ch11.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "np.random.seed(12345)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "PREVIOUS_MAX_ROWS = pd.options.display.max_rows\n",
    "pd.options.display.max_rows = 20\n",
    "np.set_printoptions(precision=4, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Date and Time Data Types and Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2019, 6, 11, 10, 20, 35, 951527)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "now = datetime.now()\n",
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2019, 6, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now.year, now.month, now.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(926, 56700)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta = datetime(2011, 1, 7) - datetime(2008, 6, 24, 8, 15)\n",
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "926"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56700"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "delta = datetime(2011, 1, 7) - datetime(2011, 1, 5, 23, 59, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86430.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 19, 0, 0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "start = datetime(2011, 1, 7)\n",
    "start + timedelta(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2010, 12, 14, 0, 0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start - 2 * timedelta(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting Between String and Datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03 00:00:00'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = datetime(2011, 1, 3)\n",
    "str(stamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = '2011-01-03'\n",
    "datetime.strptime(value, '%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2011, 7, 6, 0, 0), datetime.datetime(2011, 8, 6, 0, 0)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datestrs = ['7/6/2011', '8/6/2011']\n",
    "[datetime.strptime(x, '%m/%d/%Y') for x in datestrs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil.parser import parse\n",
    "parse('2011-01-03')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1997, 1, 31, 22, 45)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse('Jan 31, 1997 10:45 PM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 12, 6, 0, 0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse('6/12/2011', dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-07-06 12:00:00', '2011-08-06 00:00:00'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datestrs = ['2011-07-06 12:00:00', '2011-08-06 00:00:00']\n",
    "pd.to_datetime(datestrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-07-06 12:00:00', '2011-08-06 00:00:00', 'NaT'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.to_datetime(datestrs + [None])\n",
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaT"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.204708\n",
       "2011-01-05    0.478943\n",
       "2011-01-07   -0.519439\n",
       "2011-01-08   -0.555730\n",
       "2011-01-10    1.965781\n",
       "2011-01-12    1.393406\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "dates = [datetime(2011, 1, 2), datetime(2011, 1, 5),\n",
    "         datetime(2011, 1, 7), datetime(2011, 1, 8),\n",
    "         datetime(2011, 1, 10), datetime(2011, 1, 12)]\n",
    "ts = pd.Series(np.random.randn(6), index=dates)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-02', '2011-01-05', '2011-01-07', '2011-01-08',\n",
       "               '2011-01-10', '2011-01-12'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.409415\n",
       "2011-01-05         NaN\n",
       "2011-01-07   -1.038877\n",
       "2011-01-08         NaN\n",
       "2011-01-10    3.931561\n",
       "2011-01-12         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts + ts[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-01-02 00:00:00')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = ts.index[0]\n",
    "stamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing, Selection, Subsetting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5194387150567381"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = ts.index[2]\n",
    "ts[stamp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9657805725027142"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['1/10/2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9657805725027142"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['20110110']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0.092908\n",
       "2000-01-02    0.281746\n",
       "2000-01-03    0.769023\n",
       "2000-01-04    1.246435\n",
       "2000-01-05    1.007189\n",
       "2000-01-06   -1.296221\n",
       "2000-01-07    0.274992\n",
       "2000-01-08    0.228913\n",
       "2000-01-09    1.352917\n",
       "2000-01-10    0.886429\n",
       "                ...   \n",
       "2002-09-17   -0.139298\n",
       "2002-09-18   -1.159926\n",
       "2002-09-19    0.618965\n",
       "2002-09-20    1.373890\n",
       "2002-09-21   -0.983505\n",
       "2002-09-22    0.930944\n",
       "2002-09-23   -0.811676\n",
       "2002-09-24   -1.830156\n",
       "2002-09-25   -0.138730\n",
       "2002-09-26    0.334088\n",
       "Freq: D, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts = pd.Series(np.random.randn(1000),\n",
    "                      index=pd.date_range('1/1/2000', periods=1000))\n",
    "longer_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-01-01    1.599534\n",
       "2001-01-02    0.474071\n",
       "2001-01-03    0.151326\n",
       "2001-01-04   -0.542173\n",
       "2001-01-05   -0.475496\n",
       "2001-01-06    0.106403\n",
       "2001-01-07   -1.308228\n",
       "2001-01-08    2.173185\n",
       "2001-01-09    0.564561\n",
       "2001-01-10   -0.190481\n",
       "                ...   \n",
       "2001-12-22    0.000369\n",
       "2001-12-23    0.900885\n",
       "2001-12-24   -0.454869\n",
       "2001-12-25   -0.864547\n",
       "2001-12-26    1.129120\n",
       "2001-12-27    0.057874\n",
       "2001-12-28   -0.433739\n",
       "2001-12-29    0.092698\n",
       "2001-12-30   -1.397820\n",
       "2001-12-31    1.457823\n",
       "Freq: D, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts['2001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-05-01   -0.622547\n",
       "2001-05-02    0.936289\n",
       "2001-05-03    0.750018\n",
       "2001-05-04   -0.056715\n",
       "2001-05-05    2.300675\n",
       "2001-05-06    0.569497\n",
       "2001-05-07    1.489410\n",
       "2001-05-08    1.264250\n",
       "2001-05-09   -0.761837\n",
       "2001-05-10   -0.331617\n",
       "                ...   \n",
       "2001-05-22    0.503699\n",
       "2001-05-23   -1.387874\n",
       "2001-05-24    0.204851\n",
       "2001-05-25    0.603705\n",
       "2001-05-26    0.545680\n",
       "2001-05-27    0.235477\n",
       "2001-05-28    0.111835\n",
       "2001-05-29   -1.251504\n",
       "2001-05-30   -2.949343\n",
       "2001-05-31    0.634634\n",
       "Freq: D, Length: 31, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts['2001-05']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07   -0.519439\n",
       "2011-01-08   -0.555730\n",
       "2011-01-10    1.965781\n",
       "2011-01-12    1.393406\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[datetime(2011, 1, 7):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.204708\n",
       "2011-01-05    0.478943\n",
       "2011-01-07   -0.519439\n",
       "2011-01-08   -0.555730\n",
       "2011-01-10    1.965781\n",
       "2011-01-12    1.393406\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07   -0.519439\n",
       "2011-01-08   -0.555730\n",
       "2011-01-10    1.965781\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['1/6/2011':'1/11/2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.204708\n",
       "2011-01-05    0.478943\n",
       "2011-01-07   -0.519439\n",
       "2011-01-08   -0.555730\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.truncate(after='1/9/2011')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-05-02</th>\n",
       "      <td>-0.006045</td>\n",
       "      <td>0.490094</td>\n",
       "      <td>-0.277186</td>\n",
       "      <td>-0.707213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-09</th>\n",
       "      <td>-0.560107</td>\n",
       "      <td>2.735527</td>\n",
       "      <td>0.927335</td>\n",
       "      <td>1.513906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-16</th>\n",
       "      <td>0.538600</td>\n",
       "      <td>1.273768</td>\n",
       "      <td>0.667876</td>\n",
       "      <td>-0.969206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-23</th>\n",
       "      <td>1.676091</td>\n",
       "      <td>-0.817649</td>\n",
       "      <td>0.050188</td>\n",
       "      <td>1.951312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-30</th>\n",
       "      <td>3.260383</td>\n",
       "      <td>0.963301</td>\n",
       "      <td>1.201206</td>\n",
       "      <td>-1.852001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2001-05-02 -0.006045  0.490094 -0.277186 -0.707213\n",
       "2001-05-09 -0.560107  2.735527  0.927335  1.513906\n",
       "2001-05-16  0.538600  1.273768  0.667876 -0.969206\n",
       "2001-05-23  1.676091 -0.817649  0.050188  1.951312\n",
       "2001-05-30  3.260383  0.963301  1.201206 -1.852001"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('1/1/2000', periods=100, freq='W-WED')\n",
    "long_df = pd.DataFrame(np.random.randn(100, 4),\n",
    "                       index=dates,\n",
    "                       columns=['Colorado', 'Texas',\n",
    "                                'New York', 'Ohio'])\n",
    "long_df.loc['5-2001']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Series with Duplicate Indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0\n",
       "2000-01-02    1\n",
       "2000-01-02    2\n",
       "2000-01-02    3\n",
       "2000-01-03    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.DatetimeIndex(['1/1/2000', '1/2/2000', '1/2/2000',\n",
    "                          '1/2/2000', '1/3/2000'])\n",
    "dup_ts = pd.Series(np.arange(5), index=dates)\n",
    "dup_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts['1/3/2000']  # not duplicated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02    1\n",
       "2000-01-02    2\n",
       "2000-01-02    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts['1/2/2000']  # duplicated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0\n",
