02-Time_Series_Handling_with_Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Time Series Analysis` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chapter 02: Time Series Handling with Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `01-Array and Data Frames` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([1,2,3,4])\n",
    "print(x)\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Series مثال على الـ\n",
    "d = {'a': 1, 'b': 2, 'c': 3}\n",
    "ser = pd.Series(data=d, index=['a', 'b', 'c'])\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cake</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Orange</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eggs</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     col1  col2  col3\n",
       "0    Cake     2   3.5\n",
       "1  Orange     3   4.0\n",
       "2    Eggs     5  10.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe  مثال على أطر البيانات\n",
    "d = {'col1': [\"Cake\", \"Orange\", \"Eggs\"], 'col2': [2, 3, 5], 'col3':[3.5, 4, 10]}\n",
    "df = pd.DataFrame(data=d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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       "    }\n",
       "\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>Type</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cake</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Orange</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eggs</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apple</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fish</td>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type  Quantity  Price\n",
       "0    Cake         2    3.5\n",
       "1  Orange         3    4.0\n",
       "2    Eggs         5   10.0\n",
       "3   apple         4    5.0\n",
       "4    Fish         5   20.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'Type': [\"Cake\", \"Orange\", \"Eggs\", \"apple\", \"Fish\"], 'Quantity': [2, 3, 5, 4, 5], 'Price':[3.5, 4, 10, 5, 20]}\n",
    "df = pd.DataFrame(data=d)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `02-Dates and time creation using datetime module`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arbitrary date:  2021-04-15 00:00:00\n",
      "Date and time:   2021-04-15 08:22:43\n",
      "Date right now:  2022-05-06 06:42:43.236795\n",
      "Date yesterday:  2022-05-05 06:42:43.236795\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "# إنشاء تاريخ عشوائي\n",
    "print('Arbitrary date: ', end=' ')\n",
    "print(datetime(year=2021, month=4, day=15))\n",
    "\n",
    "# إنشاء تاريخ وتوقيت عشوائيين\n",
    "print('Date and time:  ', end=' ')\n",
    "print(datetime(year=2021, month=4, day=15, hour=8, minute=22, second=43))\n",
    "\n",
    "# التاريخ والتوقيت الآن\n",
    "print('Date right now: ', end=' ')\n",
    "print(datetime.now())\n",
    "\n",
    "#  استخراج التاريخ التوقيت في الأمس \n",
    "print('Date yesterday: ', end=' ')\n",
    "print(datetime.now() - timedelta(days=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `03-Datetime variable methods`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current Time: 2022-05-06 06:42:44.215827\n",
      "2022 5 6 6 42 44\n"
     ]
    }
   ],
   "source": [
    "now = datetime.now()\n",
    "print(f\"Current Time: {now}\")\n",
    "print(now.year, now.month, now.day, now.hour, now.minute, now.second)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `04-Datetime Ranges `"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2022, 2, 13, 0, 0),\n",
       " datetime.datetime(2022, 2, 14, 0, 0),\n",
       " datetime.datetime(2022, 2, 15, 0, 0)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "date_list = [\n",
    "    datetime(2022, 2, 13),\n",
    "    datetime(2022, 2, 14),\n",
    "    datetime(2022, 2, 15) ]\n",
    "\n",
    "date_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2022-02-13', '2022-02-14', '2022-02-15'], dtype='datetime64[D]')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "date_list = np.array(['2022-02-13', '2022-02-14', '2022-02-15'], \n",
    "                      dtype='datetime64')\n",
    "date_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2022-02-13T00:00:00', '2022-02-14T00:00:00',\n",
       "       '2022-02-15T00:00:00'], dtype='datetime64[s]')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_list = np.array(['2022-02-13', '2022-02-14', '2022-02-15'], \n",
    "            dtype='datetime64[s]')\n",
    "date_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2022-02-13T00:00:00.000000000', '2022-02-14T00:00:00.000000000',\n",
       "       '2022-02-15T00:00:00.000000000'], dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "date_list = np.array(['2022-02-13', '2022-02-14', '2022-02-15'], \n",
    "            dtype='datetime64[ns]')\n",
    "date_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2022-07-01', '2022-07-02', '2022-07-03', '2022-07-04',\n",
       "       '2022-07-05', '2022-07-06', '2022-07-07', '2022-07-08',\n",
       "       '2022-07-09'], dtype='datetime64[D]')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_list = np.arange('2022-07-01', '2022-07-10', \n",
    "                      dtype='datetime64[D]')\n",
    "date_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-07-01', '2022-07-02', '2022-07-03', '2022-07-04',\n",
       "               '2022-07-05', '2022-07-06', '2022-07-07', '2022-07-08',\n",
       "               '2022-07-09', '2022-07-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "date_list = pd.date_range(start='2022-07-01', \n",
    "                          end='2022-07-10')\n",
    "date_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-07-01', '2022-07-02', '2022-07-03', '2022-07-04',\n",
       "               '2022-07-05', '2022-07-06', '2022-07-07', '2022-07-08',\n",
       "               '2022-07-09', '2022-07-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_list = pd.date_range(start='2022-07-01', periods=10, freq='D')\n",
    "date_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-12-31', '2023-12-31', '2024-12-31', '2025-12-31',\n",
       "               '2026-12-31', '2027-12-31', '2028-12-31', '2029-12-31',\n",
       "               '2030-12-31', '2031-12-31'],\n",
       "              dtype='datetime64[ns]', freq='A-DEC')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_list = pd.date_range(start='2022-07-01', periods=10, freq='Y')\n",
    "date_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Get Min(oldest) & Max(newest) dates`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum date: 2022-07-01 00:00:00\n",
      "Maximum date: 2022-07-10 00:00:00\n"
     ]
    }
   ],
   "source": [
    "date_list = pd.date_range(start='2022-07-01', periods=10, freq='D')\n",
    "\n",
    "\n",
    "print(f'Minimum date: {date_list.min()}')\n",
    "print(f'Maximum date: {date_list.max()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `05-Resampling`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>LTOTALNSA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>864.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-02-01</th>\n",
       "      <td>973.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-03-01</th>\n",
       "      <td>1216.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-04-01</th>\n",
       "      <td>1163.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>1176.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-06-01</th>\n",
       "      <td>1224.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-07-01</th>\n",
       "      <td>1130.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LTOTALNSA\n",
       "DATE                 \n",
       "1976-01-01      864.6\n",
       "1976-02-01      973.3\n",
       "1976-03-01     1216.1\n",
       "1976-04-01     1163.2\n",
       "1976-05-01     1176.1\n",
       "1976-06-01     1224.9\n",
       "1976-07-01     1130.1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('datasets/LTOTALNSA.csv', index_col='DATE', parse_dates=True)\n",
    "df.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1-Resampling : Upesampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>LTOTALNSA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-12-31</th>\n",
       "      <td>12969.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-12-31</th>\n",
       "      <td>14481.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12-31</th>\n",
       "      <td>14981.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-12-31</th>\n",
       "      <td>13758.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-31</th>\n",
       "      <td>11197.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-12-31</th>\n",
       "      <td>10563.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-12-31</th>\n",
       "      <td>10357.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LTOTALNSA\n",
       "DATE                 \n",
       "1976-12-31    12969.8\n",
       "1977-12-31    14481.9\n",
       "1978-12-31    14981.0\n",
       "1979-12-31    13758.4\n",
       "1980-12-31    11197.5\n",
       "1981-12-31    10563.6\n",
       "1982-12-31    10357.3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_totals = df.resample(rule='Y').sum()\n",
    "yearly_totals.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2-Resampling : Downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "        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>LTOTALNSA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-12-31</th>\n",
       "      <td>12969.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-31</th>\n",
       "      <td>13120.378969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-02-28</th>\n",
       "      <td>13254.038620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-03-31</th>\n",
       "      <td>13399.044409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-04-30</th>\n",
       "      <td>13536.009623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-05-31</th>\n",
       "      <td>13673.652110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-06-30</th>\n",
       "      <td>13802.696216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-07-31</th>\n",
       "      <td>13931.331712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-08-31</th>\n",
       "      <td>14054.759973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09-30</th>\n",
       "      <td>14168.848857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               LTOTALNSA\n",
       "DATE                    \n",
       "1976-12-31  12969.800000\n",
       "1977-01-31  13120.378969\n",
       "1977-02-28  13254.038620\n",
       "1977-03-31  13399.044409\n",
       "1977-04-30  13536.009623\n",
       "1977-05-31  13673.652110\n",
       "1977-06-30  13802.696216\n",
