Naira - Dollar Rate Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c756759",
   "metadata": {},
   "source": [
    "## Importing Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56487cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da831778",
   "metadata": {},
   "outputs": [],
   "source": [
    "naira_rate = pd.read_csv('exchange05082023.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad44dc47",
   "metadata": {},
   "source": [
    "## viewing the first few rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "38f37e40",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Rate Year  Buying Rate  Central Rate  Selling Rate\n",
      "count  5295.000000  5295.000000   5295.000000   5295.000000\n",
      "mean   2012.267611   214.453896    215.210633    215.500479\n",
      "std       6.245237   112.864427    113.965834    112.836876\n",
      "min    2001.000000    15.590000     66.090000    112.850000\n",
      "25%    2007.000000   131.800000    132.300000    132.800000\n",
      "50%    2012.000000   154.740000    155.240000    155.740000\n",
      "75%    2018.000000   304.950000    305.450000    305.950000\n",
      "max    2023.000000   789.510000   1376.800000    790.510000\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5295 entries, 0 to 5294\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Rate Date     5295 non-null   object \n",
      " 1   Currency      5295 non-null   object \n",
      " 2   Rate Year     5295 non-null   int64  \n",
      " 3   Rate Month    5295 non-null   object \n",
      " 4   Buying Rate   5295 non-null   float64\n",
      " 5   Central Rate  5295 non-null   float64\n",
      " 6   Selling Rate  5295 non-null   float64\n",
      "dtypes: float64(3), int64(1), object(3)\n",
      "memory usage: 289.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "naira_rate.head(10)\n",
    "print(naira_rate.describe())\n",
    "print(naira_rate.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d7c836",
   "metadata": {},
   "source": [
    "##### The summary seem to show an abnormal value (outlier) in the max value returned"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdcd4069",
   "metadata": {},
   "source": [
    "## Investigating the outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d5a3983",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rate Date       1/10/2002\n",
      "Currency        US DOLLAR\n",
      "Rate Year            2001\n",
      "Rate Month              8\n",
      "Buying Rate         15.59\n",
      "Central Rate        66.09\n",
      "Selling Rate       112.85\n",
      "dtype: object\n",
      "Rate Date         9/9/2022\n",
      "Currency        US DOLLAR \n",
      "Rate Year             2023\n",
      "Rate Month       September\n",
      "Buying Rate         789.51\n",
      "Central Rate        1376.8\n",
      "Selling Rate        790.51\n",
      "dtype: object\n"
     ]
    },
    {
     "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>Rate Date</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Rate Year</th>\n",
       "      <th>Rate Month</th>\n",
       "      <th>Buying Rate</th>\n",
       "      <th>Central Rate</th>\n",
       "      <th>Selling Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4804</th>\n",
       "      <td>12/9/2003</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2003</td>\n",
       "      <td>December</td>\n",
       "      <td>136.3</td>\n",
       "      <td>1376.8</td>\n",
       "      <td>137.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rate Date   Currency  Rate Year Rate Month  Buying Rate  Central Rate  \\\n",
       "4804  12/9/2003  US DOLLAR       2003   December        136.3        1376.8   \n",
       "\n",
       "      Selling Rate  \n",
       "4804         137.3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(naira_rate.min())\n",
