1. Dataset Walkthrough.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cde97e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98df1dc2",
   "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>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>1968</td>\n",
       "      <td>2.576068e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>1969</td>\n",
       "      <td>2.843420e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>1970</td>\n",
       "      <td>3.138550e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>1971</td>\n",
       "      <td>3.642691e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>1972</td>\n",
       "      <td>4.331606e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country Name Country Code  Year         Value\n",
       "0   Arab World          ARB  1968  2.576068e+10\n",
       "1   Arab World          ARB  1969  2.843420e+10\n",
       "2   Arab World          ARB  1970  3.138550e+10\n",
       "3   Arab World          ARB  1971  3.642691e+10\n",
       "4   Arab World          ARB  1972  4.331606e+10"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('gdp.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8eb7b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     11507\n",
       "unique      256\n",
       "top         HKG\n",
       "freq         57\n",
       "Name: Country Code, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country Code'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e52524b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                    11507\n",
       "unique                     256\n",
       "top       Hong Kong SAR, China\n",
       "freq                        57\n",
       "Name: Country Name, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country Name'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad39663d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Year'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8bc46a3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Year'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "98f92d41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1960"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Year'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b27768",
   "metadata": {},
   "source": [
    "## Analysing India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f44a3b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pr = df[df['Country Code'] == 'IND']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "34088b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q2bKlbrnlFo98icTERElyBxJ4TrNmzdztCwDwMTs+MZedRlhbRz3Va9Kzw4cPKz4+XqtXr1b//tVPMzt58mQtWbJE27Ztc2+bOHGivv/+e61bt65Gn1PTSVMqKiqq3BwO9RMaGkqPCAD4qoLd0uu9paAQ6bGfpSZ2qys6T01/v+t1o7zCwkJJUosWLS64z7p16zR06NAq24YNG6bp06errKys2rEIpaWlKi0tdT93OBw1qic4OJgfTwBA47DzdK9I22t9MojURp0HsBqGoYyMDF177bVKS0u74H75+flKSEiosi0hIUHl5eUqKCio9j1Tp06V3W53P1JSUupaJgAAganyFE1H/z5FI9UjjNx///364YcfNHfu3Evue+6A0sozQxcaaDplyhQVFha6H/v27atrmQAABJ7io1LO6aEOnYZbW4sH1Ok0zQMPPKAlS5ZozZo1atWq1UX3TUxMVH5+fpVthw4dUkhIiGJjY6t9T3h4uMLDw+tSGgAAgW/XCslwSfFdpeZtra6m3mrVM2IYhu6//34tXLhQK1euVGpq6iXf07dvX61YsaLKts8++0x9+vRh7goAAOqi8pJeP7+KplKtwsh9992nOXPm6O9//7uio6OVn5+v/Px8lZSUuPeZMmWKxo8f734+ceJE7d27VxkZGdq2bZtmzJih6dOna9KkSZ77FgAANBblpdLuf5vrjTGMvPXWWyosLNSAAQOUlJTkfsyfP9+9T15ennJyctzPU1NTtWzZMq1atUo9evTQn//8Z7322msem2MEAIBGZc+XkvOEFBUvJfeyuhqPqNWYkZpMSTJr1qzztl133XXatGlTbT4KAABUZ+dyc9lpuBTk93d1kRQA96YBAKDRMIyAuqS3EmEEAAB/cfAnqXCfFNJEunyA1dV4DGEEAAB/seP0KZrLB0phkdbW4kGEEQAA/EWAXdJbiTACAIA/cORJB05fDNJxmLW1eBhhBAAAf1B5Fc1lvaXoRGtr8TDCCAAA/sB9SW9gnaKRCCMAAPg+Z5H08ypzvdNIS0vxBsIIAAC+7udVUvkpyd5aiu9idTUeRxgBAMDXVU501mmEZLNZW4sXEEYAAPBlLldAjxeRCCMAAPi23I1S0WEpPEZqc43V1XgFYQQAAF9WOdFZ+0FSSJi1tXhJre7aCwAAGkhFmbRptrRxpvk8AK+iqUQYAQDAlxiGtGWRtPIv0tEsc1t8l4AdLyIRRgAA8B0/r5I+f1o6kGk+j4yTrntM6v2bgD1FIxFGAACwXt73ZgjJWmk+D2sq9XtA6nufFB5taWkNgTACAIA3lZVIh3dIxQVSUeXj8JnnJw+e6QkJCpX63CP1f1Rq2tLauhsQYQQAAG+pKJfeuubM2I8LskndbpUGPi61SG2Q0nwJYQQAAG/J+doMIkGhUstOUlScFNXSHAtSuR7V0nwttp3V1VqGMAIAgLds/ae57D5WGvOGtbX4MCY9AwDAG1wuadvH5nqXGy0txdcRRgAA8IZ935qDU8PtUup1Vlfj0wgjAAB4w7Yl5rLTiICeI8QTCCMAAHiaYUhbT4eRLr+ythY/QBgBAMDTcjdJjv3m5GXtrre6Gp9HGAEAwNO2nb6KpsNQKTTC2lr8AGEEAABPMowzl/RyiqZGCCMAAHhS/o/SsT1SSITUfojV1fgFwggAAJ5U2SvSfpAU3tTaWvwEYQQAAE+pcormRktL8SeEEQAAPOXwdunILik4TOo4zOpq/AZhBAAAT6mcW6Td9VKTGGtr8SOEEQAAPKVy1tXOXEVTG4QRAAA84UiWdPAnKSjEnAIeNUYYAQDAEyoHrqb2lyJbWFuLnyGMAADgCZVhhFM0tUYYAQCgvo7tlfI2S7Yg6YpRVlfjdwgjAADUV+XA1TbXSE1bWluLHyKMAABQX5WX9HYZY20dfoowAgBAfTgOSPvXm+ucoqkTwggAAPWx7WNzmXK1FJNkbS1+ijACAEB9uE/RcBVNXRFGAACoq1MOKedrc73zaGtr8WOEEQAA6qpgp2S4pOgkqVlrq6vxW4QRAADq6vB2c9myk7V1+DnCCAAAdVUZRuIII/VBGAEAoK4O7zSX9IzUC2EEAIC6cp+mucLaOvwcYQQAgLpwFknHc8x1wki9EEYAAKiLgl2SDCkyVoqKtboav0YYAQCgLgoqx4vQK1JfhBEAAOqCy3o9hjACAEBdHN5hLrmst94IIwAA1AU9Ix5DGAEAoLbKS6Wj2eY6Y0bqjTACAEBtHcmSjAop3C5FJ1pdjd8jjAAAUFvuUzQdJZvN2loCAGEEAIDaqhy8yngRjyCMAABQWwWVYYTxIp5AGAEAoLYOE0Y8iTACAEBtVJSfngpeUlxHa2sJEIQRAABq49geyVUmhUZK9hSrqwkItQ4ja9as0ejRo5WcnCybzabFixdfdP9Vq1bJZrOd99i+fXtdawYAwDqVV9LEdZSC+De9J4TU9g1FRUXq3r27fvOb3+iWW26p8ft27NihmJgY9/OWLVvW9qMBALCe+7Jexot4Sq3DyIgRIzRixIhaf1B8fLyaNWtW6/cBAOBT3INXGS/iKQ3Wv9SzZ08lJSVp0KBB+uKLLy66b2lpqRwOR5UHAAA+gct6Pc7rYSQpKUnTpk3TggULtHDhQnXq1EmDBg3SmjVrLvieqVOnym63ux8pKQwQAgD4AJdLOrzTXCeMeIzNMAyjzm+22bRo0SLdeOONtXrf6NGjZbPZtGTJkmpfLy0tVWlpqfu5w+FQSkqKCgsLq4w7AQCgQR3bI73aXQoOlx4/IAXXerRDo+JwOGS32y/5+23JMOCrr75au3btuuDr4eHhiomJqfIAAMByleNFYtsTRDzIkjCSmZmppKQkKz4aAIC64540XlHrWHfy5Ent3r3b/Tw7O1ubN29WixYt1Lp1a02ZMkW5ubmaPXu2JOmVV15R27Zt1bVrVzmdTs2ZM0cLFizQggULPPctAABoCEwD7xW1DiMbNmzQwIED3c8zMjIkSXfddZdmzZqlvLw85eTkuF93Op2aNGmScnNzFRERoa5du2rp0qUaOXKkB8oHAKABuecYoWfEk+o1gLWh1HQADAAAXmMY0tQUyXlC+u9vpPjOVlfk83x6ACsAAH7nRJ4ZRGzBUot2VlcTUAgjAADUROUpmth2UkiYtbUEGMIIAAA1wZU0XkMYAQCgJirDSBxhxNMIIwAA1ASX9XoNYQQAgEsxDOnwNnOd0zQeRxgBAOBSigqkkmOSbFJcB6urCTiEEQAALqXg9Cma5m2k0AhrawlAhBEAAC7FPfMq40W8gTACAMClcFmvVxFGAAC4FHpGvIowAgDApRzeaS6ZY8QrCCMAAFxMyTHpZL653rKjtbUEKMIIAAAXU9krEtNKCo+2tpYARRgBAOBi3ONFOEXjLYQRAAAupuB0zwhhxGsIIwAAXAw9I15HGAEA4GK4QZ7XEUYAALiQU4VS4T5zPY4rabyFMAIAwIVsWWwuYztIkS0sLSWQEUYAALiQjbPMZa/xlpYR6AgjAABUJ+8H6cAmKShU6nGH1dUENMIIAADV2fSeuew8SoqKs7aWAEcYAQDgXM5i6YcPzfVed1lbSyNAGAEA4FxbF0ulhVKzNlLqdVZXE/AIIwAAnKty4Grvu6Qgfiq9jRYGAOBsh7ZJ+76VbMFSjzutrqZRIIwAAHC2TbPNZacRUnSitbU0EoQRAAAqlZ2Svp9rrve+29JSGhPCCAAAlbZ9LJUck+wpUrvrra6m0SCMAABQqXLgas9fS0HBlpbSmBBGAACQpILd0t4vJVuQGUbQYAgjAABIZ2ZcbT9EsreytpZGhjACAEC5U9r8d3OdgasNjjACAMCOpVJxgRSdJHUYanU1jQ5hBACAyoGrPe6UgkMsLaUxosUBAIGrvFRa+J9S+Skptb95n5n4LlWneD+aLf28SpJN6jXOqkobNcIIACBwZa8xb3onSTuXm