       "2000-01-02    2\n",
       "2000-01-03    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = dup_ts.groupby(level=0)\n",
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    1\n",
       "2000-01-02    3\n",
       "2000-01-03    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0\n",
       "2000-01-02    6\n",
       "2000-01-03    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Date Ranges, Frequencies, and Shifting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.204708\n",
       "2011-01-05    0.478943\n",
       "2011-01-07   -0.519439\n",
       "2011-01-08   -0.555730\n",
       "2011-01-10    1.965781\n",
       "2011-01-12    1.393406\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndexResampler [freq=<Day>, axis=0, closed=left, label=left, convention=start, base=0]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampler = ts.resample('D')\n",
    "resampler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating Date Ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-04-01', '2012-04-02', '2012-04-03', '2012-04-04',\n",
       "               '2012-04-05', '2012-04-06', '2012-04-07', '2012-04-08',\n",
       "               '2012-04-09', '2012-04-10', '2012-04-11', '2012-04-12',\n",
       "               '2012-04-13', '2012-04-14', '2012-04-15', '2012-04-16',\n",
       "               '2012-04-17', '2012-04-18', '2012-04-19', '2012-04-20',\n",
       "               '2012-04-21', '2012-04-22', '2012-04-23', '2012-04-24',\n",
       "               '2012-04-25', '2012-04-26', '2012-04-27', '2012-04-28',\n",
       "               '2012-04-29', '2012-04-30', '2012-05-01', '2012-05-02',\n",
       "               '2012-05-03', '2012-05-04', '2012-05-05', '2012-05-06',\n",
       "               '2012-05-07', '2012-05-08', '2012-05-09', '2012-05-10',\n",
       "               '2012-05-11', '2012-05-12', '2012-05-13', '2012-05-14',\n",
       "               '2012-05-15', '2012-05-16', '2012-05-17', '2012-05-18',\n",
       "               '2012-05-19', '2012-05-20', '2012-05-21', '2012-05-22',\n",
       "               '2012-05-23', '2012-05-24', '2012-05-25', '2012-05-26',\n",
       "               '2012-05-27', '2012-05-28', '2012-05-29', '2012-05-30',\n",
       "               '2012-05-31', '2012-06-01'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.date_range('2012-04-01', '2012-06-01')\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-04-01', '2012-04-02', '2012-04-03', '2012-04-04',\n",
       "               '2012-04-05', '2012-04-06', '2012-04-07', '2012-04-08',\n",
       "               '2012-04-09', '2012-04-10', '2012-04-11', '2012-04-12',\n",
       "               '2012-04-13', '2012-04-14', '2012-04-15', '2012-04-16',\n",
       "               '2012-04-17', '2012-04-18', '2012-04-19', '2012-04-20'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2012-04-01', periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-13', '2012-05-14', '2012-05-15', '2012-05-16',\n",
       "               '2012-05-17', '2012-05-18', '2012-05-19', '2012-05-20',\n",
       "               '2012-05-21', '2012-05-22', '2012-05-23', '2012-05-24',\n",
       "               '2012-05-25', '2012-05-26', '2012-05-27', '2012-05-28',\n",
       "               '2012-05-29', '2012-05-30', '2012-05-31', '2012-06-01'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(end='2012-06-01', periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31', '2000-04-28',\n",
       "               '2000-05-31', '2000-06-30', '2000-07-31', '2000-08-31',\n",
       "               '2000-09-29', '2000-10-31', '2000-11-30'],\n",
       "              dtype='datetime64[ns]', freq='BM')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-01-01', '2000-12-01', freq='BM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-02 12:56:31', '2012-05-03 12:56:31',\n",
       "               '2012-05-04 12:56:31', '2012-05-05 12:56:31',\n",
       "               '2012-05-06 12:56:31'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2012-05-02 12:56:31', periods=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-02', '2012-05-03', '2012-05-04', '2012-05-05',\n",
       "               '2012-05-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2012-05-02 12:56:31', periods=5, normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequencies and Date Offsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Hour>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import Hour, Minute\n",
    "hour = Hour()\n",
    "hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4 * Hours>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "four_hours = Hour(4)\n",
    "four_hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 04:00:00',\n",
       "               '2000-01-01 08:00:00', '2000-01-01 12:00:00',\n",
       "               '2000-01-01 16:00:00', '2000-01-01 20:00:00',\n",
       "               '2000-01-02 00:00:00', '2000-01-02 04:00:00',\n",
       "               '2000-01-02 08:00:00', '2000-01-02 12:00:00',\n",
       "               '2000-01-02 16:00:00', '2000-01-02 20:00:00',\n",
       "               '2000-01-03 00:00:00', '2000-01-03 04:00:00',\n",
       "               '2000-01-03 08:00:00', '2000-01-03 12:00:00',\n",
       "               '2000-01-03 16:00:00', '2000-01-03 20:00:00'],\n",
       "              dtype='datetime64[ns]', freq='4H')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-01-01', '2000-01-03 23:59', freq='4h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<150 * Minutes>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hour(2) + Minute(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 01:30:00',\n",
       "               '2000-01-01 03:00:00', '2000-01-01 04:30:00',\n",
       "               '2000-01-01 06:00:00', '2000-01-01 07:30:00',\n",
       "               '2000-01-01 09:00:00', '2000-01-01 10:30:00',\n",
       "               '2000-01-01 12:00:00', '2000-01-01 13:30:00'],\n",
       "              dtype='datetime64[ns]', freq='90T')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-01-01', periods=10, freq='1h30min')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Week of month dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2012-01-20 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-02-17 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-03-16 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-04-20 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-05-18 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-06-15 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-07-20 00:00:00', freq='WOM-3FRI'),\n",
       " Timestamp('2012-08-17 00:00:00', freq='WOM-3FRI')]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('2012-01-01', '2012-09-01', freq='WOM-3FRI')\n",
    "list(rng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shifting (Leading and Lagging) Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.066748\n",
       "2000-02-29    0.838639\n",
       "2000-03-31   -0.117388\n",
       "2000-04-30   -0.517795\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(4),\n",
    "               index=pd.date_range('1/1/2000', periods=4, freq='M'))\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29         NaN\n",
       "2000-03-31   -0.066748\n",
       "2000-04-30    0.838639\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.117388\n",
       "2000-02-29   -0.517795\n",
       "2000-03-31         NaN\n",
       "2000-04-30         NaN\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31          NaN\n",
       "2000-02-29   -13.564241\n",
       "2000-03-31    -1.139975\n",
       "2000-04-30     3.410958\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts / ts.shift(1) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-03-31   -0.066748\n",
       "2000-04-30    0.838639\n",
       "2000-05-31   -0.117388\n",
       "2000-06-30   -0.517795\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2, freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-02-03   -0.066748\n",
       "2000-03-03    0.838639\n",
       "2000-04-03   -0.117388\n",
       "2000-05-03   -0.517795\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(3, freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31 01:30:00   -0.066748\n",
       "2000-02-29 01:30:00    0.838639\n",
       "2000-03-31 01:30:00   -0.117388\n",
       "2000-04-30 01:30:00   -0.517795\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(1, freq='90T')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shifting dates with offsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-20 00:00:00')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import Day, MonthEnd\n",
    "now = datetime(2011, 11, 17)\n",
    "now + 3 * Day()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now + MonthEnd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-31 00:00:00')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now + MonthEnd(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset = MonthEnd()\n",
    "offset.rollforward(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-10-31 00:00:00')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset.rollback(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-15   -0.116696\n",
       "2000-01-19    2.389645\n",
       "2000-01-23   -0.932454\n",
       "2000-01-27   -0.229331\n",
       "2000-01-31   -1.140330\n",
       "2000-02-04    0.439920\n",
       "2000-02-08   -0.823758\n",
       "2000-02-12   -0.520930\n",
       "2000-02-16    0.350282\n",
       "2000-02-20    0.204395\n",
       "2000-02-24    0.133445\n",
       "2000-02-28    0.327905\n",
       "2000-03-03    0.072153\n",
       "2000-03-07    0.131678\n",
       "2000-03-11   -1.297459\n",
       "2000-03-15    0.997747\n",
       "2000-03-19    0.870955\n",
       "2000-03-23   -0.991253\n",
       "2000-03-27    0.151699\n",
       "2000-03-31    1.266151\n",
       "Freq: 4D, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(20),\n",
    "               index=pd.date_range('1/15/2000', periods=20, freq='4d'))\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.005833\n",
       "2000-02-29    0.015894\n",
       "2000-03-31    0.150209\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.groupby(offset.rollforward).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.005833\n",
       "2000-02-29    0.015894\n",
       "2000-03-31    0.150209\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Zone Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytz\n",
