While counting the number of values in a bin is straightforward, it is also time-consuming. How long do you think it would take you to count the number of values in each bin for:

- an exercise class of 50 people?
- a grocery store with 300 loaves of bread?

Most of the data you will analyze with histograms includes far more than ten values.

For these situations, we can use the `numpy.histogram()`

function. In the example below, we use this function to find the counts for a twenty-person exercise class.

exercise_ages = np.array([22, 27, 45, 62, 34, 52, 42, 22, 34, 26, 24, 65, 34, 25, 45, 23, 45, 33, 52, 55]) np.histogram(exercise_ages, range = (20, 70), bins = 5)

Below, we explain each of the function’s inputs:

`exercise_ages`

is the input array`range = (20, 70)`

— is the range of values we expect in our array. Range includes everything from 20, up until but not including 70.`bins = 5`

is the number of bins. Python will automatically calculate equally-sized bins based on the range and number of bins.

Below, you can see the output of the `numpy.histogram()`

function:

(array([7, 4, 4, 3, 2]), array([20., 30., 40., 50., 60., 70.]))

The first array, `array([7, 4, 4, 3, 2])`

, is the counts for each bin. The second array, `array([20., 30., 40., 50., 60., 70.])`

, includes the minimum and maximum values for each bin:

**Bin 1:**20 to <30**Bin 2:**30 to <40**Bin 3:**40 to <50**Bin 4:**50 to <60**Bin 5:**60 to <70

### Instructions

**1.**

Use the `np.histogram()`

function to determine the busiest six hours of the day. Save the result to `times_hist`

on line 12.

Use the following range and number of bins.

**Range:**0 to 24**Bins:**4

Can you determine which six-hour period is the busiest?

Check the hint if you need help, or want to see the correct answer.