8vIODOYXH6dGU4y3ze3txsoNW9rRZWNHmEEABC48jaby4RuUnSCtPdrc2zIloXmQ5JkMxe97rKiQogwAgAIZHk/mMvuY6V+D5hXzeRuMHtMfl4t7f9OcpVJMZdJnUZaW2sjRhgBAASuvO/NZVJ3cxkSJrXpZz4G/EFyFkm5G6XmqeZrsARhBAAQmEqOS8f3muuJ3arfJyzKHD8CS3FpLwAgMOX/aC6btZYimltbCy6KMAIACEznnqKBzyKMAAACU/7pwauJhBFfRxgBAAQmekb8BmEEABB4nMVSwU5zPelKa2vBJRFGAACB5+AWyXBJTROk6ESrq8ElEEYAAIEn//QpmkR6RfwBYQQAEHgYL+JXCCMAgMBTOQ0840X8AmEEABBYKsqkQ1vNdU7T+AXCCAAgsBzeLlU4pXC71Lyt1dWgBggjAIDA4h4vcqVks1lbC2qEMAIACCzu8SIMXvUXhBEAQGDJ47Jef0MYAQAEDpfrzN166RnxG4QRAEDgOJollRVJIRFSXAerq0ENEUYAAIHDfYomTQoKtrYW1BhhBAAQOPJPD15lvIhfIYwAAAIH08D7JcIIACAwGAbTwPspwggAIDAU7pdKjkpBIVJ8F6urQS3UOoysWbNGo0ePVnJysmw2mxYvXnzJ96xevVq9e/dWkyZNdPnll+vtt9+uS60AAFxY5XiRlp2lkHBra0Gt1DqMFBUVqXv37nr99ddrtH92drZGjhyp9PR0ZWZm6vHHH9eDDz6oBQsW1LpYAAAu6Oxp4OFXQmr7hhEjRmjEiBE13v/tt99W69at9corr0iSOnfurA0bNujFF1/ULbfcUtuPBwCgekwD77e8PmZk3bp1Gjp0aJVtw4YN04YNG1RWVlbte0pLS+VwOKo8AAC4KKaB91teDyP5+flKSEiosi0hIUHl5eUqKCio9j1Tp06V3W53P1JSUrxdJgDAn508LJ04IMlmTngGv9IgV9PYzrmFs2EY1W6vNGXKFBUWFrof+/bt83qNAAA/ln+6VyS2nRQebW0tqLVajxmprcTEROXn51fZdujQIYWEhCg2Nrba94SHhys8nJHQAIAaYryIX/N6z0jfvn21YsWKKts+++wz9enTR6Ghod7+eABAY8B4Eb9W6zBy8uRJbd68WZs3b5ZkXrq7efNm5eTkSDJPsYwfP969/8SJE7V3715lZGRo27ZtmjFjhqZPn65JkyZ55hsAAJDPzKv+rNanaTZs2KCBAwe6n2dkZEiS7rrrLs2aNUt5eXnuYCJJqampWrZsmR555BG98cYbSk5O1muvvcZlvQAAzzhVKB392VxP5DSNP7IZlaNJfZjD4ZDdbldhYaFiYmKsLgcA4Ev2fCXNGinFtJIytlhdDc5S099v7k0DAPBv3KnX7xFGAAD+jfEifo8wAgDwb/SM+D3CCADAf5WVSId3mOtc1uu3CCMAAP91IFMyKqTIOCkm2epqUEeEEQCA/1r3hrlsP1i6wC1G4PsIIwAA/3Rwi7T9X5JsUnqG1dWgHggjAAD/tOZFc9lljNSyk7W1oF4IIwAA/1OwS9qyyFzv/6i1taDeCCMAAP+z9iVJhtRppJSYZnU1qCfCCADAvxzNln74h7nen5uuBgLCCADAv3z5f+blvO0GSZf1troaeABhBADgP47vkzb/3Vy/7jFra4HHEEYAAP7jq1clV5nUNl1qfbXV1cBDCCMAAP9wIl/aNNtc5wqagEIYAQD4h6//n1RRKqVcJaX2t7oaeBBhBADg+4oKpA0zzPX+jzH1e4AhjAAAfN+6N6SyYim5p9R+kNXVwMMIIwAA31Z8VFr/jrne/1F6RQIQYQQA4NvWT5OcJ6SENKnjCKurgRcQRgAAvuuUQ/rmTXM9/fdSED9bgYj/qgAA37V+mnSqUIrraN6dFwGJMAIA8E2lJ6R1r5vr/R+VgoKtrQdeQxgBAPim9e9IJcek2PZS2i1WVwMvIowAAHxP6UlzkjOJXpFGgDACAPA9370jlRyVWrST0v7D6mrgZYQRAIBvObdXJDjE2nrgdYQRAIBv2TBdKj4itbhc6nar1dWgARBGAAC+w1kkffWauZ4+iV6RRoIwAgDwHRtmSMUFUvO20pVjra4GDYQwAgDwDc5i6atXzXV6RRoVwggAwDdsmCEVHZaatZG6/39WV4MGRBgBAFjv7F6R/pOk4FBr60GDIowAAKy3cZZUdEhq1lrqfrvV1aCBEUYAANYqK5G+esVcT/89vSKNEGEEAGCtje9JJw9K9hSp+x1WVwMLEEYAANYpOyV9+X/menqGFBJmbT2wBGEEAGCdDdOlk/lSTCupx6+trgYWIYwAAKxxqlBa84K5ft1j9Io0YoQRAIA1vnxFKjkmxXWSetxpdTWwEGEEANDwHAekb94y1wc/zWyrjRxhBADQ8L54TiovkVr3lTqNsLoaWIwwAgBoWIe2S5s/MNcHPyPZbNbWA8sRRgAADevfz0iGS7pilNT6KqurgQ8gjAAAGs7eddKOZZIt2BwrAogwAgBoKIYhrfiTud5rvBTXwdp64DMIIwCAhrHtY2n/d1JopDTgD1ZXAx9CGAEAeF9FmTlWRJL63idFJ1pbD3wKYQQA4H2Z70tHdkuRsVK/B62uBj6GMAIA8C5nkbTqeXP9uslSkxhr64HPIYwAALxr3RvSyYNS87ZS799YXQ18EGEEAOA9Jw9LX71qrl//J26Gh2oRRgAA3vPFXyTnSSmph9T1ZqurgY8ijAAAvCN3k7TxPXN9+PNSED85qB5/GQAAz3O5pGWPSjKkK8dKbfpaXRF8GGEEAOB538+VcjdIYU2lIc9aXQ18HGEEAOBZpwqlz58y16+bzARnuCTCCADAs1Y9LxUdluI6SldNtLoa+AHCCADAcw5ulb79m7k+4q9cyosaIYwAADzDMKRPHpOMCqnzaKnd9VZXBD9BGAEAeMaWRdKetVJIE2nYc1ZXAz9CGAEA1F/pSemzP5rr6b+XmrW2th74FcIIAKD+1r4kOXLN+89wV17UUp3CyJtvvqnU1FQ1adJEvXv31tq1ay+476pVq2Sz2c57bN++vc5FAwB8yJEsad3r5vqwqVJoE2vrgd+pdRiZP3++Hn74YT3xxBPKzMxUenq6RowYoZycnIu+b8eOHcrLy3M/OnToUOeiAQA+wjCkTyZLFU6p/RCp0wirK4IfqnUYefnllzVhwgT99re/VefOnfXKK68oJSVFb7311kXfFx8fr8TERPcjODi4zkUDAHxA8VHp0yek3Suk4DDzUl6bzeqq4IdqFUacTqc2btyooUOHVtk+dOhQff311xd9b8+ePZWUlKRBgwbpiy++uOi+paWlcjgcVR4AAB/hLJbWviy91kP65g1zW/okKbadpWXBf4XUZueCggJVVFQoISGhyvaEhATl5+dX+56kpCRNmzZNvXv3Vmlpqd5//30NGjRIq1atUv/+/at9z9SpU/XMM8/UpjQAgLdVlEub55gzrJ7IM7clpEmDn5baD7a0NPi3WoWRSrZzuuEMwzhvW6VOnTqpU6dO7ud9+/bVvn379OKLL14wjEyZMkUZGRnu5w6HQykpKXUpFQBQX4YhbVsi/fvP0pFd5jZ7a+n6P0rdbpWCuDAT9VOrMBIXF6fg4ODzekEOHTp0Xm/JxVx99dWaM2fOBV8PDw9XeHh4bUoDAHjayUPSjk+kTe9JuRvNbZGxUv9HpT73SCH8/zQ8o1ZhJCwsTL1799aKFSt00003ubevWLFCY8aMqfFxMjMzlZSUVJuPBgA0hILd0o6l0val0r71kgxze2iU1Pc+qd8DUpMYS0tE4Kn1aZqMjAyNGzdOffr0Ud++fTVt2jTl5ORo4kTzzoxTpkxRbm6uZs+eLUl65ZVX1LZtW3Xt2lVOp1Nz5szRggULtGDBAs9+EwBA7ZU7pbzvzwSQgp1VX0/uKXW6Qeo1XoqueQ84UBu1DiNjx47VkSNH9OyzzyovL09paWlatmyZ2rRpI0nKy8urMueI0+nUpEmTlJubq4iICHXt2lVLly7VyJEjPfctAAAX53JJx/dIh7aZd9Y9dPpxZLfkKj+zX1CIlNpf6jTSfNgvs6xkNB42wzAMq4u4FIfDIbvdrsLCQsXE0D0IADViGNJPC6Rv3jKDR1lx9fs1aSa1H2SGjw5DpCb2Bi0Tgaumv991upoGAODjigqkfz1iXgVTKThcatlJiu8iJXSR4rtK8Z2lmGQmK4OlCCMAEGi2L5U+fkgqOmyedkmfJHX7D6l5qhTM/+3D9/BXCQCBouS4tPwP0vdzzectO0s3vS0l97CyKuCSCCMAEAiyVkr/vF9y5EqySdc8KA18grlA4BcIIwDgj1wuqbjAnJZ902zpu3fN7S0ul258W2p9lbX1AbVAGAEAX2UYUu4mKevfkuOAdPKgGT5OHDTXjYqq+//yP837xIRFWVIuUFeEEQDwNWUl5iW569+R8jZfZEeb1DReat5WGjBFajewgQoEPIswAgC+4mi2tGG6lDlHKjlmbgsOl64YKcV1lKITpaaJ5jI6UYqK5+oYBAT+igHASi6XtPtz6bt3pF0r5L4XjL219It7pJ7jpahYS0sEvI0wAgBW2btOWvaodPDHM9vaDZJ++Tupw1ApKNi62oAGRBgBgIZ2Il9a8aT0w3zzeXiM1HOc9IsJUmw7a2sDLEAYAYCGUlEmffu2tOp5yXlSks28G+6gJ6WoOKurAyxDGAGAhpD1hfTJZKlgh/n8st7SyBfMJdDIEUYAwJuO75M+e0La+k/zeWScNOQZqfsdUlCQtbUBPoIwAgDeUF4qrXtdWv2CVF4i2YLMSckGTJEimlldHeBTCCMA4Gm7P5eWPSYdzTKft+5nnpJJTLO2LsBHEUYAwFOO75M+nSJt+9h83jRBGvoXqdutks1mbW2ADyOMAEB9nXdKJli6aqI04A9SkxirqwN8HmEEAOoje6308UNnTsm0ucY8JZPQ1dq6AD9CGAHgv0qOSd/Pk6JaSt3+o+E/f+N70r8eMe+eyykZoM4IIwD8z7G90jdvSZtmS2VF5rZD26Tr/9gwQcAwpJV/lta+ZD7vdpt0w4tSE7v3PxsIQIQRAP4jd5P09f+Tti6WDJe5rXlb6dgeae2LUslRaeSL3r2nS3mp9M/7pB8/NJ9fN9m8XJfeEKDOCCMAfJvLJe1eYYaQPWvPbL98oNTvAand9dKGGdLS35vLkuPSTX+TQsI8X0vJMWner6W9X0pBIdKoV6Re4zz/OUAjQxgB0PDKSszp0bf/S9rxifkjbwsyezRswWet28xTIqUO831BIVLaf0j97pcSu5053i8mmBOJLbxX2rJQOlUojX1fCovyXM3H9kof3GpO5x4WLd32ntR+kOeODzRihBEADeOUQ9r1mTkHx64VZ8Z6VDIqpIqK6t8bHiP1vtu8XNZ+WfX7pN1ijtmYP07K+rc0+0bpjvlSZIvq9y8rMScn2/axVHxUiusotex05hHR/My+uZukv4+Vig5J0cnSnR8ygRngQTbDMAyri7gUh8Mhu92uwsJCxcRwzT7gNyrKzLEVWxZJP6+SKpxnXotpJXUeJXUebQYBV4U5DsQ4vXRVmL0iRoVkb1XzXo59680ejFPHpfgu0rhFUnSi+ZqzyAxCW/8p7fz0/EB0tqh4M5S0SJV+/EgqK5YS0qQ7/nHhQASgipr+fhNGAHieYZg9Dp8/fWb+DUmKbS91/pUZQJJ7em/Q58Gt0vs3SSfzpWZtpP6PmuNOdq0wQ0Ule4rUZYwU204q2CUd3i4d3ik59p9/zMsHSrfNZhIzoBYIIwCskfON9NmfpP3rzeeRceYN4rqMMXsaGuqqk6PZZiA5ll11e7M2UtcbzXqSe1VfT+kJqWCndHiHGVCik6Rf/FYKDm2Q0oFAQRgB0LAKdpk9Idv/ZT4PjZT63m9e8WJVb8KJg9KHd0nFR6QrRpkBJKk7l+ECDaSmv98MYAVQPycPSauelzbOMsd32IKknuPMuTdikqytLTpBume5tTUAuCTCCIDacxyQdi43B4H+vEoqP2Vu7zhCGvy0FH+FldUB8DOEEQCX5nJJeZvNALLjEyn/h6qvX9ZbGvJnqe01lpQHwL8RRgBUz+WScr6WfviH2QNyMv+sF21Sq19InYZLHYebl9AyDgNAHRFGAFR19GfzTrjfz5WO55zZHtbUnHq943Cpw1CpaUvragQQUAgjQKByVZgTgO35UgpvKsUkmxONxSRLTeOr3kzuVKE5Mdn386ScdWe2h0Wbl8F2vUlqe60UEt7gXwNA4COMAIGk3CntWWNOOLZ9mTl9eXWCQsy5M2IuM4PKni/PDEK1BZkTfHW/XbriBikssuHqB9AoEUYAf+csknb/2wwgOz+VSgvPvNbELrUbZE6v7jggOXKlE3mSq1wq3Gc+KrW8wgwgV95m9p4AQAMhjAD+oPSkdHyvdGzP6cfp9eN7zZlGK0rP7BsVb97z5YpRUtt0KSSs6rEqyqWTB0+Hk/1SUYF5NYw3p2cHgIsgjAC+oKLcDAbnBo3K9eKCi7+/Wesz93xp9Yuq40HOFRxi3ujNfpmkX3juOwBAHRFGAKuUO6Vv3pA2vmdetWJUXHz/iObmfVWatz39OHs9lV4NAH6LMAJYIXuNtPT35s3YKgWHmwGjWZszQaNyvVkbKaKZVdUCgFcRRoCGdCJf+vQJ6aePzOdRLc3p09sNkpomSEFBlpYHAFYgjAANoaJc+u4daeX/SM4T5uWzfSZI1/+RHg8AjR5hBPC2nG/NUzIHfzSfX9ZbuuFlKbmHpWUBgK8gjACeZhjmjeR2rZB2f35mRtMmzcxTMr3u4nQMAJyFMAJ4QskxKesLM3zs/tycx+NsPcdJg5+RomKtqQ8AfBhhBKiLs3s/dq2Q9n9X9dLc0Cjp8uuk9oOlDkPMeUAAANUijAA1darQ7P2oPP1yMr/q6y2vOBM+WvflpnIAUEOEEeBiCnZJ2/9lBpCcb+j9AAAvIIwA5zqaLW1ZKP206MwVMJXiOkodhpoBpE0/ej8AwAMII4AkFe6XtiyWflogHdh0ZntQiHT5AKnjcDOAtEi1qkIACFiEEfg3Z7FUdMgMDUGh5jL49Hrw6eeucqn4qFRyVCo+cs7jmBk+Ki+/lcwJydqmS2m3mDeei2xh3fcDgEaAMAL/UnZK2r9eyl4r7Vkr7d8guco8c+zW/aS0m6UuY6Sm8Z45JgDgkggj8G3lTil3oxk8stdI+9ZLFaVV9wmJkAyXGUoM1wUOZDN7OCJaSJGxpx8tzIc9Reo0UrJf5vWvAwA4H2EEvqWiXMr7XspebQaQnG+ksuKq+zRNlFLTzVMpqf3Nu9vabOZrrtOhpKLs9LJcCgqWmtjNJQDA5xBGYC1XhXRwi9nrsWettOcr80ZyZ4uMPR080qXU66TY9mfCx7mCgqSgcK5yAQA/QhiBZ5WVSCcPSUWHzcfJQ1JxwekBpMfMZfGR04NJj0qnjp9/aqWJ3QwflQGkZWfu5QIAAYww4ktcLrNXoPSEdMphLsuKpLhO1oxncFWYgaL4yJkwUXJWqCg5Zj6KCswrWk4ePr9XoybCmppzdlSedknsxikVAGhECCMNxTCkE/nS0SzpyG7pSJb5OJYtlRw3g8fFfsibp0ptrz3dY3CNZG9Vu893uSTnSfNz3I/TgafosHljtxP5VZdFhy8yIPQigsOkqHipaUtzGRV31uDRs5aRsWcGlAbzpwgAjRW/AJ7icpmnIwr3S45cqTBXcuyXju87HUB+Nns5aiIoVGoSI4VHmz/sR3aboeVYtpT5vrlP87ZmOGlzjblPZe9FlTk0Tj8vdZhBpC5sQVJEczM0RDQ/EyYimkuRzU8v48xLYSuDRxP7hcd0AABwDsKIYUjlp8yxDs4ic1lWfNay+HQvwskzPQnuHoaT5pgHR67kOCBVOC/+WbZg8/4lse2l2HbmskWq+WMeHi2Fx5gh5NzBl6cc5lUle9ZKe76U8jZLx/aYj8w5tfu+QSHm54RHm4+wpmaAaJogRSeev4yMo9cCAOBVjftX5qMJ5vTfMjx0QJv5Ax5zmTnGw55irre43AwfzdpIIWG1P2yTGKnjUPMhmeFk37dmONn3nTm+wj1vRmzVeTQiWkgRzaSw0+EjJJxeCwCAT2ncYSQoWFWCSHCYFBp5+hFxZlnZixDe1OxVCGt61rZoKSbZDB3RSXULG7XVJMa8S2yHId7/LAAAvMxmGEatuwXefPNNvfDCC8rLy1PXrl31yiuvKD09/YL7r169WhkZGdqyZYuSk5P12GOPaeLEiTX+PIfDIbvdrsLCQsXExNS23AsrOmLeEj40wpzFk9MRAAB4TE1/v2s9ecP8+fP18MMP64knnlBmZqbS09M1YsQI5eTkVLt/dna2Ro4cqfT0dGVmZurxxx/Xgw8+qAULFtT2oz0vKtYceBkeTRABAMAite4Zueqqq9SrVy+99dZb7m2dO3fWjTfeqKlTp563/+TJk7VkyRJt27bNvW3ixIn6/vvvtW7duvP2r443ekYMw1BJWYVHjgUAgL+LCA2WzcNjCmv6+12r7gCn06mNGzfqD3/4Q5XtQ4cO1ddff13te9atW6ehQ4dW2TZs2DBNnz5dZWVlCg0NPe89paWlKi09czM0h8NRmzJrpKSsQl2e/NTjxwUAwB9tfXaYIsOsOUtQq9M0BQUFqqioUEJCQpXtCQkJys/Pr/Y9+fn51e5fXl6ugoKCat8zdepU2e129yMlJaU2ZQIAAD9Spwh0bjeOYRgX7dqpbv/qtleaMmWKMjIy3M8dDofHA0lEaLC2PjvMo8cEAMBfRYRadxuOWoWRuLg4BQcHn9cLcujQofN6PyolJiZWu39ISIhiY2OrfU94eLjCw71711WbzWZZdxQAADijVqdpwsLC1Lt3b61YsaLK9hUrVqhfv37Vvqdv377n7f/ZZ5+pT58+1Y4XAQAAjUutL+3NyMjQu+++qxkzZmjbtm165JFHlJOT4543ZMqUKRo/frx7/4kTJ2rv3r3KyMjQtm3bNGPGDE2fPl2TJk3y3LcAAAB+q9bnKcaOHasjR47o2WefVV5entLS0rRs2TK1adNGkpSXl1dlzpHU1FQtW7ZMjzzyiN544w0lJyfrtdde0y233OK5bwEAAPxWnWZgbWhem4EVAAB4jddmYAUAAPAkwggAALAUYQQAAFiKMAIAACxFGAEAAJYijAAAAEsRRgAAgKUIIwAAwFKEEQAAYCm/uG1t5SSxDofD4koAAEBNVf5uX2qyd78IIydOnJAkpaSkWFwJAACorRMnTshut1/wdb+4N43L5dKBAwcUHR0tm81mdTk+xeFwKCUlRfv27eO+PXVA+9UfbVg/tF/90Yb14832MwxDJ06cUHJysoKCLjwyxC96RoKCgtSqVSury/BpMTEx/I+wHmi/+qMN64f2qz/asH681X4X6xGpxABWAABgKcIIAACwFGHEz4WHh+upp55SeHi41aX4Jdqv/mjD+qH96o82rB9faD+/GMAKAAACFz0jAADAUoQRAABgKcIIAACwFGEEAABYijDiA3Jzc/XrX/9asbGxioyMVI8ePbRx40b364Zh6Omnn1ZycrIiIiI0YMAAbdmypcox8vPzNW7cOCUmJioqKkq9evXSRx99VGWfY8eOady4cbLb7bLb7Ro3bpyOHz/eEF/RqzzRfllZWbrpppvUsmVLxcTE6LbbbtPBgwer7BOo7Sddug0XLlyoYcOGKS4uTjabTZs3bz7vGKWlpXrggQcUFxenqKgo/epXv9L+/fur7EMbXrwNp02bpgEDBigmJkY2m63atgnUNqxv+x09elQPPPCAOnXqpMjISLVu3VoPPvigCgsLq+wXqO0neeZv8N5771W7du0UERGhli1basyYMdq+fXuVfbzRhoQRix07dkzXXHONQkND9cknn2jr1q166aWX1KxZM/c+//u//6uXX35Zr7/+ur777jslJiZqyJAh7nv2SNK4ceO0Y8cOLVmyRD/++KNuvvlmjR07VpmZme597rjjDm3evFnLly/X8uXLtXnzZo0bN64hv67HeaL9ioqKNHToUNlsNq1cuVJfffWVnE6nRo8eLZfL5T5OILafVLM2LCoq0jXXXKPnn3/+gsd5+OGHtWjRIs2bN09ffvmlTp48qVGjRqmiosK9D2148TYsLi7W8OHD9fjjj19wn0BsQ0+034EDB3TgwAG9+OKL+vHHHzVr1iwtX75cEyZMqLJfILaf5Lm/wd69e2vmzJnatm2bPv30UxmGoaFDh3r/f8cGLDV58mTj2muvveDrLpfLSExMNJ5//nn3tlOnThl2u914++233duioqKM2bNnV3lvixYtjHfffdcwDMPYunWrIcn45ptv3K+vW7fOkGRs377dU1+nwXmi/T799FMjKCjIKCwsdO9z9OhRQ5KxYsUKwzACt/0M49JteLbs7GxDkpGZmVll+/Hjx43Q0FBj3rx57m25ublGUFCQsXz5csMwaMNKF2rDs33xxReGJOPYsWNVtgdqG3q6/Sr94x//MMLCwoyysjLDMAK3/QzDe234/fffG5KM3bt3G4bhvTakZ8RiS5YsUZ8+fXTrrbcqPj5ePXv21DvvvON+PTs7W/n5+Ro6dKh7W3h4uK677jp9/fXX7m3XXnut5s+fr6NHj8rlcmnevHkqLS3VgAEDJEnr1q2T3W7XVVdd5X7P1VdfLbvdXuU4/sYT7VdaWiqbzVZlwp8mTZooKChIX375paTAbT/p0m1YExs3blRZWVmVdk5OTlZaWpq7fWjD+gvUNvRW+xUWFiomJkYhIeZt2AK1/STvtGFRUZFmzpyp1NRUpaSkSPJeGxJGLPbzzz/rrbfeUocOHfTpp59q4sSJevDBBzV79mxJ5lgQSUpISKjyvoSEBPdrkjR//nyVl5crNjZW4eHhuvfee7Vo0SK1a9fOfZz4+PjzPj8+Pr7KcfyNJ9rv6quvVlRUlCZPnqzi4mIVFRXp0UcflcvlUl5envs4gdh+0qXbsCby8/MVFham5s2bV9l+djvThvUXqG3ojfY7cuSI/vznP+vee+91bwvU9pM824ZvvvmmmjZtqqZNm2r58uVasWKFwsLCJHmvDf3irr2BzOVyqU+fPnruueckST179tSWLVv01ltvafz48e79bDZblfcZhlFl2x//+EcdO3ZMn3/+ueLi4rR48WLdeuutWrt2rbp161btMao7jr/xRPu1bNlSH374of7rv/5Lr732moKCgnT77berV69eCg4OvuAxzj2Ov6ppG9bFue1DG9ZfILahp9vP4XDohhtuUJcuXfTUU09VeS0Q20/ybBveeeedGjJkiPLy8vTiiy/qtttu01dffaUmTZpI8k4b0jNisaSkJHXp0qXKts6dOysnJ0eSlJiYKEnnJc5Dhw65/7WflZWl119/XTNmzNCgQYPUvXt3PfXUU+rTp4/eeOMN93HOvTpEkg4fPnxer4E/8UT7SdLQoUOVlZWlQ4cOqaCgQO+//75yc3OVmprqPk4gtp906TasicTERDmdTh07dqzK9rPbmTasv0BtQ0+234kTJzR8+HA1bdpUixYtUmhoqPu1QG0/ybNtaLfb1aFDB/Xv318fffSRtm/frkWLFknyXhsSRix2zTXXaMeOHVW27dy5U23atJEkpaamKjExUStWrHC/7nQ6tXr1avXr10+SOQJfkoKCqv7nDA4Odl8N0rdvXxUWFmr9+vXu17/99lsVFha6j+OPPNF+Z4uLi1OzZs20cuVKHTp0SL/61a8kBW77SZduw5ro3bu3QkNDq7RzXl6efvrpJ3f70Ib1F6ht6Kn2czgcGjp0qMLCwrRkyRL3v+QrBWr7Sd79GzQMQ6WlpZK82IZ1HvoKj1i/fr0REhJi/M///I+xa9cu44MPPjAiIyONOXPmuPd5/vnnDbvdbixcuND48ccfjdtvv91ISkoyHA6HYRiG4XQ6jfbt2xvp6enGt99+a+zevdt48cUXDZvNZixdutR9nOHDhxtXXnmlsW7dOmPdunVGt27djFGjRjX4d/YkT7SfYRjGjBkzjHXr1hm7d+823n//faNFixZGRkZGlc8KxPYzjJq14ZEjR4zMzExj6dKlhiRj3rx5RmZmppGXl+feZ+LEiUarVq2Mzz//3Ni0aZNx/fXXG927dzfKy8vd+9CGF2/DvLw8IzMz03jnnXcMScaaNWuMzMxM48iRI+59ArENPdF+DofDuOqqq4xu3boZu3fvNvLy8twP/gZNl2rDrKws47nnnjM2bNhg7N271/j666+NMWPGGC1atDAOHjzoPo432pAw4gM+/vhjIy0tzQgPDzeuuOIKY9q0aVVed7lcxlNPPWUkJiYa4eHhRv/+/Y0ff/yxyj47d+40br75ZiM+Pt6IjIw0rrzyyvMu9T1y5Ihx5513GtHR0UZ0dLRx5513nnfpoD/yRPtNnjzZSEhIMEJDQ40OHToYL730kuFyuarsE6jtZxiXbsOZM2caks57PPXUU+59SkpKjPvvv99o0aKFERERYYwaNcrIycmpchza8OJt+NRTT1W7z8yZM937BGob1rf9Ki+Hru6RnZ3tPk6gtp9h1L8Nc3NzjREjRhjx8fFGaGio0apVK+OOO+4475Jdb7ShzTAMo+79KgAAAPXDmBEAAGApwggAALAUYQQAAFiKMAIAACxFGAEAAJYijAAAAEsRRgAAgKUIIwAAwFKEEQAAYCnCCAAAsBRhBAAAWIowAgAALPX/A8jjbUa7BJapAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pr.