    "print(naira_rate.max())\n",
    "naira_rate[naira_rate['Central Rate'] > 1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "505c2a13",
   "metadata": {},
   "source": [
    "##### The outlier willbe dealt with later in the cleaning process"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df8c6eaf",
   "metadata": {},
   "source": [
    "## Dropping the blank columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f369b369",
   "metadata": {},
   "outputs": [],
   "source": [
    "#naira_rate.drop(columns=['Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e69b2f",
   "metadata": {},
   "source": [
    "## Converting the date column to date format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "925f683d",
   "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>Rate Date</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Rate Year</th>\n",
       "      <th>Rate Month</th>\n",
       "      <th>Buying Rate</th>\n",
       "      <th>Central Rate</th>\n",
       "      <th>Selling Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-08-04</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2023</td>\n",
       "      <td>August</td>\n",
       "      <td>749.615</td>\n",
       "      <td>750.115</td>\n",
       "      <td>750.615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-08-03</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2023</td>\n",
       "      <td>August</td>\n",
       "      <td>765.949</td>\n",
       "      <td>766.449</td>\n",
       "      <td>766.949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-08-02</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2023</td>\n",
       "      <td>August</td>\n",
       "      <td>740.665</td>\n",
       "      <td>741.165</td>\n",
       "      <td>741.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2023</td>\n",
       "      <td>August</td>\n",
       "      <td>774.855</td>\n",
       "      <td>775.355</td>\n",
       "      <td>775.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-07-31</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2023</td>\n",
       "      <td>July</td>\n",
       "      <td>757.022</td>\n",
       "      <td>757.522</td>\n",
       "      <td>758.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5290</th>\n",
       "      <td>2001-12-13</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2001</td>\n",
       "      <td>December</td>\n",
       "      <td>111.850</td>\n",
       "      <td>112.350</td>\n",
       "      <td>112.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5291</th>\n",
       "      <td>2001-12-12</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2001</td>\n",
       "      <td>December</td>\n",
       "      <td>111.850</td>\n",
       "      <td>112.350</td>\n",
       "      <td>112.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5292</th>\n",
       "      <td>2001-12-11</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2001</td>\n",
       "      <td>December</td>\n",
       "      <td>111.850</td>\n",
       "      <td>112.350</td>\n",
       "      <td>112.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5293</th>\n",
       "      <td>2001-12-10</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2001</td>\n",
       "      <td>December</td>\n",
       "      <td>111.850</td>\n",
       "      <td>112.350</td>\n",
       "      <td>112.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5294</th>\n",
       "      <td>2012-08-09</td>\n",
       "      <td>US DOLLAR</td>\n",
       "      <td>2012</td>\n",
       "      <td>August</td>\n",
       "      <td>154.830</td>\n",
       "      <td>155.330</td>\n",
       "      <td>155.830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5295 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rate Date    Currency  Rate Year Rate Month  Buying Rate  Central Rate  \\\n",
       "0     2023-08-04   US DOLLAR       2023     August      749.615       750.115   \n",
       "1     2023-08-03   US DOLLAR       2023     August      765.949       766.449   \n",
       "2     2023-08-02   US DOLLAR       2023     August      740.665       741.165   \n",
       "3     2023-08-01   US DOLLAR       2023     August      774.855       775.355   \n",
       "4     2023-07-31   US DOLLAR       2023       July      757.022       757.522   \n",
       "...          ...         ...        ...        ...          ...           ...   \n",
       "5290  2001-12-13   US DOLLAR       2001   December      111.850       112.350   \n",
       "5291  2001-12-12   US DOLLAR       2001   December      111.850       112.350   \n",
       "5292  2001-12-11   US DOLLAR       2001   December      111.850       112.350   \n",
       "5293  2001-12-10   US DOLLAR       2001   December      111.850       112.350   \n",
       "5294  2012-08-09  US DOLLAR        2012     August      154.830       155.330   \n",
       "\n",
       "      Selling Rate  \n",
       "0          750.615  \n",
       "1          766.949  \n",
       "2          741.665  \n",
       "3          775.855  \n",
       "4          758.022  \n",
       "...            ...  \n",
       "5290       112.850  \n",
       "5291       112.850  \n",
       "5292       112.850  \n",
       "5293       112.850  \n",
       "5294       155.830  \n",
       "\n",
       "[5295 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naira_rate['Rate Date'] = pd.to_datetime(naira_rate['Rate Date']).dt.date\n",
    "naira_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff6b30aa",
   "metadata": {},
   "source": [
    "## Converting the columns to lower case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31647672",
   "metadata": {},
   "outputs": [],
   "source": [
    "naira_rate.columns = naira_rate.columns.map(str.lower)\n",
    "naira_rate.columns = naira_rate.columns.str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ec31d62",
   "metadata": {},
   "source": [
    "## Filtering the dataset to include only US Dollar rates and correcting the outlier values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6fc8f412",
   "metadata": {},
   "outputs": [],
   "source": [
    "naira_us_rate = naira_rate[naira_rate['currency'] == 'US DOLLAR'].reset_index().drop(columns=['currency', 'index'])\n",
    "naira_us_rate['central_rate'].min()\n",
    "naira_us_rate[naira_us_rate['central_rate'] > 1000]\n",
    "naira_us_rate.iloc[4804, 4] = 136.8\n",
    "naira_us_rate[naira_us_rate['central_rate'] < 100]\n",
    "naira_us_rate.iloc[3674:3680]\n",
    "naira_us_rate.iloc[3676, 4] = 116.09\n",
    "naira_us_rate.iloc[3904, 4] = 124.52\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac56e4d",
   "metadata": {},
   "source": [
    "## Segmenting by president tenures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ffe5484e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14592\\3288208942.py:1: FutureWarning: Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior. In a future version these will be considered non-comparable. Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.\n",
      "  Obasanjo = naira_us_rate[naira_us_rate['rate_date'] < pd.to_datetime('2007-05-29')].sort_values(by='rate_date').reset_index()\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14592\\3288208942.py:2: FutureWarning: Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior. In a future version these will be considered non-comparable. Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.\n",
      "  Yar_Adua = naira_us_rate[(naira_us_rate['rate_date'] >= pd.to_datetime('2007-05-29')) & (naira_us_rate['rate_date'] < pd.to_datetime('2010-05-06'))].sort_values(by='rate_date').reset_index()\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14592\\3288208942.py:3: FutureWarning: Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior. In a future version these will be considered non-comparable. Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.\n",
