DataTalks_Time Series I.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series\n",
    "import dataset from https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2014-07-03</td>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>92.10</td>\n",
       "      <td>93.73</td>\n",
       "      <td>92.09</td>\n",
       "      <td>92.93</td>\n",
       "      <td>49482300</td>\n",
       "      <td>92.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2014-06-27</td>\n",
       "      <td>90.82</td>\n",
       "      <td>92.00</td>\n",
       "      <td>90.77</td>\n",
       "      <td>91.98</td>\n",
       "      <td>64006800</td>\n",
       "      <td>91.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2014-06-26</td>\n",
       "      <td>90.37</td>\n",
       "      <td>91.05</td>\n",
       "      <td>89.80</td>\n",
       "      <td>90.90</td>\n",
       "      <td>32595800</td>\n",
       "      <td>90.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2014-06-25</td>\n",
       "      <td>90.21</td>\n",
       "      <td>90.70</td>\n",
       "      <td>89.65</td>\n",
       "      <td>90.36</td>\n",
       "      <td>36852200</td>\n",
       "      <td>90.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2014-06-24</td>\n",
       "      <td>90.75</td>\n",
       "      <td>91.74</td>\n",
       "      <td>90.19</td>\n",
       "      <td>90.28</td>\n",
       "      <td>38988300</td>\n",
       "      <td>90.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low  Close    Volume  Adj Close\n",
       "0  2014-07-08  96.27  96.80  93.92  95.35  65130000      95.35\n",
       "1  2014-07-07  94.14  95.99  94.10  95.97  56305400      95.97\n",
       "2  2014-07-03  93.67  94.10  93.20  94.03  22891800      94.03\n",
       "3  2014-07-02  93.87  94.06  93.09  93.48  28420900      93.48\n",
       "4  2014-07-01  93.52  94.07  93.13  93.52  38170200      93.52\n",
       "5  2014-06-30  92.10  93.73  92.09  92.93  49482300      92.93\n",
       "6  2014-06-27  90.82  92.00  90.77  91.98  64006800      91.98\n",
       "7  2014-06-26  90.37  91.05  89.80  90.90  32595800      90.90\n",
       "8  2014-06-25  90.21  90.70  89.65  90.36  36852200      90.36\n",
       "9  2014-06-24  90.75  91.74  90.19  90.28  38988300      90.28"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check out the type of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date          object\n",
       "Open         float64\n",
       "High         float64\n",
       "Low          float64\n",
       "Close        float64\n",
       "Volume         int64\n",
       "Adj Close    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transform the Date column as a datetime type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2014-07-08\n",
       "1   2014-07-07\n",
       "2   2014-07-03\n",
       "3   2014-07-02\n",
       "4   2014-07-01\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Date = pd.to_datetime(df.Date)\n",
    "\n",
    "df['Date'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the date as the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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",
       "      <td>2014-07-08</td>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-03</td>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980-12-18</td>\n",
       "      <td>26.63</td>\n",
       "      <td>26.75</td>\n",
       "      <td>26.63</td>\n",
       "      <td>26.63</td>\n",
       "      <td>18362400</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980-12-17</td>\n",
       "      <td>25.87</td>\n",
       "      <td>26.00</td>\n",
       "      <td>25.87</td>\n",
       "      <td>25.87</td>\n",
       "      <td>21610400</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>25.37</td>\n",
       "      <td>25.37</td>\n",
       "      <td>25.25</td>\n",
       "      <td>25.25</td>\n",
       "      <td>26432000</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980-12-15</td>\n",
       "      <td>27.38</td>\n",
       "      <td>27.38</td>\n",
       "      <td>27.25</td>\n",
       "      <td>27.25</td>\n",
       "      <td>43971200</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980-12-12</td>\n",
       "      <td>28.75</td>\n",
       "      <td>28.87</td>\n",
       "      <td>28.75</td>\n",
       "      <td>28.75</td>\n",
       "      <td>117258400</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8465 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume  Adj Close\n",
       "Date                                                        \n",
       "2014-07-08  96.27  96.80  93.92  95.35   65130000      95.35\n",
       "2014-07-07  94.14  95.99  94.10  95.97   56305400      95.97\n",
       "2014-07-03  93.67  94.10  93.20  94.03   22891800      94.03\n",
       "2014-07-02  93.87  94.06  93.09  93.48   28420900      93.48\n",
       "2014-07-01  93.52  94.07  93.13  93.52   38170200      93.52\n",
       "...           ...    ...    ...    ...        ...        ...\n",
       "1980-12-18  26.63  26.75  26.63  26.63   18362400       0.41\n",
       "1980-12-17  25.87  26.00  25.87  25.87   21610400       0.40\n",
       "1980-12-16  25.37  25.37  25.25  25.25   26432000       0.39\n",
       "1980-12-15  27.38  27.38  27.25  27.25   43971200       0.42\n",
