Nov30_process_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "7e8ec5fb-a549-40df-a7b3-606ca6c9ccbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "945d9bb7-5227-433c-9aba-ff2ab633e43f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pd.read_csv('/Users/scottye/Desktop/InfoVis/Final_Project/Nov30/OxCGRT_nat_latest.csv')\n",
    "df_population = pd.read_csv('/Users/scottye/Desktop/InfoVis/Final_Project/Nov30/population_by_country_2020.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "686639d0-8f87-4a86-b944-765e2e01ce80",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/scottye/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:5042: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing\n",
    "df = df_raw[['CountryName', 'Date', 'V1_Vaccine Prioritisation (summary)', \n",
    "             'V2A_Vaccine Availability (summary)', 'V3_Vaccine Financial Support (summary)', \n",
    "             'V4_Mandatory Vaccination (summary)', 'ConfirmedCases', 'ConfirmedDeaths', \n",
    "             'MajorityVaccinated', 'PopulationVaccinated']]\n",
    "\n",
    "df.rename(columns={'V1_Vaccine Prioritisation (summary)':'V1', 'V2A_Vaccine Availability (summary)':'V2',\n",
    "                  'V3_Vaccine Financial Support (summary)':'V3', 'V4_Mandatory Vaccination (summary)':'V4',\n",
    "                  'PopulationVaccinated':'PercentageVaccinated'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ae5c2de-4b38-4b3e-8480-3f78562377d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "a7e698b2-f8b0-4d19-860e-6dbaf7235f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add total population for each country\n",
    "df = df[~df['CountryName'].isin([\"Cote d'Ivoire\", \"Cape Verde\", \"Kosovo\", \"United States Virgin Islands\"])]\n",
    "df_population = df_population[['Country (or dependency)', 'Population (2020)']].rename(columns={'Country (or dependency)':'CountryName',\n",
    "                                                                                       'Population (2020)':'Population'})\n",
    "df = pd.merge(df, df_population, how='left')\n",
    "df['PopulationVaccinated'] = df['PercentageVaccinated'] / 100 * df['Population'] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "63d6d78b-29ec-4818-9cba-099078a34225",
   "metadata": {},
   "outputs": [],
   "source": [
    "# More processing\n",
    "df['V1'] = df['V1'] / 2 * 100\n",
    "df['V2'] = df['V2'] / 3 * 100\n",
    "df['V3'] = df['V3'] / 5 * 100\n",
    "df['V4'] = df['V4'].map({0:'False', 1:'True'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "f0121ac9-6cf0-4dea-95ae-82b7fe3a6768",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create world statistics\n",
    "df_world = df[['Date', 'V1', 'V2', 'V3', 'V4', 'ConfirmedCases', \n",
    "                   'ConfirmedDeaths', 'PopulationVaccinated']].copy()\n",
    "df_world = df_world.groupby('Date').agg({'V1':'mean', 'V2':'mean', 'V3':'mean', 'V4':pd.Series.mode,\n",
    "                                            'ConfirmedCases':'sum', 'ConfirmedDeaths':'sum',\n",
    "                                            'PopulationVaccinated':'sum'})\n",
    "\n",
    "df_world['Population'] = 7808449406\n",
    "df_world['PercentageVaccinated'] = df_world['PopulationVaccinated'] / df_world['Population'] * 100\n",
    "df_world['MajorityVaccinated'] = 'NV'\n",
    "df_world.loc[df_world.PercentageVaccinated > 50, 'MajorityVaccinated'] = 'V'\n",
    "df_world['CountryName'] = 'World'\n",
    "df_world.reset_index(inplace=True)\n",
    "\n",
    "df = pd.concat([df, df_world])\n",
    "df['PopulationVaccinated'] = df['PopulationVaccinated'].round()\n",
    "df['PercentageVaccinated'] = df['PercentageVaccinated'].round(decimals = 2)\n",
    "df['V2'] = df['V2'].round(decimals = 2)\n",
    "#df.to_csv('/Users/scottye/Desktop/InfoVis/Final_Project/Nov30/daily.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3d5428f-515a-4fe1-8055-891d712cbaf9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "0915d55b-b011-4dd9-9dd6-9af478acbe5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process monthly statistics\n",
    "df_m = df.copy()\n",
    "df_m['Date'] = df_m['Date']//100\n",
    "df_m.drop_duplicates(subset=['CountryName', 'Date'], keep='last', inplace=True)\n",
    "df_m = df_m[df_m.Date != 202211]\n",
    "#df_m.to_csv('/Users/scottye/Desktop/InfoVis/Final_Project/Nov30/monthly.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "46a0368e-03b1-4413-b1fe-8782df229772",
   "metadata": {},
   "outputs": [
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       "      <th>CountryName</th>\n",
       "      <th>Date</th>\n",