      "  Jonathan = naira_us_rate[(naira_us_rate['rate_date'] >= pd.to_datetime('2010-05-06')) & (naira_us_rate['rate_date'] < pd.to_datetime('2015-05-29'))].sort_values(by='rate_date').reset_index()\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14592\\3288208942.py:4: FutureWarning: Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior. In a future version these will be considered non-comparable. Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.\n",
      "  Buhari = naira_us_rate[(naira_us_rate['rate_date'] >= pd.to_datetime('2015-05-09')) & (naira_us_rate['rate_date'] < pd.to_datetime('2023-05-29'))].sort_values(by='rate_date').reset_index()\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14592\\3288208942.py:5: FutureWarning: Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior. In a future version these will be considered non-comparable. Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.\n",
      "  Tinubu = naira_us_rate[naira_us_rate['rate_date'] >= pd.to_datetime('2023-05-29')].reset_index()\n"
     ]
    }
   ],
   "source": [
    "Obasanjo = naira_us_rate[naira_us_rate['rate_date'] < pd.to_datetime('2007-05-29')].sort_values(by='rate_date').reset_index()\n",
    "Yar_Adua = naira_us_rate[(naira_us_rate['rate_date'] >= pd.to_datetime('2007-05-29')) & (naira_us_rate['rate_date'] < pd.to_datetime('2010-05-06'))].sort_values(by='rate_date').reset_index()\n",
    "Jonathan = naira_us_rate[(naira_us_rate['rate_date'] >= pd.to_datetime('2010-05-06')) & (naira_us_rate['rate_date'] < pd.to_datetime('2015-05-29'))].sort_values(by='rate_date').reset_index()\n",
    "Buhari = naira_us_rate[(naira_us_rate['rate_date'] >= pd.to_datetime('2015-05-09')) & (naira_us_rate['rate_date'] < pd.to_datetime('2023-05-29'))].sort_values(by='rate_date').reset_index()\n",
    "Tinubu = naira_us_rate[naira_us_rate['rate_date'] >= pd.to_datetime('2023-05-29')].reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e64872d",
   "metadata": {},
   "source": [
    "## Calculating the rolling mean to smoothen the chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "729f18b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Obasanjo['rolling_mean'] = Obasanjo['central_rate'].rolling(30).mean()\n",
    "Yar_Adua['rolling_mean'] = Yar_Adua['central_rate'].rolling(30).mean()\n",
    "Jonathan['rolling_mean'] = Jonathan['central_rate'].rolling(30).mean()\n",
    "Buhari['rolling_mean'] = Buhari['central_rate'].rolling(30).mean()\n",
    "Tinubu['rolling_mean'] = Tinubu['central_rate'].rolling(30).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1091cc9",
   "metadata": {},
   "source": [
    "## Visualizing the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cf40a49e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14592\\1631315213.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax1.set_yticklabels(ax1.get_yticklabels(), alpha=0.3, fontsize= 22)\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14592\\1631315213.py:11: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax1.set_xticklabels(ax1.get_xticklabels(), alpha=0.5)\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14592\\1631315213.py:17: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax2.set_xticklabels(ax2.get_xticklabels(), rotation=45, alpha=0.5)\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14592\\1631315213.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax3.set_xticklabels(ax3.get_xticklabels(), alpha=0.5)\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_14592\\1631315213.py:29: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax4.set_xticklabels(ax4.get_xticklabels(), rotation=45, alpha=0.5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2800x850 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.style as style\n",