    "pytz.common_timezones[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DstTzInfo 'America/New_York' LMT-1 day, 19:04:00 STD>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz = pytz.timezone('America/New_York')\n",
    "tz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Zone Localization and Conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00   -0.202469\n",
       "2012-03-10 09:30:00    0.050718\n",
       "2012-03-11 09:30:00    0.639869\n",
       "2012-03-12 09:30:00    0.597594\n",
       "2012-03-13 09:30:00   -0.797246\n",
       "2012-03-14 09:30:00    0.472879\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('3/9/2012 9:30', periods=6, freq='D')\n",
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(ts.index.tz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00+00:00   -0.514551\n",
       "2012-03-10 09:30:00+00:00   -0.559782\n",
       "2012-03-11 09:30:00+00:00   -0.783408\n",
       "2012-03-12 09:30:00+00:00   -1.797685\n",
       "2012-03-13 09:30:00+00:00   -0.172670\n",
       "2012-03-14 09:30:00+00:00    0.680215\n",
       "2012-03-15 09:30:00+00:00    1.607578\n",
       "2012-03-16 09:30:00+00:00    0.200381\n",
       "2012-03-17 09:30:00+00:00   -0.834068\n",
       "2012-03-18 09:30:00+00:00   -0.302988\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = pd.date_range('3/9/2012 9:30', periods=10, freq='D', tz='UTC')\n",
    "dss = pd.Series(np.random.randn(len(ds)), index=ds)\n",
    "dss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UTC\n"
     ]
    }
   ],
   "source": [
    "print(dss.index.tz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00   -0.202469\n",
       "2012-03-10 09:30:00    0.050718\n",
       "2012-03-11 09:30:00    0.639869\n",
       "2012-03-12 09:30:00    0.597594\n",
       "2012-03-13 09:30:00   -0.797246\n",
       "2012-03-14 09:30:00    0.472879\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00+00:00   -0.202469\n",
       "2012-03-10 09:30:00+00:00    0.050718\n",
       "2012-03-11 09:30:00+00:00    0.639869\n",
       "2012-03-12 09:30:00+00:00    0.597594\n",
       "2012-03-13 09:30:00+00:00   -0.797246\n",
       "2012-03-14 09:30:00+00:00    0.472879\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc = ts.tz_localize('UTC')\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',\n",
       "               '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='D')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 04:30:00-05:00   -0.202469\n",
       "2012-03-10 04:30:00-05:00    0.050718\n",
       "2012-03-11 05:30:00-04:00    0.639869\n",
       "2012-03-12 05:30:00-04:00    0.597594\n",
       "2012-03-13 05:30:00-04:00   -0.797246\n",
       "2012-03-14 05:30:00-04:00    0.472879\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00-05:00   -0.202469\n",
       "2012-03-10 09:30:00-05:00    0.050718\n",
       "2012-03-11 09:30:00-04:00    0.639869\n",
       "2012-03-12 09:30:00-04:00    0.597594\n",
       "2012-03-13 09:30:00-04:00   -0.797246\n",
       "2012-03-14 09:30:00-04:00    0.472879\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern = ts.tz_localize('America/New_York')\n",
    "ts_eastern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 14:30:00+00:00   -0.202469\n",
       "2012-03-10 14:30:00+00:00    0.050718\n",
       "2012-03-11 13:30:00+00:00    0.639869\n",
       "2012-03-12 13:30:00+00:00    0.597594\n",
       "2012-03-13 13:30:00+00:00   -0.797246\n",
       "2012-03-14 13:30:00+00:00    0.472879\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern.tz_convert('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 15:30:00+01:00   -0.202469\n",
       "2012-03-10 15:30:00+01:00    0.050718\n",
       "2012-03-11 14:30:00+01:00    0.639869\n",
       "2012-03-12 14:30:00+01:00    0.597594\n",
       "2012-03-13 14:30:00+01:00   -0.797246\n",
       "2012-03-14 14:30:00+01:00    0.472879\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern.tz_convert('Europe/Berlin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+08:00', '2012-03-10 09:30:00+08:00',\n",
       "               '2012-03-11 09:30:00+08:00', '2012-03-12 09:30:00+08:00',\n",
       "               '2012-03-13 09:30:00+08:00', '2012-03-14 09:30:00+08:00'],\n",
       "              dtype='datetime64[ns, Asia/Shanghai]', freq='D')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index.tz_localize('Asia/Shanghai')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations with Time Zone−Aware Timestamp Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-12 04:00:00+0000', tz='UTC')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = pd.Timestamp('2011-03-12 04:00')\n",
    "stamp_utc = stamp.tz_localize('utc')\n",
    "stamp_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-11 23:00:00-0500', tz='America/New_York')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.tz_convert('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-12 04:00:00+0300', tz='Europe/Moscow')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_moscow = pd.Timestamp('2011-03-12 04:00', tz='Europe/Moscow')\n",
    "stamp_moscow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.tz_convert('America/New_York').value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-12 01:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import Hour\n",
    "stamp = pd.Timestamp('2012-03-12 01:30', tz='US/Eastern')\n",
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-12 02:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp + Hour()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 00:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = pd.Timestamp('2012-11-04 00:30', tz='US/Eastern')\n",
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 01:30:00-0500', tz='US/Eastern')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp + 2 * Hour()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations Between Different Time Zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-07 09:30:00    1.663261\n",
       "2012-03-08 09:30:00   -0.996206\n",
       "2012-03-09 09:30:00    1.521760\n",
       "2012-03-12 09:30:00    0.244175\n",
       "2012-03-13 09:30:00    0.423331\n",
       "2012-03-14 09:30:00   -0.654040\n",
       "2012-03-15 09:30:00    2.089154\n",
       "2012-03-16 09:30:00   -0.060220\n",
       "2012-03-19 09:30:00   -0.167933\n",
       "2012-03-20 09:30:00    0.631634\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('3/7/2012 9:30', periods=10, freq='B')\n",
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-07 09:30:00+00:00', '2012-03-08 09:30:00+00:00',\n",
       "               '2012-03-09 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',\n",
       "               '2012-03-15 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='B')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1 = ts[:7].tz_localize('Europe/London')\n",
    "ts2 = ts1[2:].tz_convert('Europe/Moscow')\n",
    "result = ts1 + ts2\n",
    "result.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Periods and Period Arithmetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-DEC')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period(2007, freq='A-DEC')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012', 'A-DEC')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2005', 'A-DEC')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Period('2014', freq='A-DEC') - p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06'], dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.period_range('2000-01-01', '2000-06-30', freq='M')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01   -1.594313\n",
       "2000-02   -1.519937\n",
       "2000-03    1.108752\n",
       "2000-04    1.255853\n",
       "2000-05   -0.024330\n",
       "2000-06   -2.047939\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.random.randn(6), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2001Q3', '2002Q2', '2003Q1'], dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = ['2001Q3', '2002Q2', '2003Q1']\n",
    "index = pd.PeriodIndex(values, freq='Q-DEC')\n",
    "index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Period Frequency Conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-DEC')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period('2007', freq='A-DEC')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-01', 'M')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M', how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-12', 'M')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M', how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-12', 'M')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-JUN')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period('2007', freq='A-JUN')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2006-07', 'M')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M', 'start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-06', 'M')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M', 'end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2008', 'A-JUN')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period('Aug-2007', 'M')\n",
    "p.asfreq('A-JUN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006   -0.272657\n",
       "2007   -1.692615\n",
       "2008    1.423830\n",
       "2009   -0.407890\n",
       "Freq: A-DEC, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.period_range('2006', '2009', freq='A-DEC')\n",
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-01   -0.272657\n",
       "2007-01   -1.692615\n",
       "2008-01    1.423830\n",
       "2009-01   -0.407890\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('M', how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-12-29   -0.272657\n",
       "2007-12-31   -1.692615\n",
       "2008-12-31    1.423830\n",
       "2009-12-31   -0.407890\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('B', how='end')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quarterly Period Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012Q4', 'Q-JAN')"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period('2012Q4', freq='Q-JAN')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-11-01', 'D')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D', 'start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-31', 'D')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D', 'end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-30 16:00', 'T')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4pm = (p.asfreq('B', 'e') - 1).asfreq('T', 's') + 16 * 60\n",
    "p4pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-30 16:00', 'H')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4pm = (p.asfreq('B', 'e') - 1).asfreq('H', 's') + 16\n",
