{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# jupyter notebook TIP\n",
    "#### [jupyter_dashboards](https://pypi.python.org/pypi/jupyter_dashboards/0.7.0) : 코드를 제외한 결과물만 보여주는 확장 프로그램\n",
    "#### [jupyterlab](https://pypi.python.org/pypi/jupyterlab/0.22.0) : jupyterlab. notebook 이후는 jupyterlab일듯..! [관련 영상](https://www.youtube.com/watch?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6&time_continue=16&v=Ejh0ftSjk6g)\n",
    "#### [jupyterhub](https://pypi.python.org/pypi/jupyterhub/0.7.2) : jupyterhub. 다양한 사람들이 함께 사용할 때 사용 [관령 링크](https://ansuchan.com/gettings-started-with-jupyterhub/)\n",
    "\n",
    "\n",
    "[기타 Tip 링크](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/)\n",
    "\n",
    "[기타 Tip 링크2](https://dymaxionkim.github.io/beautiful-jekyll/2017-01-23-Jupyter/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### %store\n",
    "- 다른 노트북간에 변수 저장\n",
    "%store data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pdb (python debug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T01:37:07.238590Z",
     "start_time": "2017-09-24T01:36:55.368945Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatic pdb calling has been turned ON\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'numpy' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-0f6b26649b2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mpick_and_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-1-0f6b26649b2e>\u001b[0m in \u001b[0;36mpick_and_take\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpick_and_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mpicked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'numpy' is not defined"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m<ipython-input-1-0f6b26649b2e>\u001b[0m(4)\u001b[0;36mpick_and_take\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m      2 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m      3 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mpick_and_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m----> 4 \u001b[0;31m    \u001b[0mpicked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m      5 \u001b[0;31m    \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m      6 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> d\n",
