ch11.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 9, 20, 11, 14, 40, 183835)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(2021, 9, 20)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "now = datetime.now()\n",
    "now\n",
    "now.year, now.month, now.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=926, seconds=56700)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "926"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "56700"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta = datetime(2011, 1, 7) - datetime(2008, 6, 24, 8, 15)\n",
    "delta\n",
    "delta.days\n",
    "delta.seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 19, 0, 0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2010, 12, 14, 0, 0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "start = datetime(2011, 1, 7)\n",
    "start + timedelta(12)\n",
    "start - 2 * timedelta(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting Between String and Datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03 00:00:00'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "'2011-01-03'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = datetime(2011, 1, 3)\n",
    "str(stamp)\n",
    "stamp.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2011, 7, 6, 0, 0), datetime.datetime(2011, 8, 6, 0, 0)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = '2011-01-03'\n",
    "datetime.strptime(value, '%Y-%m-%d')\n",
    "datestrs = ['7/6/2011', '8/6/2011']\n",
    "[datetime.strptime(x, '%m/%d/%Y') for x in datestrs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil.parser import parse\n",
    "parse('2011-01-03')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1997, 1, 31, 22, 45)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse('Jan 31, 1997 10:45 PM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 12, 6, 0, 0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse('6/12/2011', dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "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": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "NaT"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([False, False,  True])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.to_datetime(datestrs + [None])\n",
    "idx\n",
    "idx[2]\n",
    "pd.isnull(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "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": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts + ts[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-01-02 00:00:00')"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5194387150567381"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = ts.index[2]\n",
    "ts[stamp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9657805725027142"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1.9657805725027142"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['1/10/2011']\n",
    "ts['20110110']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0.488675\n",
       "2000-01-02   -0.178098\n",
       "2000-01-03    2.122315\n",
       "2000-01-04    0.061192\n",
       "2000-01-05    0.884111\n",
       "                ...   \n",
       "2002-09-22    1.847446\n",
       "2002-09-23    0.453739\n",
       "2002-09-24   -0.302450\n",
       "2002-09-25    1.402558\n",
       "2002-09-26    1.212354\n",
       "Freq: D, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2001-01-01    0.715359\n",
       "2001-01-02   -0.345805\n",
       "2001-01-03    0.470886\n",
       "2001-01-04   -1.121515\n",
       "2001-01-05   -1.544365\n",
       "                ...   \n",
       "2001-12-27   -0.850688\n",
       "2001-12-28    0.207830\n",
       "2001-12-29   -0.127188\n",
       "2001-12-30    0.279477\n",
       "2001-12-31    0.718729\n",
       "Freq: D, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 21,
     "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\n",
    "longer_ts['2001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-05-01    1.167858\n",
       "2001-05-02    0.969001\n",
       "2001-05-03   -2.536487\n",
       "2001-05-04    0.362754\n",
       "2001-05-05   -1.196146\n",
       "                ...   \n",
       "2001-05-27   -1.333576\n",
       "2001-05-28   -0.309119\n",
       "2001-05-29    0.028558\n",
       "2001-05-30    1.129605\n",
       "2001-05-31   -0.374173\n",
       "Freq: D, Length: 31, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer_ts['2001-05']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[datetime(2011, 1, 7):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts\n",
    "ts['1/6/2011':'1/11/2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.truncate(after='1/9/2011')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>2001-05-02</th>\n",
       "      <td>-0.924368</td>\n",
       "      <td>-0.020641</td>\n",
       "      <td>-0.826849</td>\n",
       "      <td>-0.439442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-09</th>\n",
       "      <td>0.289190</td>\n",
       "      <td>0.317978</td>\n",
       "      <td>0.035787</td>\n",
       "      <td>1.034372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-16</th>\n",
       "      <td>-0.506926</td>\n",
       "      <td>1.228659</td>\n",
       "      <td>-1.079650</td>\n",
       "      <td>-1.503665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-23</th>\n",
       "      <td>-0.167360</td>\n",
       "      <td>-0.183889</td>\n",
       "      <td>-1.003073</td>\n",
       "      <td>-0.292718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-30</th>\n",
       "      <td>-0.830605</td>\n",
       "      <td>-0.271502</td>\n",
       "      <td>0.825583</td>\n",
       "      <td>1.121559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2001-05-02 -0.924368 -0.020641 -0.826849 -0.439442\n",
       "2001-05-09  0.289190  0.317978  0.035787  1.034372\n",
       "2001-05-16 -0.506926  1.228659 -1.079650 -1.503665\n",
       "2001-05-23 -0.167360 -0.183889 -1.003073 -0.292718\n",
       "2001-05-30 -0.830605 -0.271502  0.825583  1.121559"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "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: int32"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01-02    1\n",
