tools/a7_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the final assigment. Please import this notebook into IBM Watson Studio. This Notebook displays growth of Corona cases. Please change the Country to anything other than \"United_States_of_America\". A list of available countries is given in the next cell. You can change the country in the cell after the next cell. Once finished, please share the notebook for grading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Angola', 'Albania', 'Andorra', 'Argentina',\n",
       "       'Antigua_and_Barbuda', 'Algeria', 'Armenia', 'Aruba', 'Australia',\n",
       "       'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh',\n",
       "       'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bermuda',\n",
       "       'Bhutan', 'Bolivia', 'Bosnia_and_Herzegovina', 'Brazil',\n",
       "       'Brunei_Darussalam', 'Bulgaria', 'Burkina_Faso', 'Cambodia',\n",
       "       'Cameroon', 'Canada', 'Cape_Verde',\n",
       "       'Cases_on_an_international_conveyance_Japan', 'Cayman_Islands',\n",
       "       'Central_African_Republic', 'Chad', 'Chile', 'China', 'Colombia',\n",
       "       'Congo', 'Costa_Rica', 'Cote_dIvoire', 'Croatia', 'Cuba',\n",
       "       'Curaçao', 'Cyprus', 'Czech_Republic',\n",
       "       'Democratic_Republic_of_the_Congo', 'Denmark', 'Djibouti',\n",
       "       'Dominica', 'Dominican_Republic', 'Ecuador', 'Egypt',\n",
       "       'El_Salvador', 'Equatorial_Guinea', 'Eritrea', 'Estonia',\n",
       "       'Eswatini', 'Ethiopia', 'Faroe_Islands', 'Fiji', 'Finland',\n",
       "       'France', 'French_Polynesia', 'Gabon', 'Gambia', 'Georgia',\n",
       "       'Germany', 'Ghana', 'Gibraltar', 'Greece', 'Greenland', 'Grenada',\n",
       "       'Guam', 'Guatemala', 'Guernsey', 'Guinea', 'Guyana', 'Haiti',\n",
       "       'Holy_See', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia',\n",
       "       'Iran', 'Iraq', 'Ireland', 'Isle_of_Man', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jersey', 'Jordan', 'Kazakhstan', 'Kenya',\n",
       "       'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon',\n",
       "       'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg',\n",
       "       'Madagascar', 'Malaysia', 'Maldives', 'Mali', 'Malta',\n",
       "       'Mauritania', 'Mauritius', 'Mexico', 'Moldova', 'Monaco',\n",
       "       'Mongolia', 'Montenegro', 'Montserrat', 'Morocco', 'Mozambique',\n",
       "       'Myanmar', 'Namibia', 'Nepal', 'Netherlands', 'New_Caledonia',\n",
       "       'New_Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'North_Macedonia',\n",
       "       'Norway', 'Oman', 'Pakistan', 'Palestine', 'Panama',\n",
       "       'Papua_New_Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',\n",
       "       'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda',\n",
       "       'Saint_Kitts_and_Nevis', 'Saint_Lucia',\n",
       "       'Saint_Vincent_and_the_Grenadines', 'San_Marino', 'Saudi_Arabia',\n",
