On Blackboard, you will find data on movie ratings, movie metadata (genres and year), and demographic data about the users (age. zip code. gender identification, and occupation). These datasets contain I a million ratings collected from 6.000 was on 4.000 movies. It is spread across three datasets: ^–ratings.csv”, users.csv” and “movies.csv”. Such data is often of interest in the development of recommendations system.

In this assessment. we are going to handle these datasets and make some responses regarding movie preference by children and general audiences. Your tasks are as follows:

1. First, load each of the three datasets into pandas data frames. Once the data is loaded, verify that everything is loaded properly by looking at the first five rows of each data frame.

2. Analyzing the data spread across three tables is not a simple task. particularly when you wanted to compute statistics based on a particular variable. This is much easier to do with all of the data merged together into a single data frame. Using pandits’ merge function, merge ratings with users and then merge that result with the movies Use how-‘inner’ argument and call the new data frame as ‘merged1’. Note that the panda’s merge method requires a common column to use as the merge key.

4. Using the data frame ‘merged:’, get the average movie ratings for five specific movies. Create a data frame to report the results as follows.

5. Now get the total number of ratings submitted by male and female audiences for the above Similar as before. create a data frame to report the results as follows. (5]

7. Between ‘children’ and ‘Fantasy’ movies, which group has the highest total number of rating submissions by users! How many is the difference?

8. Between ‘children’ and ‘Fantasy’ movies, which group has the highest average rating? %Tut is the average difference in the rating?

9. In this section. we are going to focus on movies rated by a gametal adult audience. Using the ‘merged ((macrame. select observations where age is greater than 18. Call the new data frame as ‘merged3’. Print out the average as well as the maximum age of this group of audience.