    "p4pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-01-30 16:00:00')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4pm.to_timestamp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011Q3    0\n",
       "2011Q4    1\n",
       "2012Q1    2\n",
       "2012Q2    3\n",
       "2012Q3    4\n",
       "2012Q4    5\n",
       "Freq: Q-JAN, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.period_range('2011Q3', '2012Q4', freq='Q-JAN')\n",
    "ts = pd.Series(np.arange(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010-10-28 16:00:00    0\n",
       "2011-01-28 16:00:00    1\n",
       "2011-04-28 16:00:00    2\n",
       "2011-07-28 16:00:00    3\n",
       "2011-10-28 16:00:00    4\n",
       "2012-01-30 16:00:00    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_rng = (rng.asfreq('B', 'e') - 1).asfreq('T', 's') + 16 * 60\n",
    "ts.index = new_rng.to_timestamp()\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting Timestamps to Periods (and Back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    0.756332\n",
       "2000-02-29   -1.288602\n",
       "2000-03-31    0.867534\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('2000-01-01', periods=3, freq='M')\n",
    "ts = pd.Series(np.random.randn(3), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    0.756332\n",
       "2000-02   -1.288602\n",
       "2000-03    0.867534\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts = ts.to_period()\n",
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29    0.575283\n",
       "2000-01-30    0.304205\n",
       "2000-01-31    1.814582\n",
       "2000-02-01    1.634858\n",
       "2000-02-02   -1.645693\n",
       "2000-02-03   -0.407164\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/29/2000', periods=6, freq='D')\n",
    "ts2 = pd.Series(np.random.randn(6), index=rng)\n",
    "ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    0.575283\n",
       "2000-01    0.304205\n",
       "2000-01    1.814582\n",
       "2000-02    1.634858\n",
       "2000-02   -1.645693\n",
       "2000-02   -0.407164\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29    0.575283\n",
       "2000-01-30    0.304205\n",
       "2000-01-31    1.814582\n",
       "2000-02-01    1.634858\n",
       "2000-02-02   -1.645693\n",
       "2000-02-03   -0.407164\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts = ts2.to_period()\n",
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29    0.575283\n",
       "2000-01-30    0.304205\n",
       "2000-01-31    1.814582\n",
       "2000-02-01    1.634858\n",
       "2000-02-02   -1.645693\n",
       "2000-02-03   -0.407164\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts.to_timestamp(how='end')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a PeriodIndex from Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('examples/macrodata.csv')\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1959.0\n",
       "1      1959.0\n",
       "2      1959.0\n",
       "3      1959.0\n",
       "4      1960.0\n",
       "5      1960.0\n",
       "6      1960.0\n",
       "7      1960.0\n",
       "8      1961.0\n",
       "9      1961.0\n",
       "        ...  \n",
       "193    2007.0\n",
       "194    2007.0\n",
       "195    2007.0\n",
       "196    2008.0\n",
       "197    2008.0\n",
       "198    2008.0\n",
       "199    2008.0\n",
       "200    2009.0\n",
       "201    2009.0\n",
       "202    2009.0\n",
       "Name: year, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.0\n",
       "1      2.0\n",
       "2      3.0\n",
       "3      4.0\n",
       "4      1.0\n",
       "5      2.0\n",
       "6      3.0\n",
       "7      4.0\n",
       "8      1.0\n",
       "9      2.0\n",
       "      ... \n",
       "193    2.0\n",
       "194    3.0\n",
       "195    4.0\n",
       "196    1.0\n",
       "197    2.0\n",
       "198    3.0\n",
       "199    4.0\n",
       "200    1.0\n",
       "201    2.0\n",
       "202    3.0\n",
       "Name: quarter, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1959Q1', '1959Q2', '1959Q3', '1959Q4', '1960Q1', '1960Q2',\n",
       "             '1960Q3', '1960Q4', '1961Q1', '1961Q2',\n",
       "             ...\n",
       "             '2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3',\n",
       "             '2008Q4', '2009Q1', '2009Q2', '2009Q3'],\n",
       "            dtype='period[Q-DEC]', length=203, freq='Q-DEC')"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.PeriodIndex(year=data.year, quarter=data.quarter,\n",
    "                       freq='Q-DEC')\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1959Q1    0.00\n",
       "1959Q2    2.34\n",
       "1959Q3    2.74\n",
       "1959Q4    0.27\n",
       "1960Q1    2.31\n",
       "1960Q2    0.14\n",
       "1960Q3    2.70\n",
       "1960Q4    1.21\n",
       "1961Q1   -0.40\n",
       "1961Q2    1.47\n",
       "          ... \n",
       "2007Q2    2.75\n",
       "2007Q3    3.45\n",
       "2007Q4    6.38\n",
       "2008Q1    2.82\n",
       "2008Q2    8.53\n",
       "2008Q3   -3.16\n",
       "2008Q4   -8.79\n",
       "2009Q1    0.94\n",
       "2009Q2    3.37\n",
       "2009Q3    3.56\n",
       "Freq: Q-DEC, Name: infl, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = index\n",
    "data.infl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling and Frequency Conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -0.252765\n",
       "2000-01-02   -0.894590\n",
       "2000-01-03    0.955842\n",
       "2000-01-04   -1.653984\n",
       "2000-01-05   -0.262528\n",
       "2000-01-06   -0.976094\n",
       "2000-01-07   -1.493407\n",
       "2000-01-08    1.167858\n",
       "2000-01-09    0.969001\n",
       "2000-01-10   -2.536487\n",
       "                ...   \n",
       "2000-03-31    1.397286\n",
       "2000-04-01    1.507055\n",
       "2000-04-02   -1.179442\n",
       "2000-04-03    0.443171\n",
       "2000-04-04    1.395676\n",
       "2000-04-05   -0.529658\n",
       "2000-04-06    0.787358\n",
       "2000-04-07    0.248845\n",
       "2000-04-08    0.743239\n",
       "2000-04-09    1.267746\n",
       "Freq: D, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('2000-01-01', periods=100, freq='D')\n",
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.057495\n",
       "2000-02-29    0.074833\n",
       "2000-03-31    0.186405\n",
       "2000-04-30    0.520443\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01   -0.057495\n",
       "2000-02    0.074833\n",
       "2000-03    0.186405\n",
       "2000-04    0.520443\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M', kind='period').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00     0\n",
       "2000-01-01 00:01:00     1\n",
       "2000-01-01 00:02:00     2\n",
       "2000-01-01 00:03:00     3\n",
       "2000-01-01 00:04:00     4\n",
       "2000-01-01 00:05:00     5\n",
       "2000-01-01 00:06:00     6\n",
       "2000-01-01 00:07:00     7\n",
       "2000-01-01 00:08:00     8\n",
       "2000-01-01 00:09:00     9\n",
       "2000-01-01 00:10:00    10\n",
       "2000-01-01 00:11:00    11\n",
       "Freq: T, dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('2000-01-01', periods=12, freq='T')\n",
    "ts = pd.Series(np.arange(12), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999-12-31 23:55:00     0\n",
       "2000-01-01 00:00:00    15\n",
       "2000-01-01 00:05:00    40\n",
       "2000-01-01 00:10:00    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min', closed='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00    10\n",
       "2000-01-01 00:05:00    35\n",
       "2000-01-01 00:10:00    21\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min', closed='left', label='left').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999-12-31 23:55:00     0\n",
       "2000-01-01 00:00:00    15\n",
       "2000-01-01 00:05:00    40\n",
       "2000-01-01 00:10:00    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min', closed='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00     0\n",
       "2000-01-01 00:05:00    15\n",
       "2000-01-01 00:10:00    40\n",
       "2000-01-01 00:15:00    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min', closed='right', label='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999-12-31 23:55:00     0\n",
       "2000-01-01 00:00:00    15\n",
       "2000-01-01 00:05:00    40\n",
       "2000-01-01 00:10:00    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min', closed='right', label='left').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999-12-31 23:59:59     0\n",
       "2000-01-01 00:04:59    15\n",
       "2000-01-01 00:09:59    40\n",
       "2000-01-01 00:14:59    11\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min', closed='right',\n",
    "            label='right', loffset='-1s').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Open-High-Low-Close (OHLC) resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:05:00</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:10:00</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     open  high  low  close\n",
       "2000-01-01 00:00:00     0     4    0      4\n",
       "2000-01-01 00:05:00     5     9    5      9\n",
       "2000-01-01 00:10:00    10    11   10     11"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min').ohlc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upsampling and Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-1.040816</td>\n",
       "      <td>0.426419</td>\n",
       "      <td>0.312945</td>\n",
       "      <td>-1.115689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-12 -1.040816  0.426419  0.312945 -1.115689"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.random.randn(2, 4),\n",
    "                     index=pd.date_range('1/1/2000', periods=2,\n",
    "                                         freq='W-WED'),\n",
    "                     columns=['Colorado', 'Texas', 'New York', 'Ohio'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-1.040816</td>\n",
       "      <td>0.426419</td>\n",
       "      <td>0.312945</td>\n",
       "      <td>-1.115689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-06       NaN       NaN       NaN       NaN\n",
       "2000-01-07       NaN       NaN       NaN       NaN\n",
       "2000-01-08       NaN       NaN       NaN       NaN\n",
       "2000-01-09       NaN       NaN       NaN       NaN\n",
       "2000-01-10       NaN       NaN       NaN       NaN\n",