       "1980-12-12  28.75  28.87  28.75  28.75  117258400       0.45\n",
       "\n",
       "[8465 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(\"Date\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is there any duplicate dates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.is_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It seems the index is from the most recent date. Make the first entry the oldest date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1980-12-12</td>\n",
       "      <td>28.75</td>\n",
       "      <td>28.87</td>\n",
       "      <td>28.75</td>\n",
       "      <td>28.75</td>\n",
       "      <td>117258400</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980-12-15</td>\n",
       "      <td>27.38</td>\n",
       "      <td>27.38</td>\n",
       "      <td>27.25</td>\n",
       "      <td>27.25</td>\n",
       "      <td>43971200</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>25.37</td>\n",
       "      <td>25.37</td>\n",
       "      <td>25.25</td>\n",
       "      <td>25.25</td>\n",
       "      <td>26432000</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980-12-17</td>\n",
       "      <td>25.87</td>\n",
       "      <td>26.00</td>\n",
       "      <td>25.87</td>\n",
       "      <td>25.87</td>\n",
       "      <td>21610400</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980-12-18</td>\n",
       "      <td>26.63</td>\n",
       "      <td>26.75</td>\n",
       "      <td>26.63</td>\n",
       "      <td>26.63</td>\n",
       "      <td>18362400</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume  Adj Close\n",
       "Date                                                        \n",
       "1980-12-12  28.75  28.87  28.75  28.75  117258400       0.45\n",
       "1980-12-15  27.38  27.38  27.25  27.25   43971200       0.42\n",
       "1980-12-16  25.37  25.37  25.25  25.25   26432000       0.39\n",
       "1980-12-17  25.87  26.00  25.87  25.87   21610400       0.40\n",
       "1980-12-18  26.63  26.75  26.63  26.63   18362400       0.41"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(ascending = True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the last business day of each month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1980-12-31</td>\n",
       "      <td>30.481538</td>\n",
       "      <td>30.567692</td>\n",
       "      <td>30.443077</td>\n",
       "      <td>30.443077</td>\n",
       "      <td>2.586252e+07</td>\n",
       "      <td>0.473077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1981-01-30</td>\n",
       "      <td>31.754762</td>\n",
       "      <td>31.826667</td>\n",
       "      <td>31.654762</td>\n",
       "      <td>31.654762</td>\n",
       "      <td>7.249867e+06</td>\n",
       "      <td>0.493810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1981-02-27</td>\n",
       "      <td>26.480000</td>\n",
       "      <td>26.572105</td>\n",
       "      <td>26.407895</td>\n",
       "      <td>26.407895</td>\n",
       "      <td>4.231832e+06</td>\n",
       "      <td>0.411053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1981-03-31</td>\n",
       "      <td>24.937727</td>\n",
       "      <td>25.016818</td>\n",
       "      <td>24.836364</td>\n",
       "      <td>24.836364</td>\n",
       "      <td>7.962691e+06</td>\n",
       "      <td>0.387727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1981-04-30</td>\n",
       "      <td>27.286667</td>\n",
       "      <td>27.368095</td>\n",
       "      <td>27.227143</td>\n",
       "      <td>27.227143</td>\n",
       "      <td>6.392000e+06</td>\n",
       "      <td>0.423333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close        Volume  \\\n",
       "Date                                                                   \n",
       "1980-12-31  30.481538  30.567692  30.443077  30.443077  2.586252e+07   \n",
       "1981-01-30  31.754762  31.826667  31.654762  31.654762  7.249867e+06   \n",
       "1981-02-27  26.480000  26.572105  26.407895  26.407895  4.231832e+06   \n",
       "1981-03-31  24.937727  25.016818  24.836364  24.836364  7.962691e+06   \n",
       "1981-04-30  27.286667  27.368095  27.227143  27.227143  6.392000e+06   \n",
       "\n",
       "            Adj Close  \n",
       "Date                   \n",
       "1980-12-31   0.473077  \n",
       "1981-01-30   0.493810  \n",
       "1981-02-27   0.411053  \n",
       "1981-03-31   0.387727  \n",
       "1981-04-30   0.423333  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month = df.resample(\"BM\").mean()\n",
    "df_month.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the difference in days between the first day and the oldest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12261"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.index.max() - df.index.min()).days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many months in the data we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "404"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the 'Adj Close' value. Set the size of the figure to 13.5 x 9 inches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000.8x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = df[\"Adj Close\"].plot(title = \"Apple Stock\")\n",
    "fig = g.get_figure()\n",
    "fig.set_size_inches(13.9, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}