Demo20190303.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Basic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 5\n",
    "b = 3\n",
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python 數值運算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 + 2 * 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 3\n",
    "b = 2\n",
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a - b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = 1.5\n",
    "d = 2.5\n",
    "c + d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c - d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = '2'\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(int(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float('3.2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 10\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = a + a\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "price = 25.6\n",
    "exchange_rate = 32.33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntd_price = price * exchange_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "827.648"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ntd_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 字串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 'hello world'\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = \"hello world\"\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-25-255da4b258eb>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-25-255da4b258eb>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    s = 'Now I'm ready to use the single quotes inside a string!'\u001b[0m\n\u001b[1;37m               ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "s = 'Now I'm ready to use the single quotes inside a string!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Now I'm ready to use the single quotes inside a string!\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"Now I'm ready to use the single quotes inside a string!\"\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'haha'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'hello world'\n",
    "'haha'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n",
      "Ha ha\n"
     ]
    }
   ],
   "source": [
    "print('Hello World')\n",
    "print('Ha ha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is the first line \n",
      " this is the second line\n"
     ]
    }
   ],
   "source": [
    "print('this is the first line \\n this is the second line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 2 3\n",
      "100 200 300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('''\n",
    "1 2 3\n",
    "100 200 300\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1\t2\t3\n",
      "100\t200\t300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('''\n",
    "1\\t2\\t3\n",
    "100\\t200\\t300\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Now I'm ready to use the single quotes inside a string! I'm ready to use the single quotes inside a string! I'm ready to use the single quotes inside a string!\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"Now I'm ready to use the single quotes inside a string! \\\n",
    "I'm ready to use the single quotes inside a string! \\\n",
    "I'm ready to use the single quotes inside a string!\"\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now I'm ready to use the single quotes inside a string! \n",
      "I'm ready to use the single quotes inside a string! \n",
      "I'm ready to use the single quotes inside a string!\n"
     ]
    }
   ],
   "source": [
    "s = \"\"\"Now I'm ready to use the single quotes inside a string! \n",
    "I'm ready to use the single quotes inside a string! \n",
    "I'm ready to use the single quotes inside a string!\"\"\"\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'H'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'Hello'\n",
    "s[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'e'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'o'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[len(s)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'o'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'l'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ell'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ello'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hel'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ell'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for(i = 1; i < 4; i++)\n",
    "s[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ell'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for(i = 1; i < 4; i++)\n",
    "s[1:4:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'el'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for(i = 1; i < 4; i = i + 2)\n",
    "s[1:4:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'olleH'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for(i = len(s) - 1; i > 0; i = i - 1)\n",
    "s[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'上海自來水來自海上'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = '上海自來水來自海上'\n",
    "s[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s  == s[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello world'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'Hello '\n",
    "s  + 'world'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'capitalize', 'casefold', 'center', 'count', 'encode', 'endswith', 'expandtabs', 'find', 'format', 'format_map', 'index', 'isalnum', 'isalpha', 'isdecimal', 'isdigit', 'isidentifier', 'islower', 'isnumeric', 'isprintable', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'maketrans', 'partition', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']\n"
     ]
    }
   ],
   "source": [
    "print(dir(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello '"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HELLO '"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello '"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello', 'World']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'Hello World'\n",
    "s.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function split:\n",
      "\n",
      "split(...) method of builtins.str instance\n",
      "    S.split(sep=None, maxsplit=-1) -> list of strings\n",
      "    \n",
      "    Return a list of the words in S, using sep as the\n",
      "    delimiter string.  If maxsplit is given, at most maxsplit\n",
      "    splits are done. If sep is not specified or is None, any\n",
      "    whitespace string is a separator and empty strings are\n",
