26-Important Methods for Time Series in Pandas.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Important Methods for Time Series in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to talk about important methods for time series in Pandas.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd ; import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## resampling method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb=pd.read_csv(\"FB.csv\", \n",
    "               parse_dates=[\"Date\"], \n",
    "               index_col=\"Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Date</th>\n",
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       "      <th></th>\n",
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       "      <th>2018-07-30</th>\n",
       "      <td>175.300003</td>\n",
       "      <td>175.300003</td>\n",
       "      <td>166.559998</td>\n",
       "      <td>171.059998</td>\n",
       "      <td>171.059998</td>\n",
       "      <td>65280800</td>\n",
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       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>170.669998</td>\n",
       "      <td>174.240005</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>172.580002</td>\n",
       "      <td>172.580002</td>\n",
       "      <td>40356500</td>\n",
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       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>173.929993</td>\n",
       "      <td>175.080002</td>\n",
       "      <td>170.899994</td>\n",
       "      <td>171.649994</td>\n",
       "      <td>171.649994</td>\n",
       "      <td>34042100</td>\n",
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       "    <tr>\n",
       "      <th>2018-08-02</th>\n",
       "      <td>170.679993</td>\n",
       "      <td>176.789993</td>\n",
       "      <td>170.270004</td>\n",
       "      <td>176.369995</td>\n",
       "      <td>176.369995</td>\n",
       "      <td>32400000</td>\n",
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       "    <tr>\n",
       "      <th>2018-08-03</th>\n",
       "      <td>177.690002</td>\n",
       "      <td>178.850006</td>\n",
       "      <td>176.149994</td>\n",
       "      <td>177.779999</td>\n",
       "      <td>177.779999</td>\n",
       "      <td>24763400</td>\n",
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       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2018-07-30  175.300003  175.300003  166.559998  171.059998  171.059998   \n",
       "2018-07-31  170.669998  174.240005  170.000000  172.580002  172.580002   \n",
       "2018-08-01  173.929993  175.080002  170.899994  171.649994  171.649994   \n",
       "2018-08-02  170.679993  176.789993  170.270004  176.369995  176.369995   \n",
       "2018-08-03  177.690002  178.850006  176.149994  177.779999  177.779999   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2018-07-30  65280800  \n",
       "2018-07-31  40356500  \n",
       "2018-08-01  34042100  \n",
       "2018-08-02  32400000  \n",
       "2018-08-03  24763400  "
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Close</th>\n",
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       "      <th>2018-07-31</th>\n",
       "      <td>172.985001</td>\n",
       "      <td>174.770004</td>\n",
       "      <td>168.279999</td>\n",
       "      <td>171.820000</td>\n",
       "      <td>171.820000</td>\n",
       "      <td>5.281865e+07</td>\n",
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       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>177.598695</td>\n",
       "      <td>179.433914</td>\n",
       "      <td>175.680868</td>\n",
       "      <td>177.492172</td>\n",
       "      <td>177.492172</td>\n",
       "      <td>2.386229e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>164.233158</td>\n",
       "      <td>166.399473</td>\n",
       "      <td>162.416843</td>\n",
       "      <td>164.377368</td>\n",
       "      <td>164.377368</td>\n",
       "      <td>2.634046e+07</td>\n",
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       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>154.873479</td>\n",
       "      <td>157.124784</td>\n",
       "      <td>152.103045</td>\n",
       "      <td>154.187826</td>\n",
       "      <td>154.187826</td>\n",
       "      <td>2.706288e+07</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>141.762857</td>\n",
       "      <td>143.658096</td>\n",
       "      <td>139.593336</td>\n",
       "      <td>141.635715</td>\n",
       "      <td>141.635715</td>\n",
       "      <td>2.467389e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>137.529475</td>\n",
       "      <td>140.493684</td>\n",
       "      <td>134.814209</td>\n",
       "      <td>137.161052</td>\n",
       "      <td>137.161052</td>\n",
       "      <td>2.940980e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>144.551904</td>\n",
       "      <td>147.329999</td>\n",
       "      <td>142.938095</td>\n",
       "      <td>145.422857</td>\n",
       "      <td>145.422857</td>\n",