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       "      <td>Aruba</td>\n",
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      "text/plain": [
       "     CountryName    Date         V1     V2         V3     V4  ConfirmedCases  \\\n",
       "30         Aruba  202001   0.000000   0.00   0.000000    NaN             0.0   \n",
       "59         Aruba  202002   0.000000   0.00   0.000000    NaN             0.0   \n",
       "90         Aruba  202003   0.000000   0.00   0.000000    NaN            55.0   \n",
       "120        Aruba  202004   0.000000   0.00   0.000000    NaN           100.0   \n",
       "151        Aruba  202005   0.000000   0.00   0.000000    NaN           101.0   \n",
       "...          ...     ...        ...    ...        ...    ...             ...   \n",
       "911        World  202206  98.888889  85.56  92.666667  False     545701276.0   \n",
       "942        World  202207  98.603352  85.69  94.350282  False     575228934.0   \n",
       "973        World  202208  98.888889  87.19  94.689266  False     600836185.0   \n",
       "1003       World  202209  98.603352  86.17  93.750000  False     615547717.0   \n",
       "1034       World  202210  98.809524  85.45  92.848485  False     628282310.0   \n",
       "\n",
       "      ConfirmedDeaths MajorityVaccinated  PercentageVaccinated  Population  \\\n",
       "30                0.0                 NV                  0.00      106845   \n",
       "59                0.0                 NV                  0.00      106845   \n",
       "90                0.0                 NV                  0.00      106845   \n",
       "120               2.0                 NV                  0.00      106845   \n",
       "151               3.0                 NV                  0.00      106845   \n",
       "...               ...                ...                   ...         ...   \n",
       "911         6333723.0                  V                 60.60  7808449406   \n",
       "942         6394828.0                  V                 61.45  7808449406   \n",
       "973         6469770.0                  V                 62.05  7808449406   \n",
       "1003        6520960.0                  V                 62.47  7808449406   \n",
       "1034        6566016.0                  V                 62.98  7808449406   \n",
       "\n",
       "      PopulationVaccinated  \n",
       "30            0.000000e+00  \n",
       "59            0.000000e+00  \n",
       "90            0.000000e+00  \n",
       "120           0.000000e+00  \n",
       "151           0.000000e+00  \n",
       "...                    ...  \n",
       "911           4.732141e+09  \n",
       "942           4.798493e+09  \n",
       "973           4.845153e+09  \n",
       "1003          4.878150e+09  \n",
       "1034          4.917916e+09  \n",
       "\n",
       "[6256 rows x 12 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "96e391db-0753-423f-9286-cf4dbf759200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process statistics for every 10 days\n",
    "# We will have 202xxx0, 202xxx1, 202xxx2 to separate each month into three date ranges\n",
    "df_t = df.copy()\n",
    "df_t['Date'] = df_t['Date']//10\n",
    "df_t.drop_duplicates(subset=['CountryName', 'Date'], keep='last', inplace=True)\n",
    "df_t = df_t[df_t['Date'].astype(str).str.strip().str[-1] != '3']\n",
    "df_t = df_t[df_t.Date != 2022112]\n",
    "#df_t.to_csv('/Users/scottye/Desktop/InfoVis/Final_Project/Nov30/ten_days.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "2e7a1f88-9b2e-4ea6-a87c-54553c6536f6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Aruba</td>\n",
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       "      <td>106845</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <th>39</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>106845</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NV</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106845</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>World</td>\n",
       "      <td>2022100</td>\n",
       "      <td>98.295455</td>\n",
       "      <td>87.02</td>\n",
       "      <td>94.186047</td>\n",
       "      <td>False</td>\n",
       "      <td>619174120.0</td>\n",
       "      <td>6532463.0</td>\n",
       "      <td>V</td>\n",
       "      <td>62.70</td>\n",
       "      <td>7808449406</td>\n",
       "      <td>4.895546e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>World</td>\n",
       "      <td>2022101</td>\n",
       "      <td>98.857143</td>\n",
       "      <td>86.35</td>\n",