      "    removed from the result.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(s.split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "?s.split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'banana'.count('a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "name1 = 'marry'\n",
    "name2 = 'john'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['asap', 3]\n"
     ]
    }
   ],
   "source": [
    "print(['asap', 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['w', 'o', 'r', 'd']\n"
     ]
    }
   ],
   "source": [
    "print(list(\"word\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['list'], ['of', 'lists']]\n"
     ]
    }
   ],
   "source": [
    "print( [['list'], ['of', 'lists']] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 1]\n"
     ]
    }
   ],
   "source": [
    "print(list(('a', 1)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = ['heterogeneous', 3]\n",
    "a[1] + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "must be str, not int",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-4492ba6b8a7f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: must be str, not int"
     ]
    }
   ],
   "source": [
    "a[0] + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'heterogeneoushello'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0] + 'hello'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [5,6,7,'s']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 's']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['s', 7, 6, 5]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 7]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [5,6,7,8]\n",
    "a.pop()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.append(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 7, 2]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 5, 6, 7]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 6, 5, 2]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list('hello world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'e' in list('hello world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'a' in list('hello world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'marry'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 'marry'\n",
    "b = a\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b= a\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2000, 3]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b= a\n",
    "a[1] = 2000\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b = a.copy()\n",
    "a[1] = 2000\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 100, 'b': 'yes', 'c': 0.98}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic={'a':100, 'b':\"yes\", 'c':0.98}\n",
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['a', 'b', 'c'])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([100, 'yes', 0.98])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'d'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-2b1b63ca196d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdic\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'd'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: 'd'"
     ]
    }
   ],
   "source": [
    "dic['d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic.get('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic.get('d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'default'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic.get('d', 'default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic.get('a', 'default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 100, 'b': 'yes', 'c': 0.98}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 100, 'b': 'yes', 'c': 0.98, 'd': 'new'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic['d'] = 'new'\n",
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 100, 'b': 'yes', 'c': 0.98, 'd': 'old', 'e': 123}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic.update({'d':'old','e':123})\n",
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 100\n",
      "b yes\n",
      "c 0.98\n",
      "d old\n",
      "e 123\n"
     ]
    }
   ],
   "source": [
    "# for (int i = 0 ; i < 10; i++){\n",
    "#   System.our.println(i);\n",
    "#}\n",
    "for rec in dic:\n",
    "    print(rec, dic.get(rec)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "請說出你的需求:大大安安\n",
      "我現在還在學習, 等我變聰明以後就會告訴你答案\n"
     ]
    }
   ],
   "source": [
    "qa = {\n",
    "  '早安': '你也早安',\n",
    "  '晚安': '該睡了',\n",
    "  '再見': '掰掰'\n",
    "}\n",
    "\n",
    "question = input('請說出你的需求:')\n",
    "print(qa.get(question, '我現在還在學習, 等我變聰明以後就會告訴你答案' ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple1 = (1,2,3)\n",
    "tuple1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple2 = 1,2,3\n",
    "tuple2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple3 = tuple([1,2,3])\n",
    "tuple3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "list1 = [1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']\n"
     ]
    }
   ],
   "source": [
    "print(dir(list1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'count', 'index']\n"
     ]
    }
   ],
   "source": [
    "print(dir(tuple1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = ('one', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('one', 2)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'one'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1\n",
    "b = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "a , b = 1 , 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = (1 , 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = (1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c == d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 迴圈與控制流程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 is really greater than 3\n",
      "whatever i write, i will execute this\n"
     ]
    }
   ],
   "source": [
    "if 5 > 3:\n",
    "    print('5 is really greater than 3')\n",
    "print('whatever i write, i will execute this')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for qoo in range(1,10):\n",
    "    print(qoo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 was not in the list\n",
      "9 was not in the list\n",
      "9 was not in the list\n",
      "9 was not in the list\n",
      "9 was not in the list\n",
      "9 was not in the list\n",
      "9 was not in the list\n",
      "8 found\n",
      "9 found!\n"
     ]
    }
   ],
   "source": [
    "#if - else\n",
    "a = 9\n",
    "b = 8\n",
    "for i in range(1,10):\n",
    "    if i == a:\n",
    "        print(str(a) + ' found!')\n",