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01e3c9ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Years', ylabel='Value'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3AAC4oKWZS1VWWaZOkZ3UPbq72eW4FcINAAAu6LOdn0mqOmpjsVjMLcbNEG4AAHAx5bZy/SfzP5I4JXU+CDcAALiYH4t+VHF5sVqFt1K/hH5ml+N2CDcAALiY7wu+lyRd0/Ua+Vj4U91QdAwAABdirbTqh4IfJHFK6nwRbgAAcCHfZH2josoiRYdEa1DrQWaX45YINwAAuJDqq6TGdR4nXx9fc4txU4QbAABchM2w6fNdn0uSxncZb24xboxwAwCAi1h/aL2yi7MV4hOioW2Gml2O2yLcAADgIpbuWSpJ6hveV4F+gSZX474INwAAuIitx7dKkjqGdDS5EvdGuAEAwEVsz90uSWoV1MrkStwb4QYAABdQYavQ7rzdkqRWgYQbRxBuAABwAZknMmW1WRXiH6LogGizy3FrhBsAAFxA9SmpLlFdeOSCg+geAAAuYPvxqnDTNaqryZW4P8INAAAuoPrITdeWhBtHEW4AAHAB9nDDkRuHEW4AADCZYRjakbtDEkduGgPhBgAAkx0qPKTi8mL5+fipYwtu4Ocowg0AACbbdnybJKljZEf5+/qbXI37I9wAAGCy6vk2SS2TTK7EMxBuAAAwWfVl4ISbxkG4AQDAZPYjN9GEm8ZAuAEAwGSclmpchBsAAEyUW5qr3NJcSVwG3lgINwAAmKh6vk3riNYKDQg1uRrPQLgBAMBE1aekukV3M7kSz0G4AQDARFwp1fhMDTdr1qzRuHHjlJCQIIvFos8+++ys49PS0mSxWGq9duzY0TQFAwDQyJhM3Pj8zPzlJSUl6tWrl26++WZNmDCh3p/buXOnwsPD7e+jo6OdUR4AAE7HZeCNz9RwM3r0aI0ePbrBn4uJiVHz5s0bvyAAAJpQcXmxsgqyJHHkpjGZGm7OV+/evXX69Gl169ZN06dP19ChQ884tqysTGVlZfb3hYWFkiSr1Sqr1dqodVVvr7G36y3on+PooePooWPoX8NszdkqSYoOiVa4f3iNv030sKaG9MOtwk18fLxmz56tvn37qqysTB988IGGDRumtLQ0XX755XV+ZsaMGXrqqadqLV++fLlCQkKcUmdqaqpTtust6J/j6KHj6KFj6F/9pJ1IkyTFWGK0ZMmSGuvoYU2lpaX1HmsxDMNwYi31ZrFYtGjRIo0fP75Bnxs3bpwsFosWL15c5/q6jtwkJiYqNze3xrydxmC1WpWamqoRI0bI35+nujYU/XMcPXQcPXQM/WuYv6T9Rc9995xu7327Xh/9uiR6eCaFhYVq2bKlCgoKzvn3262O3NTl0ksv1YcffnjG9YGBgQoMDKy13N/f32lfGmdu2xvQP8fRQ8fRQ8fQv/rZdWKXJKl7TPda/aKHNTWkF25/n5v09HTFx8ebXQYAAA3GlVLOYeqRm+LiYu3Zs8f+ft++fcrIyFBkZKRat26tadOm6fDhw3r//fclSTNnzlTbtm3VvXt3lZeX68MPP9SCBQu0YMECs3YBAIDzUl5Zrj0nqv4GcqVU4zI13GzcuLHGlU4pKSmSpBtvvFFz5sxRdna2srKy7OvLy8v10EMP6fDhwwoODlb37t311VdfacyYMU1eOwAAjthzYo8qbBVqFtBMrcJbmV2ORzE13AwZMkRnm888Z86cGu+nTp2qqVOnOrkqAACcr/qxC11bdpXFYjG5Gs/i9nNuAABwRzx2wXkINwAAmICngTsP4QYAABPwNHDnIdwAANDEbIZNO3J3SOIycGcg3AAA0MSyCrJ0quKUAnwD1L5Fe7PL8TiEGwAAmlj1KalOkZ3k5+P2DwtwOYQbAACaGHcmdi7CDQAATYzJxM5FuAEAoIlxjxvnItwAANCEDMPgtJSTEW4AAGhCx0uP68SpE7LIoi5RXcwuxyMRbgAAaELV823aNm+rYP9gk6vxTIQbAACaEKeknI9wAwBAE+JKKecj3AAA0IS4Usr5CDcAADQhngbufIQbAACaSGFZoQ4VHpLEnBtnItwAANBEqp8EHtcsTs2DmptbjAcj3AAA0ESYTNw0CDcAADSRzdmbJRFunI1wAwBAEyivLNfcn+dKkkZ2HGlyNZ6NcAMAQBP4YucXOl56XPHN4jWm0xizy/FohBsAAJrA2+lvS5Juuugm+fn4mVyNZyPcAADgZAcLDmrZnmWSpFt632JyNZ6PcAMAgJO9l/GeDBka2naoOkZ2NLscj0e4AQDAiSptlXon/R1J0q29bzW5Gu9AuAEAwIlW7FuhrIIsNQ9qrmuTrjW7HK9AuAEAwIne3lw1kfiPPf+oYP9gk6vxDoQbAACcJLc0V5/t+EySdFuf28wtxoucV7ipqKjQ119/rX/9618qKiqSJB05ckTFxcWNWhwAAO7sgx8/kNVmVd/4vuoV18vscrxGgy+0P3DggEaNGqWsrCyVlZVpxIgRCgsL0/PPP6/Tp09r1qxZzqgTAAC3YhiG/d42HLVpWg0+cnPfffepX79+ys/PV3Dw/84dXnPNNVqxYkWjFgcAgLv6/tD32nZ8m4L9gnV9j+vNLserNPjIzdq1a/Xtt98qICCgxvI2bdro8OHDjVYYAADurPry74ndJyoiKMLkarxLg4/c2Gw2VVZW1lp+6NAhhYWFNUpRAAC4s6KyIn3888eSuLeNGRocbkaMGKGZM2fa31ssFhUXF+uJJ57QmDE8CAwAgHlb56nEWqLOUZ01sPVAs8vxOg0+LfXKK69o6NCh6tatm06fPq0//OEP2r17t1q2bKm5c+c6o0YAANxK9b1tbut9mywWi8nVeJ8Gh5uEhARlZGRo7ty52rx5s2w2m2699VbdcMMNNSYYAwDgjX4+9rPWH14vPx8/Te412exyvNJ5PXM9ODhYt9xyi265hSebAgC8x6p9q/Tst8+qZ0xPDW8/XINaD1JoQGiNMe9srppIPK7zOMU2izWjTK/X4HDz/vvvn3X95MmkVACAZ3r222e1PHO5lmcu10vrXpK/j7/6J/bX8HbDNbz9cF0Ye6He/6nq7yT3tjFPg8PNfffdV+O91WpVaWmpAgICFBISQrgBAHgkwzCUnp0uSbqqy1XKyMlQVkGW1hxYozUH1uivaX9VoG+gyirLdEHYBRrZYaTJFXuvBoeb/Pz8Wst2796tP//5z3r44YcbpSgAAFxNdnG2jpcel6/FVx9P+FhBfkHKzM/Uir0r9PW+r7Vi7wrln676G3l7n9vl6+NrcsXe67zm3Pxap06d9Oyzz+qPf/yjduzY0RibBADApVQftenasqv96d4dIzuqY2RH3dHvDlXaKpWRk6HM/Exd0/UaM0v1eo0SbiTJ19dXR44caazNAQDgUtJzqsJN7/jeda739fFV34S+6pvQtynLQh0aHG4WL15c471hGMrOztZrr72myy67rNEKAwDAlWTkZEiSLoq9yNQ6cG4NDjfjx4+v8d5isSg6OlpXXHGFXnrppcaqCwAAl3KuIzdwHQ0ONzabzRl1AADgsgpOF2hv/l5J0kVxF5lbDM6pwc+WAgDA2/x49EdJUuuI1ooMjjS5GpxLvY7cpKSk1HuDL7/88nkXAwCAK6q+Uqp3HKek3EG9wk16enq9NsbDwQAAnsg+34Zw4xbqFW5WrVrl7DoAAHBZ9iulmG/jFphzAwDAWZRVlGnr8a2SuFLKXZzXTfw2bNig+fPnKysrS+Xl5TXWLVy4sFEKAwDAFWw9vlUVtgpFBkcqMTzR7HJQDw0+cvPxxx/rsssu07Zt27Ro0SJZrVZt27ZNK1euVEREhDNqBADANL88JcXcUvfQ4HDz//7f/9Mrr7yiL7/8UgEBAXr11Ve1fft2TZw4Ua1bt3ZGjQAAmIYrpdxPg8NNZmamrrzySklSYGCgSkpKZLFY9MADD2j27NmNXiAAAGbiSin30+BwExkZqaKiIknSBRdcoJ9//lmSdPLkSZWWljZudQAAmMhm2Ow38ONKKfdR73CTkZEhSRo0aJBSU1MlSRMnTtR9992n22+/Xddff72GDRvmlCIBADBD5olMFZcXK8gvSF1adjG7HNRTva+W6tOnj3r37q3x48fr+uuvlyRNmzZN/v7+Wrt2ra699lr95S9/cVqhAAA0tepTUhfGXig/n/O6wBgmqPeRm2+//VZ9+vTRiy++qA4dOuiPf/yjVq9eralTp2rx4sV6+eWX1aJFC2fWCgBAk7JfKRV7kal1oGHqHW769++vt956Szk5OXrzzTd16NAhDR8+XB06dNAzzzyjQ4cOObNOAACanH0yMTfvcysNnlAcHBysG2+8UWlpadq1a5euv/56/etf/1K7du00ZswYZ9QIAIApuAzcPTn0+IUOHTro0Ucf1eOPP67w8HAtW7asseoCAMBUOcU5OlpyVD4WH/WM7Wl2OWiA854dtXr1ar377rtasGCBfH19NXHiRN16662NWRsAAKapPmrTJaqLQvxDTK4GDdGgcHPw4EHNmTNHc+bM0b59+zRgwAD985//1MSJExUaGuqsGgEAaHLMt3Ff9T4tNWLECLVr105vvPGGfvvb32r79u1au3atbr755vMONmvWrNG4ceOUkJAgi8Wizz777JyfWb16tfr27augoCC1b99es2bNOq/fDQDA2XCllPuqd7gJDg7WggULdOjQIT333HPq0sXxmxmVlJSoV69eeu211+o1ft++fRozZowGDRqk9PR0PfbYY7r33nu1YMECh2sBAOCXOHLjvup9Wmrx4sWN/stHjx6t0aNH13v8rFmz1Lp1a82cOVOSlJSUpI0bN+rFF1/UhAkTGr0+AIB3Kiwr1J4TeyTx2AV35NDVUk1t3bp1Sk5OrrFs5MiR2rhxo6xWq0lVAQA8zU9Hf5IktQpvpZYhLU2uBg3lVveSzsnJUWxsbI1lsbGxqqioUG5uruLj42t9pqysTGVlZfb3hYWFkiSr1drogah6ewSt80P/HEcPHUcPHeMp/dt4aKMkqVdsrybfF0/pYWNrSD/cKtxIksViqfHeMIw6l1ebMWOGnnrqqVrLly9frpAQ51zaV/1gUZwf+uc4eug4eugYd+/fl1lfSpKaFTbTkiVLTKnB3XvY2EpLS+s91q3CTVxcnHJycmosO3bsmPz8/BQVFVXnZ6ZNm6aUlBT7+8LCQiUmJio5OVnh4eGNWp/ValVqaqpGjBghf3//Rt22N6B/jqOHjqOHjvGU/j35zpOSpAkDJ2hMl6a9+76n9LCxVZ95qQ+3Cjf9+/fXF198UWPZ8uXL1a9fvzN+AQIDAxUYGFhrub+/v9O+NM7ctjegf46jh46jh45x5/6VV5Zr6/GtkqSLW11s2n64cw+doSG9MHVCcXFxsTIyMpSRkSGp6lLvjIwMZWVlSao66jJ58mT7+ClTpujAgQNKSUnR9u3b9e677+qdd97RQw89ZEb5AAAPtO34NlltVjUPaq42EW3MLgfnwdQjNxs3btTQoUPt76tPH914442aM2eOsrOz7UFHktq1a6clS5bogQce0Ouvv66EhAT94x//4DJwAECjsd+8L+6iM87nhGszNdwMGTLEPiG4LnPmzKm1bPDgwdq8ebMTqwIAeDOeBO7+3Oo+NwAAOJv9zsSEG7dFuAEA4L9shq3GaSm4J8INAAD/tS9/n4rKixToG6iuLbuaXQ7OE+EGAID/qj4l1TO2p/x9uQzbXRFuAAD4L/spqdiLTK0DjiHcAACgqsf5pO6teuRB73gmE7szwg0AAJLS9qfph8M/KNA3UNcmXWt2OXAA4QYAAEn/b+3/kyTd2vtWxTWLM7kaOIJwAwDwej8c/kFf7/1afj5+mnrZVLPLgYMINwAAr/fMN89Ikv544R/VpjnPk3J3hBsAgFfbcnSLFu9cLIssevSyR80uB42AcAMA8Goz1s6QJP2222/VpWUXk6tBYyDcAAC81p4TezRv6zxJ0mODHjO5GjQWwg0AwGs9t/Y52QybxnQaw7OkPAjhBgDglQ4WHNS/f/y3JOnxQY+bXA0aE+EGAOCVXvzuRVltVg1pO0QDEgeYXQ4aEeEGAOB1jpUc01ub35IkPTaQuTaehnADAPA6M7+fqVMVp3RxwsUa3n642eWgkRFuAABe5eTpk3p9w+uSqubaWCwWkytCYyPcAAC8yus/vK7CskJ1j+6ucV3GmV0OnIBwAwDwGiXlJXrl+1ckVd3XxsfCn0FPxH9VAIDXmL1ptvJO5al9i/aa2H2i2eXASQg3AACvcLritF747gVJ0qOXPSo/Hz+TK4KzEG4AAF7h3fR3lV2crVbhrTS512Szy4ETEW4AAB6vvLJcz659VpL0yGWPKNAv0OSK4EyEGwCAx3v/x/d1sPCg4prF6dbet5pdDpyMcAMA8GgVtgrNWDtDkjR1wFQF+webXBGcjXADAPBoH235SHvz9yo6JFp39LvD7HLQBAg3AACPVWmr1DPfPCNJerD/gwrxDzG5IjQFwg0AwGN9svUT7crbpcjgSN158Z1ml4MmQrgBAHgkm2GzH7W5/5L7FRYYZnJFaCqEGwCAR1q0fZG2Ht+q8MBw3XPJPWaXgyZEuAEAeBzDMPT3b/4uSbr3N/eqeVBzcwtCkyLcAAA8zpe7vlRGToaaBTTT/Zfeb3Y5aGKEGwCARzEMQ0+veVqSdNfFdykqJMrkitDUCDcAAI+yPHO5NhzZoGC/YKX0TzG7HJiAcAMA8BiGYehva/4