Pandas - Time - Series - Analysis.ipynb

{
 "cells": [
  {
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       "      <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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-07</th>\n",
       "      <td>142.90</td>\n",
       "      <td>144.75</td>\n",
       "      <td>142.90</td>\n",
       "      <td>144.18</td>\n",
       "      <td>19201712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-06</th>\n",
       "      <td>143.02</td>\n",
       "      <td>143.50</td>\n",
       "      <td>142.41</td>\n",
       "      <td>142.73</td>\n",
       "      <td>24128782</td>\n",
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      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-07-07  142.90  144.75  142.90  144.18  19201712\n",
       "2017-07-06  143.02  143.50  142.41  142.73  24128782"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"data/aapl.csv\",parse_dates=[\"Date\"], index_col=\"Date\")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb237655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-07-07', '2017-07-06', '2017-07-05', '2017-07-03',\n",
       "               '2017-06-30', '2017-06-29', '2017-06-28', '2017-06-27',\n",
       "               '2017-06-26', '2017-06-23',\n",
       "               ...\n",
       "               '2016-07-22', '2016-07-21', '2016-07-20', '2016-07-19',\n",
       "               '2016-07-18', '2016-07-15', '2016-07-14', '2016-07-13',\n",
       "               '2016-07-12', '2016-07-11'],\n",
       "              dtype='datetime64[ns]', name='Date', length=251, freq=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aca82876",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_122825/3188288194.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df['2017-06'].head()\n"
     ]
    },
    {
     "data": {
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       "  <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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-30</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",
       "    <tr>\n",
       "      <th>2017-06-29</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>2017-06-28</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>2017-06-27</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>2017-06-26</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",
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      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-06-30  144.45  144.96  143.78  144.02  23024107\n",
       "2017-06-29  144.71  145.13  142.28  143.68  31499368\n",
       "2017-06-28  144.49  146.11  143.16  145.83  22082432\n",
       "2017-06-27  145.01  146.16  143.62  143.73  24761891\n",
       "2017-06-26  147.17  148.28  145.38  145.82  25692361"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-06'].head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ba43e2c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/tmp/ipykernel_122825/3632472639.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df[\"2017-01\"]\n"
     ]
    },
    {
     "data": {
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       "  <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",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>121.15</td>\n",
       "      <td>121.39</td>\n",
       "      <td>120.62</td>\n",
       "      <td>121.35</td>\n",
       "      <td>49200993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-30</th>\n",
       "      <td>120.93</td>\n",
       "      <td>121.63</td>\n",
       "      <td>120.66</td>\n",
       "      <td>121.63</td>\n",
       "      <td>30377503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-27</th>\n",
       "      <td>122.14</td>\n",
       "      <td>122.35</td>\n",
       "      <td>121.60</td>\n",
       "      <td>121.95</td>\n",
       "      <td>20562944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-26</th>\n",
       "      <td>121.67</td>\n",
       "      <td>122.44</td>\n",
       "      <td>121.60</td>\n",
       "      <td>121.94</td>\n",
       "      <td>26337576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>120.42</td>\n",
       "      <td>122.10</td>\n",
       "      <td>120.28</td>\n",
       "      <td>121.88</td>\n",
       "      <td>32586673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-24</th>\n",
       "      <td>119.55</td>\n",
       "      <td>120.10</td>\n",
       "      <td>119.50</td>\n",
       "      <td>119.97</td>\n",
       "      <td>23211038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>120.00</td>\n",
       "      <td>120.81</td>\n",
       "      <td>119.77</td>\n",
       "      <td>120.08</td>\n",
       "      <td>22050218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-20</th>\n",
       "      <td>120.45</td>\n",
       "      <td>120.45</td>\n",
       "      <td>119.73</td>\n",
       "      <td>120.00</td>\n",
       "      <td>32597892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-19</th>\n",
       "      <td>119.40</td>\n",
       "      <td>120.09</td>\n",
       "      <td>119.37</td>\n",
       "      <td>119.78</td>\n",
       "      <td>25597291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>120.00</td>\n",
       "      <td>120.50</td>\n",
       "      <td>119.71</td>\n",
       "      <td>119.99</td>\n",
       "      <td>23712961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>118.34</td>\n",
       "      <td>120.24</td>\n",
       "      <td>118.22</td>\n",
       "      <td>120.00</td>\n",
       "      <td>34439843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-13</th>\n",
       "      <td>119.11</td>\n",
       "      <td>119.62</td>\n",
       "      <td>118.81</td>\n",
       "      <td>119.04</td>\n",
       "      <td>26111948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>118.90</td>\n",
       "      <td>119.30</td>\n",
       "      <td>118.21</td>\n",
       "      <td>119.25</td>\n",
       "      <td>27086220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>118.74</td>\n",
       "      <td>119.93</td>\n",
       "      <td>118.60</td>\n",
       "      <td>119.75</td>\n",
       "      <td>27588593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>118.77</td>\n",
       "      <td>119.38</td>\n",
       "      <td>118.30</td>\n",
       "      <td>119.11</td>\n",
       "      <td>24462051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>117.95</td>\n",
       "      <td>119.43</td>\n",
       "      <td>117.94</td>\n",
       "      <td>118.99</td>\n",
       "      <td>33561948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>116.78</td>\n",
       "      <td>118.16</td>\n",
       "      <td>116.47</td>\n",
       "      <td>117.91</td>\n",
       "      <td>31751900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>115.92</td>\n",
       "      <td>116.86</td>\n",
       "      <td>115.81</td>\n",
       "      <td>116.61</td>\n",
       "      <td>22193587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>115.85</td>\n",
       "      <td>116.51</td>\n",
       "      <td>115.75</td>\n",
       "      <td>116.02</td>\n",
       "      <td>21118116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>115.80</td>\n",
       "      <td>116.33</td>\n",
       "      <td>114.76</td>\n",
       "      <td>116.15</td>\n",
       "      <td>28781865</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-01-31  121.15  121.39  120.62  121.35  49200993\n",
       "2017-01-30  120.93  121.63  120.66  121.63  30377503\n",
       "2017-01-27  122.14  122.35  121.60  121.95  20562944\n",
       "2017-01-26  121.67  122.44  121.60  121.94  26337576\n",
       "2017-01-25  120.42  122.10  120.28  121.88  32586673\n",
       "2017-01-24  119.55  120.10  119.50  119.97  23211038\n",
       "2017-01-23  120.00  120.81  119.77  120.08  22050218\n",
       "2017-01-20  120.45  120.45  119.73  120.00  32597892\n",
       "2017-01-19  119.40  120.09  119.37  119.78  25597291\n",
       "2017-01-18  120.00  120.50  119.71  119.99  23712961\n",
       "2017-01-17  118.34  120.24  118.22  120.00  34439843\n",
       "2017-01-13  119.11  119.62  118.81  119.04  26111948\n",
       "2017-01-12  118.90  119.30  118.21  119.25  27086220\n",
       "2017-01-11  118.74  119.93  118.60  119.75  27588593\n",
       "2017-01-10  118.77  119.38  118.30  119.11  24462051\n",
       "2017-01-09  117.95  119.43  117.94  118.99  33561948\n",
       "2017-01-06  116.78  118.16  116.47  117.91  31751900\n",
       "2017-01-05  115.92  116.86  115.81  116.61  22193587\n",
       "2017-01-04  115.85  116.51  115.75  116.02  21118116\n",
       "2017-01-03  115.80  116.33  114.76  116.15  28781865"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"2017-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "da933316",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_122825/3891989140.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df[\"2017-01\"].Close.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "119.57000000000001"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[\"2017-01\"].Close.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "92b0d72a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_122825/3139111208.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df['2017'].head(2)\n"
     ]
    },
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       "      <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",
       "    <tr>\n",
       "      <th>Date</th>\n",
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       "    <tr>\n",
       "      <th>2017-07-07</th>\n",
       "      <td>142.90</td>\n",
       "      <td>144.75</td>\n",
       "      <td>142.90</td>\n",
       "      <td>144.18</td>\n",
       "      <td>19201712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-06</th>\n",
       "      <td>143.02</td>\n",
       "      <td>143.50</td>\n",
       "      <td>142.41</td>\n",
       "      <td>142.73</td>\n",
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      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-07-07  142.90  144.75  142.90  144.18  19201712\n",
       "2017-07-06  143.02  143.50  142.41  142.73  24128782"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['2017'].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a4cb8cae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_122825/1936286170.py:1: FutureWarning: Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.\n",
      "  df['2017-01-08':'2017-01-03']\n"
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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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Open, High, Low, Close, Volume]\n",
       "Index: []"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['2017-01-08':'2017-01-03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6d8953c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_122825/1372776413.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df['2017-01']\n"
