Every dimension of data in a Series or DataFrame is labeled through an Index object. It is this Index that separates pandas data structures from NumPy's n-dimensional array. Indexes provide meaningful labels for each row and column of data, and pandas users have the ability to select data through the use of these labels. Additionally, pandas allows its users to select data by the integer location of the rows and columns. This dual selection capability, one using labels and the other using integer location, makes for powerful yet confusing syntax to select subsets of data.

Selecting data through the use of labels or integer location is not unique to pandas. Python dictionaries and lists are built-in data structures that select their data in exactly one of these ways. Both dictionaries and lists have precise instructions and limited use-cases for what may be passed to the indexing operator. A dictionary's key (its label) must be an immutable object, such as a string, integer, or tuple. Lists must either use integers or slice objects for selection. Dictionaries can only select one object at a time by passing the key to the indexing operator. In some sense, pandas is combining the ability to select data using integers, as with lists, and labels, as with dictionaries.