mSZrSb4piQmNMrghmINwAADzGqv2r9N3B7xToG6iHBzxsdjkwCeEGAOARDMPQU6ufkiTd1uc2xYfFm1wRzEK4AQB4hGWZy7TmwBoF+gbqkcseMbscmIhwAwBwezbDpmkrpkmqukIqMSLR5IpgJsINAMDtzft5njJyMhQeGK7HBj1mdjkwGeEGAODWyivLNX3VdEnSwwMe5r42INwAANzb25vf1t78vYoNjeVuxJBEuAEAuLGS8hL9bXXVfW3+cvlf1CygmckVwRUQbgAAbmvm9zN1tOSo2rdor9v73m52OXARhBsAgFvKK83T8989L0l6eujTCvANMLkiuArCDQDALc1YO0OFZYXqFdtLv+/xe7PLgQsh3AAA3M7BgoN67YfXJEkzhs2Qj4U/Z/gfvg0AALfzZNqTKqss0+A2gzWq4yizy4GLIdwAANzK9uPbNefHOZKqjtpYLBZzC4LLIdwAANzK4ysfl82w6eouV6t/Yn+zy4ELItwAANzG+kPrtWjHIvlYfPTMFc+YXQ5cFOEGAOAWDMPQI19XPe17cq/J6h7T3eSK4KoINwAAt7Bg+wKtPrBaQX5BemrIU2aXAxdGuAEAuLxSa6keXP6gJGnqgKlqHdHa5Irgygg3AACX98K3LyirIEuJ4Yl6ZOAjZpcDF0e4AQC4tAMnD+jZb5+VJL2Y/KJC/ENMrgiujnADAHBpD6c+rNMVpzW4zWD9rtvvzC4HboBwAwBwWWn70zR/23z5WHz06qhXuWEf6oVwAwBwSRW2Ct239D5J0h1971CvuF4mVwR3QbgBALik2Ztm66ejP6lFUAs9PfRps8uBGyHcAABczolTJ/SXVX+RJP1t6N8UFRJlckVwJ4QbAIDL+euqv+rEqRPqEdNDU/pNMbscuBnCDQDApfx09Ce9ufFNSdI/Rv1Dfj5+JlcEd2N6uHnjjTfUrl07BQUFqW/fvvrmm2/OODYtLU0Wi6XWa8eOHU1YMQDAWQzD0H1L75PNsGlC0gQNbTfU7JLghkwNN/PmzdP999+vxx9/XOnp6Ro0aJBGjx6trKyss35u586dys7Otr86derURBUDAJxpwfYFStufpiC/IL2Y/KLZ5cBNmRpuXn75Zd1666267bbblJSUpJkzZyoxMVFvvvnmWT8XExOjuLg4+8vX17eJKgYAOENhWaGmr5yuyYsmS6p6flTb5m3NLQpuy7QTmeXl5dq0aZMeffTRGsuTk5P13XffnfWzvXv31unTp9WtWzdNnz5dQ4ee+bBlWVmZysrK7O8LCwslSVarVVar1YE9qK16e429XW9B/xxHDx1HDx3T0P6VV5brrc1v6Zm1zyj3VK4kaUibIUq5JMVr/xvwHaxbQ/phWrjJzc1VZWWlYmNjayyPjY1VTk5OnZ+Jj4/X7Nmz1bdvX5WVlemDDz7QsGHDlJaWpssvv7zOz8yYMUNPPfVUreXLly9XSIhznk+SmprqlO16C/rnOHroOHromHP1zzAMfVfwnT448oFyyqv+Pz8hMEGT4yfrkohLlJaa1gRVuja+gzWVlpbWe6zpU9B/fSttwzDOeHvtLl26qEuXLvb3/fv318GDB/Xiiy+eMdxMmzZNKSkp9veFhYVKTExUcnKywsPDG2EP/sdqtSo1NVUjRoyQv79/o27bG9A/x9FDx9FDx9Snf2sOrNG0VdO04cgGSVJsaKz+MugvurnXzfL3ped8B+tWfealPkwLNy1btpSvr2+tozTHjh2rdTTnbC699FJ9+OGHZ1wfGBiowMDAWsv9/f2d9qVx5ra9Af1zHD10HD10zK/7d6jwkD7b8Zk+3fapVh9YLUkK9Q/VwwMe1oMDHlSzgGZmleqy+A7W1JBemBZuAgIC1LdvX6Wmpuqaa66xL09NTdXVV19d7+2kp6crPj7eGSUCAByw/fh2LdqxSJ/t+Mx+lEaSfC2+ur3P7XpiyBOKaxZnYoXwVKaelkpJSdGkSZPUr18/9e/fX7Nnz1ZWVpamTKm6G+W0adN0+PBhvf/++5KkmTNnqm3bturevbvKy8v14YcfasGCBVqwYIGZuwEAkJRXmqfNhzfrgyMfaOqsqdp1Ypd9nUUWDUgcoGu6XqMJ3SZwJRScytRwc9111ykvL09/+9vflJ2drR49emjJkiVq06aNJCk7O7vGPW/Ky8v10EMP6fDhwwoODlb37t311VdfacyYMWbtAgB4ndMVp7X9+HZtObZFPx39SVuObdGWo1uUXZxdY1yAb4CGtRuma7peo3FdxnGUBk3G9AnFd955p+688846182ZM6fG+6lTp2rq1KlNUBUA4Nd25e3SlC+naM2BNao0Kusc0655O7VSK90x5A6N6zpO4YGNe+EGUB+mhxsAgGszDENvbHhDD6c+rFMVpyRJkcGR6hnTUz1jeurC2AvVM7anukd3V5BPkJYsWaIx3cYwGRamIdwAAM7oSNER3fL5LVqWuUySNLz9cL155Zvq0KJDnbft4MZzcAWEGwBAneb9PE9//urPyj+dryC/ID0//Hnd9Zu75GMx/ZnLwFkRbgAANeSfytddS+7S3J/nSpL6xvfVh9d+qK4tu5pcGVA/hBsA8HI2w6bc0lwdKTqibce3aWrqVB0uOixfi68eH/S4pl8+nTsHw60QbgDAS5w8fVIfbflIW49t1ZHiI8ouytaRoiPKLs5Wha2ixthOkZ30wTUf6JJWl5hULXD+CDcA4OG2HN2i1ze8rg9++kCl1rofPmiRRTGhMUoIS9CwdsP05JAnFRoQ2sSVAo2DcAMAHqjCVqHPd3yu1za8prT9afblPWJ66KrOV+mC8AuUEJZgf8WGxnLqCR6DcAMAHuR4yXG9vfltvbnxTR0sPCip6llO47uO1z2/uUeXt7m8zku4AU9CuAEAD1BeWa6X172sp9c8bT/11DKkpf7U50+a0m+KEiMSTa4QaDqEGwBwc1/v/Vp3L7lbO/N2SpL6xPfRvb+5V9f1uE5BfkEmVwc0PcINALipQ4WH9ODyB/XJ1k8kSbGhsXox+UXd0PMGTj3BqxFuAMDNlFeW69XvX9VTq59SibVEPhYf3X3x3frb0L8pIijC7PIA0xFuAMCNrNq3SnctuUvbc7dLkgYkDtAbY95Qr7heJlcGuA7CDQC4gZziHD20/CH935b/kyRFh0TrhREvaFKvSTzrCfgVwg0AuLBKW6Xe3PimHl/5uArLCmWRRX/u92f9/Yq/q0VwC7PLA1wS4QYAXNQPh3/Qn7/6szZnb5Yk9Uvop1lXzlLfhL4mVwa4NsINALiY/FP5mrZimmZvmi1DhpoHNdeMYTN0e5/b5evja3Z5gMsj3ACAizAMQ+//+L4eTn1Yx0uPS5Im95qs54c/r9hmsSZXB7gPwg0A/FdeaZ5W7l0pW6WtyX93ha1Cd311l2Zvni1J6hbdTW+MeUOD2w5u8loAd0e4AeD19p/cr5fXvax30t9RqbVUnUI6aXDpYMVFxDXJ7y8pL9HvF/xeX+76UhZZ9Pcr/q6HBzzMgyyB80S4AeC10rPT9cJ3L+iTrZ+o0qiUVPWQyd2luzXsw2H6evLXig+Ld2oNx0uOa+zcsfrh8A8K8gvSR9d+pGuSrnHq7wQ8HTdHAOBVDMPQir0rlPxBsvrM7qO5P89VpVGpEe1HKHVSqjbetlEt/FpoW+42DXxvoPbl73NaLZknMjXg3QH64fAPigyO1IrJKwg2QCPgyA0At2cYhtJz0rVg2wKtO7ROhgz5WnzlY/GRr89///3v+/0n9+vHoz9KknwsPprYfaKmDpiq3vG9JUlWq1UzOs3Q89nPa2/+Xg16b5BSJ6UqKTqpUWvecHiDrvzoSh0vPa62zdtq6Q1L1aVll0b9HYC3ItwAcEs2w6YfDv+gT7d9qoXbF2rfyfofYQn2C9ZtfW7TA5c+oHYt2tVaHxcYp5WTVmrMx2O07fg2DXpvkJb9cdlZ7y9THbBW7F2h6NBodYvupqSWSQoLDKs19qtdX2nipxNVai1V77jeWnLDEsU1a5r5PYA3INwAcBs2w6ZvDnyjBdsXaOH2hTpcdNi+LtgvWKM7jdaYjmPULKCZKo1K2QybKm3//fe/7wN8AzS281i1DGl51t+VEJag1Tet1uj/G62NRzbqivev0JfXf6lBbQbZxxiGoYycDH2y9RPN3zZfmfmZtbaTGJ6o7jHd1a1lN3WL7qaTp0/qka8fUaVRqZEdRmr+7+bXGYAAnD/CDQC38PXer/XQ8ofsp5QkKSwgTGM7j9WEpAka1XGUQgNCG/V3tgxpqRWTV2jc3HFac2CNRn44UgsmLlB8WLw90Ow5scc+PtgvWMPbD1eJtUTbjm9TTnGODhYe1MHCg1q6Z2mNbd900U2aPXY2V0QBTkC4AeDSth7bqodTH9Z/9vxHkhQeGK5rk67VhKQJGt5+uIL8gpz6+8MDw7X0hqX67fzfasnuJRrz0Zga64P8gnRlpyv1u26/05Wdr1SzgGb2dSdOndD249u17fg2bT2+VduOb9OhwkOadOEkPTrwUVksFqfWDngrwg0Al5RTnKMnVj2ht9Pfls2wyc/HT3ddfJf+cvlfFBUS1aS1BPsHa9F1izR50WTN2zpPQX5BGt1xtCZ2n6ixncfWCDS/FBkcqctaX6bLWl/WpPUC3o5wA8CllFpL9dJ3L+m5b59TibVEkjQhaYJmDJuhTlGdTKsrwDdAH034SA/2f1BdW3Zlngzgwgg3AExnGIa2Hd+mz3d+rtc3vK4jRUckSb+54Dd6KfklDWw90OQKq/hYfHTxBRebXQaAcyDcADCFtdKqtVlrtXjnYi3etVh78/fa17Vt3lbPDntWE7tPZF4KgAYj3ABoMkVlRfpq91f6YtcXWrJ7iU6ePmlfF+gbqOHth2t81/H644V/dPpEYQCei3ADwKlshk1p+9M0J2OOFmxfoFJrqX1ddEi0xnYeq6u6XKUR7Uc0+qXcALwT4QZAvVXYKlRSXqLwwPBzni7am79X/874t/794791oOCAfXmnyE66NulaXdXlKl1ywSXy9fF1dtkAvAzhBsBZlVWUKXVvqhZuX6jPd36uE6dOKNQ/VIkRiUoM/+8r4n//Hik6ojkZc7T6wGr7NiICI/T7Hr/XTRfdpEsuuIR5NACcinADoJZSa6mW7lmqBdsX6IudX6iovKjG+hJriXbk7tCO3B1n3IZFFo3oMEI3X3Szru5ytYL9g51dNgBIItwAXq+sokwHCg5ob/5eZZ7I1Kr9q/SfPf+pMTcmISxB13a9VhO6TVDf+L7KLs7WwYKqxwpU/3uo8JAOFh6Ur8VX13W/TpN6TVKr8FYm7hkAb0W4ATycYRjKLc3V3vy9NV8nq8LMocJDMmTU+lybiDb6bbffakLSBF3S6hL5WHzs68ICw9Q5qnNT7gYA1BvhBvAgGUcz9NXxr5T2dZr2F+63B5ni8uKzfi7UP1TtW7RX+xbt1TOmp8Z3Ha8+8X2YGwPALRFuAA+QV5qnR75+RO+kv1O14HDN9RZZdEH4BWrfor3aNW+nDi062MNMh8gOig6JJsgA8BiEG8CN2Qyb/p3xbz2c+rDyTuVJkvqE9dHgboPVIfJ/AaZN8zbcFA+A1yDcAG5q67Gt+vNXf9Y3Wd9IknrG9NQ/R/5TJ7ec1JhhY+Tv729yhQBgDp9zDwHgSkrKS/To14/qon9dpG+yvlGIf4heGPGCNv1pkwYkDjC7PAAwHUduADdRUl6iL3d9qUe+fsR+x9/xXcfr1VGvqnVEa0mS1WY1s0QAcAmEG8CF7TmxR0t2L9FXu79S2v40lVeWS5JaR7TWP0f/U1d1ucrkCgHA9RBuABdSVlGmNQfW2APN7hO7a6xvE9FGky6cpEcHPspDJgHgDAg3gMkOFx62h5mv936tEmuJfZ2fj58GtR6kMZ3GaEynMUpqmcQl2wBwDoQboIlV2iq1/vB6fbXrK321+yv9ePTHGuvjm8Xbw8zw9sMVHhhuUqUA4J4IN0ATsFZatTxzueZtnaclu5fY70kjVd1g75JWl+jKTlfqyk5X6qK4izg6AwAOINwATlJpq9SaA2v08c8f69Ptn+rEqRP2dc2Dmmtkh5G6stOVGtVxlKJDo02sFAA8C+EGaESGYWjDkQ2au2Wu5m2dp+zibPu62NBYXdf9Ok3oNkEDEgfIz4f/+QGAM/D/roCqQsmuvF369uC3Kqsok7+vv/x9/Gv96+fjp8KyQuWW5v7vdep/Px8pOqKc4hz7dpsHNdeEpAm6vsf1GtJ2iHx9fE3cSwDwDoQbeK2sgiyt2LtCK/ev1Mp9K3Wk6EijbDfEP0RXd7la1/e4XiM7jlSAb0CjbBcAUD+EG3iNYyXHtHLfSvsrMz+zxvpA30D1T+yvyOBIWSutstqsdf4bFhimliEtFR0SrZYhLWu9ukd35x40AGAiwg08VsHpAq0+sFor963Uin0r9POxn2us97X46uILLtawdsN0Rbsr1L9VfwX7B5tULQCgsRBu4PaslVblncpTXmmeDhUe0uoDq7Vi3wptPLJRNsNWY+yFsRfaw8zlbS7nHjIA4IEIN3A5lbZKnSg+oZziHB0tOaqjxUf/93PJUeWW5iqvNE95p/KUW5qrwrLCM26rU2Qne5gZ0nYIl1wDgBcg3HgQwzB0quKUCssKVXC6oOrfsoIa7/19/XVZ4mXqGdtTPhafJqnrdMVpZRdlK7s4WznFOfZgcuLUCfvP1Udejpw8oqIfi2odcTkXiyyKDI5UdGi0fnPBb3RF2yt0RbsrlBiR6KS9AgC4KsKNGyopL9GeE3u0M2+nduXtqvHKP51fr21EBUdpcNvBGtp2qIa2Hapu0d3OeVfcCluF8k/lq7Cs8IyvE6dOKLu4KshkF2XrSNGRetf0SxZZFB0ardjQWMU1i1Nss1jFhla9okOjFRUcpaiQKLUMaamo4Cg1D2rOZdYAAEmEG5dUcLpAWQVZOlh4UFkFWTV+3pu/V4cKD5318z4WH4UHhis8MFwRgRFV/wZV/Zt/Kl9rs9Yq71SeFm5fqIXbF0qSokOiNaTtEPWK7aWCsoKa93EpzdXx0uM6efrkee9TgG+AEsISFNcszh5IqgNKZHCkooKjFBEQoW2btmnCyAmKj4jnJncAgPPCX49GUmmr1OGiwzp8+rAyjmbIalhVUl6iUmupSq2lKrFW/VxcXlzraEdReZH956PFR1VUXnTO39cypKU6R3WuekV2tv/cpnkbhQWEnfUojLXSqo1HNmrV/lVatX+Vvs36VsdLj2v+tvmav23+OX93s4Bm9vD061dEYITim8UrPixeCWEJ9p9bBLU455Ehq9WqU9tOKa5ZHMEGAHDe+AvSSA4XHVa7f7arerPD8e1FBUepdURrJUYkqnV4a/vPbZu3VeeozooMjjzvbfv7+qt/Yn/1T+yvxwY9prKKMm04skGr9q1SZn6mooKj6rx/S3RotFoEteD0DwDApRFuGkmof6h8Lb4KtAQqIiRCIf4hCvEPUWhA6P9+9g9VqH+o/RRRXa+o4CglRiQqxD+kyWoP9AvUwNYDNbD1wCb7nQAAOIvp4eaNN97QCy+8oOzsbHXv3l0zZ87UoEGDzjh+9erVSklJ0datW5WQkKCpU6dqypQpTVhx3SKDI3Vq2iktWbJEY8aMkb+/v9klAQDglZrmWuAzmDdvnu6//349/vjjSk9P16BBgzR69GhlZWXVOX7fvn0aM2aMBg0apPT0dD322GO69957tWDBgiauvLZzzScBAABNw9Rw8/LLL+vWW2/VbbfdpqSkJM2cOVOJiYl688036xw/a9YstW7dWjNnzlRSUpJuu+023XLLLXrxxRebuHIAAOCqTDstVV5erk2bNunRRx+tsTw5OVnfffddnZ9Zt26dkpOTaywbOXKk3nnnHVmt1jpPBZWVlamsrMz+vrCw6m62VqtVVqvV0d2ooXp7jb1db0H/HEcPHUcPHUP/HEcP69aQfpgWbnJzc1VZWanY2Ngay2NjY5WTk1PnZ3JycuocX1FRodzcXMXHx9f6zIwZM/TUU0/VWr58+XKFhDhn0m5qaqpTtust6J/j6KHj6KFj6J/j6GFNpaWl9R5r+oTiX89VMQzjrPNX6hpf1/Jq06ZNU0pKiv19YWGhEhMTlZycrPDwxn1ootVqVWpqqkaMGMGE4vNA/xxHDx1HDx1D/xxHD+tWfealPkwLNy1btpSvr2+tozTHjh2rdXSmWlxcXJ3j/fz8FBUVVednAgMDFRgYWGu5v7+/0740zty2N6B/jqOHjqOHjqF/jqOHNTWkF6ZNKA4ICFDfvn1rHXZLTU3VgAED6vxM//79a41fvny5+vXrxxcAAABIMvlqqZSUFL399tt69913tX37dj3wwAPKysqy37dm2rRpmjx5sn38lClTdODAAaWkpGj79u1699139c477+ihhx4yaxcAAICLMXXOzXXXXae8vDz97W9/U3Z2tnr06KElS5aoTZs2kqTs7Owa97xp166dlixZogceeECvv/66EhIS9I9//EMTJkwwaxcAAICLMX1C8Z133qk777yzznVz5syptWzw4MHavHmzk6sCAADuytTTUgAAAI2NcAMAADwK4QYAAHgUwg0AAPAopk8obmrVdzRuyJ0O68tqtaq0tFSFhYXcd+c80D/H0UPH0UPH0D/H0cO6Vf/drv47fjZeF26KiookSYmJiSZXAgAAGqqoqEgRERFnHWMx6hOBPIjNZtORI0cUFhZ21mdYnY/q51YdPHiw0Z9b5Q3on+PooePooWPon+PoYd0Mw1BRUZESEhLk43P2WTVed+TGx8dHrVq1curvCA8P5wvpAPrnOHroOHroGPrnOHpY27mO2FRjQjEAAPAohBsAAOBRCDeNKDAwUE888YQCAwPNLsUt0T/H0UPH0UPH0D/H0UPHed2EYgAA4Nk4cgMAADwK4QYAAHgUwg0AAPAohJtfWbNmjcaNG6eEhARZLBZ99tlnNdYfPXpUN910kxISEhQSEqJRo0Zp9+7dtbazbt06XXHFFQoNDVXz5s01ZMgQnTp1yr4+Pz9fkyZNUkREhCIiIjRp0iSdPHnSyXvnfI72b//+/bJYLHW+5s+fbx/nqf2TGuc7mJOTo0mTJikuLk6hoaHq06ePPv300xpjPLWHjdG/zMxMXXPNNYqOjlZ4eLgmTpyoo0eP1hjjqf2bMWOGLr74YoWFhSkmJkbjx4/Xzp07a4wxDENPPvmkEhISFBwcrCFDhmjr1q01xpSVlemee+5Ry5YtFRoaqquuukqHDh2qMYYenr2Hs2fP1pAhQxQeHi6LxVJnbzy1h44i3PxKSUmJevXqpddee63WOsMwNH78eO3du1eff/650tPT1aZNGw0fPlwlJSX2cevWrdOoUaOUnJysH374QRs2bNDdd99d446Kf/jDH5SRkaGlS5dq6dKlysjI0KRJk5pkH53J0f4lJiYqOzu7xuupp55SaGioRo8ebd+Wp/ZPapzv4KRJk7Rz504tXrxYW7Zs0bXXXqvrrrtO6enp9jGe2kNH+1dSUqLk5GRZLBatXLlS3377rcrLyzVu3DjZbDb7tjy1f6tXr9Zdd92l77//XqmpqaqoqFBycnKN79fzzz+vl19+Wa+99po2bNiguLg4jRgxwv54G0m6//77tWjRIn388cdau3atiouLNXbsWFVWVtrH0MOz97C0tFSjRo3SY489dsbf5ak9dJiBM5JkLFq0yP5+586dhiTj559/ti+rqKgwIiMjjbfeesu+7JJLLjGmT59+xu1u27bNkGR8//339mXr1q0zJBk7duxo3J0w0fn279cuuugi45ZbbrG/95b+Gcb59zA0NNR4//33a2wrMjLSePvttw3D8J4enk//li1bZvj4+BgFBQX2MSdOnDAkGampqYZheE//DMMwjh07ZkgyVq9ebRiGYdhsNiMuLs549tln7WNOnz5tREREGLNmzTIMwzBOnjxp+Pv7Gx9//LF9zOHDhw0fHx9j6dKlhmHQw3P18JdWrVplSDLy8/NrLPemHjYUR24aoKysTJIUFBRkX+br66uAgACtXbtWknTs2DGtX79eMTExGjBggGJjYzV48GD7eqnqyE5ERIQuueQS+7JLL71UERER+u6775pob5peffr3a5s2bVJGRoZuvfVW+zJv7Z9U/x4OHDhQ8+bN04kTJ2Sz2fTxxx+rrKxMQ4YMkeS9PaxP/8rKymSxWGrcYyQoKEg+Pj72Md7Uv4KCAklSZGSkJGnfvn3KyclRcnKyfUxgYKAGDx5s3/dNmzbJarXWGJOQkKAePXrYx9DDs/ewPryphw1FuGmArl27qk2bNpo2bZry8/NVXl6uZ599Vjk5OcrOzpYk7d27V5L05JNP6vbbb9fSpUvVp08fDRs2zH5ePycnRzExMbW2HxMTo5ycnKbboSZWn/792jvvvKOkpCQNGDDAvsxb+yfVv4fz5s1TRUWFoqKiFBgYqDvuuEOLFi1Shw4dJHlvD+vTv0svvVShoaF65JFHVFpaqpKSEj388MOy2Wz2Md7SP8MwlJKSooEDB6pHjx6SZN+/2NjYGmNjY2Pt63JychQQEKAWLVqcdQw9PHMP68Nbeng+CDcN4O/vrwULFmjXrl2KjIxUSEiI0tLSNHr0aPn6+kqS/Zz8HXfcoZtvvlm9e/fWK6+8oi5duujdd9+1b6uuJ5IbhtHoTyp3JfXp3y+dOnVKH330UY2jNtW8sX9S/Xs4ffp05efn6+uvv9bGjRuVkpKi3/3ud9qyZYt9jDf2sD79i46O1vz58/XFF1+oWbNmioiIUEFBgfr06VOjx97Qv7vvvls//fST5s6dW2vdr/ezPvv+6zH0sOE9PNc2znc7nsbrngruqL59+yojI0MFBQUqLy9XdHS0LrnkEvXr10+SFB8fL0nq1q1bjc8lJSUpKytLkhQXF1frygtJOn78eK0k72nO1b9f+vTTT1VaWqrJkyfXWO7N/ZPO3cPMzEy99tpr+vnnn9W9e3dJUq9evfTNN9/o9ddf16xZs7y6h/X5DiYnJyszM1O5ubny8/NT8+bNFRcXp3bt2knyju/gPffco8WLF2vNmjVq1aqVfXlcXJykqqMG1f9/J1Wdkq/e97i4OJWXlys/P7/G0Ztjx47Zj8LSw7P3sD68oYfniyM35ykiIkLR0dHavXu3Nm7cqKuvvlqS1LZtWyUkJNS67G/Xrl1q06aNJKl///4qKCjQDz/8YF+/fv16FRQU1Dj94snO1L9feuedd3TVVVcpOjq6xnL6V+VMPSwtLZWkGlfnSVVzS6qPLNLD+n0HW7ZsqebNm2vlypU6duyYrrrqKkme3T/DMHT33Xdr4cKFWrlypT3QVWvXrp3i4uKUmppqX1ZeXq7Vq1fb971v377y9/evMSY7O1s///yzfQw9PHsP68OTe+gwU6Yxu7CioiIjPT3dSE9PNyQZL7/8spGenm4cOHDAMAzD+OSTT4xVq1YZmZmZxmeffWa0adPGuPbaa2ts45VXXjHCw8ON+fPnG7t37zamT59uBAUFGXv27LGPGTVqlHHhhRca69atM9atW2f07NnTGDt2bJPuqzM0Rv8MwzB2795tWCwW4z//+U+dv8dT+2cYjvewvLzc6NixozFo0CBj/fr1xp49e4wXX3zRsFgsxldffWUf56k9bIzv4LvvvmusW7fO2LNnj/HBBx8YkZGRRkpKSo0xntq/P//5z0ZERISRlpZmZGdn21+lpaX2Mc8++6wRERFhLFy40NiyZYtx/fXXG/Hx8UZhYaF9zJQpU4xWrVoZX3/9tbF582bjiiuuMHr16mVUVFTYx9DDs/cwOzvbSE9PN9566y1DkrFmzRojPT3dyMvLs4/x1B46inDzK9WX3P36deONNxqGYRivvvqq0apVK8Pf399o3bq1MX36dKOsrKzWdmbMmGG0atXKCAkJMfr372988803Ndbn5eUZN9xwgxEWFmaEhYUZN9xwQ63L/NxRY/Vv2rRpRqtWrYzKyso6f4+n9s8wGqeHu3btMq699lojJibGCAkJMS688MJal4Z7ag8bo3+PPPKIERsba/j7+xudOnUyXnrpJcNms9UY46n9q6t3koz33nvPPsZmsxlPPPGEERcXZwQGBhqXX365sWXLlhrbOXXqlHH33XcbkZGRRnBwsDF27FgjKyurxhh6ePYePvHEE+fcjqf20FE8FRwAAHgU5twAAACPQrgBAAAehXADAAA8CuEGAAB4FMINAADwKIQbAADgUQg3AADAoxBuAACARyHcAAAAj0K4AeASDMPQ8OHDNXLkyFrr3njjDUVERCgrK8uEygC4G8INAJdgsVj03nvvaf369frXv/5lX75v3z498sgjevXVV9W6detG/Z1Wq7VRtwfANRBuALiMxMREvfrqq3rooYe0b98+GYahW2+9VcOGDdNvfvMbjRkzRs2aNVNsbKwmTZqk3Nxc+2eXLl2qgQMHqnnz5oqKitLYsWOVmZlpX79//35ZLBZ98sknGjJkiIKCgvThhx/qwIEDGjdunFq0aKHQ0FB1795dS5YsMWP3ATQSHpwJwOWMHz9eJ0+e1IQJE/T0009rw4YN6tevn26//XZNnjxZp06d0iOPPKKKigqtXLlSkrRgwQJZLBb17NlTJSUl+utf/6r9+/crIyNDPj4+2r9/v9q1a6e2bdvqpZdeUu/evRUYGKg//elPKi8v10svvaTQ0FBt27ZN4eHhuvzyy03uAoDzRbgB4HKOHTumHj16KC8vT59++qnS09O1fv16LVu2zD7m0KFDSkxM1M6dO9W5c+da2zh+/LhiYmK0ZcsW9ejRwx5uZs6cqfvuu88+7sILL9SECRP0xBNPNMm+AXA+TksBcDkxMTH605/+pKSkJF1zzTXatGmTVq1apWbNmtlfXbt2lST7qafMzEz94Q9/UPv27RUeHq527dpJUq1JyP369avx/t5779Xf//53XXbZZXriiSf0008/NcEeAnAmwg0Al+Tn5yc/Pz9Jks1m07hx45SRkVHjtXv3bvvpo3HjxikvL09vvfWW1q9fr/Xr10uSysvLa2w3NDS0xvvbbrtNe/fu1aRJk7Rlyxb169dP//znP5tgDwE4C+EGgMvr06ePtm7dqrZt26pjx441XqGhocrLy9P27ds1ffp0DRs2TElJScrPz6/39hMTEzVlyhQtXLhQDz74oN566y0n7g0AZyPcAHB5d911l06cOKHrr79eP/zwg/bu3avly5frlltuUWVlpVq0aKGoqCjNnj1be/bs0cqVK5WSklKvbd9///1atmyZ9u3bp82bN2vlypVKSkpy8h4BcCbCDQCXl5CQoG+//VaVlZUaOXKkevToofvuu08RERHy8fGRj4+PPv74Y23atEk9evTQAw88oBdeeKFe266srNRdd92lpKQkjRo1Sl26dNEbb7zh5D0C4ExcLQUAADwKR24AAIBHIdwAAACPQrgBAAAehXADAAA8CuEGAAB4FMINAADwKIQbAADgUQg3AADAoxBuAACARyHcAAAAj0K4AQAAHoVwAwAAPMr/Bwt3sh9RhOpSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pr.plot(kind = 'line', x = 'Year', y = 'Value', legend = False,\n",