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>121.15</td>\n",
       "      <td>121.39</td>\n",
       "      <td>120.62</td>\n",
       "      <td>121.35</td>\n",
       "      <td>49200993</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-30</th>\n",
       "      <td>120.93</td>\n",
       "      <td>121.63</td>\n",
       "      <td>120.66</td>\n",
       "      <td>121.63</td>\n",
       "      <td>30377503</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-27</th>\n",
       "      <td>122.14</td>\n",
       "      <td>122.35</td>\n",
       "      <td>121.60</td>\n",
       "      <td>121.95</td>\n",
       "      <td>20562944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-26</th>\n",
       "      <td>121.67</td>\n",
       "      <td>122.44</td>\n",
       "      <td>121.60</td>\n",
       "      <td>121.94</td>\n",
       "      <td>26337576</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>120.42</td>\n",
       "      <td>122.10</td>\n",
       "      <td>120.28</td>\n",
       "      <td>121.88</td>\n",
       "      <td>32586673</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-24</th>\n",
       "      <td>119.55</td>\n",
       "      <td>120.10</td>\n",
       "      <td>119.50</td>\n",
       "      <td>119.97</td>\n",
       "      <td>23211038</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>120.00</td>\n",
       "      <td>120.81</td>\n",
       "      <td>119.77</td>\n",
       "      <td>120.08</td>\n",
       "      <td>22050218</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-20</th>\n",
       "      <td>120.45</td>\n",
       "      <td>120.45</td>\n",
       "      <td>119.73</td>\n",
       "      <td>120.00</td>\n",
       "      <td>32597892</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-19</th>\n",
       "      <td>119.40</td>\n",
       "      <td>120.09</td>\n",
       "      <td>119.37</td>\n",
       "      <td>119.78</td>\n",
       "      <td>25597291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>120.00</td>\n",
       "      <td>120.50</td>\n",
       "      <td>119.71</td>\n",
       "      <td>119.99</td>\n",
       "      <td>23712961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>118.34</td>\n",
       "      <td>120.24</td>\n",
       "      <td>118.22</td>\n",
       "      <td>120.00</td>\n",
       "      <td>34439843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-13</th>\n",
       "      <td>119.11</td>\n",
       "      <td>119.62</td>\n",
       "      <td>118.81</td>\n",
       "      <td>119.04</td>\n",
       "      <td>26111948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>118.90</td>\n",
       "      <td>119.30</td>\n",
       "      <td>118.21</td>\n",
       "      <td>119.25</td>\n",
       "      <td>27086220</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>118.74</td>\n",
       "      <td>119.93</td>\n",
       "      <td>118.60</td>\n",
       "      <td>119.75</td>\n",
       "      <td>27588593</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>118.77</td>\n",
       "      <td>119.38</td>\n",
       "      <td>118.30</td>\n",
       "      <td>119.11</td>\n",
       "      <td>24462051</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>117.95</td>\n",
       "      <td>119.43</td>\n",
       "      <td>117.94</td>\n",
       "      <td>118.99</td>\n",
       "      <td>33561948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>116.78</td>\n",
       "      <td>118.16</td>\n",
       "      <td>116.47</td>\n",
       "      <td>117.91</td>\n",
       "      <td>31751900</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>115.92</td>\n",
       "      <td>116.86</td>\n",
       "      <td>115.81</td>\n",
       "      <td>116.61</td>\n",
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       "      <th>2017-01-04</th>\n",
       "      <td>115.85</td>\n",
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       "      <td>116.02</td>\n",
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       "      <th>2017-01-03</th>\n",
       "      <td>115.80</td>\n",
       "      <td>116.33</td>\n",
       "      <td>114.76</td>\n",
       "      <td>116.15</td>\n",
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       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-01-31  121.15  121.39  120.62  121.35  49200993\n",
       "2017-01-30  120.93  121.63  120.66  121.63  30377503\n",
       "2017-01-27  122.14  122.35  121.60  121.95  20562944\n",
       "2017-01-26  121.67  122.44  121.60  121.94  26337576\n",
       "2017-01-25  120.42  122.10  120.28  121.88  32586673\n",
       "2017-01-24  119.55  120.10  119.50  119.97  23211038\n",
       "2017-01-23  120.00  120.81  119.77  120.08  22050218\n",
       "2017-01-20  120.45  120.45  119.73  120.00  32597892\n",
       "2017-01-19  119.40  120.09  119.37  119.78  25597291\n",
       "2017-01-18  120.00  120.50  119.71  119.99  23712961\n",
       "2017-01-17  118.34  120.24  118.22  120.00  34439843\n",
       "2017-01-13  119.11  119.62  118.81  119.04  26111948\n",
       "2017-01-12  118.90  119.30  118.21  119.25  27086220\n",
       "2017-01-11  118.74  119.93  118.60  119.75  27588593\n",
       "2017-01-10  118.77  119.38  118.30  119.11  24462051\n",
       "2017-01-09  117.95  119.43  117.94  118.99  33561948\n",
       "2017-01-06  116.78  118.16  116.47  117.91  31751900\n",
       "2017-01-05  115.92  116.86  115.81  116.61  22193587\n",
       "2017-01-04  115.85  116.51  115.75  116.02  21118116\n",
       "2017-01-03  115.80  116.33  114.76  116.15  28781865"
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     "execution_count": 21,
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   "source": [
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   "id": "ca0ada00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-07-31     99.473333\n",
       "2016-08-31    107.665217\n",
       "2016-09-30    110.857143\n",
       "2016-10-31    115.707143\n",
       "2016-11-30    110.154286\n",
       "Freq: M, Name: Close, dtype: float64"
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['Close'].resample('M').mean().head()"
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  {
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   "execution_count": 23,
   "id": "c78884a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/tmp/ipykernel_122825/2659590539.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df['2016-07']\n"
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       "    <tr>\n",
       "      <th>2016-07-29</th>\n",
       "      <td>104.19</td>\n",
       "      <td>104.55</td>\n",
       "      <td>103.68</td>\n",
       "      <td>104.21</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-28</th>\n",
       "      <td>102.83</td>\n",
       "      <td>104.45</td>\n",
       "      <td>102.82</td>\n",
       "      <td>104.34</td>\n",
       "      <td>39869839</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-27</th>\n",
       "      <td>104.26</td>\n",
       "      <td>104.35</td>\n",
       "      <td>102.75</td>\n",
       "      <td>102.95</td>\n",
       "      <td>92344820</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-26</th>\n",
       "      <td>96.82</td>\n",
       "      <td>97.97</td>\n",
       "      <td>96.42</td>\n",
       "      <td>96.67</td>\n",
       "      <td>56239822</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>98.25</td>\n",
       "      <td>98.84</td>\n",
       "      <td>96.92</td>\n",
       "      <td>97.34</td>\n",
       "      <td>40382921</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-22</th>\n",
       "      <td>99.26</td>\n",
       "      <td>99.30</td>\n",
       "      <td>98.31</td>\n",
       "      <td>98.66</td>\n",
       "      <td>28313669</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-21</th>\n",
       "      <td>99.83</td>\n",
       "      <td>101.00</td>\n",
       "      <td>99.13</td>\n",
       "      <td>99.43</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-20</th>\n",
       "      <td>100.00</td>\n",
       "      <td>100.46</td>\n",
       "      <td>99.74</td>\n",
       "      <td>99.96</td>\n",
       "      <td>26275968</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-19</th>\n",
       "      <td>99.56</td>\n",
       "      <td>100.00</td>\n",
       "      <td>99.34</td>\n",
       "      <td>99.87</td>\n",
       "      <td>23779924</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-18</th>\n",
       "      <td>98.70</td>\n",
       "      <td>100.13</td>\n",
       "      <td>98.60</td>\n",
       "      <td>99.83</td>\n",
       "      <td>36493867</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-15</th>\n",
       "      <td>98.92</td>\n",
       "      <td>99.30</td>\n",
       "      <td>98.50</td>\n",
       "      <td>98.78</td>\n",
       "      <td>30136990</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-14</th>\n",
       "      <td>97.39</td>\n",
       "      <td>98.99</td>\n",
       "      <td>97.32</td>\n",
       "      <td>98.79</td>\n",
       "      <td>38918997</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-13</th>\n",
       "      <td>97.41</td>\n",
       "      <td>97.67</td>\n",
       "      <td>96.84</td>\n",
       "      <td>96.87</td>\n",
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       "      <th>2016-07-12</th>\n",
       "      <td>97.17</td>\n",
       "      <td>97.70</td>\n",
       "      <td>97.12</td>\n",
       "      <td>97.42</td>\n",
       "      <td>24167463</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-11</th>\n",
       "      <td>96.75</td>\n",
       "      <td>97.65</td>\n",
       "      <td>96.73</td>\n",
       "      <td>96.98</td>\n",
       "      <td>23794945</td>\n",
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       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2016-07-29  104.19  104.55  103.68  104.21  27733688\n",
       "2016-07-28  102.83  104.45  102.82  104.34  39869839\n",
       "2016-07-27  104.26  104.35  102.75  102.95  92344820\n",
       "2016-07-26   96.82   97.97   96.42   96.67  56239822\n",
       "2016-07-25   98.25   98.84   96.92   97.34  40382921\n",
       "2016-07-22   99.26   99.30   98.31   98.66  28313669\n",
       "2016-07-21   99.83  101.00   99.13   99.43  32702028\n",
       "2016-07-20  100.00  100.46   99.74   99.96  26275968\n",
       "2016-07-19   99.56  100.00   99.34   99.87  23779924\n",
       "2016-07-18   98.70  100.13   98.60   99.83  36493867\n",
       "2016-07-15   98.92   99.30   98.50   98.78  30136990\n",
       "2016-07-14   97.39   98.99   97.32   98.79  38918997\n",
       "2016-07-13   97.41   97.67   96.84   96.87  25892171\n",
       "2016-07-12   97.17   97.70   97.12   97.42  24167463\n",