       "      <td>2.512133e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>164.754735</td>\n",
       "      <td>166.534211</td>\n",
       "      <td>163.293158</td>\n",
       "      <td>164.813684</td>\n",
       "      <td>164.813684</td>\n",
       "      <td>1.590754e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>166.840477</td>\n",
       "      <td>168.799524</td>\n",
       "      <td>165.379525</td>\n",
       "      <td>167.411428</td>\n",
       "      <td>167.411428</td>\n",
       "      <td>1.847315e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>180.315239</td>\n",
       "      <td>182.084285</td>\n",
       "      <td>178.955712</td>\n",
       "      <td>180.544285</td>\n",
       "      <td>180.544285</td>\n",
       "      <td>1.818978e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>185.974545</td>\n",
       "      <td>187.902727</td>\n",
       "      <td>184.217273</td>\n",
       "      <td>186.082273</td>\n",
       "      <td>186.082273</td>\n",
       "      <td>1.303734e+07</td>\n",
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       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>181.664500</td>\n",
       "      <td>183.892500</td>\n",
       "      <td>178.924001</td>\n",
       "      <td>181.274500</td>\n",
       "      <td>181.274500</td>\n",
       "      <td>2.132143e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31</th>\n",
       "      <td>199.628499</td>\n",
       "      <td>201.783017</td>\n",
       "      <td>197.694002</td>\n",
       "      <td>200.097500</td>\n",
       "      <td>200.097500</td>\n",
       "      <td>1.543907e+07</td>\n",
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       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2018-07-31  172.985001  174.770004  168.279999  171.820000  171.820000   \n",
       "2018-08-31  177.598695  179.433914  175.680868  177.492172  177.492172   \n",
       "2018-09-30  164.233158  166.399473  162.416843  164.377368  164.377368   \n",
       "2018-10-31  154.873479  157.124784  152.103045  154.187826  154.187826   \n",
       "2018-11-30  141.762857  143.658096  139.593336  141.635715  141.635715   \n",
       "2018-12-31  137.529475  140.493684  134.814209  137.161052  137.161052   \n",
       "2019-01-31  144.551904  147.329999  142.938095  145.422857  145.422857   \n",
       "2019-02-28  164.754735  166.534211  163.293158  164.813684  164.813684   \n",
       "2019-03-31  166.840477  168.799524  165.379525  167.411428  167.411428   \n",
       "2019-04-30  180.315239  182.084285  178.955712  180.544285  180.544285   \n",
       "2019-05-31  185.974545  187.902727  184.217273  186.082273  186.082273   \n",
       "2019-06-30  181.664500  183.892500  178.924001  181.274500  181.274500   \n",
       "2019-07-31  199.628499  201.783017  197.694002  200.097500  200.097500   \n",
       "\n",
       "                  Volume  \n",
       "Date                      \n",
       "2018-07-31  5.281865e+07  \n",
       "2018-08-31  2.386229e+07  \n",
       "2018-09-30  2.634046e+07  \n",
       "2018-10-31  2.706288e+07  \n",
       "2018-11-30  2.467389e+07  \n",
       "2018-12-31  2.940980e+07  \n",
       "2019-01-31  2.512133e+07  \n",
       "2019-02-28  1.590754e+07  \n",
       "2019-03-31  1.847315e+07  \n",
       "2019-04-30  1.818978e+07  \n",
       "2019-05-31  1.303734e+07  \n",
       "2019-06-30  2.132143e+07  \n",
       "2019-07-31  1.543907e+07  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.resample(\"M\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fb.Close.resample(\"M\").mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fb.Close.resample(\"Q\").mean().plot(\n",
    "    kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shifting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb1=pd.DataFrame(fb.Close[\"2019-03\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>162.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>167.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>171.259995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>172.509995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>169.130005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close\n",
       "Date                  \n",
       "2019-03-01  162.279999\n",
       "2019-03-04  167.369995\n",
       "2019-03-05  171.259995\n",
       "2019-03-06  172.509995\n",
       "2019-03-07  169.130005"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>162.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>167.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>171.259995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-08</th>\n",
       "      <td>172.509995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-11</th>\n",
       "      <td>169.130005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-12</th>\n",
       "      <td>169.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-13</th>\n",
       "      <td>172.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-14</th>\n",
