?

In your scatterplot, did you notice that there is a ratio that is higher than the others? In this part of the case study, let's take a closer look to determine the name of the company.

3. Step 1: Make a histogram

Remember that histograms can help you look at the spread of data. As a first step to taking a closer look at the IT sector, let's make a histogram of its price to earnings ratios. To plot a histogram, you can use the hist() function from the pyplot module. You'll also need to define the number of bins for the histogram plot.

4. Step 2: Identify the Outlier

Based on the histogram, you'll look to identify the P/E ratio outlier. Based on this PE ratio, you can subset this company's specific data. The final step in this case study is to identify the name of the company that is associated with an abnormally high P/E ratio within the IT sector.

5. Let's practice!

It's now time for you to finish this case study!

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