18 Pandas Time Series Analysis Part 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic:\n",
    "### Date-range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%HTML\n",
    "<style type=\"text/css\">\n",
    "table.dataframe td, table.dataframe th {\n",
    "    border: 1px  black solid !important;\n",
    "  color: black !important;\n",
    "}\n",
    "</style>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153.58</td>\n",
       "      <td>155.45</td>\n",
       "      <td>152.89</td>\n",
       "      <td>155.45</td>\n",
       "      <td>27770715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153.90</td>\n",
       "      <td>155.81</td>\n",
       "      <td>153.78</td>\n",
       "      <td>154.45</td>\n",
       "      <td>26624926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155.02</td>\n",
       "      <td>155.98</td>\n",
       "      <td>154.48</td>\n",
       "      <td>155.37</td>\n",
       "      <td>21069647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Open    High     Low   Close    Volume\n",
       "0  153.17  153.33  152.22  153.18  16404088\n",
       "1  153.58  155.45  152.89  155.45  27770715\n",
       "2  154.34  154.45  153.46  153.93  25331662\n",
       "3  153.90  155.81  153.78  154.45  26624926\n",
       "4  155.02  155.98  154.48  155.37  21069647"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"aapl_no_dates.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-06-01', '2016-06-02', '2016-06-03', '2016-06-06',\n",
       "               '2016-06-07', '2016-06-08', '2016-06-09', '2016-06-10',\n",
       "               '2016-06-13', '2016-06-14', '2016-06-15', '2016-06-16',\n",
       "               '2016-06-17', '2016-06-20', '2016-06-21', '2016-06-22',\n",
       "               '2016-06-23', '2016-06-24', '2016-06-27', '2016-06-28',\n",
       "               '2016-06-29', '2016-06-30'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range(start=\"6/1/2016\",end=\"6/30/2016\",freq='B')  \n",
    "## freq=\"B\": Only business days, weekends excluded\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-02</th>\n",
       "      <td>153.58</td>\n",
       "      <td>155.45</td>\n",
       "      <td>152.89</td>\n",
       "      <td>155.45</td>\n",
       "      <td>27770715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-03</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-06</th>\n",
       "      <td>153.90</td>\n",
       "      <td>155.81</td>\n",
       "      <td>153.78</td>\n",
       "      <td>154.45</td>\n",
       "      <td>26624926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-07</th>\n",
       "      <td>155.02</td>\n",
       "      <td>155.98</td>\n",
       "      <td>154.48</td>\n",
       "      <td>155.37</td>\n",
       "      <td>21069647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "2016-06-01  153.17  153.33  152.22  153.18  16404088\n",
       "2016-06-02  153.58  155.45  152.89  155.45  27770715\n",
       "2016-06-03  154.34  154.45  153.46  153.93  25331662\n",
       "2016-06-06  153.90  155.81  153.78  154.45  26624926\n",
       "2016-06-07  155.02  155.98  154.48  155.37  21069647"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(rng, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-02</th>\n",
       "      <td>153.58</td>\n",
       "      <td>155.45</td>\n",
       "      <td>152.89</td>\n",
       "      <td>155.45</td>\n",
       "      <td>27770715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-03</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-06</th>\n",
       "      <td>153.90</td>\n",
       "      <td>155.81</td>\n",
       "      <td>153.78</td>\n",
       "      <td>154.45</td>\n",
       "      <td>26624926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-07</th>\n",
       "      <td>155.02</td>\n",
       "      <td>155.98</td>\n",
       "      <td>154.48</td>\n",
       "      <td>155.37</td>\n",
       "      <td>21069647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-08</th>\n",
       "      <td>155.25</td>\n",
       "      <td>155.54</td>\n",
       "      <td>154.40</td>\n",
       "      <td>154.99</td>\n",
       "      <td>21250798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-09</th>\n",
