data/cleaning_data.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d1902c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b917a96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing file to clean\n",
    "confirmed = pd.read_csv('confirmed_global.csv')\n",
    "deaths = pd.read_csv('deaths_global.csv')\n",
    "recovered = pd.read_csv('recovered_global.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9f13c55b",
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       "    Province/State Country/Region        Lat       Long  1/22/20  1/23/20  \\\n",
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# Show only COVID german and venezuelan confirmed cases\n",
    "countries_confirmed_cases = confirmed.loc[(confirmed['Country/Region']=='Germany') | (confirmed['Country/Region']=='Venezuela')]\n",
    "countries_confirmed_cases"
   ]
  },
  {
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       "    Country/Region        Lat       Long  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping Province/State column from confirmed table\n",
    "confirmed_cases = countries_confirmed_cases.drop(['Province/State'], axis=1)\n",
    "confirmed_cases\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df85e105",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the new csv file of confirmed german and venezuelan cases\n",
    "confirmed_cases.to_csv('confirmed_cases.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7f516ae1",
   "metadata": {},
   "outputs": [
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       "    Province/State Country/Region        Lat       Long  1/22/20  1/23/20  \\\n",
       "133            NaN        Germany  51.165691  10.451526        0        0   \n",
       "268            NaN      Venezuela   6.423800 -66.589700        0        0   \n",
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     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show only COVID german and venezuelan deaths\n",
    "countries_deaths_cases = deaths.loc[(deaths['Country/Region']=='Germany') | (deaths['Country/Region']=='Venezuela')]\n",
    "countries_deaths_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f2bee5db",
   "metadata": {},
   "outputs": [
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       "    Country/Region        Lat       Long  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
       "133        Germany  51.165691  10.451526        0        0        0        0   \n",
       "268      Venezuela   6.423800 -66.589700        0        0        0        0   \n",
       "\n",
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       "133        0        0        0  ...   76823   76940   77010   77060   77136   \n",
       "268        0        0        0  ...    1615    1629    1647    1662    1678   \n",
       "\n",
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       "133   77245   77755   78049   78295    78402  \n",
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       "\n",
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Dropping Province/State column from deaths table\n",
    "deaths_cases = countries_deaths_cases.drop(['Province/State'], axis=1)\n",
    "deaths_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d4153ca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the new csv file of confirmed german and venezuelan deaths\n",
    "deaths_cases.to_csv('deaths_cases.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e48d1254",
   "metadata": {},
   "outputs": [
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       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
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       "      <td>Venezuela</td>\n",
       "      <td>6.423800</td>\n",
       "      <td>-66.589700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>150374</td>\n",
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       "<p>2 rows × 449 columns</p>\n",
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      ],
      "text/plain": [
       "    Province/State Country/Region        Lat       Long  1/22/20  1/23/20  \\\n",
       "118            NaN        Germany  51.165691  10.451526        0        0   \n",
       "253            NaN      Venezuela   6.423800 -66.589700        0        0   \n",
       "\n",
       "     1/24/20  1/25/20  1/26/20  1/27/20  ...   4/1/21   4/2/21   4/3/21  \\\n",
       "118        0        0        0        0  ...  2552020  2566530  2575740   \n",
       "253        0        0        0        0  ...   148656   149459   150374   \n",
       "\n",
       "      4/4/21   4/5/21   4/6/21   4/7/21   4/8/21   4/9/21  4/10/21  \n",
       "118  2585850  2587580  2604890  2638320  2640300  2654930  2668910  \n",
       "253   150374   152201   153063   154023   154811   155662   156472  \n",
       "\n",
       "[2 rows x 449 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show only COVID german and venezuelan recovered cases\n",
    "countries_recovered_cases = recovered.loc[(recovered['Country/Region']=='Germany') | (recovered['Country/Region']=='Venezuela')]\n",
    "countries_recovered_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "68f4b89b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>2 rows × 448 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country/Region        Lat       Long  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
       "118        Germany  51.165691  10.451526        0        0        0        0   \n",
       "253      Venezuela   6.423800 -66.589700        0        0        0        0   \n",
       "\n",
       "     1/26/20  1/27/20  1/28/20  ...   4/1/21   4/2/21   4/3/21   4/4/21  \\\n",
       "118        0        0        0  ...  2552020  2566530  2575740  2585850   \n",
       "253        0        0        0  ...   148656   149459   150374   150374   \n",
       "\n",
       "      4/5/21   4/6/21   4/7/21   4/8/21   4/9/21  4/10/21  \n",
       "118  2587580  2604890  2638320  2640300  2654930  2668910  \n",
       "253   152201   153063   154023   154811   155662   156472  \n",
       "\n",
       "[2 rows x 448 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping Province/State column from deaths table\n",
    "recovered_cases = countries_recovered_cases.drop(['Province/State'], axis=1)\n",
    "recovered_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6f0e8582",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the new csv file of confirmed german and venezuelan recovered\n",
    "recovered_cases.to_csv('recovered_cases.csv')"
   ]
  }
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