Take a look at the file called results.csv. You’ll plot that data soon, but before you plot it, take a minute to understand the context behind that data, which is based on a hypothetical situation we have created:

Suppose we are analyzing data from a survey: we asked 1,000 patients at a hospital how satisfied they were with their experience. Their response was measured on a scale of 1 - 10, with 1 being extremely unsatisfied, and 10 being extremely satisfied. We have summarized that data in a CSV file called results.csv.

To plot this data using Matplotlib, you would write the following:

df = pd.read_csv("results.csv")
ax = plt.subplot()
plt.bar(range(len(df)),
        df["Mean Satisfaction"])
ax.set_xticks(range(len(df)))
ax.set_xticklabels(df.Gender)
plt.xlabel("Gender")
plt.ylabel("Mean Satisfaction")

That’s a lot of work for a simple bar chart! Seaborn gives us a much simpler option. With Seaborn, you can use the sns.barplot() command to do the same thing.

The Seaborn function sns.barplot(), takes at least three keyword arguments:

• data: a Pandas DataFrame that contains the data (in this example, data=df)
• x: a string that tells Seaborn which column in the DataFrame contains other x-labels (in this case, x="Gender")
• y: a string that tells Seaborn which column in the DataFrame contains the heights we want to plot for each bar (in this case y="Mean Satisfaction")

By default, Seaborn will aggregate and plot the mean of each category. In the next exercise you will learn more about aggregation and how Seaborn handles it.