diff --git a/notebooks/Play.ipynb b/notebooks/Play.ipynb
index fd34756b9b546785942cec51d4b5d4dacaf5e74a..5ab6e9cc2b984fc7c96f86f730e9fc8217e77031 100644
--- a/notebooks/Play.ipynb
+++ b/notebooks/Play.ipynb
@@ -20,7 +20,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -34,36 +34,240 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
- "confirmed_df = read_jhu_covid_df(\"Confirmed\")\n",
- "deaths_df = read_jhu_covid_df(\"Deaths\")\n",
- "recovered_df = read_jhu_covid_df(\"Recovered\")"
+ "frames_map = {\n",
+ " \"confirmed\": read_jhu_covid_df(\"Confirmed\"),\n",
+ " \"deaths\": read_jhu_covid_df(\"Deaths\"),\n",
+ " \"recovered\": read_jhu_covid_df(\"Recovered\")\n",
+ "}"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
- "def summarize_df(df, name):\n",
- " ser = df.groupby(level='Country/Region').sum().iloc[:,-1].sort_values(ascending=False)\n",
- " ser.name = f\"Total {name}\"\n",
- " return ser"
+ "def current_region_totals_df(frames_map):\n",
+ " sers = [df.groupby(level='Country/Region').sum().iloc[:,-1].sort_values(ascending=False)\n",
+ " for name, df in frames_map.items()]\n",
+ " for name, ser in zip(frames_map, sers):\n",
+ " ser.name = name\n",
+ " return pd.concat(sers, axis=1)"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 23,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>confirmed</th>\n",
+ " <th>deaths</th>\n",
+ " <th>recovered</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Mainland China</th>\n",
+ " <td>80757</td>\n",
+ " <td>3136</td>\n",
+ " <td>60106</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Italy</th>\n",
+ " <td>10149</td>\n",
+ " <td>631</td>\n",
+ " <td>724</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Iran (Islamic Republic of)</th>\n",
+ " <td>8042</td>\n",
+ " <td>291</td>\n",
+ " <td>2731</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Republic of Korea</th>\n",
+ " <td>7513</td>\n",
+ " <td>54</td>\n",
+ " <td>247</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>France</th>\n",
+ " <td>1784</td>\n",
+ " <td>33</td>\n",
+ " <td>12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Spain</th>\n",
+ " <td>1695</td>\n",
+ " <td>35</td>\n",
+ " <td>32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>US</th>\n",
+ " <td>1670</td>\n",
+ " <td>56</td>\n",
+ " <td>15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Germany</th>\n",
+ " <td>1457</td>\n",
+ " <td>2</td>\n",
+ " <td>18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Others</th>\n",
+ " <td>696</td>\n",
+ " <td>6</td>\n",
+ " <td>40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Japan</th>\n",
+ " <td>581</td>\n",
+ " <td>10</td>\n",
+ " <td>101</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Switzerland</th>\n",
+ " <td>491</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Norway</th>\n",
+ " <td>400</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>UK</th>\n",
+ " <td>382</td>\n",
+ " <td>6</td>\n",
+ " <td>18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Netherlands</th>\n",
+ " <td>382</td>\n",
+ " <td>4</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Sweden</th>\n",
+ " <td>355</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Belgium</th>\n",
+ " <td>267</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Denmark</th>\n",
+ " <td>262</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Austria</th>\n",
+ " <td>182</td>\n",
+ " <td>0</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Singapore</th>\n",
+ " <td>160</td>\n",
+ " <td>0</td>\n",
+ " <td>78</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Malaysia</th>\n",
+ " <td>129</td>\n",
+ " <td>0</td>\n",
+ " <td>24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Hong Kong SAR</th>\n",
+ " <td>120</td>\n",
+ " <td>3</td>\n",
+ " <td>65</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Bahrain</th>\n",
+ " <td>110</td>\n",
+ " <td>0</td>\n",
+ " <td>22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Australia</th>\n",
+ " <td>107</td>\n",
+ " <td>3</td>\n",
+ " <td>21</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " confirmed deaths recovered\n",
+ "Mainland China 80757 3136 60106\n",
+ "Italy 10149 631 724\n",
+ "Iran (Islamic Republic of) 8042 291 2731\n",
+ "Republic of Korea 7513 54 247\n",
+ "France 1784 33 12\n",
+ "Spain 1695 35 32\n",
+ "US 1670 56 15\n",
+ "Germany 1457 2 18\n",
+ "Others 696 6 40\n",
+ "Japan 581 10 101\n",
+ "Switzerland 491 3 3\n",
+ "Norway 400 0 1\n",
+ "UK 382 6 18\n",
+ "Netherlands 382 4 0\n",
+ "Sweden 355 0 1\n",
+ "Belgium 267 0 1\n",
+ "Denmark 262 0 1\n",
+ "Austria 182 0 4\n",
+ "Singapore 160 0 78\n",
+ "Malaysia 129 0 24\n",
+ "Hong Kong SAR 120 3 65\n",
+ "Bahrain 110 0 22\n",
+ "Australia 107 3 21"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "confirmed_ser = summarize_df(confirmed_df, \"Confirmed\")\n",
- "deaths_ser = summarize_df(deaths_df, \"Deaths\")\n",
- "recovered_ser = summarize_df(recovered_df, \"Recovered\")"
+ "current_totals_df = current_region_totals_df(frames_map)\n",
+ "current_totals_df[current_totals_df['confirmed'] > 100]"
]
},
{
@@ -75,7 +279,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@@ -83,6 +287,279 @@
"zf = zipfile.ZipFile(\"../data/worldbank/SP.POP.TOTL.zip\")\n",
"pop_df = pd.read_csv(zf.open(\"API_SP.POP.TOTL_DS2_en_csv_v2_821007.csv\"), skiprows=4)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Country Name</th>\n",
+ " <th>Country Code</th>\n",
+ " <th>Indicator Name</th>\n",
+ " <th>Indicator Code</th>\n",
+ " <th>1960</th>\n",
+ " <th>1961</th>\n",
+ " <th>1962</th>\n",
+ " <th>1963</th>\n",
+ " <th>1964</th>\n",
+ " <th>1965</th>\n",
+ " <th>...</th>\n",
+ " <th>2011</th>\n",
+ " <th>2012</th>\n",
+ " <th>2013</th>\n",
+ " <th>2014</th>\n",
+ " <th>2015</th>\n",
+ " <th>2016</th>\n",
+ " <th>2017</th>\n",
+ " <th>2018</th>\n",
+ " <th>2019</th>\n",
+ " <th>Unnamed: 64</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>67</th>\n",
+ " <td>Eritrea</td>\n",
+ " <td>ERI</td>\n",
+ " <td>Population, total</td>\n",
+ " <td>SP.POP.TOTL</td>\n",
+ " <td>1007590.0</td>\n",
+ " <td>1033328.0</td>\n",
+ " <td>1060486.0</td>\n",
+ " <td>1088854.0</td>\n",
+ " <td>1118159.0</td>\n",
+ " <td>1148189.0</td>\n",
+ " <td>...</td>\n",
+ " <td>3213972.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108</th>\n",
+ " <td>Not classified</td>\n",
+ " <td>INX</td>\n",
+ " <td>Population, total</td>\n",
+ " <td>SP.POP.TOTL</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2 rows × 65 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Country Name Country Code Indicator Name Indicator Code 1960 \\\n",
+ "67 Eritrea ERI Population, total SP.POP.TOTL 1007590.0 \n",
+ "108 Not classified INX Population, total SP.POP.TOTL NaN \n",
+ "\n",
+ " 1961 1962 1963 1964 1965 ... 2011 \\\n",
+ "67 1033328.0 1060486.0 1088854.0 1118159.0 1148189.0 ... 3213972.0 \n",
+ "108 NaN NaN NaN NaN NaN ... NaN \n",
+ "\n",
+ " 2012 2013 2014 2015 2016 2017 2018 2019 Unnamed: 64 \n",
+ "67 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
+ "108 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
+ "\n",
+ "[2 rows x 65 columns]"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# There is 2018 pop data for all countries/regions except Eritrea\n",
+ "pop_df[pd.isna(pop_df['2018'])]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Fix the country/region names that differ"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "region_wb_jhu_map = {\n",
+ " 'China': 'Mainland China',\n",
+ " 'Iran, Islamic Rep.': 'Iran (Islamic Republic of)',\n",
+ " 'Korea, Rep.': 'Republic of Korea',\n",
+ " 'United States': 'US',\n",
+ " 'United Kingdom': 'UK',\n",
+ " 'Hong Kong SAR, China': 'Hong Kong SAR',\n",
+ " 'Egypt, Arab Rep.': 'Egypt',\n",
+ " 'Vietnam': 'Viet Nam',\n",
+ " 'Macao SAR, China': 'Macao SAR',\n",
+ " 'Slovak Republic': 'Slovakia',\n",
+ " 'Moldova': 'Republic of Moldova',\n",
+ " 'St. Martin (French part)': 'Saint Martin',\n",
+ " 'Brunei Darussalam': 'Brunei'\n",
+ "}\n",
+ "current_pop_ser = pop_df[['Country Name', '2018']].copy().replace(region_wb_jhu_map).set_index('Country Name')\n",
+ "data_pop_ser = current_pop_ser[current_pop_ser.index.isin(current_totals_df.index)]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are some regions that we cannot resolve, but we will just ignore these."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>confirmed</th>\n",
+ " <th>deaths</th>\n",
+ " <th>recovered</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Others</th>\n",
+ " <td>696</td>\n",
+ " <td>6</td>\n",
+ " <td>40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Taipei and environs</th>\n",
+ " <td>47</td>\n",
+ " <td>1</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>occupied Palestinian territory</th>\n",
+ " <td>25</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>French Guiana</th>\n",
+ " <td>5</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Martinique</th>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Holy See</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Saint Barthelemy</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " confirmed deaths recovered\n",
+ "Others 696 6 40\n",
+ "Taipei and environs 47 1 17\n",
+ "occupied Palestinian territory 25 0 0\n",
+ "French Guiana 5 0 0\n",
+ "Martinique 2 0 0\n",
+ "Holy See 1 0 0\n",
+ "Saint Barthelemy 1 0 0"
+ ]
+ },
+ "execution_count": 112,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "current_totals_df[current_totals_df.index.isin(data_pop_ser.index) == False]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {