2.tour/05-Built-in-Scalar-Types.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "# Built-In Types: Simple Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"fig/cover-small.jpg\">\n",
    "*This notebook contains an excerpt from the [Whirlwind Tour of Python](http://www.oreilly.com/programming/free/a-whirlwind-tour-of-python.csp) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/WhirlwindTourOfPython).*\n",
    "\n",
    "*The text and code are released under the [CC0](https://github.com/jakevdp/WhirlwindTourOfPython/blob/master/LICENSE) license; see also the companion project, the [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "<!--NAVIGATION-->\n",
    "< [Basic Python Semantics: Operators](04-Semantics-Operators.ipynb) | [Contents](Index.ipynb) | [Built-In Data Structures](06-Built-in-Data-Structures.ipynb) >\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "When discussing Python variables and objects, \n",
    "- we mentioned the fact that all Python objects have type information attached. \n",
    "\n",
    "Here we'll briefly walk through the **built-in simple types** offered by Python.\n",
    "- We say \"simple types\" to contrast with several compound types, \n",
    "    - which will be discussed in the following section.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "source": [
    "\n",
    "Python's simple types are summarized in the following table:\n",
    "\n",
    "<center>**Python Scalar Types**</center>\n",
    "\n",
    "| Type        | Example        | Description                                                  |\n",
    "|-------------|----------------|--------------------------------------------------------------|\n",
    "| ``int``     | ``x = 1``      | integers (i.e., whole numbers)                               |\n",
    "| ``float``   | ``x = 1.0``    | floating-point numbers (i.e., real numbers)                  |\n",
    "| ``complex`` | ``x = 1 + 2j`` | Complex numbers (i.e., numbers with real and imaginary part) |\n",
    "| ``bool``    | ``x = True``   | Boolean: True/False values                                   |\n",
    "| ``str``     | ``x = 'abc'``  | String: characters or text                                   |\n",
    "| ``NoneType``| ``x = None``   | Special object indicating nulls                              |\n",
    "\n",
    "We'll take a quick look at each of these in turn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Integers\n",
    "The most basic numerical type is the integer.\n",
    "- Any number without a decimal point is an integer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 1\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Python integers are actually quite a bit more sophisticated than integers in languages like ``C``.\n",
    "- C integers are fixed-precision, and usually overflow at some value (often near $2^{31}$ or $2^{63}$, depending on your system).\n",
    "- Python integers are **variable-precision**, so you can do computations that would overflow in other languages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1606938044258990275541962092341162602522202993782792835301376"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Another convenient feature of Python integers is that by default, \n",
    "\n",
    "**division up-casts to floating-point type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 / 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Note that this upcasting is a feature of Python 3; in Python 2, like in many statically-typed languages such as C, integer division truncates any decimal and always returns an integer:\n",
    "``` python\n",
    "# Python 2 behavior\n",
    ">>> 5 / 2\n",
    "2\n",
    "```\n",
    "To recover this behavior in Python 3, you can use the floor-division operator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 // 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Finally, note that although Python *2.x* had both an ``int`` and ``long`` type, Python 3 combines the behavior of these two into a single ``int`` type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Floating-Point Numbers\n",
    "The floating-point type can store fractional numbers.\n",
    "- They can be defined either in standard decimal notation, or in exponential notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "x = 0.000005\n",
    "y = 5e-6\n",
    "print(x == y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "x = 1400000.00\n",
    "y = 1.4e6\n",
    "print(x == y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In the exponential notation, the ``e`` or ``E`` can be read \"...times ten to the...\",\n",
    "so that ``1.4e6`` is interpreted as $~1.4 \\times 10^6$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "An integer can be explicitly converted to a float with the ``float`` constructor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Aside: Floating-point precision\n",
    "One thing to be aware of with floating point arithmetic is that its ``precision is limited``, \n",
    "- which can cause equality tests to be unstable. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-10T07:26:56.421345Z",
     "start_time": "2018-10-10T07:26:56.416640Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.1,\n",
       " 0.2,\n",
       " 0.30000000000000004,\n",
       " 0.4,\n",
       " 0.5,\n",
       " 0.6000000000000001,\n",
       " 0.7000000000000001,\n",
       " 0.8,\n",
       " 0.9]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for i in range(10):\n",
    "#     print(i)\n",
    "[i*0.1 for i in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-10T07:26:30.976524Z",
     "start_time": "2018-10-10T07:26:30.972231Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30000000000000004"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.1 + 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.1 + 0.2 == 0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Why is this the case? \n",
    "- It turns out that it is not a behavior unique to Python, but is due to the fixed-precision format of the binary floating-point storage used by most, if not all, scientific computing platforms.\n",
    "- All programming languages using floating-point numbers store them in a fixed number of bits, and this leads some numbers to be represented only approximately.\n",
    "\n",
    "We can see this by printing the three values to high precision:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1 = 0.10000000000000001\n",
      "0.2 = 0.20000000000000001\n",
      "0.3 = 0.29999999999999999\n"
     ]
    }
   ],
   "source": [
    "print(\"0.1 = {0:.17f}\".format(0.1))\n",
    "print(\"0.2 = {0:.17f}\".format(0.2))\n",
    "print(\"0.3 = {0:.17f}\".format(0.3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We're accustomed to thinking of numbers in decimal (base-10) notation, so that each fraction must be expressed as a sum of powers of 10:\n",
    "$$\n",
    "1 /8 = 1\\cdot 10^{-1} + 2\\cdot 10^{-2} + 5\\cdot 10^{-3}\n",
    "$$\n",
    "In the familiar base-10 representation, we represent this in the familiar decimal expression: $0.125$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Computers usually store values in binary notation, so that each number is expressed as a sum of powers of 2:\n",
    "$$\n",
    "1/8 = 0\\cdot 2^{-1} + 0\\cdot 2^{-2} + 1\\cdot 2^{-3}\n",
    "$$\n",
    "In a base-2 representation, we can write this $0.001_2$, where the subscript 2 indicates binary notation.\n",
    "The value $0.125 = 0.001_2$ happens to be one number which both binary and decimal notation can represent in a finite number of digits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In the familiar base-10 representation of numbers, you are probably familiar with numbers that can't be expressed in a finite number of digits.\n",
    "For example, dividing $1$ by $3$ gives, in standard decimal notation:\n",
    "$$\n",
    "1 / 3 = 0.333333333\\cdots\n",
    "$$\n",