       "2016-07-11   96.75   97.65   96.73   96.98  23794945"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['2016-07']"
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  {
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   "execution_count": 24,
   "id": "dba6871c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dcf150d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].resample('M').mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4f9e339f",
   "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>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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/aapl_no_dates.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e242da9d",
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range(start=\"6/1/2016\",end=\"6/30/2016\",freq='B')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a1e52359",
   "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>2016-06-06</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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(rng, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fec643d5",
   "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": 30,
     "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": 31,
   "id": "53081be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df.Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d64c5e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152.72125"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"2016-06-01\":\"2016-06-10\"].Close.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4a145cec",
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5e83360d",
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.asfreq('D',method='pad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6d481ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.asfreq('W',method='pad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9313c1e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
       "      <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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.asfreq('H',method='pad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f21b7a33",
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2011', periods=72, freq='H')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e475046b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-01 00:00:00    7\n",
       "2011-01-01 01:00:00    4\n",
       "2011-01-01 02:00:00    4\n",
       "2011-01-01 03:00:00    8\n",
       "2011-01-01 04:00:00    3\n",
       "2011-01-01 05:00:00    9\n",
       "2011-01-01 06:00:00    8\n",
       "2011-01-01 07:00:00    1\n",
       "2011-01-01 08:00:00    8\n",
       "2011-01-01 09:00:00    1\n",
       "2011-01-01 10:00:00    8\n",
       "2011-01-01 11:00:00    1\n",
       "2011-01-01 12:00:00    0\n",
       "2011-01-01 13:00:00    5\n",
       "2011-01-01 14:00:00    0\n",
       "2011-01-01 15:00:00    7\n",
       "2011-01-01 16:00:00    3\n",
       "2011-01-01 17:00:00    8\n",
       "2011-01-01 18:00:00    9\n",
       "2011-01-01 19:00:00    6\n",
       "Freq: H, dtype: int64"
      ]
     },
     "execution_count": 38,
     "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": 51,
   "id": "220fe7c2",
   "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>0</th>\n",
       "      <td>144.88</td>\n",
       "      <td>145.30</td>\n",
       "      <td>143.10</td>\n",
       "      <td>143.50</td>\n",
       "      <td>14277848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>143.69</td>\n",
       "      <td>144.79</td>\n",
       "      <td>142.72</td>\n",
       "      <td>144.09</td>\n",
       "      <td>21569557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>143.02</td>\n",
       "      <td>143.50</td>\n",
       "      <td>142.41</td>\n",
       "      <td>142.73</td>\n",
       "      <td>24128782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>142.90</td>\n",
       "      <td>144.75</td>\n",
       "      <td>142.90</td>\n",
       "      <td>144.18</td>\n",
       "      <td>19201712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>144.11</td>\n",
       "      <td>145.95</td>\n",
       "      <td>143.37</td>\n",
       "      <td>145.06</td>\n",
       "      <td>21090636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Open    High     Low   Close    Volume\n",
       "0  144.88  145.30  143.10  143.50  14277848\n",
       "1  143.69  144.79  142.72  144.09  21569557\n",
       "2  143.02  143.50  142.41  142.73  24128782\n",
       "3  142.90  144.75  142.90  144.18  19201712\n",
       "4  144.11  145.95  143.37  145.06  21090636"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/aapl_no_dates1.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1772b750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-07-03', '2017-07-04', '2017-07-05', '2017-07-06',\n",
       "               '2017-07-07', '2017-07-10', '2017-07-11', '2017-07-12',\n",
       "               '2017-07-13', '2017-07-14', '2017-07-17', '2017-07-18',\n",
       "               '2017-07-19', '2017-07-20', '2017-07-21'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range(start=\"7/1/2017\", end=\"7/21/2017\", freq='B')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "729aef57",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "from pandas.tseries.offsets import CustomBusinessDay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e50d6e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<CustomBusinessDay>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_cal = CustomBusinessDay(calendar=USFederalHolidayCalendar())\n",
    "us_cal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cd0a91aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-07-03', '2017-07-05', '2017-07-06', '2017-07-07',\n",
       "               '2017-07-10', '2017-07-11', '2017-07-12', '2017-07-13',\n",
       "               '2017-07-14', '2017-07-17', '2017-07-18', '2017-07-19',\n",
       "               '2017-07-20', '2017-07-21'],\n",
       "              dtype='datetime64[ns]', freq='C')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range(start=\"7/1/2017\", end=\"7/21/2017\", freq=us_cal)\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b5f51715",
   "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>2017-07-03</th>\n",
       "      <td>144.88</td>\n",
       "      <td>145.30</td>\n",
       "      <td>143.10</td>\n",
       "      <td>143.50</td>\n",
       "      <td>14277848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-05</th>\n",
       "      <td>143.69</td>\n",
       "      <td>144.79</td>\n",
       "      <td>142.72</td>\n",
       "      <td>144.09</td>\n",
       "      <td>21569557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-06</th>\n",
       "      <td>143.02</td>\n",
       "      <td>143.50</td>\n",
       "      <td>142.41</td>\n",
       "      <td>142.73</td>\n",
       "      <td>24128782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-07</th>\n",
       "      <td>142.90</td>\n",
       "      <td>144.75</td>\n",
       "      <td>142.90</td>\n",
       "      <td>144.18</td>\n",
       "      <td>19201712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-10</th>\n",
       "      <td>144.11</td>\n",
       "      <td>145.95</td>\n",
       "      <td>143.37</td>\n",
       "      <td>145.06</td>\n",
       "      <td>21090636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-11</th>\n",
       "      <td>144.73</td>\n",
       "      <td>145.85</td>\n",
       "      <td>144.38</td>\n",
       "      <td>145.53</td>\n",
       "      <td>19781836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-12</th>\n",
       "      <td>145.87</td>\n",
       "      <td>146.18</td>\n",
       "      <td>144.82</td>\n",
       "      <td>145.74</td>\n",
       "      <td>24884478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13</th>\n",
       "      <td>145.50</td>\n",
       "      <td>148.49</td>\n",
       "      <td>145.44</td>\n",
       "      <td>147.77</td>\n",
       "      <td>25199373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-14</th>\n",
       "      <td>147.97</td>\n",
       "      <td>149.33</td>\n",
       "      <td>147.33</td>\n",
       "      <td>149.04</td>\n",
       "      <td>20132061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-17</th>\n",
       "      <td>148.82</td>\n",
       "      <td>150.90</td>\n",
       "      <td>148.57</td>\n",
       "      <td>149.56</td>\n",
       "      <td>23793456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-18</th>\n",
       "      <td>149.20</td>\n",
       "      <td>150.13</td>\n",
       "      <td>148.67</td>\n",
       "      <td>150.08</td>\n",
       "      <td>17868792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-19</th>\n",
       "      <td>150.48</td>\n",
       "      <td>151.42</td>\n",
       "      <td>149.95</td>\n",
       "      <td>151.02</td>\n",
       "      <td>20922969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20</th>\n",
       "      <td>151.50</td>\n",
       "      <td>151.74</td>\n",
       "      <td>150.19</td>\n",
       "      <td>150.34</td>\n",
       "      <td>17243748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-21</th>\n",
       "      <td>149.99</td>\n",
       "      <td>150.44</td>\n",
       "      <td>148.88</td>\n",
       "      <td>150.27</td>\n",
       "      <td>26252630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "2017-07-03  144.88  145.30  143.10  143.50  14277848\n",
       "2017-07-05  143.69  144.79  142.72  144.09  21569557\n",
       "2017-07-06  143.02  143.50  142.41  142.73  24128782\n",
       "2017-07-07  142.90  144.75  142.90  144.18  19201712\n",
       "2017-07-10  144.11  145.95  143.37  145.06  21090636\n",
       "2017-07-11  144.73  145.85  144.38  145.53  19781836\n",
       "2017-07-12  145.87  146.18  144.82  145.74  24884478\n",
       "2017-07-13  145.50  148.49  145.44  147.77  25199373\n",
       "2017-07-14  147.97  149.33  147.33  149.04  20132061\n",
       "2017-07-17  148.82  150.90  148.57  149.56  23793456\n",
       "2017-07-18  149.20  150.13  148.67  150.08  17868792\n",
       "2017-07-19  150.48  151.42  149.95  151.02  20922969\n",
       "2017-07-20  151.50  151.74  150.19  150.34  17243748\n",
       "2017-07-21  149.99  150.44  148.88  150.27  26252630"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(rng, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b12d9f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-04-03', '2017-04-04', '2017-04-05', '2017-04-06',\n",
       "               '2017-04-07', '2017-04-10', '2017-04-11', '2017-04-12',\n",
       "               '2017-04-13', '2017-04-14', '2017-04-17', '2017-04-18',\n",
       "               '2017-04-19', '2017-04-20', '2017-04-21', '2017-04-24',\n",
       "               '2017-04-25', '2017-04-26', '2017-04-27', '2017-04-28'],\n",
