03.Time Series Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"HistoricalQuotes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16:00</td>\n",
       "      <td>200.99</td>\n",
       "      <td>24,619,446</td>\n",
       "      <td>201.230</td>\n",
       "      <td>202.7600</td>\n",
       "      <td>199.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019/08/09</td>\n",
       "      <td>200.99</td>\n",
       "      <td>24619750.0000</td>\n",
       "      <td>201.300</td>\n",
       "      <td>202.7600</td>\n",
       "      <td>199.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019/08/08</td>\n",
       "      <td>203.43</td>\n",
       "      <td>27009520.0000</td>\n",
       "      <td>200.200</td>\n",
       "      <td>203.5300</td>\n",
       "      <td>199.3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019/08/07</td>\n",
       "      <td>199.04</td>\n",
       "      <td>33364400.0000</td>\n",
       "      <td>195.410</td>\n",
       "      <td>199.5600</td>\n",
       "      <td>193.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019/08/06</td>\n",
       "      <td>197.00</td>\n",
       "      <td>35824790.0000</td>\n",
       "      <td>196.310</td>\n",
       "      <td>198.0670</td>\n",
       "      <td>194.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019/08/05</td>\n",
       "      <td>193.34</td>\n",
       "      <td>52392970.0000</td>\n",
       "      <td>197.990</td>\n",
       "      <td>198.6490</td>\n",
       "      <td>192.5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019/08/02</td>\n",
       "      <td>204.02</td>\n",
       "      <td>40862120.0000</td>\n",
       "      <td>205.530</td>\n",
       "      <td>206.4300</td>\n",
       "      <td>201.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019/08/01</td>\n",
       "      <td>208.43</td>\n",
       "      <td>54017920.0000</td>\n",
       "      <td>213.900</td>\n",
       "      <td>218.0300</td>\n",
       "      <td>206.7435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019/07/31</td>\n",
       "      <td>213.04</td>\n",
       "      <td>69281360.0000</td>\n",
       "      <td>216.420</td>\n",
       "      <td>221.3700</td>\n",
       "      <td>211.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019/07/30</td>\n",
       "      <td>208.78</td>\n",
       "      <td>33935720.0000</td>\n",
       "      <td>208.760</td>\n",
       "      <td>210.1600</td>\n",
       "      <td>207.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019/07/29</td>\n",
       "      <td>209.68</td>\n",
       "      <td>21673390.0000</td>\n",
       "      <td>208.460</td>\n",
       "      <td>210.6400</td>\n",
       "      <td>208.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019/07/26</td>\n",
       "      <td>207.74</td>\n",
       "      <td>17618870.0000</td>\n",
       "      <td>207.480</td>\n",
       "      <td>209.7300</td>\n",
       "      <td>207.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019/07/25</td>\n",
       "      <td>207.02</td>\n",
       "      <td>13909560.0000</td>\n",
       "      <td>208.890</td>\n",
       "      <td>209.2400</td>\n",
       "      <td>206.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019/07/24</td>\n",
       "      <td>208.67</td>\n",
       "      <td>14991570.0000</td>\n",
       "      <td>207.670</td>\n",
       "      <td>209.1500</td>\n",
       "      <td>207.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019/07/23</td>\n",
       "      <td>208.84</td>\n",
       "      <td>18355210.0000</td>\n",
       "      <td>208.460</td>\n",
       "      <td>208.9100</td>\n",
       "      <td>207.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019/07/22</td>\n",
       "      <td>207.22</td>\n",
       "      <td>22277930.0000</td>\n",
       "      <td>203.650</td>\n",
       "      <td>207.2300</td>\n",
       "      <td>203.6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019/07/19</td>\n",
       "      <td>202.59</td>\n",
       "      <td>20929310.0000</td>\n",
       "      <td>205.790</td>\n",
       "      <td>206.5000</td>\n",
       "      <td>202.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019/07/18</td>\n",
       "      <td>205.66</td>\n",
       "      <td>18582160.0000</td>\n",
       "      <td>204.000</td>\n",
       "      <td>205.8800</td>\n",
       "      <td>203.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019/07/17</td>\n",
       "      <td>203.35</td>\n",
       "      <td>14107450.0000</td>\n",
       "      <td>204.050</td>\n",
       "      <td>205.0915</td>\n",
       "      <td>203.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019/07/16</td>\n",
       "      <td>204.50</td>\n",
       "      <td>16866820.0000</td>\n",
       "      <td>204.590</td>\n",
       "      <td>206.1100</td>\n",
       "      <td>203.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019/07/15</td>\n",
       "      <td>205.21</td>\n",
       "      <td>16947420.0000</td>\n",
       "      <td>204.090</td>\n",
       "      <td>205.8700</td>\n",
       "      <td>204.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019/07/12</td>\n",
       "      <td>203.30</td>\n",
       "      <td>17595210.0000</td>\n",
       "      <td>202.450</td>\n",
       "      <td>204.0000</td>\n",
       "      <td>202.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019/07/11</td>\n",
       "      <td>201.75</td>\n",
       "      <td>20191840.0000</td>\n",
       "      <td>203.310</td>\n",
       "      <td>204.3900</td>\n",
       "      <td>201.7100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019/07/10</td>\n",
       "      <td>203.23</td>\n",
       "      <td>17897140.0000</td>\n",
       "      <td>201.850</td>\n",
       "      <td>203.7300</td>\n",
       "      <td>201.5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019/07/09</td>\n",
       "      <td>201.24</td>\n",
       "      <td>20578020.0000</td>\n",
       "      <td>199.200</td>\n",
       "      <td>201.5100</td>\n",
       "      <td>198.8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019/07/08</td>\n",
       "      <td>200.02</td>\n",
       "      <td>25338630.0000</td>\n",
       "      <td>200.810</td>\n",
       "      <td>201.4000</td>\n",
       "      <td>198.4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019/07/05</td>\n",
       "      <td>204.23</td>\n",
       "      <td>17265520.0000</td>\n",
       "      <td>203.350</td>\n",
       "      <td>205.0800</td>\n",
       "      <td>202.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019/07/03</td>\n",
       "      <td>204.41</td>\n",
       "      <td>11362050.0000</td>\n",
       "      <td>203.280</td>\n",
       "      <td>204.4400</td>\n",
       "      <td>202.6901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019/07/02</td>\n",
       "      <td>202.73</td>\n",
       "      <td>16935220.0000</td>\n",
       "      <td>201.410</td>\n",
       "      <td>203.1323</td>\n",
       "      <td>201.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019/07/01</td>\n",
       "      <td>201.55</td>\n",
       "      <td>27316740.0000</td>\n",
       "      <td>203.170</td>\n",
       "      <td>204.4900</td>\n",
       "      <td>200.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>2018/09/20</td>\n",
       "      <td>220.03</td>\n",
       "      <td>26388050.0000</td>\n",
       "      <td>220.240</td>\n",
       "      <td>222.2800</td>\n",
       "      <td>219.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>2018/09/19</td>\n",
       "      <td>218.37</td>\n",
       "      <td>26869880.0000</td>\n",
       "      <td>218.500</td>\n",
       "      <td>219.6200</td>\n",
       "      <td>215.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>2018/09/18</td>\n",
       "      <td>218.24</td>\n",
       "      <td>31495630.0000</td>\n",
       "      <td>217.790</td>\n",
       "      <td>221.8500</td>\n",
       "      <td>217.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>2018/09/17</td>\n",
       "      <td>217.88</td>\n",
       "      <td>36810120.0000</td>\n",
       "      <td>222.150</td>\n",
       "      <td>222.9500</td>\n",
       "      <td>217.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>2018/09/14</td>\n",
       "      <td>223.84</td>\n",
       "      <td>31801970.0000</td>\n",
       "      <td>225.750</td>\n",
       "      <td>226.8400</td>\n",
       "      <td>222.5220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>2018/09/13</td>\n",
       "      <td>226.41</td>\n",
       "      <td>41411350.0000</td>\n",
       "      <td>223.520</td>\n",
       "      <td>228.3500</td>\n",
       "      <td>222.5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>2018/09/12</td>\n",
       "      <td>221.07</td>\n",
       "      <td>49118510.0000</td>\n",
       "      <td>224.940</td>\n",
       "      <td>225.0000</td>\n",
       "      <td>219.8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>2018/09/11</td>\n",
       "      <td>223.85</td>\n",
       "      <td>35644510.0000</td>\n",
       "      <td>218.010</td>\n",
       "      <td>224.2990</td>\n",
       "      <td>216.5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>2018/09/10</td>\n",
       "      <td>218.33</td>\n",
       "      <td>39194720.0000</td>\n",
       "      <td>220.950</td>\n",
       "      <td>221.8500</td>\n",
       "      <td>216.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2018/09/07</td>\n",
       "      <td>221.30</td>\n",
       "      <td>37418910.0000</td>\n",
       "      <td>221.850</td>\n",
       "      <td>225.3700</td>\n",
       "      <td>220.7100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2018/09/06</td>\n",
       "      <td>223.10</td>\n",
       "      <td>34121240.0000</td>\n",
       "      <td>226.230</td>\n",
       "      <td>227.3500</td>\n",
       "      <td>221.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2018/09/05</td>\n",
       "      <td>226.87</td>\n",
       "      <td>33290830.0000</td>\n",
       "      <td>228.990</td>\n",
       "      <td>229.6700</td>\n",
       "      <td>225.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2018/09/04</td>\n",
       "      <td>228.36</td>\n",
       "      <td>27363960.0000</td>\n",
       "      <td>228.410</td>\n",
       "      <td>229.1800</td>\n",
