Introduction

Every dimension of data in a Series or DataFrame is labeled in the 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 can select data through the use of these labels. Additionally, pandas allows its users to select data according to the position of the rows and columns. This dual selection capability, one using names and the other using the position, makes for powerful yet confusing syntax to select subsets of data.

Selecting data by label or position 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 you can index with. A dictionary's key (its label) must be an immutable object, such as a string, integer, or tuple. Lists must either use integers...