    "        break\n",
    "    elif i == b:\n",
    "        print(str(b) + ' found')\n",
    "    else:\n",
    "        print(str(a) + ' was not in the list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,10):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n",
      "7\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,10,2):\n",
    "    print(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "a = [1,2,3,4,5]\n",
    "for i in a:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5050"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 0\n",
    "for i in range(1,101):\n",
    "    s = s+i\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5050\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "s = 0\n",
    "\n",
    "while(i < 101):\n",
    "    s += i\n",
    "    i +=1\n",
    "    \n",
    "print(s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-87-934d64851863>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mwhile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m101\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0ms\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "s = 0\n",
    "\n",
    "while(i < 101):\n",
    "    s += i\n",
    "    \n",
    "print(s)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 函式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def say_hello():\n",
    "    print('hello world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "say_hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def addNum(a,b):\n",
    "    return a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addNum(3,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HelloWorld'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addNum('Hello', 'World')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def addall(x, *args):\n",
    "    res = x\n",
    "    for y in args:\n",
    "        res += y\n",
    "    return(res)\n",
    "\n",
    "addall(1,2,3,4,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'david': 'M', 'Mary': 'F', 'John': 'M'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['david', 'Mary', 'John'], ['M', 'F', 'M']]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_two_lists(**kwargs):\n",
    "    keys,values = [],[]\n",
    "    print(kwargs)\n",
    "    \n",
    "    for k,v in kwargs.items():\n",
    "        keys.append(k)\n",
    "        values.append(v)\n",
    "    return([keys,values])\n",
    "\n",
    "make_two_lists(david='M', Mary = 'F', John='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('david', 'M'), ('Mary', 'F'), ('John', 'M')])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {'david': 'M', 'Mary': 'F', 'John': 'M'}\n",
    "dic.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k,v in dic.items():\n",
    "    print(k, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "qa = {\n",
    "  '早安': '你也早安',\n",
    "  '早上好': '你也早安',\n",
    "  '晚安': '該睡了',\n",
    "  '再見': '掰掰'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "x = 25\n",
    "\n",
    "def printer():\n",
    "    x = 50\n",
    "    return(x)\n",
    "\n",
    "print(x)\n",
    "\n",
    "print(printer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def square(x):\n",
    "    return x ** 2\n",
    "\n",
    "square(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "square = lambda x: x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = lambda x:x**2 # x is local\n",
    "\n",
    "name = 'This is a global name'\n",
    "\n",
    "def greet():\n",
    "    # Enclosing function\n",
    "    name = 'Sammy' # Enclosing function locals\n",
    "    def hello():\n",
    "        print('Hello '+name )\n",
    "    hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Sammy\n"
     ]
    }
   ],
   "source": [
    "greet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a global name\n"
     ]
    }
   ],
   "source": [
    "print(name) # Global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function len(obj, /)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len # Built-in function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is 50\n",
      "Changed local x to 2\n",
      "x is still 50\n"
     ]
    }
   ],
   "source": [
    "x = 50\n",
    "\n",
    "def func(x):\n",
    "    print('x is', x)\n",
    "    x = 2\n",
    "    print('Changed local x to', x )\n",
    "    \n",
    "func(x)\n",
    "print('x is still', x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This function is now using the global x!\n",
      "Because of global x is:  50\n",
      "Ran func(), changed global x to 2\n",
      "Before calling func(), x is:  2\n",
      "Value of x (outside of func()) is:  2\n"
     ]
    }
   ],
   "source": [
    "x = 50\n",
    "\n",
    "def func():\n",
    "    global x\n",
    "    print('This function is now using the global x!' )\n",
    "    print('Because of global x is: ', x)\n",
    "    x = 2\n",
    "    print('Ran func(), changed global x to', x )\n",
    "    print('Before calling func(), x is: ', x)\n",
    "    \n",
    "func()\n",
    "print('Value of x (outside of func()) is: ', x )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 物件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1680305216"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'list'>\n",
      "<class 'tuple'>\n",
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "print(type(1))\n",
    "print(type([]))\n",
    "print(type(()))\n",
    "print(type({}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1\n",
    "b = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1680305216"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1680305216"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89687944 89688072\n"
     ]
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b = [1,2,3]\n",
    "print(id(a), id(b))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1680305216 1680305216\n"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "b = 1\n",
    "print(id(a), id(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.Sample'>\n"
     ]
    }
   ],
   "source": [
    "class Sample(object):\n",
    "    pass\n",
    "\n",
    "x = Sample() # new instance\n",
    "print(type(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Huskie'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Dog(object):\n",
    "    def __init__(self,breed):\n",
    "        self.breed = breed\n",
    "\n",
    "sam   = Dog(breed='Lab')\n",
    "\n",
    "frank = Dog(breed='Huskie')\n",
    "\n",
    "sam.breed\n",
