├── README.md
└── Corona.ipynb


/README.md:
--------------------------------------------------------------------------------
1 | # Estimating-Covid-19-Death-Rate
2 | In this Project we will do some analysis on the Death rate of the pandemic Covid-19 using python
3 | 


--------------------------------------------------------------------------------
/Corona.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 2,
   6 |    "id": "b3014b89",
   7 |    "metadata": {},
   8 |    "outputs": [],
   9 |    "source": [
  10 |     "import numpy as np\n",
  11 |     "import pandas as pd\n",
  12 |     "import seaborn as sns\n",
  13 |     "import matplotlib.pyplot as plt\n",
  14 |     "from datetime import datetime\n"
  15 |    ]
  16 |   },
  17 |   {
  18 |    "cell_type": "code",
  19 |    "execution_count": 4,
  20 |    "id": "fa078280",
  21 |    "metadata": {
  22 |     "scrolled": false
  23 |    },
  24 |    "outputs": [
  25 |     {
  26 |      "data": {
  27 |       "text/html": [
  28 |        "<div>\n",
  29 |        "<style scoped>\n",
  30 |        "    .dataframe tbody tr th:only-of-type {\n",
  31 |        "        vertical-align: middle;\n",
  32 |        "    }\n",
  33 |        "\n",
  34 |        "    .dataframe tbody tr th {\n",
  35 |        "        vertical-align: top;\n",
  36 |        "    }\n",
  37 |        "\n",
  38 |        "    .dataframe thead th {\n",
  39 |        "        text-align: right;\n",
  40 |        "    }\n",
  41 |        "</style>\n",
  42 |        "<table border=\"1\" class=\"dataframe\">\n",
  43 |        "  <thead>\n",
  44 |        "    <tr style=\"text-align: right;\">\n",
  45 |        "      <th></th>\n",
  46 |        "      <th>Date</th>\n",
  47 |        "      <th>Country</th>\n",
  48 |        "      <th>Population</th>\n",
  49 |        "      <th>Total Tests</th>\n",
  50 |        "      <th>Total Cases</th>\n",
  51 |        "      <th>Total Deaths</th>\n",
  52 |        "      <th>Total Recovered</th>\n",
  53 |        "      <th>Serious or Critical</th>\n",
  54 |        "      <th>Active Cases</th>\n",
  55 |        "    </tr>\n",
  56 |        "  </thead>\n",
  57 |        "  <tbody>\n",
  58 |        "    <tr>\n",
  59 |        "      <th>0</th>\n",
  60 |        "      <td>2020-04-18</td>\n",
  61 |        "      <td>USA</td>\n",
  62 |        "      <td>330774664</td>\n",
  63 |        "      <td>3722145.0</td>\n",
  64 |        "      <td>738792.0</td>\n",
  65 |        "      <td>39014.0</td>\n",
  66 |        "      <td>68269.0</td>\n",
  67 |        "      <td>13551.0</td>\n",
  68 |        "      <td>631509.0</td>\n",
  69 |        "    </tr>\n",
  70 |        "    <tr>\n",
  71 |        "      <th>1</th>\n",
  72 |        "      <td>2020-04-18</td>\n",
  73 |        "      <td>Russia</td>\n",
  74 |        "      <td>145927292</td>\n",
  75 |        "      <td>1831892.0</td>\n",
  76 |        "      <td>36793.0</td>\n",
  77 |        "      <td>313.0</td>\n",
  78 |        "      <td>3057.0</td>\n",
  79 |        "      <td>8.0</td>\n",
  80 |        "      <td>33423.0</td>\n",
  81 |        "    </tr>\n",
  82 |        "    <tr>\n",
  83 |        "      <th>2</th>\n",
  84 |        "      <td>2020-04-18</td>\n",
  85 |        "      <td>Spain</td>\n",
  86 |        "      <td>46752703</td>\n",
  87 |        "      <td>930230.0</td>\n",
  88 |        "      <td>194416.0</td>\n",
  89 |        "      <td>20043.0</td>\n",
  90 |        "      <td>74797.0</td>\n",
  91 |        "      <td>7371.0</td>\n",
  92 |        "      <td>99576.0</td>\n",
  93 |        "    </tr>\n",
  94 |        "    <tr>\n",
  95 |        "      <th>3</th>\n",
  96 |        "      <td>2020-04-18</td>\n",
  97 |        "      <td>Brazil</td>\n",
  98 |        "      <td>212380932</td>\n",
  99 |        "      <td>62985.0</td>\n",
 100 |        "      <td>36722.0</td>\n",
 101 |        "      <td>2361.0</td>\n",
 102 |        "      <td>14026.0</td>\n",
 103 |        "      <td>6634.0</td>\n",
 104 |        "      <td>20335.0</td>\n",
 105 |        "    </tr>\n",
 106 |        "    <tr>\n",
 107 |        "      <th>4</th>\n",
 108 |        "      <td>2020-04-18</td>\n",
 109 |        "      <td>UK</td>\n",
 110 |        "      <td>67844241</td>\n",
 111 |        "      <td>460437.0</td>\n",
 112 |        "      <td>114217.0</td>\n",
 113 |        "      <td>15464.0</td>\n",
 114 |        "      <td>NaN</td>\n",
 115 |        "      <td>1559.0</td>\n",
 116 |        "      <td>98409.0</td>\n",
 117 |        "    </tr>\n",
 118 |        "    <tr>\n",
 119 |        "      <th>...</th>\n",
 120 |        "      <td>...</td>\n",
 121 |        "      <td>...</td>\n",
 122 |        "      <td>...</td>\n",
 123 |        "      <td>...</td>\n",
 124 |        "      <td>...</td>\n",
 125 |        "      <td>...</td>\n",
 126 |        "      <td>...</td>\n",
 127 |        "      <td>...</td>\n",
 128 |        "      <td>...</td>\n",
 129 |        "    </tr>\n",
 130 |        "    <tr>\n",
 131 |        "      <th>6598</th>\n",
 132 |        "      <td>2020-05-18</td>\n",
 133 |        "      <td>St. Barth</td>\n",
 134 |        "      <td>9874</td>\n",
 135 |        "      <td>NaN</td>\n",
 136 |        "      <td>6.0</td>\n",
 137 |        "      <td>NaN</td>\n",
 138 |        "      <td>6.0</td>\n",
 139 |        "      <td>NaN</td>\n",
 140 |        "      <td>0.0</td>\n",
 141 |        "    </tr>\n",
 142 |        "    <tr>\n",
 143 |        "      <th>6599</th>\n",
 144 |        "      <td>2020-05-18</td>\n",
 145 |        "      <td>Western Sahara</td>\n",
 146 |        "      <td>595462</td>\n",
 147 |        "      <td>NaN</td>\n",
 148 |        "      <td>6.0</td>\n",
 149 |        "      <td>NaN</td>\n",
 150 |        "      <td>6.0</td>\n",
 151 |        "      <td>NaN</td>\n",
 152 |        "      <td>0.0</td>\n",
 153 |        "    </tr>\n",
 154 |        "    <tr>\n",
 155 |        "      <th>6600</th>\n",
 156 |        "      <td>2020-05-18</td>\n",
 157 |        "      <td>Anguilla</td>\n",
 158 |        "      <td>14987</td>\n",
 159 |        "      <td>NaN</td>\n",
 160 |        "      <td>3.0</td>\n",
 161 |        "      <td>NaN</td>\n",
 162 |        "      <td>3.0</td>\n",
 163 |        "      <td>NaN</td>\n",
 164 |        "      <td>0.0</td>\n",
 165 |        "    </tr>\n",
 166 |        "    <tr>\n",
 167 |        "      <th>6601</th>\n",
 168 |        "      <td>2020-05-18</td>\n",
 169 |        "      <td>Lesotho</td>\n",
 170 |        "      <td>2140235</td>\n",
 171 |        "      <td>NaN</td>\n",
 172 |        "      <td>1.0</td>\n",
 173 |        "      <td>NaN</td>\n",
 174 |        "      <td>NaN</td>\n",
 175 |        "      <td>NaN</td>\n",
 176 |        "      <td>1.0</td>\n",
 177 |        "    </tr>\n",
 178 |        "    <tr>\n",
 179 |        "      <th>6602</th>\n",
 180 |        "      <td>2020-05-18</td>\n",
 181 |        "      <td>Saint Pierre Miquelon</td>\n",
 182 |        "      <td>5797</td>\n",
 183 |        "      <td>NaN</td>\n",
 184 |        "      <td>1.0</td>\n",
 185 |        "      <td>NaN</td>\n",
 186 |        "      <td>1.0</td>\n",
 187 |        "      <td>NaN</td>\n",
 188 |        "      <td>0.0</td>\n",
 189 |        "    </tr>\n",
 190 |        "  </tbody>\n",
 191 |        "</table>\n",
 192 |        "<p>6603 rows × 9 columns</p>\n",
 193 |        "</div>"
 194 |       ],
 195 |       "text/plain": [
 196 |        "            Date                Country  Population  Total Tests  Total Cases  \\\n",
 197 |        "0     2020-04-18                    USA   330774664    3722145.0     738792.0   \n",
 198 |        "1     2020-04-18                 Russia   145927292    1831892.0      36793.0   \n",
 199 |        "2     2020-04-18                  Spain    46752703     930230.0     194416.0   \n",
 200 |        "3     2020-04-18                 Brazil   212380932      62985.0      36722.0   \n",
 201 |        "4     2020-04-18                     UK    67844241     460437.0     114217.0   \n",
 202 |        "...          ...                    ...         ...          ...          ...   \n",
 203 |        "6598  2020-05-18              St. Barth        9874          NaN          6.0   \n",
 204 |        "6599  2020-05-18         Western Sahara      595462          NaN          6.0   \n",
 205 |        "6600  2020-05-18               Anguilla       14987          NaN          3.0   \n",
 206 |        "6601  2020-05-18                Lesotho     2140235          NaN          1.0   \n",
 207 |        "6602  2020-05-18  Saint Pierre Miquelon        5797          NaN          1.0   \n",
 208 |        "\n",
 209 |        "      Total Deaths  Total Recovered  Serious or Critical  Active Cases  \n",
 210 |        "0          39014.0          68269.0              13551.0      631509.0  \n",
 211 |        "1            313.0           3057.0                  8.0       33423.0  \n",
 212 |        "2          20043.0          74797.0               7371.0       99576.0  \n",
 213 |        "3           2361.0          14026.0               6634.0       20335.0  \n",
 214 |        "4          15464.0              NaN               1559.0       98409.0  \n",
 215 |        "...            ...              ...                  ...           ...  \n",
 216 |        "6598           NaN              6.0                  NaN           0.0  \n",
 217 |        "6599           NaN              6.0                  NaN           0.0  \n",
 218 |        "6600           NaN              3.0                  NaN           0.0  \n",
 219 |        "6601           NaN              NaN                  NaN           1.0  \n",
 220 |        "6602           NaN              1.0                  NaN           0.0  \n",
 221 |        "\n",
 222 |        "[6603 rows x 9 columns]"
 223 |       ]
 224 |      },
 225 |      "execution_count": 4,
 226 |      "metadata": {},
 227 |      "output_type": "execute_result"
 228 |     }
 229 |    ],
 230 |    "source": [
 231 |     "#Now load the main data table and display it\n",
 232 |     "worldometer_df = pd.read_csv('worldometer_snapshots_April18_to_May18.csv')\n",
 233 |     "worldometer_df"
 234 |    ]
 235 |   },
 236 |   {
 237 |    "cell_type": "code",
 238 |    "execution_count": 11,
 239 |    "id": "9a6ffd44",
 240 |    "metadata": {},
 241 |    "outputs": [
 242 |     {
 243 |      "data": {
 244 |       "text/html": [
 245 |        "<div>\n",
 246 |        "<style scoped>\n",
 247 |        "    .dataframe tbody tr th:only-of-type {\n",
 248 |        "        vertical-align: middle;\n",
 249 |        "    }\n",
 250 |        "\n",
 251 |        "    .dataframe tbody tr th {\n",
 252 |        "        vertical-align: top;\n",
 253 |        "    }\n",
 254 |        "\n",
 255 |        "    .dataframe thead th {\n",
 256 |        "        text-align: right;\n",
 257 |        "    }\n",
 258 |        "</style>\n",
 259 |        "<table border=\"1\" class=\"dataframe\">\n",
 260 |        "  <thead>\n",
 261 |        "    <tr style=\"text-align: right;\">\n",
 262 |        "      <th></th>\n",
 263 |        "      <th>Date</th>\n",
 264 |        "      <th>Country</th>\n",
 265 |        "      <th>Population</th>\n",
 266 |        "      <th>Total Tests</th>\n",
 267 |        "      <th>Total Cases</th>\n",
 268 |        "      <th>Total Deaths</th>\n",
 269 |        "      <th>Total Recovered</th>\n",
 270 |        "      <th>Serious or Critical</th>\n",
