For quantitative variables, we often want to describe the *central tendency*, or the “typical” value of a variable. For example, what is the typical cost of rent in New York City?

There are several common measures of central tendency:

**Mean**: The average value of the variable, calculated as the sum of all values divided by the number of values.**Median**: The middle value of the variable when sorted.**Mode**: The most frequent value of the variable.**Trimmed mean**: The mean excluding x percent of the lowest and highest data points.

For our `rentals`

DataFrame with a column named `rent`

that contains rental prices, we can calculate the central tendency statistics listed above as follows:

# Mean rentals.rent.mean() # Median rentals.rent.median() # Mode rentals.rent.mode() # Trimmed mean from scipy.stats import trim_mean trim_mean(rentals.rent, proportiontocut=0.1) # trim extreme 10%

### Instructions

**1.**

Using the same `movies`

DataFrame from the last exercise, find the mean `production_budget`

for all movies and save it to a variable called `mean_budget`

. Print `mean_budget`

to see the result.

**2.**

Save the median budget to a variable called `med_budget`

and print the result.

**3.**

Save the mode to a variable called `mode_budget`

and print the result.

How do the mean, median, and mode of movie budgets compare to each other?

**4.**

Find the mean of the budget after removing 20% of the lowest and highest data points. Save the trimmed mean to a variable called `trmean_budget`

and print the result.

How does trimming the most extreme data points affect the mean budget?