       "2000-01-02    2\n",
       "2000-01-02    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts['1/3/2000']  # not duplicated\n",
    "dup_ts['1/2/2000']  # duplicated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0\n",
       "2000-01-02    2\n",
       "2000-01-03    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01-01    1\n",
       "2000-01-02    3\n",
       "2000-01-03    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = dup_ts.groupby(level=0)\n",
    "grouped.mean()\n",
    "grouped.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Date Ranges, Frequencies, and Shifting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts\n",
    "resampler = ts.resample('D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating Date Ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.date_range('2012-04-01', '2012-06-01')\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2012-04-01', periods=20)\n",
    "pd.date_range(end='2012-06-01', periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-01-01', '2000-12-01', freq='BM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2012-05-02 12:56:31', periods=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Hour>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import Hour, Minute\n",
    "hour = Hour()\n",
    "hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4 * Hours>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "four_hours = Hour(4)\n",
    "four_hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-01-01', '2000-01-03 23:59', freq='4h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<150 * Minutes>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hour(2) + Minute(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 41,
     "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": 42,
   "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": 42,
     "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -1.677765\n",
       "2000-02-29   -0.034771\n",
       "2000-03-31   -1.943194\n",
       "2000-04-30    0.405850\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29         NaN\n",
       "2000-03-31   -1.677765\n",
       "2000-04-30   -0.034771\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01-31   -1.943194\n",
       "2000-02-29    0.405850\n",
       "2000-03-31         NaN\n",
       "2000-04-30         NaN\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 43,
     "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\n",
    "ts.shift(2)\n",
    "ts.shift(-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ts / ts.shift(1) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-03-31   -1.677765\n",
       "2000-04-30   -0.034771\n",
       "2000-05-31   -1.943194\n",
       "2000-06-30    0.405850\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2, freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-02-03   -1.677765\n",
       "2000-03-03   -0.034771\n",
       "2000-04-03   -1.943194\n",
       "2000-05-03    0.405850\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01-31 01:30:00   -1.677765\n",
       "2000-02-29 01:30:00   -0.034771\n",
       "2000-03-31 01:30:00   -1.943194\n",
       "2000-04-30 01:30:00    0.405850\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(3, freq='D')\n",
    "ts.shift(1, freq='90T')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shifting dates with offsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-20 00:00:00')"
      ]
     },
     "execution_count": 46,
     "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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-31 00:00:00')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now + MonthEnd()\n",
    "now + MonthEnd(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-10-31 00:00:00')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset = MonthEnd()\n",
    "offset.rollforward(now)\n",
    "offset.rollback(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-15    0.680251\n",
       "2000-01-19    1.357221\n",
       "2000-01-23   -1.386823\n",
       "2000-01-27   -0.181085\n",
       "2000-01-31   -0.658086\n",
       "2000-02-04    0.476751\n",
       "2000-02-08   -0.160366\n",
       "2000-02-12   -1.522472\n",
       "2000-02-16    0.438110\n",
       "2000-02-20    1.171833\n",
       "2000-02-24    2.319066\n",
       "2000-02-28    1.188635\n",
       "2000-03-03    1.173413\n",
       "2000-03-07    0.839666\n",
       "2000-03-11   -0.295329\n",
       "2000-03-15    1.539478\n",
       "2000-03-19   -1.034012\n",
       "2000-03-23    1.102609\n",
       "2000-03-27   -0.082710\n",
       "2000-03-31    1.397644\n",
       "Freq: 4D, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.037705\n",
       "2000-02-29    0.558794\n",
       "2000-03-31    0.580095\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "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\n",
    "ts.groupby(offset.rollforward).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.037705\n",
       "2000-02-29    0.558794\n",
       "2000-03-31    0.580095\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Zone Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytz\n",
    "pytz.common_timezones[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DstTzInfo 'America/New_York' LMT-1 day, 19:04:00 STD>"
      ]
     },
     "execution_count": 52,
     "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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00    0.217985\n",
       "2012-03-10 09:30:00   -0.254538\n",
       "2012-03-11 09:30:00   -0.490485\n",
       "2012-03-12 09:30:00   -1.606192\n",
       "2012-03-13 09:30:00   -0.250439\n",
       "2012-03-14 09:30:00   -0.151218\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(ts.index.tz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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",
       "               '2012-03-15 09:30:00+00:00', '2012-03-16 09:30:00+00:00',\n",
       "               '2012-03-17 09:30:00+00:00', '2012-03-18 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='D')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('3/9/2012 9:30', periods=10, freq='D', tz='UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00    0.217985\n",