    "           grid = True, xlabel = 'Years', ylabel = 'Value', color = 'green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c0bd4a38",
   "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>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6074</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1960</td>\n",
       "      <td>3.653593e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6075</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1961</td>\n",
       "      <td>3.870910e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6076</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1962</td>\n",
       "      <td>4.159907e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6077</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1963</td>\n",
       "      <td>4.777600e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6078</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1964</td>\n",
       "      <td>5.572687e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6079</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1965</td>\n",
       "      <td>5.876042e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6080</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1966</td>\n",
       "      <td>4.525364e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6081</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1967</td>\n",
       "      <td>4.946617e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6082</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1968</td>\n",
       "      <td>5.237732e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6083</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1969</td>\n",
       "      <td>5.766833e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6084</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1970</td>\n",
       "      <td>6.158980e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6085</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1971</td>\n",
       "      <td>6.645256e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6086</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1972</td>\n",
       "      <td>7.050991e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6087</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1973</td>\n",
       "      <td>8.437454e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6088</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1974</td>\n",
       "      <td>9.819828e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6089</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1975</td>\n",
       "      <td>9.715922e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6090</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1976</td>\n",
       "      <td>1.013470e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6091</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1977</td>\n",
       "      <td>1.198667e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6092</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1978</td>\n",
       "      <td>1.354688e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6093</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1979</td>\n",
       "      <td>1.509508e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6094</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1980</td>\n",
       "      <td>1.838399e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6095</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1981</td>\n",
       "      <td>1.909095e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6096</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1982</td>\n",
       "      <td>1.980377e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6097</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1983</td>\n",
       "      <td>2.153508e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6098</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1984</td>\n",
       "      <td>2.093282e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6099</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1985</td>\n",
       "      <td>2.294103e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6100</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1986</td>\n",
       "      <td>2.456647e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6101</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1987</td>\n",
       "      <td>2.753114e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6102</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1988</td>\n",
       "      <td>2.926327e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6103</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1989</td>\n",
       "      <td>2.920933e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6104</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1990</td>\n",
       "      <td>3.166973e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6105</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1991</td>\n",
       "      <td>2.665023e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6106</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1992</td>\n",
       "      <td>2.843639e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6107</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1993</td>\n",
       "      <td>2.755704e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6108</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1994</td>\n",
       "      <td>3.229099e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6109</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.554760e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6110</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1996</td>\n",
       "      <td>3.876560e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6111</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1997</td>\n",
