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%



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.


Save the median budget to a variable called med_budget and print the result.


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?


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?

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