       "2012-03-10 09:30:00   -0.254538\n",
       "2012-03-11 09:30:00   -0.490485\n",
       "2012-03-12 09:30:00   -1.606192\n",
       "2012-03-13 09:30:00   -0.250439\n",
       "2012-03-14 09:30:00   -0.151218\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00+00:00    0.217985\n",
       "2012-03-10 09:30:00+00:00   -0.254538\n",
       "2012-03-11 09:30:00+00:00   -0.490485\n",
       "2012-03-12 09:30:00+00:00   -1.606192\n",
       "2012-03-13 09:30:00+00:00   -0.250439\n",
       "2012-03-14 09:30:00+00:00   -0.151218\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts\n",
    "ts_utc = ts.tz_localize('UTC')\n",
    "ts_utc\n",
    "ts_utc.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 04:30:00-05:00    0.217985\n",
       "2012-03-10 04:30:00-05:00   -0.254538\n",
       "2012-03-11 05:30:00-04:00   -0.490485\n",
       "2012-03-12 05:30:00-04:00   -1.606192\n",
       "2012-03-13 05:30:00-04:00   -0.250439\n",
       "2012-03-14 05:30:00-04:00   -0.151218\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 14:30:00+00:00    0.217985\n",
       "2012-03-10 14:30:00+00:00   -0.254538\n",
       "2012-03-11 13:30:00+00:00   -0.490485\n",
       "2012-03-12 13:30:00+00:00   -1.606192\n",
       "2012-03-13 13:30:00+00:00   -0.250439\n",
       "2012-03-14 13:30:00+00:00   -0.151218\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2012-03-09 15:30:00+01:00    0.217985\n",
       "2012-03-10 15:30:00+01:00   -0.254538\n",
       "2012-03-11 14:30:00+01:00   -0.490485\n",
       "2012-03-12 14:30:00+01:00   -1.606192\n",
       "2012-03-13 14:30:00+01:00   -0.250439\n",
       "2012-03-14 14:30:00+01:00   -0.151218\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern = ts.tz_localize('America/New_York')\n",
    "ts_eastern.tz_convert('UTC')\n",
    "ts_eastern.tz_convert('Europe/Berlin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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=None)"
      ]
     },
     "execution_count": 59,
     "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-11 23:00:00-0500', tz='America/New_York')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = pd.Timestamp('2011-03-12 04:00')\n",
    "stamp_utc = stamp.tz_localize('utc')\n",
    "stamp_utc.tz_convert('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-12 04:00:00+0300', tz='Europe/Moscow')"
      ]
     },
     "execution_count": 61,
     "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": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.value\n",
    "stamp_utc.tz_convert('America/New_York').value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-12 01:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-12 02:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 63,
     "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\n",
    "stamp + Hour()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 00:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 01:30:00-0500', tz='US/Eastern')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = pd.Timestamp('2012-11-04 00:30', tz='US/Eastern')\n",
    "stamp\n",
    "stamp + 2 * Hour()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations Between Different Time Zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-07 09:30:00    0.166420\n",
       "2012-03-08 09:30:00   -0.718974\n",
       "2012-03-09 09:30:00   -0.823032\n",
       "2012-03-12 09:30:00    0.477885\n",
       "2012-03-13 09:30:00   -0.573430\n",
       "2012-03-14 09:30:00    0.373544\n",
       "2012-03-15 09:30:00    0.606128\n",
       "2012-03-16 09:30:00    0.483893\n",
       "2012-03-19 09:30:00    0.447850\n",
       "2012-03-20 09:30:00    0.321877\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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=None)"
      ]
     },
     "execution_count": 65,
     "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\n",
    "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-DEC')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period(2007, freq='A-DEC')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012', 'A-DEC')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Period('2005', 'A-DEC')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + 5\n",
    "p - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<7 * YearEnds: month=12>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Period('2014', freq='A-DEC') - p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 69,
     "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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01   -1.295372\n",
       "2000-02    0.355685\n",
       "2000-03   -0.697173\n",
       "2000-04    0.727872\n",
       "2000-05   -1.256619\n",
       "2000-06   -0.349921\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.random.randn(6), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2001Q3', '2002Q2', '2003Q1'], dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 71,
     "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": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-DEC')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Period('2007-01', 'M')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Period('2007-12', 'M')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period('2007', freq='A-DEC')\n",
    "p\n",
    "p.asfreq('M', how='start')\n",
    "p.asfreq('M', how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-JUN')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Period('2006-07', 'M')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Period('2007-06', 'M')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period('2007', freq='A-JUN')\n",
    "p\n",
    "p.asfreq('M', 'start')\n",
    "p.asfreq('M', 'end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2008', 'A-JUN')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period('Aug-2007', 'M')\n",
    "p.asfreq('A-JUN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006    0.831740\n",
       "2007   -0.197357\n",
       "2008    2.169756\n",
