What do the different actions of the OpenAI gym's environment of 'Pong-v0' represent? Printing action_space for Pong-v0 gives Discrete(6) as output, i.e. $0, 1, 2, 3, 4, 5$ are actions defined in the environment as per the documentation. However, the game needs only 2 controls. Why do we have this discrepancy? Further, is that necessary to identify which number from 0 to 5 corresponds to which action in a gym environment?
You can try the actions yourselves, but if you want another reference, check out the documentation for ALE at GitHub.

In particular, 0 means no action, 1 means fire, which is why they don't have an effect on the racket.

Here's a better way:

env.unwrapped.get_action_meanings()