       "      <td>93.099415</td>\n",
       "      <td>False</td>\n",
       "      <td>624034208.0</td>\n",
       "      <td>6548681.0</td>\n",
       "      <td>V</td>\n",
       "      <td>62.81</td>\n",
       "      <td>7808449406</td>\n",
       "      <td>4.904261e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>World</td>\n",
       "      <td>2022102</td>\n",
       "      <td>98.816568</td>\n",
       "      <td>85.45</td>\n",
       "      <td>92.848485</td>\n",
       "      <td>False</td>\n",
       "      <td>627813026.0</td>\n",
       "      <td>6564226.0</td>\n",
       "      <td>V</td>\n",
       "      <td>62.87</td>\n",
       "      <td>7808449406</td>\n",
       "      <td>4.909211e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>World</td>\n",
       "      <td>2022110</td>\n",
       "      <td>99.342105</td>\n",
       "      <td>86.17</td>\n",
       "      <td>96.054422</td>\n",
       "      <td>False</td>\n",
       "      <td>631537475.0</td>\n",
       "      <td>6580375.0</td>\n",
       "      <td>V</td>\n",
       "      <td>63.03</td>\n",
       "      <td>7808449406</td>\n",
       "      <td>4.921828e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>World</td>\n",
       "      <td>2022111</td>\n",
       "      <td>99.115044</td>\n",
       "      <td>86.06</td>\n",
       "      <td>95.636364</td>\n",
       "      <td>False</td>\n",
       "      <td>635591527.0</td>\n",
       "      <td>6594065.0</td>\n",
       "      <td>V</td>\n",
       "      <td>63.17</td>\n",
       "      <td>7808449406</td>\n",
       "      <td>4.932245e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19136 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CountryName     Date         V1     V2         V3     V4  ConfirmedCases  \\\n",
       "8          Aruba  2020010   0.000000   0.00   0.000000    NaN             NaN   \n",
       "18         Aruba  2020011   0.000000   0.00   0.000000    NaN             NaN   \n",
       "28         Aruba  2020012   0.000000   0.00   0.000000    NaN             0.0   \n",
       "39         Aruba  2020020   0.000000   0.00   0.000000    NaN             0.0   \n",
       "49         Aruba  2020021   0.000000   0.00   0.000000    NaN             0.0   \n",
       "...          ...      ...        ...    ...        ...    ...             ...   \n",
       "1012       World  2022100  98.295455  87.02  94.186047  False     619174120.0   \n",
       "1022       World  2022101  98.857143  86.35  93.099415  False     624034208.0   \n",
       "1032       World  2022102  98.816568  85.45  92.848485  False     627813026.0   \n",
       "1043       World  2022110  99.342105  86.17  96.054422  False     631537475.0   \n",
       "1053       World  2022111  99.115044  86.06  95.636364  False     635591527.0   \n",
       "\n",
       "      ConfirmedDeaths MajorityVaccinated  PercentageVaccinated  Population  \\\n",
       "8                 NaN                NaN                   NaN      106845   \n",
       "18                NaN                NaN                   NaN      106845   \n",
       "28                0.0                 NV                  0.00      106845   \n",
       "39                0.0                 NV                  0.00      106845   \n",
       "49                0.0                 NV                  0.00      106845   \n",
       "...               ...                ...                   ...         ...   \n",
       "1012        6532463.0                  V                 62.70  7808449406   \n",
       "1022        6548681.0                  V                 62.81  7808449406   \n",
       "1032        6564226.0                  V                 62.87  7808449406   \n",
       "1043        6580375.0                  V                 63.03  7808449406   \n",
       "1053        6594065.0                  V                 63.17  7808449406   \n",
       "\n",
       "      PopulationVaccinated  \n",
       "8                      NaN  \n",
       "18                     NaN  \n",
       "28            0.000000e+00  \n",
       "39            0.000000e+00  \n",
       "49            0.000000e+00  \n",
       "...                    ...  \n",
       "1012          4.895546e+09  \n",
       "1022          4.904261e+09  \n",
       "1032          4.909211e+09  \n",
       "1043          4.921828e+09  \n",
       "1053          4.932245e+09  \n",
       "\n",
       "[19136 rows x 12 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8dc2e5b-832d-41e0-90f1-95193a2de136",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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