02-array-seq/00_chapter02_My_file.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import abc\n",
    "\n",
    "issubclass(tuple, abc.Sequence)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issubclass(list, abc.MutableSequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[36, 162, 163, 165, 8364, 164]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols = '$¢£¥€¤'\n",
    "codes = []\n",
    "\n",
    "for symbol in symbols:\n",
    "    codes.append(ord(symbol))\n",
    "\n",
    "codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[36, 162, 163, 165, 8364, 164]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codes = [ord(symbol) for symbol in symbols]\n",
    "codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[162, 163, 165, 8364, 164]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ">>> symbols = '$¢£¥€¤'\n",
    ">>> beyond_ascii = [ord(s) for s in symbols if ord(s) > 127]\n",
    ">>> beyond_ascii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "beyond_ascii = list(filter(lambda c: c > 127, map(ord, symbols)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[162, 163, 165, 8364, 164]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beyond_ascii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('black', 'S'),\n",
       " ('black', 'M'),\n",
       " ('black', 'L'),\n",
       " ('white', 'S'),\n",
       " ('white', 'M'),\n",
       " ('white', 'L')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors = ['black', 'white']\n",
    "sizes = ['S', 'M', 'L']\n",
    "\n",
    "tshirts = [(color , size) for color in colors for size in sizes]\n",
    "\n",
    "tshirts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 162, 163, 165, 8364, 164)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols = '$¢£¥€¤'\n",
    "tuple(ord(symbol) for symbol in symbols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('I', [36, 162, 163, 165, 8364, 164])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import array\n",
    "array.array('I', (ord(symbol) for symbol in symbols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "black S\n",
      "black M\n",
      "black L\n",
      "white S\n",
      "white M\n",
      "white L\n"
     ]
    }
   ],
   "source": [
    "colors = ['black', 'white']\n",
    "sizes = ['S', 'M', 'L']\n",
    "\n",
    "for tshirt in (f'{c} {s}' for c in colors for s in sizes):\n",
    "    print(tshirt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fixed(o):\n",
    "    try:\n",
    "        hash(o)\n",
    "    except TypeError:\n",
    "        return False\n",
    "    return True\n",
    "\n",
    "tf = (10, 'alpha', (1, 2))\n",
    "tm = (10, 'alpha', [1, 2])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fixed(tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fixed(tm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1, [2, 3, 4])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a, b, *rest = range(5)\n",
    "a, b, rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1, 2, 3, 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "*range(4), 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1, 2, 3, 4, 5, 6, 7}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{*range(4), 4, *(5, 6, 7)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                |  latitude | longitude\n",
      "Mexico City     |   19.4333 |  -99.1333\n",
      "New York-Newark |   40.8086 |  -74.0204\n",
      "São Paulo       |  -23.5478 |  -46.6358\n"
     ]
    }
   ],
   "source": [
    "metro_areas = [\n",
    "    ('Tokyo', 'JP', 36.933, (35.689722, 139.691667)),\n",
    "    ('Delhi NCR', 'IN', 21.935, (28.613889, 77.208889)),\n",
    "    ('Mexico City', 'MX', 20.142, (19.433333, -99.133333)),\n",
    "    ('New York-Newark', 'US', 20.104, (40.808611, -74.020386)),\n",
    "    ('São Paulo', 'BR', 19.649, (-23.547778, -46.635833)),\n",
    "]\n",
    "\n",
    "def main():\n",
    "    print(f'{\"\":15} | {\"latitude\":>9} | {\"longitude\":>9}')\n",
    "    for x1, x2, _, (lat, lon) in metro_areas:\n",
    "        if lon <= 0:\n",
    "            print(f'{x1:15} | {lat:9.4f} | {lon:9.4f}')\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_command(self, message):\n",
    "    match message:\n",
    "        case ['BEEPER', frequency, times]:\n",
    "            self.beep(times, frequency)\n",
    "        case ['NECK', angle]:\n",
    "            self.rotate_neck(angle)\n",
    "        case ['LED', ident, intensity]:\n",
    "            self.leds[ident].set_brightness(ident, intensity)\n",
    "        case ['LED', ident, red, green, blue]:\n",
    "            self.leds[ident].set_color(ident, red, green, blue)\n",
    "        case _:\n",
    "            raise InvalidCommand(message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                |  latitude | longitude\n",
      "Mexico City     |   19.4333 |  -99.1333\n",
      "New York-Newark |   40.8086 |  -74.0204\n",
      "São Paulo       |  -23.5478 |  -46.6358\n"
     ]
    }
   ],
   "source": [
    "def main1():\n",
    "    print(f'{\"\":15} | {\"latitude\":>9} | {\"longitude\":>9}')\n",
    "    for record in metro_areas:\n",
    "        match record:\n",
    "            case [name, _, _, (lat, lon)] if lon <= 0:\n",
    "                print(f'{name:15} | {lat:9.4f} | {lon:9.4f}')\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "collections.abc.Sequence"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import collections\n",
    "collections.abc.Sequence\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1           0 LOAD_NAME                0 (s)\n",
      "              2 LOAD_NAME                1 (a)\n",
      "              4 DUP_TOP_TWO\n",
      "              6 BINARY_SUBSCR\n",
      "              8 LOAD_NAME                2 (b)\n",
      "             10 INPLACE_ADD\n",
      "             12 ROT_THREE\n",
      "             14 STORE_SUBSCR\n",
      "             16 LOAD_CONST               0 (None)\n",
      "             18 RETURN_VALUE\n"
     ]
    }
   ],
   "source": [
    "import dis\n",
    "dis.dis('s[a] += b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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