       "      <td>226.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2018/08/31</td>\n",
       "      <td>227.63</td>\n",
       "      <td>43177540.0000</td>\n",
       "      <td>226.510</td>\n",
       "      <td>228.8700</td>\n",
       "      <td>226.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2018/08/30</td>\n",
       "      <td>225.03</td>\n",
       "      <td>48696960.0000</td>\n",
       "      <td>223.250</td>\n",
       "      <td>228.2600</td>\n",
       "      <td>222.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2018/08/29</td>\n",
       "      <td>222.98</td>\n",
       "      <td>27074260.0000</td>\n",
       "      <td>220.150</td>\n",
       "      <td>223.4900</td>\n",
       "      <td>219.4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2018/08/28</td>\n",
       "      <td>219.70</td>\n",
       "      <td>22751390.0000</td>\n",
       "      <td>219.010</td>\n",
       "      <td>220.5400</td>\n",
       "      <td>218.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>2018/08/27</td>\n",
       "      <td>217.94</td>\n",
       "      <td>20400770.0000</td>\n",
       "      <td>217.150</td>\n",
       "      <td>218.7400</td>\n",
       "      <td>216.3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2018/08/24</td>\n",
       "      <td>216.16</td>\n",
       "      <td>18457720.0000</td>\n",
       "      <td>216.600</td>\n",
       "      <td>216.9000</td>\n",
       "      <td>215.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2018/08/23</td>\n",
       "      <td>215.49</td>\n",
       "      <td>18505780.0000</td>\n",
       "      <td>214.650</td>\n",
       "      <td>217.0500</td>\n",
       "      <td>214.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2018/08/22</td>\n",
       "      <td>215.05</td>\n",
       "      <td>18965930.0000</td>\n",
       "      <td>214.100</td>\n",
       "      <td>216.3600</td>\n",
       "      <td>213.8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2018/08/21</td>\n",
       "      <td>215.04</td>\n",
       "      <td>25682700.0000</td>\n",
       "      <td>216.800</td>\n",
       "      <td>217.1900</td>\n",
       "      <td>214.0250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2018/08/20</td>\n",
       "      <td>215.46</td>\n",
       "      <td>30149410.0000</td>\n",
       "      <td>218.100</td>\n",
       "      <td>219.1800</td>\n",
       "      <td>215.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2018/08/17</td>\n",
       "      <td>217.58</td>\n",
       "      <td>35034410.0000</td>\n",
       "      <td>213.440</td>\n",
       "      <td>217.9500</td>\n",
       "      <td>213.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2018/08/16</td>\n",
       "      <td>213.32</td>\n",
       "      <td>27595310.0000</td>\n",
       "      <td>211.750</td>\n",
       "      <td>213.8121</td>\n",
       "      <td>211.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2018/08/15</td>\n",
       "      <td>210.24</td>\n",
       "      <td>28595230.0000</td>\n",
       "      <td>209.220</td>\n",
       "      <td>210.7400</td>\n",
       "      <td>208.3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2018/08/14</td>\n",
       "      <td>209.75</td>\n",
       "      <td>20679270.0000</td>\n",
       "      <td>210.155</td>\n",
       "      <td>210.5600</td>\n",
       "      <td>208.2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2018/08/13</td>\n",
       "      <td>208.87</td>\n",
       "      <td>25864510.0000</td>\n",
       "      <td>207.700</td>\n",
       "      <td>210.9520</td>\n",
       "      <td>207.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2018/08/10</td>\n",
       "      <td>207.53</td>\n",
       "      <td>24592460.0000</td>\n",
       "      <td>207.360</td>\n",
       "      <td>209.1000</td>\n",
       "      <td>206.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2018/08/09</td>\n",
       "      <td>208.88</td>\n",
       "      <td>23389530.0000</td>\n",
       "      <td>207.280</td>\n",
       "      <td>209.7800</td>\n",
       "      <td>207.2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date   close         volume     open      high       low\n",
       "0         16:00  200.99     24,619,446  201.230  202.7600  199.2900\n",
       "1    2019/08/09  200.99  24619750.0000  201.300  202.7600  199.2900\n",
       "2    2019/08/08  203.43  27009520.0000  200.200  203.5300  199.3900\n",
       "3    2019/08/07  199.04  33364400.0000  195.410  199.5600  193.8200\n",
       "4    2019/08/06  197.00  35824790.0000  196.310  198.0670  194.0400\n",
       "5    2019/08/05  193.34  52392970.0000  197.990  198.6490  192.5800\n",
       "6    2019/08/02  204.02  40862120.0000  205.530  206.4300  201.6300\n",
       "7    2019/08/01  208.43  54017920.0000  213.900  218.0300  206.7435\n",
       "8    2019/07/31  213.04  69281360.0000  216.420  221.3700  211.3000\n",
       "9    2019/07/30  208.78  33935720.0000  208.760  210.1600  207.3100\n",
       "10   2019/07/29  209.68  21673390.0000  208.460  210.6400  208.4400\n",
       "11   2019/07/26  207.74  17618870.0000  207.480  209.7300  207.1400\n",
       "12   2019/07/25  207.02  13909560.0000  208.890  209.2400  206.7300\n",
       "13   2019/07/24  208.67  14991570.0000  207.670  209.1500  207.1700\n",
       "14   2019/07/23  208.84  18355210.0000  208.460  208.9100  207.2900\n",
       "15   2019/07/22  207.22  22277930.0000  203.650  207.2300  203.6100\n",
       "16   2019/07/19  202.59  20929310.0000  205.790  206.5000  202.3600\n",
       "17   2019/07/18  205.66  18582160.0000  204.000  205.8800  203.7000\n",
       "18   2019/07/17  203.35  14107450.0000  204.050  205.0915  203.2700\n",
       "19   2019/07/16  204.50  16866820.0000  204.590  206.1100  203.5000\n",
       "20   2019/07/15  205.21  16947420.0000  204.090  205.8700  204.0000\n",
       "21   2019/07/12  203.30  17595210.0000  202.450  204.0000  202.2000\n",
       "22   2019/07/11  201.75  20191840.0000  203.310  204.3900  201.7100\n",
       "23   2019/07/10  203.23  17897140.0000  201.850  203.7300  201.5600\n",
       "24   2019/07/09  201.24  20578020.0000  199.200  201.5100  198.8100\n",
       "25   2019/07/08  200.02  25338630.0000  200.810  201.4000  198.4100\n",
       "26   2019/07/05  204.23  17265520.0000  203.350  205.0800  202.9000\n",
       "27   2019/07/03  204.41  11362050.0000  203.280  204.4400  202.6901\n",
       "28   2019/07/02  202.73  16935220.0000  201.410  203.1323  201.3600\n",
       "29   2019/07/01  201.55  27316740.0000  203.170  204.4900  200.6500\n",
       "..          ...     ...            ...      ...       ...       ...\n",
       "223  2018/09/20  220.03  26388050.0000  220.240  222.2800  219.1500\n",
       "224  2018/09/19  218.37  26869880.0000  218.500  219.6200  215.3000\n",
       "225  2018/09/18  218.24  31495630.0000  217.790  221.8500  217.1200\n",
       "226  2018/09/17  217.88  36810120.0000  222.150  222.9500  217.2700\n",
       "227  2018/09/14  223.84  31801970.0000  225.750  226.8400  222.5220\n",
       "228  2018/09/13  226.41  41411350.0000  223.520  228.3500  222.5700\n",
       "229  2018/09/12  221.07  49118510.0000  224.940  225.0000  219.8400\n",
       "230  2018/09/11  223.85  35644510.0000  218.010  224.2990  216.5600\n",
       "231  2018/09/10  218.33  39194720.0000  220.950  221.8500  216.4700\n",
       "232  2018/09/07  221.30  37418910.0000  221.850  225.3700  220.7100\n",
       "233  2018/09/06  223.10  34121240.0000  226.230  227.3500  221.3000\n",
       "234  2018/09/05  226.87  33290830.0000  228.990  229.6700  225.1000\n",
       "235  2018/09/04  228.36  27363960.0000  228.410  229.1800  226.6300\n",
       "236  2018/08/31  227.63  43177540.0000  226.510  228.8700  226.0000\n",
       "237  2018/08/30  225.03  48696960.0000  223.250  228.2600  222.4000\n",
       "238  2018/08/29  222.98  27074260.0000  220.150  223.4900  219.4100\n",
       "239  2018/08/28  219.70  22751390.0000  219.010  220.5400  218.9200\n",
       "240  2018/08/27  217.94  20400770.0000  217.150  218.7400  216.3300\n",
       "241  2018/08/24  216.16  18457720.0000  216.600  216.9000  215.1100\n",
       "242  2018/08/23  215.49  18505780.0000  214.650  217.0500  214.6000\n",
       "243  2018/08/22  215.05  18965930.0000  214.100  216.3600  213.8400\n",
       "244  2018/08/21  215.04  25682700.0000  216.800  217.1900  214.0250\n",
       "245  2018/08/20  215.46  30149410.0000  218.100  219.1800  215.1100\n",
       "246  2018/08/17  217.58  35034410.0000  213.440  217.9500  213.1600\n",
       "247  2018/08/16  213.32  27595310.0000  211.750  213.8121  211.4700\n",
       "248  2018/08/15  210.24  28595230.0000  209.220  210.7400  208.3300\n",
       "249  2018/08/14  209.75  20679270.0000  210.155  210.5600  208.2600\n",
       "250  2018/08/13  208.87  25864510.0000  207.700  210.9520  207.7000\n",
       "251  2018/08/10  207.53  24592460.0000  207.360  209.1000  206.6700\n",
       "252  2018/08/09  208.88  23389530.0000  207.280  209.7800  207.2000\n",
       "\n",
       "[253 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16:00</td>\n",
       "      <td>200.99</td>\n",
       "      <td>24,619,446</td>\n",
       "      <td>201.23</td>\n",
       "      <td>202.760</td>\n",
       "      <td>199.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019/08/09</td>\n",
       "      <td>200.99</td>\n",
       "      <td>24619750.0000</td>\n",
       "      <td>201.30</td>\n",
       "      <td>202.760</td>\n",
       "      <td>199.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019/08/08</td>\n",
       "      <td>203.43</td>\n",
       "      <td>27009520.0000</td>\n",
       "      <td>200.20</td>\n",
       "      <td>203.530</td>\n",
       "      <td>199.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019/08/07</td>\n",
       "      <td>199.04</td>\n",
       "      <td>33364400.0000</td>\n",
       "      <td>195.41</td>\n",
       "      <td>199.560</td>\n",
       "      <td>193.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019/08/06</td>\n",
       "      <td>197.00</td>\n",
       "      <td>35824790.0000</td>\n",
       "      <td>196.31</td>\n",
       "      <td>198.067</td>\n",
       "      <td>194.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   close         volume    open     high     low\n",