       "      <td>4.103203e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6112</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1998</td>\n",
       "      <td>4.157309e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6113</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1999</td>\n",
       "      <td>4.527000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6114</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2000</td>\n",
       "      <td>4.621468e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6115</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2001</td>\n",
       "      <td>4.789655e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6116</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2002</td>\n",
       "      <td>5.080690e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6117</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2003</td>\n",
       "      <td>5.995929e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6118</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2004</td>\n",
       "      <td>6.996889e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6119</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2005</td>\n",
       "      <td>8.089011e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6120</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2006</td>\n",
       "      <td>9.203165e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6121</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2007</td>\n",
       "      <td>1.201112e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6122</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2008</td>\n",
       "      <td>1.186953e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6123</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.323940e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6124</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.656617e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6125</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.823050e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6126</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.827638e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6127</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.856722e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6128</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2014</td>\n",
       "      <td>2.035393e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6129</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2015</td>\n",
       "      <td>2.089865e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6130</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.263792e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country Name Country Code  Year         Value\n",
       "6074        India          IND  1960  3.653593e+10\n",
       "6075        India          IND  1961  3.870910e+10\n",
       "6076        India          IND  1962  4.159907e+10\n",
       "6077        India          IND  1963  4.777600e+10\n",
       "6078        India          IND  1964  5.572687e+10\n",
       "6079        India          IND  1965  5.876042e+10\n",
       "6080        India          IND  1966  4.525364e+10\n",
       "6081        India          IND  1967  4.946617e+10\n",
       "6082        India          IND  1968  5.237732e+10\n",
       "6083        India          IND  1969  5.766833e+10\n",
       "6084        India          IND  1970  6.158980e+10\n",
       "6085        India          IND  1971  6.645256e+10\n",
       "6086        India          IND  1972  7.050991e+10\n",
       "6087        India          IND  1973  8.437454e+10\n",
       "6088        India          IND  1974  9.819828e+10\n",
       "6089        India          IND  1975  9.715922e+10\n",
       "6090        India          IND  1976  1.013470e+11\n",
       "6091        India          IND  1977  1.198667e+11\n",
       "6092        India          IND  1978  1.354688e+11\n",
       "6093        India          IND  1979  1.509508e+11\n",
       "6094        India          IND  1980  1.838399e+11\n",
       "6095        India          IND  1981  1.909095e+11\n",
       "6096        India          IND  1982  1.980377e+11\n",
       "6097        India          IND  1983  2.153508e+11\n",
       "6098        India          IND  1984  2.093282e+11\n",
       "6099        India          IND  1985  2.294103e+11\n",
       "6100        India          IND  1986  2.456647e+11\n",
       "6101        India          IND  1987  2.753114e+11\n",
       "6102        India          IND  1988  2.926327e+11\n",
       "6103        India          IND  1989  2.920933e+11\n",
       "6104        India          IND  1990  3.166973e+11\n",
       "6105        India          IND  1991  2.665023e+11\n",
       "6106        India          IND  1992  2.843639e+11\n",
       "6107        India          IND  1993  2.755704e+11\n",
       "6108        India          IND  1994  3.229099e+11\n",
       "6109        India          IND  1995  3.554760e+11\n",
       "6110        India          IND  1996  3.876560e+11\n",
       "6111        India          IND  1997  4.103203e+11\n",
       "6112        India          IND  1998  4.157309e+11\n",
       "6113        India          IND  1999  4.527000e+11\n",
       "6114        India          IND  2000  4.621468e+11\n",
       "6115        India          IND  2001  4.789655e+11\n",
       "6116        India          IND  2002  5.080690e+11\n",
       "6117        India          IND  2003  5.995929e+11\n",
       "6118        India          IND  2004  6.996889e+11\n",
       "6119        India          IND  2005  8.089011e+11\n",
       "6120        India          IND  2006  9.203165e+11\n",
       "6121        India          IND  2007  1.201112e+12\n",
       "6122        India          IND  2008  1.186953e+12\n",
       "6123        India          IND  2009  1.323940e+12\n",
       "6124        India          IND  2010  1.656617e+12\n",
       "6125        India          IND  2011  1.823050e+12\n",
       "6126        India          IND  2012  1.827638e+12\n",
       "6127        India          IND  2013  1.856722e+12\n",
       "6128        India          IND  2014  2.035393e+12\n",
       "6129        India          IND  2015  2.089865e+12\n",
       "6130        India          IND  2016  2.263792e+12"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "584bd2e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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