       "2000-01-11       NaN       NaN       NaN       NaN\n",
       "2000-01-12 -1.040816  0.426419  0.312945 -1.115689"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily = frame.resample('D').asfreq()\n",
    "df_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
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       "      <th>Ohio</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>1.302395</td>\n",
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       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-1.040816</td>\n",
       "      <td>0.426419</td>\n",
       "      <td>0.312945</td>\n",
       "      <td>-1.115689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-06  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-07  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-08  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-09  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-10  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-11  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-12 -1.040816  0.426419  0.312945 -1.115689"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.resample('D').ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-1.040816</td>\n",
       "      <td>0.426419</td>\n",
       "      <td>0.312945</td>\n",
       "      <td>-1.115689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-06  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-07  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-08       NaN       NaN       NaN       NaN\n",
       "2000-01-09       NaN       NaN       NaN       NaN\n",
       "2000-01-10       NaN       NaN       NaN       NaN\n",
       "2000-01-11       NaN       NaN       NaN       NaN\n",
       "2000-01-12 -1.040816  0.426419  0.312945 -1.115689"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.resample('D').ffill(limit=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>1.302395</td>\n",
       "      <td>-0.272154</td>\n",
       "      <td>-0.051532</td>\n",
       "      <td>-0.467740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>-1.040816</td>\n",
       "      <td>0.426419</td>\n",
       "      <td>0.312945</td>\n",
       "      <td>-1.115689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-06  1.302395 -0.272154 -0.051532 -0.467740\n",
       "2000-01-13 -1.040816  0.426419  0.312945 -1.115689"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.resample('W-THU').ffill()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling with Periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01</th>\n",
       "      <td>-1.920361</td>\n",
       "      <td>0.020459</td>\n",
       "      <td>-1.350049</td>\n",
       "      <td>0.411061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02</th>\n",
       "      <td>1.592783</td>\n",
       "      <td>1.909133</td>\n",
       "      <td>1.755214</td>\n",
       "      <td>-0.271815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03</th>\n",
       "      <td>1.290008</td>\n",
       "      <td>-0.232239</td>\n",
       "      <td>0.428683</td>\n",
       "      <td>-0.723039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04</th>\n",
       "      <td>0.390677</td>\n",
       "      <td>1.659682</td>\n",
       "      <td>0.873921</td>\n",
       "      <td>-1.180212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05</th>\n",
       "      <td>-0.208885</td>\n",
       "      <td>-0.549671</td>\n",
       "      <td>-1.252880</td>\n",
       "      <td>-1.276761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-06</th>\n",
       "      <td>1.881156</td>\n",
       "      <td>1.108227</td>\n",
       "      <td>-1.751994</td>\n",
       "      <td>-0.973899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-07</th>\n",
       "      <td>0.908732</td>\n",
       "      <td>-0.509226</td>\n",
       "      <td>-1.023400</td>\n",
       "      <td>-0.412273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-08</th>\n",
       "      <td>-1.073039</td>\n",
       "      <td>-0.601411</td>\n",
       "      <td>0.222178</td>\n",
       "      <td>0.949363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-09</th>\n",
       "      <td>0.704186</td>\n",
       "      <td>-1.358964</td>\n",
       "      <td>1.148475</td>\n",
       "      <td>-0.161873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-10</th>\n",
       "      <td>-0.627426</td>\n",
       "      <td>0.617612</td>\n",
       "      <td>-1.449028</td>\n",
       "      <td>1.011090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-03</th>\n",
       "      <td>-0.897339</td>\n",
       "      <td>0.989615</td>\n",
       "      <td>1.096456</td>\n",
       "      <td>0.454319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-04</th>\n",
       "      <td>-1.018280</td>\n",
       "      <td>2.238237</td>\n",
       "      <td>0.406254</td>\n",
       "      <td>1.295960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05</th>\n",
       "      <td>0.469002</td>\n",
       "      <td>-2.195325</td>\n",
       "      <td>-1.221725</td>\n",
       "      <td>0.963414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-06</th>\n",
       "      <td>0.107341</td>\n",
       "      <td>-1.255465</td>\n",
       "      <td>-0.534739</td>\n",
       "      <td>0.959506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-07</th>\n",
       "      <td>-0.807201</td>\n",
       "      <td>-0.005354</td>\n",
       "      <td>0.175242</td>\n",
       "      <td>1.840241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-08</th>\n",
       "      <td>-1.822233</td>\n",
       "      <td>-0.374730</td>\n",
       "      <td>1.765640</td>\n",
       "      <td>-0.214612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-09</th>\n",
       "      <td>-0.309315</td>\n",
       "      <td>0.359124</td>\n",
       "      <td>0.787222</td>\n",
       "      <td>-0.299719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-10</th>\n",
       "      <td>-0.501940</td>\n",
       "      <td>0.238768</td>\n",
       "      <td>0.188189</td>\n",
       "      <td>-1.118327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-11</th>\n",
       "      <td>0.386220</td>\n",
       "      <td>0.224123</td>\n",
       "      <td>-1.359184</td>\n",
       "      <td>1.093890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12</th>\n",
       "      <td>-1.254319</td>\n",
       "      <td>-2.925113</td>\n",
       "      <td>0.654578</td>\n",
       "      <td>1.227245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Colorado     Texas  New York      Ohio\n",
       "2000-01 -1.920361  0.020459 -1.350049  0.411061\n",
       "2000-02  1.592783  1.909133  1.755214 -0.271815\n",
       "2000-03  1.290008 -0.232239  0.428683 -0.723039\n",
       "2000-04  0.390677  1.659682  0.873921 -1.180212\n",
       "2000-05 -0.208885 -0.549671 -1.252880 -1.276761\n",
       "2000-06  1.881156  1.108227 -1.751994 -0.973899\n",
       "2000-07  0.908732 -0.509226 -1.023400 -0.412273\n",
       "2000-08 -1.073039 -0.601411  0.222178  0.949363\n",
       "2000-09  0.704186 -1.358964  1.148475 -0.161873\n",
       "2000-10 -0.627426  0.617612 -1.449028  1.011090\n",
       "...           ...       ...       ...       ...\n",
       "2001-03 -0.897339  0.989615  1.096456  0.454319\n",
       "2001-04 -1.018280  2.238237  0.406254  1.295960\n",
       "2001-05  0.469002 -2.195325 -1.221725  0.963414\n",
       "2001-06  0.107341 -1.255465 -0.534739  0.959506\n",
       "2001-07 -0.807201 -0.005354  0.175242  1.840241\n",
       "2001-08 -1.822233 -0.374730  1.765640 -0.214612\n",
       "2001-09 -0.309315  0.359124  0.787222 -0.299719\n",
       "2001-10 -0.501940  0.238768  0.188189 -1.118327\n",
       "2001-11  0.386220  0.224123 -1.359184  1.093890\n",
       "2001-12 -1.254319 -2.925113  0.654578  1.227245\n",
       "\n",
       "[24 rows x 4 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.random.randn(24, 4),\n",
    "                     index=pd.period_range('1-2000', '12-2001',\n",
    "                                           freq='M'),\n",
    "                     columns=['Colorado', 'Texas', 'New York', 'Ohio'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.328431</td>\n",
       "      <td>-0.346026</td>\n",
       "      <td>0.153892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Colorado     Texas  New York      Ohio\n",
       "2000  0.530229  0.108171  0.036965 -0.314991\n",
       "2001 -0.001540  0.328431 -0.346026  0.153892"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame = frame.resample('A-DEC').mean()\n",
    "annual_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000Q1</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q2</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q3</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q4</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.328431</td>\n",
       "      <td>-0.346026</td>\n",
       "      <td>0.153892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.328431</td>\n",
       "      <td>-0.346026</td>\n",
       "      <td>0.153892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.328431</td>\n",
       "      <td>-0.346026</td>\n",
       "      <td>0.153892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.328431</td>\n",
       "      <td>-0.346026</td>\n",
       "      <td>0.153892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q1  0.530229  0.108171  0.036965 -0.314991\n",
       "2000Q2  0.530229  0.108171  0.036965 -0.314991\n",
       "2000Q3  0.530229  0.108171  0.036965 -0.314991\n",
       "2000Q4  0.530229  0.108171  0.036965 -0.314991\n",
       "2001Q1 -0.001540  0.328431 -0.346026  0.153892\n",
       "2001Q2 -0.001540  0.328431 -0.346026  0.153892\n",
       "2001Q3 -0.001540  0.328431 -0.346026  0.153892\n",
       "2001Q4 -0.001540  0.328431 -0.346026  0.153892"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q-DEC: Quarterly, year ending in December\n",
    "annual_frame.resample('Q-DEC').ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000Q4</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.328431</td>\n",
       "      <td>-0.346026</td>\n",
       "      <td>0.153892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q4  0.530229  0.108171  0.036965 -0.314991\n",
       "2001Q1  0.530229  0.108171  0.036965 -0.314991\n",
       "2001Q2  0.530229  0.108171  0.036965 -0.314991\n",
       "2001Q3  0.530229  0.108171  0.036965 -0.314991\n",
