24-Working with Methods in Pandas - Part 1 .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Methods in Pandas - Part 1 "
   ]
  },
  {
   "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 working with indexes in Time Series \n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd ; import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "date=pd.date_range(\n",
    "    start=\"2018\",end=\"2019\", freq=\"BM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-31   -1.977003\n",
       "2018-02-28   -0.339459\n",
       "2018-03-30   -0.587687\n",
       "2018-04-30    1.141997\n",
       "2018-05-31   -0.125199\n",
       "2018-06-29   -1.090406\n",
       "2018-07-31   -0.435640\n",
       "2018-08-31    0.181651\n",
       "2018-09-28   -2.518869\n",
       "2018-10-31    1.428868\n",
       "2018-11-30   -0.357551\n",
       "2018-12-31    0.612771\n",
       "Freq: BM, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts=pd.Series(\n",
    "    np.random.randn(len(date)),index=date)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-30', '2018-04-30',\n",
       "               '2018-05-31', '2018-06-29', '2018-07-31', '2018-08-31',\n",
       "               '2018-09-28', '2018-10-31', '2018-11-30', '2018-12-31'],\n",
       "              dtype='datetime64[ns]', freq='BM')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-30', '2018-04-30',\n",
       "               '2018-05-31'],\n",
       "              dtype='datetime64[ns]', freq='BM')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[:5].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading a Time Series Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb=pd.read_csv(\"FB.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-07-30</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-31</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-08-01</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-02</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-03</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2018-07-30  175.300003  175.300003  166.559998  171.059998  171.059998   \n",
       "1  2018-07-31  170.669998  174.240005  170.000000  172.580002  172.580002   \n",
       "2  2018-08-01  173.929993  175.080002  170.899994  171.649994  171.649994   \n",
       "3  2018-08-02  170.679993  176.789993  170.270004  176.369995  176.369995   \n",
       "4  2018-08-03  177.690002  178.850006  176.149994  177.779999  177.779999   \n",
       "\n",
       "     Volume  \n",
       "0  65280800  \n",
       "1  40356500  \n",
       "2  34042100  \n",
       "3  32400000  \n",
       "4  24763400  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting date to index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date          object\n",
       "Open         float64\n",
       "High         float64\n",
       "Low          float64\n",
       "Close        float64\n",
       "Adj Close    float64\n",
       "Volume         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb=pd.read_csv(\n",
    "    \"FB.csv\", parse_dates=[\"Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb=pd.read_csv(\n",
    "    \"FB.csv\", \n",
    "    parse_dates=[\"Date\"], \n",
    "    index_col=\"Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-07-30', '2018-07-31', '2018-08-01', '2018-08-02',\n",
       "               '2018-08-03', '2018-08-06', '2018-08-07', '2018-08-08',\n",
       "               '2018-08-09', '2018-08-10',\n",
       "               ...\n",
       "               '2019-07-16', '2019-07-17', '2019-07-18', '2019-07-19',\n",
       "               '2019-07-22', '2019-07-23', '2019-07-24', '2019-07-25',\n",
       "               '2019-07-26', '2019-07-29'],\n",
       "              dtype='datetime64[ns]', name='Date', length=251, freq=None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  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  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-06-03</th>\n",
       "      <td>175.000000</td>\n",
       "      <td>175.050003</td>\n",
       "      <td>161.009995</td>\n",
       "      <td>164.149994</td>\n",
       "      <td>164.149994</td>\n",
       "      <td>56059600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-04</th>\n",
       "      <td>163.710007</td>\n",
       "      <td>168.279999</td>\n",
       "      <td>160.839996</td>\n",
       "      <td>167.500000</td>\n",
       "      <td>167.500000</td>\n",
       "      <td>46044300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-05</th>\n",
       "      <td>167.479996</td>\n",
       "      <td>168.720001</td>\n",
       "      <td>164.630005</td>\n",
       "      <td>168.169998</td>\n",
       "      <td>168.169998</td>\n",
       "      <td>19758300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-06</th>\n",
       "      <td>168.300003</td>\n",
       "      <td>169.699997</td>\n",
       "      <td>167.229996</td>\n",
       "      <td>168.330002</td>\n",
       "      <td>168.330002</td>\n",
       "      <td>12446400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07</th>\n",
