Exercise

# Promoting ebooks with conviction

In the previous exercise, we defined a function to compute conviction. We were asked to apply that function to all two-book permutations of the goodreads-10k dataset. In this exercise, we'll test the function by applying it to the three most popular books, which we used in earlier exercises: *The Hunger Games*, *Harry Potter*, and *Twilight*.

The function has been defined for you and is available as `conviction`

. Recall that it takes an antecedent and a consequent as its two arguments. Additionally, the columns of the `books`

DataFrame from earlier exercises are available as three separate DataFrames: `potter`

, `twilight`

, and `hunger`

.

Instructions

**100 XP**

- Compute conviction for
*{Twilight}*\(\rightarrow\)*{Potter}*and*{Potter}*\(\rightarrow\)*{Twilight}*. - Compute conviction for
*{Twilight}*\(\rightarrow\)*{Hunger}*and*{Hunger}*\(\rightarrow\)*{Twilight}*. - Compute conviction for
*{Potter}*\(\rightarrow\)*{Hunger}*and*{Hunger}*\(\rightarrow\)*{Potter}*.