ipython-notebook-demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iPython使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算用时 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 554 µs, sys: 708 µs, total: 1.26 ms\n",
      "Wall time: 3.01 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from time import sleep\n",
    "\n",
    "sleep(1)\n",
    "sleep(1)\n",
    "sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 9.79 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "1000000 loops, best of 3: 166 ns per loop\n",
      "The slowest run took 6.54 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "10000000 loops, best of 3: 162 ns per loop\n"
     ]
    }
   ],
   "source": [
    "xs = [1, 2, 3, 4, 5, 6]\n",
    "ys = [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "%timeit 6 in xs\n",
    "%timeit 6 in ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 5.96 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "1000000 loops, best of 3: 306 ns per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "\n",
    "xs = [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "6 in xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "latex": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cat": "Other",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "colors": "BasicMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "cp": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "install_default_config": "DeprecatedMagics",
        "install_ext": "ExtensionMagics",
        "install_profiles": "DeprecatedMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "lf": "Other",
        "lk": "Other",
        "ll": "Other",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "lx": "Other",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "man": "KernelMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "mv": "Other",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "profile": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rm": "Other",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %install_default_config  %install_ext  %install_profiles  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from numpy.random import randint\n",
    "\n",
    "xs = randint(1, 100, size=10**6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    }
   ],
   "source": [
    "%%prun\n",
    "\n",
    "freq = {}\n",
    "for x in xs:\n",
    "    if x not in freq:\n",
    "        freq[x] = 0\n",
    "    freq[x] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    }
   ],
   "source": [
    "%%prun\n",
    "from collections import Counter\n",
    "freq = Counter(xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 10172.,  10163.,  10045.,   9955.,  10055.,  10065.,  10022.,\n",
       "         10021.,   9997.,  10224.,  10112.,   9994.,   9975.,  10036.,\n",
       "         10099.,  10161.,   9773.,  10135.,  10204.,  10172.,  10088.,\n",
       "          9967.,  10052.,  10168.,  10265.,  10191.,  10155.,  10037.,\n",
       "         10136.,  10069.,  10127.,  10121.,  10048.,  10039.,  10104.,\n",
       "         10468.,  10107.,   9985.,  10150.,  10006.,   9926.,  10025.,\n",
       "         10154.,  10021.,   9956.,  10228.,  10136.,  10097.,  10083.,\n",
       "         10006.,   9949.,  10076.,  10050.,  10208.,  10276.,  10183.,\n",
       "         10069.,  10176.,  10426.,  10093.,  10069.,  10145.,  10160.,\n",
       "          9989.,  10063.,  10039.,  10039.,  10042.,  10162.,  10068.,\n",
       "         10154.,  10330.,  10069.,   9970.,  10092.,  10219.,  10140.,\n",
       "         10268.,  10086.,  10121.,  10080.,  10156.,  10090.,   9928.,\n",
       "         10059.,  10128.,  10242.,  10139.,   9910.,  10158.,  10222.,\n",
       "         10142.,  10048.,  10136.,  10141.,  10143.,  10142.,   9973.,\n",
       "         10137.]),\n",
       " array([  1.        ,   1.98989899,   2.97979798,   3.96969697,\n",
       "          4.95959596,   5.94949495,   6.93939394,   7.92929293,\n",
       "          8.91919192,   9.90909091,  10.8989899 ,  11.88888889,\n",
       "         12.87878788,  13.86868687,  14.85858586,  15.84848485,\n",
       "         16.83838384,  17.82828283,  18.81818182,  19.80808081,\n",
       "         20.7979798 ,  21.78787879,  22.77777778,  23.76767677,\n",
       "         24.75757576,  25.74747475,  26.73737374,  27.72727273,\n",
       "         28.71717172,  29.70707071,  30.6969697 ,  31.68686869,\n",
       "         32.67676768,  33.66666667,  34.65656566,  35.64646465,\n",
       "         36.63636364,  37.62626263,  38.61616162,  39.60606061,\n",
       "         40.5959596 ,  41.58585859,  42.57575758,  43.56565657,\n",
       "         44.55555556,  45.54545455,  46.53535354,  47.52525253,\n",
       "         48.51515152,  49.50505051,  50.49494949,  51.48484848,\n",
       "         52.47474747,  53.46464646,  54.45454545,  55.44444444,\n",
       "         56.43434343,  57.42424242,  58.41414141,  59.4040404 ,\n",
       "         60.39393939,  61.38383838,  62.37373737,  63.36363636,\n",
       "         64.35353535,  65.34343434,  66.33333333,  67.32323232,\n",
       "         68.31313131,  69.3030303 ,  70.29292929,  71.28282828,\n",