       "      <td>170.169998</td>\n",
       "      <td>173.869995</td>\n",
       "      <td>168.839996</td>\n",
       "      <td>173.350006</td>\n",
       "      <td>173.350006</td>\n",
       "      <td>16917300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10</th>\n",
       "      <td>174.750000</td>\n",
       "      <td>177.860001</td>\n",
       "      <td>173.800003</td>\n",
       "      <td>174.820007</td>\n",
       "      <td>174.820007</td>\n",
       "      <td>14767900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-11</th>\n",
       "      <td>178.479996</td>\n",
       "      <td>179.979996</td>\n",
       "      <td>176.789993</td>\n",
       "      <td>178.100006</td>\n",
       "      <td>178.100006</td>\n",
       "      <td>15266600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-12</th>\n",
       "      <td>178.380005</td>\n",
       "      <td>179.270004</td>\n",
       "      <td>172.880005</td>\n",
       "      <td>175.039993</td>\n",
       "      <td>175.039993</td>\n",
       "      <td>17699800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-13</th>\n",
       "      <td>175.529999</td>\n",
       "      <td>178.029999</td>\n",
       "      <td>174.610001</td>\n",
       "      <td>177.470001</td>\n",
       "      <td>177.470001</td>\n",
       "      <td>12253600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-14</th>\n",
       "      <td>180.509995</td>\n",
       "      <td>181.839996</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>181.330002</td>\n",
       "      <td>181.330002</td>\n",
       "      <td>16773700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2019-06-03  175.000000  175.050003  161.009995  164.149994  164.149994   \n",
       "2019-06-04  163.710007  168.279999  160.839996  167.500000  167.500000   \n",
       "2019-06-05  167.479996  168.720001  164.630005  168.169998  168.169998   \n",
       "2019-06-06  168.300003  169.699997  167.229996  168.330002  168.330002   \n",
       "2019-06-07  170.169998  173.869995  168.839996  173.350006  173.350006   \n",
       "2019-06-10  174.750000  177.860001  173.800003  174.820007  174.820007   \n",
       "2019-06-11  178.479996  179.979996  176.789993  178.100006  178.100006   \n",
       "2019-06-12  178.380005  179.270004  172.880005  175.039993  175.039993   \n",
       "2019-06-13  175.529999  178.029999  174.610001  177.470001  177.470001   \n",
       "2019-06-14  180.509995  181.839996  180.000000  181.330002  181.330002   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2019-06-03  56059600  \n",
       "2019-06-04  46044300  \n",
       "2019-06-05  19758300  \n",
       "2019-06-06  12446400  \n",
       "2019-06-07  16917300  \n",
       "2019-06-10  14767900  \n",
       "2019-06-11  15266600  \n",
       "2019-06-12  17699800  \n",
       "2019-06-13  12253600  \n",
       "2019-06-14  16773700  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb[\"2019-06\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181.27450025000002"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb[\"2019-06\"].Close.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-07-05</th>\n",
       "      <td>196.179993</td>\n",
       "      <td>197.070007</td>\n",
       "      <td>194.169998</td>\n",
       "      <td>196.399994</td>\n",
       "      <td>196.399994</td>\n",
       "      <td>11164100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-08</th>\n",
       "      <td>195.190002</td>\n",
       "      <td>196.679993</td>\n",
       "      <td>193.639999</td>\n",
       "      <td>195.759995</td>\n",
       "      <td>195.759995</td>\n",
       "      <td>9723900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-09</th>\n",
       "      <td>194.970001</td>\n",
       "      <td>199.460007</td>\n",
       "      <td>194.889999</td>\n",
       "      <td>199.210007</td>\n",
       "      <td>199.210007</td>\n",
       "      <td>14698600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-10</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>202.960007</td>\n",
       "      <td>199.669998</td>\n",
       "      <td>202.729996</td>\n",
       "      <td>202.729996</td>\n",
       "      <td>20571700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2019-07-05  196.179993  197.070007  194.169998  196.399994  196.399994   \n",
       "2019-07-08  195.190002  196.679993  193.639999  195.759995  195.759995   \n",
       "2019-07-09  194.970001  199.460007  194.889999  199.210007  199.210007   \n",
       "2019-07-10  200.000000  202.960007  199.669998  202.729996  202.729996   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2019-07-05  11164100  \n",
       "2019-07-08   9723900  \n",
       "2019-07-09  14698600  \n",
       "2019-07-10  20571700  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb[\"2019-07-05\":\"2019-07-10\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-07-22 00:00:00')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=pd.to_datetime(\"7/22/2019\")\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-07-22</th>\n",