       "0       16:00  200.99     24,619,446  201.23  202.760  199.29\n",
       "1  2019/08/09  200.99  24619750.0000  201.30  202.760  199.29\n",
       "2  2019/08/08  203.43  27009520.0000  200.20  203.530  199.39\n",
       "3  2019/08/07  199.04  33364400.0000  195.41  199.560  193.82\n",
       "4  2019/08/06  197.00  35824790.0000  196.31  198.067  194.04"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.date[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"HistoricalQuotes.csv\", parse_dates=[\"date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslib.Timestamp"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.date[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"HistoricalQuotes.csv\", parse_dates=[\"date\"], index_col=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</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>2019-08-12 16:00:00</th>\n",
       "      <td>200.99</td>\n",
       "      <td>24,619,446</td>\n",
       "      <td>201.230</td>\n",
       "      <td>202.7600</td>\n",
       "      <td>199.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-09 00:00:00</th>\n",
       "      <td>200.99</td>\n",
       "      <td>24619750.0000</td>\n",
       "      <td>201.300</td>\n",
       "      <td>202.7600</td>\n",
       "      <td>199.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 00:00:00</th>\n",
       "      <td>203.43</td>\n",
       "      <td>27009520.0000</td>\n",
       "      <td>200.200</td>\n",
       "      <td>203.5300</td>\n",
       "      <td>199.3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-07 00:00:00</th>\n",
       "      <td>199.04</td>\n",
       "      <td>33364400.0000</td>\n",
       "      <td>195.410</td>\n",
       "      <td>199.5600</td>\n",
       "      <td>193.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-06 00:00:00</th>\n",
       "      <td>197.00</td>\n",
       "      <td>35824790.0000</td>\n",
       "      <td>196.310</td>\n",
       "      <td>198.0670</td>\n",
       "      <td>194.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05 00:00:00</th>\n",
       "      <td>193.34</td>\n",
       "      <td>52392970.0000</td>\n",
       "      <td>197.990</td>\n",
       "      <td>198.6490</td>\n",
       "      <td>192.5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02 00:00:00</th>\n",
       "      <td>204.02</td>\n",
       "      <td>40862120.0000</td>\n",
       "      <td>205.530</td>\n",
       "      <td>206.4300</td>\n",
       "      <td>201.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 00:00:00</th>\n",
       "      <td>208.43</td>\n",
       "      <td>54017920.0000</td>\n",
       "      <td>213.900</td>\n",
       "      <td>218.0300</td>\n",
       "      <td>206.7435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31 00:00:00</th>\n",
       "      <td>213.04</td>\n",
       "      <td>69281360.0000</td>\n",
       "      <td>216.420</td>\n",
       "      <td>221.3700</td>\n",
       "      <td>211.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-30 00:00:00</th>\n",
       "      <td>208.78</td>\n",
       "      <td>33935720.0000</td>\n",
       "      <td>208.760</td>\n",
       "      <td>210.1600</td>\n",
       "      <td>207.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-29 00:00:00</th>\n",
       "      <td>209.68</td>\n",
       "      <td>21673390.0000</td>\n",
       "      <td>208.460</td>\n",
       "      <td>210.6400</td>\n",
       "      <td>208.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-26 00:00:00</th>\n",
       "      <td>207.74</td>\n",
       "      <td>17618870.0000</td>\n",
       "      <td>207.480</td>\n",
       "      <td>209.7300</td>\n",
       "      <td>207.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-25 00:00:00</th>\n",
       "      <td>207.02</td>\n",
       "      <td>13909560.0000</td>\n",
       "      <td>208.890</td>\n",
       "      <td>209.2400</td>\n",
       "      <td>206.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-24 00:00:00</th>\n",
       "      <td>208.67</td>\n",
       "      <td>14991570.0000</td>\n",
       "      <td>207.670</td>\n",
       "      <td>209.1500</td>\n",
       "      <td>207.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-23 00:00:00</th>\n",
       "      <td>208.84</td>\n",
       "      <td>18355210.0000</td>\n",
       "      <td>208.460</td>\n",
       "      <td>208.9100</td>\n",
       "      <td>207.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-22 00:00:00</th>\n",
       "      <td>207.22</td>\n",
       "      <td>22277930.0000</td>\n",
       "      <td>203.650</td>\n",
       "      <td>207.2300</td>\n",
       "      <td>203.6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-19 00:00:00</th>\n",
       "      <td>202.59</td>\n",
       "      <td>20929310.0000</td>\n",
       "      <td>205.790</td>\n",
       "      <td>206.5000</td>\n",
       "      <td>202.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-18 00:00:00</th>\n",
       "      <td>205.66</td>\n",
       "      <td>18582160.0000</td>\n",
       "      <td>204.000</td>\n",
       "      <td>205.8800</td>\n",
       "      <td>203.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-17 00:00:00</th>\n",
       "      <td>203.35</td>\n",
       "      <td>14107450.0000</td>\n",
       "      <td>204.050</td>\n",
       "      <td>205.0915</td>\n",
       "      <td>203.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-16 00:00:00</th>\n",
       "      <td>204.50</td>\n",
       "      <td>16866820.0000</td>\n",
       "      <td>204.590</td>\n",
       "      <td>206.1100</td>\n",
       "      <td>203.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-15 00:00:00</th>\n",
       "      <td>205.21</td>\n",
       "      <td>16947420.0000</td>\n",
       "      <td>204.090</td>\n",
       "      <td>205.8700</td>\n",
       "      <td>204.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-12 00:00:00</th>\n",
       "      <td>203.30</td>\n",
       "      <td>17595210.0000</td>\n",
       "      <td>202.450</td>\n",
       "      <td>204.0000</td>\n",
       "      <td>202.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-11 00:00:00</th>\n",
       "      <td>201.75</td>\n",
       "      <td>20191840.0000</td>\n",
       "      <td>203.310</td>\n",
       "      <td>204.3900</td>\n",
       "      <td>201.7100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-10 00:00:00</th>\n",
       "      <td>203.23</td>\n",
       "      <td>17897140.0000</td>\n",
       "      <td>201.850</td>\n",
       "      <td>203.7300</td>\n",
       "      <td>201.5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-09 00:00:00</th>\n",
       "      <td>201.24</td>\n",
       "      <td>20578020.0000</td>\n",
       "      <td>199.200</td>\n",
       "      <td>201.5100</td>\n",
       "      <td>198.8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-08 00:00:00</th>\n",
       "      <td>200.02</td>\n",
       "      <td>25338630.0000</td>\n",
       "      <td>200.810</td>\n",
       "      <td>201.4000</td>\n",
       "      <td>198.4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-05 00:00:00</th>\n",
       "      <td>204.23</td>\n",
       "      <td>17265520.0000</td>\n",
       "      <td>203.350</td>\n",
       "      <td>205.0800</td>\n",
       "      <td>202.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-03 00:00:00</th>\n",
       "      <td>204.41</td>\n",
       "      <td>11362050.0000</td>\n",
       "      <td>203.280</td>\n",
       "      <td>204.4400</td>\n",
       "      <td>202.6901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-02 00:00:00</th>\n",
       "      <td>202.73</td>\n",
       "      <td>16935220.0000</td>\n",
       "      <td>201.410</td>\n",
       "      <td>203.1323</td>\n",
       "      <td>201.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01 00:00:00</th>\n",
       "      <td>201.55</td>\n",
       "      <td>27316740.0000</td>\n",
       "      <td>203.170</td>\n",
       "      <td>204.4900</td>\n",
       "      <td>200.6500</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>2018-09-20 00:00:00</th>\n",
       "      <td>220.03</td>\n",
       "      <td>26388050.0000</td>\n",
       "      <td>220.240</td>\n",
       "      <td>222.2800</td>\n",
       "      <td>219.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-19 00:00:00</th>\n",
       "      <td>218.37</td>\n",
       "      <td>26869880.0000</td>\n",
       "      <td>218.500</td>\n",
       "      <td>219.6200</td>\n",
       "      <td>215.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-18 00:00:00</th>\n",
       "      <td>218.24</td>\n",
       "      <td>31495630.0000</td>\n",
       "      <td>217.790</td>\n",
       "      <td>221.8500</td>\n",
       "      <td>217.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-17 00:00:00</th>\n",
       "      <td>217.88</td>\n",
       "      <td>36810120.0000</td>\n",
       "      <td>222.150</td>\n",
       "      <td>222.9500</td>\n",
       "      <td>217.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-14 00:00:00</th>\n",
       "      <td>223.84</td>\n",
       "      <td>31801970.0000</td>\n",
       "      <td>225.750</td>\n",
       "      <td>226.8400</td>\n",
       "      <td>222.5220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-13 00:00:00</th>\n",
       "      <td>226.41</td>\n",
       "      <td>41411350.0000</td>\n",
       "      <td>223.520</td>\n",
       "      <td>228.3500</td>\n",
       "      <td>222.5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-12 00:00:00</th>\n",
       "      <td>221.07</td>\n",
       "      <td>49118510.0000</td>\n",
       "      <td>224.940</td>\n",
       "      <td>225.0000</td>\n",
       "      <td>219.8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-11 00:00:00</th>\n",
       "      <td>223.85</td>\n",
       "      <td>35644510.0000</td>\n",
       "      <td>218.010</td>\n",
       "      <td>224.2990</td>\n",
       "      <td>216.5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-10 00:00:00</th>\n",
       "      <td>218.33</td>\n",
       "      <td>39194720.0000</td>\n",
       "      <td>220.950</td>\n",
       "      <td>221.8500</td>\n",
       "      <td>216.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-07 00:00:00</th>\n",
       "      <td>221.30</td>\n",
