the million song dataset taste profile the million song dataset is very popular dataset and is available at millionsongthe original dataset contained quantified audio features of around million songs ranging over multiple years the dataset was created as collaborative project between the echonest (using this dataset directlywe will be using some parts of it several other datasets were spawned from the original million song dataset one of those datasets was called the echonest taste profile subset this particular dataset was created by the echonest with some undisclosed partners the dataset contains play counts by anonymous users for songs contained in the million songs dataset the taste profile dataset is quite bigas it contains around million lines of triplets the triplets contain the following information(user idsong idplay countseach row gives the play counts of song identified by the song id for the user identified by the user id the overall dataset contains around million unique users and around , songs from the million song dataset are contained in it the readers can download the dataset from default/files/challenge/train_triplets txt zip the size of the compressed dataset is around mb andupon decompressionyou need space of around gb once you have the data downloaded and uncompressedyou will see how to subset the dataset to reduce its size #the million song dataset has several other useful auxiliary datasets we will not be covering them in detail herebut we encourage the reader to explore these datasets and use their imagination for developing innovative use cases exploratory data analysis exploratory data analysis is an important part of any data analysis workflow by this timewe have established this fact firmly in the mindset of our readers the exploratory data analysis becomes even more important in the cases of large datasetsas this will often lead us to information that we can use to trim down the dataset little as we will seesometimes we will also go beyond the traditional data access tools to bypass the problems posed by the large size of data loading and trimming data the first step in the process is loading the data from the uncompressed files as the data size is around gbwe will not load the complete data but we will only load specified number of rows from the dataset this can be achieved by using the nrows parameter of the read_csv function provided by pandas in [ ]triplet_dataset pd read_csv(filepath_or_buffer=data_home+'train_triplets txt'nrows= ,sep='\ 'header=nonenames=['user','song','play_count']