       "      <td>199.910004</td>\n",
       "      <td>202.570007</td>\n",
       "      <td>198.809998</td>\n",
       "      <td>202.320007</td>\n",
       "      <td>202.320007</td>\n",
       "      <td>13589000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-23</th>\n",
       "      <td>202.839996</td>\n",
       "      <td>204.240005</td>\n",
       "      <td>200.960007</td>\n",
       "      <td>202.360001</td>\n",
       "      <td>202.360001</td>\n",
       "      <td>14583700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-24</th>\n",
       "      <td>197.630005</td>\n",
       "      <td>204.809998</td>\n",
       "      <td>197.220001</td>\n",
       "      <td>204.660004</td>\n",
       "      <td>204.660004</td>\n",
       "      <td>32532500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-25</th>\n",
       "      <td>206.699997</td>\n",
       "      <td>208.660004</td>\n",
       "      <td>198.259995</td>\n",
       "      <td>200.710007</td>\n",
       "      <td>200.710007</td>\n",
       "      <td>39889900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-26</th>\n",
       "      <td>200.190002</td>\n",
       "      <td>202.880005</td>\n",
       "      <td>196.250000</td>\n",
       "      <td>199.750000</td>\n",
       "      <td>199.750000</td>\n",
       "      <td>24426700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-29</th>\n",
       "      <td>199.000000</td>\n",
       "      <td>199.590302</td>\n",
       "      <td>197.880005</td>\n",
       "      <td>198.059998</td>\n",
       "      <td>198.059998</td>\n",
       "      <td>754198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2019-07-22  199.910004  202.570007  198.809998  202.320007  202.320007   \n",
       "2019-07-23  202.839996  204.240005  200.960007  202.360001  202.360001   \n",
       "2019-07-24  197.630005  204.809998  197.220001  204.660004  204.660004   \n",
       "2019-07-25  206.699997  208.660004  198.259995  200.710007  200.710007   \n",
       "2019-07-26  200.190002  202.880005  196.250000  199.750000  199.750000   \n",
       "2019-07-29  199.000000  199.590302  197.880005  198.059998  198.059998   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2019-07-22  13589000  \n",
       "2019-07-23  14583700  \n",
       "2019-07-24  32532500  \n",
       "2019-07-25  39889900  \n",
       "2019-07-26  24426700  \n",
       "2019-07-29    754198  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.loc[fb.index>=t,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dating the Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb1=pd.read_csv(\"FB-no-date.csv\",sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>163899994</td>\n",
       "      <td>167500000</td>\n",
       "      <td>163830002</td>\n",
       "      <td>167369995</td>\n",
       "      <td>167369995</td>\n",
       "      <td>18894700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>167369995</td>\n",
       "      <td>171880005</td>\n",
       "      <td>166550003</td>\n",
       "      <td>171259995</td>\n",
       "      <td>171259995</td>\n",
       "      <td>28187900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>172899994</td>\n",
       "      <td>173570007</td>\n",
       "      <td>171270004</td>\n",
       "      <td>172509995</td>\n",
       "      <td>172509995</td>\n",
       "      <td>21531700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>171500000</td>\n",
       "      <td>171740005</td>\n",
       "      <td>167610001</td>\n",
       "      <td>169130005</td>\n",
       "      <td>169130005</td>\n",
       "      <td>18306500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Open       High        Low      Close  Adj Close    Volume\n",
       "0  162600006  163130005  161690002  162279999  162279999  11097800\n",
       "1  163899994  167500000  163830002  167369995  167369995  18894700\n",
       "2  167369995  171880005  166550003  171259995  171259995  28187900\n",
       "3  172899994  173570007  171270004  172509995  172509995  21531700\n",
       "4  171500000  171740005  167610001  169130005  169130005  18306500"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates=pd.date_range(start=\"03/01/2019\", \n",
    "                    end=\"03/29/2019\", \n",
    "                    freq=\"B\") \n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fb1.set_index(dates,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>163899994</td>\n",
       "      <td>167500000</td>\n",
       "      <td>163830002</td>\n",
       "      <td>167369995</td>\n",
       "      <td>167369995</td>\n",
       "      <td>18894700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>167369995</td>\n",
       "      <td>171880005</td>\n",
       "      <td>166550003</td>\n",
       "      <td>171259995</td>\n",
       "      <td>171259995</td>\n",
       "      <td>28187900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>172899994</td>\n",
       "      <td>173570007</td>\n",
       "      <td>171270004</td>\n",
       "      <td>172509995</td>\n",