       "      <td>37418910.0000</td>\n",
       "      <td>221.850</td>\n",
       "      <td>225.3700</td>\n",
       "      <td>220.7100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-06 00:00:00</th>\n",
       "      <td>223.10</td>\n",
       "      <td>34121240.0000</td>\n",
       "      <td>226.230</td>\n",
       "      <td>227.3500</td>\n",
       "      <td>221.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-05 00:00:00</th>\n",
       "      <td>226.87</td>\n",
       "      <td>33290830.0000</td>\n",
       "      <td>228.990</td>\n",
       "      <td>229.6700</td>\n",
       "      <td>225.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-04 00:00:00</th>\n",
       "      <td>228.36</td>\n",
       "      <td>27363960.0000</td>\n",
       "      <td>228.410</td>\n",
       "      <td>229.1800</td>\n",
       "      <td>226.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31 00:00:00</th>\n",
       "      <td>227.63</td>\n",
       "      <td>43177540.0000</td>\n",
       "      <td>226.510</td>\n",
       "      <td>228.8700</td>\n",
       "      <td>226.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-30 00:00:00</th>\n",
       "      <td>225.03</td>\n",
       "      <td>48696960.0000</td>\n",
       "      <td>223.250</td>\n",
       "      <td>228.2600</td>\n",
       "      <td>222.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-29 00:00:00</th>\n",
       "      <td>222.98</td>\n",
       "      <td>27074260.0000</td>\n",
       "      <td>220.150</td>\n",
       "      <td>223.4900</td>\n",
       "      <td>219.4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-28 00:00:00</th>\n",
       "      <td>219.70</td>\n",
       "      <td>22751390.0000</td>\n",
       "      <td>219.010</td>\n",
       "      <td>220.5400</td>\n",
       "      <td>218.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-27 00:00:00</th>\n",
       "      <td>217.94</td>\n",
       "      <td>20400770.0000</td>\n",
       "      <td>217.150</td>\n",
       "      <td>218.7400</td>\n",
       "      <td>216.3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-24 00:00:00</th>\n",
       "      <td>216.16</td>\n",
       "      <td>18457720.0000</td>\n",
       "      <td>216.600</td>\n",
       "      <td>216.9000</td>\n",
       "      <td>215.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23 00:00:00</th>\n",
       "      <td>215.49</td>\n",
       "      <td>18505780.0000</td>\n",
       "      <td>214.650</td>\n",
       "      <td>217.0500</td>\n",
       "      <td>214.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-22 00:00:00</th>\n",
       "      <td>215.05</td>\n",
       "      <td>18965930.0000</td>\n",
       "      <td>214.100</td>\n",
       "      <td>216.3600</td>\n",
       "      <td>213.8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21 00:00:00</th>\n",
       "      <td>215.04</td>\n",
       "      <td>25682700.0000</td>\n",
       "      <td>216.800</td>\n",
       "      <td>217.1900</td>\n",
       "      <td>214.0250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-20 00:00:00</th>\n",
       "      <td>215.46</td>\n",
       "      <td>30149410.0000</td>\n",
       "      <td>218.100</td>\n",
       "      <td>219.1800</td>\n",
       "      <td>215.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-17 00:00:00</th>\n",
       "      <td>217.58</td>\n",
       "      <td>35034410.0000</td>\n",
       "      <td>213.440</td>\n",
       "      <td>217.9500</td>\n",
       "      <td>213.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-16 00:00:00</th>\n",
       "      <td>213.32</td>\n",
       "      <td>27595310.0000</td>\n",
       "      <td>211.750</td>\n",
       "      <td>213.8121</td>\n",
       "      <td>211.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-15 00:00:00</th>\n",
       "      <td>210.24</td>\n",
       "      <td>28595230.0000</td>\n",
       "      <td>209.220</td>\n",
       "      <td>210.7400</td>\n",
       "      <td>208.3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-14 00:00:00</th>\n",
       "      <td>209.75</td>\n",
       "      <td>20679270.0000</td>\n",
       "      <td>210.155</td>\n",
       "      <td>210.5600</td>\n",
       "      <td>208.2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-13 00:00:00</th>\n",
       "      <td>208.87</td>\n",
       "      <td>25864510.0000</td>\n",
       "      <td>207.700</td>\n",
       "      <td>210.9520</td>\n",
       "      <td>207.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10 00:00:00</th>\n",
       "      <td>207.53</td>\n",
       "      <td>24592460.0000</td>\n",
       "      <td>207.360</td>\n",
       "      <td>209.1000</td>\n",
       "      <td>206.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-09 00:00:00</th>\n",
       "      <td>208.88</td>\n",
       "      <td>23389530.0000</td>\n",
       "      <td>207.280</td>\n",
       "      <td>209.7800</td>\n",
       "      <td>207.2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      close         volume     open      high       low\n",
       "date                                                                   \n",
       "2019-08-12 16:00:00  200.99     24,619,446  201.230  202.7600  199.2900\n",
       "2019-08-09 00:00:00  200.99  24619750.0000  201.300  202.7600  199.2900\n",
       "2019-08-08 00:00:00  203.43  27009520.0000  200.200  203.5300  199.3900\n",
       "2019-08-07 00:00:00  199.04  33364400.0000  195.410  199.5600  193.8200\n",
       "2019-08-06 00:00:00  197.00  35824790.0000  196.310  198.0670  194.0400\n",
       "2019-08-05 00:00:00  193.34  52392970.0000  197.990  198.6490  192.5800\n",
       "2019-08-02 00:00:00  204.02  40862120.0000  205.530  206.4300  201.6300\n",
       "2019-08-01 00:00:00  208.43  54017920.0000  213.900  218.0300  206.7435\n",
       "2019-07-31 00:00:00  213.04  69281360.0000  216.420  221.3700  211.3000\n",
       "2019-07-30 00:00:00  208.78  33935720.0000  208.760  210.1600  207.3100\n",
       "2019-07-29 00:00:00  209.68  21673390.0000  208.460  210.6400  208.4400\n",
       "2019-07-26 00:00:00  207.74  17618870.0000  207.480  209.7300  207.1400\n",
       "2019-07-25 00:00:00  207.02  13909560.0000  208.890  209.2400  206.7300\n",
       "2019-07-24 00:00:00  208.67  14991570.0000  207.670  209.1500  207.1700\n",
       "2019-07-23 00:00:00  208.84  18355210.0000  208.460  208.9100  207.2900\n",
       "2019-07-22 00:00:00  207.22  22277930.0000  203.650  207.2300  203.6100\n",
       "2019-07-19 00:00:00  202.59  20929310.0000  205.790  206.5000  202.3600\n",
       "2019-07-18 00:00:00  205.66  18582160.0000  204.000  205.8800  203.7000\n",
       "2019-07-17 00:00:00  203.35  14107450.0000  204.050  205.0915  203.2700\n",
       "2019-07-16 00:00:00  204.50  16866820.0000  204.590  206.1100  203.5000\n",
       "2019-07-15 00:00:00  205.21  16947420.0000  204.090  205.8700  204.0000\n",
       "2019-07-12 00:00:00  203.30  17595210.0000  202.450  204.0000  202.2000\n",
       "2019-07-11 00:00:00  201.75  20191840.0000  203.310  204.3900  201.7100\n",
       "2019-07-10 00:00:00  203.23  17897140.0000  201.850  203.7300  201.5600\n",
       "2019-07-09 00:00:00  201.24  20578020.0000  199.200  201.5100  198.8100\n",
       "2019-07-08 00:00:00  200.02  25338630.0000  200.810  201.4000  198.4100\n",
       "2019-07-05 00:00:00  204.23  17265520.0000  203.350  205.0800  202.9000\n",
       "2019-07-03 00:00:00  204.41  11362050.0000  203.280  204.4400  202.6901\n",
       "2019-07-02 00:00:00  202.73  16935220.0000  201.410  203.1323  201.3600\n",
       "2019-07-01 00:00:00  201.55  27316740.0000  203.170  204.4900  200.6500\n",
       "...                     ...            ...      ...       ...       ...\n",
       "2018-09-20 00:00:00  220.03  26388050.0000  220.240  222.2800  219.1500\n",
       "2018-09-19 00:00:00  218.37  26869880.0000  218.500  219.6200  215.3000\n",
       "2018-09-18 00:00:00  218.24  31495630.0000  217.790  221.8500  217.1200\n",
       "2018-09-17 00:00:00  217.88  36810120.0000  222.150  222.9500  217.2700\n",
       "2018-09-14 00:00:00  223.84  31801970.0000  225.750  226.8400  222.5220\n",
       "2018-09-13 00:00:00  226.41  41411350.0000  223.520  228.3500  222.5700\n",
       "2018-09-12 00:00:00  221.07  49118510.0000  224.940  225.0000  219.8400\n",
       "2018-09-11 00:00:00  223.85  35644510.0000  218.010  224.2990  216.5600\n",
       "2018-09-10 00:00:00  218.33  39194720.0000  220.950  221.8500  216.4700\n",
       "2018-09-07 00:00:00  221.30  37418910.0000  221.850  225.3700  220.7100\n",
       "2018-09-06 00:00:00  223.10  34121240.0000  226.230  227.3500  221.3000\n",
       "2018-09-05 00:00:00  226.87  33290830.0000  228.990  229.6700  225.1000\n",
       "2018-09-04 00:00:00  228.36  27363960.0000  228.410  229.1800  226.6300\n",
       "2018-08-31 00:00:00  227.63  43177540.0000  226.510  228.8700  226.0000\n",
       "2018-08-30 00:00:00  225.03  48696960.0000  223.250  228.2600  222.4000\n",
       "2018-08-29 00:00:00  222.98  27074260.0000  220.150  223.4900  219.4100\n",
       "2018-08-28 00:00:00  219.70  22751390.0000  219.010  220.5400  218.9200\n",
       "2018-08-27 00:00:00  217.94  20400770.0000  217.150  218.7400  216.3300\n",
       "2018-08-24 00:00:00  216.16  18457720.0000  216.600  216.9000  215.1100\n",
       "2018-08-23 00:00:00  215.49  18505780.0000  214.650  217.0500  214.6000\n",
       "2018-08-22 00:00:00  215.05  18965930.0000  214.100  216.3600  213.8400\n",
       "2018-08-21 00:00:00  215.04  25682700.0000  216.800  217.1900  214.0250\n",
       "2018-08-20 00:00:00  215.46  30149410.0000  218.100  219.1800  215.1100\n",
       "2018-08-17 00:00:00  217.58  35034410.0000  213.440  217.9500  213.1600\n",
       "2018-08-16 00:00:00  213.32  27595310.0000  211.750  213.8121  211.4700\n",
       "2018-08-15 00:00:00  210.24  28595230.0000  209.220  210.7400  208.3300\n",
       "2018-08-14 00:00:00  209.75  20679270.0000  210.155  210.5600  208.2600\n",
       "2018-08-13 00:00:00  208.87  25864510.0000  207.700  210.9520  207.7000\n",
       "2018-08-10 00:00:00  207.53  24592460.0000  207.360  209.1000  206.6700\n",
       "2018-08-09 00:00:00  208.88  23389530.0000  207.280  209.7800  207.2000\n",
       "\n",
       "[253 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-08-12 16:00:00', '2019-08-09 00:00:00',\n",
