Histograms are helpful for understanding how your data is distributed. While the average time a customer may arrive at the grocery store is 3 pm, the manager knows 3 pm is not the busiest time of day.

Before identifying the busiest times of the day, it’s important to understand the extremes of your data: the minimum and maximum values in your dataset. With the minimums and maximums, you can calculate the *range*.

The range of your data is the difference between the maximum value and the minimum value in your dataset.

`$range = max(data)\ -\ min(data)$`

#### Exercise Class Example

In the example below, we have a NumPy array with the ages of people in an exercise class. Before looking at the data, let’s think about what minimum, maximum, and range values are reasonable for a group of people in an exercise class:

- The minimum cannot be below 0, because people don’t have negative ages
- The maximum is probably lower than 122 (the oldest person ever).

Now, let’s take a look at our data.

exercise_ages = np.array([22, 27, 45, 62, 34, 52, 42, 22, 34, 26])

The minimum age in `exercise_ages`

is 22, the maximum age is 62, and the range is 40.

You can use the following Python commands to verify this result:

min_age = np.amin(exercise_ages) # Answer is 22 max_age = np.amax(exercise_ages) # Answer is 62 age_range = max_age - min_age

### Instructions

**1.**

Find the minimum transaction time and save it to `min_time`

.

**2.**

Find the maximum transaction time and save it to `max_time`

.

**3.**

Find the range, and save it to `range_time`

.

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