    "The 3s go on forever: that is, to truly represent this quotient, the number of required digits is infinite!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Similarly, there are numbers for which binary representations require an infinite number of digits.\n",
    "For example:\n",
    "$$\n",
    "1 / 10 = 0.00011001100110011\\cdots_2\n",
    "$$\n",
    "Just as decimal notation requires an infinite number of digits to perfectly represent $1/3$, binary notation requires an infinite number of digits to represent $1/10$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### **Python internally truncates these representations at 52 bits beyond the first nonzero bit on most systems**.\n",
    "\n",
    "This rounding error for floating-point values is a necessary evil of working with floating-point numbers.\n",
    "- The best way to deal with it is to always keep in mind that floating-point arithmetic is approximate, and \n",
    "- *never* rely on exact equality tests with floating-point values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Complex Numbers\n",
    "Complex numbers are numbers with real and imaginary (floating-point) parts.\n",
    "\n",
    "We've seen integers and real numbers before; we can use these to construct a complex number:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1+2j)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complex(1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Alternatively, we can use the \"``j``\" suffix in expressions to indicate the imaginary part:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1+2j)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 2j"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Complex numbers have a variety of interesting attributes and methods, which we'll briefly demonstrate here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "c = 3 + 4j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.real  # real part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.imag  # imaginary part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3-4j)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.conjugate()  # complex conjugate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(c)  # magnitude, i.e. sqrt(c.real ** 2 + c.imag ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## String Type\n",
    "Strings in Python are created with single or double quotes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "message = \"what do you like?\"\n",
    "response = 'spam'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Python has many extremely useful string functions and methods; here are a few of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# length of string\n",
    "len(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SPAM'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make upper-case. See also str.lower()\n",
    "response.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'What do you like?'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Capitalize. See also str.title()\n",
    "message.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'what do you like? spam'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenation with +\n",
    "message + ' ' + response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'spamspamspamspamspamspamspamspamspamspam'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiplication is multiple concatenation\n",
    "response * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'w'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access individual characters (zero-based indexing)\n",
    "message[0]\n",
    "#message[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "source": [
    "For more discussion of indexing in Python, see [\"Lists\"](06-Built-in-Data-Structures.ipynb#Lists)."
   ]
  },
  {
   "cell_type": "markdown",
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     "slide_type": "slide"
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   },
   "source": [
    "## None Type\n",
    "Python includes a special type, the ``NoneType``, which has only a single possible value: ``None``. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(None)"
   ]
  },
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     "slide_type": "subslide"
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   "source": [
    "You'll see ``None`` used in many places, but perhaps most commonly it is used as the default return value of a function.\n",
    "For example, the ``print()`` function in Python 3 does not return anything, but we can still catch its value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abc\n"
     ]
    }
   ],
   "source": [
    "return_value = print('abc')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(return_value)"
   ]
  },
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     "slide_type": "subslide"
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   },
   "source": [
    "Likewise, any function in Python with no return value is, in reality, returning ``None``."
   ]
  },
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   "source": [
    "## Boolean Type\n",
    "The Boolean type is a simple type with two possible values: ``True`` and ``False``, and is returned by comparison operators discussed previously:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
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     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = (4 < 5)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bool"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(result)"
   ]
  },
  {
   "cell_type": "markdown",
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     "slide_type": "subslide"
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   "source": [
    "Keep in mind that the Boolean values are case-sensitive: unlike some other languages, ``True`` and ``False`` must be capitalized!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True False\n"
     ]
    }
   ],
   "source": [
    "print(True, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Booleans can also be constructed using the ``bool()`` object constructor: values of any other type can be converted to Boolean via predictable rules.\n",
    "For example, any numeric type is False if equal to zero, and True otherwise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(0)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(3.1415)"
   ]
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   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
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   "source": [
    "The Boolean conversion of ``None`` is always False:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(None)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
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   "source": [
    "For strings, ``bool(s)`` is False for empty strings and True otherwise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
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     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
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     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "bool(\"abc\")"
   ]
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   "cell_type": "markdown",
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     "slide_type": "subslide"
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   "source": [
    "For sequences, which we'll see in the next section, the Boolean representation is False for empty sequences and True for any other sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
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     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool([1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
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   "source": [
    "<!--NAVIGATION-->\n",
    "< [Basic Python Semantics: Operators](04-Semantics-Operators.ipynb) | [Contents](Index.ipynb) | [Built-In Data Structures](06-Built-in-Data-Structures.ipynb) >"
   ]
  }
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