list comprehension tutorial

list_comprehensions.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# list comprehensions\n",
    "\n",
    "*are awesome*\n",
    "\n",
    "Creating a list with a loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
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     "execution_count": 1,
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   "source": [
    "out_list = []\n",
    "\n",
    "for i in range(10):\n",
    "    out_list.append(i)\n",
    "    \n",
    "out_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a list comprehension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
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     "execution_count": 2,
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   "source": [
    "out_list = [i for i in range(10)]\n",
    "\n",
    "out_list"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can use conditionals within them\n",
    "\n",
    "I.e a list of even numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
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     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
      ]
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     "execution_count": 3,
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   "source": [
    "even_nums = []\n",
    "for i in range(20):\n",
    "    if i % 2 == 0:\n",
    "        even_nums.append(i)\n",
    "        \n",
    "even_nums"
   ]
  },
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   "execution_count": 4,
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       "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
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     "execution_count": 4,
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   "source": [
    "even_nums = [i for i in range(20) if i % 2 == 0]\n",
    "\n",
    "even_nums"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can use functions within them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "def is_prime(n):\n",
    "    if n < 2:\n",
    "        return False\n",
    "    if n == 2: \n",
    "        return True    \n",
    "    if not n & 1: \n",
    "        return False\n",
    "    for x in range(3, int(n**0.5) + 1, 2):\n",
    "        if n % x == 0:\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2,\n",
       " 3,\n",
       " 5,\n",
       " 7,\n",
       " 11,\n",
       " 13,\n",
       " 17,\n",
       " 19,\n",
       " 23,\n",
       " 29,\n",
       " 31,\n",
       " 37,\n",
       " 41,\n",
       " 43,\n",
       " 47,\n",
       " 53,\n",
       " 59,\n",
       " 61,\n",
       " 67,\n",
       " 71,\n",
       " 73,\n",
       " 79,\n",
       " 83,\n",
       " 89,\n",
       " 97]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "primes = [i for i in range(100) if is_prime(i)]\n",
    "\n",
    "primes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can use nested list comprehensions.\n",
    "\n",
    "E.g if we want to create a list of lists of all combinations of two lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['red', 'S'],\n",
       " ['red', 'M'],\n",
       " ['red', 'L'],\n",
       " ['blue', 'S'],\n",
       " ['blue', 'M'],\n",
       " ['blue', 'L']]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "colours = [\"red\", \"blue\"]\n",
    "sizes = [\"S\", \"M\", \"L\"]\n",
    "\n",
    "t_shirts = [[colour, size] for colour in colours for size in sizes]\n",
    "\n",
    "t_shirts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['red', 'S'],\n",
       " ['red', 'M'],\n",
       " ['red', 'L'],\n",
       " ['blue', 'S'],\n",
       " ['blue', 'M'],\n",
       " ['blue', 'L']]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "t_shirts = []\n",
    "\n",
    "for colour in colours:\n",
    "    for size in sizes:\n",
    "        t_shirts.append([colour, size])\n",
    "        \n",
    "t_shirts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As pointed out by Jon, list comprehensions can also be used to flatten a nested list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['red', 'S', 'red', 'M', 'red', 'L', 'blue', 'S', 'blue', 'M', 'blue', 'L']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat = [i for sublist in t_shirts for i in sublist]\n",
    "flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['red', 'S', 'red', 'M', 'red', 'L', 'blue', 'S', 'blue', 'M', 'blue', 'L']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as a loop\n",
    "flat = []\n",
    "for sublist in t_shirts:\n",
    "    for i in sublist:\n",
    "        flat.append(i)\n",
    "flat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dict comprehensions\n",
    "\n",
    "The same technique can also be used to create dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = [\"Tim\", \"Angela\", \"Bob\"]\n",
    "fav_food = [\"chips\", \"pizza\", \"wasabi?\"]\n",
    "\n",
    "my_dict = {name:food for name, food in zip(names, fav_food)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Angela': 'pizza', 'Bob': 'wasabi?', 'Tim': 'chips'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  }
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