       "      <td>171.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-15</th>\n",
       "      <td>173.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-18</th>\n",
       "      <td>170.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-19</th>\n",
       "      <td>165.979996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-20</th>\n",
       "      <td>160.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-21</th>\n",
       "      <td>161.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-22</th>\n",
       "      <td>165.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>166.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>164.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>166.289993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>167.679993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>165.869995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close\n",
       "Date                  \n",
       "2019-03-01         NaN\n",
       "2019-03-04         NaN\n",
       "2019-03-05  162.279999\n",
       "2019-03-06  167.369995\n",
       "2019-03-07  171.259995\n",
       "2019-03-08  172.509995\n",
       "2019-03-11  169.130005\n",
       "2019-03-12  169.600006\n",
       "2019-03-13  172.070007\n",
       "2019-03-14  171.919998\n",
       "2019-03-15  173.369995\n",
       "2019-03-18  170.169998\n",
       "2019-03-19  165.979996\n",
       "2019-03-20  160.470001\n",
       "2019-03-21  161.570007\n",
       "2019-03-22  165.440002\n",
       "2019-03-25  166.080002\n",
       "2019-03-26  164.339996\n",
       "2019-03-27  166.289993\n",
       "2019-03-28  167.679993\n",
       "2019-03-29  165.869995"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1.shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>171.259995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>172.509995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>169.130005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>169.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>172.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-08</th>\n",
       "      <td>171.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-11</th>\n",
       "      <td>173.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-12</th>\n",
       "      <td>170.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-13</th>\n",
       "      <td>165.979996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-14</th>\n",
       "      <td>160.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-15</th>\n",
       "      <td>161.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-18</th>\n",
       "      <td>165.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-19</th>\n",
       "      <td>166.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-20</th>\n",
       "      <td>164.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-21</th>\n",
       "      <td>166.289993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-22</th>\n",
       "      <td>167.679993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>165.869995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>165.550003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>166.690002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close\n",
       "Date                  \n",
       "2019-03-01  171.259995\n",
       "2019-03-04  172.509995\n",
       "2019-03-05  169.130005\n",
       "2019-03-06  169.600006\n",
       "2019-03-07  172.070007\n",
       "2019-03-08  171.919998\n",
       "2019-03-11  173.369995\n",
       "2019-03-12  170.169998\n",
       "2019-03-13  165.979996\n",
       "2019-03-14  160.470001\n",
       "2019-03-15  161.570007\n",
       "2019-03-18  165.440002\n",
       "2019-03-19  166.080002\n",
       "2019-03-20  164.339996\n",
       "2019-03-21  166.289993\n",
       "2019-03-22  167.679993\n",
       "2019-03-25  165.869995\n",
       "2019-03-26  165.550003\n",
       "2019-03-27  166.690002\n",
       "2019-03-28         NaN\n",
       "2019-03-29         NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1.shift(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb1[\"Previous Price\"]=fb1.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>162.279999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>167.369995</td>\n",
       "      <td>162.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>171.259995</td>\n",
       "      <td>167.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>172.509995</td>\n",
       "      <td>171.259995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>169.130005</td>\n",
       "      <td>172.509995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-08</th>\n",
       "      <td>169.600006</td>\n",
       "      <td>169.130005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-11</th>\n",
       "      <td>172.070007</td>\n",
       "      <td>169.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-12</th>\n",
       "      <td>171.919998</td>\n",
       "      <td>172.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-13</th>\n",