       "      <td>172509995</td>\n",
       "      <td>21531700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>171500000</td>\n",
       "      <td>171740005</td>\n",
       "      <td>167610001</td>\n",
       "      <td>169130005</td>\n",
       "      <td>169130005</td>\n",
       "      <td>18306500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "2019-03-01  162600006  163130005  161690002  162279999  162279999  11097800\n",
       "2019-03-04  163899994  167500000  163830002  167369995  167369995  18894700\n",
       "2019-03-05  167369995  171880005  166550003  171259995  171259995  28187900\n",
       "2019-03-06  172899994  173570007  171270004  172509995  172509995  21531700\n",
       "2019-03-07  171500000  171740005  167610001  169130005  169130005  18306500"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Time Series Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": [
    "fb1.Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:00:00</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 01:00:00</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 02:00:00</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 03:00:00</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 04:00:00</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Open       High        Low      Close  Adj Close  \\\n",
       "2019-03-01 00:00:00  162600006  163130005  161690002  162279999  162279999   \n",
       "2019-03-01 01:00:00  162600006  163130005  161690002  162279999  162279999   \n",
       "2019-03-01 02:00:00  162600006  163130005  161690002  162279999  162279999   \n",
       "2019-03-01 03:00:00  162600006  163130005  161690002  162279999  162279999   \n",
       "2019-03-01 04:00:00  162600006  163130005  161690002  162279999  162279999   \n",
       "\n",
       "                       Volume  \n",
       "2019-03-01 00:00:00  11097800  \n",
       "2019-03-01 01:00:00  11097800  \n",
       "2019-03-01 02:00:00  11097800  \n",
       "2019-03-01 03:00:00  11097800  \n",
       "2019-03-01 04:00:00  11097800  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1.asfreq(\"H\",method=\"pad\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-03</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-10</th>\n",
       "      <td>166199997</td>\n",
       "      <td>169619995</td>\n",
       "      <td>165970001</td>\n",
       "      <td>169600006</td>\n",
       "      <td>169600006</td>\n",
       "      <td>13184800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-17</th>\n",
       "      <td>167160004</td>\n",
       "      <td>167580002</td>\n",
       "      <td>162509995</td>\n",
       "      <td>165979996</td>\n",
       "      <td>165979996</td>\n",
       "      <td>37135400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-24</th>\n",
       "      <td>165649994</td>\n",
       "      <td>167419998</td>\n",
       "      <td>164089996</td>\n",
       "      <td>164339996</td>\n",
       "      <td>164339996</td>\n",
       "      <td>16389200</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "2019-03-03  162600006  163130005  161690002  162279999  162279999  11097800\n",
       "2019-03-10  166199997  169619995  165970001  169600006  169600006  13184800\n",
       "2019-03-17  167160004  167580002  162509995  165979996  165979996  37135400\n",
       "2019-03-24  165649994  167419998  164089996  164339996  164339996  16389200"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1.asfreq(\"W\", method=\"pad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
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       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:00:00</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 01:00:00</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 02:00:00</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-01 03:00:00</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 04:00:00</th>\n",
       "      <td>162600006</td>\n",
       "      <td>163130005</td>\n",
       "      <td>161690002</td>\n",
       "      <td>162279999</td>\n",
       "      <td>162279999</td>\n",
       "      <td>11097800</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>2019-03-28 20:00:00</th>\n",
       "      <td>164570007</td>\n",
       "      <td>166720001</td>\n",
       "      <td>163330002</td>\n",
       "      <td>165550003</td>\n",
       "      <td>165550003</td>\n",
       "      <td>10443000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28 21:00:00</th>\n",
       "      <td>164570007</td>\n",
       "      <td>166720001</td>\n",
       "      <td>163330002</td>\n",
       "      <td>165550003</td>\n",
       "      <td>165550003</td>\n",
       "      <td>10443000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28 22:00:00</th>\n",
       "      <td>164570007</td>\n",
       "      <td>166720001</td>\n",
       "      <td>163330002</td>\n",
       "      <td>165550003</td>\n",
       "      <td>165550003</td>\n",
       "      <td>10443000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28 23:00:00</th>\n",