       "              dtype='datetime64[ns]', freq='C')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.holiday import AbstractHolidayCalendar, nearest_workday, Holiday\n",
    "class myCalendar(AbstractHolidayCalendar):\n",
    "    rules = [\n",
    "        Holiday('My Birth Day', month=4, day=15),#, observance=nearest_workday),\n",
    "    ]\n",
    "    \n",
    "my_bday = CustomBusinessDay(calendar=myCalendar())\n",
    "pd.date_range('4/1/2017','4/30/2017',freq=my_bday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "28fdd85f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-07-02', '2017-07-03', '2017-07-04', '2017-07-05',\n",
       "               '2017-07-06', '2017-07-09', '2017-07-10', '2017-07-11',\n",
       "               '2017-07-12', '2017-07-13', '2017-07-16', '2017-07-17',\n",
       "               '2017-07-18', '2017-07-19', '2017-07-20', '2017-07-23',\n",
       "               '2017-07-24', '2017-07-25', '2017-07-26', '2017-07-27'],\n",
       "              dtype='datetime64[ns]', freq='C')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "egypt_weekdays = \"Sun Mon Tue Wed Thu\"\n",
    "\n",
    "b = CustomBusinessDay(weekmask=egypt_weekdays)\n",
    "\n",
    "pd.date_range(start=\"7/1/2017\",periods=20,freq=b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7bb81704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-07-02', '2017-07-03', '2017-07-05', '2017-07-06',\n",
       "               '2017-07-09', '2017-07-11', '2017-07-12', '2017-07-13',\n",
       "               '2017-07-16', '2017-07-17', '2017-07-18', '2017-07-19',\n",
       "               '2017-07-20', '2017-07-23', '2017-07-24', '2017-07-25',\n",
       "               '2017-07-26', '2017-07-27', '2017-07-30', '2017-07-31'],\n",
       "              dtype='datetime64[ns]', freq='C')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = CustomBusinessDay(holidays=['2017-07-04', '2017-07-10'], weekmask=egypt_weekdays)\n",
    "\n",
    "pd.date_range(start=\"7/1/2017\",periods=20,freq=b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c86c14d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2017, 7, 9, 0, 0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "dt = datetime(2017,7,9)\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ba2e5969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-07-11 00:00:00')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt + 1*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ead3a0ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-01-05', '2017-01-05', '2017-01-05', '2017-01-05',\n",
       "               '2017-01-05', '2017-01-05'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = ['2017-01-05', 'Jan 5, 2017', '01/05/2017', '2017.01.05', '2017/01/05','20170105']\n",
    "pd.to_datetime(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a508e20f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-01-05 14:30:00', '2017-01-05 14:30:00',\n",
       "               '2016-01-05 00:00:00', '2017-01-05 00:00:00',\n",
       "               '2017-01-05 00:00:00', '2017-01-05 00:00:00'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = ['2017-01-05 2:30:00 PM', 'Jan 5, 2017 14:30:00', '01/05/2016', '2017.01.05', '2017/01/05','20170105']\n",
    "pd.to_datetime(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b06c18b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_122825/2598061570.py:1: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
      "  pd.to_datetime('30-12-2016')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-12-30 00:00:00')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('30-12-2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cddb88a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-01-05 00:00:00')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('5-1-2016', dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "754752a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-01-05 00:00:00')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('2017$01$05', format='%Y$%m$%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "18b9b9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-01-05 00:00:00')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('2017#01#05', format='%Y#%m#%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1f4b03f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2017-01-05', 'Jan 6, 2017', 'abc'], dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(['2017-01-05', 'Jan 6, 2017', 'abc'], errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "142dbf32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-01-05', '2017-01-06', 'NaT'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(['2017-01-05', 'Jan 6, 2017', 'abc'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cb547a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-07-29 10:34:38')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_epoch = 1501324478\n",
    "pd.to_datetime(current_epoch, unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a06bc313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-07-29 10:34:38')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(current_epoch*1000, unit='ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8a9be47e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-07-29 10:34:38'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.to_datetime([current_epoch], unit='s')\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "eb4e3143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1501324478000000000])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.view('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "53a4993e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2016', 'A-DEC')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# year period\n",
    "y = pd.Period('2016')\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ca976e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-01-01 00:00:00')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6cca53f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-12-31 23:59:59.999999999')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e63ab2ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.is_leap_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "685b4b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2017-12', 'M')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# monthly period\n",
    "m = pd.Period('2017-12')\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "257177ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-12-01 00:00:00')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4b52db13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-12-31 23:59:59.999999999')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "dedb021d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2018-01', 'M')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "601f810b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2016-02-28', 'D')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Daily period\n",
    "d = pd.Period('2016-02-28', freq='D')\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9fc3cc75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-02-28 00:00:00')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3b7a686e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-02-28 23:59:59.999999999')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3404fd73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2016-02-29', 'D')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "642d632d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2017-08-15 23:00', 'H')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hrourly period\n",
    "h = pd.Period('2017-08-15 23:00:00',freq='H')\n",
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b93e0add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2017-08-16 00:00', 'H')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "200b8c09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2017-08-16 00:00', 'H')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h+pd.offsets.Hour(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "47477fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2017Q1', 'Q-JAN')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#quarterly Period\n",
    "q1= pd.Period('2017Q1', freq='Q-JAN')\n",
    "q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "2b499d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2017Q1', 'Q-JAN')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1= pd.Period('2017Q1', freq='Q-JAN')\n",
    "q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b9db1305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-02-01 00:00:00')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1.start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "561c3388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-04-30 23:59:59.999999999')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1.end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2878e9af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2016-02', 'M')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1.asfreq('M',how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "38690c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2016-04', 'M')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1.asfreq('M',how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e6fde950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2017-07-03/2017-07-09', 'W-SUN')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#weekly Period\n",
    "w = pd.Period('2017-07-05',freq='W')\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "db3e6c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2017-06-26/2017-07-02', 'W-SUN')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1af9d58e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2017-08-14/2017-08-20', 'W-SUN')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2 = pd.Period('2017-08-15',freq='W')\n",
    "w2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "dc57c270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<6 * Weeks: weekday=6>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2-w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5980462b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2011Q1', '2011Q2', '2011Q3', '2011Q4', '2012Q1', '2012Q2',\n",
       "             '2012Q3', '2012Q4', '2013Q1', '2013Q2', '2013Q3', '2013Q4',\n",
       "             '2014Q1', '2014Q2', '2014Q3', '2014Q4', '2015Q1', '2015Q2',\n",
       "             '2015Q3', '2015Q4', '2016Q1', '2016Q2', '2016Q3', '2016Q4',\n",
       "             '2017Q1'],\n",
       "            dtype='period[Q-DEC]')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PeriodIndex and period_range\n",