       "      <td>173.369995</td>\n",
       "      <td>171.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-14</th>\n",
       "      <td>170.169998</td>\n",
       "      <td>173.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-15</th>\n",
       "      <td>165.979996</td>\n",
       "      <td>170.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-18</th>\n",
       "      <td>160.470001</td>\n",
       "      <td>165.979996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-19</th>\n",
       "      <td>161.570007</td>\n",
       "      <td>160.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-20</th>\n",
       "      <td>165.440002</td>\n",
       "      <td>161.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-21</th>\n",
       "      <td>166.080002</td>\n",
       "      <td>165.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-22</th>\n",
       "      <td>164.339996</td>\n",
       "      <td>166.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>166.289993</td>\n",
       "      <td>164.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>167.679993</td>\n",
       "      <td>166.289993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>165.869995</td>\n",
       "      <td>167.679993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>165.550003</td>\n",
       "      <td>165.869995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>166.690002</td>\n",
       "      <td>165.550003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close  Previous Price\n",
       "Date                                  \n",
       "2019-03-01  162.279999             NaN\n",
       "2019-03-04  167.369995      162.279999\n",
       "2019-03-05  171.259995      167.369995\n",
       "2019-03-06  172.509995      171.259995\n",
       "2019-03-07  169.130005      172.509995\n",
       "2019-03-08  169.600006      169.130005\n",
       "2019-03-11  172.070007      169.600006\n",
       "2019-03-12  171.919998      172.070007\n",
       "2019-03-13  173.369995      171.919998\n",
       "2019-03-14  170.169998      173.369995\n",
       "2019-03-15  165.979996      170.169998\n",
       "2019-03-18  160.470001      165.979996\n",
       "2019-03-19  161.570007      160.470001\n",
       "2019-03-20  165.440002      161.570007\n",
       "2019-03-21  166.080002      165.440002\n",
       "2019-03-22  164.339996      166.080002\n",
       "2019-03-25  166.289993      164.339996\n",
       "2019-03-26  167.679993      166.289993\n",
       "2019-03-27  165.869995      167.679993\n",
       "2019-03-28  165.550003      165.869995\n",
       "2019-03-29  166.690002      165.550003"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb1[\"One Day Difference\"]=fb1[\n",
    "    \"Close\"]-fb1[\"Previous Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>162.279999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>167.369995</td>\n",
       "      <td>162.279999</td>\n",
       "      <td>5.089996</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
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       "      <td>167.369995</td>\n",
       "      <td>3.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>172.509995</td>\n",
       "      <td>171.259995</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>169.130005</td>\n",
       "      <td>172.509995</td>\n",
       "      <td>-3.379990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                 Close  Previous Price  One Day Difference\n",
       "Date                                                      \n",
       "2019-03-01  162.279999             NaN                 NaN\n",
       "2019-03-04  167.369995      162.279999            5.089996\n",
       "2019-03-05  171.259995      167.369995            3.890000\n",
       "2019-03-06  172.509995      171.259995            1.250000\n",
       "2019-03-07  169.130005      172.509995           -3.379990"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb1[\"Percentage Change\"]=(\n",
    "    fb1[\"Close\"]-fb1[\n",
    "        \"Previous Price\"])*100/fb1[\n",
    "    \"Previous Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb2=fb1[[\"Close\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>162.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>167.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>171.259995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>172.509995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>169.130005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close\n",
       "Date                  \n",
       "2019-03-01  162.279999\n",
       "2019-03-04  167.369995\n",
       "2019-03-05  171.259995\n",
       "2019-03-06  172.509995\n",
       "2019-03-07  169.130005"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-03-01', '2019-03-04', '2019-03-05', '2019-03-06',\n",
       "               '2019-03-07', '2019-03-08', '2019-03-11', '2019-03-12',\n",
       "               '2019-03-13', '2019-03-14', '2019-03-15', '2019-03-18',\n",
       "               '2019-03-19', '2019-03-20', '2019-03-21', '2019-03-22',\n",