    "\n",
    "style.use('fivethirtyeight')\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1, ncols=4, figsize=(28,8.5))\n",
    "ax1.plot(Obasanjo['rate_date'], Obasanjo['rolling_mean'])\n",
    "ax1.set_ylim(110, 500)\n",
    "ax1.set_title('Obasanjo', color='blue', alpha=0.5, fontsize=25, weight='bold')\n",
    "ax1.set_yticklabels(ax1.get_yticklabels(), alpha=0.3, fontsize= 22)\n",
    "ax1.set_xticklabels(ax1.get_xticklabels(), alpha=0.5)\n",
    "\n",
    "\n",
    "ax2.plot(Yar_Adua['rate_date'], Yar_Adua['rolling_mean'], color='green', alpha=0.7)\n",
    "ax2.set_ylim(110, 500)\n",
    "ax2.set_yticklabels([])\n",
    "ax2.set_xticklabels(ax2.get_xticklabels(), rotation=45, alpha=0.5)\n",
    "ax2.set_title('Yar Adua', color='green', fontsize=25, weight='bold')\n",
    "\n",
    "ax3.plot(Jonathan['rate_date'], Jonathan['rolling_mean'], color='red', alpha=0.6)\n",
    "ax3.set_ylim(110, 500)\n",
    "ax3.set_yticklabels([])\n",
    "ax3.set_xticklabels(ax3.get_xticklabels(), alpha=0.5)\n",
    "ax3.set_title('Jonathan', color='red', alpha=0.5, fontsize=25, weight='bold')\n",
    "\n",
    "ax4.plot(Buhari['rate_date'], Buhari['rolling_mean'], color='black')\n",
    "ax4.set_ylim(110, 500)\n",
    "ax4.set_yticklabels([])\n",
    "ax4.set_xticklabels(ax4.get_xticklabels(), rotation=45, alpha=0.5)\n",
    "ax4.set_title('Buhari', color='black', alpha=0.7, fontsize=25, weight='bold')\n",
    "ax1.text(11350, 570.0, 'NAIRA-USD rate recorded a 300% increase under the last four Nigeria presidents',\n",
    "         fontsize=26, weight='bold')\n",
    "ax1.text(11350, 550.0, '''NAIRA-USD exchange rates under Olusegun Obasanjo (2001-2007), Yar'Adua (2007-2010), Goodluck Jonathan (2010-2015) and Muhammadu Buhari (2021-2023)''',\n",
    "        fontsize=22)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41cdf521",
   "metadata": {},
   "source": [
    "## Visualizing the chart from 2001 till date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0ace43f3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ghvjnf/7nNm/snopkMhnf//7345FHHjnpUpERRwOLr3zlK3H48OHTVkO2Mo3NmWZTbNy4Ma1t5MiRGc+ZaRbesY/ftGlT2mth0KBBGW9gZ7rGli1b2v3ao+t11u8xr2EA6NnMbAGAPqagoCA+8IEPxI9+9KPWtl/96ldx+eWXn/LMhYULF6a1zZgxIyKOhhPHhysLFiyI22677ZSu1VFHjhyJ3//+92ntc+fOPeHjdu/enbYme3l5eUyaNCny8vLi/PPPT1kOZe/evbF8+fK46KKLTkvdnWXy5Mnxb//2byltTU1NsXDhwtbv46BBg+Kcc86JSZMmxfTp0ztliZdJkyalfdJ8586dsW/fvigvLz8t11iwYEFs2rSpzeMVFRXxH/7Df0hpKy4ujk9/+tPxpS99qfUGbUvA8vWvfz1++ctfpv08f+ADH4gJEyaktDU0NMS3v/3tlE89txg6dGhMmDAhSkpKYs+ePbFu3bo4cOBAu55TyxJHw4cPjylTpsTevXvjpZdeSrmp29jYGE899VR87GMfy3iOfv36xVlnnRUDBw6MsrKyKCkpifr6+tixY0e89tprKTd4N27cGFVVVRlvKh6vZRPwU6ntsccey/i9OvPMM2Py5MmtS/odv8RTW5YuXRq//e1v09rPOuusOOuss1qXVKupqWk9VlNTE9///vfjy1/+cruucapWrlyZFsCVlpaedIbGkCFDYvjw4VFWVta63Nz+/fvj9ddfT9sg/eGHH04Jha+66qqora2NrVu3xooVK1L6tuz/cXxbi3nz5kVVVVVaPRMmTIiRI0fG4cOHY8WKFVFbW9t6bOvWrfHjH/84/vzP//yEz6mrHL/cU0RkHKt37NiR1tbWeJSp/djHZ/pZ7ci5jhw5Ert3746hQ4dmfAy519HfY17DAND7CVsAoA965zvfGb/5zW/i7bffjoijywstXLiwXTdUj/f666+n3aAaNWpUjBkzJiKOhi3HryVeVVUVf/Inf9IpeyQsW7YsDh06FBFHb/6uXbs27SbG2WefHe9973tPeJ7j1+OPOPpcWgKpyy67LG3t+YULF3b7sOWCCy6ICRMmxLp169rss3fv3li6dGksXbo0Hnzwwbj44ovjIx/5SNrG09lo6wbi6QxbVqxYkXZT6lhjx45NC1sijt6kuummm+KRRx5pbduwYUP84Ac/SLthde6558YHPvCBtHM8+eSTsWvXrpS2RCIRd9xxR1x77bUpP/uNjY2xYMGCdj/vWbNmxT333BMFBUffyj/22GNpm3of/7MZcTRc+vznPx+TJk1qc1bXhg0b4gtf+ELKz/6SJUvaPTacSm3Nzc3xxBNPpJ3r8ssvj7/4i79o3S/nzTffjC9/+cvtCqb+9V//NeXfiUQiPvWpT6Us09bQ0BDf+c53Uj6Jvnr16li+fHnGpZ5O1QMPPNB6verq6li5cmXK1zeRSMTHPvaxNpc1vPbaa+NP/uRP2pz50tTUFP/7f//vlE/Cv/XWW7F58+bW5QFblsF77rnn0l4TU6dOzbi3Q0vNx4ezhYWF8V//63+NqVOntrbV1tbGV7/61ZSZSQsWLIibbrrptI4bp2LlypVpr9uCgoJ4z3vek9a35XfHsYqKijKeN9Nr6NjHZwpaj93PpT3XyFQP3Ud7f495DQNA3yFsAYA+qKCgIG655ZaUDa4feeSRmDVrVofP1dZSWy3GjBkTI0eOjDfffLO1bdeuXbFmzZqYNGlSh693Mq+//nra/jEtCgoK4oorroiPfOQjJ92vJdNsnWOf12WXXRb/8i//knLT9MUXX4y6uro2b6h1F5/5zGfiW9/6VsYlc46XTCbj5ZdfjjVr1sQXvvCFOO+8805LDW19jbrLsiU333xzLF++POVn6fif9ZKSkpQw4FiZloC59tprM4Z8BQUFcdVVV7Wrrn79+sUdd9zRGmZERLznPe+Jn/70pylLJR0f9EREDBgwoDUM3L59e2zcuDHefvvtOHz4cDQ2Nrb+LBcVFaUsvdWen5Nsatu0aVPasloFBQXxkY98JOVrO3LkyLjmmmvSwtvjbdu2LW320dSpU9P2wyksLIybb745bdmfxYsXn9aw5fHHH2/z2KhRo+LDH/5w6z4fmbSMk/X19bFu3brYvn177N+/P+rr61u/rpluym/atKldezGdyOrVq2P//v0pbVdeeWXKTdqIo0sqve9974vvf//7rW3JZDJeeOGFuOmmm1L63nPPPXHPPfdkVVd7bdq0Kb773e+mBecf/vCH05YLjMi8j0tbMz6P/TnP9Pjj9/uKiIxjRVvnisi8xxbdR3t/j/W21zAA0DZhCwD0UXPmzIlf//rXsX379og4uvzJ/PnzY8qUKe0+R2NjYzz//PNp7S1LiLWYPn162icr58+f3ylhy4mcd955cc0115w0aNm0aVPa3hEtS4i1qKioiPPOOy9lhkhdXV3GWQALFy6M9evXn/Cad9xxR3ufRtYqKiri61//ejz99NPx5JNPtmt/j8OHD8d9990X3/ve99q8YdgRbYUqJ/vedJX8/Pz49Kc/HZ/73OfavOH58Y9/PIYNG5bW3tzcnPH7/a53vSvrui644IK0GTD9+vWLsrKylBtqbX0ifsmSJfHzn/+8Q3u6tHeJs1OtbfPmzWnnOvvsszNuVp9pptzxMn3tX3311bj11ltP+LgWbYW1p1thYWFceeWVJ50Nt3///vjXf/3XWLBgQcYb+Cd6XLYyfS3++Mc/xh//+Md2Pf507lfUUatXr47vfOc7aTNM3vve98Z1112X8TFFRUVpY1Nb+6Zk2vvi2BkqmWa+tLVfRlvt3T247+va+3vMaxgA+g5hCwD0UXl5eXHzzTenfIrxkUceSdt74kSWLVuWdjNg5MiRrUuItZgxY0Za2LJkyZL4xCc+Ef369TuF6k/N6tWr44tf/GJ87nOfO2GoNH/+/LS26dOnp33CecaMGWnLcWVajm358uXx3HPPnbC248OWjgYamTYlP9E5WpbRec973hNvvfVWrF69OtatWxevv/56awB3vOrq6lizZk2HArm2ZNobISJi4MCBWZ+7xT333JM2m6Ejhg8fHrfffnv88z//c9qxSy65pM1zHzhwIG1D7kQi0eZG2x3R1rI1x7+OMv08PPnkk/HjH/+4w9fM9Gn/01lbphuKgwcPzniuTAHM8fbu3XvSPp35+PZqaGiIhx56KNasWRN//dd/nXFZxYMHD8aXvvSleOutt07p/NnK9mZvyz4+Xe2ll16K//W//lfa1+Dd7373CYPt/v37p91Ab+vnP9PX99ib7KWlpWnH2wpu22rvLuEzmbXn95jXMAD0LcIWAOjDZs2aFb/+9a9bl9zZuXNnPPPMM+1+fKYlxPLz81v3KDhWQUFByqd3Dx48GC+99FLaLJhs3XLLLfHBD34wDh48GGvWrIkHHnigdW+aiKM3L/7P//k/8Xd/93etG9QeK5lMxqJFi9Lad+7cmfa8Mt2EWLFiRezduzcGDRqU1fMoKSlJazvRTe9MxzLd7MvkzDPPjDPPPDPmzp0bEUef6+9+97t48skn0/pu3rz5tIQtmfYUGTp06Gnbr+V0aWtvm40bN8aBAwcy/gx1prKysoztbS111OLAgQPx05/+tDNKanWqtWXS1p4ynbHP0/FO99JNP//5z6OpqSl27twZjz/+eNrr6uWXX45HH320dU+GY/3bv/3bKd2kjcgcuHW1XCyD9dxzz8UPf/jDtBkpN954Y9x+++0nfOzw4cPT9vhqK3zL1H7s0mSZZr21deM6U3u/fv3aFS6SO+35PeY1DAB9i7AFAPqwRCIRt9xyS3zve99rbct0gz2TQ4cOxUsvvZTWvmXLlrQluNqyYMGC0x62tCgtLY1p06bFyJEj43Of+1xKGLFv37549NFHM37CecWKFVFTU5PW/uqrr8arr7560us2NzdHVVVVXH/99VnVX1FRkbZXRqZ9OFocf4Ow5RynYsiQIXHnnXfGli1bYs2aNSnHjhw5ckrnPNb+/fszzh46nftknA6LFi3KGChGRNTU1MQ//dM/xV/91V+lHRswYEDk5eWlzG5JJpPx5ptvxqhRozqt3hNZtmxZ2qeky8vL44477ogpU6a01hwRcffdd3fZ7I6IzLOZMv08n6j9WJmCznHjxsX555/frnpOJRw6mfz8/Bg+fHjceeedceTIkXj66adTjv/mN7+Jq6++Ou01u3Tp0rRzXXHFFXHDDTfEsGHDWpeZeuqpp+JHP/rRaa870/dm8uTJMXbs2HY9vqvD08ceeywefPDBlJvUiUQibr/99rjhhhtO+vhx48bFqlWrUtramul37D5kLcaPH9/697POOisSiURKLfv27csY0mY615gxY07Lko10jvb+HvMaBoC+RdgCAH3cZZddFmeddVZs2rQpItq/ZNCSJUuyXuLi1Vdf7fTZAWeeeWbccMMN8cgjj6S0/+EPf4gbb7wxKisrU9rburneEQsXLkwJW05lQ+hzzz03Xn755ZS2FStWZOzb2NiY8RO255xzTlrb8uXLo1+/fu268ZzpJk1bsxfaq7GxMf7+7/8+7dOyiUQi4+bxubJ79+6My4cda8mSJfHMM8+kbW6fl5cXZ599dtpa+U899VR87GMfO92ltkumkOLGG2+MWbNmpbTt3LmzS4OWiMh40++1116Lurq6tD0rlixZctLznX322Wlt+fn57d4X6fgl4E63D3/4w/HCCy+k7CVSX18fjz76aHziE59I6Xt8wFpSUhL33HNP2gyf9u6rkGlm0Imeb6YxZODAge36Wnb1p/L/9V//NW25yoKCgrj77rvTlnZsy8UXXxy///3vU9qWL1+e1q+mpqZ1RmiL4uLilHG1f//+cf7556eNzStWrIiZM2ee9BqXXnppu2qm63Xk95jXMAD0Laf/Y1sAQI+SSCTigx/8YIcfdzpCicbGxnj++eezPs/JXHfddWk3bRsbG9NuqtXX17frZu7JbNiwoUMbkGeS6Ubbhg0b0ja1TSaT8a//+q9py9AUFhZmnCmyefPm+MpXvhJf/OIX49///d+jtrY24/XXrVsXy5YtS2s/9pPbHbV+/fr42te+lnGG0Dvf+c6czfo4XjKZjPvuuy/lZngikYi//Mu/TJs18S//8i8Z1+2/7LLL0tqeeOKJmDdvXtoNrKampliwYEG88sorp+cJZJDpE/LHb0y/b9++uO+++zqthracddZZaaFnXV1d2gyF7du3x7x58056vlGjRqX9LL3xxhvx0EMPtRkQ7969O5588sn43Oc+lzab63QrKyuL97znPWntzz77bFrQVVCQ+tm4urq6tCWJFixYkPET9plk2nA906yKFpMmTUoLw59//vl4/PHH27zBu3379vjNb34Tn/nMZzLOxvuHf/iHuPXWW1P+HD+bpCOSyWT86Ec/SgtaiouL47Of/Wy7g5aIo5/4P375rzfffDOeeuqplOv99Kc/TXsdz5w5M+3r+853vjPtGo888kjK2LJp06a0719eXl6H6qbrdPT3WG98DQMAbTOzBQCIadOmxdlnnx3r169vV/89e/ak3RzLz8+PH/7whyecpfL000/HD3/4w5S2hQsXZrzxeDqVlZXFu971rrRw5amnnor3ve99rctkvPjii2mfVB03blz8j//xP054/u9///uxcOHClLaFCxfGf/yP//GUax47dmxccsklabNb/vmf/zmefvrpGD9+fDQ3N8e6desy3miZO3fuCTebf+211+K1116LH/3oR603pwcMGBD19fXx1ltvZfyU7YgRI2LcuHHtqn/BggWxadOmaGpqitra2tiwYUOb69aPGzcu7rzzznadtyNaajiRs88+O2bPnp3S9pvf/Cbt0+jvfve7Y+bMmVFUVBTf/va3W9vr6urivvvui69+9aspgcY111wT8+bNS5lRkkwm41/+5V9i3rx5MWHChCguLo59+/bF2rVrY9++fR2e/dQRmUKyBQsWxJtvvhnjx4+PAwcOxMqVK1NuAneVvLy8uPbaa+NnP/tZSvtTTz0Va9eujYkTJ8bBgwfj5ZdfbvfMu1tvvTX+7u/+LqXtt7/9bTz77LNx3nnnRUVFRTQ1NcW+ffti27ZtUV1dfdqeT3u8973vjccffzzl+TQ0NMTvf//7lH1Fzj777Fi5cmXrv5PJZHzhC1+Iiy++OIqLi2PTpk2xYcOGdl/32D1FWixZsiS+/vWvx4gRI1p/hm+99dYoLi6OoqKiuOmmm+InP/lJymMeeOCB+P3vfx/nnHNOlJeXx5EjR6Kmpia2bNkSe/bsaXc9p8PPf/7zlDCkxfjx42PZsmUZQ+MW11xzTcrXJJFIxEc+8pH4zne+k9Lvn//5n+OFF16IoUOHxuuvv54WVPbv3z8+9KEPpZ1/zpw58cQTT6R8j7Zt2xZ/9Vd/FRdddFHU19fHSy+9lBYCXnfddTFkyJC08x04cCAeffTRlLbjZ9BFHF028NChQyltH/jAB7p8j6me7nT8HvMaBoC+RdgCAERExIc+9KH45je/2a6+CxYsSPtUb6ZPTx5v2rRp8aMf/Sjl05Tr1q2L6urqjJsJn0433nhjPPnkkyl7jtTX18e8efNaQ5FMs3UyzVA43vTp0zOGLbfeemtWG3rffffd8YUvfCFtCagNGzac8ObMWWedFbfddlu7rpFMJmPr1q1pS+Icr6CgIO688852P58VK1a0uezZsS666KL49Kc/3eaG6NloTw1XXnllStiyYcOG+OUvf5nSZ+jQofHhD384Io7OOLryyivjueeeaz3++uuvx6OPPpoyQ6ywsDD++q//Or7yla/E4cOHU85XXV3d5Tf3zz///JTlAlsc/7M0duzY2Lt3b5sbeXeW66+/PhYtWpRW37Zt21JmiY0ePfqkP6sRR1+TN954Y/zud79Lad+/f3/GPRS6Wnl5eVx99dVpM3X++Mc/xvvf//7W5fquvfbalBu1ERGHDx+ORYsWpbRNnTr1hKFCi9GjR8eQIUNi586drW3JZDLttXLTTTe1foL+uuuuizfeeCPtmrt3727XHjqdra0bw6tXr864vOKxpk2blnbz+h3veEdcc801KfuXJZPJjEt9RRwNC+++++6Me2Tl5eXFn//5n8fXvva1lNdUTU1NPPPMMxnPN2HChIzBTcTRcPfxxx8/4XOKODomHR/CXHfddcKWDjodv8e8hgGgb7GMGAAQEUdvFkyYMKFdfY8PFiKiXRvdDxw4MCZOnNiu851ugwYNyrikyx/+8Ic4ePBg7Nu3L+NNlenTp5/03BdddFEUFRWltO3cuTPWrl17yvVGHK35G9/4RkyZMqXdj5k1a1Z85StfaTO8GDp0aIc3vC0vL4+/+qu/igsuuKBDjzuRoUOHxsc+9rH4b//tv3WbG4D19fXx/e9/PxobG1vbEolEfOpTn0pZuuVjH/tYnHHGGSmPffTRR9Nubp511llx7733Zlwzv6slEon4zGc+k1b3sUaNGhV//dd/3SnB18kUFBTE5z//+RgzZkybfUaPHh2f+9zn0tr79euXsf/tt98ef/ZnfxYlJSXtrmPMmDEZb5p3hhtvvDHjEkNPPPFE67/f8Y53xPve9742z5FIJOK2225L23vnRP1vv/32DoXAiUQi/uIv/iI+9KEPtfm1zuS8887LuORRT/Dxj388br755rTvz/HKy8vjv/yX/3LC33+jRo2KL37xiyf82W5x2WWXxec///m03yd0T+35PeY1DAB9i5ktAECrD33oQ/G3f/u3J+yzefPm2LJlS0pbIpGIadOmtesa06dPT/u08cKFC+Pmm2/uWLGn4D/8h/8QTz/9dDQ1NbW2HTx4MP7whz9EcXFxSnvE0ZtkI0eOPOl5i4qK4qKLLooXXnghpX3hwoXt2oj+RCoqKuKLX/xivPbaa/H888/Ha6+9Fjt37oxDhw5FIpGI/v37x5lnnhnnnXdezJkzJ0aPHn3C81122WUxffr0eOONN2LdunWxYcOG2LFjR+zatSsOHz4cjY2NUVxcHAMHDoyxY8fGxRdfHDNmzOjQDesWeXl5UVBQECUlJTFgwIAYPHhwjBo1Ki644IKYOnVqVrN+OsNPfvKT2L59e0rbtddeG5MmTUpp69+/f3zyk5+Me++9t3WGV3Nzc9x3333xrW99K+Xm1IgRI+Ib3/hGrFy5MhYvXhyvvfZa7NmzJw4dOhT9+/eP8vLyGDNmTFx44YVxySWXdOrzGz58eHzrW9+K3/3ud7F06dJ4++23o7CwMIYOHRqXX355XHvttTm9yTto0KD45je/GfPmzYuqqqp46623IpFIxPDhw+Pyyy+P9773vbF///60x51oubx3vetdMXPmzJg/f36sWLEiNm/eHAcOHIiGhoYoKSmJioqKGDFiRJx33nlx0UUXnfT1czqdccYZMWfOnLQZDk888UTccMMNrT9Ht912W0yaNCnmzZsX69evj8OHD0d5eXlMnDgxrrnmmpgwYULKTKuTmTFjRnzlK1+Jxx9/PF5//fXYv39/SsCYSV5eXtx8883xrne9K5599tlYvXp1bN26NWpra6OxsTFKSkqisrIyRo0aFeeff35MnTq102crdqZEIhEf+tCHWr8/y5Yti927d8fhw4ejrKwsRowYEZdccklcffXVUVpaetLzjR49Or71rW9FVVVVLFmyJDZs2BD79++P/Pz8qKioiIkTJ8bs2bNj8uTJXfDs6IjT8XvMaxgA+o7E3r17kyfvBgAAkFuPP/54PPDAAylt11xzTafs+QMAANARlhEDAABy7oknnoiqqqq0zcJbvPLKK/Hzn/88rf3SSy/t7NIAAABOyjJiAABAzm3atCmeeeaZKCoqivHjx8fw4cOjqKgoDh48GOvXr09b4i3i6GbiU6dOzUG1AAAAqYQtAABAt1FfXx9r1qyJNWvWnLDfGWecEX/5l3/ZRVUBAACcmLAFAADoUS655JK46667oqKiItelAAAAREREYu/evclcFwEAAPRtBw4ciJdeeilWrlwZ27Zti3379sWBAwcikUhEaWlpnHnmmXHOOefE7NmzY+zYsbkuFwAAIIWwBQAAAAAAIAt5uS4AAAAAAACgJxO2AAAAAAAAZEHYAgAAAAAAkAVhCxB1dXWxYcOGqKury3UpQDdibAAyMTYAmRgbgEyMDUAmvXVsELYAERHR1NSU6xKAbsjYAGRibAAyMTYAmRgbgEx649ggbAEAAAAAAMiCsAUAAAAAACALwhYAAAAAAIAsCFsAAAAAAACyIGwBAAAAAADIgrAFAAAAAAAgC8IWAAAAAACALAhbAAAAAAAAsiBsAQAAAAAAyIKwBQAAAAAAIAvCFgAAAAAAgCwUdPQByWQyXnvttVi6dGns2bMn6uvrY8CAATFmzJi4/PLLY9CgQSn96+vrY+HChbF27do4ePBglJaWxsSJE2P27NlRVFSU8RqrVq2KpUuXxs6dOyM/Pz9GjhwZc+bMiTPPPPOUniQAAAAAAEBn6fDMlqeffjoeffTR2LNnT5x77rlx6aWXxqBBg2LZsmXx4x//OHbu3Nnat6GhIR566KFYsmRJnHHGGTF9+vQYPHhwLFmyJB566KFoaGhIO/+iRYvit7/9bdTW1sbFF18cEydOjG3btsWDDz4Ymzdvzu7ZAgAAAAAAnGYdmtlSW1sbL774YpSXl8cnPvGJlJkpL774Yjz11FOxZMmSuP766yMiYvHixVFdXR0zZsyIq666qrXv/Pnzo6qqKhYvXhxXXHFFa/uePXtiwYIFUVlZGXfccUcUFxdHRMS0adPigQceiHnz5sVdd90VeXlWPwMAAAAAALqHDqUW+/bti2QyGaNGjUpbAuzss8+OiIhDhw5FxNHlxpYtWxaFhYUxa9aslL4zZ86MkpKSWL58eSSTydb2FStWRHNzc8ycObM1aImIGDJkSEyZMiVqamrMbgEAAAAAALqVDoUtFRUVkZ+fH9u2bYv6+vqUY+vXr4+IiLFjx0Z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      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 8))\n",