       "      <td>155.19</td>\n",
       "      <td>155.19</td>\n",
       "      <td>146.02</td>\n",
       "      <td>148.98</td>\n",
       "      <td>64882657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-10</th>\n",
       "      <td>145.74</td>\n",
       "      <td>146.09</td>\n",
       "      <td>142.51</td>\n",
       "      <td>145.42</td>\n",
       "      <td>72307330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-13</th>\n",
       "      <td>147.16</td>\n",
       "      <td>147.45</td>\n",
       "      <td>145.15</td>\n",
       "      <td>146.59</td>\n",
       "      <td>34165445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-14</th>\n",
       "      <td>147.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>143.84</td>\n",
       "      <td>145.16</td>\n",
       "      <td>31531232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-15</th>\n",
       "      <td>143.32</td>\n",
       "      <td>144.48</td>\n",
       "      <td>142.21</td>\n",
       "      <td>144.29</td>\n",
       "      <td>32165373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-16</th>\n",
       "      <td>143.78</td>\n",
       "      <td>144.50</td>\n",
       "      <td>142.20</td>\n",
       "      <td>142.27</td>\n",
       "      <td>50361093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-17</th>\n",
       "      <td>143.66</td>\n",
       "      <td>146.74</td>\n",
       "      <td>143.66</td>\n",
       "      <td>146.34</td>\n",
       "      <td>32541404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-20</th>\n",
       "      <td>146.87</td>\n",
       "      <td>146.87</td>\n",
       "      <td>144.94</td>\n",
       "      <td>145.01</td>\n",
       "      <td>24900073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-21</th>\n",
       "      <td>145.52</td>\n",
       "      <td>146.07</td>\n",
       "      <td>144.61</td>\n",
       "      <td>145.87</td>\n",
       "      <td>21265751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-22</th>\n",
       "      <td>145.77</td>\n",
       "      <td>146.70</td>\n",
       "      <td>145.12</td>\n",
       "      <td>145.63</td>\n",
       "      <td>19106294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-23</th>\n",
       "      <td>145.13</td>\n",
       "      <td>147.16</td>\n",
       "      <td>145.11</td>\n",
       "      <td>146.28</td>\n",
       "      <td>35439389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-24</th>\n",
       "      <td>147.17</td>\n",
       "      <td>148.28</td>\n",
       "      <td>145.38</td>\n",
       "      <td>145.82</td>\n",
       "      <td>25692361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>145.01</td>\n",
       "      <td>146.16</td>\n",
       "      <td>143.62</td>\n",
       "      <td>143.73</td>\n",
       "      <td>24761891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
       "      <td>144.49</td>\n",
       "      <td>146.11</td>\n",
       "      <td>143.16</td>\n",
       "      <td>145.83</td>\n",
       "      <td>22082432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>144.71</td>\n",
       "      <td>145.13</td>\n",
       "      <td>142.28</td>\n",
       "      <td>143.68</td>\n",
       "      <td>31499368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>144.45</td>\n",
       "      <td>144.96</td>\n",
       "      <td>143.78</td>\n",
       "      <td>144.02</td>\n",
       "      <td>23024107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "2016-06-01  153.17  153.33  152.22  153.18  16404088\n",
       "2016-06-02  153.58  155.45  152.89  155.45  27770715\n",
       "2016-06-03  154.34  154.45  153.46  153.93  25331662\n",
       "2016-06-06  153.90  155.81  153.78  154.45  26624926\n",
       "2016-06-07  155.02  155.98  154.48  155.37  21069647\n",
       "2016-06-08  155.25  155.54  154.40  154.99  21250798\n",
       "2016-06-09  155.19  155.19  146.02  148.98  64882657\n",
       "2016-06-10  145.74  146.09  142.51  145.42  72307330\n",
       "2016-06-13  147.16  147.45  145.15  146.59  34165445\n",
       "2016-06-14  147.50  147.50  143.84  145.16  31531232\n",
       "2016-06-15  143.32  144.48  142.21  144.29  32165373\n",
