17 Pandas Time series analysis-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic:\n",
    "### 1. Date-time index\n",
    "### 2. Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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    "%%HTML\n",
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    "</style>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"aapl.csv\",parse_dates=[\"Date\"], index_col=\"Date\")\n",
    "df.head(2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is DatetimeIndex? Benefits of it\n",
    "#### (1) Partial Date Index: Select Specific Months Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <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",
       "  </tbody>\n",
       "</table>\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-01']\n",
    "## all the datas of Jan 2017\n",
    "## it's easy to find datas according date if you set date as index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find out the mean 'Close' price of Jan 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2017-01-31    121.35\n",
       "2017-01-30    121.63\n",
       "2017-01-27    121.95\n",
       "2017-01-26    121.94\n",
       "2017-01-25    121.88\n",
       "2017-01-24    119.97\n",
       "2017-01-23    120.08\n",
       "2017-01-20    120.00\n",
       "2017-01-19    119.78\n",
       "2017-01-18    119.99\n",
       "2017-01-17    120.00\n",
       "2017-01-13    119.04\n",
       "2017-01-12    119.25\n",
       "2017-01-11    119.75\n",
       "2017-01-10    119.11\n",
       "2017-01-09    118.99\n",
       "2017-01-06    117.91\n",
       "2017-01-05    116.61\n",
       "2017-01-04    116.02\n",
       "2017-01-03    116.15\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-01'].Close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119.57000000000001"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-01'].Close.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\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",