       "1977-07-31  13931.331712\n",
       "1977-08-31  14054.759973\n",
       "1977-09-30  14168.848857"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_totals = yearly_totals.resample(rule='M')\n",
    "monthly_totals = monthly_totals.interpolate(method='spline', order=3)\n",
    "monthly_totals.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `06-Shifting`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>LTOTALNSA</th>\n",
       "      <th>Shift_1</th>\n",
       "      <th>Shift_2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>864.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-02-01</th>\n",
       "      <td>973.3</td>\n",
       "      <td>864.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-03-01</th>\n",
       "      <td>1216.1</td>\n",
       "      <td>973.3</td>\n",
       "      <td>864.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-04-01</th>\n",
       "      <td>1163.2</td>\n",
       "      <td>1216.1</td>\n",
       "      <td>973.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>1176.1</td>\n",
       "      <td>1163.2</td>\n",
       "      <td>1216.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LTOTALNSA  Shift_1  Shift_2\n",
       "DATE                                   \n",
       "1976-01-01      864.6      NaN      NaN\n",
       "1976-02-01      973.3    864.6      NaN\n",
       "1976-03-01     1216.1    973.3    864.6\n",
       "1976-04-01     1163.2   1216.1    973.3\n",
       "1976-05-01     1176.1   1163.2   1216.1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shift = df.copy()\n",
    "df_shift['Shift_1'] = df_shift['LTOTALNSA'].shift(1)\n",
    "df_shift['Shift_2'] = df_shift['LTOTALNSA'].shift(2)\n",
    "df_shift.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>LTOTALNSA</th>\n",
       "      <th>Shift_Neg1</th>\n",
       "      <th>Shift_Neg2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>1014.411</td>\n",
       "      <td>1203.993</td>\n",
       "      <td>989.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>1203.993</td>\n",
       "      <td>989.560</td>\n",
       "      <td>1045.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>989.560</td>\n",
       "      <td>1045.307</td>\n",
       "      <td>1246.336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-01</th>\n",
       "      <td>1045.307</td>\n",
       "      <td>1246.336</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>1246.336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LTOTALNSA  Shift_Neg1  Shift_Neg2\n",
       "DATE                                         \n",
       "2021-11-01   1014.411    1203.993     989.560\n",
       "2021-12-01   1203.993     989.560    1045.307\n",
       "2022-01-01    989.560    1045.307    1246.336\n",
       "2022-02-01   1045.307    1246.336         NaN\n",
       "2022-03-01   1246.336         NaN         NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shift = df.copy()\n",
    "df_shift['Shift_Neg1'] = df_shift['LTOTALNSA'].shift(-1)\n",
    "df_shift['Shift_Neg2'] = df_shift['LTOTALNSA'].shift(-2)\n",
    "df_shift.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `07-Rolling`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>LTOTALNSA</th>\n",
       "      <th>QuarterRolling</th>\n",
       "      <th>YearRolling</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>864.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-02-01</th>\n",
       "      <td>973.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-03-01</th>\n",
       "      <td>1216.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-04-01</th>\n",
       "      <td>1163.2</td>\n",
       "      <td>1054.300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>1176.1</td>\n",
       "      <td>1132.175</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-06-01</th>\n",
       "      <td>1224.9</td>\n",
       "      <td>1195.075</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-07-01</th>\n",
       "      <td>1130.1</td>\n",
       "      <td>1173.575</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-01</th>\n",
       "      <td>994.9</td>\n",
       "      <td>1131.500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-09-01</th>\n",
       "      <td>1024.9</td>\n",
       "      <td>1093.700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-10-01</th>\n",
       "      <td>1103.7</td>\n",
       "      <td>1063.400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-11-01</th>\n",
       "      <td>1062.0</td>\n",
       "      <td>1046.375</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-12-01</th>\n",
       "      <td>1036.0</td>\n",
       "      <td>1056.650</td>\n",
       "      <td>1080.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-01</th>\n",
       "      <td>945.0</td>\n",
       "      <td>1036.675</td>\n",
       "      <td>1087.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-02-01</th>\n",
       "      <td>1065.4</td>\n",
       "      <td>1027.100</td>\n",
       "      <td>1095.191667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-03-01</th>\n",
       "      <td>1414.0</td>\n",
       "      <td>1115.100</td>\n",
       "      <td>1111.683333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LTOTALNSA  QuarterRolling  YearRolling\n",
       "DATE                                              \n",