       "2001Q4 -0.001540  0.328431 -0.346026  0.153892"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame.resample('Q-DEC', convention='end').ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000Q4</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>0.530229</td>\n",
       "      <td>0.108171</td>\n",
       "      <td>0.036965</td>\n",
       "      <td>-0.314991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.328431</td>\n",
       "      <td>-0.346026</td>\n",
       "      <td>0.153892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q1</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.328431</td>\n",
       "      <td>-0.346026</td>\n",
       "      <td>0.153892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q2</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.328431</td>\n",
       "      <td>-0.346026</td>\n",
       "      <td>0.153892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q3</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.328431</td>\n",
       "      <td>-0.346026</td>\n",
       "      <td>0.153892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q4  0.530229  0.108171  0.036965 -0.314991\n",
       "2001Q1  0.530229  0.108171  0.036965 -0.314991\n",
       "2001Q2  0.530229  0.108171  0.036965 -0.314991\n",
       "2001Q3  0.530229  0.108171  0.036965 -0.314991\n",
       "2001Q4 -0.001540  0.328431 -0.346026  0.153892\n",
       "2002Q1 -0.001540  0.328431 -0.346026  0.153892\n",
       "2002Q2 -0.001540  0.328431 -0.346026  0.153892\n",
       "2002Q3 -0.001540  0.328431 -0.346026  0.153892"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame.resample('Q-MAR').ffill()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving Window Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>7.40</td>\n",
       "      <td>21.11</td>\n",
       "      <td>29.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>7.45</td>\n",
       "      <td>21.14</td>\n",
       "      <td>29.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>7.45</td>\n",
       "      <td>21.52</td>\n",
       "      <td>29.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>7.43</td>\n",
       "      <td>21.93</td>\n",
       "      <td>28.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>7.28</td>\n",
       "      <td>21.31</td>\n",
       "      <td>28.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-09</th>\n",
       "      <td>7.34</td>\n",
       "      <td>21.93</td>\n",
       "      <td>29.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-10</th>\n",
       "      <td>7.36</td>\n",
       "      <td>21.97</td>\n",
       "      <td>29.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-13</th>\n",
       "      <td>7.32</td>\n",
       "      <td>22.16</td>\n",
       "      <td>28.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-14</th>\n",
       "      <td>7.30</td>\n",
       "      <td>22.39</td>\n",
       "      <td>29.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-15</th>\n",
       "      <td>7.22</td>\n",
       "      <td>22.11</td>\n",
       "      <td>28.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>374.60</td>\n",
       "      <td>24.53</td>\n",
       "      <td>71.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>372.50</td>\n",
       "      <td>25.34</td>\n",
       "      <td>72.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>378.25</td>\n",
       "      <td>25.89</td>\n",
       "      <td>73.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>377.37</td>\n",
       "      <td>26.34</td>\n",
       "      <td>73.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>369.80</td>\n",
       "      <td>26.25</td>\n",
       "      <td>73.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>388.81</td>\n",
       "      <td>26.94</td>\n",
       "      <td>76.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>400.29</td>\n",
       "      <td>27.00</td>\n",
       "      <td>76.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>402.19</td>\n",
       "      <td>26.96</td>\n",
       "      <td>77.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>408.43</td>\n",
       "      <td>27.18</td>\n",
       "      <td>76.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>422.00</td>\n",
       "      <td>27.27</td>\n",
       "      <td>78.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2292 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL   MSFT    XOM\n",
       "2003-01-02    7.40  21.11  29.22\n",
       "2003-01-03    7.45  21.14  29.24\n",
       "2003-01-06    7.45  21.52  29.96\n",
       "2003-01-07    7.43  21.93  28.95\n",
       "2003-01-08    7.28  21.31  28.83\n",
       "2003-01-09    7.34  21.93  29.44\n",
       "2003-01-10    7.36  21.97  29.03\n",
       "2003-01-13    7.32  22.16  28.91\n",
       "2003-01-14    7.30  22.39  29.17\n",
       "2003-01-15    7.22  22.11  28.77\n",
       "...            ...    ...    ...\n",
       "2011-10-03  374.60  24.53  71.15\n",
       "2011-10-04  372.50  25.34  72.83\n",
       "2011-10-05  378.25  25.89  73.95\n",
       "2011-10-06  377.37  26.34  73.89\n",
       "2011-10-07  369.80  26.25  73.56\n",
       "2011-10-10  388.81  26.94  76.28\n",
       "2011-10-11  400.29  27.00  76.27\n",
       "2011-10-12  402.19  26.96  77.16\n",
       "2011-10-13  408.43  27.18  76.37\n",
       "2011-10-14  422.00  27.27  78.11\n",
       "\n",
       "[2292 rows x 3 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px_all = pd.read_csv('examples/stock_px_2.csv',\n",
    "                           parse_dates=True, index_col=0)\n",
    "close_px = close_px_all[['AAPL', 'MSFT', 'XOM']]\n",
    "close_px = close_px.resample('B').ffill()\n",
    "close_px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f136c7c5828>"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_px.AAPL.plot()\n",
    "close_px.AAPL.rolling(250).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003-01-09         NaN\n",
       "2003-01-10         NaN\n",
       "2003-01-13         NaN\n",
       "2003-01-14         NaN\n",
       "2003-01-15    0.077496\n",
       "2003-01-16    0.074760\n",
       "2003-01-17    0.112368\n",
       "Freq: B, Name: AAPL, dtype: float64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appl_std250 = close_px.AAPL.rolling(250, min_periods=10).std()\n",
    "appl_std250[5:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f136c6d35c0>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "appl_std250.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f136c3c9780>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "expanding_mean = appl_std250.expanding().mean()\n",
    "expanding_mean.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f136c5b2780>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JzWRHQjb3D2xdqRb2XX0DySwsY815najlRjO/7jvFyPBmle541DSN8T0D2JWYTVKW9YY+HjuTz9zIBCZEtOLOPoFWu66oGEnsQlRSVmEZTy8+QNumHpVOaoNDmxLQ2I0FO0+ee2xNdBrZReVM6FW15QJu6dESBw1+2ZtcpedXllKKl34/jJerE89d194q1xSVI4ldiEowmRUzfjlEbrGBTyf2wN2lcsstOTpoTOwdyLb4LOIzCjGbFZ9viKOVjxuDQi8/quZK/L3dGBjalF/2plhlTPvPe1LYfSKHmde2p7GHS41fT1SeJHYhKshsVjz/2yHWH0vnpRs6EN6iaqst3h7RCicHjc/Wx/H7gVNEn87nX6PCKtQBezm39QwgNa+UbfGZVT5HRRw+lcdLSw/Tt41PrViQrL6SxC5EBZQaTLz4+2EW70nhiWEh3NMvuMrnaurZgAcGt2HJ/lM8vfgg4f5ejO1SsSGOlzMyvBmerk4s2V9znahx6QU88N0efD1c+OzOHueWOxD2x26W7RXCXsWcKWD6T/s5dqaAh4e05amR7ap9zmdHh9Hcy5Xf9qXw5i2dq50kXZ0dGdPJnxVRpzGYzOcWIbOU9PxSJn2zC4NJMf++XjK71M5JYhfiMnKKynlhSRQrj5zB282Z/5sSwbD2lhnWp2kak/sHM7l/sEXOBzC0fVMW7UnmYHIuEcE+FjtvSbmJqd/tIbfEwOKH+tGxhbfFzi1qhiR2IS4hr8TAxLk7SMgs4tFr2nL/wDb42HlHYd82vmgabIvPsmhif3npYaJO5TH3ngg6tZSkXhtIjV2IS/hqczwxaQX83+RezBjd3u6TOkAjdxc6tvBia5zlOlD3nszh570pPDKkrUxCqkUksQvxD7nF5czfdpIxnf0ZGNrE1uFUSv+2TdiflGuxTbTnbzuBp6sTjw8Lscj5hHVIYhfiH+ZtPUFhmZFptTCZ9WvjS7nJzP6k6u+ulFFQxl+HTzO+Z0Clx+sL25LELsR58ksNzNuayOiOzWjfvGrj1G0pIrgxDhrsSMiq9rkW7DyJwaS4q0+QBSIT1iSJXYjzfLftBPmlRqYNu/IyvPbK09WZzi292ZGQXa3zZBWW8XVkIqPCmxHi19BC0QlrkcQuxFnZReXMjUxkeHu/Wj36o08bXw4k51JqqHqd/bMNcRSXG3n2WlkLpjaSxC7EWR+sjqGwzFjrF7bq28aHcpOZfVWssydnF/PDjpNM6NVKWuu1lCR2IYAjqXks3JXEPX2DCG3maetwqiUi2Odsnb1q5ZgPVsfg6KAxfXj1Z9gK27BYV7emaaOAWwGUUg9Z6rxC1DSlFK8tj8bbzZmnRtT+ZObl6kynlt5V6kA9fCqP3w+k8ug1bWnuLTsi1VYVarFrmuasadqy8z531TRtuaZpBzVN+17TNzrMAZ4CPGooViFqxLqj6exIyOapke3wdne2dTgW0a+tL/uTcsgpKq/U895fHYO3mzMPDWlbQ5EJa7hqYtc0zQ3YC4w87+G7gRSlVFeg8dmvHQZeAmZbPkwhaobRZOatv47SpokHE3vXnZ2AbuzaEoNJ8eu+lAo/Z8+JbDbGZPDINW3xdqsbb3D11VUTu1KqRCnVBTj/N2QYsObsx+uBocBrgDtwh6ZpFy39pmnag5qm7dE0bU9GRkb1IxfCAhbuTiY+o4jnrmtv8RURbSm8hRe9g334ZksiZcaKjY75cM1xmjRswKR+Mm69tqvqb7IvkHf243zARyn1L6XUdKXU60qpsn8+QSn1lVIqQikV0bRp1XaKEcKS0vNLeW/lMfq28WFkHVwH5fFhIZzOK+XnPVdvtR9IzmVbfBYPDW4js0zrgKom9kzg74G+3mc/F6LWUErx798PU2Y08+bNndG7ieqWQaFN6BHYiLf/OsZHa45ftuWulOLTdbF4uToxUTamrhOqmtjXAaPOfjwM2GCZcISwjj8OprI6Oo2nRrajTdO6OVZb0zQ+ntCdXsGN+c+6WK7/ZAtJWcWAviyxUvr+qF9HJrLuWDqPDg2hYQNprdcFVf0pLgBu0TTtEHAQPdELUSvsS8ph5q+H6B7YiKkDW9s6nBoV6OvOvHt7szEmnek/HeC++bsJ9WvIX4fP0Njdmd6tfVh1JI3rO/vz4KA2tg5XWIj297u2VS6maWOBsSEhIQ/ExsZa7bqi/lFKXVBeiTlTwBcb48gvNbI1LpNmXq789mj/erXFW2RsBvfP34PBZObO3oEkZRcTGZvJoNAmzJ0Ugauzo61DFFehadpepVTEVY+zZmL/W0REhNqzZ4/VryvqtvxSA5+sjWVDTDonsooJ9/diYGgT8koMLN6djJuLI828XOnT2odpw0Lr5QScnKJyyoxmmnu7opQiLb+MZl4N6mQfQ11U0cQuBTVRJ+QVG7j1v9tIzCxiUGgThrX340ByLl9tTsDRQePWHgHMvK527IRUkxqf9/o1TauXb271gSR2Uetti8vk+SVRpOaWMP/e3hfsevT3CofWKDMopSgxllBsLCa9OJ3mHs3xca383qM5pTnE5MQA4O3iTZhPGA5a3RljX1sVG4rZnLKZlMIUWnu1pnPTzjRxa2KXPxtJ7KLWMpsVLy09zIKdSQT6uLNgal96t74wkdZkQs8ry+Pn4z+zN20vKQUpnCk6Q6mp9IJj2jVuxzWtriG0cSg+DXwI9AqkuUfzC44pN5Wz5dQWNqdsZsfpHZwqPHXB13v49WB2/9m09q7bHb32Krs0m4/2fsSqE6soMZZc9PW23m0ZFjiMcN9wwnzCCGgYYPPSltTYRa31zspjfLExnvsHtmbG6DCrtMozSzJZmbiS6Kxo1iatpcRYQrvG7QjyCsLfwx9fN18aODbAz92PpPwkIk9Fsj99P2ZlPneOgS0HMqXjFHo3782uM7t4bcdrnMw/SUPnhvTx70PXpl1p79MeZwdn4nLj+GT/J5QaS3moy0M82OVBmyeN+mTNyTW8vuN1CsoLuCnkJq5rfR0dfDoQlxvHkawj5JTmsC99H3vT9p77GXu5eDEiaATPRDyDl4tld+Gyy85TGRUjLOVQSi43fr6VCRGteOuWmplgZFZmig3FZJRkkFqYyvbU7fx47EcMZgM+rj4MbDmQSeGTCPMJu+J5CsoLSCtKI6s0i71pe1kUs4js0mx8XH3ILs2mlWcrZkTMYGDAQJwdLl6jJbMkk7d3vc2qE6u4u8PdzOg1wy5v/+uSUmMpr25/lWUJy+jg04E3Br5BaOPL76pVbCgmIS+Bo9lHOZh+kBUJK/Bz9+P1ga/Tq3kvi8Vll4n9b9JiF9VhNJm5ac5W0vPLWPvMELxcLbtglcFk4PMDn7MkbgnZpf9b01xDY2zbsTzQ+QGCvYOrfP4yUxnL45ez88xOOvl24vaw23F1unInplKKd3e/yw9Hf2BQy0G8OuBVmrg1ueJzRNUYzUae2vgUm5I38UjXR5jaZeol33Cv5FDGIWZunklKYQpDAoYwtfNUuvl1q3ZskthFnfXB6hg+XR/H53f24Pou/hY9d2phKs9ufpaDGQcZETiCrk274uvmS8uGLQnyCsLXzdei16sMpRQLjy3kw70f4ubkxqx+sxgRNMJm8dRFSile3fEqvxz/hRf6vMDE9hOrfK5iQzELji5g3pF550o5M3vNpKFL1Wc6S2IXVnXsTD6HkvMoKDMyskMzAn3da+Q6G2PSmTJvN7dHBPDu+K4WO6/JbOKnmJ/4/MDnmJWZ2f1nc23wtRY7vyUl5CbwwpYXOJJ1hNva3cZTPZ/C06V27/pkD5RSfLT3I+YdmcfUzlOZ3mO6Rc5bWF7I11FfM+/IPJq6NWVm75mMDBp59SdegiR2YTU/7kzipaWHMZn136UGTg68dmMnbu/VyqLXScws4uY5W2nu5crvjw2wWGdpWlEaz25+ln3p++jj34dZfWfRysuysVuawWTg430f88PRH/Bz