Loading with pandas

The MovieLens dataset is in a good shape; however, there are some changes from the default options in pandas.read_csv that we need to make. To start with, the data is separated by tabs, not commas. Next, there is no heading line. This means the first line in the file is actually data and we need to manually set the column names.

When loading the file, we set the delimiter parameter to the tab character, tell pandas not to read the first row as the header (with header=None) and to set the column names with given values. Let's look at the following code:

all_ratings = pd.read_csv(ratings_filename, delimiter="t", header=None, names
 = ["UserID", "MovieID", "Rating", "Datetime"])

While we won't use it in this chapter, you can properly parse the date timestamp using the following line. Dates for reviews can be an important feature in recommendation prediction, as movies that are rated together often have more similar rankings than movies ranked separately. Accounting for this can improve models significantly.

all_ratings["Datetime"] = pd.to_datetime(all_ratings['Datetime'], unit='s')

You can view the first few records by running the following in a new cell:

all_ratings.head()

The result will come out looking something like this: