14.3 Basics of Time Series.ipynb

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   "source": [
    "![title](img/cover4.png)"
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   "metadata": {},
   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.3 Basics of Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import pandas and datetime libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets read this time series from a csv file using the function **pd.read_csv()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
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       "      <th>date</th>\n",
       "      <th>google</th>\n",
       "      <th>apple</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30-Apr-19</td>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29-May-19</td>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28-Jun-19</td>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27-Jul-19</td>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26-Apr-20</td>\n",
       "      <td>2326.74</td>\n",
       "      <td>134.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        date   google   apple\n",
       "0  30-Apr-19  2410.12  131.24\n",
       "1  29-May-19  2429.89  133.25\n",
       "2  28-Jun-19  2379.91  133.35\n",
       "3  27-Jul-19  2307.12  134.16\n",
       "4  26-Apr-20  2326.74  134.49"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/ex15.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the data type of all columns in this dataframe we use the attribute **dtypes**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date       object\n",
       "google    float64\n",
       "apple     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert the 'date' column into datetime format using the function **pd.to_datetime()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check **again** the data types using the attribute **dtypes**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      datetime64[ns]\n",
       "google           float64\n",
       "apple            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the head of this dataframe we use the function **head()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
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       "      <td>131.24</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-29</td>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-27</td>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>2326.74</td>\n",
       "      <td>134.49</td>\n",
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       "        date   google   apple\n",
       "0 2019-04-30  2410.12  131.24\n",
       "1 2019-05-29  2429.89  133.25\n",
       "2 2019-06-28  2379.91  133.35\n",
       "3 2019-07-27  2307.12  134.16\n",
       "4 2020-04-26  2326.74  134.49"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For time series, we set the index to be the date column using the function **set_index()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
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   "outputs": [
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       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29</th>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-27</th>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26</th>\n",
       "      <td>2326.74</td>\n",
       "      <td>134.49</td>\n",
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      "text/plain": [
       "             google   apple\n",
       "date                       \n",
       "2019-04-30  2410.12  131.24\n",
       "2019-05-29  2429.89  133.25\n",
       "2019-06-28  2379.91  133.35\n",
       "2019-07-27  2307.12  134.16\n",
       "2020-04-26  2326.74  134.49"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df = df.set_index('date')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can combine the above steps in one code line, like this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
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       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29</th>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-27</th>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26</th>\n",
       "      <td>2326.74</td>\n",
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      "text/plain": [
       "             google   apple\n",
       "date                       \n",
       "2019-04-30  2410.12  131.24\n",
       "2019-05-29  2429.89  133.25\n",
       "2019-06-28  2379.91  133.35\n",
       "2019-07-27  2307.12  134.16\n",
       "2020-04-26  2326.74  134.49"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/ex15.csv', parse_dates = ['date'], index_col = 'date')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In time series, we can select data that belong to specific year using the function **loc [ ]**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
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       "      <th>google</th>\n",
       "      <th>apple</th>\n",
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       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-20</th>\n",
       "      <td>2293.63</td>\n",
       "      <td>132.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-30</th>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-29</th>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-28</th>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-27</th>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
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      "text/plain": [
       "             google   apple\n",
       "date                       \n",
       "2021-04-20  2293.63  132.88\n",
       "2021-05-30  2410.12  131.24\n",
       "2021-06-29  2429.89  133.25\n",
       "2021-07-28  2379.91  133.35\n",
       "2021-04-27  2307.12  134.16"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2021']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also select data that belong to a specif month like this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-20</th>\n",
       "      <td>2293.63</td>\n",
       "      <td>132.88</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-27</th>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
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      "text/plain": [
       "             google   apple\n",
       "date                       \n",
       "2021-04-20  2293.63  132.88\n",
       "2021-04-27  2307.12  134.16"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2021-4']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also select data in a range of dates, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29</th>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-27</th>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26</th>\n",
       "      <td>2326.74</td>\n",
       "      <td>134.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-23</th>\n",
       "      <td>2315.30</td>\n",
       "      <td>134.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-24</th>\n",
       "      <td>2267.92</td>\n",
       "      <td>131.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20</th>\n",
       "      <td>2293.29</td>\n",
       "      <td>133.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             google   apple\n",
       "date                       \n",
       "2019-04-30  2410.12  131.24\n",
       "2019-05-29  2429.89  133.25\n",
       "2019-06-28  2379.91  133.35\n",
       "2019-07-27  2307.12  134.16\n",
       "2020-04-26  2326.74  134.49\n",
       "2020-05-23  2315.30  134.09\n",
       "2020-06-24  2267.92  131.72\n",
       "2020-07-20  2293.29  133.27"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2019':'2020']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select from the very begining to a specific date, we leave the start befor the colon empty:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>google</th>\n",
       "      <th>apple</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29</th>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-27</th>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26</th>\n",
       "      <td>2326.74</td>\n",
       "      <td>134.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-23</th>\n",
       "      <td>2315.30</td>\n",
       "      <td>134.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-24</th>\n",
       "      <td>2267.92</td>\n",
       "      <td>131.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20</th>\n",
       "      <td>2293.29</td>\n",
       "      <td>133.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             google   apple\n",
       "date                       \n",
       "2019-04-30  2410.12  131.24\n",
       "2019-05-29  2429.89  133.25\n",
       "2019-06-28  2379.91  133.35\n",
       "2019-07-27  2307.12  134.16\n",
       "2020-04-26  2326.74  134.49\n",
       "2020-05-23  2315.30  134.09\n",
       "2020-06-24  2267.92  131.72\n",
       "2020-07-20  2293.29  133.27"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:'2020']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check for duplicate index dates, we use the attribute **is_unique** for the index like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.is_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
 ],
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