       "       'Senegal', 'Serbia', 'Seychelles', 'Singapore', 'Sint_Maarten',\n",
       "       'Slovakia', 'Slovenia', 'Somalia', 'South_Africa', 'South_Korea',\n",
       "       'Spain', 'Sri_Lanka', 'Sudan', 'Suriname', 'Sweden', 'Switzerland',\n",
       "       'Syria', 'Taiwan', 'Thailand', 'Timor_Leste', 'Togo',\n",
       "       'Trinidad_and_Tobago', 'Tunisia', 'Turkey',\n",
       "       'Turks_and_Caicos_islands', 'Uganda', 'Ukraine',\n",
       "       'United_Arab_Emirates', 'United_Kingdom',\n",
       "       'United_Republic_of_Tanzania', 'United_States_of_America',\n",
       "       'United_States_Virgin_Islands', 'Uruguay', 'Uzbekistan',\n",
       "       'Venezuela', 'Vietnam', 'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "url = 'https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-2020-03-26.xlsx'\n",
    "df_raw = pd.read_excel(url)\n",
    "\n",
    "df_raw['Countries and territories'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_raw\n",
    "\n",
    "df = df[df['Countries and territories']=='United_States_of_America']\n",
    "\n",
    "df = df.sort_values(['Year', 'Month' ,'Day'], ascending=[1, 1, 1])\n",
    "\n",
    "df['ts'] = pd.to_datetime(df[['Year', 'Month' ,'Day']])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f37f0439f28>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEKCAYAAACopKobAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xt8FPW5+PHPkxshhGsIiHItpWqAgIqId9SKKCJatdX6q3ivFm/1HCsej8Vra9WjgNpaWkG869FaaQ+KQAHRohJBUQIKyC2CkAuQG7ns5vn9Md8Nm2U3CYFkd5Pn/Xrta3e/852Z77OzO8/OzHdmRFUxxhhjYlFCtBtgjDHGRGJJyhhjTMyyJGWMMSZmWZIyxhgTsyxJGWOMiVmWpIwxxsQsS1LGGGNiliUpY4wxMcuSlDHGmJiVFO0GtLTu3btr//79o90MY4yJK5999lmBqma29HzbXJLq378/OTk50W6GMcbEFRHZHI352u4+Y4wxMcuSlDHGmJhlScoYY0zManPHpMKprq4mLy+PioqKaDfFGABSU1Pp3bs3ycnJ0W6KMVHVbElKRGYC5wM7VXVIyLD/BB4DMlW1QEQEmAacB5QDV6nqCld3IvDfbtSHVHW2Kz8OeB5oD8wFbtMm3hwrLy+Pjh070r9/f7ymGBM9qkphYSF5eXkMGDAg2s0xJqqac3ff88DY0EIR6QOcDWwJKj4XGOQeNwB/cnW7AVOAE4CRwBQR6erG+ZOrGxhvv3k1VkVFBRkZGZagTEwQETIyMmzL3hiaMUmp6gdAUZhBTwK/AYK3eiYAL6jnY6CLiPQCzgHmq2qRqu4C5gNj3bBOqrrMbT29AFx4MO21BGViiX0fjfG0aMcJEbkA+E5VvwgZdASwNeh9niurrzwvTLkxxpgQn24s4sn531Dp80e7KQesxZKUiKQB9wC/DTc4TJk2oTzSvG8QkRwRycnPz29Mc1vc999/z2WXXcbAgQPJysrivPPO45tvvjmk81i8eDH//ve/Iw6fM2cOjzzyyCGdZ1Okp6fvV7Z7927++Mc/HpLpX3fddeTm5gLwu9/9rknTOO+889i9e/chaY8xze2TbwuZtnAdCXG4hd6SW1IDgQHAFyKyCegNrBCRw/C2hPoE1e0NbGugvHeY8rBUdYaqjlDVEZmZLX5VjwapKhdddBGjR49mw4YN5Obm8rvf/Y4dO3Yc0vnUl6R8Ph8XXHABkydPPqTzPFSakqRUlZqamjplfr+fv/71r2RlZQEHnqQC05w7dy5dunQ5oHGNiZayKj8pSQkkJ8bfWUct1mJV/VJVe6hqf1Xtj5dojlXV74E5wJXiGQXsUdXtwDxgjIh0dR0mxgDz3LASERnlegZeCbzTUrEcaosWLSI5OZkbb7yxtmz48OGceuqpqCp33nknQ4YMYejQobz++uuAl3DOP//82vo333wzzz//POBd+mnKlCkce+yxDB06lLVr17Jp0yaeffZZnnzySYYPH87SpUu56qqruOOOOzjjjDO46667eP7557n55psByM/P5+KLL+b444/n+OOP56OPPgJgyZIlDB8+nOHDh3PMMcdQUlKyXzwXXnghxx13HIMHD2bGjBm15enp6dxzzz0MGzaMUaNG1SbhjRs3cuKJJ3L88cdz7733hv2MJk+ezIYNGxg+fDh33nknAI899hjHH3882dnZTJkyBYBNmzZx9NFH86tf/Ypjjz2WrVu3kp6ezm9/+1tOOOEEli1bxujRo8nJyWHy5Mns3buX4cOHc8UVVwDwxBNPMGTIEIYMGcLUqVMjTrN///