       "               '2019-08-08 00:00:00', '2019-08-07 00:00:00',\n",
       "               '2019-08-06 00:00:00', '2019-08-05 00:00:00',\n",
       "               '2019-08-02 00:00:00', '2019-08-01 00:00:00',\n",
       "               '2019-07-31 00:00:00', '2019-07-30 00:00:00',\n",
       "               ...\n",
       "               '2018-08-22 00:00:00', '2018-08-21 00:00:00',\n",
       "               '2018-08-20 00:00:00', '2018-08-17 00:00:00',\n",
       "               '2018-08-16 00:00:00', '2018-08-15 00:00:00',\n",
       "               '2018-08-14 00:00:00', '2018-08-13 00:00:00',\n",
       "               '2018-08-10 00:00:00', '2018-08-09 00:00:00'],\n",
       "              dtype='datetime64[ns]', name='date', length=253, freq=None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</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>2019-08-12 16:00:00</th>\n",
       "      <td>200.99</td>\n",
       "      <td>24,619,446</td>\n",
       "      <td>201.23</td>\n",
       "      <td>202.760</td>\n",
       "      <td>199.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-09 00:00:00</th>\n",
       "      <td>200.99</td>\n",
       "      <td>24619750.0000</td>\n",
       "      <td>201.30</td>\n",
       "      <td>202.760</td>\n",
       "      <td>199.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08 00:00:00</th>\n",
       "      <td>203.43</td>\n",
       "      <td>27009520.0000</td>\n",
       "      <td>200.20</td>\n",
       "      <td>203.530</td>\n",
       "      <td>199.3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-07 00:00:00</th>\n",
       "      <td>199.04</td>\n",
       "      <td>33364400.0000</td>\n",
       "      <td>195.41</td>\n",
       "      <td>199.560</td>\n",
       "      <td>193.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-06 00:00:00</th>\n",
       "      <td>197.00</td>\n",
       "      <td>35824790.0000</td>\n",
       "      <td>196.31</td>\n",
       "      <td>198.067</td>\n",
       "      <td>194.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05 00:00:00</th>\n",
       "      <td>193.34</td>\n",
       "      <td>52392970.0000</td>\n",
       "      <td>197.99</td>\n",
       "      <td>198.649</td>\n",
       "      <td>192.5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02 00:00:00</th>\n",
       "      <td>204.02</td>\n",
       "      <td>40862120.0000</td>\n",
       "      <td>205.53</td>\n",
       "      <td>206.430</td>\n",
       "      <td>201.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01 00:00:00</th>\n",
       "      <td>208.43</td>\n",
       "      <td>54017920.0000</td>\n",
       "      <td>213.90</td>\n",
       "      <td>218.030</td>\n",
       "      <td>206.7435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      close         volume    open     high       low\n",
       "date                                                                 \n",
       "2019-08-12 16:00:00  200.99     24,619,446  201.23  202.760  199.2900\n",
       "2019-08-09 00:00:00  200.99  24619750.0000  201.30  202.760  199.2900\n",
       "2019-08-08 00:00:00  203.43  27009520.0000  200.20  203.530  199.3900\n",
       "2019-08-07 00:00:00  199.04  33364400.0000  195.41  199.560  193.8200\n",
       "2019-08-06 00:00:00  197.00  35824790.0000  196.31  198.067  194.0400\n",
       "2019-08-05 00:00:00  193.34  52392970.0000  197.99  198.649  192.5800\n",
       "2019-08-02 00:00:00  204.02  40862120.0000  205.53  206.430  201.6300\n",
       "2019-08-01 00:00:00  208.43  54017920.0000  213.90  218.030  206.7435"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-08']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2019-08-12 16:00:00    200.99\n",
       "2019-08-09 00:00:00    200.99\n",
       "2019-08-08 00:00:00    203.43\n",
       "2019-08-07 00:00:00    199.04\n",
       "2019-08-06 00:00:00    197.00\n",
       "2019-08-05 00:00:00    193.34\n",
       "2019-08-02 00:00:00    204.02\n",
       "2019-08-01 00:00:00    208.43\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-08'].close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200.905"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['2019-08'].close.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200.99"
      ]
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['2019-08'].close.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>204.02</td>\n",
       "      <td>40862120.0000</td>\n",
       "      <td>205.53</td>\n",
       "      <td>206.43</td>\n",
       "      <td>201.63</td>\n",
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      "text/plain": [
       "             close         volume    open    high     low\n",
       "date                                                     \n",
       "2019-08-02  204.02  40862120.0000  205.53  206.43  201.63"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   "source": [
    "df['2019-08-02']"
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  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-09</th>\n",
       "      <td>200.99</td>\n",
       "      <td>24619750.0000</td>\n",
       "      <td>201.30</td>\n",
       "      <td>202.7600</td>\n",
       "      <td>199.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-08</th>\n",
       "      <td>203.43</td>\n",
       "      <td>27009520.0000</td>\n",
       "      <td>200.20</td>\n",
       "      <td>203.5300</td>\n",
       "      <td>199.3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-07</th>\n",
       "      <td>199.04</td>\n",
       "      <td>33364400.0000</td>\n",
       "      <td>195.41</td>\n",
       "      <td>199.5600</td>\n",
       "      <td>193.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-06</th>\n",
       "      <td>197.00</td>\n",
       "      <td>35824790.0000</td>\n",
       "      <td>196.31</td>\n",
       "      <td>198.0670</td>\n",
       "      <td>194.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05</th>\n",
       "      <td>193.34</td>\n",
       "      <td>52392970.0000</td>\n",
       "      <td>197.99</td>\n",
       "      <td>198.6490</td>\n",
       "      <td>192.5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>204.02</td>\n",
       "      <td>40862120.0000</td>\n",
       "      <td>205.53</td>\n",
       "      <td>206.4300</td>\n",
       "      <td>201.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>208.43</td>\n",
       "      <td>54017920.0000</td>\n",
       "      <td>213.90</td>\n",
       "      <td>218.0300</td>\n",
       "      <td>206.7435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31</th>\n",
       "      <td>213.04</td>\n",
       "      <td>69281360.0000</td>\n",
       "      <td>216.42</td>\n",
       "      <td>221.3700</td>\n",
       "      <td>211.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-30</th>\n",
       "      <td>208.78</td>\n",
       "      <td>33935720.0000</td>\n",
       "      <td>208.76</td>\n",
       "      <td>210.1600</td>\n",
       "      <td>207.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-29</th>\n",
       "      <td>209.68</td>\n",
       "      <td>21673390.0000</td>\n",
       "      <td>208.46</td>\n",
       "      <td>210.6400</td>\n",
       "      <td>208.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-26</th>\n",
       "      <td>207.74</td>\n",
       "      <td>17618870.0000</td>\n",
       "      <td>207.48</td>\n",
       "      <td>209.7300</td>\n",
       "      <td>207.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-25</th>\n",
       "      <td>207.02</td>\n",
       "      <td>13909560.0000</td>\n",
       "      <td>208.89</td>\n",
       "      <td>209.2400</td>\n",
       "      <td>206.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-24</th>\n",
       "      <td>208.67</td>\n",
       "      <td>14991570.0000</td>\n",
       "      <td>207.67</td>\n",
       "      <td>209.1500</td>\n",
       "      <td>207.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-23</th>\n",
       "      <td>208.84</td>\n",
       "      <td>18355210.0000</td>\n",
       "      <td>208.46</td>\n",
       "      <td>208.9100</td>\n",
       "      <td>207.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-22</th>\n",
       "      <td>207.22</td>\n",
       "      <td>22277930.0000</td>\n",
       "      <td>203.65</td>\n",
       "      <td>207.2300</td>\n",
       "      <td>203.6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-19</th>\n",
       "      <td>202.59</td>\n",
       "      <td>20929310.0000</td>\n",
       "      <td>205.79</td>\n",
       "      <td>206.5000</td>\n",
       "      <td>202.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-18</th>\n",
       "      <td>205.66</td>\n",
       "      <td>18582160.0000</td>\n",
       "      <td>204.00</td>\n",
       "      <td>205.8800</td>\n",
       "      <td>203.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-17</th>\n",
       "      <td>203.35</td>\n",
       "      <td>14107450.0000</td>\n",
       "      <td>204.05</td>\n",
       "      <td>205.0915</td>\n",
       "      <td>203.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-16</th>\n",
       "      <td>204.50</td>\n",
       "      <td>16866820.0000</td>\n",
       "      <td>204.59</td>\n",
       "      <td>206.1100</td>\n",
       "      <td>203.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-15</th>\n",
       "      <td>205.21</td>\n",
       "      <td>16947420.0000</td>\n",
       "      <td>204.09</td>\n",
       "      <td>205.8700</td>\n",
       "      <td>204.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-12</th>\n",
       "      <td>203.30</td>\n",
       "      <td>17595210.0000</td>\n",
       "      <td>202.45</td>\n",
       "      <td>204.0000</td>\n",
       "      <td>202.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-11</th>\n",
       "      <td>201.75</td>\n",
       "      <td>20191840.0000</td>\n",
       "      <td>203.31</td>\n",
       "      <td>204.3900</td>\n",
       "      <td>201.7100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-10</th>\n",
       "      <td>203.23</td>\n",
       "      <td>17897140.0000</td>\n",
       "      <td>201.85</td>\n",
       "      <td>203.7300</td>\n",
       "      <td>201.5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-09</th>\n",