    "frank.breed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sam'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Dog(object):\n",
    "    species = 'mammal' # 類別屬性\n",
    "    def __init__(self,breed,name):\n",
    "        self.breed = breed\n",
    "        self.name = name\n",
    "        \n",
    "sam = Dog('Lab','Sam')\n",
    "sam.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mammal'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sam.species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mammal'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dog.species # 類別屬性是公開的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Circle(object):\n",
    "    pi = 3.14\n",
    "    def __init__(self, radius=1):\n",
    "        self.radius = radius\n",
    "        \n",
    "    def area(self):\n",
    "        return self.radius * self.radius * Circle.pi\n",
    "    \n",
    "    def setRadius(self, radius):\n",
    "        self.radius = radius\n",
    "        \n",
    "    def getRadius(self):\n",
    "        return self.radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.14"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Circle()\n",
    "c.area()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.setRadius(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.56"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.area()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 規則模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function sklearn.datasets.base.load_iris(return_X_y=False)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.datasets.load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DESCR', 'data', 'feature_names', 'target', 'target_names']\n"
     ]
    }
   ],
   "source": [
    "print(dir(iris))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "12\n",
      "30\n",
      "56\n",
      "90\n"
     ]
    }
   ],
   "source": [
    "a = [1, 3, 5, 7, 9]\n",
    "b = [2, 4, 6, 8, 10]\n",
    "for i in range(0, len(a)):\n",
    "    print(a[i] * b[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "12\n",
      "30\n",
      "56\n",
      "90\n"
     ]
    }
   ],
   "source": [
    "for i, j in zip(a,b):\n",
    "    print(i * j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1, 3, 5, 7, 9]\n",
    "b = [2, 4, 6, 8, 10]\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "na = numpy.array(a)\n",
    "nb = numpy.array(b)\n",
    "type(na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 7, 9]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2, 12, 30, 56, 90])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na * nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "na = numpy.array([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False,  True,  True])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na > 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na[na > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "na = numpy.array([[1,2,3,4,5],[6,7,8,9,10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 6])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  6],\n",
       "       [ 2,  7],\n",
       "       [ 3,  8],\n",
       "       [ 4,  9],\n",
       "       [ 5, 10]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "clf = tree.DecisionTreeClassifier(max_depth=2)\n",
    "clf.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,\n",
       "       2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = clf.predict(iris.data)\n",
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True, False,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True, False,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True, False,  True,  True,  True, False, False,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target == predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "tree.export_graphviz(clf, out_file='tree.dot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn. datasets import load_iris\n",
    "iris = load_iris()\n",
    "X = iris.data[:, [2, 3]]\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "model = tree.DecisionTreeClassifier(max_depth=2)\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot()\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, alpha=1, cmap = plt.cm.RdYlBu)\n",
    "plt.title('Decision Tree')\n",
    "plt.xlabel('Petal.Length')\n",
    "plt.ylabel('Petal.Width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),np.arange(y_min, y_max, 0.1))\n",
    "Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot()\n",
    "plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.rainbow)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, alpha=1, cmap = plt.cm.RdYlBu)\n",
    "plt.title('Decision Tree')\n",
    "plt.xlabel('Petal.Length')\n",
    "plt.ylabel('Petal.Width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 邏輯式迴歸"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = model.predict(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(predicted == iris.target) / len(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "model2 = tree.DecisionTreeClassifier(max_depth=2)\n",
    "model2.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted2 = model2.predict(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(predicted2 == iris.target) / len(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "\n",
    "X = iris.data[:, [2, 3]]\n",
    "y = iris.target\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "plt.plot()\n",
    "plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.RdYlBu)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap = plt.cm.brg)\n",
    "plt.title('Logistic Regression')\n",
    "plt.xlabel('Petal.Length')\n",
    "plt.ylabel('Petal.Width')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 線性迴歸\n",
    "- https://github.com/ywchiu/tibamepy/blob/master/Demo20181013.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas\n",
    "df = pandas.read_csv('https://raw.githubusercontent.com/ywchiu/tibamepy/master/data/salary.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% pylab inline\n",
    "from matplotlib import pyplot as plt\n",
    "X = df[['year']]\n",
    "Y = df['salary'].values\n",
    "\n",
    "plt.scatter(X, Y, color='black')\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regr = LinearRegression()\n",
    "\n",
    "regr.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([712.59413615])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5049.009899813836"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color='black')\n",
    "plt.plot(X, regr.predict(X), color='blue', linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}