       "         72.27272727,  73.26262626,  74.25252525,  75.24242424,\n",
       "         76.23232323,  77.22222222,  78.21212121,  79.2020202 ,\n",
       "         80.19191919,  81.18181818,  82.17171717,  83.16161616,\n",
       "         84.15151515,  85.14141414,  86.13131313,  87.12121212,\n",
       "         88.11111111,  89.1010101 ,  90.09090909,  91.08080808,\n",
       "         92.07070707,  93.06060606,  94.05050505,  95.04040404,\n",
       "         96.03030303,  97.02020202,  98.01010101,  99.        ]),\n",
       " <a list of 99 Patch objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEACAYAAACtVTGuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEVPGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106c8f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import hist\n",
    "\n",
    "hist(xs, bins=99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Python日期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__radd__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rsub__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " 'ctime',\n",
       " 'day',\n",
       " 'fromordinal',\n",
       " 'fromtimestamp',\n",
       " 'isocalendar',\n",
       " 'isoformat',\n",
       " 'isoweekday',\n",
       " 'max',\n",
       " 'min',\n",
       " 'month',\n",
       " 'replace',\n",
       " 'resolution',\n",
       " 'strftime',\n",
       " 'timetuple',\n",
       " 'today',\n",
       " 'toordinal',\n",
       " 'weekday',\n",
       " 'year']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import date\n",
    "\n",
    "new_years = date(2000, 1, 1)\n",
    "dir(new_years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_years.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_years.weekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "helloween = date(1999, 10, 31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helloween == new_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(-62)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helloween - new_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2000, 1, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "new_years + timedelta(days=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1999, 12, 31, 23, 59, 59)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "datetime(1999, 12, 31, 23, 59, 59)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"250\"\n",
       "            src=\"http://pandas.pydata.org\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1042798d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "\n",
    "IFrame('http://pandas.pydata.org', width='100%', height=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目-可视化微软股票数据 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from requests import get\n",
    "url = 'https://www.quandl.com/api/v3/datasets/GOOG/NASDAQ_MSFT.json?trim_start=1986-03-13&trim_end=2016-07-13&order=asc'\n",
    "response = get(url)\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['database_code', 'name', 'column_names', 'limit', 'refreshed_at', 'collapse', 'oldest_available_date', 'dataset_code', 'start_date', 'end_date', 'newest_available_date', 'description', 'type', 'data', 'id', 'column_index', 'transform', 'premium', 'order', 'frequency', 'database_id'])\n"
     ]
    }
   ],
   "source": [
    "print(response.json().get('dataset').keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = response.json().get('dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1986-03-13', 0.0, None, None, 0.1, 30758400.0]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['data'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-03-13</th>\n",
       "      <td>1986-03-13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>30758400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-14</th>\n",
       "      <td>1986-03-14</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7852896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-17</th>\n",
       "      <td>1986-03-17</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>33036768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-18</th>\n",
       "      <td>1986-03-18</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>66403296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-19</th>\n",
       "      <td>1986-03-19</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>47846304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  Open  High  Low  Close    Volume\n",
       "Date                                                    \n",
       "1986-03-13  1986-03-13     0   NaN  NaN    0.1  30758400\n",
       "1986-03-14  1986-03-14     0   NaN  NaN    0.1   7852896\n",
       "1986-03-17  1986-03-17     0   NaN  NaN    0.1  33036768\n",
       "1986-03-18  1986-03-18     0   NaN  NaN    0.1  66403296\n",
       "1986-03-19  1986-03-19     0   NaN  NaN    0.1  47846304"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "df = DataFrame(dataset['data'], columns=dataset['column_names'])\n",
    "df.index = df.Date\n",
    "df.head() #显示前五行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-07</th>\n",
       "      <td>2016-07-07</td>\n",
       "      <td>51.42</td>\n",
       "      <td>51.61</td>\n",