       "      <td>201.24</td>\n",
       "      <td>20578020.0000</td>\n",
       "      <td>199.20</td>\n",
       "      <td>201.5100</td>\n",
       "      <td>198.8100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close         volume    open      high       low\n",
       "date                                                         \n",
       "2019-08-09  200.99  24619750.0000  201.30  202.7600  199.2900\n",
       "2019-08-08  203.43  27009520.0000  200.20  203.5300  199.3900\n",
       "2019-08-07  199.04  33364400.0000  195.41  199.5600  193.8200\n",
       "2019-08-06  197.00  35824790.0000  196.31  198.0670  194.0400\n",
       "2019-08-05  193.34  52392970.0000  197.99  198.6490  192.5800\n",
       "2019-08-02  204.02  40862120.0000  205.53  206.4300  201.6300\n",
       "2019-08-01  208.43  54017920.0000  213.90  218.0300  206.7435\n",
       "2019-07-31  213.04  69281360.0000  216.42  221.3700  211.3000\n",
       "2019-07-30  208.78  33935720.0000  208.76  210.1600  207.3100\n",
       "2019-07-29  209.68  21673390.0000  208.46  210.6400  208.4400\n",
       "2019-07-26  207.74  17618870.0000  207.48  209.7300  207.1400\n",
       "2019-07-25  207.02  13909560.0000  208.89  209.2400  206.7300\n",
       "2019-07-24  208.67  14991570.0000  207.67  209.1500  207.1700\n",
       "2019-07-23  208.84  18355210.0000  208.46  208.9100  207.2900\n",
       "2019-07-22  207.22  22277930.0000  203.65  207.2300  203.6100\n",
       "2019-07-19  202.59  20929310.0000  205.79  206.5000  202.3600\n",
       "2019-07-18  205.66  18582160.0000  204.00  205.8800  203.7000\n",
       "2019-07-17  203.35  14107450.0000  204.05  205.0915  203.2700\n",
       "2019-07-16  204.50  16866820.0000  204.59  206.1100  203.5000\n",
       "2019-07-15  205.21  16947420.0000  204.09  205.8700  204.0000\n",
       "2019-07-12  203.30  17595210.0000  202.45  204.0000  202.2000\n",
       "2019-07-11  201.75  20191840.0000  203.31  204.3900  201.7100\n",
       "2019-07-10  203.23  17897140.0000  201.85  203.7300  201.5600\n",
       "2019-07-09  201.24  20578020.0000  199.20  201.5100  198.8100"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-08-09':'2019-07-09']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2018-08-31    215.685294\n",
       "2018-09-30    222.073684\n",
       "2018-10-31    220.845652\n",
       "2018-11-30    191.235714\n",
       "2018-12-31    164.266316\n",
       "2019-01-31    154.166190\n",
       "2019-02-28    171.727895\n",
       "2019-03-31    183.293810\n",
       "2019-04-30    200.516190\n",
       "2019-05-31    191.273636\n",
       "2019-06-30    192.969000\n",
       "2019-07-31    205.216364\n",
       "2019-08-31    200.905000\n",
       "Freq: M, Name: close, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.close.resample(\"M\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6f5ec5518>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6f87c37b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df.close.resample(\"M\").mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6f5edc4e0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6f71a0be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.close.resample(\"W\").mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6f3d587b8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6f3da5a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.close.resample(\"D\").mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6f3e520f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Quarterly_resampled_data = df.open.resample('Q').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.Series(range(3), index=pd.date_range('2019', freq='D', periods=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-01    0\n",
       "2019-01-02    1\n",
       "2019-01-03    2\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-01    0\n",
       "2019-01-02    1\n",
       "2019-01-03    2\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mx=pd.Series(range(3), index=pd.date_range('2019', freq='D', periods=3))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6f3bfbf98>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6f3d9fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.open.resample('QS').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndexResampler [freq=<BusinessHour: BH=09:00-17:00>, axis=0, closed=left, label=left, convention=start, base=0]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.open.resample('BH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6f3d4dc18>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6f3b815f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.open.resample('BQ').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: FutureWarning: \n",
      ".resample() is now a deferred operation\n",
      "You called plot(...) on this deferred object which materialized it into a series\n",
      "by implicitly taking the mean.  Use .resample(...).mean() instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6f3c045f8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6f3be3240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.open.resample('BQ').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6f3bbf400>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6f3affda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.open.resample('Y').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6f3aff080>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6f3a95898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.open.resample('M').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6f3a14c88>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6f3a3d4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.open.resample('M').mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng=pd.date_range(start=\"2018-08-09\", end=\"2019/08/09\",freq=\"B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-08-09', '2018-08-10', '2018-08-13', '2018-08-14',\n",
       "               '2018-08-15', '2018-08-16', '2018-08-17', '2018-08-20',\n",
       "               '2018-08-21', '2018-08-22',\n",
       "               ...\n",
       "               '2019-07-29', '2019-07-30', '2019-07-31', '2019-08-01',\n",
       "               '2019-08-02', '2019-08-05', '2019-08-06', '2019-08-07',\n",
       "               '2019-08-08', '2019-08-09'],\n",
       "              dtype='datetime64[ns]', length=262, freq='B')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</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>2018-12-07</th>\n",
       "      <td>168.49</td>\n",
       "      <td>41678680.0000</td>\n",
       "      <td>173.490</td>\n",
       "      <td>174.4900</td>\n",
       "      <td>168.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-06</th>\n",
       "      <td>174.72</td>\n",
       "      <td>42704910.0000</td>\n",
       "      <td>171.760</td>\n",
       "      <td>174.7800</td>\n",
       "      <td>170.4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-04</th>\n",
       "      <td>176.69</td>\n",
       "      <td>41141250.0000</td>\n",
       "      <td>180.950</td>\n",
       "      <td>182.3899</td>\n",
       "      <td>176.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-03</th>\n",
       "      <td>184.82</td>\n",
       "      <td>40537700.0000</td>\n",
       "      <td>184.460</td>\n",
       "      <td>184.9400</td>\n",
       "      <td>181.2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>178.58</td>\n",
       "      <td>39424260.0000</td>\n",
       "      <td>180.290</td>\n",
       "      <td>180.3300</td>\n",
       "      <td>177.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-29</th>\n",
       "      <td>179.55</td>\n",
       "      <td>41523580.0000</td>\n",
       "      <td>182.660</td>\n",
       "      <td>182.8000</td>\n",
       "      <td>177.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-28</th>\n",
       "      <td>180.94</td>\n",
       "      <td>45941750.0000</td>\n",
       "      <td>176.730</td>\n",
       "      <td>181.2900</td>\n",
       "      <td>174.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-27</th>\n",
       "      <td>174.24</td>\n",
       "      <td>41156140.0000</td>\n",
       "      <td>171.510</td>\n",
       "      <td>174.7700</td>\n",
       "      <td>170.8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-26</th>\n",
       "      <td>174.62</td>\n",
       "      <td>44662320.0000</td>\n",
       "      <td>174.240</td>\n",
       "      <td>174.9500</td>\n",
       "      <td>170.2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-23</th>\n",
       "      <td>172.29</td>\n",
       "      <td>23623970.0000</td>\n",
       "      <td>174.940</td>\n",
       "      <td>176.5950</td>\n",
       "      <td>172.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-21</th>\n",
       "      <td>176.78</td>\n",
       "      <td>31096240.0000</td>\n",
       "      <td>179.730</td>\n",
       "      <td>180.2700</td>\n",
       "      <td>176.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-20</th>\n",
       "      <td>176.98</td>\n",
       "      <td>67678680.0000</td>\n",
       "      <td>178.370</td>\n",
       "      <td>181.4700</td>\n",
       "      <td>175.5100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-19</th>\n",
       "      <td>185.86</td>\n",
       "      <td>41626820.0000</td>\n",
       "      <td>190.000</td>\n",
       "      <td>190.7000</td>\n",
       "      <td>184.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-16</th>\n",
       "      <td>193.53</td>\n",
       "      <td>36191330.0000</td>\n",
       "      <td>190.500</td>\n",
       "      <td>194.9695</td>\n",
       "      <td>189.4600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-15</th>\n",
       "      <td>191.41</td>\n",
       "      <td>46271660.0000</td>\n",
       "      <td>188.390</td>\n",
       "      <td>191.9700</td>\n",
       "      <td>186.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-14</th>\n",
       "      <td>186.80</td>\n",
       "      <td>60547340.0000</td>\n",
       "      <td>193.900</td>\n",
       "      <td>194.4800</td>\n",
       "      <td>185.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-13</th>\n",
       "      <td>192.23</td>\n",
       "      <td>46725710.0000</td>\n",
       "      <td>191.630</td>\n",
