Live Code, Data Ingestion How to Load and Analyze a MovieLens Data Set Using Pandas Are you looking for ways to load and analyze a MovieLens data set using Pandas? Well, you have come to the right place! In this article, we will show you how to load a data set, which includes movies, ratings, tags, and genome tags into a Pandas DataFrame. You will also learn how to view the contents of the data files and check the data formatting. Let's begin by checking the contents of the MovieLens directory. If you are following along, please make sure that you have downloaded the data set and located it in the correct directory. To view the contents of the directory, we will use the ls command. In this case, we will specify the MovieLens directory using the ./movielens command. Once we have verified the contents of the directory, we can start to load the data into a Pandas DataFrame. We will start by loading the movies.csv file. We will use the read_csv function and specify the separator for the data as a comma. This will create a new DataFrame object that we will call movies. import pandas as pd movies = pd.read_csv('./movielens/movies.csv', sep=',') Now that we have loaded the data into a DataFrame, let's take a look at the contents of the file. We can use the head function to display the first five elements in the DataFrame. print(movies.head()) This will display the first five elements in the movies DataFrame. movieId 0 1 2 3 4 0 1 2 3 4 1 2 3 4 5 Toy Story Jumanji Grumpier Old Men Waiting to Exhale Father of the Bride Part II title \ (1995) (1995) (1995) (1995) (1995) genres Adventure|Animation|Children|Comedy|Fantasy Adventure|Children|Fantasy Comedy|Romance Comedy|Drama|Romance Comedy As you can see, there are three columns in the movies DataFrame: movieId, title, and genres. The labels for the columns are found in the first row of the DataFrame. These labels will be indexed numerically if they are not present in the data file. Next, we will load the tags.csv file into a new DataFrame object that we will call tags. tags = pd.read_csv('./movielens/tags.csv', sep=',') We can view the contents of the tags DataFrame using the head function. print(tags.head()) This will display the first five elements in the tags DataFrame. 0 1 2 3 4 userId movieId 15 339 15 1955 15 7478 15 32892 15 34162 tag sandra 'boring dentist' Cambodia' Russian' forgettable' timestamp 1138537770 1193435061 1170560997 1170626366 1141391765 The tags DataFrame contains four columns: userId, movieId, tag, and timestamp. We will also load the ratings.csv file into a new DataFrame object that we will call ratings. ratings = pd.read_csv('./movielens/ratings.csv', sep=',