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      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-01-04  115.85  116.51  115.75  116.02  21118116"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "df['2017-01-04']\n",
    "## Datas of a particular date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>2017-06-30</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",
       "</table>\n",
       "</div>"
      ],
      "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-06'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    <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>2017-07-07</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",
       "    </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>2017-07-03</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>2017-06-30</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",
       "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\n",
       "2017-07-05  143.69  144.79  142.72  144.09  21569557\n",
       "2017-07-03  144.88  145.30  143.10  143.50  14277848\n",
       "2017-06-30  144.45  144.96  143.78  144.02  23024107"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (2) Select Date Range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "    <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>2017-01-06</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-01-08':'2017-01-03']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find out the monthly average 'Close' price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "2016-12-31    114.335714\n",
       "2017-01-31    119.570000\n",
       "2017-02-28    133.713684\n",
       "2017-03-31    140.617826\n",
       "2017-04-30    142.886842\n",
       "2017-05-31    152.227727\n",
       "2017-06-30    147.831364\n",
       "2017-07-31    143.625000\n",
       "Freq: M, Name: Close, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Close.resample('M').mean()\n",
    "## Reasampled a column according to month, then run mean operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x73347b0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df.Close.resample('M').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x83e44b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cjQkw+wtLmfXySgqKy5lzw3gm9Ovq65CMH7LkbkwA2ZlfwqyXVnK8vIp//exURvfq7OuQjJ+y5G5MgDheXsWsl1ZS6XDyzi0TGNyjk69DMn7MkrsxAeKFZTs5UFTG+7dZYjf1s+8lGxMAco6e4IW0XVw8sidje3fxdTgmAFhyNyYAPLJwKwD3TBvo40hMoLDkboyfW7vvCPPX5XLTGX1J7GxrmpqGseRujB9TVf7yyWbiO0Zy25R+vg7HBBBL7sb4sY83HGDNvqPcfd5AOkTa+AfTcJbcjfFTZZUOHv50C0N6dOLKsYm+DscEGEvuxvipl77aRW5RGX+4aAihNi+7aSRL7sb4obxjZTy3dCdTh3a36QVMk1hyN8YPPbp4G5UOJ/eeP9jXoZgAZcndGD+zKaeId91L5SXHdfB1OCZAWXI3xs+8sWIvHSLCuOOsFF+HYgKYJXdj/EiVw8nizQc5a1A3YqLCfR2OCWCW3I3xI6v2FHKktJLzh53i61BMgKs3uYvIKyKSJyKbPMr+IiIbRGSdiCwWkZ4e2+4VkSwR2SYiU1sqcGOC0aJNB2kXHsLkgfG+DsUEuIa03OcA004q+7uqjlDVUcAnwP0AIjIEmAEMdR/znIiEei9cY4KX06ksyjzE5AHxtI+wb6Oa5qk3uatqGlB4Utkxj4cdAHX/fCnwtqqWq+puIAsY76VYjQlq67KPcvBYGdOsS8Z4QZObByLyIHAtUASc6S5OAL712C3bXVbT8TcDNwP06tWrqWEYEzQWbTpIeKhw1qDuvg7FBIEm31BV1ftUNQl4E7jDXVzTd6S1hjJUdbaqpqpqany89S+atk1VWZh5kIn94myUjPEKb4yWeQu40v1zNpDksS0RyPVCHcYEtS0Hitl7uNS6ZIzXNCm5i4jntysuAba6f14AzBCRSBHpA6QAq5oXojHBb2HmQUIEzh1iXTLGO+rtcxeRucAUIE5EsoEHgAtEZCDgBPYCtwKoaqaIzAM2A1XA7arqaKHYjQkaizYdZFxyF+KiI30digkS9SZ3VZ1ZQ/HLdez/IPBgc4Iypi3ZlV/CtkPFPHDxEF+HYoKIfUPVGB9bmHkQgKlDrb/deI8ld2N8bNGmg4xMiqVnbJSvQzFBxJK7MT6Uc/QE67OLmGatduNlltyN8aFFm1xdMjYE0nibJXdjfGhh5kEGndKRPrYoh/EyS+7G+Eh+cTmr9xTajVTTIiy5G+MjSzYfQhXOH27J3XifJXdjfGRh5kGSu7ZnYPeOvg7FBCFL7sb4QOHxCpZnFTB12CmI1DTfnjHNY8ndGB94PyObKqdy5ZhEX4digpQld2Namaoyd/U+xvbuzADrkjEtxJK7Ma1s1e5CduUfZ+Z4W6TGtBxL7sa0srmr9tGxXRgXDu/h61BMELPkbkwrOlpawaebDnLF6ASiImzteNNyLLkb04o+WJNDRZWTGdYlY1qYJXdjWomqMnfVPkYlxTK4Rydfh2OCnCV3Y1pJxt4j7Mgr4cfWajetwJK7Ma3krVX7iI4M46KRdiPVtDxL7sa0gqLSSv694QCXjupJ+4h6V7c0ptksuRvTCj5al0N5ldPGtptWY8ndmBZWfSN1RGIMwxJifB2OaSMsuRvTwtbuP8rWg8XMGGetdtN6LLkb08LeXrWP9hGhXDKqp69DMW2IJXdjWtCxsko+Xu+6kRodaTdSTeux5G5MC3pjxV5OVDrsRqppddaUMKYF5BeX88CCTXy68SBnpMQx3G6kmlZWb8tdRF4RkTwR2eRR9ncR2SoiG0TkQxGJ9dh2r4hkicg2EZnaUoEb449UlQ/XZnPuE8v4fEsed08dyCvXj7PVlkyra0i3zBxg2kllS4BhqjoC2A7cCyAiQ4AZwFD3Mc+JiE19Z9qE3KMnuHHOan71znr6xUfz6Z1ncPuZ/QkPtd5P0/rq7ZZR1TQRST6pbLHHw2+Bq9w/Xwq8rarlwG4RyQLGAyu8Eq0xfqiiysm89P08/NlWHE7lgYuHcO2EZEJDrLVufMcbfe43Au+4f07AleyrZbvLfkBEbgZuBujVy242mcCz5cAx3k3P5qN1ORQer+BH/bvy0OUj6NW