       "2016-06-16  143.78  144.50  142.20  142.27  50361093\n",
       "2016-06-17  143.66  146.74  143.66  146.34  32541404\n",
       "2016-06-20  146.87  146.87  144.94  145.01  24900073\n",
       "2016-06-21  145.52  146.07  144.61  145.87  21265751\n",
       "2016-06-22  145.77  146.70  145.12  145.63  19106294\n",
       "2016-06-23  145.13  147.16  145.11  146.28  35439389\n",
       "2016-06-24  147.17  148.28  145.38  145.82  25692361\n",
       "2016-06-27  145.01  146.16  143.62  143.73  24761891\n",
       "2016-06-28  144.49  146.11  143.16  145.83  22082432\n",
       "2016-06-29  144.71  145.13  142.28  143.68  31499368\n",
       "2016-06-30  144.45  144.96  143.78  144.02  23024107"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benefits of having DatetimeIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x731d0f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df.Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152.72125"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"2016-06-01\":\"2016-06-10\"].Close.mean()\n",
    "## Average close price of 1st 10 days of June"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding missing dates from datetimeindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-06-01', '2016-06-02', '2016-06-03', '2016-06-04',\n",
       "               '2016-06-05', '2016-06-06', '2016-06-07', '2016-06-08',\n",
       "               '2016-06-09', '2016-06-10', '2016-06-11', '2016-06-12',\n",
       "               '2016-06-13', '2016-06-14', '2016-06-15', '2016-06-16',\n",
       "               '2016-06-17', '2016-06-18', '2016-06-19', '2016-06-20',\n",
       "               '2016-06-21', '2016-06-22', '2016-06-23', '2016-06-24',\n",
       "               '2016-06-25', '2016-06-26', '2016-06-27', '2016-06-28',\n",
       "               '2016-06-29', '2016-06-30'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_index = pd.date_range(start=\"6/1/2016\",end=\"6/30/2016\",freq='D')\n",
    "daily_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-06-04', '2016-06-05', '2016-06-11', '2016-06-12',\n",
       "               '2016-06-18', '2016-06-19', '2016-06-25', '2016-06-26'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_index.difference(df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## asfreq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### If you want to add all the missing holiday dates and insert the datas of previous row,\n",
    "#### you can use this method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-06-01', '2016-06-02', '2016-06-03', '2016-06-06',\n",
       "               '2016-06-07', '2016-06-08', '2016-06-09', '2016-06-10',\n",
       "               '2016-06-13', '2016-06-14', '2016-06-15', '2016-06-16',\n",
       "               '2016-06-17', '2016-06-20', '2016-06-21', '2016-06-22',\n",
       "               '2016-06-23', '2016-06-24', '2016-06-27', '2016-06-28',\n",
       "               '2016-06-29', '2016-06-30'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-02</th>\n",
       "      <td>153.58</td>\n",
       "      <td>155.45</td>\n",
       "      <td>152.89</td>\n",
       "      <td>155.45</td>\n",
       "      <td>27770715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-03</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-04</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-05</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-06</th>\n",
       "      <td>153.90</td>\n",
       "      <td>155.81</td>\n",
       "      <td>153.78</td>\n",
       "      <td>154.45</td>\n",
       "      <td>26624926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-07</th>\n",
       "      <td>155.02</td>\n",
       "      <td>155.98</td>\n",
       "      <td>154.48</td>\n",
       "      <td>155.37</td>\n",
       "      <td>21069647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-08</th>\n",
       "      <td>155.25</td>\n",
       "      <td>155.54</td>\n",
       "      <td>154.40</td>\n",
       "      <td>154.99</td>\n",
       "      <td>21250798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-09</th>\n",
       "      <td>155.19</td>\n",
       "      <td>155.19</td>\n",
       "      <td>146.02</td>\n",