      "*** Newest frame\n",
      "ipdb> 1\n",
      "1\n",
      "ipdb> 1\n",
      "1\n",
      "ipdb> 2\n",
      "2\n",
      "ipdb> 100\n",
      "100\n",
      "ipdb> \n",
      "100\n",
      "ipdb> 0\n",
      "0\n",
      "ipdb> exit\n"
     ]
    }
   ],
   "source": [
    "%pdb\n",
    "\n",
    "def pick_and_take():\n",
    "    picked = numpy.random.randint(0, 1000)\n",
    "    raise NotImplementedError()\n",
    "    \n",
    "pick_and_take()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T01:37:52.994031Z",
     "start_time": "2017-09-24T01:37:52.710180Z"
    }
   },
   "source": [
    "### retrina 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T01:38:01.432833Z",
     "start_time": "2017-09-24T01:38:01.250036Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yvX7/45wr8t7fBrwCxAEfei+AyccYQ0QiyBc5u5g4LVNBEgSs5pRD+MvIyHBZWVl+lyEi\njURB0jzMbJFzLuN4/fQNeBEJOf9YV6ggCTIKExEJKXNX7+SWaVmc3KmNgiSI6P4CIhIyPli+nbtm\nLmFgSgLTbhxBQry+2R4stGciIiHhncX5/GTGYob2SOTViQqSYKM9ExEJem8s2MIDf1nBGSd34IXx\nGbppYxDSFhGRoPbyPzfym7+u5uy+nXjuunTdRj5IKUxEJGg999l6HvkomwsHdOHpccP0YKsgpjAR\nkaDjnOOJj3N4cl4Olw9J5g/fH6JH7QY5hYmIBBXnHJM/zOb5+Ru4Oj2VyVcOJqqF+V2WHIfCRESC\nRlW144F3VzAzM4/rR/bkN5cPoIWCJCQoTEQkKJRVVvGzN5cye8UOfnJOb+4+/xS8J3VLCFCYiIjv\nDpRV8qPXFvF5zi7+65JTuflbJ/ldktSTwkREfLW3tJwbXs5kef5eHr1qMFdndD/+QhJ0FCYi4pud\n+w4x/qWFbNx1gOeuS+fCAV39LkkaSGEiIr7YvPsA1720gKL95bx843DO7N3R75IkAAoTEWl2a7bv\nY/zUhVRWVfPGLSMZ0j3R75IkQPoWkIg0q8xNRfzg+S+JMuOtW0cpSMKE9kxEpNl8tHI7d85cSmpi\nHNNuGkH39vF+lySNRGEiIs1i+pebePD9VZzWPZGXJgzXQ63CjMJERJqUc45H56zl2c/Wc96pnXl6\n3DDiYnXn33CjMBGRJlNRVc0v/7ycdxZvZdyI7vx2zECidcPGsKQwEZEmsb+sktteX8z8dYXcff4p\n/OSc3ro9ShhTmIhIoyssKeOmVzJZvX0fj1w5iB8M7+F3SdLEFCYi0qg2FO7nhpczKSwp44Xx6ZzT\nr4vfJUkzUJiISKNZsGE3t762iBZmzJg0ktP0HZKIoTARkUbx7pJ87p21nO7t43n5huH07NDa75Kk\nGSlMRCQgtR+xO+qkDvzpunQS4mP8LkuamcJERBqsrLKKX85azl+WbuOq9FR+971BxEbr0t9IpDAR\nkQbZc6CcW19dxMJNRfziwr7c9t2TdelvBFOYiEi9bdx