1va9DM6Z5yV1E7gOqgDeri2rYTWs6VlVnA7MBUlNTa9zHGH9TVFrJgvU5zEvPZmNOERGhIZw7pDtXpyYyeUC89a0bv9Hk5C4i1wEXAWeranVyzgaSPHZLBHKbHp4x/uPJz7fz3NKdVFQ5GdKjE3+8eAiXjkqgc4cIX4dmzA80KbmLyDTgHmCyqpZ6bFoAvCUijwM9gRRgVbOjNMbHFm46wJOf7+CC4afw8yn9bY4Y4/fqTe4iMheYAsSJSDbwAK7RMZHAEvfH0G9V9VZVzRSRecBmXN01t6uqo6WCN6Y1ZB8p5bfvbWBkYgxPXjOaiDAb/WL8X0NGy8ysofjlOvZ/EHiwOUEZ4y+qHE7uensdToVnZo6xxG4Chn1D1Zg6PPXFDtL3HuGpGaNsFIwJKNYMMaYWy7MKePbLLKanJnLpqBpH9Brjtyy5G1ODwyXl3PXOOvrGdeCPlwz1dTjGNJp1y7QBqsqhY+Vk5ZWQlVdMWGgI01OTrP+4FqrK3e9t4OiJSubcMN7WPDUByZ61QUhVeS8jm5W7C8nKK2FnXgnF5VXf2+eNFXt55KoRjEyKreUsbdcr3+zhP1vz+NMlQxnSs5OvwzGmSSy5B6EvtuRx93sbiIuOJKVbNJeNTiClezT946Pp3y2ajTlF3PfhJi5/7htuOqMvvzp3AO3Cg29+t9eW7+GVb3YjQERYCOGhIUSEhRDh/h/A4VSqnOrxv5NtB4s5d0h3rp3Q27cXYEwzWHIPMk6n8ujibSR3bc+SX0+ucUbCszu1Y1yfLjz06RZeSNvF4s2H+H9XjmB8ny4+iNj7qhxO/vLJZl5bsZfU3p3pGRtFRZWTSoeTCoeT8ionxWVViEBYiBAaIkSEhxIaIoSHCkN7xHDvBYNsKgET0Cy5B5mPN+Sy9WAxT88cXedUs53ahfPQFSO4eERP7vlgA9NfWMG1E3rzu/MHBXQfc0l5FXe8tYal2/K56Yw+/O78wTY7o2mTAvdVbH6g0uHk8SXbGdyjExcN79GgYyb2j2PRXZP4+6JtzFm+B1X4y2XDWjjSllE9n/qOvBL+etkwZp1m3Sqm7bLhEkHkndX72Xu4lLunDiCkEa3V9hFhPHDxUC4fncBHa3M4URF4M0ZszC7isn98Q86RE7x6/ThL7KbNs+QeJMoqHTz9xQ5Se3fmzIHdmnSO6alJFJdXsTDzgJeja1mLMw8y/YUVhIeG8N5tE5k0IN7XIRnjc5bcg8Rry/eQV1zO3VMHNvlG4Kl9utC7a3vmrc72cnQtJ/tIKbe/tYYB3aP58PaJDDylo69DMsYvWHIPAsfKKvnnsp1MHhDPqX27Nvk8IsLVYxNZsesw+w6X1n+AH3hh2S4A/jlrLN06tvNxNMb4D0vuQeDFtF0cLa3k7qkDm32uK8cmIgLvZez3QmQt69CxMt5J389VYxPpGRvl63CM8SuW3ANcQUk5L3+9mwuH9/DKAhI9YqKYlBLPexnZOJz+vfrhi2m7cDiV2yb393UoxvgdS+5+QlX5brXChvvHl1mUVTr49XkDvBbL9NQkcovK+CarwGvn9LbC4xW8uXIfl47saVPxGlMDG+fuB8qrHMyc/S17D5dy9uBunDO4O2ekxBMVUfeUANlHSnnz231cNTaRfvHRXovnnCHdiG0fzrz0/X478uSVr3dTVuXg52f283UoxvglS+5+4KFPt7Jm31HOGtSNzzYdZF56Nu3CQzi9fzznDenO6SlxFJ2oZE/BcXYVHGdPwXF2FxwnK78EgF+e471WO0BkWCiXjUrgrZX7OFpaQWx7/1oAuuhEJa8t38MFw3rQv5uNjjGmJpbcfezfGw4wZ/kefnp6H/5w0RAqqpys2l3Iks0HWbL5EJ9vOfSDY+I7RtInrgPThp7C1GGnkNACNxOnpyYxZ/ke5q/L5bqJyV4/f3O8vnwPxeVV3H6m9bUbUxtL7j60p+A497y/gdG9Yrln2iDANXvh6SlxnJ4Sxx8vGUpm7jFW7S78b0JPjutAdGTL/9mG9OzEsIROzEvf71fJ/Xh5FS9/s5tzBnez6XiNqYMldx8pq3Tw8zfXEBYqPPvjmhdeFhGGJcR4ZRRMU0xPTeL++ZlsyinyWQwne3PlXo6WVlqr3Zh62GgZH/nzJ5vZfOAYj08f2SLdKt5wycieRISF8F6Gf3xjtazSwey03ZyREsfoXp19HY4xfs2Suw/MX5fDWyv3cevkfpw1qLuvw6lVbPsIpg49hQ/X5lBW6b3JxCqqnE063zur91NQUs4d1mo3pl6W3FtZVl4J936wkXHJnfmNF8emt5TpqYkUnahkyeYf3thtClXlhjmrOPVvX/DK17upqHI26LiKKifPL9vJ+OQuzZpiwZi2wpJ7KzpR4eD2N9fQLjyUZ2aOIayOxTT8xcR+cSTERjEv3TvTEXy4Nodvsg7TrWMkf/5kM+c9sYxFmQdr/QKXqrLlwDH+8NEmDhSVcftZ1mo3piH8P7sEkbmr9rHtUDGPTx/JKTGBMclVaIhw5dhEvs4q4IVlO3E2Y0qCohOV/O3TLYzuFcuiuybx6g3jCA8N4ZY3Mpgx+1s25RT9d99d+SU8/cUOzn0ijfOf+or31mRz1dhEJqXEeeOyjAl69Y6WEZFXgIuAPFUd5i67GvgjMBgYr6rpHvvfC/wUcAB3quqiFog7IL2bkc3IxBimNHG+dV+5eVJfth8s5qHPtvLVjgIemz6S7p0a/+b02OJtFB6vYM4N4wkJEc4c2I0z+sfx9ur9PLFkOxc/+zXThp7C/iOlbMo5hgiMS+7CXy4bxgXDTqFrdGQLXJ0xwakhLfc5wLSTyjYBVwBpnoUiMgSYAQx1H/OciNT9Hfo2YlNOEVsOHOOqsYm+DqXRoiPD+OesMTx8xXAy9h5h2pNpje6D35hdxBvf7uXaCcnfG1YZFhrCrNN68+XdU7h1cj+WbssnNCSE3184mOW/O4t5t0zgJ6f1tsRuTCPV23JX1TQRST6pbAtQ06IQlwJvq2o5sFtEsoDxwApvBBvI3svIJiI0hEtGJvg6lCYREWaM70VqchfunLuWm15P5yen9ea+CwfTLrzu92+HU/n9RxuJi46sdYKzTu3CuWfaIH7bjMVGjDHf8XafewLgeect2132AyJys4iki0h6fn6+l8PwLxVVTuavy+Hcod2JaR/u63CapX8314pHN53Rhze+3cslz37N5txjdR7z9up9rM8u4vcXDqZTu7qv3xK7Md7h7eRe0yuzxjtwqjpbVVNVNTU+3j9nHvSWL7Yc4khpJVcHYJdMTSLDQrnvwiG8fuN4Co9XcvGzX/PgvzdzvLzqB/sWlJTzyMJtTOjblUtG9vRBtMa0Td5O7tlAksfjRCDXy3V41c78Em56Pb1BLdCmejcjm+6dIjkjJbjexCYNiGfJryYxPTWRF7/azTmPL2Phpu8Pa3z4s62UVlTxl8uGWqvcmFbk7eS+AJghIpEi0gdIAVZ5uQ6vKDxewQPzNzH1iTRW7DxM7tETXPaPb3gxbVezhvudLO9YGcu253PFmERCQ4IvuXXuEMFDV4zg/dsmEBMVzq3/yuBnr6Wzv7CU1XsKeS8jm5vO6GtT8xrTyhoyFHIuMAWIE5Fs4AGgEHgGiAf+LSLrVHWqqmaKyDxgM1AF3K6q3vveuheUVzl4bfkenvlPFsfLq5g5vhe/OncAISLc8/4GHvx0C0u35/HY1aO8Mhb9w7U5OJwaNF0ytRnbuwsf/+J05nyzhyc+3865Tyyjc/sIEmKjuMO+eGRMq5OmLO3mbampqZqenl7/js306cYDPPTZFvYXnmDKwHj+74LBDOj+XYtSVXl79X7+/PFmIsNDeOjy4Zw/vEeT61NVzn0ijZiocN6/baI3LiEg5B49wZ8+zmTx5kPM/kkq5w7x3/lzjAlkIpKhqqk1bWszU/7OXbWPez/YyKBTOvLGT8fX2P8tIswc34tT+3ThrnfWcduba7h6bCIPXDK0SXOor88uIiuvhIeuGO6NSwgYPWOjeOEnqRSVVgb86CBjAlWbmNMK9R4AABFRSURBVH4gY28h98/fxKQB8Xzyi9PrvbHZNz6a92+byO1n9uO9Ndlc8NRXZOwtbHS976bvp114CBeNaHrrP5BZYjfGd4I+uR8sKuPWf62hZ2wUz8wY3eDJusJDQ7h76iDm3TIBpypXP7+CRxdto9LRsFkMyyodLFify/nDetCxnrHdxhjjbUGd3MsqHdzyrwyOl1fx4rWpTWpJjkvuwme/PIMrxiTy7JdZXPHccrLySuo9bvHmQxSXVQXkdAPGmMAXtMldVbl//ibW7z/K49NHfu/GaWN1bBfOo1eP5PlZY8g+UsqFT3/Fa8v31DpNLbi6ZBJio5hgc48bY3wgaJP7G9/uZV56Nnee1Z9pw7zT5z1tWA8W3TWJCf268sCCTK6Z/S1vrNjDrvyS7yX63KMn+DqrgCvHJhIShGPbjTH+LyhHy3y76zB//ngzZw/qxl3neHe1o26d2vHq9eP418p9PL90J3+YnwlAz5h2/Kh/HKenxLE59xiqcNUY65IxxvhG0CX3nKMnuP3NNfTq2p4nZoxqkZaziPCT03oz69Re7Css5asdBXyTVcDizYd4172Y9Kl9utCra3uv122MMQ0RdMn9/z7YSEWVkxevTa13BsLmEhF6d+1A764dmHVabxxOJTO3iJW7CjndVgwyxvhQUCX3/YWlLNuez6/PHUC/+OhWrz80RBiRGMuIxNhWr9sYYzwF1Q3Vd9P3I4INPzTGtHlBk9wdTuXdjGwmpcTTMzbK1+EYY4xPBU1y/2pHPgeKyrhmXFL9OxtjTJALmuQ+L30/XTpEcM5gm4HQGGOCIrkfLilnyeZDXD46gYiwoLgkY4xplqDIhB+uzaHSodYlY4wxbgGf3FWVeen7GZUU26z5Y4wxJpgEfHJft/8o2w+VWKvdGGM8BHxyn5e+n6jw0Da7IIYxxtQkoJP78fIqFqzL5cIRtiCGMcZ4Cujk/u+NBzhe4bAuGWOMOUlAJ/d5q/fTN74Dqb07+zoUY4zxKwGb3LPySkjfe4TpqUmI2IIYxhjjKWCT+7vp+wkNEa4Yk+DrUIwxxu8EZHKvdDh5f002Zw3qRreO7XwdjjHG+J2AS+6VDid/XJBJQUkFM+xGqjHG1Kje5C4ir4hInohs8ijrIiJLRGSH+//OHtvuFZEsEdkmIlO9GWxRaSXXv7qKN1fu46Yz+nDWoG7ePL0xxgSNhrTc5wDTTir7HfCFqqYAX7gfIyJDgBnAUPcxz4lIqDcC3ZlfwmXPfcOq3YU8ctUI7rtwiN1INcaYWtSb3FU1DSg8qfhS4DX3z68Bl3mUv62q5aq6G8gCxjc3yLTt+Vz2j284dqKSuTedxvRU644xxpi6NLXPvbuqHgBw/1/dP5IA7PfYL9td9gMicrOIpItIen5+fo2VqCpzvtnNDXNWkxAbxUe3/4jU5C5NDNkYY9oOby+QXVM/ida0o6rOBmYDpKam/mAfh1N5YMEm/vXtPs4Z3J0nZ4wiOjKo1vM2xpgW09RseUhEeqjqARHpAeS5y7MBzz6TRCC3sSevdDj5zbvrmb8ul1sm9eWeaYMICbH+dWOMaaimdsssAK5z/3wdMN+jfIaIRIpIHyAFWNWYE5dXOfj5m2uYvy6Xe6YN4t4LBltiN8aYRqq35S4ic4EpQJyIZAMPAA8D80Tkp8A+4GoAVc0UkXnAZqAKuF1VHQ0N5kSFg5vfSOerHQX86ZKhXDcxubHXY4wxhgYkd1WdWcums2vZ/0HgwcYGUlxWyU/npJO+1zXU0UbEGGNM0/nFHUqHU5n10koyc4/x9MzRXDSip69DMsaYgOYXyX1X/nGOHyzmhZ+M5ezB3X0djjHGBDy/SO4VDievXj+OH/WP83UoxhgTFPxi4rCUbtGW2I0xxov8IrlHhPlFGMYYEzQsqxpjTBCy5G6MMUHIkrsxxgQhS+7GGBOELLkbY0wQsuRujDFByJK7McYEIVGtcS2N1g1CpBjYVsvmGKCoEeUAcUCBF89X27bWqqeuuppyPrtW79fjDzG0pddEW7rWuuoaqKodazxCVX3+D0ivY9vsxpS30PlqO6ZV6qmrribGbdfq5Xr8IYa29JpoS9daz/Ok1hgCoVvm40aWt8T5mlKXr+vxhxj8/Vq9WY8/xNCWXhNt6VqbxF+6ZdJVNdVfz+frelq7Ll/XH4x/P1/HEGz1+EMM/vCaqCsGf2m5z/bz8/m6ntauy9f1B+PfrzbBdq32O23dumqNwS9a7sYYY7zLX1ruxhhjvMiSuzHGBKGATO4icrmIqIgMaoW67hORTBHZICLrROTUFqonUUTmi8gOEdkpIk+JSEQd+98lIu29WL+KyGMej38jIn/01vk9zutw/x4zRWS9iPxaRFr0eSgiJS15/nrqrr7e6n/Jdey7VESadIPO/fd7w+NxmIjki8gnTTlfA+prtdfgSfW26nW662jV50999TX0eRKQyR2YCXwNzGjJSkRkAnARMEZVRwDnAPtboB4BPgA+UtUUYAAQDTxYx2F3AV5L7kA5cIWItPSSWCdUdZSqDgXOBS4AHmjhOn2p+nqr/+1poXqOA8NEJMr9+FwgpzEnEJHGLLvZpNegiIQ2Zv8aNPs624qAS+4iEg38CPgp7ieWiEzxfOcWkWdF5Hr3zxeIyFYR+VpEnm7kO3wPoEBVywFUtUBVc0VkrIgsE5EMEVkkIj3cdS0VkSdFZLmIbBKR8Q2s5yygTFVfddfjAH4F3CgiHUTkURHZ6P708AsRuRPoCXwpIl824nrqUoXrzvuvTt4gIr1F5At3/V+ISC8RiRGRPdWtbhFpLyL7RSS8oRWqah5wM3CHuISKyN9FZLW7rls8Yvit+3ewXkQebuzFiUi0O/Y17vNc6i5PFpEtIvKi+9PEYo/E0SJqe/64zWrC86faZ8CF7p9nAnM96hzvPu9a9/8D3eXXi8i7IvIxsLiB8df2GkwTkQ9FZLOIPO/x3CgRkT+LyEpgQiOvyVvX+ZWIjPLY7xsRGdHQCuvJMXtE5E8ez61mf5qpq76GCrjkDlwGLFTV7UChiIypbUcRaQe8AJyvqqcD8Y2sazGQJCLbReQ5EZnsTl7PAFep6ljgFb7fwu6gqhOBn7u3NcRQIMOzQFWPAfuAnwF9gNHuTw9vqurTQC5wpqqe2chrqss/gP8RkZiTyp8FXq+uH3haVYuA9cBk9z4XA4tUtbIxFarqLlzPw264kkWRqo4DxgE3iUgfETkf19/9VFUdCTzShGsrAy5X1THAmcBjIiLubSnAP9yfJo4CVzbh/LWJku+6ZD5soedPtbeBGe7n/Qhgpce2rcAkVR0N3A/8zWPbBOA6VT2rgfXU9hocD/wvMBzoB1xRfU3AJlU9VVW/buQ11aQp1/kScD2AiAwAIlV1gxdiqVbgfm79E/iNF8/bZI35GOYvZgJPun9+2/3437XsOwjYpaq73Y/n4mopNoiqlojIWOAMXAnhHeCvwDBgiTs3hAIHPA6b6z42TUQ6iUisqh6tpyoBahqTKsAk4HlVrXKft7Ch8TeWqh4TkdeBO4ETHpsm8N0L9Q2+S67vANcAX+JqwT3XxKqrk+x5wAgRucr9OAZX4j0HeFVVS91xNuV3IMDfRGQS4AQSgO7ubbtVdZ375wwguQnnr80JVfVsMQ7D+88f3MdsEFef/kzg05M2xwCviUgKruea5yesJY38ndb2GlzlfrNGROYCpwPvAQ7g/Uacv05NvM53gT+IyN3AjcAcb8Xj9oH7/wy+e634VEAldxHpiqsLY5iIKK4XhgIL+P6nkHbVhzS3TncXyVJgqYhsBG4HMlW1to+XJyfphnyRIJOTWosi0glIAnY18Bze8iSwBni1jn2q41kAPCQiXYCxwH8aW5mI9MX14s/D9ff6haouOmmfaTT/d/A/uD65jVXVShHZw3fPk3KP/RxAS3bLCN5//nhaADwKTAG6epT/BfhSVS93J8alHtuON/TkdbwGP60h1urHZe7XkTc16jpVtVRElgCXAtOBxt64rqLmHFOt+jnkwDt5tb766hVo3TJX4eoe6K2qyaqaBFS3yoeISKS7S+Fsd9lWoK98N0LhmsZUJiID3S2AaqOALUC8uG62IiLhIjLUY59r3OWn4+piqG2WN09fAO1F5Fr3saHAY7haF4uBW8V9s8udSAGKgZpng2sGdwtuHq4ukmrL+e7G2f/gupGGqpYAq4CngE8a+wIWkXjgeeBZdX2bbhFwm7vrAhEZICIdcP0ObhT36CCP30FjxAB57sR+JtC7Cefwhm14//nj6RXgz6q68aTyGL678Xh9o6P+Tm2vwdOB8e5utBBc1+GNLpjaNOU6XwKeBlY34dPfXmrOMS2l2fUFVMsd18ewk2+mvQ/8GFdC2gDsANYCqOoJEfk5sFBECnAlosaIBp4RkVhc76RZuLp1ZgNPu3/pYbhau5nuY46IyHKgE66Pf/VSVRWRy4HnROQPuN50PwX+D1dLYACwQUQqgRdx9YHPBj4TkQNe7