14.4 Generating Date Ranges.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/cover4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "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": [
    "## 14.4 Generating Date Ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import pandas library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "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": [
    {
     "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>google</th>\n",
       "      <th>apple</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2326.74</td>\n",
       "      <td>134.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2315.30</td>\n",
       "      <td>134.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2267.92</td>\n",
       "      <td>131.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2293.29</td>\n",
       "      <td>133.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2293.63</td>\n",
       "      <td>132.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2326.74</td>\n",
       "      <td>134.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2315.30</td>\n",
       "      <td>134.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     google   apple\n",
       "0   2410.12  131.24\n",
       "1   2429.89  133.25\n",
       "2   2379.91  133.35\n",
       "3   2307.12  134.16\n",
       "4   2326.74  134.49\n",
       "5   2315.30  134.09\n",
       "6   2267.92  131.72\n",
       "7   2293.29  133.27\n",
       "8   2293.63  132.88\n",
       "9   2410.12  131.24\n",
       "10  2429.89  133.25\n",
       "11  2379.91  133.35\n",
       "12  2307.12  134.16\n",
       "13  2326.74  134.49\n",
       "14  2315.30  134.09"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/ex16.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time series has no date index, we can generate a date index for any specific period of time using the function **pd.date_range()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-05-01', '2020-05-02', '2020-05-03', '2020-05-04',\n",
       "               '2020-05-05', '2020-05-06', '2020-05-07', '2020-05-08',\n",
       "               '2020-05-09', '2020-05-10', '2020-05-11', '2020-05-12',\n",
       "               '2020-05-13', '2020-05-14', '2020-05-15'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.date_range(start = '2020-05-01', end = '2020-05-15')\n",
    "index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After generating the date index, we cann attch the index to the time series using the function **set_index()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": false
   },
   "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>google</th>\n",
       "      <th>apple</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-01</th>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-02</th>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-03</th>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-04</th>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05</th>\n",
       "      <td>2326.74</td>\n",
       "      <td>134.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06</th>\n",
       "      <td>2315.30</td>\n",
       "      <td>134.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07</th>\n",
       "      <td>2267.92</td>\n",
       "      <td>131.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-08</th>\n",
       "      <td>2293.29</td>\n",
       "      <td>133.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-09</th>\n",
       "      <td>2293.63</td>\n",
       "      <td>132.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-10</th>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-11</th>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-12</th>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-13</th>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>2326.74</td>\n",
       "      <td>134.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-15</th>\n",
       "      <td>2315.30</td>\n",
       "      <td>134.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             google   apple\n",
       "2020-05-01  2410.12  131.24\n",
       "2020-05-02  2429.89  133.25\n",
       "2020-05-03  2379.91  133.35\n",
       "2020-05-04  2307.12  134.16\n",
       "2020-05-05  2326.74  134.49\n",
       "2020-05-06  2315.30  134.09\n",
       "2020-05-07  2267.92  131.72\n",
       "2020-05-08  2293.29  133.27\n",
       "2020-05-09  2293.63  132.88\n",
       "2020-05-10  2410.12  131.24\n",
       "2020-05-11  2429.89  133.25\n",
       "2020-05-12  2379.91  133.35\n",
       "2020-05-13  2307.12  134.16\n",
       "2020-05-14  2326.74  134.49\n",
       "2020-05-15  2315.30  134.09"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TS = df.set_index(index)\n",
    "TS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also generate date index by specifying a start date and the number of periods, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-05-01', '2020-05-02', '2020-05-03', '2020-05-04',\n",
       "               '2020-05-05', '2020-05-06', '2020-05-07', '2020-05-08',\n",
       "               '2020-05-09', '2020-05-10', '2020-05-11', '2020-05-12',\n",
       "               '2020-05-13', '2020-05-14', '2020-05-15'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index2 = pd.date_range(start = '2020-05-01', periods = 15)\n",
    "index2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the function **date_range()** generates daily periods, but we can generate any frequency, for example by adding the argument **freq = 'BM'** we can generate frequency as **the last business day of the month**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-31', '2020-02-28', '2020-03-31', '2020-04-30',\n",
       "               '2020-05-29', '2020-06-30', '2020-07-31', '2020-08-31',\n",
       "               '2020-09-30', '2020-10-30', '2020-11-30', '2020-12-31'],\n",
       "              dtype='datetime64[ns]', freq='BM')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_last = pd.date_range('2020-01-01', '2020-12-31', freq = 'BM')\n",
    "index_last"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By adding the argument **freq = 'B'** , we can generate frequency as **the business days**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-06',\n",
       "               '2020-01-07', '2020-01-08', '2020-01-09', '2020-01-10',\n",
       "               '2020-01-13', '2020-01-14', '2020-01-15', '2020-01-16',\n",
       "               '2020-01-17', '2020-01-20', '2020-01-21', '2020-01-22',\n",
       "               '2020-01-23', '2020-01-24', '2020-01-27', '2020-01-28',\n",
       "               '2020-01-29', '2020-01-30', '2020-01-31'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_BD = pd.date_range('2020-01-01', '2020-1-31', freq = 'B')\n",
    "index_BD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By adding the argument **freq = '4h'** , we can generate frequency as **four-hour periods**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-01 00:00:00', '2020-01-01 04:00:00',\n",
       "               '2020-01-01 08:00:00', '2020-01-01 12:00:00',\n",
       "               '2020-01-01 16:00:00', '2020-01-01 20:00:00',\n",
       "               '2020-01-02 00:00:00'],\n",
       "              dtype='datetime64[ns]', freq='4H')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_4h = pd.date_range('2020-01-01', '2020-1-02', freq = '4h')\n",
    "index_4h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By adding the argument **freq = '1h30min'** , we can generate frequency as **one hour and a half  periods**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-01 00:00:00', '2020-01-01 01:30:00',\n",
       "               '2020-01-01 03:00:00', '2020-01-01 04:30:00',\n",
       "               '2020-01-01 06:00:00', '2020-01-01 07:30:00',\n",
       "               '2020-01-01 09:00:00', '2020-01-01 10:30:00',\n",
       "               '2020-01-01 12:00:00', '2020-01-01 13:30:00',\n",
       "               '2020-01-01 15:00:00', '2020-01-01 16:30:00',\n",
       "               '2020-01-01 18:00:00', '2020-01-01 19:30:00',\n",
       "               '2020-01-01 21:00:00', '2020-01-01 22:30:00',\n",
       "               '2020-01-02 00:00:00'],\n",
       "              dtype='datetime64[ns]', freq='90T')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_1h30 = pd.date_range('2020-01-01', '2020-1-02', freq = '1h30min')\n",
    "index_1h30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example by adding the argument **freq = 'WOM-3FRI'** , we can generate a frequency with **the third Friday for each month**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-01-15', '2021-02-19', '2021-03-19', '2021-04-16',\n",
       "               '2021-05-21', '2021-06-18', '2021-07-16', '2021-08-20',\n",
       "               '2021-09-17', '2021-10-15', '2021-11-19', '2021-12-17'],\n",
       "              dtype='datetime64[ns]', freq='WOM-3FRI')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_3fri = pd.date_range('2021-01-01', '2021-12-31', freq = 'WOM-3FRI')\n",
    "index_3fri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}