1gBtfXsi24kM8PW4olw1J9rsk8ZnCRETq\n5RtXbN1yOuk92/tdkgSBgA9umlmUmS0xs795073MbIGZ5ZrZm2YW67W39KZzvflptT7jfq99rZld\nWKt9tNeWa2b31Wo/6hgi0rRmLNzCtS8uoH3rWN697QwFiXytMc6U3QWsqTX9CPC4c643sAeY6LVP\nBPZ47Y97/TCz/sA1wABgNPCsF1BRwDPARUB/YJzXt64xRKQJVFZV8+B7K7n/nRWc2bsj7952pi79\nlW8IKEzMLBW4BHjRmzbgHGCW12UaMNZ7P8abxpt/rtd/DDDTOVfmnNsI5AIjvFeuc26Dc64cmAmM\nOc4YItLI9paWM+HlhUz7cjM3n9WLqTcMJyFOl/7KNwV6zuQJ4F6grTfdAdjrnKv0pvOBFO99CpAH\n4JyrNLNir38K8FWtz6y9TN4R7acfZwwRaUS5BSXcPC2LbXsP8ehVg7k6o7vfJUmQavCeiZldChQ4\n5xY1Yj2NyswmmVmWmWUVFhb6XY5ISPk0u4Cxz/yL/WVVzJh0uoJE6hTInsmZwOVmdjHQCmgHPAkk\nmlm0t+eQCmz1+m8FugP5ZhYNJAC7a7UfVnuZo7XvrmOMb3DOTQGmAGRkZLgA1lUkYjjnmDJ/A5M/\nymZAcjumXJ9BcmKc32VJkGvwnolz7n7nXKpzLo2aE+ifOOeuBT4FrvK6TQDe896/703jzf/EOee8\n9mu8q716AX2AhUAm0Me7civWG+N9b5ljjSEiAThUUcU9by3j4Q+zuXhQN96+9QwFiZyQpvieyS+B\nmWb2ELAEeMlrfwl41cxygSJqwgHn3CozewtYDVQCtzvnqgDM7A5gDhAFTHXOrTrOGCLSQHlFpdz6\n6iLW7NjHPeefwh16mJXUg9X8oR/+MjIyXFZWlt9liASlz9YWcNfMpTjneHLcUM7u29nvkiRImNki\n51zG8frpG/AiEay62vHMp7k89vE6+nZpy/PXp+v7I9IgChORCLXvUAV3v7mMj9fsZOxpyTx8xWDi\nYqP8LktClMJEJAKt3VHCj15bRF5RKb++rD8TzkjT+REJiMJEJML8bfk27p21nNYto5kxaSTD03R/\nLQmcwkQkQpRVVvHw7Gxe+dcmMnom8ey1w+jcrpXfZUmYUJiIRIAtu0u5Y8ZilucXc/NZvbh3dD89\nEVEalcJEJMx9tHIHv5i1DAOmXJ/OBQO6+l2ShCGFiUiYKq+s5uEP1/DyPzcxJDWBP/5wGN3bx/td\nloQphYlIGMorKuWOGUtYlreXG89M4/6LTtVhLWlSChORMPP3VTv4+dvLcMCfrhvG6IHd/C5JIoDC\nRCRMHKqo4nez1zD9y80MSkngmR8Oo0cHHdaS5qEwEQkDa3eUcOeMJazdWcLEs3px7+i+tIzWt9ml\n+ShMREKYc45Xv9rMQx+soV2rGKbdNILvnNLJ77IkAilMRELU7v1l3DtrOfOyC/hu3078/uohdGzT\n0u+yJEIpTERC0Oc5hdz91jKKSyt48LL+3KB7a4nPFCYiIeRQRRWPzV3HlPkb6N25DdNuHEH/5HZ+\nlyWiMBEJFau2FXP3m8tYu7OEa0/vwX9d0l+3jJegoTARCXKVVdX86R/reeLjHNq3juXlG4Zzdj89\nCVGCi8JEJIitL9zPPW8tY2neXi4bksxvxwwgMT7W77JE/oPCRCQIVVc7pn+5ickfZdMqJoqnxw3l\nsiHJfpclckwKE5Egs3XvQe6dtYx/5u7m7L6deOTKwXruiAQ9hYlIkKiudszI3MLDs7NxzvHwFYO4\nZnh3XfIrIUFhIhIENu06wH3vLOerDUWc2bsDk68YrNvFS0hRmIj4qKra8dIXG/jD39cRG9WCyVcM\n4gfaG5EQpDAR8cnaHSXcO2sZy/KLOe/ULjw0diBdE3RuREKTwkSkmZVXVvPsZ7k882kubVvF8NS4\noVw2uJv2RiSkKUxEmlHmpiIeeHcF63bu5/IhyTx4WX866OaMEgYUJiLNoOhAOZM/XMNbWfmkJMbx\n4vgMzuvfxe+yRBqNwkSkCTnnmLUon9/NXsO+Q5Xc+u2TuOu