       "      <td>148.98</td>\n",
       "      <td>64882657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-10</th>\n",
       "      <td>145.74</td>\n",
       "      <td>146.09</td>\n",
       "      <td>142.51</td>\n",
       "      <td>145.42</td>\n",
       "      <td>72307330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-11</th>\n",
       "      <td>145.74</td>\n",
       "      <td>146.09</td>\n",
       "      <td>142.51</td>\n",
       "      <td>145.42</td>\n",
       "      <td>72307330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-12</th>\n",
       "      <td>145.74</td>\n",
       "      <td>146.09</td>\n",
       "      <td>142.51</td>\n",
       "      <td>145.42</td>\n",
       "      <td>72307330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-13</th>\n",
       "      <td>147.16</td>\n",
       "      <td>147.45</td>\n",
       "      <td>145.15</td>\n",
       "      <td>146.59</td>\n",
       "      <td>34165445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-14</th>\n",
       "      <td>147.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>143.84</td>\n",
       "      <td>145.16</td>\n",
       "      <td>31531232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-15</th>\n",
       "      <td>143.32</td>\n",
       "      <td>144.48</td>\n",
       "      <td>142.21</td>\n",
       "      <td>144.29</td>\n",
       "      <td>32165373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-16</th>\n",
       "      <td>143.78</td>\n",
       "      <td>144.50</td>\n",
       "      <td>142.20</td>\n",
       "      <td>142.27</td>\n",
       "      <td>50361093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-17</th>\n",
       "      <td>143.66</td>\n",
       "      <td>146.74</td>\n",
       "      <td>143.66</td>\n",
       "      <td>146.34</td>\n",
       "      <td>32541404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-18</th>\n",
       "      <td>143.66</td>\n",
       "      <td>146.74</td>\n",
       "      <td>143.66</td>\n",
       "      <td>146.34</td>\n",
       "      <td>32541404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-19</th>\n",
       "      <td>143.66</td>\n",
       "      <td>146.74</td>\n",
       "      <td>143.66</td>\n",
       "      <td>146.34</td>\n",
       "      <td>32541404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-20</th>\n",
       "      <td>146.87</td>\n",
       "      <td>146.87</td>\n",
       "      <td>144.94</td>\n",
       "      <td>145.01</td>\n",
       "      <td>24900073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-21</th>\n",
       "      <td>145.52</td>\n",
       "      <td>146.07</td>\n",
       "      <td>144.61</td>\n",
       "      <td>145.87</td>\n",
       "      <td>21265751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-22</th>\n",
       "      <td>145.77</td>\n",
       "      <td>146.70</td>\n",
       "      <td>145.12</td>\n",
       "      <td>145.63</td>\n",
       "      <td>19106294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-23</th>\n",
       "      <td>145.13</td>\n",
       "      <td>147.16</td>\n",
       "      <td>145.11</td>\n",
       "      <td>146.28</td>\n",
       "      <td>35439389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-24</th>\n",
       "      <td>147.17</td>\n",
       "      <td>148.28</td>\n",
       "      <td>145.38</td>\n",
       "      <td>145.82</td>\n",
       "      <td>25692361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-25</th>\n",
       "      <td>147.17</td>\n",
       "      <td>148.28</td>\n",
       "      <td>145.38</td>\n",
       "      <td>145.82</td>\n",
       "      <td>25692361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-26</th>\n",
       "      <td>147.17</td>\n",
       "      <td>148.28</td>\n",
       "      <td>145.38</td>\n",
       "      <td>145.82</td>\n",
       "      <td>25692361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>145.01</td>\n",
       "      <td>146.16</td>\n",
       "      <td>143.62</td>\n",
       "      <td>143.73</td>\n",
       "      <td>24761891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
       "      <td>144.49</td>\n",
       "      <td>146.11</td>\n",
       "      <td>143.16</td>\n",