       "               '2019-03-25', '2019-03-26', '2019-03-27', '2019-03-28',\n",
       "               '2019-03-29'],\n",
       "              dtype='datetime64[ns]', name='Date', freq=None)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb2.index=pd.date_range(\n",
    "    \"2019-03-01\", periods=21, freq=\"B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-03-01', '2019-03-04', '2019-03-05', '2019-03-06',\n",
       "               '2019-03-07', '2019-03-08', '2019-03-11', '2019-03-12',\n",
       "               '2019-03-13', '2019-03-14', '2019-03-15', '2019-03-18',\n",
       "               '2019-03-19', '2019-03-20', '2019-03-21', '2019-03-22',\n",
       "               '2019-03-25', '2019-03-26', '2019-03-27', '2019-03-28',\n",
       "               '2019-03-29'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-93d300b30aef>:1: FutureWarning: tshift is deprecated and will be removed in a future version. Please use shift instead.\n",
      "  fb2.tshift(1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>162.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>167.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>171.259995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>172.509995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-08</th>\n",
       "      <td>169.130005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-11</th>\n",
       "      <td>169.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-12</th>\n",
       "      <td>172.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-13</th>\n",
       "      <td>171.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-14</th>\n",
       "      <td>173.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-15</th>\n",
       "      <td>170.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-18</th>\n",
       "      <td>165.979996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-19</th>\n",
       "      <td>160.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-20</th>\n",
       "      <td>161.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-21</th>\n",
       "      <td>165.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-22</th>\n",
       "      <td>166.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>164.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>166.289993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>167.679993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>165.869995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>165.550003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>166.690002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close\n",
       "2019-03-04  162.279999\n",
       "2019-03-05  167.369995\n",
       "2019-03-06  171.259995\n",
       "2019-03-07  172.509995\n",
       "2019-03-08  169.130005\n",
       "2019-03-11  169.600006\n",
       "2019-03-12  172.070007\n",
       "2019-03-13  171.919998\n",
       "2019-03-14  173.369995\n",
       "2019-03-15  170.169998\n",
       "2019-03-18  165.979996\n",
       "2019-03-19  160.470001\n",
       "2019-03-20  161.570007\n",
       "2019-03-21  165.440002\n",
       "2019-03-22  166.080002\n",
       "2019-03-25  164.339996\n",
       "2019-03-26  166.289993\n",
       "2019-03-27  167.679993\n",
       "2019-03-28  165.869995\n",
       "2019-03-29  165.550003\n",
       "2019-04-01  166.690002"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb2.tshift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-23-2455d462e22f>:1: FutureWarning: tshift is deprecated and will be removed in a future version. Please use shift instead.\n",
      "  fb2.tshift(-2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-27</th>\n",
       "      <td>162.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>167.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>171.259995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>172.509995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>169.130005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>169.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>172.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-08</th>\n",
       "      <td>171.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-11</th>\n",
       "      <td>173.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-12</th>\n",
       "      <td>170.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-13</th>\n",
       "      <td>165.979996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-14</th>\n",
       "      <td>160.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-15</th>\n",
       "      <td>161.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-18</th>\n",
       "      <td>165.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-19</th>\n",
       "      <td>166.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-20</th>\n",
       "      <td>164.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-21</th>\n",
       "      <td>166.289993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-22</th>\n",