       "2009    0.466215\n",
       "Freq: A-DEC, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2006-01    0.831740\n",
       "2007-01   -0.197357\n",
       "2008-01    2.169756\n",
       "2009-01    0.466215\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 75,
     "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\n",
    "ts.asfreq('M', how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-12-29    0.831740\n",
       "2007-12-31   -0.197357\n",
       "2008-12-31    2.169756\n",
       "2009-12-31    0.466215\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('B', how='end')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quarterly Period Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012Q4', 'Q-JAN')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.Period('2012Q4', freq='Q-JAN')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-11-01', 'D')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Period('2012-01-31', 'D')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D', 'start')\n",
    "p.asfreq('D', 'end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-30 16:00', 'T')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-01-30 16:00:00')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4pm = (p.asfreq('B', 'e') - 1).asfreq('T', 's') + 16 * 60\n",
    "p4pm\n",
    "p4pm.to_timestamp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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: int32"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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: int32"
      ]
     },
     "execution_count": 80,
     "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\n",
    "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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    0.695790\n",
       "2000-02-29   -1.394431\n",
       "2000-03-31   -0.862790\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01    0.695790\n",
       "2000-02   -1.394431\n",
       "2000-03   -0.862790\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 81,
     "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\n",
    "pts = ts.to_period()\n",
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29    1.349069\n",
       "2000-01-30    0.656649\n",
       "2000-01-31   -0.811808\n",
       "2000-02-01   -0.334579\n",
       "2000-02-02    0.858363\n",
       "2000-02-03   -0.925223\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01    1.349069\n",
       "2000-01    0.656649\n",
       "2000-01   -0.811808\n",
       "2000-02   -0.334579\n",
       "2000-02    0.858363\n",
       "2000-02   -0.925223\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 82,
     "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\n",
    "ts2.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29    1.349069\n",
       "2000-01-30    0.656649\n",
       "2000-01-31   -0.811808\n",
       "2000-02-01   -0.334579\n",
       "2000-02-02    0.858363\n",
       "2000-02-03   -0.925223\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01-29 23:59:59.999999999    1.349069\n",
       "2000-01-30 23:59:59.999999999    0.656649\n",
       "2000-01-31 23:59:59.999999999   -0.811808\n",
       "2000-02-01 23:59:59.999999999   -0.334579\n",
       "2000-02-02 23:59:59.999999999    0.858363\n",
       "2000-02-03 23:59:59.999999999   -0.925223\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts = ts2.to_period()\n",
    "pts\n",
    "pts.to_timestamp(how='end')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a PeriodIndex from Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0      1959.0\n",
       "1      1959.0\n",
       "2      1959.0\n",
       "3      1959.0\n",
       "4      1960.0\n",
       "        ...  \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": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0      1.0\n",
       "1      2.0\n",
       "2      3.0\n",
       "3      4.0\n",
       "4      1.0\n",
       "      ... \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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('examples/macrodata.csv')\n",
    "data.head(5)\n",
    "data.year\n",
    "data.quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1959Q1    0.00\n",
       "1959Q2    2.34\n",
       "1959Q3    2.74\n",
       "1959Q4    0.27\n",
       "1960Q1    2.31\n",
       "          ... \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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.PeriodIndex(year=data.year, quarter=data.quarter,\n",
    "                       freq='Q-DEC')\n",
    "index\n",
    "data.index = index\n",
    "data.infl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling and Frequency Conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0.172077\n",
       "2000-01-02   -0.128474\n",
       "2000-01-03    0.783874\n",
       "2000-01-04    0.016520\n",
       "2000-01-05   -0.662316\n",
       "                ...   \n",
       "2000-04-05    0.299367\n",
       "2000-04-06    0.115907\n",
       "2000-04-07   -1.274368\n",
       "2000-04-08   -1.048946\n",
       "2000-04-09   -0.609616\n",
       "Freq: D, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.035854\n",
       "2000-02-29    0.116268\n",
       "2000-03-31   -0.143370\n",
       "2000-04-30   -0.913684\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2000-01   -0.035854\n",
       "2000-02    0.116268\n",
       "2000-03   -0.143370\n",
       "2000-04   -0.913684\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 86,
     "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\n",
    "ts.resample('M').mean()\n",
    "ts.resample('M', kind='period').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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: int32"
      ]
     },
     "execution_count": 87,
     "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": 88,