    "plt.plot(naira_us_rate['rate_date'], naira_us_rate['central_rate'], color='grey', alpha=0.3)\n",
    "plt.yticks(alpha=0.5)\n",
    "plt.xticks(alpha=0.5)\n",
    "plt.plot(Obasanjo['rate_date'], Obasanjo['central_rate'])\n",
    "plt.plot(Yar_Adua['rate_date'], Yar_Adua['central_rate'], color='green', alpha=0.7)\n",
    "plt.plot(Jonathan['rate_date'], Jonathan['central_rate'], color='red', alpha=0.6)\n",
    "plt.plot(Buhari['rate_date'], Buhari['central_rate'], color='black')\n",
    "plt.title('NAIRA-USD Exchange Rate: 2001 Till Date', weight='bold', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "49864a3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-15.460845175766636\n",
      "-6.270351617440227\n",
      "-8.214919678714864\n",
      "-133.61473467005072\n"
     ]
    }
   ],
   "source": [
    "print((Obasanjo['central_rate'].iloc[0]) -Obasanjo['central_rate'].mean())\n",
    "print((Yar_Adua['central_rate'].iloc[0]) - Yar_Adua['central_rate'].mean())\n",
    "print((Jonathan['central_rate'].iloc[0]) - Jonathan['central_rate'].mean())\n",
    "print((Buhari['central_rate'].iloc[0]) - Buhari['central_rate'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6badf787",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe4711c4",
   "metadata": {},
   "source": [
    "The Naira has only gone one direction since the time of President Olusegun Obasanjo and that is upwards. Although, the naira on very few ocassions in the past record some gains against the dollar, it is almost always immediately followed by an upward trend.\n",
    "\n",
    "In summary the following is how the past presidents have been able to sustain the value of the Naira against the dollar during their tenures:\n",
    "| President | Average change in naira |\n",
    "|:-----------|---------------------:|\n",
    "| Obasanjo   |                 -15 |\n",
    "| Yar'Adua   |                  -6 |\n",
    "| Jonathan   |                  -8 |\n",
    "| Buhari     |                -134 |\n",
    "\n",
    "According to the stats Yar'Adua's tenure seems to have had the most stability which is closely followed by Jonathan's, while Buhari's tenure have seen the highest surge in the value of the Naira to the Dollar.\n",
    "\n",
    "Empirically, there is enough evidence to predict that there will be a continued surge in the trend of the Naira to dollar. The Naira has continuously risen since the Obasanjo era and does not look like it would make a change in the foreseeable future with Tinubu Era continuing the trend."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6660fcd3",
   "metadata": {},
   "source": [
    "## Recommendation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33e8cd18",
   "metadata": {},
   "source": [
    "I have always been excited about finding this dataset that explains the value of the naira against the dollar at different periods in time. Finally, I am happy that I have been able to achieve this goal. \n",
    "\n",
    "A part of taking this work further is to use the most applicable machine learning methods to predict the future value of the naira.\n",
    "\n",
    "Lastly, a story can be told about how the Naira has been affected by fared different major political and economic event that have happened over the years."
   ]
  }
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