       "      <td>197.1800</td>\n",
       "      <td>191.4501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12</th>\n",
       "      <td>194.17</td>\n",
       "      <td>50991030.0000</td>\n",
       "      <td>199.000</td>\n",
       "      <td>199.8500</td>\n",
       "      <td>193.7900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09</th>\n",
       "      <td>204.47</td>\n",
       "      <td>34317760.0000</td>\n",
       "      <td>205.550</td>\n",
       "      <td>206.0100</td>\n",
       "      <td>202.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08</th>\n",
       "      <td>208.49</td>\n",
       "      <td>25289270.0000</td>\n",
       "      <td>209.980</td>\n",
       "      <td>210.1200</td>\n",
       "      <td>206.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-07</th>\n",
       "      <td>209.95</td>\n",
       "      <td>33291640.0000</td>\n",
       "      <td>205.970</td>\n",
       "      <td>210.0600</td>\n",
       "      <td>204.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>203.77</td>\n",
       "      <td>31774720.0000</td>\n",
       "      <td>201.920</td>\n",
       "      <td>204.7200</td>\n",
       "      <td>201.6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>201.59</td>\n",
       "      <td>66072170.0000</td>\n",
       "      <td>204.300</td>\n",
       "      <td>204.3900</td>\n",
       "      <td>198.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>207.48</td>\n",
       "      <td>91046560.0000</td>\n",
       "      <td>209.550</td>\n",
       "      <td>213.6500</td>\n",
       "      <td>205.4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>222.22</td>\n",
       "      <td>52954070.0000</td>\n",
       "      <td>219.050</td>\n",
       "      <td>222.3600</td>\n",
       "      <td>216.8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>218.86</td>\n",
       "      <td>38016810.0000</td>\n",
       "      <td>216.880</td>\n",
       "      <td>220.4500</td>\n",
       "      <td>216.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-30</th>\n",
       "      <td>213.30</td>\n",
       "      <td>36487930.0000</td>\n",
       "      <td>211.150</td>\n",
       "      <td>215.1800</td>\n",
       "      <td>209.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-29</th>\n",
       "      <td>212.24</td>\n",
       "      <td>45713690.0000</td>\n",
       "      <td>219.190</td>\n",
       "      <td>219.6900</td>\n",
       "      <td>206.0900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-26</th>\n",
       "      <td>216.30</td>\n",
       "      <td>47191700.0000</td>\n",
       "      <td>215.900</td>\n",
       "      <td>220.1900</td>\n",
       "      <td>212.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-25</th>\n",
       "      <td>219.80</td>\n",
       "      <td>29027340.0000</td>\n",
       "      <td>217.710</td>\n",
       "      <td>221.3800</td>\n",
       "      <td>216.7500</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>2018-09-20</th>\n",
       "      <td>220.03</td>\n",
       "      <td>26388050.0000</td>\n",
       "      <td>220.240</td>\n",
       "      <td>222.2800</td>\n",
       "      <td>219.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-19</th>\n",
       "      <td>218.37</td>\n",
       "      <td>26869880.0000</td>\n",
       "      <td>218.500</td>\n",
       "      <td>219.6200</td>\n",
       "      <td>215.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-18</th>\n",
       "      <td>218.24</td>\n",
       "      <td>31495630.0000</td>\n",
       "      <td>217.790</td>\n",
       "      <td>221.8500</td>\n",
       "      <td>217.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-17</th>\n",
       "      <td>217.88</td>\n",
       "      <td>36810120.0000</td>\n",
       "      <td>222.150</td>\n",
       "      <td>222.9500</td>\n",
       "      <td>217.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-14</th>\n",
       "      <td>223.84</td>\n",
       "      <td>31801970.0000</td>\n",
       "      <td>225.750</td>\n",
       "      <td>226.8400</td>\n",
       "      <td>222.5220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-13</th>\n",
       "      <td>226.41</td>\n",
       "      <td>41411350.0000</td>\n",
       "      <td>223.520</td>\n",
       "      <td>228.3500</td>\n",
       "      <td>222.5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-12</th>\n",
       "      <td>221.07</td>\n",
       "      <td>49118510.0000</td>\n",
       "      <td>224.940</td>\n",
       "      <td>225.0000</td>\n",
       "      <td>219.8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-11</th>\n",
       "      <td>223.85</td>\n",
       "      <td>35644510.0000</td>\n",
       "      <td>218.010</td>\n",
       "      <td>224.2990</td>\n",
       "      <td>216.5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-10</th>\n",
       "      <td>218.33</td>\n",
       "      <td>39194720.0000</td>\n",
       "      <td>220.950</td>\n",
       "      <td>221.8500</td>\n",
       "      <td>216.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-07</th>\n",
       "      <td>221.30</td>\n",
       "      <td>37418910.0000</td>\n",
       "      <td>221.850</td>\n",
       "      <td>225.3700</td>\n",
       "      <td>220.7100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-06</th>\n",
       "      <td>223.10</td>\n",
       "      <td>34121240.0000</td>\n",
       "      <td>226.230</td>\n",
       "      <td>227.3500</td>\n",
       "      <td>221.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-05</th>\n",
       "      <td>226.87</td>\n",
       "      <td>33290830.0000</td>\n",
       "      <td>228.990</td>\n",
       "      <td>229.6700</td>\n",
       "      <td>225.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-04</th>\n",
       "      <td>228.36</td>\n",
       "      <td>27363960.0000</td>\n",
       "      <td>228.410</td>\n",
       "      <td>229.1800</td>\n",
       "      <td>226.6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>227.63</td>\n",
       "      <td>43177540.0000</td>\n",
       "      <td>226.510</td>\n",
       "      <td>228.8700</td>\n",
       "      <td>226.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-30</th>\n",
       "      <td>225.03</td>\n",
       "      <td>48696960.0000</td>\n",
       "      <td>223.250</td>\n",
       "      <td>228.2600</td>\n",
       "      <td>222.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-29</th>\n",
       "      <td>222.98</td>\n",
       "      <td>27074260.0000</td>\n",
       "      <td>220.150</td>\n",
       "      <td>223.4900</td>\n",
       "      <td>219.4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-28</th>\n",
       "      <td>219.70</td>\n",
       "      <td>22751390.0000</td>\n",
       "      <td>219.010</td>\n",
       "      <td>220.5400</td>\n",
       "      <td>218.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-27</th>\n",
       "      <td>217.94</td>\n",
       "      <td>20400770.0000</td>\n",
       "      <td>217.150</td>\n",
       "      <td>218.7400</td>\n",
       "      <td>216.3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-24</th>\n",
       "      <td>216.16</td>\n",
       "      <td>18457720.0000</td>\n",
       "      <td>216.600</td>\n",
       "      <td>216.9000</td>\n",
       "      <td>215.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>215.49</td>\n",
       "      <td>18505780.0000</td>\n",
       "      <td>214.650</td>\n",
       "      <td>217.0500</td>\n",
       "      <td>214.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-22</th>\n",
       "      <td>215.05</td>\n",
       "      <td>18965930.0000</td>\n",
       "      <td>214.100</td>\n",
       "      <td>216.3600</td>\n",
       "      <td>213.8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21</th>\n",
       "      <td>215.04</td>\n",
       "      <td>25682700.0000</td>\n",
       "      <td>216.800</td>\n",
       "      <td>217.1900</td>\n",
       "      <td>214.0250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-20</th>\n",
       "      <td>215.46</td>\n",
       "      <td>30149410.0000</td>\n",
       "      <td>218.100</td>\n",
       "      <td>219.1800</td>\n",
       "      <td>215.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-17</th>\n",
       "      <td>217.58</td>\n",
       "      <td>35034410.0000</td>\n",
       "      <td>213.440</td>\n",
       "      <td>217.9500</td>\n",
       "      <td>213.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-16</th>\n",
       "      <td>213.32</td>\n",
       "      <td>27595310.0000</td>\n",
       "      <td>211.750</td>\n",
       "      <td>213.8121</td>\n",
       "      <td>211.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-15</th>\n",
       "      <td>210.24</td>\n",
       "      <td>28595230.0000</td>\n",
       "      <td>209.220</td>\n",
       "      <td>210.7400</td>\n",
       "      <td>208.3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-14</th>\n",
       "      <td>209.75</td>\n",
       "      <td>20679270.0000</td>\n",
       "      <td>210.155</td>\n",
       "      <td>210.5600</td>\n",
       "      <td>208.2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-13</th>\n",
       "      <td>208.87</td>\n",
       "      <td>25864510.0000</td>\n",
       "      <td>207.700</td>\n",
       "      <td>210.9520</td>\n",
       "      <td>207.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10</th>\n",
       "      <td>207.53</td>\n",
       "      <td>24592460.0000</td>\n",
       "      <td>207.360</td>\n",
       "      <td>209.1000</td>\n",
       "      <td>206.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-09</th>\n",
       "      <td>208.88</td>\n",
       "      <td>23389530.0000</td>\n",
       "      <td>207.280</td>\n",
       "      <td>209.7800</td>\n",
       "      <td>207.2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             close         volume     open      high       low\n",
       "date                                                          \n",
       "2018-12-07  168.49  41678680.0000  173.490  174.4900  168.3000\n",
       "2018-12-06  174.72  42704910.0000  171.760  174.7800  170.4200\n",
       "2018-12-04  176.69  41141250.0000  180.950  182.3899  176.2700\n",
       "2018-12-03  184.82  40537700.0000  184.460  184.9400  181.2100\n",