9+OtgW8R0fzKf+9pRWksjV/KyhMrKTYUMyNiBsODhlspYvv3xcEvmHNgDhPCJvBinxct3m8TlRHF7O2zCWscxpuD3qzSOSSxC6v4M+o0jy7Yx5B2TXntxk5oGjz/WxRb4jK5PSKAt27pgqND9f9AsovKuWXOVvJLjSx5tD9BvpbZqOtM0RnuW3UfWSVZ/LvvvxnbdqxFzmstURlRPL/leVILU3l1wKtc3/r6ixKSwWzgx6M/8vmBzykxltC1aVcKygtIyEtgSscpPBPxjI2itx/zj8zn/T3vM67tOF4b8FqNdU6bzCaKjcVVvsOSxC5qnNmsGPXxZpwdHfjj8QHnNqowmRUfrTnOZxviePSatjx7bftqX2vq/N1Exmby4wN96RnUuNrnA72kMW39NLJKs/hy5Jd0bWq50o415ZXl8fi6xzmQcYAgryBGB4+mo29HDmYcJK04jYRcfbTG4IDBzOw1k0CvQAwmA2/vepvFxxdzT/g9TO8xnQaONbdujlLq3Dh/szLTzL1ZtWrNloxrbtRcPt3/KaOCRvHO4HdwcrDf6T12uaTAecMdrXlZUUM2xWYQl17IxxO6XbD7kKODxr9Gh5FVVMacjfF0bdWI0R2bX+FMV7b5eAZrj6bz3HXtK5zUDSYDmSWZeDfwxt35wnp/dFY0C44uYEXCChq6NKzVSR3Au4E3X4/+mj/i/2D1idXMPTQXhcLJwYnm7s1xcnBidr/Z3BJ6y7nWvLOjMy/0eQGA76O/Z9upbfxn2H8I8rLs7klGs5E/4v/g66ivSS5IPve4k+bEgJYDeKTbI3T07WjRa1aUUooP9nzA/Oj53NDmBl4d8KpdJ/XKkBa7qLK7vt5BXHohkc8Ow8Xp4lvXMqOJ2/+7ndj0Qp4e2Y60/FJaN2nI+J4Blzz+UowmM2M+iaTUYGbN04Np4HTpuvrxnOOsSFjBkawjxOfGk1WShULh6ujK8KDh3NDmBnJKc1h4bCFRmVG4Oroyvt147u98f50bNphSkEJacRrhvuG4Obld9fitp7YyM3ImZcYynu39LLe1u+2qzyk2FJNXloeHiweezp7n3jBMZhOpRakk5iWyP30/KxJWcLroNJ18OzEuZBzeLt44aA5EZ0WzJG4JuWW53Nn+Tp6JeAYXR+ut46OU4q1db7Hw2EImtp/Ic72fqxVzA6QUI2rUkdQ8rv9kC89d156Hr7CjfXp+KVO/28OhlLxzj7Vs5MbE3q2YMqD1VTd2+H77CV5aeoT/3t2TaztdutW/OGYxb+18CzR9Cn9Y47Bzs0BjsmP4K/EvCgwFAAR7BXNH+zsY23asxWcF1mZpRWnM2jaLralbebrn09zb6V5AX+7gj/g/OJJ1hFJjKUazkcS8RGJzY8/NtGzo3JCOTTqSW5rLifwTlJn0nTEdNUf6+vdlQtgErml1zUW1/8LyQj4/8Dk/HP2BcN9w3h/yPq08a77T2qzMvLnzTRbFLGJKxyk83fPpWjObVxK7qFHTf9rP2ug0tj0//Ko72pvMiujUfNr6ebAjIYtvtiSyNS4LV2cHRoU3Z/a4jpdcdfF0XgmjPtxM5wBvFkztc9Efn1KKLw5+wRcHv2BwwGDeGPAGjVwbXXSeUmMpu87swsvFiy5Nu9SKlpktGMwGXoh8gZUnVjKm9RiCvYP5LfY3zhSdoXGDxrg7u+OoOdKiYQu6Nu2Kv4c/hYZCTuaf5EjWEXxdfWnj3YbW3q1p06gNIY1CKtRJuC5pHS9tfQmlFNe1vg5/D3+8G3jj4exBYl4ihzIO4eDgQESzCCaETajW0E6zMvPajtf45fgv3NfpPp7s8WStSeogiV3UoONpBYz+eDMPDmrD82M6VOkcB5Nz+WVvCov2JNPB34sfp/bB47zWu1KKe7/dzY6ELFY9OfiiUTBmZebd3e+y4OgCbgq5iVn9ZtWZ+qgtGc1GPt77MYuPL6bEWEKXpl14rNtj9PPvV6MJ8FThKV7b8RpHMo+QW5Z77nEHzYF2jduhlCImJwYvFy/uCb+HCWETaOxauU7085P6A50fYFr3abUqqYMkdlGDHv5+L1viMol8dugF63tXxZroNB76fg8DQ5vy9aSIc7X3LzfF89Zfx5g9NpwpA/63qmFyfjJrktbwe9zvJOYlck/4PcyImFHr/kDtnclsotBQiJdMbT9iAAAgAElEQVSLl9W/t2WmMvLK8igsL6S5R/Nznd/RWdHMOTCHTSmbcHV05a1Bb1V45m2psZS3d73Nr7G/MrXzVJ7o/kSt/J2RxC5qxN+19adGtGP6iMsvilQZi3YnMfPXKEaFN+PGbi1ZEZXKn1FnGNO5OZ9N7M7R7KOsS1rH+qT1xOfFA9C1aVcmtp/ImNZjauUfqKi6uJw4Zm2fxeHMw7w+4PWrzj3YlrqN17a/RkphSq1tqf/NLoc7itrvm8hEPFwcmTIg2GLnnNArkPwSI2+vPMbq6DTcnB15ckQoHdqe5Pol15NSmIKj5kjPZj0Z3248Q1oNsUonm7BPIY1DmDtyLtPWT+OFLS+QXJDMw10fvqjvJCY7hs8OfMbG5I0EeQXxzahv6O3f20ZRW5cs2ysqLC2/lIHvrOeuPkHMHmf5scfp+aXkFBto5tmARXHz+OzAZ7T3ac/dHe5mSMCQS3aMivqr3FTOq9tfZWn8UjydPWnm0QxXR1ecHZ0xmo1EZUbh6ezJlE5TmNxxco1OwLIWKcUIi3tv1THmbIxn47+usdiU/n8qN5Uza9sslics54Y2N/BK/1esOr5Z1C5KKdYmrWVH6g5yynIoMZZgMBlQKLr5dWNS+CS8G3jbOkyLkVKMsKiSchMLdiYxskOzaid1o9lIuan8ohmhZ4rOMHPzTPal7+Pxbo/LbkHiqjRNY2TQyCqvllhXSWIXFfLb/hRyiw3cP7By+24azUbic+M5nHmY3Wm72Ze2j7TiNJRStPdpT7vG7SgxlnC66DRHso7g4uDCe0Pes9slc4WoDSSxi4sUGYrYnrodTxdPmrk3w9+9JV9HJtKppddFm0VfSl5ZHjtO7+CX479wIP3AuQ2efVx96N28N4FegWho7Evfx/bT23F3cqeJWxMe6PwAN4feTMuGLWv6JQpRp0lir6OUUqQXp+Po4FihtVCUUpzMP8nG5I3MOzLvgi3hXBzcKHb3p29gW+ZGHeX6NtdfkHzzyvJYFLOInad3cqboDMkFySgULTxaML7deDo26UjnJp0J9AyU0ooQViCdp3VQdFY0b+96m/3p+9HQuKvDXTze/XE8nC+ujacUpPB11NdEpkSSXpIOQO/mvXmgywM4ao4k5ibz9vo1OLim0MizhLTiNFwcXJjccTL9WvRjXdI6fov9jRJjCR19OxLgGUBoo1B6NOtBd7/uMhtUCAuSztN66s+EP3lhywt4N/Dm6Z5Pc6rwFD8c/YHlCcsZEjAEVydXjGYjRrORhLwEojKjcHFwYXjgcHr596JXs14EeQWda1lvPOhJdlIDfnm4HxHBPpwpOsPH+z5mbtRc5kbNxUlzYkybMUzpOOWKu7gLIaxHWux1yLqkdTy98Wl6NuvJx0M/Prd64eHMw8w/Mp8dp3cA4OzgjKODI35ufgwNHMoNbW6gucfFKycePZ3PjZ9t5YYu/nw44cId1pPykziZf5L2Pu1p6t605l+cEMI+x7HLBKWasy11G4+ve5wOPh2YO2ruRUMJK6ug1MC4z7ZSVGbkz+mDaNKw9k/uEKK2q2hit+r6pUqpZUqpB729686EAXtwOPMw09dPp413G+aMmFPtpK6U4oUlhzmZVcSnE7tLUheilpGFqWu5wvJCZmyaQWPXxnw58kuLzLKLjM1k2cFUnhrRjj5tfC0QpRDCmqTztBZTSvH6ztdJLUrl22u/xdet+klYKcW7q44R0NiNB4e0sUCUQghrkxZ7LVVYXshzkc+xImEFj3Z9lO5+3S1y3lVHznD4VD5Pjmh32f1FhRD2TVrstUhaURoxOTFsObWFtSfXklWaxRPdn2Bq56kWOb/JrPhg9XHaNvXg5u4y+1OI2koSu507nnOclYkr2ZC8gbjcOABcHV3p49+H+zvfb7GWOsAfB08Rm17IZ3d2x9FBZogKUVtJYrdTmSWZvLPrHVaeWImj5kiPZj34V8S/CPcNp3OTzrg6uVr0euVGMx+vjaWDvxdjOvlb9NxCCOuSxG5nDCYDS+KW8J99/6HEWMIjXR9hYvuJFd64N6eonI/WHsdgUozr2oJ+ba/eoWo2K2b+eoiTWcXMu7cXDtJaF6JWk8RuJ/LK8lh4bCGLYxaTUZJBD78ezOo/izbeFR+ZUmowMfW7Pew9mYNnAycW7kpifM8A3r21yxWT9TurjrFk/yn+NaodQ8P8LPFyhBA2JIndxjJLMvku+jsWHVtEsbGYAS0H8GqHVxnQYkClV0J8f1UMe0/m8PmdPRjewY+P18by303xNGnYgBmjwy5ZN/+/LYl8uSmBe/oG8djQEEu9LCGEDVk1sZ+3pIA1L2t3CssL2ZO2h7Un17LyxEoMZgOjg0dzf6f7CfMJq9I5o1PzmbftBHf2CeT6LnqNfOa1YeQUlfPfTfEs2HmS+wa05skRoefeMJYfSuW1FdGM7tiM2eM6ypK6QtQRsgiYlZjMJnan7WZp3FLWnlxLqakUD2cPrmt9HVM6TiHIK6jK5zaazNz63+2kZBez/plr8HZ3Pvc1s1nx1+EzLD1witXRadzYrQVv3tyZQyl5TP6/XXQJ8OaHqX1wdZYx60LYO1m2105kFGew4OgCfo/7nazSLDydPbkx5EZGB4+mS9MuFtk5fc7GeA4m5/LpxO4XJHUABweN67v4M6Zzcz5bH8eHa4+zNS6LojIjgb7ufD05QpK6EHWMJHYLKzOVcarwFMezj7MxZSOrT6zGpExcE3ANY9qMObcmuqUcPpXHf9bFcmO3Fozt2uKyx2maxrThofQMasysP44Q3sKLt2/pTCN3F4vFIoSwD5LYLSCvLI9VJ1axPGE5B9IPoNDLW94NvLkl9BYmhU8i0CuwRq798dpYGjZw4tUbO1Xo+P4hTVjz9JAaiUUIYR8ksVeBUorlCctZfWI1p4tOE58Xj9FsJKRRCFM7TyXYO5i2jdoS1jisRreGi07NZ+3RNJ4a0Q5vN+erP0EIUS9IYq+kUmMpr2x/heUJy2nl2YrW3q0Z0HIAo4NH08Gng1VHlny+MY6GDZyY0j/YatcUQtg/SeyVcLrwNNM3TOdY9jGmdZ/GA50fsNkQwbj0Qv6MOs0jQ9pe1GEqhKjfJLFX0N60vTy98WnKTGV8MuwTrml1jU3jmbMxjgZODtw/sLVN4xBC2B9J7FdhMBlYeGwhH+39iJaeLZk3dB5tGtl2A4qEjEJ+33+Kewe0xle2rRNC/EO9T+wms4mozCgKDYUENAyglWcrHB0cKSgv4Nfjv/LD0R9IK05jcMBg3hr0Fl4uXrYOmU/WxdLAyZGHh7S1dSjiUpSCffNh/Rvg3RKu/xBa9rB1VKIeqbeJ3WA2sPbkWr48+CXxefHnHvd08aStd1ticmIoMZbQq3kvZvefXaW1W2pCXHoBSw+m8uDgNjT1lNa6Xdr4Fmx6BwJ6QW4yzB0GYz+GnlNsHZmoJ+p0Ys8ryyO5IJm0ojTSS9JJL9b/JeUnEZcbR6GhkLbebXlz4JsEeAZwMv8k+9L2kVyQzLi247gl9BbCfcNt/TIu8J91cbg7O/LQYGmt26U98/Sk3v1uGPsplOXDr1Nh2XTQHKHHPbaOUNQDdSqxpxen82fCn0SeiiQuN47s0uwLvu6oOdLErQktG7ZkTOsxDGw5kCGthuCg6Vu/dvfrzk0hN9ki9Ao5nlbA8kOpPDKkLT4eMmPU7kT9AsufgpCRcMPH4OAAbo1gwg/w00T4Yxo4OEG3ibaOVNRxtX51R4PJwPbT2/kr8S9Wn1hNubmc9j7tGdpqKMFewQR6BdLcozl+7n74uPqcS+K10TeRibg6OTJ1kG07b8UlHFsBvz0IQQPg9u/A8bwhqM6ucMeP8OME+ONx8GkDgX1sF6uo82rV6o7lpnJOFZ6i3FROenE665LWsebkGvLL8/Fy8eLa4GuZ3HFyjU3ft6XCMiO931jLDV38eXd8V1uHI84Xv15P2s07w6Sl0MDz0seV5MBXQ8FYBo9sBXcf68ZpT6KXwub3IS8ZWvaE0FHQojs06wQu7v87zmSEY8shdZ/+tU7j9TuheqpOru6YmJfI+GXjz33u7uTO0MChjGk9hn7+/XB2rLsTdZYfTKW43MSEXq1sHUr9UJoHLp5XTyLJu+Cnu6BJO7jrl8sndQC3xnDbPPh6pF6WmfAD2EGHvFUpBX/NhF1fgl9H6DAWEiMh7ln965ojNO8ErfqAiwcc+hnyU0BzAGWG/T/AzV+Cl+zLeyW1KrH7N/TnnUHv4OLogncDbzo16YSbk5utw7KKRXuSCfFrSI/Aiu19Wq+VF+uJ2aPJhSWRK1EKTmyBmD8hfgNkHAV3X2h3HfR9WG+N/1NpPvxyHzRsBvcsqVgLvEV3GDELVv8btnwIg56p3Gur7SI/0JN6n0dg1Gv6z0cpyEuB0wf1lnnKbjjwI5QXQevBMOY9vUV/YAGsfA6+6A83fgbtr6+ZGE1GyEkE71Z6Ga0WqlWJ3cvFizFtxtg6DKs7kVnE/qRcnr+uvV0MubRbGTGw+T2I/gNMZXrrzztATwD9HtfHlF/K6YOw+iVI3ASODSCoH3S8GbIT4MhvcOAHPSG36quPR28cDM5uepLKPwX3rYaGldgrtu9jkHoA1r0KZYUw7CVQJkjaAenR4OAIviHg01aPv678zPd9B+tfg863wbVv/e91aRo0aqX/63CD/pjZrP8Mnc9ruPWcDEH99TfTn+6Em7+CrhMsE5vJCCciIfp3OLocijP1O7Ze98Ggf4Gr7eevVEatqrHXV59viOO9VTFsfW4YLRvVjzuUSkk7ok8GilkBzh76UMOmYVBwGtKPwvGVeqswdCR0uxPaXQtODSDjuJ6cDy3SR68MmamPNT8/mZTk6Lf/x1bobwCG4guvPXwWDHq68jGbDPDnv2Dvt+AVoA+LLMu/+LgW3fXSQ9OqbZloN479qSfjkOF6R7JTNeZgGMvgh1shZQ88uBH82lcvtsTNsOxJyI7Xf3/CroXgQXqiP/yrfkc24hXoMsHm9f2K1tglsdcCN3waibOjA0seHWDrUOxLYQasegGiftZr230fhd4P6CWY8+WchD3/pyfwgtN6rbtxa/2238kV+jwEA5/Wk/uVmIyQcQwKzkB5od7CbNmz6vErBQcXQuwacPWGkBHQqree9LPi9Desze+BoUQvR3S/u3a23lMPwLzr9DenKSv02nl1FZyBLwbod0oPrL/wzbiilIItH+l3To2DYcRsaDf6wnOl7IW/noVTe/QJZ8Ne0stDFf05lObB7m/0Oz9Pf/33rllH/e6jCiSx1xEZBWX0emMtM0aH8djQ+r0J+AUSI/Vb8tI86Pco9H/i6jVuswkSNuj129xkCLsOut8DDZtaJ+aqKDijT3A6EQkdxuk1ec/m4OFn89ZjheSdgq+H6+P3p64Dz2aWO3fsWlhwK/SYBGM/qdybXnkR/PEEHP4FOt4CN35+4Wic85nNcOgnWDsbCtP05Bxxnz7ZzO0ffV7GMji+Ck4fgJwTeoxleRDQW78jKzijv3nc8lWVXrIk9jpiyf4Unlp0kGWPD6RzgLetw7EPsWv12/rGQXDbt3oLqC4zm2DbJ3q5yWzQH3Pz0ctGwQP0j+1xLZqyAvi/6/QEd/9qaFYDs7jXvqJ3Qne8WV+T52pv7uXFELUYNr6j370Nf0m/W6vIm0J5sT5Mc993kLRNH6njF65/7/3C9bu5v+vzmiN4tYQW3fQ34xbdLPJyJbHXEU8tOsCm4xnseXEEDg52dhuuFJzcpv+SF5zRO/uC+uujCXJPQGYs5KfqdWqPJnrruLpjt4+vhkV3QdP2+pjx+jQWPDsRzkTprcaYvyBho97pCnprfvjL0CTUpiGeYzLqs23j1sFdi/UyU034u5yy4Q1wbwK9pkKX2/TSSmEGmMr1VrXZAAcW6qWt4ky9hDb6TQjsW7Xrph7QfwYpu+HUXijN1evz7UbpJbPWQyo+IqsSJLHXAWazoveb6+jf1pdPJnb/3xfKCvRk2bA5ONlgaYHyIji0GHZ/DWmH9ccaeF268w/0kSamMvBsAbfOheCBVbtuzEpYfI/eOqro8MK6rDRPTyqxa/UOXhTc8zsEVKPubwlKwYpnYM83eiu61/01f83U/frIphOR+udeAfr4939qPRgGP6v/Dlqqv0IpvWFTmeG1VSSJvQ44kprH9Z9s4f3bujI+uExPpgkb9NEAyqQn09BR+hCx0FHV65RSSr+VjF2td+bln4LA/vpQwaD+ehJJ3Kxf/++6YbPO0HuqPhuwQUN9LHLyLr1F6emvl0i8Wuq1y9T9eq04O1GvL3Yef/WYzndsBSyerI8nv+e3i2ub9V1uMnx7vd7R+ugO8PC1TRyJm/Uyx8kter/HqNese/2ck3BkCSTvhBY99O9DaZ5eNvHvVrmOTzskib0O+GJjPF+u3M22frtxP/itPvOuRXdocw00CtJvA2P+hOIsPdH1exz6PVbxEQJK6W8SB3/Uk3lesv64X0f9VvbEFj2Bn69hM/22usckfXZgZf5Iygrgxzvg5FZ9gkn3uyv2vKPL4Ocp+h/m3b9effRKfXXmMHw1BDrdWuXOuapfO+rsHIKl+p3ZoKf1skgtTqL2qE4uKVCvGMvw3DeHLa4LcT9QAj0mwzXPXziqoOdkvWPt5DbY/pk++WP/99D7Ib0F79v2f39Y5UX6OOzTB/WWdW4SJG2Hogxwaai/WQz+l74y4d8TeYzlehI+c0g/JrCvXgap6h9rA0+462dYdDcsfQwcXaDL7Vd+TvRSffRLix5w9y/6sEBxac07wYAnIfJ96P0gBFz177/6Tu2Fv56DlF16jfmaF2DA9Fo7Y7OukBa72aTfPmYn6GOaQ0dWbhZhVRRl6i3lU3sh96Q+y7BpGDi7663ywjTMkR/hkJNAnHc/Qu76CPw6XP28CRv1OuOZQ/rnDZuBb6ievLNi9XODfh2PpnqJJbDv/0op1vL3BJPkXTBluT52+1KiftFXTAzopSf1K63DInRlBfBJD/136t4/a7bFHPUL/P6o/rvU9xHofpeUyGqYlGKupjRfr8Vt/wwyj//vcc1Bv5UdPOPi2X5lhRC7Si9RuHhAt7srPutNKX1s67ZP4cjveo1cc9A7QAtSLzq8yLM1j2TdzsP3P0j/tk0uccIryDmhl1ZO7dNn03k01VvaARF6OcOSY4mrqjhb31moJEdP7v9ci2XPPH1t86ABcOdPktQrY/fXeuflxJ/0sfo1YddcfeZsYD+YsMB2Nf16RhL75ZiMsHsurHsNDEXQtINegggaoNeqDy7UZykaivXWblA/fULCqb0QtxaMpdDAG4wlYDbqQ/iGvqBPGrmchE2w8nlIP6KvP9Fzst4p6d9Vf4MoK9RnGpqNegvLyY3ntxhYdugM+14aiYtTLZiIUhU5J2DeGP17OuVP/U2yrECf5r/lI72cdPt3VZtVWJ+ZDDCnrz4p6OGt4GjhiuuBH+H3R/QF0m77VsouViSJ/VJO7YWl0/QEGzpKXxukZc+Lb1eLMvVf3hNb9NphSY7eIRQ+DsJv1DsNS/Ng07v6m4RjA+g/TV8F8Pxb0fIivTSy5xu9M7Lf43pN+Sp1YqUU/d5aT/fARnxxt42HrtW0rHh9urmhFNpeo88oLcmGbnfB2P/U+PCxOit6KSyeBKPf0mfmWsrJ7from9aDYOIiSepWJondbNaHPGXG6JNkUvboGyJ4+sN1b+sTOipafyzJ1YcWXmoKd1a8vtZE9O/65+6+0CRMb8En7dDLLH0f02e4VbDlGZ2az5hPInn31i7cXh/WX0+L1icdmYzQqpe+5os1Ov7qMqXgx9v1jvUnDlhm2YTcZPhmlL6A10Oba92Kh3VB3R0VU1Z4cUdfSa6+SE/6MX2aeUaM3rGTcfTsAZr++OAZ+nDAyg6Xu9Lxvm3h9vn63cDfnbAZx/V4/LvCrV/r074rYUNMOgDXtLfjNUwsqVk4PLHf1lHULZqmz6z8vI8+DHHMu9U7X0kufH+zfhd612JJ6naudu15mhWvby3W50F9WFfBGdj6sV4XNxsvPLZFd7jpv3pS9fSv+Vv6lj2rt9LfedYfS6dzS2/8POU2V1RDk1B9vsGeb/Q5A/5dqn6uNS/pm09MXnbpTUeEXbFqYldKLQOWRUREPFClEzi76cMRN78H2+fonZ+ODaDnvfrsy6bt9fHZjQKv3Jlpx9LzS9mXlMMTw+xkzQ9Ruw17SV+PfsF4uPcv/Q6zspJ36wtf9Z+mD5EVdq92lWK8WsD4b6DPw/oGBY0CIeLeC8ed19KE/reVR86gFFzfRfZ0FBbg4auvH/PtGJg/Du77S/+7qSiTEVY8pQ8eGPJczcUpLKp2Jfa/teql/6uD/ow6TYhfQ9o1k3HbwkL82uvJff4N8N2N+sYUFZ1ItOcbfbmA2+ZbdxKbqJY6OkC6dsooKGNXYjZjOktrXViYfxe4c7E+suXzvrBwoj7Y4EoK0mD969B2mD7MV9QaktjtyMojZzArGNO5dpeThJ0K7KvPRm3RXZ+j8c1IfajppZjN+sxfYymMeV8W86plJLHbCYPJzPfbTxDi15AwKcOImhI6Ql+i4dHt+mCExffoG1KcLzMW/nxG3xx8xCtV63AVNlU7a+x10LytiRxPK2TupAg0aR2JmuYdAOPnwQ+3wH8H6PM7Wg+G7Z/rm4ODvkJk30dsG6eoEknsdiA5u5iP18YyokMzRobbwQJdon4IHqBvMP3nDFjzsv6Yg7M+ka/HpMqNnhF2RRK7jcWmFfDogn04OmjMHlcDm/0KcSXNO+lDIFMP6CuBNu8KTao4gVDYDUnsNvTbvhReWBKFu4sTX9zVk4DG7rYOSdRXLbrp/0SdIIndBsqMJl5dFs2CnUn0ae3DpxO74+clywcIISxDEruVnc4r4eHv93IwJY+HBrdhxugwnBxlcJIQwnIksVtRam4JE77aTm6RgS/v6cnojjJeXQhheZLYreRUbgkTv9pBbrGBH6b2oWurSi4dLIQQFSSJvYaZzYr3V8fw1eYE3Jwd+V6SuhCihklir2HvrDrGl5sSuLFbCx4bGiKLewkhapwk9hr0/Y6TfLkpgXv6BvHqjR1lRqkQwioksdcAo8nMnI3xfLz2OMPb+zFrbLgkdSGE1Uhit7DsonIe/3Ef2+KzGNHBj08mdpfhjEIIq5LEbkGxaQXc++1u0gvK+OC2rtzaM8DWIQkh6iFJ7BaSlFXMnV/vBODnh/rJyBchhM1IYreA9PxS7v5mJwaTmZ8f6keojHwRQtiQFH+rKa/YwKT/20VmYRnf3ttbkroQwuYksVdDmdHE1O92k5BRxNxJEXST8osQwg5IKaYaXlkWze4TOXx2Z3cGhDSxdThCCAFIi73Klh44xY87k3j0mrbc0KWFrcMRQohzJLGfVW40cyKzCLNZXfXYknITb/55lK4B3jwzKswK0QkhRMVJKQbYkZDFtIX7ySgoI8SvIQ8Mas3Yri1wd7n421NmNPHGn9Gk5Zfx2Z09cHSQGaVCCPtSrxO7yaz4z9rjfLYhjmBfDx4a3Iaf96Qw89co3lhxlOEdmnF330A6+HthMisOJOfywpIokrNLmNwviF7BPrZ+CUIIcZF6m9gzCsp4/rco1h5N49YeAcweF46nqzP3D2zNnpM5/LgziQ0x6SzZf+qC5wX7uvP9/b0ZFNrURpELIcSVWTSxa5rWAvgAmKGUSrHkuS1pxaHTPPPzAQwmxSvjOjK5f/C5r2maRq9gH3oF+1BcbmTpgVTySwxoGni5OjO2aws8GtTb90MhRC1Q4QylaZoz8JtSauzZz12BX4BWwCFgklIqVdO0mBqJ1EI+3xDHe6ti6BnUmDdv7kxY88tPKHJ3cWJi70ArRieEENVXoVExmqa5AXuBkec9fDeQopTqCjT+x9fsUmRsBu+timFc1xb8+ECfKyZ1IYSorSqU2JVSJUqpLsD55ZVhwJqzH68Hhl7pHJqmPahp2h5N0/ZkZGRUKdjqyCs28PxvUbRp4sG747vQwMnR6jEIIYQ1VGccuy+Qd/bjfMAHQCk1+1L1daXUV0qpCKVURNOm1u143JeUw8S5O0jLL+W927rg6ixJXQhRd1WnFzAT8D77sffZz+2KUopXlkXz7bYT+Hq48NWkCHoGyRBFIUTdVp0W+zpg1NmPhwEbqh+OZUXGZvLtthPc3TeQyJlDGRrmZ+uQhBCixlUnsS8AWmqadgjIRk/0dmXOxjiaeTXgpRvCLzmLVAgh6qJKZTulVMh5H5cBN1g8IgvZl5TDjoRs/n19B+koFULUK1ZdBEzTtLGapn2Vl5d39YOrac6GeBq5O8s4dCFEvWPVxK6UWqaUetDb2/vqB1fDweRc1h5NY0r/YJklKoSod+rcsr1KKd5ZeQxfDxfuH9ja1uEIIYTV1bnEHhmbybb4LB4fFoKnq7OtwxFCCKurU4ndZNZb66183Lizj9TWhRD1U53pPDWbFTN/PcSR1HyeHd1eRsIIIeqtOtN5+tmGOH7Zm8KTI0IZ21X2IBVC1F91ohSzIyGLj9Ye5+buLZk+PNTW4QghhE3V+sReUGrgmcUHCfJx542bO6FpsgepEKJ+q1WDvDMKynj2l4O0a+5J++aeDA5tylt/HeN0Xgm/PNJflg0QQghqWWLPLirndF4pW+OyKDeZzz3++NAQegQ2tmFkQghhP2pVYg9r7snKJwdjNJk5dqaAFVGn0YDpI6SuLoQQf7NqYtc0bSwwNiQk5KrHXomTowOdWnrTqWXNLk0ghBC1UZ0Z7iiEEEJX60fFCCGEuJAkdiGEqGMksQshRB0jiV0IIeoYSexCCFHHSGIXQog6ps4s2yuEEEKnKaWsf1FNKwBiLvPlJkDmVU7hDVzt3cFSx9S2eOwpFnuLx55ikXjkZ1WVeMKUUp5Xeb6+R6i1/wF7qvK18475yorH1Kp47CkWe4vHnmKReORnVZV4KhKrUqrW1qqnT0kAAASVSURBVNiXWfGYirCneOwpFkteS7431TumIuwpHnuKxZLXslQ8V2SrUswepVREZb9mCxLP5dlTLGBf8dhTLCDxXIk9xQKWyY+2arF/VcWv2YLEc3n2FAvYVzz2FAtIPFdiT7GABfKjTVrsQgghak5trbELIYS4DGuPY9c0TZuvadoOTdP+0DStoaZpyzVNO6hp2vdnv+56icecNE37WdO0rZqm/Z+t4znv+U9pmrbWlrFomnatpmkpmqZtOfsvzJbxnH3us5qmRWqa9pemaS62ikXTtGvO+74ka5o2ufrfmWrF46Fp2lJN/z1+18axNNY0bePZWF6yRCwVjefscc6api0773mX/TuzRTyXe8wWsVzieVfdR8PaLfYBgJNSqi/gBdwHpCilugKNgZHA3Zd47CbgoFJqAOCvaVo3G8eDpmlBwBQLxVGtWIAvlFIDz/673PwAq8SjaVoboKNSahDwFxBgq1iUUhv//r4Ah4D9FoilyvEAdwE7zv4ed9Q0rYMNY7kTOHI2lgGaprW2QCwVikfTNDdgL//7/eUyMdosnsvEaJNYLvG8UVe7kLUTexrwn7MflwOzgTVnP18PDAWGXeKxlcCHZ9+pGgH5No6Hs8973kJxVDeWWzVN26Vp2q+WaulUI57hQGNN0zYDg4BEG8YCgKZp7kCIUuqQBWKpTjxlgPvZn5Hr2efaKhYAz7OxaIClGktXjUcpVaKU6gKknPe8y/78bBHPZWK0SSyXeN5VWXsHpVil1C5N024GXNDfmf6ehZUP+AC+/3xMKVWolCoGtgJpSqkEW8ajadqdwEEg2hJxVCcWIB54SSnVG/AHhtg4nqZAhlJqMHprfaANY/nbSGBddeOwQDw/AtcBR4FjSql4G8ayAL2R9Cv6G45bdWOpRDyXcqWfny3isbiqxnKJ56262rWs3nmqado4YDowFkhHn2LL2f8zz/674DFN03w1TWsA9EdvDVrq3bxK8QA3oLdMfwJ6apr2uA1jyQb+rvOfAPwsEUs14snnf8tFJAAtbRjL38YCyy0RRzXjeR74r1KqPXoDob8NYwG4Xyl1C3piT7dELBWM51Ku9POzRTw1oqqxnP88pZTpatexdudpc2AGcL1SqgC9FfV3vWgYsOEyjz0D3Hb2BRVjodZFVeNRSt15tm57B7BXKfWZrWIBngbu0DTNAegEHK5uLNWMZy/Q6+xjIejJ3VaxcLbUMBT9VtciqhGPJ1B69rEyoKENYxkM/Pdsg6krsKO6sVQinkup6HHWisfiqhrLJZ53VdZusU9GLxes0jRtC+AMtNQ07f/bt1schGE4DONPJQfiQMxxD47AzoFEoRAL4HAcAofA/RGd4SNhaTZImucnl3R7Rfdm7dIz+ctzR14ivl5rgSal1AFXBixFJs4zhdIsa2ABHIBNRIy1PVSUJyI68irrBFwi4vivLP3YOfkn4f39tj/P0wLLfh7PGGculWbZkvf598AqIm4jZBma55Op3rPSPFMozfI0LqXUfHuQB5QkqTIeUJKkyljsklQZi12SKmOxS1JlLHZJqozFLkmVsdglqTIPdnLiJ6zuWzIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_px.rolling(60).mean().plot(logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>21.110000</td>\n",
       "      <td>29.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>7.425000</td>\n",
       "      <td>21.125000</td>\n",
       "      <td>29.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>7.433333</td>\n",
       "      <td>21.256667</td>\n",
       "      <td>29.473333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>7.432500</td>\n",
       "      <td>21.425000</td>\n",
       "      <td>29.342500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>7.402000</td>\n",
       "      <td>21.402000</td>\n",
       "      <td>29.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-09</th>\n",
       "      <td>7.391667</td>\n",
       "      <td>21.490000</td>\n",
       "      <td>29.273333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-10</th>\n",
       "      <td>7.387143</td>\n",
       "      <td>21.558571</td>\n",
       "      <td>29.238571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-13</th>\n",
       "      <td>7.378750</td>\n",
       "      <td>21.633750</td>\n",
       "      <td>29.197500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-14</th>\n",
       "      <td>7.370000</td>\n",
       "      <td>21.717778</td>\n",
       "      <td>29.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-15</th>\n",
       "      <td>7.355000</td>\n",
       "      <td>21.757000</td>\n",
       "      <td>29.152000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>398.002143</td>\n",
       "      <td>25.890714</td>\n",
       "      <td>72.413571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>396.802143</td>\n",
       "      <td>25.807857</td>\n",
       "      <td>72.427143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>395.751429</td>\n",
       "      <td>25.729286</td>\n",
       "      <td>72.422857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>394.099286</td>\n",
       "      <td>25.673571</td>\n",
       "      <td>72.375714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>392.479333</td>\n",
       "      <td>25.712000</td>\n",
       "      <td>72.454667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>389.351429</td>\n",
       "      <td>25.602143</td>\n",
       "      <td>72.527857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>388.505000</td>\n",
       "      <td>25.674286</td>\n",
       "      <td>72.835000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>388.531429</td>\n",
       "      <td>25.810000</td>\n",
       "      <td>73.400714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>388.826429</td>\n",
       "      <td>25.961429</td>\n",
       "      <td>73.905000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>391.038000</td>\n",
       "      <td>26.048667</td>\n",
       "      <td>74.185333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2292 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL       MSFT        XOM\n",
       "2003-01-02    7.400000  21.110000  29.220000\n",
       "2003-01-03    7.425000  21.125000  29.230000\n",
       "2003-01-06    7.433333  21.256667  29.473333\n",
       "2003-01-07    7.432500  21.425000  29.342500\n",
       "2003-01-08    7.402000  21.402000  29.240000\n",
       "2003-01-09    7.391667  21.490000  29.273333\n",
       "2003-01-10    7.387143  21.558571  29.238571\n",
       "2003-01-13    7.378750  21.633750  29.197500\n",
       "2003-01-14    7.370000  21.717778  29.194444\n",
       "2003-01-15    7.355000  21.757000  29.152000\n",
       "...                ...        ...        ...\n",
       "2011-10-03  398.002143  25.890714  72.413571\n",
       "2011-10-04  396.802143  25.807857  72.427143\n",
       "2011-10-05  395.751429  25.729286  72.422857\n",
       "2011-10-06  394.099286  25.673571  72.375714\n",
       "2011-10-07  392.479333  25.712000  72.454667\n",
       "2011-10-10  389.351429  25.602143  72.527857\n",
       "2011-10-11  388.505000  25.674286  72.835000\n",
       "2011-10-12  388.531429  25.810000  73.400714\n",
       "2011-10-13  388.826429  25.961429  73.905000\n",
       "2011-10-14  391.038000  26.048667  74.185333\n",
       "\n",
       "[2292 rows x 3 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px.rolling('20D').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0\n",
       "0   NaN\n",
       "1   NaN\n",
       "2   6.0\n",
       "3   9.0\n",
       "4  12.0\n",
       "5  15.0\n",
       "6  18.0"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_roll = pd.DataFrame(np.array([1, 2, 3, 4, 5, 6, 7]))\n",
    "my_roll.rolling(3).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exponentially Weighted Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f136c063198>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aapl_px = close_px.AAPL['2006':'2007']\n",
    "ma60 = aapl_px.rolling(30, min_periods=20).mean()\n",
    "ewma60 = aapl_px.ewm(span=60).mean()\n",
    "ma60.plot(style='k--', label='Simple MA')\n",
    "ewma60.plot(style='k-', label='EW MA')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary Moving Window Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "spx_px = close_px_all['SPX']\n",
    "spx_rets = spx_px.pct_change()\n",
    "returns = close_px.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f136bfaa438>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = returns.AAPL.rolling(125, min_periods=100).corr(spx_rets)\n",
    "corr.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f136bee3d30>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = returns.rolling(125, min_periods=100).corr(spx_rets)\n",
    "corr.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User-Defined Moving Window Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: Currently, 'apply' passes the values as ndarrays to the applied function. In the future, this will change to passing it as Series objects. You need to specify 'raw=True' to keep the current behaviour, and you can pass 'raw=False' to silence this warning\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1363d1acc0>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import percentileofscore\n",
    "score_at_2percent = lambda x: percentileofscore(x, 0.02)\n",
    "result = returns.AAPL.rolling(250).apply(score_at_2percent)\n",
    "result.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = PREVIOUS_MAX_ROWS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  }
 ],
 "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": "3.6.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}