       "      <td>145.83</td>\n",
       "      <td>22082432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>144.71</td>\n",
       "      <td>145.13</td>\n",
       "      <td>142.28</td>\n",
       "      <td>143.68</td>\n",
       "      <td>31499368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>144.45</td>\n",
       "      <td>144.96</td>\n",
       "      <td>143.78</td>\n",
       "      <td>144.02</td>\n",
       "      <td>23024107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "2016-06-01  153.17  153.33  152.22  153.18  16404088\n",
       "2016-06-02  153.58  155.45  152.89  155.45  27770715\n",
       "2016-06-03  154.34  154.45  153.46  153.93  25331662\n",
       "2016-06-04  154.34  154.45  153.46  153.93  25331662\n",
       "2016-06-05  154.34  154.45  153.46  153.93  25331662\n",
       "2016-06-06  153.90  155.81  153.78  154.45  26624926\n",
       "2016-06-07  155.02  155.98  154.48  155.37  21069647\n",
       "2016-06-08  155.25  155.54  154.40  154.99  21250798\n",
       "2016-06-09  155.19  155.19  146.02  148.98  64882657\n",
       "2016-06-10  145.74  146.09  142.51  145.42  72307330\n",
       "2016-06-11  145.74  146.09  142.51  145.42  72307330\n",
       "2016-06-12  145.74  146.09  142.51  145.42  72307330\n",
       "2016-06-13  147.16  147.45  145.15  146.59  34165445\n",
       "2016-06-14  147.50  147.50  143.84  145.16  31531232\n",
       "2016-06-15  143.32  144.48  142.21  144.29  32165373\n",
       "2016-06-16  143.78  144.50  142.20  142.27  50361093\n",
       "2016-06-17  143.66  146.74  143.66  146.34  32541404\n",
       "2016-06-18  143.66  146.74  143.66  146.34  32541404\n",
       "2016-06-19  143.66  146.74  143.66  146.34  32541404\n",
       "2016-06-20  146.87  146.87  144.94  145.01  24900073\n",
       "2016-06-21  145.52  146.07  144.61  145.87  21265751\n",
       "2016-06-22  145.77  146.70  145.12  145.63  19106294\n",
       "2016-06-23  145.13  147.16  145.11  146.28  35439389\n",
       "2016-06-24  147.17  148.28  145.38  145.82  25692361\n",
       "2016-06-25  147.17  148.28  145.38  145.82  25692361\n",
       "2016-06-26  147.17  148.28  145.38  145.82  25692361\n",
       "2016-06-27  145.01  146.16  143.62  143.73  24761891\n",
       "2016-06-28  144.49  146.11  143.16  145.83  22082432\n",
       "2016-06-29  144.71  145.13  142.28  143.68  31499368\n",
       "2016-06-30  144.45  144.96  143.78  144.02  23024107"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.asfreq('D',method='pad')\n",
    "## sets frequency as \"Daily\"=\"D\",\n",
    "## 'pad' method will insert the datas of pervious row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-05</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-12</th>\n",
       "      <td>145.74</td>\n",
       "      <td>146.09</td>\n",
       "      <td>142.51</td>\n",
       "      <td>145.42</td>\n",
       "      <td>72307330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-19</th>\n",
       "      <td>143.66</td>\n",
       "      <td>146.74</td>\n",
       "      <td>143.66</td>\n",
       "      <td>146.34</td>\n",
       "      <td>32541404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-26</th>\n",
       "      <td>147.17</td>\n",
       "      <td>148.28</td>\n",
       "      <td>145.38</td>\n",
       "      <td>145.82</td>\n",
       "      <td>25692361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "2016-06-05  154.34  154.45  153.46  153.93  25331662\n",
       "2016-06-12  145.74  146.09  142.51  145.42  72307330\n",
       "2016-06-19  143.66  146.74  143.66  146.34  32541404\n",
       "2016-06-26  147.17  148.28  145.38  145.82  25692361"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.asfreq('W',method='pad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-01 00:00:00</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01 01:00:00</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01 02:00:00</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01 03:00:00</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01 04:00:00</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29 20:00:00</th>\n",
       "      <td>144.71</td>\n",
       "      <td>145.13</td>\n",