   "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: int32"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min', closed='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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: int32"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min', closed='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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: int32"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min', closed='right', label='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-91-b63ded3800c4>:1: FutureWarning: 'loffset' in .resample() and in Grouper() is deprecated.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", loffset=\"8H\")\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> from pandas.tseries.frequencies import to_offset\n",
      ">>> df = df.resample(freq=\"3s\").mean()\n",
      ">>> df.index = df.index.to_timestamp() + to_offset(\"8H\")\n",
      "\n",
      "  ts.resample('5min', closed='right',\n"
     ]
    },
    {
     "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: int32"
      ]
     },
     "execution_count": 91,
     "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": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>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": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min').ohlc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upsampling and Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>0.956764</td>\n",
       "      <td>0.629398</td>\n",
       "      <td>0.661080</td>\n",
       "      <td>0.853042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.133227</td>\n",
       "      <td>-0.208423</td>\n",
       "      <td>0.312951</td>\n",
       "      <td>-1.004685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-12 -0.133227 -0.208423  0.312951 -1.004685"
      ]
     },
     "execution_count": 93,
     "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": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>0.956764</td>\n",
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       "    <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>-0.133227</td>\n",
       "      <td>-0.208423</td>\n",
       "      <td>0.312951</td>\n",
       "      <td>-1.004685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  0.956764  0.629398  0.661080  0.853042\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 -0.133227 -0.208423  0.312951 -1.004685"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily = frame.resample('D').asfreq()\n",
    "df_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.956764</td>\n",
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       "      <td>0.853042</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>0.956764</td>\n",
       "      <td>0.629398</td>\n",
       "      <td>0.661080</td>\n",
       "      <td>0.853042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>0.956764</td>\n",
       "      <td>0.629398</td>\n",
       "      <td>0.661080</td>\n",
       "      <td>0.853042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>0.956764</td>\n",
       "      <td>0.629398</td>\n",
       "      <td>0.661080</td>\n",
       "      <td>0.853042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.133227</td>\n",
       "      <td>-0.208423</td>\n",
       "      <td>0.312951</td>\n",
       "      <td>-1.004685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-06  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-07  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-08  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-09  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-10  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-11  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-12 -0.133227 -0.208423  0.312951 -1.004685"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.resample('D').ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <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",
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       "      <td>0.853042</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.956764</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.956764</td>\n",
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       "      <td>0.661080</td>\n",
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       "    <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>-0.133227</td>\n",
       "      <td>-0.208423</td>\n",
       "      <td>0.312951</td>\n",
       "      <td>-1.004685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-06  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-07  0.956764  0.629398  0.661080  0.853042\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 -0.133227 -0.208423  0.312951 -1.004685"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.resample('D').ffill(limit=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>2000-01-06</th>\n",
       "      <td>0.956764</td>\n",
       "      <td>0.629398</td>\n",
       "      <td>0.661080</td>\n",
       "      <td>0.853042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>-0.133227</td>\n",
       "      <td>-0.208423</td>\n",
       "      <td>0.312951</td>\n",
       "      <td>-1.004685</td>\n",
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      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-06  0.956764  0.629398  0.661080  0.853042\n",
       "2000-01-13 -0.133227 -0.208423  0.312951 -1.004685"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.resample('W-THU').ffill()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling with Periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <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>-1.428740</td>\n",
       "      <td>-0.800789</td>\n",
       "      <td>-0.314256</td>\n",
       "      <td>0.578314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02</th>\n",
       "      <td>0.953520</td>\n",
       "      <td>-1.412194</td>\n",
       "      <td>1.141900</td>\n",
       "      <td>-1.607601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03</th>\n",
       "      <td>0.969296</td>\n",
       "      <td>-0.794957</td>\n",
       "      <td>0.831329</td>\n",
       "      <td>-0.834459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04</th>\n",
       "      <td>0.876430</td>\n",
       "      <td>-1.382301</td>\n",
       "      <td>-1.351052</td>\n",