 271 |        "      <th>Active Cases</th>\n",
 272 |        "    </tr>\n",
 273 |        "  </thead>\n",
 274 |        "  <tbody>\n",
 275 |        "    <tr>\n",
 276 |        "      <th>0</th>\n",
 277 |        "      <td>2020-04-18</td>\n",
 278 |        "      <td>USA</td>\n",
 279 |        "      <td>330774664</td>\n",
 280 |        "      <td>3722145.0</td>\n",
 281 |        "      <td>738792.0</td>\n",
 282 |        "      <td>39014.0</td>\n",
 283 |        "      <td>68269.0</td>\n",
 284 |        "      <td>13551.0</td>\n",
 285 |        "      <td>631509.0</td>\n",
 286 |        "    </tr>\n",
 287 |        "    <tr>\n",
 288 |        "      <th>1</th>\n",
 289 |        "      <td>2020-04-18</td>\n",
 290 |        "      <td>Russia</td>\n",
 291 |        "      <td>145927292</td>\n",
 292 |        "      <td>1831892.0</td>\n",
 293 |        "      <td>36793.0</td>\n",
 294 |        "      <td>313.0</td>\n",
 295 |        "      <td>3057.0</td>\n",
 296 |        "      <td>8.0</td>\n",
 297 |        "      <td>33423.0</td>\n",
 298 |        "    </tr>\n",
 299 |        "    <tr>\n",
 300 |        "      <th>2</th>\n",
 301 |        "      <td>2020-04-18</td>\n",
 302 |        "      <td>Spain</td>\n",
 303 |        "      <td>46752703</td>\n",
 304 |        "      <td>930230.0</td>\n",
 305 |        "      <td>194416.0</td>\n",
 306 |        "      <td>20043.0</td>\n",
 307 |        "      <td>74797.0</td>\n",
 308 |        "      <td>7371.0</td>\n",
 309 |        "      <td>99576.0</td>\n",
 310 |        "    </tr>\n",
 311 |        "    <tr>\n",
 312 |        "      <th>3</th>\n",
 313 |        "      <td>2020-04-18</td>\n",
 314 |        "      <td>Brazil</td>\n",
 315 |        "      <td>212380932</td>\n",
 316 |        "      <td>62985.0</td>\n",
 317 |        "      <td>36722.0</td>\n",
 318 |        "      <td>2361.0</td>\n",
 319 |        "      <td>14026.0</td>\n",
 320 |        "      <td>6634.0</td>\n",
 321 |        "      <td>20335.0</td>\n",
 322 |        "    </tr>\n",
 323 |        "    <tr>\n",
 324 |        "      <th>4</th>\n",
 325 |        "      <td>2020-04-18</td>\n",
 326 |        "      <td>UK</td>\n",
 327 |        "      <td>67844241</td>\n",
 328 |        "      <td>460437.0</td>\n",
 329 |        "      <td>114217.0</td>\n",
 330 |        "      <td>15464.0</td>\n",
 331 |        "      <td>NaN</td>\n",
 332 |        "      <td>1559.0</td>\n",
 333 |        "      <td>98409.0</td>\n",
 334 |        "    </tr>\n",
 335 |        "  </tbody>\n",
 336 |        "</table>\n",
 337 |        "</div>"
 338 |       ],
 339 |       "text/plain": [
 340 |        "         Date Country  Population  Total Tests  Total Cases  Total Deaths  \\\n",
 341 |        "0  2020-04-18     USA   330774664    3722145.0     738792.0       39014.0   \n",
 342 |        "1  2020-04-18  Russia   145927292    1831892.0      36793.0         313.0   \n",
 343 |        "2  2020-04-18   Spain    46752703     930230.0     194416.0       20043.0   \n",
 344 |        "3  2020-04-18  Brazil   212380932      62985.0      36722.0        2361.0   \n",
 345 |        "4  2020-04-18      UK    67844241     460437.0     114217.0       15464.0   \n",
 346 |        "\n",
 347 |        "   Total Recovered  Serious or Critical  Active Cases  \n",
 348 |        "0          68269.0              13551.0      631509.0  \n",
 349 |        "1           3057.0                  8.0       33423.0  \n",
 350 |        "2          74797.0               7371.0       99576.0  \n",
 351 |        "3          14026.0               6634.0       20335.0  \n",
 352 |        "4              NaN               1559.0       98409.0  "
 353 |       ]
 354 |      },
 355 |      "execution_count": 11,
 356 |      "metadata": {},
 357 |      "output_type": "execute_result"
 358 |     }
 359 |    ],
 360 |    "source": [
 361 |     "worldometer_df.head()"
 362 |    ]
 363 |   },
 364 |   {
 365 |    "cell_type": "code",
 366 |    "execution_count": 6,
 367 |    "id": "c691a52f",
 368 |    "metadata": {},
 369 |    "outputs": [
 370 |     {
 371 |      "data": {
 372 |       "text/plain": [
 373 |        "Date                    object\n",
 374 |        "Country                 object\n",
 375 |        "Population               int64\n",
 376 |        "Total Tests            float64\n",
 377 |        "Total Cases            float64\n",
 378 |        "Total Deaths           float64\n",
 379 |        "Total Recovered        float64\n",
 380 |        "Serious or Critical    float64\n",
 381 |        "Active Cases           float64\n",
 382 |        "dtype: object"
 383 |       ]
 384 |      },
 385 |      "execution_count": 6,
 386 |      "metadata": {},
 387 |      "output_type": "execute_result"
 388 |     }
 389 |    ],
 390 |    "source": [
 391 |     "worldometer_df.dtypes"
 392 |    ]
 393 |   },
 394 |   {
 395 |    "cell_type": "code",
 396 |    "execution_count": 20,
 397 |    "id": "26f3fddc",
 398 |    "metadata": {},
 399 |    "outputs": [
 400 |     {
 401 |      "data": {
 402 |       "text/html": [
 403 |        "<div>\n",
 404 |        "<style scoped>\n",
 405 |        "    .dataframe tbody tr th:only-of-type {\n",
 406 |        "        vertical-align: middle;\n",
 407 |        "    }\n",
 408 |        "\n",
 409 |        "    .dataframe tbody tr th {\n",
 410 |        "        vertical-align: top;\n",
 411 |        "    }\n",
 412 |        "\n",
 413 |        "    .dataframe thead th {\n",
 414 |        "        text-align: right;\n",
 415 |        "    }\n",
 416 |        "</style>\n",
 417 |        "<table border=\"1\" class=\"dataframe\">\n",
 418 |        "  <thead>\n",
 419 |        "    <tr style=\"text-align: right;\">\n",
 420 |        "      <th></th>\n",
 421 |        "      <th>Population</th>\n",
 422 |        "      <th>Total Tests</th>\n",
 423 |        "      <th>Total Cases</th>\n",
 424 |        "      <th>Total Deaths</th>\n",
 425 |        "      <th>Total Recovered</th>\n",
 426 |        "      <th>Serious or Critical</th>\n",
 427 |        "      <th>Active Cases</th>\n",
 428 |        "    </tr>\n",
 429 |        "  </thead>\n",
 430 |        "  <tbody>\n",
 431 |        "    <tr>\n",
 432 |        "      <th>count</th>\n",
 433 |        "      <td>6.603000e+03</td>\n",
 434 |        "      <td>5.505000e+03</td>\n",
 435 |        "      <td>6.554000e+03</td>\n",
 436 |        "      <td>5367.000000</td>\n",
 437 |        "      <td>6318.000000</td>\n",
 438 |        "      <td>4117.000000</td>\n",
 439 |        "      <td>6.544000e+03</td>\n",
 440 |        "    </tr>\n",
 441 |        "    <tr>\n",
 442 |        "      <th>mean</th>\n",
 443 |        "      <td>3.637913e+07</td>\n",
 444 |        "      <td>2.167252e+05</td>\n",
 445 |        "      <td>1.692968e+04</td>\n",
 446 |        "      <td>1416.252096</td>\n",
 447 |        "      <td>5855.424185</td>\n",
 448 |        "      <td>386.748360</td>\n",
 449 |        "      <td>9.964142e+03</td>\n",
 450 |        "    </tr>\n",
 451 |        "    <tr>\n",
 452 |        "      <th>std</th>\n",
 453 |        "      <td>1.411537e+08</td>\n",
 454 |        "      <td>7.595143e+05</td>\n",
 455 |        "      <td>8.861467e+04</td>\n",
 456 |        "      <td>6645.247810</td>\n",
 457 |        "      <td>23467.641235</td>\n",
 458 |        "      <td>1658.428324</td>\n",
 459 |        "      <td>6.541025e+04</td>\n",
 460 |        "    </tr>\n",
 461 |        "    <tr>\n",
 462 |        "      <th>min</th>\n",
 463 |        "      <td>8.010000e+02</td>\n",
 464 |        "      <td>1.000000e+01</td>\n",
 465 |        "      <td>1.000000e+00</td>\n",
 466 |        "      <td>0.000000</td>\n",
 467 |        "      <td>1.000000</td>\n",
 468 |        "      <td>1.000000</td>\n",
 469 |        "      <td>0.000000e+00</td>\n",
 470 |        "    </tr>\n",
 471 |        "    <tr>\n",
 472 |        "      <th>25%</th>\n",
 473 |        "      <td>8.672680e+05</td>\n",
 474 |        "      <td>2.690000e+03</td>\n",
 475 |        "      <td>7.900000e+01</td>\n",
 476 |        "      <td>6.000000</td>\n",
 477 |        "      <td>26.000000</td>\n",
 478 |        "      <td>3.000000</td>\n",
 479 |        "      <td>2.600000e+01</td>\n",
 480 |        "    </tr>\n",
 481 |        "    <tr>\n",
 482 |        "      <th>50%</th>\n",
 483 |        "      <td>6.859926e+06</td>\n",
 484 |        "      <td>2.547800e+04</td>\n",
 485 |        "      <td>6.135000e+02</td>\n",
 486 |        "      <td>24.000000</td>\n",
 487 |        "      <td>219.000000</td>\n",
 488 |        "      <td>13.000000</td>\n",
 489 |        "      <td>2.720000e+02</td>\n",
 490 |        "    </tr>\n",
 491 |        "    <tr>\n",
 492 |        "      <th>75%</th>\n",
 493 |        "      <td>2.546422e+07</td>\n",
 494 |        "      <td>1.437810e+05</td>\n",
 495 |        "      <td>4.877500e+03</td>\n",
 496 |        "      <td>199.000000</td>\n",
 497 |        "      <td>1394.750000</td>\n",
 498 |        "      <td>89.000000</td>\n",
 499 |        "      <td>2.041000e+03</td>\n",
 500 |        "    </tr>\n",
 501 |        "    <tr>\n",
 502 |        "      <th>max</th>\n",
 503 |        "      <td>1.439324e+09</td>\n",
 504 |        "      <td>1.230074e+07</td>\n",
 505 |        "      <td>1.550294e+06</td>\n",
 506 |        "      <td>91981.000000</td>\n",
 507 |        "      <td>356383.000000</td>\n",
 508 |        "      <td>18671.000000</td>\n",
 509 |        "      <td>1.101930e+06</td>\n",
 510 |        "    </tr>\n",
 511 |        "  </tbody>\n",
 512 |        "</table>\n",
 513 |        "</div>"
 514 |       ],
 515 |       "text/plain": [
 516 |        "         Population   Total Tests   Total Cases  Total Deaths  \\\n",
 517 |        "count  6.603000e+03  5.505000e+03  6.554000e+03   5367.000000   \n",
 518 |        "mean   3.637913e+07  2.167252e+05  1.692968e+04   1416.252096   \n",
 519 |        "std    1.411537e+08  7.595143e+05  8.861467e+04   6645.247810   \n",
 520 |        "min    8.010000e+02  1.000000e+01  1.000000e+00      0.000000   \n",
 521 |        "25%    8.672680e+05  2.690000e+03  7.900000e+01      6.000000   \n",
 522 |        "50%    6.859926e+06  2.547800e+04  6.135000e+02     24.000000   \n",
 523 |        "75%    2.546422e+07  1.437810e+05  4.877500e+03    199.000000   \n",
 524 |        "max    1.439324e+09  1.230074e+07  1.550294e+06  91981.000000   \n",
 525 |        "\n",
 526 |        "       Total Recovered  Serious or Critical  Active Cases  \n",
 527 |        "count      6318.000000          4117.000000  6.544000e+03  \n",
 528 |        "mean       5855.424185           386.748360  9.964142e+03  \n",
 529 |        "std       23467.641235          1658.428324  6.541025e+04  \n",
 530 |        "min           1.000000             1.000000  0.000000e+00  \n",
 531 |        "25%          26.000000             3.000000  2.600000e+01  \n",
 532 |        "50%         219.000000            13.000000  2.720000e+02  \n",
 533 |        "75%        1394.750000            89.000000  2.041000e+03  \n",
 534 |        "max      356383.000000         18671.000000  1.101930e+06  "
 535 |       ]
 536 |      },
 537 |      "execution_count": 20,
 538 |      "metadata": {},
 539 |      "output_type": "execute_result"
 540 |     }
 541 |    ],
 542 |    "source": [
 543 |     "worldometer_df.describe()"
 544 |    ]
 545 |   },
 546 |   {
 547 |    "cell_type": "code",
 548 |    "execution_count": 21,
 549 |    "id": "5c99646f",