       "      <td>142.28</td>\n",
       "      <td>143.68</td>\n",
       "      <td>31499368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29 21:00:00</th>\n",
       "      <td>144.71</td>\n",
       "      <td>145.13</td>\n",
       "      <td>142.28</td>\n",
       "      <td>143.68</td>\n",
       "      <td>31499368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29 22:00:00</th>\n",
       "      <td>144.71</td>\n",
       "      <td>145.13</td>\n",
       "      <td>142.28</td>\n",
       "      <td>143.68</td>\n",
       "      <td>31499368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29 23:00:00</th>\n",
       "      <td>144.71</td>\n",
       "      <td>145.13</td>\n",
       "      <td>142.28</td>\n",
       "      <td>143.68</td>\n",
       "      <td>31499368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30 00:00:00</th>\n",
       "      <td>144.45</td>\n",
       "      <td>144.96</td>\n",
       "      <td>143.78</td>\n",
       "      <td>144.02</td>\n",
       "      <td>23024107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>697 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open    High     Low   Close    Volume\n",
       "2016-06-01 00:00:00  153.17  153.33  152.22  153.18  16404088\n",
       "2016-06-01 01:00:00  153.17  153.33  152.22  153.18  16404088\n",
       "2016-06-01 02:00:00  153.17  153.33  152.22  153.18  16404088\n",
       "2016-06-01 03:00:00  153.17  153.33  152.22  153.18  16404088\n",
       "2016-06-01 04:00:00  153.17  153.33  152.22  153.18  16404088\n",
       "...                     ...     ...     ...     ...       ...\n",
       "2016-06-29 20:00:00  144.71  145.13  142.28  143.68  31499368\n",
       "2016-06-29 21:00:00  144.71  145.13  142.28  143.68  31499368\n",
       "2016-06-29 22:00:00  144.71  145.13  142.28  143.68  31499368\n",
       "2016-06-29 23:00:00  144.71  145.13  142.28  143.68  31499368\n",
       "2016-06-30 00:00:00  144.45  144.96  143.78  144.02  23024107\n",
       "\n",
       "[697 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.asfreq('H',method='pad')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating DatetimeIndex with periods argument"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  When you know about start days but do not know about end date, you can insert how upto many periods you'll need dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-01 00:00:00', '2011-01-01 01:00:00',\n",
       "               '2011-01-01 02:00:00', '2011-01-01 03:00:00',\n",
       "               '2011-01-01 04:00:00', '2011-01-01 05:00:00',\n",
       "               '2011-01-01 06:00:00', '2011-01-01 07:00:00',\n",
       "               '2011-01-01 08:00:00', '2011-01-01 09:00:00',\n",
       "               '2011-01-01 10:00:00', '2011-01-01 11:00:00',\n",
       "               '2011-01-01 12:00:00', '2011-01-01 13:00:00',\n",
       "               '2011-01-01 14:00:00', '2011-01-01 15:00:00',\n",
       "               '2011-01-01 16:00:00', '2011-01-01 17:00:00',\n",
       "               '2011-01-01 18:00:00', '2011-01-01 19:00:00',\n",
       "               '2011-01-01 20:00:00', '2011-01-01 21:00:00',\n",
       "               '2011-01-01 22:00:00', '2011-01-01 23:00:00',\n",
       "               '2011-01-02 00:00:00', '2011-01-02 01:00:00',\n",
       "               '2011-01-02 02:00:00', '2011-01-02 03:00:00',\n",
       "               '2011-01-02 04:00:00', '2011-01-02 05:00:00',\n",
       "               '2011-01-02 06:00:00', '2011-01-02 07:00:00',\n",
       "               '2011-01-02 08:00:00', '2011-01-02 09:00:00',\n",
       "               '2011-01-02 10:00:00', '2011-01-02 11:00:00',\n",
       "               '2011-01-02 12:00:00', '2011-01-02 13:00:00',\n",
       "               '2011-01-02 14:00:00', '2011-01-02 15:00:00',\n",
       "               '2011-01-02 16:00:00', '2011-01-02 17:00:00',\n",
       "               '2011-01-02 18:00:00', '2011-01-02 19:00:00',\n",
       "               '2011-01-02 20:00:00', '2011-01-02 21:00:00',\n",
       "               '2011-01-02 22:00:00', '2011-01-02 23:00:00',\n",
       "               '2011-01-03 00:00:00', '2011-01-03 01:00:00',\n",
       "               '2011-01-03 02:00:00', '2011-01-03 03:00:00',\n",
