주말 오후반 - E01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AskDjango\n",
    "## 2017년 7월 오프라인 강의\n",
    "\n",
    "+ 평일 오후반\n",
    "+ 평일 저녁반\n",
    "+ 주말 오후반"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19950631168807583848837421626835850838234968318861924548520089498529438830221946631919961684036194597899331129423209124271556491349413781117593785932096323957855730046793794526765246551266059895520550086918193311542508608460618104685509074866089624888090489894838009253941633257850621568309473902556912388065225096643874441046759871626985453222868538161694315775629640762836880760732228535091641476183956381458969463899410840960536267821064621427333394036525565649530603142680234969400335934316651459297773279665775606172582031407994198179607378245683762280037302885487251900834464581454650557929601414833921615734588139257095379769119277800826957735674444123062018757836325502728323789270710373802866393031428133241401624195671690574061419654342324638801248856147305207431992259611796250130992860241708340807605932320161268492288496255841312844061536738951487114256315111089745514203313820202931640957596464756010405845841566072044962867016515061920631004186422275908670900574606417856951911456055068251250406007519842261898059237118054444788072906395242548339221982707404473162376760846613033778706039803413197133493654622700563169937455508241780972810983291314403571877524768509857276937926433221599399876886660808368837838027643282775172273657572744784112294389733810861607423253291974813120197604178281965697475898164531258434135959862784130128185406283476649088690521047580882615823961985770122407044330583075869039319604603404973156583208672105913300903752823415539745394397715257455290510212310947321610753474825740775273986348298498340756937955646638621874569499279016572103701364433135817214311791398222983845847334440270964182851005072927748364550578634501100852987812389473928699540834346158807043959118985815145779177143619698728131459483783202081474982171858011389071228250905826817436220577475921417653715687725614904582904992461028630081535583308130101987675856234343538955409175623400844887526162643568648833519463720377293240094456246923254350400678027273837755376406726898636241037491410966718557050759098100246789880178271925953381282421954028302759408448955014676668389697996886241636313376393903373455801407636741877711055384225739499110186468219696581651485130494222369947714763069155468217682876200362777257723781365331611196811280792669481887201298643660768551639860534602297871557517947385246369446923087894265948217008051120322365496288169035739121368338393591756418733850510970271613915439590991598154654417336311656936031122249937969999226781732358023111862644575299135758175008199839236284615249881088960232244362173771618086357015468484058622329792853875623486556440536962622018963571028812361567512543338303270029097668650568557157505516727518899194129711337690149916181315171544007728650573189557450920330185304847113818315407324053319038462084036421763703911550639789000742853672196280903477974533320468368795868580237952218629120080742819551317948157624448298518461509704888027274721574688131594750409732115080498190455803416826949787141316063210686391511681774304792596709376"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rBs9e3YcfJbVXuwURiZiCvwrJ2LiX+1IWk569l4t7tOKJK3vRqrGaqolI2Sj4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1074d14e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot([1, 2, 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4a8fa2ea48646c08c4abb6e81a36e7c"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact\n",
    "def f(n):\n",
    "    plt.plot([0, 1, 2], [0, 1, n])\n",
    "    plt.show()\n",
    "interact(f, n=(0, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Glp53/DYhhK0QIhDoBOzSL8S6OXymkNk/RtOznQvv3BzaoBlCdWFlacGcm0Mp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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10900c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "dataframe = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv\")\n",
    "dataframe.plot()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 파이썬 기초 문법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "message = \"Hello, Python\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, Python'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "birth = 1990"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age = 2017 - birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "message1 = 'Hello World'\n",
    "message2 = \"Hello World\"\n",
    "message3 = '''Hello World'''\n",
    "message4 = \"\"\"Hello World\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello \"Good\" World'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Hello \\\"Good\\\" World\"\n",
    "'Hello \"Good\" World'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "가사 = '''비도 오고 그래서\n",
    "네 생각이 났어\n",
    "생각이 나서 그래서\n",
    "그랬던거지'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "비도 오고 그래서\n",
      "네 생각이 났어\n",
      "생각이 나서 그래서\n",
      "그랬던거지\n"
     ]
    }
   ],
   "source": [
    "print(가사)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "나는 Tom이야. 10살이지.\n",
      "나는 Tom이야. 10살이지.\n",
      "나는 Tom이야. 10살이지. 내 이름 Tom을 기억해줘.\n",
      "나는 Tom이야. 10살이지. 내 이름 Tom을 기억해줘.\n"
     ]
    }
   ],
   "source": [
    "name = \"Tom\"\n",
    "age = 10\n",
    "\n",
    "print(\"나는 %s이야. %d살이지.\" % (name, age))\n",
    "print(\"나는 {}이야. {}살이지.\".format(name, age))\n",
    "print(\"나는 {0}이야. {1}살이지. 내 이름 {0}을 기억해줘.\".format(name, age))\n",
    "\n",
    "print(\"나는 {name}이야. {age}살이지. 내 이름 {name}을 기억해줘.\".format(name=name, age=age))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 기본 자료구조"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Tom' in ['Tom', 'Steve', 'John']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Tom' in ['tom', 'Steve', 'John']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Tom' in ['Tomi', 'Steve', 'John']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Tom' in 'Hello Tom'\n",
    "# 'Hello Tom'.contains('Tom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 'Tom', 1.1]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1, 'Tom', 1.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = ['Tom', 'Johne', 'Kane', 'Steve']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Kane'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# names[len(names)-2]\n",
    "names[-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 튜플"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1,)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytuple = (1,)\n",
    "mytuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "for i in [1,]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for i in {1, 3, 4, 5, 1, 4, 3, 1}:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "4\n",
      "3\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# 정렬이 필요하다면\n",
    "for i in sorted({1, 3, 4, 5, 1, 4, 3, 1}, reverse=True):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n",
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "# 중복을 제거한 리스트\n",
    "list_numbers = [1, 1, 2, 1, 1, 2, 2, 3, 1, 2, 3]\n",
    "result = []\n",
    "for i in list_numbers:\n",
    "    if i not in result:\n",
    "        result.append(i)\n",
    "print(result)\n",
    "\n",
    "print(list(set(list_numbers)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 슬라이싱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'abcdefghijklmnopqrstuvwxyz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bcdefghijklmnopqrstuvwxyz'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bcdefghijklmnopqrstuvw'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bdfhjlnprtv'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:-3:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'zyxwvutsrqponmlkjihgfedcba'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0:-1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcdefghij'"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:10]  # 1페이지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'klmnopqrst'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[10:20]  # page 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'uvwxyz'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[20:30] # page 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydict = {\n",
    "    'a': 1,\n",
    "    'b': 2,\n",
    "    #'c': 3,\n",
    "    #'d': 4,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mysum(x, y):\n",
    "    '2개 인자를 받아서, 더한 값을 리턴합니다.'\n",
    "    return x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mysum(1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2개 인자를 받아서, 더한 값을 리턴합니다.'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mysum.__doc__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mysum?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
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