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    "# Chapter 5: Numbers & Bits (TL;DR)"
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    "There exist three numeric types in core Python:\n",
    "- `int`: a near-perfect model for whole numbers (i.e., $\\mathbb{Z}$); inherently precise\n",
    "- `float`: the \"gold\" standard to approximate real numbers (i.e., $\\mathbb{R}$); inherently imprecise\n",
    "- `complex`: layer on top of the `float` type to approximate complex numbers (i.e., $\\mathbb{C}$); inherently imprecise\n",
    "\n",
    "Furthermore, the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides two more types that can be used as substitutes for the `float` type:\n",
    "- `Decimal`: similar to `float` but allows customizing the precision; still inherently imprecise\n",
    "- `Fraction`: a near-perfect model for rational numbers (i.e., $\\mathbb{Q}$); built on top of the `int` type and therefore inherently precise\n",
    "\n",
    "The *important* takeaways for the data science practitioner are:\n",
    "\n",
    "1. **Do not mix** precise and imprecise data types, and\n",
    "2. actively expect `nan` results when working with `float` numbers as there are no **loud failures**.\n",
    "\n",
    "The **numerical tower** is Python's way of implementing various **abstract** ideas of what numbers are in mathematics."
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