Python gives you some powerful, highly optimized data structures, both as built-ins and as part of a few modules in the standard library (lists and dicts, of course, but also tuples, sets, arrays in module array, and some other containers in module collections).

Combinations of these data structures (and maybe some of the functions from helper modules such as heapq and bisect) are generally sufficient to implement most richer structures that may be needed in real-life programming; however, that's not invariably the case.

When you need something more than the rich library provides, consider the fact that an object's attributes (and items in collections) are essentially "pointers" to other objects (without pointer arithmetic), i.e., "reseatable references", in Python just like in Java. In Python, you normally use a None value in an attribute or item to represent what NULL would mean in C++ or null would mean in Java.

So, for example, you could implement binary trees via, e.g.:

class Node(object):

  __slots__ = 'payload', 'left', 'right'

  def __init__(self, payload=None, left=None, right=None):
    self.payload = payload
    self.left = left
    self.right = right

plus methods or functions for traversal and similar operations (the __slots__ class attribute is optional -- mostly a memory optimization, to avoid each Node instance carrying its own __dict__, which would be substantially larger than the three needed attributes/references).

Other examples of data structures that may best be represented by dedicated Python classes, rather than by direct composition of other existing Python structures, include tries (see e.g. here) and graphs (see e.g. here).