    "r = pd.period_range('2011', '2017', freq='q')\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "aad4a481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-01-01 00:00:00')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[0].start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "51f3e946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-31 23:59:59.999999999')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[0].end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "33709ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2011Q4', '2012Q1', '2012Q2', '2012Q3', '2012Q4', '2013Q1',\n",
       "             '2013Q2', '2013Q3', '2013Q4', '2014Q1', '2014Q2', '2014Q3',\n",
       "             '2014Q4', '2015Q1', '2015Q2', '2015Q3', '2015Q4', '2016Q1',\n",
       "             '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2', '2017Q3',\n",
       "             '2017Q4'],\n",
       "            dtype='period[Q-JAN]')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = pd.period_range('2011', '2017', freq='q-jan')\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ee3611e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2010-11-01 00:00:00')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[0].start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ec805885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-01-31 23:59:59.999999999')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[0].end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "07987a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "#r = pd.PeriodIndex(start='2016-01', freq='3M', periods=10)\n",
    "#r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c94028",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "bc0d875d",
   "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>Line Item</th>\n",
       "      <th>2017Q1</th>\n",
       "      <th>2017Q2</th>\n",
       "      <th>2017Q3</th>\n",
       "      <th>2017Q4</th>\n",
       "      <th>2018Q1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Revenue</td>\n",
       "      <td>115904</td>\n",
       "      <td>120854</td>\n",
       "      <td>118179</td>\n",
       "      <td>130936</td>\n",
       "      <td>117542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Expenses</td>\n",
       "      <td>86544</td>\n",
       "      <td>89485</td>\n",
       "      <td>87484</td>\n",
       "      <td>97743</td>\n",
       "      <td>87688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Profit</td>\n",
       "      <td>29360</td>\n",
       "      <td>31369</td>\n",
       "      <td>30695</td>\n",
       "      <td>33193</td>\n",
       "      <td>29854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Line Item  2017Q1  2017Q2  2017Q3  2017Q4  2018Q1\n",
       "0   Revenue  115904  120854  118179  130936  117542\n",
       "1  Expenses   86544   89485   87484   97743   87688\n",
       "2    Profit   29360   31369   30695   33193   29854"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/wmt.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "43d5911c",
   "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>Line Item</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017Q1</th>\n",
       "      <td>115904</td>\n",
       "      <td>86544</td>\n",
       "      <td>29360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q2</th>\n",
       "      <td>120854</td>\n",
       "      <td>89485</td>\n",
       "      <td>31369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q3</th>\n",
       "      <td>118179</td>\n",
       "      <td>87484</td>\n",
       "      <td>30695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q4</th>\n",
       "      <td>130936</td>\n",
       "      <td>97743</td>\n",
       "      <td>33193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018Q1</th>\n",
       "      <td>117542</td>\n",
       "      <td>87688</td>\n",
       "      <td>29854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Line Item  Revenue  Expenses  Profit\n",
       "2017Q1      115904     86544   29360\n",
       "2017Q2      120854     89485   31369\n",
       "2017Q3      118179     87484   30695\n",
       "2017Q4      130936     97743   33193\n",
       "2018Q1      117542     87688   29854"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(\"Line Item\",inplace=True)\n",
    "df = df.T\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f0737803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Revenue</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017Q1</th>\n",
       "      <td>115904</td>\n",
       "      <td>86544</td>\n",
       "      <td>29360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q2</th>\n",
       "      <td>120854</td>\n",
       "      <td>89485</td>\n",
       "      <td>31369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q3</th>\n",
       "      <td>118179</td>\n",
       "      <td>87484</td>\n",
       "      <td>30695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q4</th>\n",
       "      <td>130936</td>\n",
       "      <td>97743</td>\n",
       "      <td>33193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018Q1</th>\n",
       "      <td>117542</td>\n",
       "      <td>87688</td>\n",
       "      <td>29854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Line Item  Revenue  Expenses  Profit\n",
       "2017Q1      115904     86544   29360\n",
       "2017Q2      120854     89485   31369\n",
       "2017Q3      118179     87484   30695\n",
       "2017Q4      130936     97743   33193\n",
       "2018Q1      117542     87688   29854"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = pd.PeriodIndex(df.index, freq=\"Q-JAN\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "e0e49f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1'], dtype='period[Q-JAN]')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "40a48bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-02-01 00:00:00')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[0].start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "be37481b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Revenue</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Start Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017Q1</th>\n",
       "      <td>115904</td>\n",
       "      <td>86544</td>\n",
       "      <td>29360</td>\n",
       "      <td>2016-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q2</th>\n",
       "      <td>120854</td>\n",
       "      <td>89485</td>\n",
       "      <td>31369</td>\n",
       "      <td>2016-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q3</th>\n",
       "      <td>118179</td>\n",
       "      <td>87484</td>\n",
       "      <td>30695</td>\n",
       "      <td>2016-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q4</th>\n",
       "      <td>130936</td>\n",
       "      <td>97743</td>\n",
       "      <td>33193</td>\n",
       "      <td>2016-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018Q1</th>\n",
       "      <td>117542</td>\n",
       "      <td>87688</td>\n",
       "      <td>29854</td>\n",
       "      <td>2017-02-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Line Item  Revenue  Expenses  Profit Start Date\n",
       "2017Q1      115904     86544   29360 2016-02-01\n",
       "2017Q2      120854     89485   31369 2016-05-01\n",
       "2017Q3      118179     87484   30695 2016-08-01\n",
       "2017Q4      130936     97743   33193 2016-11-01\n",
       "2018Q1      117542     87688   29854 2017-02-01"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Start Date\"]=df.index.map(lambda x: x.start_time)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "262cec4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Revenue</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>Profit</th>\n",
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       "      <th>End Date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017Q1</th>\n",
       "      <td>115904</td>\n",
       "      <td>86544</td>\n",
       "      <td>29360</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>2016-04-30 23:59:59.999999999</td>\n",
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       "    <tr>\n",
       "      <th>2017Q2</th>\n",
       "      <td>120854</td>\n",
       "      <td>89485</td>\n",
       "      <td>31369</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>2016-07-31 23:59:59.999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q3</th>\n",
       "      <td>118179</td>\n",
       "      <td>87484</td>\n",
       "      <td>30695</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>2016-10-31 23:59:59.999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017Q4</th>\n",
       "      <td>130936</td>\n",
       "      <td>97743</td>\n",
       "      <td>33193</td>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>2017-01-31 23:59:59.999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018Q1</th>\n",
       "      <td>117542</td>\n",
       "      <td>87688</td>\n",
       "      <td>29854</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>2017-04-30 23:59:59.999999999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Line Item  Revenue  Expenses  Profit Start Date                      End Date\n",
       "2017Q1      115904     86544   29360 2016-02-01 2016-04-30 23:59:59.999999999\n",
       "2017Q2      120854     89485   31369 2016-05-01 2016-07-31 23:59:59.999999999\n",
       "2017Q3      118179     87484   30695 2016-08-01 2016-10-31 23:59:59.999999999\n",
       "2017Q4      130936     97743   33193 2016-11-01 2017-01-31 23:59:59.999999999\n",
       "2018Q1      117542     87688   29854 2017-02-01 2017-04-30 23:59:59.999999999"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"End Date\"]=df.index.map(lambda x: x.end_time)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "fd71bba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-17 09:00:00</th>\n",
       "      <td>72.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 09:15:00</th>\n",
       "      <td>71.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 09:30:00</th>\n",
       "      <td>71.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 10:00:00</th>\n",
       "      <td>72.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 10:30:00</th>\n",
       "      <td>73.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 11:00:00</th>\n",
       "      <td>72.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Price\n",
       "Date Time                 \n",
       "2017-08-17 09:00:00  72.38\n",
       "2017-08-17 09:15:00  71.00\n",
       "2017-08-17 09:30:00  71.67\n",
       "2017-08-17 10:00:00  72.80\n",
       "2017-08-17 10:30:00  73.00\n",
       "2017-08-17 11:00:00  72.50"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft = pd.read_csv(\"data/msft.csv\", header=1,index_col='Date Time',parse_dates=True)\n",
    "msft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "63bb7d2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-08-17 09:00:00', '2017-08-17 09:15:00',\n",
       "               '2017-08-17 09:30:00', '2017-08-17 10:00:00',\n",