       "2018-11-30  178.58  39424260.0000  180.290  180.3300  177.0300\n",
       "2018-11-29  179.55  41523580.0000  182.660  182.8000  177.7000\n",
       "2018-11-28  180.94  45941750.0000  176.730  181.2900  174.9300\n",
       "2018-11-27  174.24  41156140.0000  171.510  174.7700  170.8800\n",
       "2018-11-26  174.62  44662320.0000  174.240  174.9500  170.2600\n",
       "2018-11-23  172.29  23623970.0000  174.940  176.5950  172.1000\n",
       "2018-11-21  176.78  31096240.0000  179.730  180.2700  176.5500\n",
       "2018-11-20  176.98  67678680.0000  178.370  181.4700  175.5100\n",
       "2018-11-19  185.86  41626820.0000  190.000  190.7000  184.9900\n",
       "2018-11-16  193.53  36191330.0000  190.500  194.9695  189.4600\n",
       "2018-11-15  191.41  46271660.0000  188.390  191.9700  186.9000\n",
       "2018-11-14  186.80  60547340.0000  193.900  194.4800  185.9300\n",
       "2018-11-13  192.23  46725710.0000  191.630  197.1800  191.4501\n",
       "2018-11-12  194.17  50991030.0000  199.000  199.8500  193.7900\n",
       "2018-11-09  204.47  34317760.0000  205.550  206.0100  202.2500\n",
       "2018-11-08  208.49  25289270.0000  209.980  210.1200  206.7500\n",
       "2018-11-07  209.95  33291640.0000  205.970  210.0600  204.1300\n",
       "2018-11-06  203.77  31774720.0000  201.920  204.7200  201.6900\n",
       "2018-11-05  201.59  66072170.0000  204.300  204.3900  198.1700\n",
       "2018-11-02  207.48  91046560.0000  209.550  213.6500  205.4300\n",
       "2018-11-01  222.22  52954070.0000  219.050  222.3600  216.8100\n",
       "2018-10-31  218.86  38016810.0000  216.880  220.4500  216.6200\n",
       "2018-10-30  213.30  36487930.0000  211.150  215.1800  209.2700\n",
       "2018-10-29  212.24  45713690.0000  219.190  219.6900  206.0900\n",
       "2018-10-26  216.30  47191700.0000  215.900  220.1900  212.6700\n",
       "2018-10-25  219.80  29027340.0000  217.710  221.3800  216.7500\n",
       "...            ...            ...      ...       ...       ...\n",
       "2018-09-20  220.03  26388050.0000  220.240  222.2800  219.1500\n",
       "2018-09-19  218.37  26869880.0000  218.500  219.6200  215.3000\n",
       "2018-09-18  218.24  31495630.0000  217.790  221.8500  217.1200\n",
       "2018-09-17  217.88  36810120.0000  222.150  222.9500  217.2700\n",
       "2018-09-14  223.84  31801970.0000  225.750  226.8400  222.5220\n",
       "2018-09-13  226.41  41411350.0000  223.520  228.3500  222.5700\n",
       "2018-09-12  221.07  49118510.0000  224.940  225.0000  219.8400\n",
       "2018-09-11  223.85  35644510.0000  218.010  224.2990  216.5600\n",
       "2018-09-10  218.33  39194720.0000  220.950  221.8500  216.4700\n",
       "2018-09-07  221.30  37418910.0000  221.850  225.3700  220.7100\n",
       "2018-09-06  223.10  34121240.0000  226.230  227.3500  221.3000\n",
       "2018-09-05  226.87  33290830.0000  228.990  229.6700  225.1000\n",
       "2018-09-04  228.36  27363960.0000  228.410  229.1800  226.6300\n",
       "2018-08-31  227.63  43177540.0000  226.510  228.8700  226.0000\n",
       "2018-08-30  225.03  48696960.0000  223.250  228.2600  222.4000\n",
       "2018-08-29  222.98  27074260.0000  220.150  223.4900  219.4100\n",
       "2018-08-28  219.70  22751390.0000  219.010  220.5400  218.9200\n",
       "2018-08-27  217.94  20400770.0000  217.150  218.7400  216.3300\n",
       "2018-08-24  216.16  18457720.0000  216.600  216.9000  215.1100\n",
       "2018-08-23  215.49  18505780.0000  214.650  217.0500  214.6000\n",
       "2018-08-22  215.05  18965930.0000  214.100  216.3600  213.8400\n",
       "2018-08-21  215.04  25682700.0000  216.800  217.1900  214.0250\n",
       "2018-08-20  215.46  30149410.0000  218.100  219.1800  215.1100\n",
       "2018-08-17  217.58  35034410.0000  213.440  217.9500  213.1600\n",
       "2018-08-16  213.32  27595310.0000  211.750  213.8121  211.4700\n",
       "2018-08-15  210.24  28595230.0000  209.220  210.7400  208.3300\n",
       "2018-08-14  209.75  20679270.0000  210.155  210.5600  208.2600\n",
       "2018-08-13  208.87  25864510.0000  207.700  210.9520  207.7000\n",
       "2018-08-10  207.53  24592460.0000  207.360  209.1000  206.6700\n",
       "2018-08-09  208.88  23389530.0000  207.280  209.7800  207.2000\n",
       "\n",
       "[84 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"2018-12-09\" :\"2018-08-09\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "191.20864628820956"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df[\"2019-08-01\" :\"2018-09-01\"].close.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng=pd.date_range(start=\"2018-08-09\", periods=100,freq=\"B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-08-09', '2018-08-10', '2018-08-13', '2018-08-14',\n",
       "               '2018-08-15', '2018-08-16', '2018-08-17', '2018-08-20',\n",
       "               '2018-08-21', '2018-08-22', '2018-08-23', '2018-08-24',\n",
       "               '2018-08-27', '2018-08-28', '2018-08-29', '2018-08-30',\n",
       "               '2018-08-31', '2018-09-03', '2018-09-04', '2018-09-05',\n",
       "               '2018-09-06', '2018-09-07', '2018-09-10', '2018-09-11',\n",
       "               '2018-09-12', '2018-09-13', '2018-09-14', '2018-09-17',\n",
       "               '2018-09-18', '2018-09-19', '2018-09-20', '2018-09-21',\n",
       "               '2018-09-24', '2018-09-25', '2018-09-26', '2018-09-27',\n",
       "               '2018-09-28', '2018-10-01', '2018-10-02', '2018-10-03',\n",
       "               '2018-10-04', '2018-10-05', '2018-10-08', '2018-10-09',\n",
       "               '2018-10-10', '2018-10-11', '2018-10-12', '2018-10-15',\n",
       "               '2018-10-16', '2018-10-17', '2018-10-18', '2018-10-19',\n",
       "               '2018-10-22', '2018-10-23', '2018-10-24', '2018-10-25',\n",
       "               '2018-10-26', '2018-10-29', '2018-10-30', '2018-10-31',\n",
       "               '2018-11-01', '2018-11-02', '2018-11-05', '2018-11-06',\n",
       "               '2018-11-07', '2018-11-08', '2018-11-09', '2018-11-12',\n",
       "               '2018-11-13', '2018-11-14', '2018-11-15', '2018-11-16',\n",
       "               '2018-11-19', '2018-11-20', '2018-11-21', '2018-11-22',\n",
       "               '2018-11-23', '2018-11-26', '2018-11-27', '2018-11-28',\n",
       "               '2018-11-29', '2018-11-30', '2018-12-03', '2018-12-04',\n",
       "               '2018-12-05', '2018-12-06', '2018-12-07', '2018-12-10',\n",
       "               '2018-12-11', '2018-12-12', '2018-12-13', '2018-12-14',\n",
       "               '2018-12-17', '2018-12-18', '2018-12-19', '2018-12-20',\n",
       "               '2018-12-21', '2018-12-24', '2018-12-25', '2018-12-26'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-08-09 00:00:00', '2018-08-09 01:00:00',\n",
       "               '2018-08-09 02:00:00', '2018-08-09 03:00:00',\n",
       "               '2018-08-09 04:00:00', '2018-08-09 05:00:00',\n",
       "               '2018-08-09 06:00:00', '2018-08-09 07:00:00',\n",
       "               '2018-08-09 08:00:00', '2018-08-09 09:00:00',\n",
       "               ...\n",
       "               '2019-08-08 15:00:00', '2019-08-08 16:00:00',\n",
       "               '2019-08-08 17:00:00', '2019-08-08 18:00:00',\n",
       "               '2019-08-08 19:00:00', '2019-08-08 20:00:00',\n",
       "               '2019-08-08 21:00:00', '2019-08-08 22:00:00',\n",
       "               '2019-08-08 23:00:00', '2019-08-09 00:00:00'],\n",
       "              dtype='datetime64[ns]', length=8761, freq='H')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng=pd.date_range(start=\"2018-08-09\", end=\"2019/08/09\",freq=\"H\")\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9 3 7 ... 1 1 3]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.random.randint(1,10,len(rng))\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts=pd.Series(np.random.randint(1,10,len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-08-09 00:00:00    2\n",
       "2018-08-09 01:00:00    1\n",
       "2018-08-09 02:00:00    9\n",
       "2018-08-09 03:00:00    8\n",
       "2018-08-09 04:00:00    8\n",
       "2018-08-09 05:00:00    5\n",
       "2018-08-09 06:00:00    5\n",
       "2018-08-09 07:00:00    4\n",
       "2018-08-09 08:00:00    8\n",
       "2018-08-09 09:00:00    1\n",
       "2018-08-09 10:00:00    7\n",
       "2018-08-09 11:00:00    3\n",
       "2018-08-09 12:00:00    1\n",
       "2018-08-09 13:00:00    6\n",
       "2018-08-09 14:00:00    8\n",
       "2018-08-09 15:00:00    5\n",
       "2018-08-09 16:00:00    6\n",
       "2018-08-09 17:00:00    3\n",
       "2018-08-09 18:00:00    6\n",
       "2018-08-09 19:00:00    9\n",
       "2018-08-09 20:00:00    7\n",
       "2018-08-09 21:00:00    6\n",
       "2018-08-09 22:00:00    3\n",
       "2018-08-09 23:00:00    3\n",
       "2018-08-10 00:00:00    8\n",
       "2018-08-10 01:00:00    8\n",
       "2018-08-10 02:00:00    7\n",
       "2018-08-10 03:00:00    1\n",
       "2018-08-10 04:00:00    7\n",
       "2018-08-10 05:00:00    9\n",
       "                      ..\n",
       "2019-08-07 19:00:00    7\n",
       "2019-08-07 20:00:00    8\n",
       "2019-08-07 21:00:00    2\n",
       "2019-08-07 22:00:00    3\n",
       "2019-08-07 23:00:00    7\n",
       "2019-08-08 00:00:00    2\n",
       "2019-08-08 01:00:00    8\n",
       "2019-08-08 02:00:00    6\n",
       "2019-08-08 03:00:00    3\n",
       "2019-08-08 04:00:00    7\n",
       "2019-08-08 05:00:00    3\n",
       "2019-08-08 06:00:00    7\n",
       "2019-08-08 07:00:00    3\n",
       "2019-08-08 08:00:00    4\n",
       "2019-08-08 09:00:00    1\n",
       "2019-08-08 10:00:00    5\n",
       "2019-08-08 11:00:00    2\n",
       "2019-08-08 12:00:00    2\n",
       "2019-08-08 13:00:00    5\n",
       "2019-08-08 14:00:00    5\n",
       "2019-08-08 15:00:00    5\n",
       "2019-08-08 16:00:00    4\n",
       "2019-08-08 17:00:00    7\n",
       "2019-08-08 18:00:00    8\n",
       "2019-08-08 19:00:00    9\n",
       "2019-08-08 20:00:00    9\n",
       "2019-08-08 21:00:00    6\n",
       "2019-08-08 22:00:00    4\n",
       "2019-08-08 23:00:00    1\n",
       "2019-08-09 00:00:00    4\n",
       "Freq: H, Length: 8761, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  }
 ],
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