 550 |    "metadata": {},
 551 |    "outputs": [
 552 |     {
 553 |      "data": {
 554 |       "text/html": [
 555 |        "<div>\n",
 556 |        "<style scoped>\n",
 557 |        "    .dataframe tbody tr th:only-of-type {\n",
 558 |        "        vertical-align: middle;\n",
 559 |        "    }\n",
 560 |        "\n",
 561 |        "    .dataframe tbody tr th {\n",
 562 |        "        vertical-align: top;\n",
 563 |        "    }\n",
 564 |        "\n",
 565 |        "    .dataframe thead th {\n",
 566 |        "        text-align: right;\n",
 567 |        "    }\n",
 568 |        "</style>\n",
 569 |        "<table border=\"1\" class=\"dataframe\">\n",
 570 |        "  <thead>\n",
 571 |        "    <tr style=\"text-align: right;\">\n",
 572 |        "      <th></th>\n",
 573 |        "      <th>Population</th>\n",
 574 |        "      <th>Total Tests</th>\n",
 575 |        "      <th>Total Cases</th>\n",
 576 |        "      <th>Total Deaths</th>\n",
 577 |        "      <th>Total Recovered</th>\n",
 578 |        "      <th>Serious or Critical</th>\n",
 579 |        "      <th>Active Cases</th>\n",
 580 |        "    </tr>\n",
 581 |        "  </thead>\n",
 582 |        "  <tbody>\n",
 583 |        "    <tr>\n",
 584 |        "      <th>Population</th>\n",
 585 |        "      <td>1.000000</td>\n",
 586 |        "      <td>0.307950</td>\n",
 587 |        "      <td>0.225890</td>\n",
 588 |        "      <td>0.177861</td>\n",
 589 |        "      <td>0.309692</td>\n",
 590 |        "      <td>0.231260</td>\n",
 591 |        "      <td>0.179400</td>\n",
 592 |        "    </tr>\n",
 593 |        "    <tr>\n",
 594 |        "      <th>Total Tests</th>\n",
 595 |        "      <td>0.307950</td>\n",
 596 |        "      <td>1.000000</td>\n",
 597 |        "      <td>0.900321</td>\n",
 598 |        "      <td>0.811128</td>\n",
 599 |        "      <td>0.825681</td>\n",
 600 |        "      <td>0.744832</td>\n",
 601 |        "      <td>0.859148</td>\n",
 602 |        "    </tr>\n",
 603 |        "    <tr>\n",
 604 |        "      <th>Total Cases</th>\n",
 605 |        "      <td>0.225890</td>\n",
 606 |        "      <td>0.900321</td>\n",
 607 |        "      <td>1.000000</td>\n",
 608 |        "      <td>0.934705</td>\n",
 609 |        "      <td>0.829137</td>\n",
 610 |        "      <td>0.890988</td>\n",
 611 |        "      <td>0.977766</td>\n",
 612 |        "    </tr>\n",
 613 |        "    <tr>\n",
 614 |        "      <th>Total Deaths</th>\n",
 615 |        "      <td>0.177861</td>\n",
 616 |        "      <td>0.811128</td>\n",
 617 |        "      <td>0.934705</td>\n",
 618 |        "      <td>1.000000</td>\n",
 619 |        "      <td>0.840124</td>\n",
 620 |        "      <td>0.829517</td>\n",
 621 |        "      <td>0.892579</td>\n",
 622 |        "    </tr>\n",
 623 |        "    <tr>\n",
 624 |        "      <th>Total Recovered</th>\n",
 625 |        "      <td>0.309692</td>\n",
 626 |        "      <td>0.825681</td>\n",
 627 |        "      <td>0.829137</td>\n",
 628 |        "      <td>0.840124</td>\n",
 629 |        "      <td>1.000000</td>\n",
 630 |        "      <td>0.722190</td>\n",
 631 |        "      <td>0.694194</td>\n",
 632 |        "    </tr>\n",
 633 |        "    <tr>\n",
 634 |        "      <th>Serious or Critical</th>\n",
 635 |        "      <td>0.231260</td>\n",
 636 |        "      <td>0.744832</td>\n",
 637 |        "      <td>0.890988</td>\n",
 638 |        "      <td>0.829517</td>\n",
 639 |        "      <td>0.722190</td>\n",
 640 |        "      <td>1.000000</td>\n",
 641 |        "      <td>0.875427</td>\n",
 642 |        "    </tr>\n",
 643 |        "    <tr>\n",
 644 |        "      <th>Active Cases</th>\n",
 645 |        "      <td>0.179400</td>\n",
 646 |        "      <td>0.859148</td>\n",
 647 |        "      <td>0.977766</td>\n",
 648 |        "      <td>0.892579</td>\n",
 649 |        "      <td>0.694194</td>\n",
 650 |        "      <td>0.875427</td>\n",
 651 |        "      <td>1.000000</td>\n",
 652 |        "    </tr>\n",
 653 |        "  </tbody>\n",
 654 |        "</table>\n",
 655 |        "</div>"
 656 |       ],
 657 |       "text/plain": [
 658 |        "                     Population  Total Tests  Total Cases  Total Deaths  \\\n",
 659 |        "Population             1.000000     0.307950     0.225890      0.177861   \n",
 660 |        "Total Tests            0.307950     1.000000     0.900321      0.811128   \n",
 661 |        "Total Cases            0.225890     0.900321     1.000000      0.934705   \n",
 662 |        "Total Deaths           0.177861     0.811128     0.934705      1.000000   \n",
 663 |        "Total Recovered        0.309692     0.825681     0.829137      0.840124   \n",
 664 |        "Serious or Critical    0.231260     0.744832     0.890988      0.829517   \n",
 665 |        "Active Cases           0.179400     0.859148     0.977766      0.892579   \n",
 666 |        "\n",
 667 |        "                     Total Recovered  Serious or Critical  Active Cases  \n",
 668 |        "Population                  0.309692             0.231260      0.179400  \n",
 669 |        "Total Tests                 0.825681             0.744832      0.859148  \n",
 670 |        "Total Cases                 0.829137             0.890988      0.977766  \n",
 671 |        "Total Deaths                0.840124             0.829517      0.892579  \n",
 672 |        "Total Recovered             1.000000             0.722190      0.694194  \n",
 673 |        "Serious or Critical         0.722190             1.000000      0.875427  \n",
 674 |        "Active Cases                0.694194             0.875427      1.000000  "
 675 |       ]
 676 |      },
 677 |      "execution_count": 21,
 678 |      "metadata": {},
 679 |      "output_type": "execute_result"
 680 |     }
 681 |    ],
 682 |    "source": [
 683 |     "worldometer_df.corr()"
 684 |    ]
 685 |   },
 686 |   {
 687 |    "cell_type": "code",
 688 |    "execution_count": 22,
 689 |    "id": "58c2caa2",
 690 |    "metadata": {},
 691 |    "outputs": [
 692 |     {
 693 |      "data": {
 694 |       "text/plain": [
 695 |        "Date                   0.000000\n",
 696 |        "Country                0.000000\n",
 697 |        "Population             0.000000\n",
 698 |        "Total Tests            0.166288\n",
 699 |        "Total Cases            0.007421\n",
 700 |        "Total Deaths           0.187188\n",
 701 |        "Total Recovered        0.043162\n",
 702 |        "Serious or Critical    0.376496\n",
 703 |        "Active Cases           0.008935\n",
 704 |        "dtype: float64"
 705 |       ]
 706 |      },
 707 |      "execution_count": 22,
 708 |      "metadata": {},
 709 |      "output_type": "execute_result"
 710 |     }
 711 |    ],
 712 |    "source": [
 713 |     "worldometer_df.isna().sum()/len(worldometer_df)"
 714 |    ]
 715 |   },
 716 |   {
 717 |    "cell_type": "code",
 718 |    "execution_count": 23,
 719 |    "id": "6c9a6cfe",
 720 |    "metadata": {},
 721 |    "outputs": [
 722 |     {
 723 |      "data": {
 724 |       "text/html": [
 725 |        "<div>\n",
 726 |        "<style scoped>\n",
 727 |        "    .dataframe tbody tr th:only-of-type {\n",
 728 |        "        vertical-align: middle;\n",
 729 |        "    }\n",
 730 |        "\n",
 731 |        "    .dataframe tbody tr th {\n",
 732 |        "        vertical-align: top;\n",
 733 |        "    }\n",
 734 |        "\n",
 735 |        "    .dataframe thead th {\n",
 736 |        "        text-align: right;\n",
 737 |        "    }\n",
 738 |        "</style>\n",
 739 |        "<table border=\"1\" class=\"dataframe\">\n",
 740 |        "  <thead>\n",
 741 |        "    <tr style=\"text-align: right;\">\n",
 742 |        "      <th></th>\n",
 743 |        "      <th>Date</th>\n",
 744 |        "      <th>Country</th>\n",
 745 |        "      <th>Population</th>\n",
 746 |        "      <th>Total Tests</th>\n",
 747 |        "      <th>Total Cases</th>\n",
 748 |        "      <th>Total Deaths</th>\n",
 749 |        "      <th>Total Recovered</th>\n",
 750 |        "      <th>Serious or Critical</th>\n",
 751 |        "      <th>Active Cases</th>\n",
 752 |        "    </tr>\n",
 753 |        "  </thead>\n",
 754 |        "  <tbody>\n",
 755 |        "    <tr>\n",
 756 |        "      <th>0</th>\n",
 757 |        "      <td>2020-04-18</td>\n",
 758 |        "      <td>Pakistan</td>\n",
 759 |        "      <td>220356788</td>\n",
 760 |        "      <td>92548.0</td>\n",
 761 |        "      <td>7638.0</td>\n",
 762 |        "      <td>143.0</td>\n",
 763 |        "      <td>1832.0</td>\n",
 764 |        "      <td>46.0</td>\n",
 765 |        "      <td>5663.0</td>\n",
 766 |        "    </tr>\n",
 767 |        "    <tr>\n",
 768 |        "      <th>1</th>\n",
 769 |        "      <td>2020-04-19</td>\n",
 770 |        "      <td>Pakistan</td>\n",
 771 |        "      <td>220356788</td>\n",
 772 |        "      <td>98522.0</td>\n",
 773 |        "      <td>8348.0</td>\n",
 774 |        "      <td>168.0</td>\n",
 775 |        "      <td>1868.0</td>\n",
 776 |        "      <td>46.0</td>\n",
 777 |        "      <td>6312.0</td>\n",
 778 |        "    </tr>\n",
 779 |        "    <tr>\n",
 780 |        "      <th>2</th>\n",
 781 |        "      <td>2020-04-20</td>\n",
 782 |        "      <td>Pakistan</td>\n",
 783 |        "      <td>220356788</td>\n",
 784 |        "      <td>104302.0</td>\n",
 785 |        "      <td>8892.0</td>\n",
 786 |        "      <td>176.0</td>\n",
 787 |        "      <td>1970.0</td>\n",
 788 |        "      <td>46.0</td>\n",
 789 |        "      <td>6746.0</td>\n",
 790 |        "    </tr>\n",
 791 |        "    <tr>\n",
 792 |        "      <th>3</th>\n",
 793 |        "      <td>2020-04-21</td>\n",
 794 |        "      <td>Pakistan</td>\n",
 795 |        "      <td>220356788</td>\n",
 796 |        "      <td>111806.0</td>\n",
 797 |        "      <td>9565.0</td>\n",
 798 |        "      <td>201.0</td>\n",
 799 |        "      <td>2073.0</td>\n",
 800 |        "      <td>46.0</td>\n",
 801 |        "      <td>7291.0</td>\n",
 802 |        "    </tr>\n",
 803 |        "    <tr>\n",
 804 |        "      <th>4</th>\n",
 805 |        "      <td>2020-04-22</td>\n",
 806 |        "      <td>Pakistan</td>\n",
 807 |        "      <td>220356788</td>\n",
 808 |        "      <td>118020.0</td>\n",
 809 |        "      <td>10076.0</td>\n",
 810 |        "      <td>212.0</td>\n",
 811 |        "      <td>2156.0</td>\n",
 812 |        "      <td>58.0</td>\n",
 813 |        "      <td>7708.0</td>\n",
 814 |        "    </tr>\n",
 815 |        "    <tr>\n",
 816 |        "      <th>5</th>\n",
 817 |        "      <td>2020-04-23</td>\n",
 818 |        "      <td>Pakistan</td>\n",
 819 |        "      <td>220356788</td>\n",
 820 |        "      <td>124549.0</td>\n",
 821 |        "      <td>11057.0</td>\n",
 822 |        "      <td>235.0</td>\n",
 823 |        "      <td>2337.0</td>\n",
 824 |        "      <td>60.0</td>\n",
 825 |        "      <td>8485.0</td>\n",
 826 |        "    </tr>\n",
 827 |        "    <tr>\n",
 828 |        "      <th>6</th>\n",
 829 |        "      <td>2020-04-24</td>\n",
 830 |        "      <td>Pakistan</td>\n",
 831 |        "      <td>220356788</td>\n",
 832 |        "      <td>138147.0</td>\n",
 833 |        "      <td>11940.0</td>\n",
 834 |        "      <td>253.0</td>\n",
 835 |        "      <td>2755.0</td>\n",
 836 |        "      <td>111.0</td>\n",
 837 |        "      <td>8932.0</td>\n",
 838 |        "    </tr>\n",
 839 |        "    <tr>\n",
 840 |        "      <th>7</th>\n",
 841 |        "      <td>2020-04-25</td>\n",
 842 |        "      <td>Pakistan</td>\n",
 843 |        "      <td>220356788</td>\n",
 844 |        "      <td>144365.0</td>\n",
 845 |        "      <td>12723.0</td>\n",
 846 |        "      <td>269.0</td>\n",
 847 |        "      <td>2866.0</td>\n",
 848 |        "      <td>111.0</td>\n",
 849 |        "      <td>9588.0</td>\n",
 850 |        "    </tr>\n",
 851 |        "    <tr>\n",
 852 |        "      <th>8</th>\n",
 853 |        "      <td>2020-04-26</td>\n",
 854 |        "      <td>Pakistan</td>\n",
 855 |        "      <td>220356788</td>\n",
 856 |        "      <td>144365.0</td>\n",
 857 |        "      <td>13328.0</td>\n",
 858 |        "      <td>281.0</td>\n",
 859 |        "      <td>2936.0</td>\n",
 860 |        "      <td>111.0</td>\n",
 861 |        "      <td>10111.0</td>\n",
 862 |        "    </tr>\n",
 863 |        "    <tr>\n",
 864 |        "      <th>9</th>\n",
 865 |        "      <td>2020-04-27</td>\n",
 866 |        "      <td>Pakistan</td>\n",
 867 |        "      <td>220356788</td>\n",
 868 |        "      <td>150756.0</td>\n",
 869 |        "      <td>13915.0</td>\n",
 870 |        "      <td>292.0</td>\n",
 871 |        "      <td>3029.0</td>\n",
 872 |        "      <td>111.0</td>\n",
 873 |        "      <td>10594.0</td>\n",
 874 |        "    </tr>\n",
 875 |        "    <tr>\n",
 876 |        "      <th>10</th>\n",
 877 |        "      <td>2020-04-28</td>\n",
 878 |        "      <td>Pakistan</td>\n",
 879 |        "      <td>220356788</td>\n",
 880 |        "      <td>157223.0</td>\n",
 881 |        "      <td>14612.0</td>\n",
 882 |        "      <td>312.0</td>\n",
 883 |        "      <td>3233.0</td>\n",
 884 |        "      <td>111.0</td>\n",
 885 |        "      <td>11067.0</td>\n",
 886 |        "    </tr>\n",
 887 |        "    <tr>\n",
 888 |        "      <th>11</th>\n",
 889 |        "      <td>2020-04-29</td>\n",
 890 |        "      <td>Pakistan</td>\n",
 891 |        "      <td>220356788</td>\n",
 892 |        "      <td>165911.0</td>\n",
 893 |        "      <td>15525.0</td>\n",
 894 |        "      <td>343.0</td>\n",
 895 |        "      <td>3425.0</td>\n",
 896 |        "      <td>111.0</td>\n",
 897 |        "      <td>11757.0</td>\n",
 898 |        "    </tr>\n",
 899 |        "    <tr>\n",
 900 |        "      <th>12</th>\n",
 901 |        "      <td>2020-04-30</td>\n",
 902 |        "      <td>Pakistan</td>\n",
 903 |        "      <td>220356788</td>\n",
 904 |        "      <td>174160.0</td>\n",
 905 |        "      <td>16473.0</td>\n",
 906 |        "      <td>361.0</td>\n",
 907 |        "      <td>4105.0</td>\n",
 908 |        "      <td>111.0</td>\n",
 909 |        "      <td>12007.0</td>\n",
 910 |        "    </tr>\n",
 911 |        "    <tr>\n",
 912 |        "      <th>13</th>\n",
 913 |        "      <td>2020-05-01</td>\n",
 914 |        "      <td>Pakistan</td>\n",
 915 |        "      <td>220356788</td>\n",
 916 |        "      <td>182131.0</td>\n",
 917 |        "      <td>18092.0</td>\n",
 918 |        "      <td>417.0</td>\n",
 919 |        "      <td>4351.0</td>\n",
 920 |        "      <td>111.0</td>\n",
 921 |        "      <td>13324.0</td>\n",
 922 |        "    </tr>\n",
 923 |        "    <tr>\n",
 924 |        "      <th>14</th>\n",
 925 |        "      <td>2020-05-02</td>\n",
 926 |        "      <td>Pakistan</td>\n",
 927 |        "      <td>220356788</td>\n",
 928 |        "      <td>203025.0</td>\n",
 929 |        "      <td>19022.0</td>\n",
 930 |        "      <td>437.0</td>\n",
 931 |        "      <td>5114.0</td>\n",
 932 |        "      <td>111.0</td>\n",
 933 |        "      <td>14513.0</td>\n",
 934 |        "    </tr>\n",
 935 |        "    <tr>\n",
 936 |        "      <th>15</th>\n",
 937 |        "      <td>2020-05-03</td>\n",
 938 |        "      <td>Pakistan</td>\n",
 939 |        "      <td>220356788</td>\n",
 940 |        "      <td>203025.0</td>\n",
 941 |        "      <td>20084.0</td>\n",
 942 |        "      <td>457.0</td>\n",
 943 |        "      <td>5114.0</td>\n",
 944 |        "      <td>111.0</td>\n",
 945 |        "      <td>14513.0</td>\n",
 946 |        "    </tr>\n",
 947 |        "    <tr>\n",
 948 |        "      <th>16</th>\n",
 949 |        "      <td>2020-05-04</td>\n",
 950 |        "      <td>Pakistan</td>\n",
 951 |        "      <td>220356788</td>\n",
 952 |        "      <td>212511.0</td>\n",
 953 |        "      <td>20941.0</td>\n",
 954 |        "      <td>476.0</td>\n",
 955 |        "      <td>5635.0</td>\n",
 956 |        "      <td>111.0</td>\n",
 957 |        "      <td>14830.0</td>\n",
 958 |        "    </tr>\n",
 959 |        "    <tr>\n",
 960 |        "      <th>17</th>\n",
 961 |        "      <td>2020-05-05</td>\n",
 962 |        "      <td>Pakistan</td>\n",
 963 |        "      <td>220356788</td>\n",
 964 |        "      <td>222404.0</td>\n",
 965 |        "      <td>22049.0</td>\n",
 966 |        "      <td>514.0</td>\n",
 967 |        "      <td>5801.0</td>\n",
 968 |        "      <td>111.0</td>\n",
 969 |        "      <td>15734.0</td>\n",
 970 |        "    </tr>\n",
 971 |        "    <tr>\n",
 972 |        "      <th>18</th>\n",
 973 |        "      <td>2020-05-06</td>\n",
 974 |        "      <td>Pakistan</td>\n",
 975 |        "      <td>220356788</td>\n",
 976 |        "      <td>232582.0</td>\n",
 977 |        "      <td>23214.0</td>\n",
 978 |        "      <td>544.0</td>\n",
 979 |        "      <td>6281.0</td>\n",
 980 |        "      <td>111.0</td>\n",
 981 |        "      <td>16389.0</td>\n",
 982 |        "    </tr>\n",
 983 |        "    <tr>\n",
 984 |        "      <th>19</th>\n",
 985 |        "      <td>2020-05-07</td>\n",
 986 |        "      <td>Pakistan</td>\n",
 987 |        "      <td>220356788</td>\n",
 988 |        "      <td>244778.0</td>\n",
 989 |        "      <td>24644.0</td>\n",
 990 |        "      <td>585.0</td>\n",
 991 |        "      <td>6464.0</td>\n",
 992 |        "      <td>111.0</td>\n",
 993 |        "      <td>17595.0</td>\n",
 994 |        "    </tr>\n",
 995 |        "    <tr>\n",
 996 |        "      <th>20</th>\n",
 997 |        "      <td>2020-05-08</td>\n",
 998 |        "      <td>Pakistan</td>\n",
 999 |        "      <td>220356788</td>\n",
1000 |        "      <td>257247.0</td>\n",
1001 |        "      <td>26435.0</td>\n",
1002 |        "      <td>599.0</td>\n",
1003 |        "      <td>7530.0</td>\n",
1004 |        "      <td>111.0</td>\n",
1005 |        "      <td>18306.0</td>\n",
1006 |        "    </tr>\n",
1007 |        "    <tr>\n",
1008 |        "      <th>21</th>\n",
1009 |        "      <td>2020-05-09</td>\n",
1010 |        "      <td>Pakistan</td>\n",
1011 |        "      <td>220356788</td>\n",
1012 |        "      <td>270025.0</td>\n",
1013 |        "      <td>28736.0</td>\n",
1014 |        "      <td>636.0</td>\n",
1015 |        "      <td>7809.0</td>\n",
1016 |        "      <td>111.0</td>\n",
1017 |        "      <td>20291.0</td>\n",
1018 |        "    </tr>\n",
1019 |        "    <tr>\n",
1020 |        "      <th>22</th>\n",
1021 |        "      <td>2020-05-10</td>\n",
1022 |        "      <td>Pakistan</td>\n",
1023 |        "      <td>220356788</td>\n",
1024 |        "      <td>283517.0</td>\n",
1025 |        "      <td>30334.0</td>\n",
1026 |        "      <td>659.0</td>\n",
1027 |        "      <td>8063.0</td>\n",
1028 |        "      <td>111.0</td>\n",
1029 |        "      <td>21612.0</td>\n",
1030 |        "    </tr>\n",
1031 |        "    <tr>\n",
1032 |        "      <th>23</th>\n",
1033 |        "      <td>2020-05-11</td>\n",
1034 |        "      <td>Pakistan</td>\n",
1035 |        "      <td>220356788</td>\n",
1036 |        "      <td>294894.0</td>\n",
1037 |        "      <td>30941.0</td>\n",
1038 |        "      <td>667.0</td>\n",
1039 |        "      <td>8212.0</td>\n",
1040 |        "      <td>111.0</td>\n",
1041 |        "      <td>22062.0</td>\n",
1042 |        "    </tr>\n",
1043 |        "    <tr>\n",
1044 |        "      <th>24</th>\n",
1045 |        "      <td>2020-05-12</td>\n",
1046 |        "      <td>Pakistan</td>\n",
1047 |        "      <td>220356788</td>\n",
1048 |        "      <td>305851.0</td>\n",
1049 |        "      <td>32674.0</td>\n",
1050 |        "      <td>724.0</td>\n",
1051 |        "      <td>8555.0</td>\n",
1052 |        "      <td>111.0</td>\n",
1053 |        "      <td>23395.0</td>\n",
1054 |        "    </tr>\n",
1055 |        "    <tr>\n",
1056 |        "      <th>25</th>\n",
1057 |        "      <td>2020-05-13</td>\n",
1058 |        "      <td>Pakistan</td>\n",
1059 |        "      <td>220356788</td>\n",
1060 |        "      <td>317699.0</td>\n",
1061 |        "      <td>35298.0</td>\n",
1062 |        "      <td>761.0</td>\n",
1063 |        "      <td>8899.0</td>\n",
1064 |        "      <td>111.0</td>\n",
1065 |        "      <td>25638.0</td>\n",
1066 |        "    </tr>\n",
1067 |        "    <tr>\n",
1068 |        "      <th>26</th>\n",
1069 |        "      <td>2020-05-14</td>\n",
1070 |        "      <td>Pakistan</td>\n",
1071 |        "      <td>220356788</td>\n",
1072 |        "      <td>330750.0</td>\n",
1073 |        "      <td>35788.0</td>\n",
1074 |        "      <td>770.0</td>\n",
1075 |        "      <td>9695.0</td>\n",
1076 |        "      <td>111.0</td>\n",
1077 |        "      <td>25323.0</td>\n",
1078 |        "    </tr>\n",
1079 |        "    <tr>\n",
1080 |        "      <th>27</th>\n",
1081 |        "      <td>2020-05-15</td>\n",
1082 |        "      <td>Pakistan</td>\n",
1083 |        "      <td>220356788</td>\n",
1084 |        "      <td>344450.0</td>\n",
1085 |        "      <td>37218.0</td>\n",
1086 |        "      <td>803.0</td>\n",
1087 |        "      <td>10155.0</td>\n",
1088 |        "      <td>111.0</td>\n",
1089 |        "      <td>26260.0</td>\n",
1090 |        "    </tr>\n",
1091 |        "    <tr>\n",
1092 |        "      <th>28</th>\n",
1093 |        "      <td>2020-05-16</td>\n",
1094 |        "      <td>Pakistan</td>\n",
1095 |        "      <td>220356788</td>\n",
1096 |        "      <td>359264.0</td>\n",
1097 |        "      <td>38799.0</td>\n",
1098 |        "      <td>834.0</td>\n",
1099 |        "      <td>10880.0</td>\n",
1100 |        "      <td>111.0</td>\n",
1101 |        "      <td>27085.0</td>\n",
1102 |        "    </tr>\n",
1103 |        "    <tr>\n",
1104 |        "      <th>29</th>\n",
1105 |        "      <td>2020-05-17</td>\n",
1106 |        "      <td>Pakistan</td>\n",
1107 |        "      <td>220356788</td>\n",
1108 |        "      <td>373410.0</td>\n",
1109 |        "      <td>40151.0</td>\n",
1110 |        "      <td>873.0</td>\n",
1111 |        "      <td>11341.0</td>\n",
1112 |        "      <td>111.0</td>\n",
1113 |        "      <td>27937.0</td>\n",
1114 |        "    </tr>\n",
1115 |        "    <tr>\n",
1116 |        "      <th>30</th>\n",
1117 |        "      <td>2020-05-18</td>\n",
1118 |        "      <td>Pakistan</td>\n",
1119 |        "      <td>220356788</td>\n",
1120 |        "      <td>387335.0</td>\n",
1121 |        "      <td>42125.0</td>\n",
1122 |        "      <td>903.0</td>\n",
1123 |        "      <td>11922.0</td>\n",
1124 |        "      <td>111.0</td>\n",
1125 |        "      <td>29300.0</td>\n",