       "      <td>51.07</td>\n",
       "      <td>51.38</td>\n",
       "      <td>19585194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-08</th>\n",
       "      <td>2016-07-08</td>\n",
       "      <td>51.73</td>\n",
       "      <td>52.36</td>\n",
       "      <td>51.55</td>\n",
       "      <td>52.30</td>\n",
       "      <td>28391026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-11</th>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>52.50</td>\n",
       "      <td>52.83</td>\n",
       "      <td>52.47</td>\n",
       "      <td>52.59</td>\n",
       "      <td>22269203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-12</th>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>52.94</td>\n",
       "      <td>53.40</td>\n",
       "      <td>52.78</td>\n",
       "      <td>53.21</td>\n",
       "      <td>27317555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-13</th>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>53.56</td>\n",
       "      <td>53.86</td>\n",
       "      <td>53.18</td>\n",
       "      <td>53.51</td>\n",
       "      <td>25356841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date   Open   High    Low  Close    Volume\n",
       "Date                                                        \n",
       "2016-07-07  2016-07-07  51.42  51.61  51.07  51.38  19585194\n",
       "2016-07-08  2016-07-08  51.73  52.36  51.55  52.30  28391026\n",
       "2016-07-11  2016-07-11  52.50  52.83  52.47  52.59  22269203\n",
       "2016-07-12  2016-07-12  52.94  53.40  52.78  53.21  27317555\n",
       "2016-07-13  2016-07-13  53.56  53.86  53.18  53.51  25356841"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail() #显示最后五行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10aad1780>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PPgqPSWXFe667zrWjRiW6lF5/PYza8Eoxikgo4+mnwwEHFDfdaaaK61OmuLBK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\nCsSUt2EYRgViytswDKMCMeVtGIZRgfx/F9YRhaHfMmYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a9e0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date       1986-03-13\n",
       "Open                0\n",
       "High              NaN\n",
       "Low               NaN\n",
       "Close             0.1\n",
       "Volume    3.07584e+07\n",
       "Name: 1986-03-13, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from dateutil.parser import parse\n",
    "\n",
    "df.index = df['Date'].apply(parse) #把字符串转化为日期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1986-03-13', '1986-03-14', '1986-03-17', '1986-03-18',\n",
       "               '1986-03-19', '1986-03-20', '1986-03-21', '1986-03-24',\n",
       "               '1986-03-25', '1986-03-26',\n",
       "               ...\n",
       "               '2016-06-29', '2016-06-30', '2016-07-01', '2016-07-05',\n",
       "               '2016-07-06', '2016-07-07', '2016-07-08', '2016-07-11',\n",
       "               '2016-07-12', '2016-07-13'],\n",
       "              dtype='datetime64[ns]', name='Date', length=7662, freq=None)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1099f68d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PPgqPSWXFe667zrWjRiW6lF5/PYza8Eoxikgo4+mnwwEHFDfdaaaK61OmuLBK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\nCsSUt2EYRgViytswDKMCMeVtGIZRgfx/F9YRhaHfMmYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109a20438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10aaa6fd0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IyVtEJIGUvEVEEkjJW0QkgZS8RUQSSMlbRCSBlLxFRBJIyVtEJIGUvEVEEkjJ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ad399e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import log\n",
    "\n",
    "# 回报率\n",
    "df['Returns'] = log(df['Close'] / df['Close'].shift(1)).cumsum()\n",
    "df['Returns'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$log(x)+log(y)=log(xy)$$\n",
    "\n",
    "$$log(x)-log(y)=log(\\frac{x}{y})$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$log(\\frac{t_1}{t_0})+log(\\frac{t_2}{t_1})+...+log(\\frac{t_n}{t_{n-1}})$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$log(t_1)-log(t_0)+log(t_2)-log(t_1)+\\ldots+log(t_n)-log(t_{n-1})$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$log(t_n)-log(t_0)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"500\"\n",
       "            src=\"https://en.wikipedia.org/wiki/Timeline_of_Microsoft_Windows\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x10b140908>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame('https://en.wikipedia.org/wiki/Timeline_of_Microsoft_Windows', width='100%', height=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from dateutil.parser import parse\n",
    "\n",
    "win95 = parse('24 August 1995')\n",
    "win98 = parse('25 June 1998')\n",
    "win2k = parse('17 February 2000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "parse('24 August 1995') == datetime(1995, 8, 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import date\n",