       "      <td>167.679993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>165.869995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>165.550003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>166.690002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close\n",
       "2019-02-27  162.279999\n",
       "2019-02-28  167.369995\n",
       "2019-03-01  171.259995\n",
       "2019-03-04  172.509995\n",
       "2019-03-05  169.130005\n",
       "2019-03-06  169.600006\n",
       "2019-03-07  172.070007\n",
       "2019-03-08  171.919998\n",
       "2019-03-11  173.369995\n",
       "2019-03-12  170.169998\n",
       "2019-03-13  165.979996\n",
       "2019-03-14  160.470001\n",
       "2019-03-15  161.570007\n",
       "2019-03-18  165.440002\n",
       "2019-03-19  166.080002\n",
       "2019-03-20  164.339996\n",
       "2019-03-21  166.289993\n",
       "2019-03-22  167.679993\n",
       "2019-03-25  165.869995\n",
       "2019-03-26  165.550003\n",
       "2019-03-27  166.690002"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb2.tshift(-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving Window Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fb.Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fb.Close.plot()\n",
    "fb.Close.rolling(30).mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Zone Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DstTzInfo 'Turkey' LMT+1:56:00 STD>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pytz.timezone(\"Turkey\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DstTzInfo 'America/New_York' LMT-1 day, 19:04:00 STD>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pytz.timezone(\"America/New_York\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['US/Arizona',\n",
       " 'US/Central',\n",
       " 'US/Eastern',\n",
       " 'US/Hawaii',\n",
       " 'US/Mountain',\n",
       " 'US/Pacific',\n",
       " 'UTC']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pytz.common_timezones[-7:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.date_range(\"12/9/2009 9:30\", \n",
    "                periods=6, freq=\"D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts=pd.Series(np.random.randn(len(x)), \n",
    "             index=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2009-12-09 09:30:00    1.321881\n",
       "2009-12-10 09:30:00    0.528298\n",
       "2009-12-11 09:30:00   -0.320733\n",
       "2009-12-12 09:30:00    1.692672\n",
       "2009-12-13 09:30:00   -0.194015\n",
       "2009-12-14 09:30:00    0.585790\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(ts.index.tz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2009-12-09 09:30:00+00:00    1.321881\n",
       "2009-12-10 09:30:00+00:00    0.528298\n",
       "2009-12-11 09:30:00+00:00   -0.320733\n",
       "2009-12-12 09:30:00+00:00    1.692672\n",
       "2009-12-13 09:30:00+00:00   -0.194015\n",
       "2009-12-14 09:30:00+00:00    0.585790\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc=ts.tz_localize(\"UTC\")\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2009-12-08 23:30:00-10:00    1.321881\n",
       "2009-12-09 23:30:00-10:00    0.528298\n",
       "2009-12-10 23:30:00-10:00   -0.320733\n",
       "2009-12-11 23:30:00-10:00    1.692672\n",
       "2009-12-12 23:30:00-10:00   -0.194015\n",
       "2009-12-13 23:30:00-10:00    0.585790\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert(\"US/Hawaii\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-06-26 05:00:00')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zstamp=pd.Timestamp(\"2019-06-26 05:00\")\n",
    "zstamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-06-26 05:00:00+0000', tz='UTC')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zstamp_utc=zstamp.tz_localize(\"utc\")\n",
    "zstamp_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-06-26 08:00:00+0300', tz='Europe/Istanbul')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zstamp_utc.tz_convert(\"Europe/Istanbul\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2009-12-09 09:30:00    1.321881\n",
       "2009-12-10 09:30:00    0.528298\n",
       "2009-12-11 09:30:00   -0.320733\n",
       "2009-12-12 09:30:00    1.692672\n",
       "2009-12-13 09:30:00   -0.194015\n",
       "2009-12-14 09:30:00    0.585790\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts1=ts[:5].tz_localize(\"Europe/Berlin\")\n",
    "ts2=ts[2:].tz_localize(\"Europe/Istanbul\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=ts1+ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2009-12-09 08:30:00+00:00', '2009-12-10 08:30:00+00:00',\n",
       "               '2009-12-11 07:30:00+00:00', '2009-12-11 08:30:00+00:00',\n",
       "               '2009-12-12 07:30:00+00:00', '2009-12-12 08:30:00+00:00',\n",
       "               '2009-12-13 07:30:00+00:00', '2009-12-13 08:30:00+00:00',\n",
       "               '2009-12-14 07:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq=None)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
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