       "      <td>0.258383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05</th>\n",
       "      <td>0.063550</td>\n",
       "      <td>-0.087024</td>\n",
       "      <td>0.599341</td>\n",
       "      <td>-1.220837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Colorado     Texas  New York      Ohio\n",
       "2000-01 -1.428740 -0.800789 -0.314256  0.578314\n",
       "2000-02  0.953520 -1.412194  1.141900 -1.607601\n",
       "2000-03  0.969296 -0.794957  0.831329 -0.834459\n",
       "2000-04  0.876430 -1.382301 -1.351052  0.258383\n",
       "2000-05  0.063550 -0.087024  0.599341 -1.220837"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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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</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.453818</td>\n",
       "      <td>-0.19461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Colorado     Texas  New York     Ohio\n",
       "2000  0.334010 -0.786824  0.012844  0.06651\n",
       "2001  0.228202  0.122498  0.453818 -0.19461"
      ]
     },
     "execution_count": 98,
     "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[:5]\n",
    "annual_frame = frame.resample('A-DEC').mean()\n",
    "annual_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>2000Q1</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q2</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q3</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q4</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.453818</td>\n",
       "      <td>-0.19461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.453818</td>\n",
       "      <td>-0.19461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.453818</td>\n",
       "      <td>-0.19461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.453818</td>\n",
       "      <td>-0.19461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York     Ohio\n",
       "2000Q1  0.334010 -0.786824  0.012844  0.06651\n",
       "2000Q2  0.334010 -0.786824  0.012844  0.06651\n",
       "2000Q3  0.334010 -0.786824  0.012844  0.06651\n",
       "2000Q4  0.334010 -0.786824  0.012844  0.06651\n",
       "2001Q1  0.228202  0.122498  0.453818 -0.19461\n",
       "2001Q2  0.228202  0.122498  0.453818 -0.19461\n",
       "2001Q3  0.228202  0.122498  0.453818 -0.19461\n",
       "2001Q4  0.228202  0.122498  0.453818 -0.19461"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.453818</td>\n",
       "      <td>-0.19461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York     Ohio\n",
       "2000Q4  0.334010 -0.786824  0.012844  0.06651\n",
       "2001Q1  0.334010 -0.786824  0.012844  0.06651\n",
       "2001Q2  0.334010 -0.786824  0.012844  0.06651\n",
       "2001Q3  0.334010 -0.786824  0.012844  0.06651\n",
       "2001Q4  0.228202  0.122498  0.453818 -0.19461"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q-DEC: Quarterly, year ending in December\n",
    "annual_frame.resample('Q-DEC').ffill()\n",
    "annual_frame.resample('Q-DEC', convention='end').ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>0.334010</td>\n",
       "      <td>-0.786824</td>\n",
       "      <td>0.012844</td>\n",
       "      <td>0.06651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.453818</td>\n",
       "      <td>-0.19461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q1</th>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.453818</td>\n",
       "      <td>-0.19461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q2</th>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.453818</td>\n",
       "      <td>-0.19461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q3</th>\n",
       "      <td>0.228202</td>\n",
       "      <td>0.122498</td>\n",
       "      <td>0.453818</td>\n",
       "      <td>-0.19461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York     Ohio\n",
       "2000Q4  0.334010 -0.786824  0.012844  0.06651\n",
       "2001Q1  0.334010 -0.786824  0.012844  0.06651\n",
       "2001Q2  0.334010 -0.786824  0.012844  0.06651\n",
       "2001Q3  0.334010 -0.786824  0.012844  0.06651\n",
       "2001Q4  0.228202  0.122498  0.453818 -0.19461\n",
       "2002Q1  0.228202  0.122498  0.453818 -0.19461\n",
       "2002Q2  0.228202  0.122498  0.453818 -0.19461\n",
       "2002Q3  0.228202  0.122498  0.453818 -0.19461"
      ]
     },
     "execution_count": 100,
     "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": 101,
   "metadata": {},
   "outputs": [],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 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": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "appl_std250 = close_px.AAPL.rolling(250, min_periods=10).std()\n",
    "appl_std250[5:12]\n",
    "appl_std250.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "expanding_mean = appl_std250.expanding().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 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": 108,
   "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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "...                ...        ...        ...\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": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px.rolling('20D').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exponentially Weighted Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2147f60fe80>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 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=30).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": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 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": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 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": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 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": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = PREVIOUS_MAX_ROWS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  }
 ],
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  "language_info": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_window_display": false
  }
 },
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