User-defined function to make plots

In the last exercise, you had to write this code to make the plot:

# Subset tech and fmcg companies
subset_dat = dataset.loc[dataset["comp_type"].isin(["tech", "fmcg"])]

# Compute yearly average gross margin ratio of tech and fmcg companies
subset_dat_avg = subset_dat.pivot_table(index=["Year", "comp_type"], values = "gross_margin").reset_index()

# Add column company
subset_dat_avg["company"] = np.where(subset_dat_avg["comp_type"]=="tech", "Avg tech", "Avg fmcg")

# Concat the DataFrames
plot_df = pd.concat([subset_dat, subset_dat_avg], axis=0)

# Make the plot
sns.relplot(data=plot_df.reset_index(drop=True), x="Year", y="gross_margin", hue="company", col="comp_type", kind="line")
plt.show()
plt.close()

Notice that we perform the same actions on the tech and FMCG DataFrames in this exercise. This is repetitive and goes against a coding principle called DRY - Don't repeat yourself. Repetitive code is bad since it increases your work and makes your code more prone to mistakes. In this exercise, you will define your function to process data and plot figures.