    "parse('24 August 1995').date() == date(1995, 8, 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b487828>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YWZnYttlmduWeq4BkNoRrK6xebe6s4cNt+2GHZfd8ERPH2bOTax5cGNLz7bf2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\njuM4ThIuDI7jOE4SLgyO4zhOEv8P2uynWYKlDhgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b43e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "# 根据产品发布时间段来看这段时间内的股价波动，以此来盈利。\n",
    "from_dt = win95 - timedelta(days=30)\n",
    "to_dt = win95 + timedelta(days=365)\n",
    "\n",
    "df[from_dt:to_dt]['Returns'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b411978>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QZLNsGTRsCDfemD+A3qpV8j7z4ovhwQdL9r28+KIVDBwzxhr93XaDq64yV9FV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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b362dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from_dt = win98 - timedelta(days=30)\n",
    "to_dt = win98 + timedelta(days=365)\n",
    "\n",
    "df[from_dt:to_dt]['Returns'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b33a588>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x0Ba4RUQEWFdJ9I/jOI6TJSL18TuO4zi5R17O3BWRjZJe57X/X0R2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\nKTD+H+PI6PJk2tzVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b3554a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from_dt = win2k - timedelta(days=30)\n",
    "to_dt = win2k + timedelta(days=365)\n",
    "\n",
    "df[from_dt:to_dt]['Returns'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adjusted Close</th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-03</th>\n",
       "      <td>1950-01-03</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>1260000</td>\n",
       "      <td>16.66</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-04</th>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>1890000</td>\n",
       "      <td>16.85</td>\n",
       "      <td>0.011340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-05</th>\n",
       "      <td>1950-01-05</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>2550000</td>\n",
       "      <td>16.93</td>\n",
       "      <td>0.016077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-06</th>\n",
       "      <td>1950-01-06</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>2010000</td>\n",
       "      <td>16.98</td>\n",
       "      <td>0.019026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-09</th>\n",
       "      <td>1950-01-09</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>2520000</td>\n",
       "      <td>17.08</td>\n",
       "      <td>0.024898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date   Open   High    Low  Close   Volume  Adjusted Close  \\\n",
       "Date                                                                          \n",
       "1950-01-03  1950-01-03  16.66  16.66  16.66  16.66  1260000           16.66   \n",
       "1950-01-04  1950-01-04  16.85  16.85  16.85  16.85  1890000           16.85   \n",
       "1950-01-05  1950-01-05  16.93  16.93  16.93  16.93  2550000           16.93   \n",
       "1950-01-06  1950-01-06  16.98  16.98  16.98  16.98  2010000           16.98   \n",
       "1950-01-09  1950-01-09  17.08  17.08  17.08  17.08  2520000           17.08   \n",
       "\n",
       "             Returns  \n",
       "Date                  \n",
       "1950-01-03       NaN  \n",
       "1950-01-04  0.011340  \n",
       "1950-01-05  0.016077  \n",
       "1950-01-06  0.019026  \n",
       "1950-01-09  0.024898  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# windows 2k发布时微软股价反而下跌了，这是为什么？\n",
    "# 这是因为2000年的时候的互联网灾难（2000-2002)，我们来看一下GSPC指数\n",
    "\n",
    "from requests import get\n",
    "from pandas import DataFrame\n",
    "from dateutil.parser import parse\n",
    "from numpy import log\n",
    "\n",
    "url = 'https://www.quandl.com/api/v3/datasets/YAHOO/INDEX_GSPC.json?trim_start=1950-01-03&trim_end=2016-07-13&order=asc'\n",
    "response = get(url)\n",
    "sp500_df = DataFrame(response.json()['dataset']['data'], columns=response.json()['dataset']['column_names'])\n",
    "sp500_df.index = sp500_df.Date.apply(parse)\n",
    "sp500_df['Returns'] = log(sp500_df['Close'] / sp500_df['Close'].shift(1)).cumsum()\n",
    "sp500_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10bec7be0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd45470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500_df['Returns'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jBla9gIVCfWHGACOEECVAMHBESpkNIIRYDAwAqpeIN6HOC5GBP/f6c7XtKxOu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\n8PDw8Egr/j9C9y9E4YCcMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c9b11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure, plot_date\n",
    "from datetime import timedelta\n",
    "\n",
    "from_dt = win2k - timedelta(days=30)\n",
    "to_dt = win2k + timedelta(days=90)\n",
    "\n",
    "fig = figure()\n",
    "plot_date(df[from_dt:to_dt].index, df[from_dt:to_dt]['Returns'], 'g-', label='msft', figure=fig)\n",
    "\n",
    "twin = fig.axes[0].twinx()\n",
    "twin.plot_date(sp500_df[from_dt:to_dt].index, sp500_df[from_dt:to_dt]['Returns'], 'b-', label='sp500')\n",
    "\n",
    "fig.axes[0].legend(loc='upper left')\n",
    "twin.legend(loc='upper right')\n",
    "\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}