1126 |        "    </tr>\n",
1127 |        "  </tbody>\n",
1128 |        "</table>\n",
1129 |        "</div>"
1130 |       ],
1131 |       "text/plain": [
1132 |        "          Date   Country  Population  Total Tests  Total Cases  Total Deaths  \\\n",
1133 |        "0   2020-04-18  Pakistan   220356788      92548.0       7638.0         143.0   \n",
1134 |        "1   2020-04-19  Pakistan   220356788      98522.0       8348.0         168.0   \n",
1135 |        "2   2020-04-20  Pakistan   220356788     104302.0       8892.0         176.0   \n",
1136 |        "3   2020-04-21  Pakistan   220356788     111806.0       9565.0         201.0   \n",
1137 |        "4   2020-04-22  Pakistan   220356788     118020.0      10076.0         212.0   \n",
1138 |        "5   2020-04-23  Pakistan   220356788     124549.0      11057.0         235.0   \n",
1139 |        "6   2020-04-24  Pakistan   220356788     138147.0      11940.0         253.0   \n",
1140 |        "7   2020-04-25  Pakistan   220356788     144365.0      12723.0         269.0   \n",
1141 |        "8   2020-04-26  Pakistan   220356788     144365.0      13328.0         281.0   \n",
1142 |        "9   2020-04-27  Pakistan   220356788     150756.0      13915.0         292.0   \n",
1143 |        "10  2020-04-28  Pakistan   220356788     157223.0      14612.0         312.0   \n",
1144 |        "11  2020-04-29  Pakistan   220356788     165911.0      15525.0         343.0   \n",
1145 |        "12  2020-04-30  Pakistan   220356788     174160.0      16473.0         361.0   \n",
1146 |        "13  2020-05-01  Pakistan   220356788     182131.0      18092.0         417.0   \n",
1147 |        "14  2020-05-02  Pakistan   220356788     203025.0      19022.0         437.0   \n",
1148 |        "15  2020-05-03  Pakistan   220356788     203025.0      20084.0         457.0   \n",
1149 |        "16  2020-05-04  Pakistan   220356788     212511.0      20941.0         476.0   \n",
1150 |        "17  2020-05-05  Pakistan   220356788     222404.0      22049.0         514.0   \n",
1151 |        "18  2020-05-06  Pakistan   220356788     232582.0      23214.0         544.0   \n",
1152 |        "19  2020-05-07  Pakistan   220356788     244778.0      24644.0         585.0   \n",
1153 |        "20  2020-05-08  Pakistan   220356788     257247.0      26435.0         599.0   \n",
1154 |        "21  2020-05-09  Pakistan   220356788     270025.0      28736.0         636.0   \n",
1155 |        "22  2020-05-10  Pakistan   220356788     283517.0      30334.0         659.0   \n",
1156 |        "23  2020-05-11  Pakistan   220356788     294894.0      30941.0         667.0   \n",
1157 |        "24  2020-05-12  Pakistan   220356788     305851.0      32674.0         724.0   \n",
1158 |        "25  2020-05-13  Pakistan   220356788     317699.0      35298.0         761.0   \n",
1159 |        "26  2020-05-14  Pakistan   220356788     330750.0      35788.0         770.0   \n",
1160 |        "27  2020-05-15  Pakistan   220356788     344450.0      37218.0         803.0   \n",
1161 |        "28  2020-05-16  Pakistan   220356788     359264.0      38799.0         834.0   \n",
1162 |        "29  2020-05-17  Pakistan   220356788     373410.0      40151.0         873.0   \n",
1163 |        "30  2020-05-18  Pakistan   220356788     387335.0      42125.0         903.0   \n",
1164 |        "\n",
1165 |        "    Total Recovered  Serious or Critical  Active Cases  \n",
1166 |        "0            1832.0                 46.0        5663.0  \n",
1167 |        "1            1868.0                 46.0        6312.0  \n",
1168 |        "2            1970.0                 46.0        6746.0  \n",
1169 |        "3            2073.0                 46.0        7291.0  \n",
1170 |        "4            2156.0                 58.0        7708.0  \n",
1171 |        "5            2337.0                 60.0        8485.0  \n",
1172 |        "6            2755.0                111.0        8932.0  \n",
1173 |        "7            2866.0                111.0        9588.0  \n",
1174 |        "8            2936.0                111.0       10111.0  \n",
1175 |        "9            3029.0                111.0       10594.0  \n",
1176 |        "10           3233.0                111.0       11067.0  \n",
1177 |        "11           3425.0                111.0       11757.0  \n",
1178 |        "12           4105.0                111.0       12007.0  \n",
1179 |        "13           4351.0                111.0       13324.0  \n",
1180 |        "14           5114.0                111.0       14513.0  \n",
1181 |        "15           5114.0                111.0       14513.0  \n",
1182 |        "16           5635.0                111.0       14830.0  \n",
1183 |        "17           5801.0                111.0       15734.0  \n",
1184 |        "18           6281.0                111.0       16389.0  \n",
1185 |        "19           6464.0                111.0       17595.0  \n",
1186 |        "20           7530.0                111.0       18306.0  \n",
1187 |        "21           7809.0                111.0       20291.0  \n",
1188 |        "22           8063.0                111.0       21612.0  \n",
1189 |        "23           8212.0                111.0       22062.0  \n",
1190 |        "24           8555.0                111.0       23395.0  \n",
1191 |        "25           8899.0                111.0       25638.0  \n",
1192 |        "26           9695.0                111.0       25323.0  \n",
1193 |        "27          10155.0                111.0       26260.0  \n",
1194 |        "28          10880.0                111.0       27085.0  \n",
1195 |        "29          11341.0                111.0       27937.0  \n",
1196 |        "30          11922.0                111.0       29300.0  "
1197 |       ]
1198 |      },
1199 |      "execution_count": 23,
1200 |      "metadata": {},
1201 |      "output_type": "execute_result"
1202 |     }
1203 |    ],
1204 |    "source": [
1205 |     "#To display a sub-table of a specific country :\n",
1206 |     "country_name = 'Pakistan'\n",
1207 |     "\n",
1208 |     "country_df = worldometer_df.loc[worldometer_df['Country'] == country_name, :].reset_index(drop=True)\n",
1209 |     "country_df"
1210 |    ]
1211 |   },
1212 |   {
1213 |    "cell_type": "code",
1214 |    "execution_count": 24,
1215 |    "id": "490165e6",
1216 |    "metadata": {},
1217 |    "outputs": [
1218 |     {
1219 |      "data": {
1220 |       "text/html": [
1221 |        "<div>\n",
1222 |        "<style scoped>\n",
1223 |        "    .dataframe tbody tr th:only-of-type {\n",
1224 |        "        vertical-align: middle;\n",
1225 |        "    }\n",
1226 |        "\n",
1227 |        "    .dataframe tbody tr th {\n",
1228 |        "        vertical-align: top;\n",
1229 |        "    }\n",
1230 |        "\n",
1231 |        "    .dataframe thead th {\n",
1232 |        "        text-align: right;\n",
1233 |        "    }\n",
1234 |        "</style>\n",
1235 |        "<table border=\"1\" class=\"dataframe\">\n",
1236 |        "  <thead>\n",
1237 |        "    <tr style=\"text-align: right;\">\n",
1238 |        "      <th></th>\n",
1239 |        "      <th>Date</th>\n",
1240 |        "      <th>Country</th>\n",
1241 |        "      <th>Population</th>\n",
1242 |        "      <th>Total Tests</th>\n",
1243 |        "      <th>Total Cases</th>\n",
1244 |        "      <th>Total Deaths</th>\n",
1245 |        "      <th>Total Recovered</th>\n",
1246 |        "      <th>Serious or Critical</th>\n",
1247 |        "      <th>Active Cases</th>\n",
1248 |        "    </tr>\n",
1249 |        "  </thead>\n",
1250 |        "  <tbody>\n",
1251 |        "    <tr>\n",
1252 |        "      <th>0</th>\n",
1253 |        "      <td>2020-05-18</td>\n",
1254 |        "      <td>USA</td>\n",
1255 |        "      <td>330774664</td>\n",
1256 |        "      <td>12300744.0</td>\n",
1257 |        "      <td>1550294.0</td>\n",
1258 |        "      <td>91981.0</td>\n",
1259 |        "      <td>356383.0</td>\n",
1260 |        "      <td>16868.0</td>\n",
1261 |        "      <td>1101930.0</td>\n",
1262 |        "    </tr>\n",
1263 |        "    <tr>\n",
1264 |        "      <th>1</th>\n",
1265 |        "      <td>2020-05-18</td>\n",
1266 |        "      <td>Russia</td>\n",
1267 |        "      <td>145927292</td>\n",
1268 |        "      <td>7147014.0</td>\n",
1269 |        "      <td>290678.0</td>\n",
1270 |        "      <td>2722.0</td>\n",
1271 |        "      <td>70209.0</td>\n",
1272 |        "      <td>2300.0</td>\n",
1273 |        "      <td>217747.0</td>\n",
1274 |        "    </tr>\n",
1275 |        "    <tr>\n",
1276 |        "      <th>2</th>\n",
1277 |        "      <td>2020-05-18</td>\n",
1278 |        "      <td>Spain</td>\n",
1279 |        "      <td>46752703</td>\n",
1280 |        "      <td>3037840.0</td>\n",
1281 |        "      <td>278188.0</td>\n",
1282 |        "      <td>27709.0</td>\n",
1283 |        "      <td>196958.0</td>\n",
1284 |        "      <td>1152.0</td>\n",
1285 |        "      <td>53521.0</td>\n",
1286 |        "    </tr>\n",
1287 |        "    <tr>\n",
1288 |        "      <th>3</th>\n",
1289 |        "      <td>2020-05-18</td>\n",
1290 |        "      <td>Brazil</td>\n",
1291 |        "      <td>212380932</td>\n",
1292 |        "      <td>735224.0</td>\n",
1293 |        "      <td>255368.0</td>\n",
1294 |        "      <td>16853.0</td>\n",
1295 |        "      <td>100459.0</td>\n",
1296 |        "      <td>8318.0</td>\n",
1297 |        "      <td>138056.0</td>\n",
1298 |        "    </tr>\n",
1299 |        "    <tr>\n",
1300 |        "      <th>4</th>\n",
1301 |        "      <td>2020-05-18</td>\n",
1302 |        "      <td>UK</td>\n",
1303 |        "      <td>67844241</td>\n",
1304 |        "      <td>2682716.0</td>\n",
1305 |        "      <td>246406.0</td>\n",
1306 |        "      <td>34796.0</td>\n",
1307 |        "      <td>NaN</td>\n",
1308 |        "      <td>1559.0</td>\n",
1309 |        "      <td>NaN</td>\n",
1310 |        "    </tr>\n",
1311 |        "    <tr>\n",
1312 |        "      <th>...</th>\n",
1313 |        "      <td>...</td>\n",
1314 |        "      <td>...</td>\n",
1315 |        "      <td>...</td>\n",
1316 |        "      <td>...</td>\n",
1317 |        "      <td>...</td>\n",
1318 |        "      <td>...</td>\n",
1319 |        "      <td>...</td>\n",
1320 |        "      <td>...</td>\n",
1321 |        "      <td>...</td>\n",
1322 |        "    </tr>\n",
1323 |        "    <tr>\n",
1324 |        "      <th>208</th>\n",
1325 |        "      <td>2020-05-18</td>\n",
1326 |        "      <td>St. Barth</td>\n",
1327 |        "      <td>9874</td>\n",
1328 |        "      <td>NaN</td>\n",
1329 |        "      <td>6.0</td>\n",
1330 |        "      <td>NaN</td>\n",
1331 |        "      <td>6.0</td>\n",
1332 |        "      <td>NaN</td>\n",
1333 |        "      <td>0.0</td>\n",
1334 |        "    </tr>\n",
1335 |        "    <tr>\n",
1336 |        "      <th>209</th>\n",
1337 |        "      <td>2020-05-18</td>\n",
1338 |        "      <td>Western Sahara</td>\n",
1339 |        "      <td>595462</td>\n",
1340 |        "      <td>NaN</td>\n",
1341 |        "      <td>6.0</td>\n",
1342 |        "      <td>NaN</td>\n",
1343 |        "      <td>6.0</td>\n",
1344 |        "      <td>NaN</td>\n",
1345 |        "      <td>0.0</td>\n",
1346 |        "    </tr>\n",
1347 |        "    <tr>\n",
1348 |        "      <th>210</th>\n",
1349 |        "      <td>2020-05-18</td>\n",
