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