8PsTH6j89CS/6Fy3SRHJ2lvDAX1ay\ncGMR6T2T+N/vDaRfV93hV8KTwkSkkR0sr+LpT3KYMn8DbVpF88iVg7g6vTstWugEu4SvFg1d0My6\nm9mnZrbazFaZ2V1ee3szm2tmOd7PJK/dzOwpM8s1s+VmNqzWZ03w+ueY2YRa7elmtsJb5inzLnc5\n1hgifnLOMXvFds577B88+9l6xg5NYd7d3+EHw3soSCTsNThMgErgHudcf2AkcLuZ9QfuA+Y55/oA\n87xpgIuAPt5rEvAc1AQD8CBwOjACeLBWODwH3FJrudFe+7HGEPFF9o59/PCFBdz2+mLatopm5qSR\n/P7qIbpSSyJGgw9zOee2A9u99yVmtgZIAcYA3/W6TQM+A37ptU93zjngKzNLNLNuXt+5zrkiADOb\nC4w2s8+Ads65r7z26cBY4MM6xhBpVntLy3l87jpe/Woz7eJi+O3YgYwb3p3oqED+ThMJPY1yzsTM\n0oChwAKgixc0ADuAw9c/pgB5tRbL99rqas8/Sjt1jHFkXZOo2QuiR48e9VwrkWOrqnbMWLiFP/x9\nLcUHK7j29J7cff4pJLXWs0YkMgUcJmbWBvgz8FPn3L7a3+J1zjkzc4GOUZe6xnDOTQGmAGRkZDRp\nHRI5/pW7i4c+WMPq7fs4vVd7fn35AE7tpqu0JLIFFCZmFkNNkLzunHvHa95pZt2cc9u9w1gFXvtW\noHutxVO9tq38+5DV4fbPvPbUo/SvawyRJpOzs4SHP8zmk+wCUhLj+OMPh3LJIN0GRQQCu5rLgJeA\nNc65x2rNeh84fEXWBOC9Wu3jvau6RgLF3qGqOcAFZpbknXi/AJjjzdtnZiO9scYf8VlHG0Ok0RXs\nO8T97yznwifmk7mpiPsu6se8e77DpYOTFSQinkD2TM4ErgdWmNlSr+1XwGTgLTObCGwGvu/Nmw1c\nDOQCpcCNAM65IjP7LZDp9fufwyfjgduAV4A4ak68f+i1H2sMkUZzoKySFz7fwJT5GyivrGb8qDTu\nPLcP7XVeROQ/WM3FVeEvIyPDZWVl+V2GhICKqmpmLcrnsbnrKCwp4+JBXbn3wn6kdWztd2kizc7M\nFjnnMo7XT9+AF/FUVzv+unwbj89dx6bdpQzrkcifrhtGes/2fpcmEvQUJhLxnHP8ffVOHvv7Otbu\nLKFf17a8MD6D807trHMiIidIYSIRyznH5zm7+MPf17Isv5iTOrbmqXFDuXRQN93+RKSeFCYSkRZs\n2M0f5q5j4cYiUhLj+L8rB3PFsBR9c12kgRQmEjGcc3yRu4un5+WycFMRndq25DeXD+CaEd1pGR3l\nd3kiIU1hImHPOccn2QU8/UkuS/P20rVdK359WX+uGdGDVjEKEZHGoDCRsFVd7ZizagdPf5LL6u37\nSE2K43ffG8SV6SnaExFpZAoTCTtllVW8v3QbU+ZvIKdgP706tubRqwYzdmgKMTonItIkFCYSNooP\nVvDGgi28/M+NFJSU0a9rW5685jQuHZxMlK7OEmlSChMJefl7Spn6xSbezNzCgfIqzurdkd9fPYRv\n9emo74mINBOFiYSs5fl7efHzjXywYjsGXDYkmZu/1YsByQl+lyYScRQmElLKKqv4cMUOpn25iSVb\n9tKmZTQ3nZnGjWf2Ijkxzu/yRCKWwkRCwvbig7yxYAszFm5h1/5yenVszYOX9efK9FTatYrxuzyR\niKcwkaDlnGPBxiKmf7mJOat2Uu0c5/brzPhRaZzVu6NueSISRBQmEnR27S/j3cVbmZm5hfWFB0iI\ni2HiWb24fmRPureP97s8ETkKhYkEhapqx+c5hbyZmcfHa3ZSUeUY1iOR/7tyMJcNSSYuVl8yFAlm\nChPxVV5RKbMW5fN2Vh7big+RFB/DhFFp/GB4d/p0aet3eSJyghQm0uz2lpbzwYrt/GXJVjI37cEM\nzurdkQcu6c95/TvrViciIUhhIs3iUEUVn2YX8O6SrXy6toCKKkfvzm34xYV9GXNaMqlJOhciEsoU\nJtJkKqqq+df63cxevp3ZK7dTcqiSTm1bMmFUGmOHpjAguZ2+oS4SJhQm0qjKKqv4ImcXs1fs4OM1\nOyk+WEGbltFcMKAL3xuawhknd9R9skTCkMJEAnawvIr5OYV8uGI789YUUFJWSbtW0ZzfvysXDezK\nWX066rkhImFOYSINkr+nlE+zC/gku4B/rd9NWWU1ifExXDyoGxcN6soZJ3ckNlq3exeJFAoTOSFV\n1Y6leXuYt6YmQLJ3lADQs0M8157ek3NP7cyIXu31vBCRCKUwkaNyzrFx1wH+mbuLL3J38eX63ew7\nVElUC2N4WhIPXHwq55zamZM6ttZJdBFRmMi/FZQc4sv1u/kiZxf/zN3FtuJDAKQkxnHRwG6c1acj\n3z6lEwlxurGiiHyTwiRCOefYsOsAWZuKyNy0h6xNRWzaXQpAQlwMZ5zcgdvO7shZvTvSs0O89j5E\npE4Kkwixv6ySVVuLWZa/l6xNe8javIeiA+UAtG8dS3rPJMaN6MGokzswIDlBl++KSL0oTMJQaXkl\nq7ftY3l+MSu2FrM8fy8bdh3AuZr5aR3iOadfZ4anJZGR1l7nPUQkYAqTEFZZVc3molLW7Shh7c4S\n1u0sYe2OEjbuOkC1Fxxd2rVkUEoiY05LYVBKAoNSE+jYpqW/hYtI2AnZMDGz0cCTQBTwonNuss8l\nNQnnHEUHytm0u5TNuw98/TNn535yC/dTXlkNgBn0bB/PKV3acsmgbgxOTWRQagJd2rXyeQ1EJBKE\nZJiYWRTwDHA+kA9kmtn7zrnV/lZWf5VV1RSUlLG9+BA7ig+xvfig9/MQm4sOsHlXKSVllV/3b2GQ\nnBhH785t+FafjpzSpS19u7bl5E5t9MwPEfFNSIYJMALIdc5tADCzmcAYwJcwcc5xqKKaA+WVlJZV\n1fwsr+RAWRXFByvYU1pO0YFy9hwop6i0gj0HytlTWs6u/WUUlpR9fUjqsFYxLUhOiKNHh3gyeran\nZ4d40jqNHh8wAAAFjElEQVS0pmeHeFKT4vXNchEJOqEaJilAXq3pfOD0phjorcw8np+/nqpqR2W1\n++bPqmoqqx0HK6q+Prldl4S4GNq3jiUpPoZuCa0YkNyOrglxdEtoRdeEVnRLaEW3dnG0i4vWCXER\nCSmhGiYnxMwmAZMAevTo0aDPSIyPoV/XdkS1MKJbGNFRRlSLFkS3sK/b4mOjiG8ZTevYKOJjo2nd\nsuZnfGwUCXExJLWOJTEuhmjdakREwlSohslWoHut6VSv7Rucc1OAKQAZGRknsO/wny4Y0JULBnRt\nyKIiIhEjVP9UzgT6mFkvM4sFrgHe97kmEZGIFZJ7Js65SjO7A5hDzaXBU51zq3wuS0QkYoVkmAA4\n52YDs/2uQ0REQvcwl4iIBBGFiYiIBExhIiIiAVOYiIhIwBQmIiISMHMnch+QMGBmhcDmBi7eEdjV\niOWEAq1zZNA6R4ZA1rmnc67T8TpFTJgEwsyynHMZftfRnLTOkUHrHBmaY511mEtERAKmMBERkYAp\nTE7MFL8L8IHWOTJonSNDk6+zzpmIiEjAtGciIiIBU5gch5mNNrO1ZpZrZvf5XU9jMbPuZvapma02\ns1VmdpfX3t7M5ppZjvczyWs3M3vK+z0sN7Nh/q5Bw5hZlJktMbO/edO9zGyBt15veo80wMxaetO5\n3vw0P+tuKDNLNLNZZpZtZmvMbFQEbOOfef+mV5rZDDNrFY7b2cymmlmBma2s1VbvbWtmE7z+OWY2\noaH1KEzqYGZRwDPARUB/YJyZ9fe3qkZTCdzjnOsPjARu99btPmCec64PMM+bhprfQR/vNQl4rvlL\nbhR3AWtqTT8CPO6c6w3sASZ67ROBPV77416/UPQk8JFzrh8whJp1D9ttbGYpwJ1AhnNuIDWPqLiG\n8NzOrwCjj2ir17Y1s/bAg9Q89nwE8ODhAKo355xex3gBo4A5tabvB+73u64mWtf3gPOBtUA3r60b\nsNZ7/zwwrlb/r/uFyouaJ3LOA84B/gYYNV/kij5ye1PzrJxR3vtor5/5vQ71XN8EYOORdYf5Nk4B\n8oD23nb7G3BhuG5nIA1Y2dBtC4wDnq/V/o1+9Xlpz6Ruh/9hHpbvtYUVb9d+KLAA6OKc2+7N2gF0\n8d6Hw+/iCeBeoNqb7gDsdc5VetO11+nr9fXmF3v9Q0kvoBB42Tu096KZtSaMt7Fzbivwe2ALsJ2a\n7baI8N7OtdV32zbaNleYRDgzawP8Gfipc25f7Xmu5k+VsLjcz8wuBQqcc4v8rqUZRQPDgOecc0OB\nA/z7sAcQXtsYwDtEM4aaIE0GWvOfh4IiQnNvW4VJ3bYC3WtNp3ptYcHMYqgJktedc+94zTvNrJs3\nvxtQ4LWH+u/iTOByM9sEzKTmUNeTQKKZHX7iaO11+np9vfkJwO7mLLgR5AP5zrkF3vQsasIlXLcx\nwHnARudcoXOuAniHmm0fztu5tvpu20bb5gqTumUCfbwrQWKpOZH3vs81NQozM+AlYI1z7rFas94H\nDl/RMYGacymH28d7V4WMBIpr7U4HPefc/c65VOdcGjXb8RPn3LXAp8BVXrcj1/fw7+Eqr39I/QXv\nnNsB5JlZX6/pXGA1YbqNPVuAkWYW7/0bP7zOYbudj1DfbTsHuMDMkry9ugu8tvrz+wRSsL+Ai4F1\nwHrgAb/racT1OouaXeDlwFLvdTE1x4vnATnAx0B7r79Rc2XbemAFNVfL+L4eDVz37wJ/896fBCwE\ncoG3gZZeeytvOtebf5LfdTdwXU8Dsrzt/BcgKdy3MfAbIBtYCbwKtAzH7QzMoOa8UAU1e6ETG7Jt\ngZu89c8FbmxoPfoGvIiIBEyHuUREJGAKExERCZjCREREAqYwERGRgClMREQkYAoTEREJmMJEREQC\npjAREZGA/X8qU6oyN3jHpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10551ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(1000)\n",
    "y = [i ** 2 for i in x]\n",
    "plt.plot(x,y)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-24T01:38:17.233806Z",
     "start_time": "2017-09-24T01:38:16.943462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rq3YMP2sQ7Vs1T7kzNWWGEkmSpCZi7KzFnP3QeErLKgDYvlNrHvnhXnRq2yLl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\nEkmSJEmF+v/3cyVrjVXqZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10551a9e8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 402
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.plot(x,y)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### markdown\n",
    "- P(A \\mid B) = \\frac{P(B \\mid A) \\, P(A)}{P(B)}  (앞뒤에 $ 2개를 붙이면 됩니다)\n",
    "\n",
    "$$ P(A \\mid B) = \\frac{P(B \\mid A) \\, P(A)}{P(B)} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### multiple cursor\n",
    "- 'ALT' 키 누르면서 클릭하면 여러 커서를 설정! 그리고 값을 동시에 입력 가능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jupyter-contrib extensions\n",
    "- extensions을 설치하면 여러가지를 사용 가능\n",
    "- 예를 들어 Variable Inspector, Nbextensions dashoard tab, Code prettify, executeTime 등"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}