1350 |        "      <td>Anguilla</td>\n",
1351 |        "      <td>14987</td>\n",
1352 |        "      <td>NaN</td>\n",
1353 |        "      <td>3.0</td>\n",
1354 |        "      <td>NaN</td>\n",
1355 |        "      <td>3.0</td>\n",
1356 |        "      <td>NaN</td>\n",
1357 |        "      <td>0.0</td>\n",
1358 |        "    </tr>\n",
1359 |        "    <tr>\n",
1360 |        "      <th>211</th>\n",
1361 |        "      <td>2020-05-18</td>\n",
1362 |        "      <td>Lesotho</td>\n",
1363 |        "      <td>2140235</td>\n",
1364 |        "      <td>NaN</td>\n",
1365 |        "      <td>1.0</td>\n",
1366 |        "      <td>NaN</td>\n",
1367 |        "      <td>NaN</td>\n",
1368 |        "      <td>NaN</td>\n",
1369 |        "      <td>1.0</td>\n",
1370 |        "    </tr>\n",
1371 |        "    <tr>\n",
1372 |        "      <th>212</th>\n",
1373 |        "      <td>2020-05-18</td>\n",
1374 |        "      <td>Saint Pierre Miquelon</td>\n",
1375 |        "      <td>5797</td>\n",
1376 |        "      <td>NaN</td>\n",
1377 |        "      <td>1.0</td>\n",
1378 |        "      <td>NaN</td>\n",
1379 |        "      <td>1.0</td>\n",
1380 |        "      <td>NaN</td>\n",
1381 |        "      <td>0.0</td>\n",
1382 |        "    </tr>\n",
1383 |        "  </tbody>\n",
1384 |        "</table>\n",
1385 |        "<p>213 rows × 9 columns</p>\n",
1386 |        "</div>"
1387 |       ],
1388 |       "text/plain": [
1389 |        "           Date                Country  Population  Total Tests  Total Cases  \\\n",
1390 |        "0    2020-05-18                    USA   330774664   12300744.0    1550294.0   \n",
1391 |        "1    2020-05-18                 Russia   145927292    7147014.0     290678.0   \n",
1392 |        "2    2020-05-18                  Spain    46752703    3037840.0     278188.0   \n",
1393 |        "3    2020-05-18                 Brazil   212380932     735224.0     255368.0   \n",
1394 |        "4    2020-05-18                     UK    67844241    2682716.0     246406.0   \n",
1395 |        "..          ...                    ...         ...          ...          ...   \n",
1396 |        "208  2020-05-18              St. Barth        9874          NaN          6.0   \n",
1397 |        "209  2020-05-18         Western Sahara      595462          NaN          6.0   \n",
1398 |        "210  2020-05-18               Anguilla       14987          NaN          3.0   \n",
1399 |        "211  2020-05-18                Lesotho     2140235          NaN          1.0   \n",
1400 |        "212  2020-05-18  Saint Pierre Miquelon        5797          NaN          1.0   \n",
1401 |        "\n",
1402 |        "     Total Deaths  Total Recovered  Serious or Critical  Active Cases  \n",
1403 |        "0         91981.0         356383.0              16868.0     1101930.0  \n",
1404 |        "1          2722.0          70209.0               2300.0      217747.0  \n",
1405 |        "2         27709.0         196958.0               1152.0       53521.0  \n",
1406 |        "3         16853.0         100459.0               8318.0      138056.0  \n",
1407 |        "4         34796.0              NaN               1559.0           NaN  \n",
1408 |        "..            ...              ...                  ...           ...  \n",
1409 |        "208           NaN              6.0                  NaN           0.0  \n",
1410 |        "209           NaN              6.0                  NaN           0.0  \n",
1411 |        "210           NaN              3.0                  NaN           0.0  \n",
1412 |        "211           NaN              NaN                  NaN           1.0  \n",
1413 |        "212           NaN              1.0                  NaN           0.0  \n",
1414 |        "\n",
1415 |        "[213 rows x 9 columns]"
1416 |       ]
1417 |      },
1418 |      "execution_count": 24,
1419 |      "metadata": {},
1420 |      "output_type": "execute_result"
1421 |     }
1422 |    ],
1423 |    "source": [
1424 |     "#To display a sub-table of a specific date \n",
1425 |     "selected_date = datetime.strptime('18/05/2020', '%d/%m/%Y')\n",
1426 |     "selected_date_df = worldometer_df.loc[worldometer_df['Date'] == selected_date.strftime('%Y-%m-%d'), :].reset_index(drop=True)\n",
1427 |     "selected_date_df"
1428 |    ]
1429 |   },
1430 |   {
1431 |    "cell_type": "code",
1432 |    "execution_count": 25,
1433 |    "id": "f0930900",
1434 |    "metadata": {},
1435 |    "outputs": [
1436 |     {
1437 |      "data": {
1438 |       "text/html": [
1439 |        "<div>\n",
1440 |        "<style scoped>\n",
1441 |        "    .dataframe tbody tr th:only-of-type {\n",
1442 |        "        vertical-align: middle;\n",
1443 |        "    }\n",
1444 |        "\n",
1445 |        "    .dataframe tbody tr th {\n",
1446 |        "        vertical-align: top;\n",
1447 |        "    }\n",
1448 |        "\n",
1449 |        "    .dataframe thead th {\n",
1450 |        "        text-align: right;\n",
1451 |        "    }\n",
1452 |        "</style>\n",
1453 |        "<table border=\"1\" class=\"dataframe\">\n",
1454 |        "  <thead>\n",
1455 |        "    <tr style=\"text-align: right;\">\n",
1456 |        "      <th></th>\n",
1457 |        "      <th>Date</th>\n",
1458 |        "      <th>Country</th>\n",
1459 |        "      <th>Population</th>\n",
1460 |        "      <th>Total Tests</th>\n",
1461 |        "      <th>Total Cases</th>\n",
1462 |        "      <th>Total Deaths</th>\n",
1463 |        "      <th>Total Recovered</th>\n",
1464 |        "      <th>Serious or Critical</th>\n",
1465 |        "      <th>Active Cases</th>\n",
1466 |        "    </tr>\n",
1467 |        "  </thead>\n",
1468 |        "  <tbody>\n",
1469 |        "    <tr>\n",
1470 |        "      <th>0</th>\n",
1471 |        "      <td>2020-05-18</td>\n",
1472 |        "      <td>USA</td>\n",
1473 |        "      <td>330774664</td>\n",
1474 |        "      <td>12300744.0</td>\n",
1475 |        "      <td>1550294.0</td>\n",
1476 |        "      <td>91981.0</td>\n",
1477 |        "      <td>356383.0</td>\n",
1478 |        "      <td>16868.0</td>\n",
1479 |        "      <td>1101930.0</td>\n",
1480 |        "    </tr>\n",
1481 |        "    <tr>\n",
1482 |        "      <th>1</th>\n",
1483 |        "      <td>2020-05-18</td>\n",
1484 |        "      <td>Russia</td>\n",
1485 |        "      <td>145927292</td>\n",
1486 |        "      <td>7147014.0</td>\n",
1487 |        "      <td>290678.0</td>\n",
1488 |        "      <td>2722.0</td>\n",
1489 |        "      <td>70209.0</td>\n",
1490 |        "      <td>2300.0</td>\n",
1491 |        "      <td>217747.0</td>\n",
1492 |        "    </tr>\n",
1493 |        "    <tr>\n",
1494 |        "      <th>2</th>\n",
1495 |        "      <td>2020-05-18</td>\n",
1496 |        "      <td>Spain</td>\n",
1497 |        "      <td>46752703</td>\n",
1498 |        "      <td>3037840.0</td>\n",
1499 |        "      <td>278188.0</td>\n",
1500 |        "      <td>27709.0</td>\n",
1501 |        "      <td>196958.0</td>\n",
1502 |        "      <td>1152.0</td>\n",
1503 |        "      <td>53521.0</td>\n",
1504 |        "    </tr>\n",
1505 |        "    <tr>\n",
1506 |        "      <th>3</th>\n",
1507 |        "      <td>2020-05-18</td>\n",
1508 |        "      <td>Brazil</td>\n",
1509 |        "      <td>212380932</td>\n",
1510 |        "      <td>735224.0</td>\n",
1511 |        "      <td>255368.0</td>\n",
1512 |        "      <td>16853.0</td>\n",
1513 |        "      <td>100459.0</td>\n",
1514 |        "      <td>8318.0</td>\n",
1515 |        "      <td>138056.0</td>\n",
1516 |        "    </tr>\n",
1517 |        "    <tr>\n",
1518 |        "      <th>4</th>\n",
1519 |        "      <td>2020-05-18</td>\n",
1520 |        "      <td>UK</td>\n",
1521 |        "      <td>67844241</td>\n",
1522 |        "      <td>2682716.0</td>\n",
1523 |        "      <td>246406.0</td>\n",
1524 |        "      <td>34796.0</td>\n",
1525 |        "      <td>NaN</td>\n",
1526 |        "      <td>1559.0</td>\n",
1527 |        "      <td>NaN</td>\n",
1528 |        "    </tr>\n",
1529 |        "    <tr>\n",
1530 |        "      <th>...</th>\n",
1531 |        "      <td>...</td>\n",
1532 |        "      <td>...</td>\n",
1533 |        "      <td>...</td>\n",
1534 |        "      <td>...</td>\n",
1535 |        "      <td>...</td>\n",
1536 |        "      <td>...</td>\n",
1537 |        "      <td>...</td>\n",
1538 |        "      <td>...</td>\n",
1539 |        "      <td>...</td>\n",
1540 |        "    </tr>\n",
1541 |        "    <tr>\n",
1542 |        "      <th>208</th>\n",
1543 |        "      <td>2020-05-18</td>\n",
1544 |        "      <td>St. Barth</td>\n",
1545 |        "      <td>9874</td>\n",
1546 |        "      <td>NaN</td>\n",
1547 |        "      <td>6.0</td>\n",
1548 |        "      <td>NaN</td>\n",
1549 |        "      <td>6.0</td>\n",
1550 |        "      <td>NaN</td>\n",
1551 |        "      <td>0.0</td>\n",
1552 |        "    </tr>\n",
1553 |        "    <tr>\n",
1554 |        "      <th>209</th>\n",
1555 |        "      <td>2020-05-18</td>\n",
1556 |        "      <td>Western Sahara</td>\n",
1557 |        "      <td>595462</td>\n",
1558 |        "      <td>NaN</td>\n",
1559 |        "      <td>6.0</td>\n",
1560 |        "      <td>NaN</td>\n",
1561 |        "      <td>6.0</td>\n",
1562 |        "      <td>NaN</td>\n",
1563 |        "      <td>0.0</td>\n",
1564 |        "    </tr>\n",
1565 |        "    <tr>\n",
1566 |        "      <th>210</th>\n",
1567 |        "      <td>2020-05-18</td>\n",
1568 |        "      <td>Anguilla</td>\n",
1569 |        "      <td>14987</td>\n",
1570 |        "      <td>NaN</td>\n",
1571 |        "      <td>3.0</td>\n",
1572 |        "      <td>NaN</td>\n",
1573 |        "      <td>3.0</td>\n",
1574 |        "      <td>NaN</td>\n",
1575 |        "      <td>0.0</td>\n",
1576 |        "    </tr>\n",
1577 |        "    <tr>\n",
1578 |        "      <th>211</th>\n",
1579 |        "      <td>2020-05-18</td>\n",
1580 |        "      <td>Lesotho</td>\n",
1581 |        "      <td>2140235</td>\n",
1582 |        "      <td>NaN</td>\n",
1583 |        "      <td>1.0</td>\n",
1584 |        "      <td>NaN</td>\n",
1585 |        "      <td>NaN</td>\n",
1586 |        "      <td>NaN</td>\n",
1587 |        "      <td>1.0</td>\n",
1588 |        "    </tr>\n",
1589 |        "    <tr>\n",
1590 |        "      <th>212</th>\n",
1591 |        "      <td>2020-05-18</td>\n",
1592 |        "      <td>Saint Pierre Miquelon</td>\n",
1593 |        "      <td>5797</td>\n",
1594 |        "      <td>NaN</td>\n",
1595 |        "      <td>1.0</td>\n",
1596 |        "      <td>NaN</td>\n",
1597 |        "      <td>1.0</td>\n",
1598 |        "      <td>NaN</td>\n",
1599 |        "      <td>0.0</td>\n",
1600 |        "    </tr>\n",
1601 |        "  </tbody>\n",
1602 |        "</table>\n",
1603 |        "<p>213 rows × 9 columns</p>\n",
1604 |        "</div>"
1605 |       ],
1606 |       "text/plain": [
1607 |        "           Date                Country  Population  Total Tests  Total Cases  \\\n",
1608 |        "0    2020-05-18                    USA   330774664   12300744.0    1550294.0   \n",
1609 |        "1    2020-05-18                 Russia   145927292    7147014.0     290678.0   \n",
1610 |        "2    2020-05-18                  Spain    46752703    3037840.0     278188.0   \n",
1611 |        "3    2020-05-18                 Brazil   212380932     735224.0     255368.0   \n",
