Finding the mode of a dataset becomes increasingly time-consuming as the size of your dataset increases — imagine finding the mode of a dataset with 10,000 observations.

The SciPy `stats.mode()`

function can do the work of finding the mode for you. In the example below, we import `stats`

then use `stats.mode()`

to calculate the mode of a dataset with ten values:

#### Example: One Mode

from scipy import stats example_array = np.array([24, 16, 12, 10, 12, 28, 38, 12, 28, 24]) example_mode = stats.mode(example_array)

The code above calculates the mode of the values in `example_array`

and saves it to `example_mode`

.

The result of `stats.mode()`

is an object with the mode value, and its count.

>>> example_mode ModeResult(mode=array([12]), count=array([3]))

#### Example: Two Modes

If there are multiple modes, the `stats.mode()`

function will always return the smallest mode in the dataset.

Let’s look at an array with two modes, `12`

and `24`

:

from scipy import stats example_array = np.array([24, 16, 12, 10, 12, 24, 38, 12, 28, 24]) example_mode = stats.mode(example_array)

The result of `stats.mode()`

is an object with the smallest mode value, and its count.

>>> example_mode ModeResult(mode=array([12]), count=array([3]))

### Instructions

**1.**

We have already imported the `stats`

library for you.

On line 13, delete the current value set to `mode_age`

.

Find the mode of the observations in the `author_ages`

array. Save the result to `mode_age`

.

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