       "      <td>164570007</td>\n",
       "      <td>166720001</td>\n",
       "      <td>163330002</td>\n",
       "      <td>165550003</td>\n",
       "      <td>165550003</td>\n",
       "      <td>10443000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-29 00:00:00</th>\n",
       "      <td>166389999</td>\n",
       "      <td>167190002</td>\n",
       "      <td>164809998</td>\n",
       "      <td>166690002</td>\n",
       "      <td>166690002</td>\n",
       "      <td>13455500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>673 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Open       High        Low      Close  Adj Close  \\\n",
       "2019-03-01 00:00:00  162600006  163130005  161690002  162279999  162279999   \n",
       "2019-03-01 01:00:00  162600006  163130005  161690002  162279999  162279999   \n",
       "2019-03-01 02:00:00  162600006  163130005  161690002  162279999  162279999   \n",
       "2019-03-01 03:00:00  162600006  163130005  161690002  162279999  162279999   \n",
       "2019-03-01 04:00:00  162600006  163130005  161690002  162279999  162279999   \n",
       "...                        ...        ...        ...        ...        ...   \n",
       "2019-03-28 20:00:00  164570007  166720001  163330002  165550003  165550003   \n",
       "2019-03-28 21:00:00  164570007  166720001  163330002  165550003  165550003   \n",
       "2019-03-28 22:00:00  164570007  166720001  163330002  165550003  165550003   \n",
       "2019-03-28 23:00:00  164570007  166720001  163330002  165550003  165550003   \n",
       "2019-03-29 00:00:00  166389999  167190002  164809998  166690002  166690002   \n",
       "\n",
       "                       Volume  \n",
       "2019-03-01 00:00:00  11097800  \n",
       "2019-03-01 01:00:00  11097800  \n",
       "2019-03-01 02:00:00  11097800  \n",
       "2019-03-01 03:00:00  11097800  \n",
       "2019-03-01 04:00:00  11097800  \n",
       "...                       ...  \n",
       "2019-03-28 20:00:00  10443000  \n",
       "2019-03-28 21:00:00  10443000  \n",
       "2019-03-28 22:00:00  10443000  \n",
       "2019-03-28 23:00:00  10443000  \n",
       "2019-03-29 00:00:00  13455500  \n",
       "\n",
       "[673 rows x 6 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb1.asfreq(\"H\", method=\"pad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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', '2019-04-01', '2019-04-02', '2019-04-03',\n",
       "               '2019-04-04', '2019-04-05', '2019-04-08', '2019-04-09',\n",
       "               '2019-04-10', '2019-04-11', '2019-04-12', '2019-04-15',\n",
       "               '2019-04-16', '2019-04-17', '2019-04-18', '2019-04-19',\n",
       "               '2019-04-22', '2019-04-23', '2019-04-24', '2019-04-25',\n",
       "               '2019-04-26', '2019-04-29', '2019-04-30', '2019-05-01',\n",
       "               '2019-05-02', '2019-05-03', '2019-05-06', '2019-05-07',\n",
       "               '2019-05-08', '2019-05-09', '2019-05-10', '2019-05-13',\n",
       "               '2019-05-14', '2019-05-15', '2019-05-16', '2019-05-17',\n",
       "               '2019-05-20', '2019-05-21', '2019-05-22', '2019-05-23'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z=pd.date_range(start=\"3/1/2019\", \n",
    "                periods=60 , freq=\"B\")\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-03-01 00:00:00', '2019-03-01 01:00:00',\n",
       "               '2019-03-01 02:00:00', '2019-03-01 03:00:00',\n",
       "               '2019-03-01 04:00:00', '2019-03-01 05:00:00',\n",
       "               '2019-03-01 06:00:00', '2019-03-01 07:00:00',\n",
       "               '2019-03-01 08:00:00', '2019-03-01 09:00:00',\n",
       "               '2019-03-01 10:00:00', '2019-03-01 11:00:00',\n",
       "               '2019-03-01 12:00:00', '2019-03-01 13:00:00',\n",
       "               '2019-03-01 14:00:00', '2019-03-01 15:00:00',\n",
       "               '2019-03-01 16:00:00', '2019-03-01 17:00:00',\n",
       "               '2019-03-01 18:00:00', '2019-03-01 19:00:00',\n",
       "               '2019-03-01 20:00:00', '2019-03-01 21:00:00',\n",
       "               '2019-03-01 22:00:00', '2019-03-01 23:00:00',\n",
       "               '2019-03-02 00:00:00', '2019-03-02 01:00:00',\n",
       "               '2019-03-02 02:00:00', '2019-03-02 03:00:00',\n",
       "               '2019-03-02 04:00:00', '2019-03-02 05:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z=pd.date_range(\n",
    "    start=\"3/1/2019\", periods=30, freq=\"H\")\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-03-01 00:00:00    7\n",
       "2019-03-01 01:00:00    1\n",
       "2019-03-01 02:00:00    6\n",
       "2019-03-01 03:00:00    2\n",
       "2019-03-01 04:00:00    7\n",
       "Freq: H, dtype: int32"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts=pd.Series(\n",
    "    np.random.randint(1,10,len(z)),index=z)\n",
    "ts.head()"
   ]
  },
  {
   "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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