1612 |        "4    2020-05-18                     UK    67844241    2682716.0     246406.0   \n",
1613 |        "..          ...                    ...         ...          ...          ...   \n",
1614 |        "208  2020-05-18              St. Barth        9874          NaN          6.0   \n",
1615 |        "209  2020-05-18         Western Sahara      595462          NaN          6.0   \n",
1616 |        "210  2020-05-18               Anguilla       14987          NaN          3.0   \n",
1617 |        "211  2020-05-18                Lesotho     2140235          NaN          1.0   \n",
1618 |        "212  2020-05-18  Saint Pierre Miquelon        5797          NaN          1.0   \n",
1619 |        "\n",
1620 |        "     Total Deaths  Total Recovered  Serious or Critical  Active Cases  \n",
1621 |        "0         91981.0         356383.0              16868.0     1101930.0  \n",
1622 |        "1          2722.0          70209.0               2300.0      217747.0  \n",
1623 |        "2         27709.0         196958.0               1152.0       53521.0  \n",
1624 |        "3         16853.0         100459.0               8318.0      138056.0  \n",
1625 |        "4         34796.0              NaN               1559.0           NaN  \n",
1626 |        "..            ...              ...                  ...           ...  \n",
1627 |        "208           NaN              6.0                  NaN           0.0  \n",
1628 |        "209           NaN              6.0                  NaN           0.0  \n",
1629 |        "210           NaN              3.0                  NaN           0.0  \n",
1630 |        "211           NaN              NaN                  NaN           1.0  \n",
1631 |        "212           NaN              1.0                  NaN           0.0  \n",
1632 |        "\n",
1633 |        "[213 rows x 9 columns]"
1634 |       ]
1635 |      },
1636 |      "execution_count": 25,
1637 |      "metadata": {},
1638 |      "output_type": "execute_result"
1639 |     }
1640 |    ],
1641 |    "source": [
1642 |     "#Now lets take the last date and continue our analysis\n",
1643 |     "last_date = datetime.strptime('18/05/2020', '%d/%m/%Y')\n",
1644 |     "last_date_df = worldometer_df.loc[worldometer_df['Date'] == last_date.strftime('%Y-%m-%d'), :].reset_index(drop=True)\n",
1645 |     "last_date_df"
1646 |    ]
1647 |   },
1648 |   {
1649 |    "cell_type": "code",
1650 |    "execution_count": 26,
1651 |    "id": "c683e59b",
1652 |    "metadata": {},
1653 |    "outputs": [
1654 |     {
1655 |      "data": {
1656 |       "image/png": "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\n",
1657 |       "text/plain": [
1658 |        "<Figure size 864x576 with 1 Axes>"
1659 |       ]
1660 |      },
1661 |      "metadata": {
1662 |       "needs_background": "light"
1663 |      },
1664 |      "output_type": "display_data"
1665 |     }
1666 |    ],
1667 |    "source": [
1668 |     "#Now calculate the naive death rate for each country and show histogram\n",
1669 |     "last_date_df['Case Fatality Ratio'] = last_date_df['Total Deaths'] / last_date_df['Total Cases']\n",
1670 |     "\n",
1671 |     "plt.figure(figsize=(12,8))\n",
1672 |     "plt.hist(100 * np.array(last_date_df['Case Fatality Ratio']), bins=np.arange(35))\n",
1673 |     "plt.xlabel('Death Rate (%)', fontsize=16)\n",
1674 |     "plt.ylabel('Number of Countries', fontsize=16)\n",
1675 |     "plt.title('Histogram of Death Rates for various Countries', fontsize=18)\n",
1676 |     "plt.show()"
1677 |    ]
1678 |   },
1679 |   {
1680 |    "cell_type": "code",
1681 |    "execution_count": 27,
1682 |    "id": "03eb9721",
1683 |    "metadata": {},
1684 |    "outputs": [
1685 |     {
1686 |      "data": {
1687 |       "image/png": "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\n",
1688 |       "text/plain": [
1689 |        "<Figure size 864x576 with 1 Axes>"
1690 |       ]
1691 |      },
1692 |      "metadata": {
1693 |       "needs_background": "light"
1694 |      },
1695 |      "output_type": "display_data"
1696 |     }
1697 |    ],
1698 |    "source": [
1699 |     "###########We see a large spread of death rates between countries##########\n",
1700 |     "#Filter out countries with small amount of cases\n",
1701 |     "min_number_of_cases = 1000\n",
1702 |     "greatly_affected_df = last_date_df.loc[last_date_df['Total Cases'] > min_number_of_cases,:]\n",
1703 |     "plt.figure(figsize=(12,8))\n",
1704 |     "plt.hist(100 * np.array(greatly_affected_df['Case Fatality Ratio']), bins=np.arange(35))\n",
1705 |     "plt.xlabel('Death Rate (%)', fontsize=16)\n",
1706 |     "plt.ylabel('Number of Countries', fontsize=16)\n",
1707 |     "plt.title('Histogram of Death Rates for various Countries', fontsize=18)\n",
1708 |     "plt.show()"
1709 |    ]
1710 |   },
1711 |   {
1712 |    "cell_type": "code",
1713 |    "execution_count": 28,
1714 |    "id": "92328cd7",
1715 |    "metadata": {},
1716 |    "outputs": [
1717 |     {
1718 |      "name": "stderr",
1719 |      "output_type": "stream",
1720 |      "text": [
1721 |       "posx and posy should be finite values\n",
1722 |       "posx and posy should be finite values\n"
1723 |      ]
1724 |     },
1725 |     {
1726 |      "data": {
1727 |       "image/png": "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\n",
1728 |       "text/plain": [
1729 |        "<Figure size 1152x864 with 2 Axes>"
1730 |       ]
1731 |      },
1732 |      "metadata": {
1733 |       "needs_background": "light"
1734 |      },
1735 |      "output_type": "display_data"
1736 |     }
1737 |    ],
1738 |    "source": [
1739 |     "#Plot scatter of death rate as function of testing quality\n",
1740 |     "last_date_df['Num Tests per Positive Case'] = last_date_df['Total Tests'] / last_date_df['Total Cases']\n",
1741 |     "\n",
1742 |     "min_number_of_cases = 1000\n",
1743 |     "greatly_affected_df = last_date_df.loc[last_date_df['Total Cases'] > min_number_of_cases,:]\n",
1744 |     "\n",
1745 |     "x_axis_limit = 80\n",
1746 |     "\n",
1747 |     "death_rate_percent = 100 * np.array(greatly_affected_df['Case Fatality Ratio'])\n",
1748 |     "num_test_per_positive = np.array(greatly_affected_df['Num Tests per Positive Case'])\n",
1749 |     "num_test_per_positive[num_test_per_positive > x_axis_limit] = x_axis_limit\n",
1750 |     "total_num_deaths = np.array(greatly_affected_df['Total Deaths'])\n",
1751 |     "population = np.array(greatly_affected_df['Population'])\n",
1752 |     "\n",
1753 |     "plt.figure(figsize=(16,12))\n",
1754 |     "plt.scatter(x=num_test_per_positive, y=death_rate_percent, \n",
1755 |     "            s=0.5*np.power(np.log(1+population),2), \n",
1756 |     "            c=np.log10(1+total_num_deaths))\n",
1757 |     "plt.colorbar()\n",
1758 |     "plt.ylabel('Death Rate (%)', fontsize=16)\n",
1759 |     "plt.xlabel('Number of Tests per Positive Case', fontsize=16)\n",
1760 |     "plt.title('Death Rate as function of Testing Quality', fontsize=18)\n",
1761 |     "plt.xlim(-1, x_axis_limit + 12)\n",
1762 |     "plt.ylim(-0.2,17)\n",
1763 |     "\n",
1764 |     "# plot on top of the figure the names of the\n",
1765 |     "#countries_to_display = greatly_affected_df['Country'].unique().tolist()\n",
1766 |     "countries_to_display = ['USA', 'Russia', 'Spain', 'Brazil', 'UK', 'Italy', 'France', \n",
1767 |     "                        'Pakistan', 'India', 'Canada', 'Belgium', 'Mexico', 'Netherlands', \n",
1768 |     "                        'Sweden', 'Portugal', 'UAE', 'Poland', 'Indonesia', 'Romania', \n",
1769 |     "                        'Israel','Thailand','Kyrgyzstan','El Salvador', 'S. Korea', \n",
1770 |     "                        'Denmark', 'Serbia', 'Norway', 'Algeria', 'Bahrain','Slovenia',\n",
1771 |     "                        'Greece','Cuba','Hong Kong','Lithuania', 'Australia', 'Morocco', \n",
1772 |     "                        'Malaysia', 'Nigeria', 'Moldova', 'Ghana', 'Armenia', 'Bolivia', \n",
1773 |     "                        'Iraq', 'Hungary', 'Cameroon', 'Azerbaijan']\n",
1774 |     "\n",
1775 |     "for country_name in countries_to_display:\n",
1776 |     "    country_index = greatly_affected_df.index[greatly_affected_df['Country'] == country_name]\n",
1777 |     "    plt.text(x=num_test_per_positive[country_index] + 0.5,\n",
1778 |     "             y=death_rate_percent[country_index] + 0.2,\n",
1779 |     "             s=country_name, fontsize=10)\n",
1780 |     "plt.show()"
1781 |    ]
1782 |   },
1783 |   {
1784 |    "cell_type": "code",
1785 |    "execution_count": 29,
1786 |    "id": "9d4682dd",
1787 |    "metadata": {},
1788 |    "outputs": [
1789 |     {
1790 |      "data": {
1791 |       "text/html": [
1792 |        "<div>\n",
1793 |        "<style scoped>\n",
1794 |        "    .dataframe tbody tr th:only-of-type {\n",
1795 |        "        vertical-align: middle;\n",
1796 |        "    }\n",
1797 |        "\n",
1798 |        "    .dataframe tbody tr th {\n",
1799 |        "        vertical-align: top;\n",
1800 |        "    }\n",
1801 |        "\n",
1802 |        "    .dataframe thead th {\n",
1803 |        "        text-align: right;\n",
1804 |        "    }\n",
1805 |        "</style>\n",
1806 |        "<table border=\"1\" class=\"dataframe\">\n",
1807 |        "  <thead>\n",
1808 |        "    <tr style=\"text-align: right;\">\n",
1809 |        "      <th></th>\n",
1810 |        "      <th>Date</th>\n",
1811 |        "      <th>Country</th>\n",
1812 |        "      <th>Population</th>\n",
1813 |        "      <th>Total Tests</th>\n",
1814 |        "      <th>Total Cases</th>\n",
1815 |        "      <th>Total Deaths</th>\n",
1816 |        "      <th>Total Recovered</th>\n",
1817 |        "      <th>Serious or Critical</th>\n",
1818 |        "      <th>Active Cases</th>\n",
1819 |        "      <th>Case Fatality Ratio</th>\n",
1820 |        "      <th>Num Tests per Positive Case</th>\n",
1821 |        "    </tr>\n",
1822 |        "  </thead>\n",
1823 |        "  <tbody>\n",
1824 |        "    <tr>\n",
1825 |        "      <th>28</th>\n",
1826 |        "      <td>2020-05-18</td>\n",
1827 |        "      <td>UAE</td>\n",
1828 |        "      <td>9875965</td>\n",
1829 |        "      <td>1600923.0</td>\n",
1830 |        "      <td>24190.0</td>\n",
1831 |        "      <td>224.0</td>\n",
1832 |        "      <td>9577.0</td>\n",
1833 |        "      <td>1.0</td>\n",
1834 |        "      <td>14389.0</td>\n",
1835 |        "      <td>0.009260</td>\n",
1836 |        "      <td>66.181191</td>\n",
1837 |        "    </tr>\n",
1838 |        "    <tr>\n",
1839 |        "      <th>43</th>\n",
1840 |        "      <td>2020-05-18</td>\n",
1841 |        "      <td>S. Korea</td>\n",
1842 |        "      <td>51264120</td>\n",
1843 |        "      <td>753211.0</td>\n",
1844 |        "      <td>11065.0</td>\n",
1845 |        "      <td>263.0</td>\n",
1846 |        "      <td>9904.0</td>\n",
1847 |        "      <td>55.0</td>\n",
1848 |        "      <td>898.0</td>\n",
1849 |        "      <td>0.023769</td>\n",
1850 |        "      <td>68.071487</td>\n",
1851 |        "    </tr>\n",
1852 |        "    <tr>\n",
1853 |        "      <th>53</th>\n",
1854 |        "      <td>2020-05-18</td>\n",
1855 |        "      <td>Australia</td>\n",
1856 |        "      <td>25464216</td>\n",
1857 |        "      <td>1062034.0</td>\n",
1858 |        "      <td>7060.0</td>\n",
1859 |        "      <td>99.0</td>\n",
1860 |        "      <td>6392.0</td>\n",
