As music streaming services dominate the music industry, the playlist is becoming an increasingly crucial element of music consumption. Consequently, the music recommendation problem is often casted as a playlist generation problem. Better understanding of the playlists is therefore necessary for developing better playlist generation algorithms. In this work, we analyse two playlist datasets to investigate some commonly assumed hypotheses about playlists. Our findings indicate that deeper understanding of playlists is needed to provide better prior information and improve machine learning algorithms in the design of recommendation systems.