       "               '2011-01-03 04:00:00', '2011-01-03 05:00:00',\n",
       "               '2011-01-03 06:00:00', '2011-01-03 07:00:00',\n",
       "               '2011-01-03 08:00:00', '2011-01-03 09:00:00',\n",
       "               '2011-01-03 10:00:00', '2011-01-03 11:00:00',\n",
       "               '2011-01-03 12:00:00', '2011-01-03 13:00:00',\n",
       "               '2011-01-03 14:00:00', '2011-01-03 15:00:00',\n",
       "               '2011-01-03 16:00:00', '2011-01-03 17:00:00',\n",
       "               '2011-01-03 18:00:00', '2011-01-03 19:00:00',\n",
       "               '2011-01-03 20:00:00', '2011-01-03 21:00:00',\n",
       "               '2011-01-03 22:00:00', '2011-01-03 23:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2011', periods=72, freq='H')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-03', '2011-01-04', '2011-01-05', '2011-01-06',\n",
       "               '2011-01-07', '2011-01-10', '2011-01-11', '2011-01-12',\n",
       "               '2011-01-13', '2011-01-14', '2011-01-17', '2011-01-18',\n",
       "               '2011-01-19', '2011-01-20', '2011-01-21', '2011-01-24',\n",
       "               '2011-01-25', '2011-01-26', '2011-01-27', '2011-01-28',\n",
       "               '2011-01-31', '2011-02-01', '2011-02-02', '2011-02-03',\n",
       "               '2011-02-04', '2011-02-07', '2011-02-08', '2011-02-09',\n",
       "               '2011-02-10', '2011-02-11', '2011-02-14', '2011-02-15',\n",
       "               '2011-02-16', '2011-02-17', '2011-02-18', '2011-02-21',\n",
       "               '2011-02-22', '2011-02-23', '2011-02-24', '2011-02-25',\n",
       "               '2011-02-28', '2011-03-01', '2011-03-02', '2011-03-03',\n",
       "               '2011-03-04', '2011-03-07', '2011-03-08', '2011-03-09',\n",
       "               '2011-03-10', '2011-03-11', '2011-03-14', '2011-03-15',\n",
       "               '2011-03-16', '2011-03-17', '2011-03-18', '2011-03-21',\n",
       "               '2011-03-22', '2011-03-23', '2011-03-24', '2011-03-25',\n",
       "               '2011-03-28', '2011-03-29', '2011-03-30', '2011-03-31',\n",
       "               '2011-04-01', '2011-04-04', '2011-04-05', '2011-04-06',\n",
       "               '2011-04-07', '2011-04-08', '2011-04-11', '2011-04-12'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2011', periods=72, freq='B')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 0, 3, 2, 8, 8, 9, 7, 8, 4, 0, 3, 1, 4, 3, 4, 6, 9, 9, 9, 8,\n",
       "       8, 3, 7, 2, 7, 7, 4, 6, 3, 4, 6, 1, 7, 7, 4, 4, 6, 3, 1, 8, 8, 1,\n",
       "       2, 4, 9, 3, 9, 2, 0, 6, 0, 1, 8, 6, 3, 5, 8, 2, 0, 9, 6, 8, 0, 3,\n",
       "       7, 0, 5, 0, 0, 8])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.randint(0,10,len(rng))\n",
    "## gave 72 random numbers between 0-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     8\n",
       "2     1\n",
       "3     5\n",
       "4     5\n",
       "5     8\n",
       "6     4\n",
       "7     2\n",
       "8     7\n",
       "9     4\n",
       "10    6\n",
       "11    0\n",
       "12    0\n",
       "13    9\n",
       "14    6\n",
       "15    7\n",
       "16    9\n",
       "17    0\n",
       "18    8\n",
       "19    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "ts = pd.Series(np.random.randint(0,10,len(rng)))\n",
    "ts.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-03    9\n",
       "2011-01-04    2\n",
       "2011-01-05    7\n",
       "2011-01-06    6\n",
       "2011-01-07    2\n",
       "2011-01-10    0\n",
       "2011-01-11    0\n",
       "2011-01-12    5\n",
       "2011-01-13    9\n",
       "2011-01-14    8\n",
       "2011-01-17    5\n",
       "2011-01-18    3\n",
       "2011-01-19    8\n",
       "2011-01-20    7\n",
       "2011-01-21    5\n",
       "2011-01-24    7\n",
       "2011-01-25    4\n",
       "2011-01-26    6\n",
       "2011-01-27    1\n",
       "2011-01-28    0\n",
       "Freq: B, dtype: int32"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "ts = pd.Series(np.random.randint(0,10,len(rng)), index=rng)\n",
    "ts.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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