ncH1xvLHR6P7wRecX+kzQdu8Nj2Dq6PvFMaeO4oEVmH6+NyFa5unsfd217C1SWyxt0fng9cpqoL3TfD0kWkgu9+N/VyvymW47pX8LGIpAPrcL3xtzpVrXB3O3nt+XPS+bNxvdme7BFc3RW/pgmfsDzU9hq8DVjh3jYcSAM+bEY9dWrKdapqhogco+5Ppd9T/fxR1f0i8oMc423erC/opx8QkWh337ngumG4Q1WfaKG6lgK/UdX0lji/aTwRGQm8qKqNHXliGkFEpuB67l/k61hqIyI9cXXTDFJVZwOPadXnjzfrC7Rumaa4yd1SzMT1ke0FH8djWomI3IrrBuXvfR2L8S13l+dK4L5GJPZWff54u76gb7kbY0xb1BZa7qaNEJEkEflSXF9KyhSRX7rLu4jIEnFN7bBERDq7y7u69y8RkWc9ztNRvj9lQIGIPFlbvcb4I2u5m6Ahrm969lDVNSLSEdeY48twjZooVNWHReR3QGdVvcc9Emc0rnHnw1T1jlrOmwH8SlXTWuVCjPECa7mboKGqB1R1jfvnYlzDVhNwjW1+zb3ba7gSPqp63P2NybLazukeCtsN+KoFQzfG6yy5m6Dk/m7DaFw30bqr6gFwvQHgStYNNRN4R+0jrgkwltxN0BHXxFbvA3e55+hpjhl4TExlTKCw5G6Civvbre/jmmCter6PQ/LdzJ09cE110JBzjQTCVDWj3p2N8TOW3E3QcH9R7WVgi6o+7rFpAXCd++frgPkNPOX3ppM1JpDYaBkTNNzzsXwFbMQ18yO4pilYiWt6il64plG+unpuEfcEYp2ACFzT/Z6nqpvd23YBF6iqT6YqMKY5LLkbY0wQsm4ZY4wJQpbcjTEmCFlyN8aYIGTJ3RhjgpAld2OMCUKW3E2bJCIO94yPmSKyXkR+7V4erq5jkkXkx60VozHNYcndtFUnVHWUqg4FzgUuAB6o55hkXEs6GuP3bJy7aZNEpERVoz0e9wVWA3G4Fs9+A+jg3nyHqi4XkW+BwbgWhH4N12LLD+NaPzYS+Ieq2kpfxi9Ycjdt0snJ3V12BBgEFANOVS1zT/k7V1VTT14nVERuBrqp6l9FJBL4Bte3X3e36sUYU4MwXwdgjB8R9//hwLMiMgpwAANq2f88YISIXOV+HAOk4GrZG+NTltyN4b/dMg5cM0Y+ABwCRuK6L1XbYh4C/EJVF7VKkMY0gt1QNW2eiMQDzwPPuhfliAEOqKoT+AkQ6t61GOjocegi4Db3NMOIyAD30n3G+Jy13E1bFSUi63B1wVThuoFaPU3wc8D7InI18CVw3F2+AagSkfXAHOApXCNo1rinG87HvYSfMb5mN1SNMSYIWbeMMcYEIUvuxhgThCy5G2NMELLkbowxQciSuzHGBCFL7sYYE4QsuRtjTBCy5G6MMUHo/wNUBZyOMFvdagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Close.resample('W').mean().plot()\n",
    "## weekly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9459450>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Close.resample('D').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x94ed050>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Close.resample('Q').mean().plot()\n",
    "#Quarterly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x953dfd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Close.resample('Q').mean().plot(kind='bar')\n",
    "#Quarterly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9585e30>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}