A counts table is one approach for exploring categorical variables, but sometimes it is useful to also look at the proportion of values in each category. For example, knowing that there are 3,539 rental listings in Manhattan is hard to interpret without any context about the counts in the other categories. On the other hand, knowing that Manhattan listings make up 71% of all New York City listings tells us a lot more about the relative frequency of this category.

We can calculate the proportion for each category by dividing its count by the total number of values for that variable:

# Proportions of rental listings in each borough rentals.borough.value_counts() / len(rentals.borough)

**Output**:

```
Manhattan 0.7078
Brooklyn 0.2026
Queens 0.0896
```

Alternatively, we could also obtain the proportions by specifying `normalize=True`

to the `.value_counts()`

method:

df.borough.value_counts(normalize=True)

### Instructions

**1.**

Using the `movies`

DataFrame, find the proportion of movies in each `genre`

and save them to a variable called `genre_props`

. Print `genre_props`

to see the result.