       "               '2017-08-17 10:30:00', '2017-08-17 11:00:00'],\n",
       "              dtype='datetime64[ns]', name='Date Time', freq=None)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "84459539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>Date Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-17 09:00:00-04:00</th>\n",
       "      <td>72.38</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17 09:15:00-04:00</th>\n",
       "      <td>71.00</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17 09:30:00-04:00</th>\n",
       "      <td>71.67</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17 10:00:00-04:00</th>\n",
       "      <td>72.80</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17 10:30:00-04:00</th>\n",
       "      <td>73.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 11:00:00-04:00</th>\n",
       "      <td>72.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Price\n",
       "Date Time                       \n",
       "2017-08-17 09:00:00-04:00  72.38\n",
       "2017-08-17 09:15:00-04:00  71.00\n",
       "2017-08-17 09:30:00-04:00  71.67\n",
       "2017-08-17 10:00:00-04:00  72.80\n",
       "2017-08-17 10:30:00-04:00  73.00\n",
       "2017-08-17 11:00:00-04:00  72.50"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft = msft.tz_localize(tz='US/Eastern')\n",
    "msft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "270c4781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-08-17 09:00:00-04:00', '2017-08-17 09:15:00-04:00',\n",
       "               '2017-08-17 09:30:00-04:00', '2017-08-17 10:00:00-04:00',\n",
       "               '2017-08-17 10:30:00-04:00', '2017-08-17 11:00:00-04:00'],\n",
       "              dtype='datetime64[ns, US/Eastern]', name='Date Time', freq=None)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de0a5cb2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f480db35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-17 15:00:00+02:00</th>\n",
       "      <td>72.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 15:15:00+02:00</th>\n",
       "      <td>71.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 15:30:00+02:00</th>\n",
       "      <td>71.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 16:00:00+02:00</th>\n",
       "      <td>72.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 16:30:00+02:00</th>\n",
       "      <td>73.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 17:00:00+02:00</th>\n",
       "      <td>72.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Price\n",
       "Date Time                       \n",
       "2017-08-17 15:00:00+02:00  72.38\n",
       "2017-08-17 15:15:00+02:00  71.00\n",
       "2017-08-17 15:30:00+02:00  71.67\n",
       "2017-08-17 16:00:00+02:00  72.80\n",
       "2017-08-17 16:30:00+02:00  73.00\n",
       "2017-08-17 17:00:00+02:00  72.50"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft = msft.tz_convert('Europe/Berlin')\n",
    "msft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "fe65a484",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Africa/Abidjan', 'Africa/Accra', 'Africa/Addis_Ababa', 'Africa/Algiers', 'Africa/Asmara', 'Africa/Asmera', 'Africa/Bamako', 'Africa/Bangui', 'Africa/Banjul', 'Africa/Bissau', 'Africa/Blantyre', 'Africa/Brazzaville', 'Africa/Bujumbura', 'Africa/Cairo', 'Africa/Casablanca', 'Africa/Ceuta', 'Africa/Conakry', 'Africa/Dakar', 'Africa/Dar_es_Salaam', 'Africa/Djibouti', 'Africa/Douala', 'Africa/El_Aaiun', 'Africa/Freetown', 'Africa/Gaborone', 'Africa/Harare', 'Africa/Johannesburg', 'Africa/Juba', 'Africa/Kampala', 'Africa/Khartoum', 'Africa/Kigali', 'Africa/Kinshasa', 'Africa/Lagos', 'Africa/Libreville', 'Africa/Lome', 'Africa/Luanda', 'Africa/Lubumbashi', 'Africa/Lusaka', 'Africa/Malabo', 'Africa/Maputo', 'Africa/Maseru', 'Africa/Mbabane', 'Africa/Mogadishu', 'Africa/Monrovia', 'Africa/Nairobi', 'Africa/Ndjamena', 'Africa/Niamey', 'Africa/Nouakchott', 'Africa/Ouagadougou', 'Africa/Porto-Novo', 'Africa/Sao_Tome', 'Africa/Timbuktu', 'Africa/Tripoli', 'Africa/Tunis', 'Africa/Windhoek', 'America/Adak', 'America/Anchorage', 'America/Anguilla', 'America/Antigua', 'America/Araguaina', 'America/Argentina/Buenos_Aires', 'America/Argentina/Catamarca', 'America/Argentina/ComodRivadavia', 'America/Argentina/Cordoba', 'America/Argentina/Jujuy', 'America/Argentina/La_Rioja', 'America/Argentina/Mendoza', 'America/Argentina/Rio_Gallegos', 'America/Argentina/Salta', 'America/Argentina/San_Juan', 'America/Argentina/San_Luis', 'America/Argentina/Tucuman', 'America/Argentina/Ushuaia', 'America/Aruba', 'America/Asuncion', 'America/Atikokan', 'America/Atka', 'America/Bahia', 'America/Bahia_Banderas', 'America/Barbados', 'America/Belem', 'America/Belize', 'America/Blanc-Sablon', 'America/Boa_Vista', 'America/Bogota', 'America/Boise', 'America/Buenos_Aires', 'America/Cambridge_Bay', 'America/Campo_Grande', 'America/Cancun', 'America/Caracas', 'America/Catamarca', 'America/Cayenne', 'America/Cayman', 'America/Chicago', 'America/Chihuahua', 'America/Coral_Harbour', 'America/Cordoba', 'America/Costa_Rica', 'America/Creston', 'America/Cuiaba', 'America/Curacao', 'America/Danmarkshavn', 'America/Dawson', 'America/Dawson_Creek', 'America/Denver', 'America/Detroit', 'America/Dominica', 'America/Edmonton', 'America/Eirunepe', 'America/El_Salvador', 'America/Ensenada', 'America/Fort_Nelson', 'America/Fort_Wayne', 'America/Fortaleza', 'America/Glace_Bay', 'America/Godthab', 'America/Goose_Bay', 'America/Grand_Turk', 'America/Grenada', 'America/Guadeloupe', 'America/Guatemala', 'America/Guayaquil', 'America/Guyana', 'America/Halifax', 'America/Havana', 'America/Hermosillo', 'America/Indiana/Indianapolis', 'America/Indiana/Knox', 'America/Indiana/Marengo', 'America/Indiana/Petersburg', 'America/Indiana/Tell_City', 'America/Indiana/Vevay', 'America/Indiana/Vincennes', 'America/Indiana/Winamac', 'America/Indianapolis', 'America/Inuvik', 'America/Iqaluit', 'America/Jamaica', 'America/Jujuy', 'America/Juneau', 'America/Kentucky/Louisville', 'America/Kentucky/Monticello', 'America/Knox_IN', 'America/Kralendijk', 'America/La_Paz', 'America/Lima', 'America/Los_Angeles', 'America/Louisville', 'America/Lower_Princes', 'America/Maceio', 'America/Managua', 'America/Manaus', 'America/Marigot', 'America/Martinique', 'America/Matamoros', 'America/Mazatlan', 'America/Mendoza', 'America/Menominee', 'America/Merida', 'America/Metlakatla', 'America/Mexico_City', 'America/Miquelon', 'America/Moncton', 'America/Monterrey', 'America/Montevideo', 'America/Montreal', 'America/Montserrat', 'America/Nassau', 'America/New_York', 'America/Nipigon', 'America/Nome', 'America/Noronha', 'America/North_Dakota/Beulah', 'America/North_Dakota/Center', 'America/North_Dakota/New_Salem', 'America/Nuuk', 'America/Ojinaga', 'America/Panama', 'America/Pangnirtung', 'America/Paramaribo', 'America/Phoenix', 'America/Port-au-Prince', 'America/Port_of_Spain', 'America/Porto_Acre', 'America/Porto_Velho', 'America/Puerto_Rico', 'America/Punta_Arenas', 'America/Rainy_River', 'America/Rankin_Inlet', 'America/Recife', 'America/Regina', 'America/Resolute', 'America/Rio_Branco', 'America/Rosario', 'America/Santa_Isabel', 'America/Santarem', 'America/Santiago', 'America/Santo_Domingo', 'America/Sao_Paulo', 'America/Scoresbysund', 'America/Shiprock', 'America/Sitka', 'America/St_Barthelemy', 'America/St_Johns', 'America/St_Kitts', 'America/St_Lucia', 'America/St_Thomas', 'America/St_Vincent', 'America/Swift_Current', 'America/Tegucigalpa', 'America/Thule', 'America/Thunder_Bay', 'America/Tijuana', 'America/Toronto', 'America/Tortola', 'America/Vancouver', 'America/Virgin', 'America/Whitehorse', 'America/Winnipeg', 'America/Yakutat', 'America/Yellowknife', 'Antarctica/Casey', 'Antarctica/Davis', 'Antarctica/DumontDUrville', 'Antarctica/Macquarie', 'Antarctica/Mawson', 'Antarctica/McMurdo', 'Antarctica/Palmer', 'Antarctica/Rothera', 'Antarctica/South_Pole', 'Antarctica/Syowa', 'Antarctica/Troll', 'Antarctica/Vostok', 'Arctic/Longyearbyen', 'Asia/Aden', 'Asia/Almaty', 'Asia/Amman', 'Asia/Anadyr', 'Asia/Aqtau', 'Asia/Aqtobe', 'Asia/Ashgabat', 'Asia/Ashkhabad', 'Asia/Atyrau', 'Asia/Baghdad', 'Asia/Bahrain', 'Asia/Baku', 'Asia/Bangkok', 'Asia/Barnaul', 'Asia/Beirut', 'Asia/Bishkek', 'Asia/Brunei', 'Asia/Calcutta', 'Asia/Chita', 'Asia/Choibalsan', 'Asia/Chongqing', 'Asia/Chungking', 'Asia/Colombo', 'Asia/Dacca', 'Asia/Damascus', 'Asia/Dhaka', 'Asia/Dili', 'Asia/Dubai', 'Asia/Dushanbe', 'Asia/Famagusta', 'Asia/Gaza', 'Asia/Harbin', 'Asia/Hebron', 'Asia/Ho_Chi_Minh', 'Asia/Hong_Kong', 'Asia/Hovd', 'Asia/Irkutsk', 'Asia/Istanbul', 'Asia/Jakarta', 'Asia/Jayapura', 'Asia/Jerusalem', 'Asia/Kabul', 'Asia/Kamchatka', 'Asia/Karachi', 'Asia/Kashgar', 'Asia/Kathmandu', 'Asia/Katmandu', 'Asia/Khandyga', 'Asia/Kolkata', 'Asia/Krasnoyarsk', 'Asia/Kuala_Lumpur', 'Asia/Kuching', 'Asia/Kuwait', 'Asia/Macao', 'Asia/Macau', 'Asia/Magadan', 'Asia/Makassar', 'Asia/Manila', 'Asia/Muscat', 'Asia/Nicosia', 'Asia/Novokuznetsk', 'Asia/Novosibirsk', 'Asia/Omsk', 'Asia/Oral', 'Asia/Phnom_Penh', 'Asia/Pontianak', 'Asia/Pyongyang', 'Asia/Qatar', 'Asia/Qostanay', 'Asia/Qyzylorda', 'Asia/Rangoon', 'Asia/Riyadh', 'Asia/Saigon', 'Asia/Sakhalin', 'Asia/Samarkand', 'Asia/Seoul', 'Asia/Shanghai', 'Asia/Singapore', 'Asia/Srednekolymsk', 'Asia/Taipei', 'Asia/Tashkent', 'Asia/Tbilisi', 'Asia/Tehran', 'Asia/Tel_Aviv', 'Asia/Thimbu', 'Asia/Thimphu', 'Asia/Tokyo', 'Asia/Tomsk', 'Asia/Ujung_Pandang', 'Asia/Ulaanbaatar', 'Asia/Ulan_Bator', 'Asia/Urumqi', 'Asia/Ust-Nera', 'Asia/Vientiane', 'Asia/Vladivostok', 'Asia/Yakutsk', 'Asia/Yangon', 'Asia/Yekaterinburg', 'Asia/Yerevan', 'Atlantic/Azores', 'Atlantic/Bermuda', 'Atlantic/Canary', 'Atlantic/Cape_Verde', 'Atlantic/Faeroe', 'Atlantic/Faroe', 'Atlantic/Jan_Mayen', 'Atlantic/Madeira', 'Atlantic/Reykjavik', 'Atlantic/South_Georgia', 'Atlantic/St_Helena', 'Atlantic/Stanley', 'Australia/ACT', 'Australia/Adelaide', 'Australia/Brisbane', 'Australia/Broken_Hill', 'Australia/Canberra', 'Australia/Currie', 'Australia/Darwin', 'Australia/Eucla', 'Australia/Hobart', 'Australia/LHI', 'Australia/Lindeman', 'Australia/Lord_Howe', 'Australia/Melbourne', 'Australia/NSW', 'Australia/North', 'Australia/Perth', 'Australia/Queensland', 'Australia/South', 'Australia/Sydney', 'Australia/Tasmania', 'Australia/Victoria', 'Australia/West', 'Australia/Yancowinna', 'Brazil/Acre', 'Brazil/DeNoronha', 'Brazil/East', 'Brazil/West', 'CET', 'CST6CDT', 'Canada/Atlantic', 'Canada/Central', 'Canada/Eastern', 'Canada/Mountain', 'Canada/Newfoundland', 'Canada/Pacific', 'Canada/Saskatchewan', 'Canada/Yukon', 'Chile/Continental', 'Chile/EasterIsland', 'Cuba', 'EET', 'EST', 'EST5EDT', 'Egypt', 'Eire', 'Etc/GMT', 'Etc/GMT+0', 'Etc/GMT+1', 'Etc/GMT+10', 'Etc/GMT+11', 'Etc/GMT+12', 'Etc/GMT+2', 'Etc/GMT+3', 'Etc/GMT+4', 'Etc/GMT+5', 'Etc/GMT+6', 'Etc/GMT+7', 'Etc/GMT+8', 'Etc/GMT+9', 'Etc/GMT-0', 'Etc/GMT-1', 'Etc/GMT-10', 'Etc/GMT-11', 'Etc/GMT-12', 