1861 |        "      <td>12.0</td>\n",
1862 |        "      <td>569.0</td>\n",
1863 |        "      <td>0.014023</td>\n",
1864 |        "      <td>150.429745</td>\n",
1865 |        "    </tr>\n",
1866 |        "    <tr>\n",
1867 |        "      <th>55</th>\n",
1868 |        "      <td>2020-05-18</td>\n",
1869 |        "      <td>Malaysia</td>\n",
1870 |        "      <td>32315733</td>\n",
1871 |        "      <td>443263.0</td>\n",
1872 |        "      <td>6941.0</td>\n",
1873 |        "      <td>113.0</td>\n",
1874 |        "      <td>5615.0</td>\n",
1875 |        "      <td>13.0</td>\n",
1876 |        "      <td>1213.0</td>\n",
1877 |        "      <td>0.016280</td>\n",
1878 |        "      <td>63.861547</td>\n",
1879 |        "    </tr>\n",
1880 |        "    <tr>\n",
1881 |        "      <th>56</th>\n",
1882 |        "      <td>2020-05-18</td>\n",
1883 |        "      <td>Kazakhstan</td>\n",
1884 |        "      <td>18749587</td>\n",
1885 |        "      <td>540708.0</td>\n",
1886 |        "      <td>6440.0</td>\n",
1887 |        "      <td>35.0</td>\n",
1888 |        "      <td>3469.0</td>\n",
1889 |        "      <td>31.0</td>\n",
1890 |        "      <td>2936.0</td>\n",
1891 |        "      <td>0.005435</td>\n",
1892 |        "      <td>83.960870</td>\n",
1893 |        "    </tr>\n",
1894 |        "    <tr>\n",
1895 |        "      <th>68</th>\n",
1896 |        "      <td>2020-05-18</td>\n",
1897 |        "      <td>Azerbaijan</td>\n",
1898 |        "      <td>10128288</td>\n",
1899 |        "      <td>235910.0</td>\n",
1900 |        "      <td>3387.0</td>\n",
1901 |        "      <td>40.0</td>\n",
1902 |        "      <td>2055.0</td>\n",
1903 |        "      <td>37.0</td>\n",
1904 |        "      <td>1292.0</td>\n",
1905 |        "      <td>0.011810</td>\n",
1906 |        "      <td>69.651609</td>\n",
1907 |        "    </tr>\n",
1908 |        "    <tr>\n",
1909 |        "      <th>69</th>\n",
1910 |        "      <td>2020-05-18</td>\n",
1911 |        "      <td>Thailand</td>\n",
1912 |        "      <td>69779718</td>\n",
1913 |        "      <td>286008.0</td>\n",
1914 |        "      <td>3031.0</td>\n",
1915 |        "      <td>56.0</td>\n",
1916 |        "      <td>2857.0</td>\n",
1917 |        "      <td>61.0</td>\n",
1918 |        "      <td>118.0</td>\n",
1919 |        "      <td>0.018476</td>\n",
1920 |        "      <td>94.360937</td>\n",
1921 |        "    </tr>\n",
1922 |        "    <tr>\n",
1923 |        "      <th>72</th>\n",
1924 |        "      <td>2020-05-18</td>\n",
1925 |        "      <td>Uzbekistan</td>\n",
1926 |        "      <td>33409960</td>\n",
1927 |        "      <td>460000.0</td>\n",
1928 |        "      <td>2791.0</td>\n",
1929 |        "      <td>13.0</td>\n",
1930 |        "      <td>2314.0</td>\n",
1931 |        "      <td>8.0</td>\n",
1932 |        "      <td>464.0</td>\n",
1933 |        "      <td>0.004658</td>\n",
1934 |        "      <td>164.815478</td>\n",
1935 |        "    </tr>\n",
1936 |        "    <tr>\n",
1937 |        "      <th>86</th>\n",
1938 |        "      <td>2020-05-18</td>\n",
1939 |        "      <td>Lithuania</td>\n",
1940 |        "      <td>2726360</td>\n",
1941 |        "      <td>231104.0</td>\n",
1942 |        "      <td>1547.0</td>\n",
1943 |        "      <td>59.0</td>\n",
1944 |        "      <td>997.0</td>\n",
1945 |        "      <td>17.0</td>\n",
1946 |        "      <td>491.0</td>\n",
1947 |        "      <td>0.038138</td>\n",
1948 |        "      <td>149.388494</td>\n",
1949 |        "    </tr>\n",
1950 |        "    <tr>\n",
1951 |        "      <th>89</th>\n",
1952 |        "      <td>2020-05-18</td>\n",
1953 |        "      <td>New Zealand</td>\n",
1954 |        "      <td>4817585</td>\n",
1955 |        "      <td>230718.0</td>\n",
1956 |        "      <td>1499.0</td>\n",
1957 |        "      <td>21.0</td>\n",
1958 |        "      <td>1433.0</td>\n",
1959 |        "      <td>NaN</td>\n",
1960 |        "      <td>45.0</td>\n",
1961 |        "      <td>0.014009</td>\n",
1962 |        "      <td>153.914610</td>\n",
1963 |        "    </tr>\n",
1964 |        "    <tr>\n",
1965 |        "      <th>90</th>\n",
1966 |        "      <td>2020-05-18</td>\n",
1967 |        "      <td>Slovakia</td>\n",
1968 |        "      <td>5459339</td>\n",
1969 |        "      <td>143433.0</td>\n",
1970 |        "      <td>1495.0</td>\n",
1971 |        "      <td>28.0</td>\n",
1972 |        "      <td>1185.0</td>\n",
1973 |        "      <td>3.0</td>\n",
1974 |        "      <td>282.0</td>\n",
1975 |        "      <td>0.018729</td>\n",
1976 |        "      <td>95.941806</td>\n",
1977 |        "    </tr>\n",
1978 |        "    <tr>\n",
1979 |        "      <th>98</th>\n",
1980 |        "      <td>2020-05-18</td>\n",
1981 |        "      <td>Hong Kong</td>\n",
1982 |        "      <td>7489763</td>\n",
1983 |        "      <td>168291.0</td>\n",
1984 |        "      <td>1056.0</td>\n",
1985 |        "      <td>4.0</td>\n",
1986 |        "      <td>1025.0</td>\n",
1987 |        "      <td>1.0</td>\n",
1988 |        "      <td>27.0</td>\n",
1989 |        "      <td>0.003788</td>\n",
1990 |        "      <td>159.366477</td>\n",
1991 |        "    </tr>\n",
1992 |        "    <tr>\n",
1993 |        "      <th>101</th>\n",
1994 |        "      <td>2020-05-18</td>\n",
1995 |        "      <td>Latvia</td>\n",
1996 |        "      <td>1888464</td>\n",
1997 |        "      <td>89123.0</td>\n",
1998 |        "      <td>1009.0</td>\n",
1999 |        "      <td>19.0</td>\n",
2000 |        "      <td>662.0</td>\n",
2001 |        "      <td>3.0</td>\n",
2002 |        "      <td>328.0</td>\n",
2003 |        "      <td>0.018831</td>\n",
2004 |        "      <td>88.328048</td>\n",
2005 |        "    </tr>\n",
2006 |        "  </tbody>\n",
2007 |        "</table>\n",
2008 |        "</div>"
2009 |       ],
2010 |       "text/plain": [
2011 |        "           Date      Country  Population  Total Tests  Total Cases  \\\n",
2012 |        "28   2020-05-18          UAE     9875965    1600923.0      24190.0   \n",
2013 |        "43   2020-05-18     S. Korea    51264120     753211.0      11065.0   \n",
2014 |        "53   2020-05-18    Australia    25464216    1062034.0       7060.0   \n",
2015 |        "55   2020-05-18     Malaysia    32315733     443263.0       6941.0   \n",
2016 |        "56   2020-05-18   Kazakhstan    18749587     540708.0       6440.0   \n",
2017 |        "68   2020-05-18   Azerbaijan    10128288     235910.0       3387.0   \n",
2018 |        "69   2020-05-18     Thailand    69779718     286008.0       3031.0   \n",
2019 |        "72   2020-05-18   Uzbekistan    33409960     460000.0       2791.0   \n",
2020 |        "86   2020-05-18    Lithuania     2726360     231104.0       1547.0   \n",
2021 |        "89   2020-05-18  New Zealand     4817585     230718.0       1499.0   \n",
2022 |        "90   2020-05-18     Slovakia     5459339     143433.0       1495.0   \n",
2023 |        "98   2020-05-18    Hong Kong     7489763     168291.0       1056.0   \n",
2024 |        "101  2020-05-18       Latvia     1888464      89123.0       1009.0   \n",
2025 |        "\n",
2026 |        "     Total Deaths  Total Recovered  Serious or Critical  Active Cases  \\\n",
2027 |        "28          224.0           9577.0                  1.0       14389.0   \n",
2028 |        "43          263.0           9904.0                 55.0         898.0   \n",
2029 |        "53           99.0           6392.0                 12.0         569.0   \n",
2030 |        "55          113.0           5615.0                 13.0        1213.0   \n",
2031 |        "56           35.0           3469.0                 31.0        2936.0   \n",
2032 |        "68           40.0           2055.0                 37.0        1292.0   \n",
2033 |        "69           56.0           2857.0                 61.0         118.0   \n",
2034 |        "72           13.0           2314.0                  8.0         464.0   \n",
2035 |        "86           59.0            997.0                 17.0         491.0   \n",
2036 |        "89           21.0           1433.0                  NaN          45.0   \n",
2037 |        "90           28.0           1185.0                  3.0         282.0   \n",
2038 |        "98            4.0           1025.0                  1.0          27.0   \n",
2039 |        "101          19.0            662.0                  3.0         328.0   \n",
2040 |        "\n",
2041 |        "     Case Fatality Ratio  Num Tests per Positive Case  \n",
2042 |        "28              0.009260                    66.181191  \n",
2043 |        "43              0.023769                    68.071487  \n",
2044 |        "53              0.014023                   150.429745  \n",
2045 |        "55              0.016280                    63.861547  \n",
2046 |        "56              0.005435                    83.960870  \n",
2047 |        "68              0.011810                    69.651609  \n",
2048 |        "69              0.018476                    94.360937  \n",
2049 |        "72              0.004658                   164.815478  \n",
2050 |        "86              0.038138                   149.388494  \n",
2051 |        "89              0.014009                   153.914610  \n",
2052 |        "90              0.018729                    95.941806  \n",
2053 |        "98              0.003788                   159.366477  \n",
2054 |        "101             0.018831                    88.328048  "
2055 |       ]
2056 |      },
2057 |      "execution_count": 29,
2058 |      "metadata": {},
2059 |      "output_type": "execute_result"
2060 |     }
2061 |    ],
2062 |    "source": [
2063 |     "#Now let’s look at data from best testing countries\n",
2064 |     "#Lets decide that the cutoff for good testing country is 50 tests per positive cases.\n",
2065 |     "good_testing_threshold = 50\n",
2066 |     "good_testing_df = greatly_affected_df.loc[greatly_affected_df['Num Tests per Positive Case'] > good_testing_threshold,:]\n",
2067 |     "good_testing_df"
2068 |    ]
2069 |   },
2070 |   {
2071 |    "cell_type": "code",
2072 |    "execution_count": 30,
2073 |    "id": "54c57800",
2074 |    "metadata": {},
2075 |    "outputs": [
2076 |     {
2077 |      "name": "stdout",
2078 |      "output_type": "stream",
2079 |      "text": [
2080 |       "Death Rate only for \"good testing countries\" is 1.36%\n"
2081 |      ]
2082 |     }
2083 |    ],
2084 |    "source": [
2085 |     "#Lets calculate the Death Rate for these countries\n",
2086 |     "estimated_death_rate_percent = 100 * good_testing_df['Total Deaths'].sum() / good_testing_df['Total Cases'].sum()\n",
2087 |     "print('Death Rate only for \"good testing countries\" is %.2f%s' %(estimated_death_rate_percent,'%'))"
2088 |    ]
2089 |   }
2090 |  ],
2091 |  "metadata": {
2092 |   "kernelspec": {
2093 |    "display_name": "Python 3",
2094 |    "language": "python",
2095 |    "name": "python3"
2096 |   },
2097 |   "language_info": {
2098 |    "codemirror_mode": {
2099 |     "name": "ipython",
2100 |     "version": 3
2101 |    },
2102 |    "file_extension": ".py",
2103 |    "mimetype": "text/x-python",
2104 |    "name": "python",
2105 |    "nbconvert_exporter": "python",
2106 |    "pygments_lexer": "ipython3",
2107 |    "version": "3.8.8"
2108 |   }
2109 |  },
2110 |  "nbformat": 4,
2111 |  "nbformat_minor": 5
2112 | }
2113 | 


--------------------------------------------------------------------------------