In most cases, we’ll want to plot the mean of our data, but sometimes, we’ll want something different:

- If our data has many outliers, we may want to plot the
*median*. - If our data is categorical, we might want to count how many times each category appears (such as in the case of survey responses).

Seaborn is flexible and can calculate any aggregate you want. To do so, you’ll need to use the keyword argument `estimator`

, which accepts any function that works on a list.

For example, to calculate the median, you can pass in `np.median`

to the `estimator`

keyword:

sns.barplot(data=df, x="x-values", y="y-values", estimator=np.median)

Consider the data in **results.csv**. To calculate the number of times a particular value appears in the `Response`

column , we pass in `len`

:

sns.barplot(data=df, x="Patient ID", y="Response", estimator=len)

### Instructions

**1.**

Consider our hospital satisfaction survey data, which is loaded into the Pandas DataFrame `df`

. Use `print`

to examine the data.

**2.**

We’d like to know how many men and women answered the survey. Use `sns.barplot()`

with:

`data`

equal to`df`

`x`

equal to`Gender`

`y`

equal to`Response`

`estimator`

equal to`len`

**3.**

Use `plt.show()`

to display the graph.

**4.**

Change `sns.barplot()`

to graph the median `Response`

aggregated by `Gender`

using `estimator=np.median`

.