'Etc/GMT-13', 'Etc/GMT-14', 'Etc/GMT-2', 'Etc/GMT-3', 'Etc/GMT-4', 'Etc/GMT-5', 'Etc/GMT-6', 'Etc/GMT-7', 'Etc/GMT-8', 'Etc/GMT-9', 'Etc/GMT0', 'Etc/Greenwich', 'Etc/UCT', 'Etc/UTC', 'Etc/Universal', 'Etc/Zulu', 'Europe/Amsterdam', 'Europe/Andorra', 'Europe/Astrakhan', 'Europe/Athens', 'Europe/Belfast', 'Europe/Belgrade', 'Europe/Berlin', 'Europe/Bratislava', 'Europe/Brussels', 'Europe/Bucharest', 'Europe/Budapest', 'Europe/Busingen', 'Europe/Chisinau', 'Europe/Copenhagen', 'Europe/Dublin', 'Europe/Gibraltar', 'Europe/Guernsey', 'Europe/Helsinki', 'Europe/Isle_of_Man', 'Europe/Istanbul', 'Europe/Jersey', 'Europe/Kaliningrad', 'Europe/Kiev', 'Europe/Kirov', 'Europe/Kyiv', 'Europe/Lisbon', 'Europe/Ljubljana', 'Europe/London', 'Europe/Luxembourg', 'Europe/Madrid', 'Europe/Malta', 'Europe/Mariehamn', 'Europe/Minsk', 'Europe/Monaco', 'Europe/Moscow', 'Europe/Nicosia', 'Europe/Oslo', 'Europe/Paris', 'Europe/Podgorica', 'Europe/Prague', 'Europe/Riga', 'Europe/Rome', 'Europe/Samara', 'Europe/San_Marino', 'Europe/Sarajevo', 'Europe/Saratov', 'Europe/Simferopol', 'Europe/Skopje', 'Europe/Sofia', 'Europe/Stockholm', 'Europe/Tallinn', 'Europe/Tirane', 'Europe/Tiraspol', 'Europe/Ulyanovsk', 'Europe/Uzhgorod', 'Europe/Vaduz', 'Europe/Vatican', 'Europe/Vienna', 'Europe/Vilnius', 'Europe/Volgograd', 'Europe/Warsaw', 'Europe/Zagreb', 'Europe/Zaporozhye', 'Europe/Zurich', 'GB', 'GB-Eire', 'GMT', 'GMT+0', 'GMT-0', 'GMT0', 'Greenwich', 'HST', 'Hongkong', 'Iceland', 'Indian/Antananarivo', 'Indian/Chagos', 'Indian/Christmas', 'Indian/Cocos', 'Indian/Comoro', 'Indian/Kerguelen', 'Indian/Mahe', 'Indian/Maldives', 'Indian/Mauritius', 'Indian/Mayotte', 'Indian/Reunion', 'Iran', 'Israel', 'Jamaica', 'Japan', 'Kwajalein', 'Libya', 'MET', 'MST', 'MST7MDT', 'Mexico/BajaNorte', 'Mexico/BajaSur', 'Mexico/General', 'NZ', 'NZ-CHAT', 'Navajo', 'PRC', 'PST8PDT', 'Pacific/Apia', 'Pacific/Auckland', 'Pacific/Bougainville', 'Pacific/Chatham', 'Pacific/Chuuk', 'Pacific/Easter', 'Pacific/Efate', 'Pacific/Enderbury', 'Pacific/Fakaofo', 'Pacific/Fiji', 'Pacific/Funafuti', 'Pacific/Galapagos', 'Pacific/Gambier', 'Pacific/Guadalcanal', 'Pacific/Guam', 'Pacific/Honolulu', 'Pacific/Johnston', 'Pacific/Kanton', 'Pacific/Kiritimati', 'Pacific/Kosrae', 'Pacific/Kwajalein', 'Pacific/Majuro', 'Pacific/Marquesas', 'Pacific/Midway', 'Pacific/Nauru', 'Pacific/Niue', 'Pacific/Norfolk', 'Pacific/Noumea', 'Pacific/Pago_Pago', 'Pacific/Palau', 'Pacific/Pitcairn', 'Pacific/Pohnpei', 'Pacific/Ponape', 'Pacific/Port_Moresby', 'Pacific/Rarotonga', 'Pacific/Saipan', 'Pacific/Samoa', 'Pacific/Tahiti', 'Pacific/Tarawa', 'Pacific/Tongatapu', 'Pacific/Truk', 'Pacific/Wake', 'Pacific/Wallis', 'Pacific/Yap', 'Poland', 'Portugal', 'ROC', 'ROK', 'Singapore', 'Turkey', 'UCT', 'US/Alaska', 'US/Aleutian', 'US/Arizona', 'US/Central', 'US/East-Indiana', 'US/Eastern', 'US/Hawaii', 'US/Indiana-Starke', 'US/Michigan', 'US/Mountain', 'US/Pacific', 'US/Samoa', 'UTC', 'Universal', 'W-SU', 'WET', 'Zulu']\n"
     ]
    }
   ],
   "source": [
    "from pytz import all_timezones\n",
    "print (all_timezones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "b72f6db9",
   "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>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-17 18:30:00+05:30</th>\n",
       "      <td>72.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 18:45:00+05:30</th>\n",
       "      <td>71.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 19:00:00+05:30</th>\n",
       "      <td>71.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 19:30:00+05:30</th>\n",
       "      <td>72.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 20:00:00+05:30</th>\n",
       "      <td>73.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 20:30:00+05:30</th>\n",
       "      <td>72.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Price\n",
       "Date Time                       \n",
       "2017-08-17 18:30:00+05:30  72.38\n",
       "2017-08-17 18:45:00+05:30  71.00\n",
       "2017-08-17 19:00:00+05:30  71.67\n",
       "2017-08-17 19:30:00+05:30  72.80\n",
       "2017-08-17 20:00:00+05:30  73.00\n",
       "2017-08-17 20:30:00+05:30  72.50"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft.index = msft.index.tz_convert('Asia/Calcutta')\n",
    "msft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "4a62d355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-06 00:09:00+00:00', '2012-03-06 01:09:00+00:00',\n",
       "               '2012-03-06 02:09:00+00:00', '2012-03-06 03:09:00+00:00',\n",
       "               '2012-03-06 04:09:00+00:00', '2012-03-06 05:09:00+00:00',\n",
       "               '2012-03-06 06:09:00+00:00', '2012-03-06 07:09:00+00:00',\n",
       "               '2012-03-06 08:09:00+00:00', '2012-03-06 09:09:00+00:00'],\n",
       "              dtype='datetime64[ns, Europe/London]', freq='H')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "london = pd.date_range('3/6/2012 00:09:00', periods=10, freq='H',tz='Europe/London')\n",
    "london"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "27e5d181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-06 00:00:00+00:00', '2012-03-06 01:00:00+00:00',\n",
       "               '2012-03-06 02:00:00+00:00', '2012-03-06 03:00:00+00:00',\n",
       "               '2012-03-06 04:00:00+00:00', '2012-03-06 05:00:00+00:00',\n",
       "               '2012-03-06 06:00:00+00:00', '2012-03-06 07:00:00+00:00',\n",
       "               '2012-03-06 08:00:00+00:00', '2012-03-06 09:00:00+00:00'],\n",
       "              dtype='datetime64[ns, tzfile('/usr/share/zoneinfo/Europe/London')]', freq='H')"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td = pd.date_range('3/6/2012 00:00', periods=10, freq='H',tz='dateutil/Europe/London')\n",
    "td"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "397f9ba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-08-22 09:00:00    0\n",
       "2017-08-22 09:30:00    1\n",
       "2017-08-22 10:00:00    2\n",
       "2017-08-22 10:30:00    3\n",
       "2017-08-22 11:00:00    4\n",
       "2017-08-22 11:30:00    5\n",
       "2017-08-22 12:00:00    6\n",
       "2017-08-22 12:30:00    7\n",
       "2017-08-22 13:00:00    8\n",
       "2017-08-22 13:30:00    9\n",
       "Freq: 30T, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range(start=\"2017-08-22 09:00:00\",periods=10, freq='30min')\n",
    "s = pd.Series(range(10),index=rng)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "0fa7c838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-08-22 09:00:00+02:00    0\n",
       "2017-08-22 09:30:00+02:00    1\n",
       "2017-08-22 10:00:00+02:00    2\n",
       "2017-08-22 10:30:00+02:00    3\n",
       "2017-08-22 11:00:00+02:00    4\n",
       "2017-08-22 11:30:00+02:00    5\n",
       "2017-08-22 12:00:00+02:00    6\n",
       "2017-08-22 12:30:00+02:00    7\n",
       "2017-08-22 13:00:00+02:00    8\n",
       "2017-08-22 13:30:00+02:00    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = s.tz_localize(tz=\"Europe/Berlin\")\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5fa3ea1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-08-22 09:00:00+02:00', '2017-08-22 09:30:00+02:00',\n",
       "               '2017-08-22 10:00:00+02:00', '2017-08-22 10:30:00+02:00',\n",
       "               '2017-08-22 11:00:00+02:00', '2017-08-22 11:30:00+02:00',\n",
       "               '2017-08-22 12:00:00+02:00', '2017-08-22 12:30:00+02:00',\n",
       "               '2017-08-22 13:00:00+02:00', '2017-08-22 13:30:00+02:00'],\n",
       "              dtype='datetime64[ns, Europe/Berlin]', freq=None)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "11431a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-08-22 09:00:00+05:30', '2017-08-22 09:30:00+05:30',\n",
       "               '2017-08-22 10:00:00+05:30', '2017-08-22 10:30:00+05:30',\n",
       "               '2017-08-22 11:00:00+05:30', '2017-08-22 11:30:00+05:30',\n",
       "               '2017-08-22 12:00:00+05:30', '2017-08-22 12:30:00+05:30',\n",
       "               '2017-08-22 13:00:00+05:30', '2017-08-22 13:30:00+05:30'],\n",
       "              dtype='datetime64[ns, Asia/Calcutta]', freq=None)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = s.tz_localize(tz=\"Asia/Calcutta\")\n",
    "m.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "4d7627a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-08-22 09:00:00+05:30    0\n",
       "2017-08-22 09:30:00+05:30    1\n",
       "2017-08-22 10:00:00+05:30    2\n",
       "2017-08-22 10:30:00+05:30    3\n",
       "2017-08-22 11:00:00+05:30    4\n",
       "2017-08-22 11:30:00+05:30    5\n",
       "2017-08-22 12:00:00+05:30    6\n",
       "2017-08-22 12:30:00+05:30    7\n",
       "2017-08-22 13:00:00+05:30    8\n",
       "2017-08-22 13:30:00+05:30    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "773be7e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-08-22 03:30:00+00:00     NaN\n",
       "2017-08-22 04:00:00+00:00     NaN\n",
       "2017-08-22 04:30:00+00:00     NaN\n",
       "2017-08-22 05:00:00+00:00     NaN\n",
       "2017-08-22 05:30:00+00:00     NaN\n",
       "2017-08-22 06:00:00+00:00     NaN\n",
       "2017-08-22 06:30:00+00:00     NaN\n",
       "2017-08-22 07:00:00+00:00     7.0\n",
       "2017-08-22 07:30:00+00:00     9.0\n",
       "2017-08-22 08:00:00+00:00    11.0\n",
       "2017-08-22 08:30:00+00:00     NaN\n",
       "2017-08-22 09:00:00+00:00     NaN\n",
       "2017-08-22 09:30:00+00:00     NaN\n",
       "2017-08-22 10:00:00+00:00     NaN\n",
       "2017-08-22 10:30:00+00:00     NaN\n",
       "2017-08-22 11:00:00+00:00     NaN\n",
       "2017-08-22 11:30:00+00:00     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "b + m "
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  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "743daf8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Price</th>\n",
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       "      <th>2017-08-15</th>\n",
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       "      <th>2017-08-21</th>\n",
       "      <td>167.78</td>\n",
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       "      <th>2017-08-22</th>\n",
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       "      <th>2017-08-25</th>\n",
       "      <td>166.32</td>\n",
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       "      <th>2017-08-28</th>\n",
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       "             Price\n",
       "Date              \n",
       "2017-08-15  171.00\n",
       "2017-08-16  170.00\n",
       "2017-08-17  166.91\n",
       "2017-08-18  167.41\n",
       "2017-08-21  167.78\n",
       "2017-08-22  169.64\n",
       "2017-08-23  168.71\n",
       "2017-08-24  167.74\n",
       "2017-08-25  166.32\n",
       "2017-08-28  167.24"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb = pd.read_csv(\"data/fb.csv\",parse_dates=['Date'],index_col='Date')\n",