       "1976-01-01      864.6             NaN          NaN\n",
       "1976-02-01      973.3             NaN          NaN\n",
       "1976-03-01     1216.1             NaN          NaN\n",
       "1976-04-01     1163.2        1054.300          NaN\n",
       "1976-05-01     1176.1        1132.175          NaN\n",
       "1976-06-01     1224.9        1195.075          NaN\n",
       "1976-07-01     1130.1        1173.575          NaN\n",
       "1976-08-01      994.9        1131.500          NaN\n",
       "1976-09-01     1024.9        1093.700          NaN\n",
       "1976-10-01     1103.7        1063.400          NaN\n",
       "1976-11-01     1062.0        1046.375          NaN\n",
       "1976-12-01     1036.0        1056.650  1080.816667\n",
       "1977-01-01      945.0        1036.675  1087.516667\n",
       "1977-02-01     1065.4        1027.100  1095.191667\n",
       "1977-03-01     1414.0        1115.100  1111.683333"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rolling = df.copy()\n",
    "df_rolling['QuarterRolling'] = df_rolling['LTOTALNSA'].rolling(window=4).mean()\n",
    "df_rolling['YearRolling'] = df_rolling['LTOTALNSA'].rolling(window=12).mean()\n",
    "df_rolling.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `08-Differencing`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>LTOTALNSA</th>\n",
       "      <th>Diff_1</th>\n",
       "      <th>Diff_2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>864.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-02-01</th>\n",
       "      <td>973.3</td>\n",
       "      <td>108.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-03-01</th>\n",
       "      <td>1216.1</td>\n",
       "      <td>242.8</td>\n",
       "      <td>351.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-04-01</th>\n",
       "      <td>1163.2</td>\n",
       "      <td>-52.9</td>\n",
       "      <td>189.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>1176.1</td>\n",
       "      <td>12.9</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LTOTALNSA  Diff_1  Diff_2\n",
       "DATE                                 \n",
       "1976-01-01      864.6     NaN     NaN\n",
       "1976-02-01      973.3   108.7     NaN\n",
       "1976-03-01     1216.1   242.8   351.5\n",
       "1976-04-01     1163.2   -52.9   189.9\n",
       "1976-05-01     1176.1    12.9   -40.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diff = df.copy()\n",
    "\n",
    "# First order:   973.3 - 864.6 = 108.7\n",
    "df_diff['Diff_1'] = df_diff['LTOTALNSA'].diff(periods=1)\n",
    "\n",
    "# Second order: 1216.1 - 864.6 = 351.5\n",
    "df_diff['Diff_2'] = df_diff['LTOTALNSA'].diff(periods=2)\n",
    "\n",
    "df_diff.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `09-Time Series Visualization`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# استيراد المكتبات\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "\n",
    "# تفضيلات نمط التصوير البياني\n",
    "sns.set_style(\"dark\")\n",
    "sns.set(color_codes=True)\n",
    "\n",
    "# تهيئة التصوير البياني و أبعاده\n",
    "ax, fig = plt.subplots(figsize=(16,8))\n",
    "\n",
    "# تصوير السلسلة الزمنية\n",
    "sns.lineplot(data=df, x=\"DATE\", y=\"LTOTALNSA\",\n",
    "             color='#0063B1', )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>LTOTALNSA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>1204.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-01</th>\n",
       "      <td>1499.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-01</th>\n",
       "      <td>1663.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-01</th>\n",
       "      <td>1495.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-01</th>\n",
       "      <td>1619.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>1341.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>1358.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>1199.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>1605.497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>1106.286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            LTOTALNSA\n",
       "DATE                 \n",
       "2000-01-01   1204.800\n",
       "2000-02-01   1499.800\n",
       "2000-03-01   1663.500\n",
       "2000-04-01   1495.400\n",
       "2000-05-01   1619.700\n",
       "...               ...\n",
       "2020-09-01   1341.099\n",
       "2020-10-01   1358.922\n",
       "2020-11-01   1199.137\n",
       "2020-12-01   1605.497\n",
       "2021-01-01   1106.286\n",
       "\n",
       "[253 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "\n",
    "# تحديد تاريخ البداية والنهاية \n",
    "start = dt.datetime(2000, 1, 1)\n",
    "end = dt.datetime(2021, 1, 1)\n",
    "\n",
    "# اقتطاع الفترة المحددة  \n",
    "df_2000_2020 = df.loc[start:end]\n",
    "df_2000_2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# تهيئة التصوير البياني و أبعاده\n",
    "ax, fig = plt.subplots(figsize=(16,8))\n",
    "\n",
    "# تصوير السلسلة الزمنية\n",
    "sns.lineplot(data=df_2000_2020, x=\"DATE\", y=\"LTOTALNSA\",\n",
    "             color='#0063B1', )\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "43798dc0310b2b2561b4c268d2ab658f1209d4eb0d367a12aa3b34e260041217"
  },
  "kernelspec": {
   "display_name": "Python 3.6.8 64-bit",
   "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": "3.6.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}