4UFBQA8NJLLzFy5EiGDx/OL3/5S/x+P36/n6uuuqp2uT355JMH8pUw5pAqr/LRISUx2s1oGlVtlgfwKrAdqMZLSNeGDN8EdHevBXgG2AB8CYwIqncNsN49rg4qHwF85cZ5GpDGtOu4447TULm5ufuVtaRp06bp7bffHnbYm2++qT/+8Y/V5/Pp999/r3369NFt27bpokWLdNy4cbX1Jk2apLNmzVJV1X79+un06dNVVfWZZ57Ra6+9VlVVp0yZoo899ljtOBMnTtRx48apz+dTVdVZs2bppEmTVFX18ssv16VLl6qq6ubNm/Woo45SVdXzzz9fP/zwQ1VVLSkp0erq6v3aXFhYqKqq5eXlOnjwYC0oKFBVVUDnzJmjqqp33nmnPvjgg6qqOn78eJ09e7aqqj799NPaoUOH/aa5ceNGHTx4cO37efPm6fXXX681NTXq9/t13LhxumTJEt24caOKiC5btqy2LqCvv/567fvTTz9dly9frqpaZ145OTk6ZMgQLS0t1ZKSEs3KytIVK1aEnWa/fv00Pz9fc3Nz9fzzz9eqqipVVb3pppt09uzZmpOToz/+8Y9r6+/atWu/mBoS7e+laT1+/fpKPen3Cw9qGkCONlO+qO/RbOdJqerlDQzvH/RagUkR6s0EZoYpzwGG7D9G6/Lhhx9y+eWXk5iYSM+ePTn99NNZvnw5nTp1qne8n/zkJwAcd9xx/O1vf4tY79JLLyUxcf9/WAsWLKg9bgNQXFxMSUkJJ598MnfccQdXXHEFP/nJT+jdu/d+406fPp23334bgK1bt7Ju3ToyMjJISUmp3fo77rjjmD9/PgAfffQRb731FgC/+MUvuOuuu+qNDeD999/n/fff55hjjgGgtLSUdevW0bdvX/r168eoUaNq6yYmJnLxxRc3OM0PP/yQiy66iA4dOgDeZ7h06VIuuOCC/aYZsHDhQj777DOOP/54APbu3UuPHj0YP3483377Lbfccgvjxo1jzJgxDc7fmOZSXuknvV18XrshPlvdygwePJg333wz7DCNcH5yUlJSneMtoefUtGvXDvBW0D6fL+K8AyvkUDU1NSxbtoz27dvXKZ88eTLjxo1j7ty5jBo1igULFnDUUUfVDl+8eDELFixg2bJlpKWlMXr06Nq2JScn13atDm3XgXa5VlXuvvtufvnLX9Yp37Rp034xpaamhk3E4aYZSaTPSVWZOHEiv//97/cb9sUXXzBv3jyeeeYZ3njjDWbO3O+/ljEtoqzKR1q7+NzdF39H0VqhM888k8rKSv7yl7/Uli1fvpwlS5Zw2mmn8frrr+P3+8nPz+eDDz5g5MiR9OvXj9zcXCorK9mzZw8LFy5scD4dO3YMewwpnDFjxvD000/Xvv/8888B2LBhA0OHDuWuu+5ixIgRrF27ts54e/bsoWvXrqSlpbF27Vo+/vjjBud18skn89prrwHw8ssvN6rt55xzDjNnzqS0tBSA7777jp07dzYqtmDJyclUV1cDcNppp/H3v/+d8vJyysrKePvttzn11FPrHf+ss87izTffrJ13UVERmzdvpqCggJqaGi6++GIefPBBVqxYccBtM+ZQKav00SElPrdJ4rPVrYyI8Pbbb3P77bfzyCOPkJqaSv/+/Zk6dSqnnXYay5YtY9iwYYgIjz76KIcddhgAP/3pT8nOzmbQoEG1u73qM378eC655BLeeecdnnrqqXrrTp8+nUmTJpGdnY3P5+O0007j2WefZerUqSxatIjExESysrI499xz64w3duxYnn32WbKzsznyyCPD7iILNW3aNH7+858zbdq0iLvlMjIyOPnkkxkyZAjnnnsujz32GGvWrOHEE08EvE4ZL730UqO2mILdcMMNZGdnc+yxx/Lyyy9z1VVXMXLkSMDrqn7MMcewadOmiONnZWXx0EMPMWbMGGpqakhOTuaZZ56hffv2XH311bVbu+G2tIxpKeVVfjI7tot2M5pE6tvF0RqNGDFCQ296uGbNGo4++ugotciY8Ox7aQ6VU/7wL0b278YTPxve5GmIyGeqOuIQNqtRbHefMca0cuVVfjrEaccJS1LGGNPKlVVaxwljjDExyOevodJXE7cdJyxJGWNMK1ZW5V1U1nb3GWOMiTnlVd75iPF6WSRLUsYY04qVVXpJKs22pIwxxsSaskpvd1+6dZwwB2PTpk0MGVL3UoT33Xcfjz/+eMRxcnJyuPXWW4GG7xUVSfDVvMN5+OGHGTx4MNnZ2QwfPpxPPvkEgKlTp1JeXt7g9Btb72Dl5+dzwgkncMwxx7B06dKI9VauXImIMG/evGZry7Zt27jkkkuabfrGHIgyt7svzTpOmJY2YsQIpk+fDjQ9SdVn2bJl/POf/2TFihWsWrWKBQsW0KePd3uvWEtSCxcu5KijjmLlypX1Xsro1Vdf5ZRTTuHVV19tlnb4fD4OP/zwiNdiNKal7duSis8kFZ+tbkb3/2M1uduKD+k0sw7vxJTxg5s8/ujRoznhhBNYtGgRu3fv5rnnnuPUU09l8eLFPP744zz99NM8++yzJCYm8tJLL/HUU09x1FFHceONN7JlyxbASxYnn3wyhYWFXH755eTn5zNy5Mh6L6q6fft2unfvXnux2u7duwPeJZO2bdvGGWecQffu3Vm0aBE33XQTy5cvZ+/evVxyySXcf//9Yeu9//77TJkyhcrKSgYOHMisWbNIT09n8uTJzJkzh6SkJMaMGRNxC3Lz5s1cc8015Ofnk5mZyaxZsygqKuI3v/lN7b2hwl0YF7yLwb755pvMnz+fU089lYqKClJTU9m0aRNjx47llFNO4eOPP2bYsGFcffXVTJkyhZ07d/Lyyy8zcuRIysrKuOWWW/jyyy/x+Xzcd999TJgwgeeff57/+7//o6KigrKyMmbOnMn555/PV199hd/v56677mLevHmICNdffz233HILDzzwAP/4xz/Yu3cvJ510En/+858P+CK7xjRGee2WlO3uM83I5/Px6aefMnXqVO6///46w/r378+NN97Ir3/9az7//HNOPfVUbrvtNn7961+zfPly3nrrLa677joA7r//fk455RRWrlzJBRdcUJvEwhkzZgxbt27lRz/6Eb/61a9YsmQJALfeeiuHH344ixYtYtGiRYC3WzAnJ4dVq1axZMkSVq1atV+9goICHnroIRYsWMCKFSsYMWIETzzxBEVFRbz99tusXr2aVatW8d///d8R23TzzTdz5ZVXsmrVKq644gpuvfVWhg8fzgMPPMDPfvYzPv/887AJCrxbggwYMICBAwcyevRo5s6dWzts/fr13HbbbaxatYq1a9fyyiuv8OGHH/L444/X3r334Ycf5swzz2T58uUsWrSIO++8k7KyMsDb6pw9ezb/+te/6sxzxowZbNy4kZUrV9a2ORDH8uXL+eqrr9i7dy///Oc/I8ZszMEodR0n4rULeny2uhkdzBbPwYj0LzpQHnx/qPoueBoQ6X5QH3zwQe39pcaNG0fXrl0jTiM9PZ3PPvuMpUuXsmjRIn72s5/xyCOPcNVVV+1X94033mDGjBn4fD62b99Obm4u2dnZdep8/PHH5ObmcvLJJwNQVVXFiSeeSKdOnUhNTeW6665j3Lhxde44HGrZsmW17f/FL37Bb37zmwY/i4BXX32Vyy67DIDLLruMF198sfZzHTBgAEOHDgW8W6ecddZZiAhDhw6t/bzff/995syZU7uVV1FRUZvkzz77bLp167bfPBcsWMCNN95IUpL3UwvUWbRoEY8++ijl5eUUFRUxePBgxo8f3+hYjGms8sr4Pk8qPlvdCmVkZLBr1646ZUVFRQwYMABo/P2hAiLdDwoO7N5NiYmJjB49mtGjRzN06FBmz569X5LauHEjjz/+OMuXL6dr165cddVV+93fCrzdbWeffXbY40GffvopCxcu5LXXXuPpp5/eb4skksbG4vf7eeutt5gzZw4PP/wwqkphYWHt7T8Cny9AQkJC7fuEhITaz1tVeeuttzjyyCPrTPuTTz6p935ToW2sqKjgV7/6FTk5OfTp04f77rsv7OdlzKEQ6DjRPtl295mDkJ6eTq9evWrvC1VUVMR7773HKaec0qjxQ++3FOl+UKeddlrtPZvefffd/RJjsK+//pp169bVmUa/fv32m19xcTEdOnSgc+fO7Nixg3fffTdsu0aNGsVHH33E+vXrASgvL+ebb76htLSUPXv2cN555zF16tTatoZz0kkn1bn3VGM/nwULFjBs2DC2bt3Kpk2b2Lx5MxdffDF///vfGzU+ePeweuqpp2qP461cubLBccaMGcOzzz5bm+iKiopqE1L37t0pLS21ThamWZVV+mifnEhiQnwe87QkFUNeeOEFHnroIYYPH86ZZ57JlClTGDhwYKPGHT9+PG+//TbDhw9n6dKlTJ8+nZycHLKzs8nKyuLZZ58FYMqUKXzwwQcce+yxvP/++/Tt2zfiNEtLS5k4cSJZWVlkZ2eTm5vLfffdB3j3YTr33HM544wzGDZsGMcccwyDBw/mmmuuqd2dF1ovMzOT559/nssvv5zs7GxGjRrF2rVrKSkp4fzzzyc7O5vTTz+dJ598MmKbpk+fzqxZs8jOzubFF19k2rRpjfp8Xn31VS666KI6ZRdffDGvvPJKo8YHuPfee6muriY7O5shQ4Zw7733NjjOddddR9++fcnOzmbYsGG88sordOnSheuvv56hQ4dy4YUX1t563pjmUBbHV0CHZryflIjMBM4HdqrqEFf2GDAeqAI2AFer6m437G7gWsAP3Kqq81z5WGAakAj8VVUfceUDgNeAbsAK4BeqWtVQu+x+UiZe2PfSHAq3v7aSlVt3s+TOMw5qOq3xflLPA2NDyuYDQ1Q1G/gGuBtARLKAy4DBbpw/ikiiiCQCzwDnAlnA5a4uwB+AJ1V1ELALL8EZY4wJUlrpj9sTeaEZO06o6gci0j+k7P2gtx8DgdPyJwCvqWolsFFE1gMj3bD1qvotgIi8BkwQkTXAmcDPXZ3ZwH3Anw59JK1fYWEhZ5111n7lCxcuJCMjIwot8rp7/+///m+dsksvvZR77rmnwXFPOOEEKisr65S9+OKLtb33jGlLyqt8cXtJJIhu775rgNfd6yPwklZAnisD2BpSfgKQAexWVV+Y+k0SrhdWW5GRkVFvZ4VouOeeexqVkMIJXLopnjXXbnjT9pRV+uiSlhLtZjRZVDpOiMg9gA94OVAUppo2oTzS/G4QkRwRycnPz99veGpqKoWFhbZiMDEh0D0+NTU12k0xrUBZlT9uL4kEUdiSEpGJeB0qztJ9WSEP6BNUrTewzb0OV14AdBGRJLc1FVx/P6o6A5gBXseJ0OG9e/cmLy+PcAnMmGhITU2ld+/e0W6GaQXKK31xe0kkaOEk5Xrq3QWcrqrBVx2dA7wiIk8AhwODgE/xtpgGuZ583+F1rvi5qqqILMI7pvUaMBF4p6ntSk5Orj1p1hhjWpPSSl9cd0Fvtt19IvIqsAw4UkTyRORa4GmgIzBfRD4XkWcBVHU18AaQC7wHTFJVv9tKuhmYB6wB3nB1wUt2d7hOFhnAc80VizHGxCNVpbzKTwfrOLE/Vb08THHERKKqDwMPhymfC8wNU/4t+3oAGmOMCVHlr8FXo3HdBd2uOGGMMa1U4F5SHeL4mJQlKWOMaaXK4vw2HWBJyhhjWq3yqvi+TQdYkjLGmFYrcMPDeO6CbknKGGNaqcCt4+P5ZF5LUsYY0wrM/XI7k15ZUaesrHZLypKUMcaYKFr89U7+b9V2Kn3+2rJA7z7bkjLGGBNVBaXe7fR2Fu+7A0Bgd19aHJ/Ma0nKGGNagYJSLzntLKmoLSutPU/KtqSMMcZEUUGJl6R2hGxJJQikJsfvqj5+W26MMQbwrtEX2N23o3jfllRZpZ8OKUlxfa88S1LGGBPniit8VPlrgLpbUmWVvrg+HgWWpIwxJu4FjkdByJZUVXzfpgMsSRljTNwLHI8SCd3d54vrThNgScoYY+Je4HjUgO4dQrak/HF9SSSwJGWMMXEvsLtv8OGd9ztPKp5P5AVLUsYYE/cKSitJEDjqsI6UVPpqL4dUVuknzZKUMcaYaCooraRbh3b06pwK7DsuVVbpI9169xljjImm/JIquqen0LNTIEl5u/zKq/xxfXFZsCRljDFxr6C0ksyO7ejZqR3gXRpJVb0u6NZxIjwRmSkiO0Xkq6CybiIyX0TWueeurlxEZLqIrBeRVSJybNA4E139dSIyMaj8OBH50o0zXeL5lGpjjDkIBaWVdE9vF7QlVcHeaj+q8X1XXmjeLanngbEhZZOBhao6CFjo3gOcCwxyjxuAP4GX1IApwAnASGBKILG5OjcEjRc6L2OMafW8SyJV0j09hfR2SaSlJLKjuLL2Nh3WcSICVf0AKAopngDMdq9nAxcGlb+gno+BLiLSCzgHmK+qRaq6C5gPjHXDOqnqMlVV4IWgaRljTJtRVuWnorqG7untEBF6dkplR3FFbQ8/2913YHqq6nYA99zDlR8BbA2ql+fK6ivPC1MelojcICI5IpKTn59/0EEYY0ysCFxtonu6dzyqR8d2XpJy95Ky3X2HRrjjSdqE8rBUdYaqjlDVEZmZmU1sojHGxJ7AibzdO3pJytuS2re7zy6LdGB2uF11uOedrjwP6BNUrzewrYHy3mHKjTGmTalNUukpAPTs1K7O7j67CvqBmQMEeuhNBN4JKr/S9fIbBexxuwPnAWNEpKvrMDEGmOeGlYjIKNer78qgaRljTJuR767bl5m+b0uq0lfD9j3eCb3xflmkZmu9iLwKjAa6i0geXi+9R4A3RORaYAtwqas+FzgPWA+UA1cDqGqRiDwILHf1HlDVQGeMm/B6ELYH3nUPY4xpUwpKKhGBbh0CW1JeN/Rv80sB4v4Cs82WpFT18giDzgpTV4FJEaYzE5gZpjwHGHIwbTTGmHhXUFpJ17QUkhK9HWOBJLWxoAyI/y2pWOk4YYwxpgkC50gFBK468a1LUnZZJGOMMVGTX1JZ2/0coEdHb0tqS1E5yYlCSlJ8r+bju/XGGNPGFZRWkdlxX5Jqn5JIp9Qk/DUa9+dIgSUpY4yJa4Hr9gULHJeK93OkwJKUMcbErfIqH+VV/v2S1GHuvlLx3rMPLEkZY0zcKijxzpEK7jgB+45L2e4+Y4wxUZMfckmkgEAPvw5xfrUJsCRljDFxK3BJpEw7JmWMMSbW7LtuX6QtKUtSxhhjoiRwTCoj9JhUJ+s4YYwxJsoKSivpkpZMcmLdVflhLknF+yWRwJKUMcbErXDnSAFkdmxHx3ZJtcem4ln8p1ljjGmjQq/bF5CcmMCC/zidrmn7D4s3tiVljDFxqqC0KuyWFHg9/OL9un1gScoYY+JWQUn43X2tiSUpY4yJQxXVfkoqfXUuLtsaWZIyxpg4tLWoHIBeneO/c0R9LEkZY0wcyt1eDEDW4Z2i3JLmZUnKGGPiUO72YlISExiYmR7tpjSrqCQpEfm1iKwWka9E5FURSRWRASLyiYisE5HXRSTF1W3n3q93w/sHTeduV/61iJwTjViMMSYacrcV86PD0vc7kbe1afHoROQI4FZghKoOARKBy4A/AE+q6iBgF3CtG+VaYJeq/hB40tVDRLLceIOBscAfRST+rwFijDENUFVytxWT1at17+qD6O3uSwLai0gSkAZsB84E3nTDZwMXutcT3Hvc8LNERFz5a6paqaobgfXAyBZqvzHGRE1+SSWFZVUcbUnq0FPV74DHgS14yWkP8BmwW1V9rloecIR7fQSw1Y3rc/UzgsvDjGOMMa3W6kCnCUtSh56IdMXbChoAHA50AM4NU1UDo0QYFqk83DxvEJEcEcnJz88/8EYbY0wMyd3mJamjW3nPPmhCkhKRBBE5mE/mx8BGVc1X1Wrgb8BJQBe3+w+gN7DNvc4D+rh5JwGdgaLg8jDj1KGqM1R1hKqOyMzMPIimG2NM9OVuL6ZPt/Z0Sk2OdlOaXaOSlIi8IiKdRKQDkAt8LSJ3NnGeW4BRIpLmji2d5aa5CLjE1ZkIvONez3HvccP/parqyi9zvf8GAIOAT5vYJmOMiRtr2kinCWj8llSWqhbjdWaYC/QFftGUGarqJ3gdIFYAX7o2zADuAu4QkfV4x5yec6M8B2S48juAyW46q4E38BLce8AkVfU3pU3GGBMvyip9bCwsI6tX52g3pUU09lYdySKSjJeknlbVahEJe/ynMVR1CjAlpPhbwvTOU9UK4NII03kYeLip7TDGmHiz9vsSVFv/lSYCGrsl9WdgE14nhw9EpB9Q3FyNMsYYE96aNnI5pIBGbUmp6nRgelDRZhE5o3maZIwxJpLc7cV0bp/M4a38wrIBje040VNEnhORd937LPZ1ZjDGGNNCcrcVc3Svjnj9zlq/xu7uex6Yh3deE8A3wO3N0SBjjDHh+WuUtd8Xt5lOE9D4JNVdVd8AaqD2yg/Wk84YY1rQxoIyKqpr2szxKGh8kioTkQzcFR1EZBTe5YmMMca0kNw2dDmkgMZ2Qb8D7+TZgSLyEZDJvhNvjTHGtIDcbcUkJwo/7NG67yEVrLG9+1aIyOnAkXjXzPvaXdLIGGNMC1m/s4QfdE8nJal130MqWGN7910KtHdXebgQeF1Ejm3WlhljjKljU2E5/bunRbsZLaqx6fheVS0RkVOAc/Du7/Sn5muWMcaYYP4aZUthOf0zOkS7KS2qsUkq0JNvHPAnVX0HSGmeJhljjAm1bfdeqvw19O9uSSqc70Tkz8BPgbki0u4AxjXGGHOQNheWA9iWVAQ/xTuZd6yq7ga6AU29VYcxxpgDtLGwDMCOSYWjquWq+jdgj4j0BZKBtc3aMmOMMbU2F5SRmpxAz45t45p9AY3t3XeBiKwDNgJL3PO7zdkwY4wx+2wqLKN/RgcSEtrGNfsCGru770FgFPCNqg7AuwX8R83WKmOMMXVsLCijX0bb2tUHjU9S1apaCCSISIKqLgKGN2O7jDHGOP4aZWvR3jbXsw8af1mk3SKSDnwAvCwiOwFf8zXLGGNMQG338zbWsw8aSFIi8kOgJzAB2Av8GrgC6Afc0uytM8YY02a7n0PDu/umAiWqWqaqNarqU9XZwFzgvqbOVES6iMibIrJWRNaIyIki0k1E5ovIOvfc1dUVEZkuIutFZFXw5ZhEZKKrv05E7CaMxphWKdD9fEAb3N3XUJLqr6qrQgtVNQfofxDznQa8p6pHAcOANcBkYKGqDgIWuvcA5wKD3OMG3OWYRKQbMAU4ARgJTAkkNmOMaU02ue7nPTogW2ZPAAAZyElEQVS2i3ZTWlxDSaq+DvntmzJDEekEnAY8B6CqVe4E4Ql41wTEPV/oXk8AXlDPx0AXEemFdw3B+apapKq7gPnA2Ka0yRhjYtnmNtr9HBpOUstF5PrQQhG5FvisifP8AZAPzBKRlSLyVxHpAPRU1e0A7rmHq38EsDVo/DxXFqncGGNalbba/Rwa7t13O/C2iFzBvqQ0Au/ishcdxDyPBW5R1U9EZBr7du2FE+6vg9ZTvv8ERG7A21VI3759D6y1xhgTRYHu5z/O6hntpkRFvVtSqrpDVU8C7gc2ucf9qnqiqn7fxHnmAXmq+ol7/yZe0trhduPhnncG1e8TNH5vYFs95eHimKGqI1R1RGZmZhObbYwxLS/Q/XxAG+zZB42/dt8iVX3KPf51MDN0yW2riBzpis4CcvFuTx/ooTcReMe9ngNc6Xr5jQL2uN2B84AxItLVdZgY48qMMabV2OR69vVro0mqsSfzHmq34J0UnAJ8C1yNlzDfcMe7tgCXurpzgfOA9UC5q4uqFonIg8ByV+8BVS1quRCMMab5bXLnSLXF7ucQpSSlqp/jHdsKdVaYugpMijCdmcDMQ9s6Y4yJHYHu5z07tb3u52A3LjTGmJi2qcDrfi7S9rqfgyUpY4yJaYFbdLRVlqSMMSZGBbqf92tjd+MNZknKGGNi1ObCsjbd/RwsSRljTMx65ZMtJCYIpwzqHu2mRI0lKWOMiUF7yqt59dMtjM/uRe+utrvPGGNMDHnx402UVfn55ekDo92UqLIkZYwxMaai2s/z/97E6CMzObpXp2g3J6osSRljTIx587M8Ckqr+OVpbXsrCixJGWNMTPHXKH9Z+i3D+nRh1A+6Rbs5UWdJyhhjYsh7X33P5sJybjr9B232KhPBLEkZY0wMeWtFHn26tefsrMOi3ZSYYEnKGGNiyMaCMrJ7dyGxDd4qPhxLUsYYEyP8NUrernL6dmu750WFsiRljDExYvuevVT7lX6WpGpZkjLGmBixxd3gsG+GJakAS1LGGBMjNhe5JGVbUrUsSRljTIzYUlROcqLQq3P7aDclZliSMsaYGLGlsJzeXdOsZ18QS1LGGBMjNheV2a6+EFFLUiKSKCIrReSf7v0AEflERNaJyOsikuLK27n3693w/kHTuNuVfy0i50QnEmOMOTS2FJbTzzpN1BHNLanbgDVB7/8APKmqg4BdwLWu/Fpgl6r+EHjS1UNEsoDLgMHAWOCPIpLYQm03xphDand5FcUVPtuSChGVJCUivYFxwF/dewHOBN50VWYDF7rXE9x73PCzXP0JwGuqWqmqG4H1wMiWicAYYw6tzYXWsy+caG1JTQV+A9S49xnAblX1ufd5wBHu9RHAVgA3fI+rX1seZpw6ROQGEckRkZz8/PxDGYcxxhwSW1z3834ZHaLcktjS4klKRM4HdqrqZ8HFYapqA8PqG6duoeoMVR2hqiMyMzMPqL3GGNMSAkmqTzfrfh4sKQrzPBm4QETOA1KBTnhbVl1EJMltLfUGtrn6eUAfIE9EkoDOQFFQeUDwOMYYE1e2FJaT2bEdaSnRWC3HrhbfklLVu1W1t6r2x+v48C9VvQJYBFziqk0E3nGv57j3uOH/UlV15Ze53n8DgEHApy0UhjHGHFKbi8rsmn1hxNJ5UncBd4jIerxjTs+58ueADFd+BzAZQFVXA28AucB7wCRV9bd4q40x5hDYUmhXPw8nqtuVqroYWOxef0uY3nmqWgFcGmH8h4GHm6+FxhjT/Cp9frYXV9iFZcOIpS0pY4xpk/J27UUVO5E3DEtSxhgTZVvsHKmILEkZY0yUbam9RYedIxXKkpQxxkTZ5sJy0lIS6Z6eEu2mxBxLUsYYE2Vb3NXPvSu+mWCWpIwxJsq2FFn380gsSRljTBSpqiWpeliSMsaYKNpZUklFdY11P4/AkpQxxkTRxoIyAPra1c/DsiRljDFR9NV3ewA4ulfHKLckNlmSMsaYKPryuz306pxKj46p0W5KTLIkZYwxUbQqbw/ZvTtHuxkxy5KUMcZEyZ691WwsKCO7d5doNyVmWZIyxpgoWe2ORw09wrakIrEkZYwxUfJFnpekbHdfZJakjDEmSr78bjd9u6XRJc2u2ReJJSljjImSL7buYahtRdXLkpQxxkRBYWkl3+3eyzBLUvWyJGWMMVHwZW2nCevZV58WT1Ii0kdEFonIGhFZLSK3ufJuIjJfRNa5566uXERkuoisF5FVInJs0LQmuvrrRGRiS8dijDFNtSpvDyIw5IhO0W5KTIvGlpQP+A9VPRoYBUwSkSxgMrBQVQcBC917gHOBQe5xA/An8JIaMAU4ARgJTAkkNmOMiXWr8vbwg+4d6JiaHO2mxLQWT1Kqul1VV7jXJcAa4AhgAjDbVZsNXOheTwBeUM/HQBcR6QWcA8xX1SJV3QXMB8a2YCjGGNNkq/J220m8jRDVY1Ii0h84BvgE6Kmq28FLZEAPV+0IYGvQaHmuLFK5McbEtB3FFewsqbSTeBshaklKRNKBt4DbVbW4vqphyrSe8nDzukFEckQkJz8//8Aba4wxh9AqdxLvsD6WpBoSlSQlIsl4CeplVf2bK97hduPhnne68jygT9DovYFt9ZTvR1VnqOoIVR2RmZl56AIxxpgmWJW3mwSBrF6WpBoSjd59AjwHrFHVJ4IGzQECPfQmAu8ElV/pevmNAva43YHzgDEi0tV1mBjjyowxJqZ9kbeHH/XsSPuUxGg3JeYlRWGeJwO/AL4Ukc9d2X8BjwBviMi1wBbgUjdsLnAesB4oB64GUNUiEXkQWO7qPaCqRS0TgjHGNE2lz8/yjUVcfJwdQm+MFk9Sqvoh4Y8nAZwVpr4CkyJMayYw89C1zhhjmtfyjbvYW+1n9I96NFzZ2BUnjDGmJS3+eicpiQmc9MOMaDclLliSMsaYFrT4m3xGDuhGWko0jrbEH0tSxhjTQvJ2lbN+Zymjj7Rexo1lScoYY1rIkm+88zQtSTWeJSljjGkhi7/O54gu7RmYmR7tpsQNS1LGGNMCqnw1/Ht9AacfmYl3uqhpDEtSxhjTAnI2F1FW5Wf0j2xX34GwJGWMMS1gydf5JCcKJ/2we7SbElcsSRljTAtY/HU+x/fvRno763p+ICxJGWNMM6qpUXI2FfH1jhLr1dcEltKNMeYQ2rO3mjXbi8ndVkzO5iKWbShkV3k17ZMTOTvrsGg3L+5YkjLGmENg4Zod3PeP1Wwt2ltb1qtzKmcc1YOTBnbnlB9257DOqVFsYXyyJGWMMQfpo/UF3PTSCn6Q2YHfjD2So3t1IqtXJ3p0bGfdzQ+SJSljjDkIK7bs4voXchjQvQOv3TCKLmkp0W5Sq2IdJ4wxponWbC/mqpmfktmxHS9eO9ISVDOwLSljjDlAqspbK77jwX/mkpaSxEvXnkCPTna8qTlYkjLGmAOwpbCc/3r7Sz5cX8CIfl35n58Oo0+3tGg3q9WyJGWMMQ3I21XOsg2FLNtQyNyvtpOUkMCDFw7hipF9SUiwjhHNyZKUMcYA/hrl8627WfJNPrnbiineW01xRTVFZVXsLKkEoFuHFM4b2os7zzmSXp3bR7nFbUPcJykRGQtMAxKBv6rqI1FukjEmTpRUVLP463zm5+7gg3X57C6vJkFgUI+OdO2QTN9uaQw9ojNZh3fixIEZ/KhHR9tyamFxnaREJBF4BjgbyAOWi8gcVc2NbsuMMdFS5ath+5695O3aS3mVn/bJibRPSSAlMZHCskp2FFewo7iSlVt28dH6Qqr8NWR0SOGso3oy+shMTh3U3XrpxZC4TlLASGC9qn4LICKvARMAS1LGNFFFtZ+SCh8dU5NITU6MWE9VKa/y46tROrZLOiRbGDU1Skmlj/IqH0kJCaQkJpCcJPhrlEpfDZW+Giqq/ZRX+il19bbt3sv6naWszy/l2/wyvi+uQLXhefXLSOPKE/sxZvBhHNevK4m2hRST4j1JHQFsDXqfB5zQHDO6bvZyNheWN8ek457irbA08MYRAREh0k8/0ngIJIhQo0pNjeJXpaYGEhIgUYSEBCGhgenWqNadZj3zr1GvviokJgiJCUKCa0O9bRUQ6o+xUW0Lmk697W7C/BpqS3BM5VV+dpVXUemrqa2TlpJI17QU2qck1s7Tr0rxXh979lZR7fcamyDQuX0yndsno4DPr1T5a6ipUZIShaSEBJITBb8qPr9S7VdqVEkQIdEt1/JqP8V7q6lpRIIJ1SElkR/2SOfEH2TQp1savbu254iu7Ulvl0RFdQ17q/1UVvvJSE+hR8dUenRqR7ukyAnYxI54T1Lhfqv7fcVF5AbgBoC+ffs2aUZ9u3UgJcnOfY5E3Bo0sBINrPy0gb+09Y0nIiSKlzREQNU7uO1XbXi6bkXe0CVpAskoUM1Lit6zhnyVBKlNvMEr99B6DQluW+h06mu3ukR6oPOrty1Bn39aSiJd0lLokpZMerskSip87Cqroqi8iopqf51xOrVPonP7FLqmJZOYIOzZW82u8ir27PWRIJCcmEByYgIJ4iWs6poafH4lMUFIShCSEhNITAB/DbV/RNJSEunSPplO7ZPp0C4JX41S7auhyl9DogipyQm0S0qkXXICaSlJdGiXSIeUJHp0asdhnVLt8kOtVLwnqTygT9D73sC20EqqOgOYATBixIgm/cJ/Oz6rKaMZY4w5CPG+abAcGCQiA0QkBbgMmBPlNhljjDlE4npLSlV9InIzMA+vC/pMVV0d5WYZY4w5ROI6SQGo6lxgbrTbYYwx5tCL9919xhhjWjFLUsYYY2KWJSljjDExy5KUMcaYmGVJyhhjTMyShs7cb21EJB/YXE+V7kBBIybV2Hqxrq3F0dbijWWhMbTGmOJVuDj6qWpmSzekzSWphohIjqqOOFT1Yl1bi6OtxRvLQmNojTHFq1iKw3b3GWOMiVmWpIwxxsQsS1L7m3GI68W6thZHW4s3loXG0BpjilcxE4cdkzLGGBOzbEvKGGNM7FJ3A7lYfeDdL2oRsAZYDdzmyrsB84F17nmIq7cBKAd8wH+G1FuLd2v51cCLwCr3+DcwLGie89z4lcDkoPLH3DRWAW8DN7nprgMmBtVbBlQDfleviytPAWYBXwJf4N2I8UtgPTAd795zfYCv3LwVeDxCvF1d+RX1xDEW+NpNf3I9n/HECHE8jHfn49IGllGdekHLbJv7HPOAz4FbQ2NwdXOBCvfYCAwLivc7oAz4FpgcKV4Xw3dAFbAjEG+4GELjBdKAd4FS97nnA4+4uv2AhW5+i/HuWXZcrC43F8v/4X1PVwfiaOJy2w6UAN+7zybfxTDAPX/vhq92MdwdFFMusCkQQ6SYwiyLia5OmWvHauAR4D2838xq4Fm8ux7sF2vQfALzXgPsAnYGLZvuLb1cXPl+MUQYf6Zr71ch68GNeOuVvXi/qfNiNQ4irLfdsGF468gvgX8Anepdv7R00jnQB9ALONa97gh8A2QBj7JvRTQZeBo4FugBnA4UujqPuuFD8H5U/4N39fflwHFu/HOBT9zrRLyV3Xj34X4BZLlhY4Ak93oasNt9SbrirUQDX5Bb8VZmpcAfgD+48knALPe6B94P8SS8ldy7rh29gJ8ARwJL8c7pChdvYJonBc03NI4NwA/wkmNtHCGfbzfX9nBxjHLtaShJ1akXWGbAVcCf61lmf3B1r3Lz7oj341vl6t7tYngU7w/CF8DPQ+MNimEjMNy9/srNM7Rt4eI9HO+He4b7rD50458L/C/7EsCZeH9uPgVOjMXlhrfiPsPVSXFtObeJy20DcLGL4bduOT7hPvPJLob73XI8F+/30tXFsA3vj0kghi2hMYWJYZN7HIH3+/sW73eyFLjYtVGAt/DuHRcu1m1uPm+6eZ4G/BL4JzAYbyV/UksvFzesU2gMEZbLaW45BCepXm6Z/Cf1rwdjIg4irLfd++XA6e71NcCD9a5fop2EDvQBvAOcjfdPoFfQB/J1SL21bqF+7YZfCrwcqAfcC/zGve4KfOden4i3JdUfb0V1N3B3mHb8D7Au6P2fgctD6pQCFwEvu/fPAP8vqM1lwEj3/nLgzyHjLwaWNCbeSHEEDYsUR535RoqjkcumNOT9VXh/Hhq1zFz5XLyTCL8GznfLopd7XyeGQLwuhrcD8boYXg2pW3oA8U4DFgDX4614ewf9KEuAtZE+v1habkGxXH8olpt7/zO8rdU6MYWJYWHQ+z8B34TGFCaGBcCC0JiCYwCS8f59/yxMHK8Dq93r1cDv3XwEKHbl5XiJN5q/p4gxBNXpT1CScmX3Af/pXh/IbypqcQTVfQc4270uZl9/iD5Abn3jxtUxKRHpDxyD90+up6puB3DPPULq9cL7JxWo9xVwPNBTRNLwNpUDt56/Fu8fMXj/4rYGzTbPlYUai/dPsaF61wRN+wtggogkubakBLUh3PipeP/M6403SFPiaGy9pvop3j+5ScBh9cXgltuJeLtweuLFvzWobmjbAvEegberMBBHHt5unQOOV0S6ABOAH+GtaL/AW6mB94cjHW93YtjxnZhYbi6W8S6OA1VnuQHt8H577+LtTQiNKTSG9KD31e4R2tbQGHxh6g0MxCAi8/B2g5XgbSmFxjoa73cO3nLr7eZxEdBRRIbj/eY+I0rLpb4YGulmEVkDnIWXoGI+jpD1NnjL6AL3+lL2rQPDipskJSLpeJuWt6tqcSPqvYe3DxoAVV2Dt1sinX37VH0icgbeQrwrMIkwk9WQedzjyr6orx7ePw0f3hYcePua84Ac4D+APW74fuO7OAYD/1NfvEH1DziOA6zXFP/Ci/cyvM88LVJFF+88vK3LOxpqW0i84erV1g2dVT3TTAJew/u3/YSqfou3e+V0EVmJtxt5J1ATaT6xstxcLK8C010cByLccqvvt5dE3RiG4CX5hmKKtNwCEvCOrUxX1W9V9Ry8P5/t8Ha9ehPfF+tcvJUmeMvtKLwtv9PxdgPOAorw9nDUq7mWS6QYGulPQDbe9/M9vC2rekU7jgjr7WuASSLyGd6uwKr6YoiLJCUiyXiBvqyqf3PFO0SklxveC9gZXA/vgF2denhf4g2qehrel7UM74sL3j+1G/F+nMGZvTewTUROEJHPRWQz8Au8YyR93PxnAbcB/y+ozRPxfrxXqKqKSCJecjoDmMO+ferrgucTEu8OvIOPYeMNmlc28FdggqoWuuKG4vhcRC6IVI8IRCQxaPwH6qmXDPwFeMkts78ACeFicHXnARnAmS6GHXgHiPsE1Q3EcAXejxTgZBdDalAcvfF+ZLXxAu2D4u0biAG4MCjeGW4aC1R1qivbgbf/XvBWDn68H+d+n1eMLbcZeLujp7p5NXW5zcL7Hs9R1b+5GHxBMZ2Jt6KaoKqFLqbr8HZVBWJKApKDlsUfgM5uvNuDYkrC+2MXcDmwOTgG4GNgBPCwiPRx09vhphVYfqjqNuBveMfQpuB9t17EW4YQxd+TqlbgrQMmBGJwjxsjLJaAIrxjpC/jJeGRsRxHhPU2qrpWVceo6nF4fy421Bt1Q/sSo/3AWzm8AEwNKX+MugcMHw2uh9t/G1LvQVevL17Plm+Bk0Kmm+TKT2Ffx4nBbthYvF5LmXgHFDfi7e/t6l53C6kXuq8/DejgXp+N969vFPsOwJ8XHC/esY0RkeJ1rwOxRIpjAPsOkA4O8/lGjCOozgEdkwqKYUbQsIvwvuShyyxw8HV3cAwu3v9yMfwBeNzFcFZovEExbMLrObQRb5fC4DBtCxsv8BDeH5u3gISg8boH3uP1hnsA78BvzC43F0udOJqy3Nz7RXhbHsExfOqe++IdP3wpJKZTw8SwNTSmMDFsdo+ubnmX4SWXdPYdd0nCO/Z0c2isIZ9dr6D5rAI+dvU3uWXaosulvhgiLJP+1O04IXgJKrB++zXeVn9MxkGE9bYb1sM9J7g619T7/WzMlziaD7xkoe6L9rl7nOe+vAvxtkQW4u0/V7zkUIX3j7cUb8W42NXbjdeh4gu8rapdQdPMCZrnYrzdcIq3S+5aV74e78cWGGeRK1sPXB00/q6g8asC03ZfvK/xVoYL8DoGfIX3T+Jpt2BPCRqvBm8f/Wdh4g2skP5aTxzn4fWq2QDcU89nfE2EOB7F+0dV457vizB+aL2ZLoadeFtDe91nPio0hqB4fUF11wXF+x3ev9+NwD2R4nUxbHOf285AvOFiCI0X71+iukeFa8MWvC2CS1x7vnHzbof3Tz4ml1tQLGuCpn1dE5dboMv2erw/VJVuegNdLHtcnF+68vygmDa4z3KDW25hYwqzLK5xy1rZ10PwK1e2Cu+P41N4K8hwsU5z8/keL4FuCZpWlXtdiLdV1ZLLpSfen5s6MUQY/1W87v/Vbrlcy77vV+A3sgdvV2hLf78aFQcR1ttu2G1u/t/gnTYg9eUAu+KEMcaYmBUXx6SMMca0TZakjDHGxCxLUsYYY2KWJSljjDExy5KUMcaYmGVJypgoEZEuIvKraLfDmFhmScqY6OkCWJIyph5J0W6AMW3YI8BAd2mf5XgXpe2E97u8SVWXRrNxxsQCO5nXmChxV4f+p6oOEZH/AFJV9WF3jbo0VS2pdwLGtAG2JWVMbFgOzHQX5fy7qn7e0AjGtAV2TMqYGKCqH+DdkfU74EURuTLKTTImJliSMiZ6SvDup4OI9AN2qupfgOfwbh9uTJtnu/uMiRL17r/0kYh8BXQAykSkGu/q/bYlZQzWccIYY0wMs919xhhjYpYlKWOMMTHLkpQxxpiYZUnKGGNMzLIkZYwxJmZZkjLGGBOzLEkZY4yJWZakjDHGxKz/D4NpxDEuC8E+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='ts', y='Cases', data=df, hue='Countries and territories')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}