    "fb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "a3197f86",
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   "outputs": [
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       "             Price\n",
       "Date              \n",
       "2017-08-15     NaN\n",
       "2017-08-16     NaN\n",
       "2017-08-17  171.00\n",
       "2017-08-18  170.00\n",
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       "2017-08-23  167.78\n",
       "2017-08-24  169.64\n",
       "2017-08-25  168.71\n",
       "2017-08-28  167.74"
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     "execution_count": 149,
     "metadata": {},
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   "source": [
    "fb.shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "7d8b214a",
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   "outputs": [
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       "             Price\n",
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       "2017-08-17  167.41\n",
       "2017-08-18  167.78\n",
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       "2017-08-22  168.71\n",
       "2017-08-23  167.74\n",
       "2017-08-24  166.32\n",
       "2017-08-25  167.24\n",
       "2017-08-28     NaN"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "fb.shift(-1)"
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  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "264e9f03",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "             Price  Prev Day Price\n",
       "Date                              \n",
       "2017-08-15  171.00             NaN\n",
       "2017-08-16  170.00          171.00\n",
       "2017-08-17  166.91          170.00\n",
       "2017-08-18  167.41          166.91\n",
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       "2017-08-25  166.32          167.74\n",
       "2017-08-28  167.24          166.32"
      ]
     },
     "execution_count": 152,
     "metadata": {},
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    }
   ],
   "source": [
    "fb['Prev Day Price'] = fb['Price'].shift(1)\n",
    "fb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "565d264c",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "             Price  Prev Day Price  Price Change\n",
       "Date                                            \n",
       "2017-08-15  171.00             NaN           NaN\n",
       "2017-08-16  170.00          171.00         -1.00\n",
       "2017-08-17  166.91          170.00         -3.09\n",
       "2017-08-18  167.41          166.91          0.50\n",
       "2017-08-21  167.78          167.41          0.37\n",
       "2017-08-22  169.64          167.78          1.86\n",
       "2017-08-23  168.71          169.64         -0.93\n",
       "2017-08-24  167.74          168.71         -0.97\n",
       "2017-08-25  166.32          167.74         -1.42\n",
       "2017-08-28  167.24          166.32          0.92"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb['Price Change'] = fb['Price'] - fb['Prev Day Price']\n",
    "fb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "c6866e0f",
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18</th>\n",
       "      <td>167.41</td>\n",
       "      <td>166.91</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-21</th>\n",
       "      <td>167.78</td>\n",
       "      <td>167.41</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-22</th>\n",
       "      <td>169.64</td>\n",
       "      <td>167.78</td>\n",
       "      <td>1.86</td>\n",
       "      <td>-0.795322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-23</th>\n",
       "      <td>168.71</td>\n",
       "      <td>169.64</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.758824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-24</th>\n",
       "      <td>167.74</td>\n",
       "      <td>168.71</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>0.497274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-25</th>\n",
       "      <td>166.32</td>\n",
       "      <td>167.74</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-0.651096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-28</th>\n",
       "      <td>167.24</td>\n",
       "      <td>166.32</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-0.321850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price  Prev Day Price  Price Change  5 day return\n",
       "Date                                                          \n",
       "2017-08-15  171.00             NaN           NaN           NaN\n",
       "2017-08-16  170.00          171.00         -1.00           NaN\n",
       "2017-08-17  166.91          170.00         -3.09           NaN\n",
       "2017-08-18  167.41          166.91          0.50           NaN\n",
       "2017-08-21  167.78          167.41          0.37           NaN\n",
       "2017-08-22  169.64          167.78          1.86     -0.795322\n",
       "2017-08-23  168.71          169.64         -0.93     -0.758824\n",
       "2017-08-24  167.74          168.71         -0.97      0.497274\n",
       "2017-08-25  166.32          167.74         -1.42     -0.651096\n",
       "2017-08-28  167.24          166.32          0.92     -0.321850"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb['5 day return'] =  (fb['Price'] - fb['Price'].shift(5))*100/fb['Price'].shift(5)\n",
    "fb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "bfa86d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Price</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-15</th>\n",
       "      <td>171.00</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-16</th>\n",
       "      <td>170.00</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17</th>\n",
       "      <td>166.91</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-18</th>\n",
       "      <td>167.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-21</th>\n",
       "      <td>167.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-22</th>\n",
       "      <td>169.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-23</th>\n",
       "      <td>168.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-24</th>\n",
       "      <td>167.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-25</th>\n",
       "      <td>166.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-28</th>\n",
       "      <td>167.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price\n",
       "Date              \n",
       "2017-08-15  171.00\n",
       "2017-08-16  170.00\n",
       "2017-08-17  166.91\n",
       "2017-08-18  167.41\n",
       "2017-08-21  167.78\n",
       "2017-08-22  169.64\n",
       "2017-08-23  168.71\n",
       "2017-08-24  167.74\n",
       "2017-08-25  166.32\n",
       "2017-08-28  167.24"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb = fb[['Price']]\n",
    "fb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "0d84bf6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-08-15', '2017-08-16', '2017-08-17', '2017-08-18',\n",
       "               '2017-08-21', '2017-08-22', '2017-08-23', '2017-08-24',\n",
       "               '2017-08-25', '2017-08-28'],\n",
       "              dtype='datetime64[ns]', name='Date', freq=None)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "1a8f78db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Price</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-15</th>\n",
       "      <td>171.00</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-16</th>\n",
       "      <td>170.00</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17</th>\n",
       "      <td>166.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18</th>\n",
       "      <td>167.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-21</th>\n",
       "      <td>167.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-22</th>\n",
       "      <td>169.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-23</th>\n",
       "      <td>168.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-24</th>\n",
       "      <td>167.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-25</th>\n",
       "      <td>166.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-28</th>\n",
       "      <td>167.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price\n",
       "2017-08-15  171.00\n",
       "2017-08-16  170.00\n",
       "2017-08-17  166.91\n",
       "2017-08-18  167.41\n",
       "2017-08-21  167.78\n",
       "2017-08-22  169.64\n",
       "2017-08-23  168.71\n",
       "2017-08-24  167.74\n",
       "2017-08-25  166.32\n",
       "2017-08-28  167.24"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.index = pd.date_range(start='2017-08-15',periods=10, freq='B')\n",
    "fb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "f849ebf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-08-15', '2017-08-16', '2017-08-17', '2017-08-18',\n",
       "               '2017-08-21', '2017-08-22', '2017-08-23', '2017-08-24',\n",
       "               '2017-08-25', '2017-08-28'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "07dedf10",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_122825/4154956941.py:1: FutureWarning: tshift is deprecated and will be removed in a future version. Please use shift instead.\n",
      "  fb.tshift(1)\n"
     ]
    },
    {
     "data": {
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       "      <th>Price</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-16</th>\n",
       "      <td>171.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17</th>\n",
       "      <td>170.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18</th>\n",
       "      <td>166.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-21</th>\n",
       "      <td>167.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-22</th>\n",
       "      <td>167.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-23</th>\n",
       "      <td>169.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-24</th>\n",
       "      <td>168.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-25</th>\n",
       "      <td>167.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-28</th>\n",
       "      <td>166.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-29</th>\n",
       "      <td>167.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price\n",
       "2017-08-16  171.00\n",
       "2017-08-17  170.00\n",
       "2017-08-18  166.91\n",
       "2017-08-21  167.41\n",
       "2017-08-22  167.78\n",
       "2017-08-23  169.64\n",
       "2017-08-24  168.71\n",
       "2017-08-25  167.74\n",
       "2017-08-28  166.32\n",
       "2017-08-29  167.24"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.tshift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39feb688",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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