diff --git a/notebooks/Preprocessing-Play.ipynb b/notebooks/Preprocessing-Play.ipynb
deleted file mode 100644
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--- a/notebooks/Preprocessing-Play.ipynb
+++ /dev/null
@@ -1,804 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Read in JHU CSSE data\n",
- "\n",
- "I will switch to [xarray](http://xarray.pydata.org/en/stable/), but ATM, it's easier like this..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "def read_jhu_covid_df(name):\n",
- " filename = f\"../data/covid-19_jhu-csse/time_series_19-covid-{name}.csv\"\n",
- " df = pd.read_csv(filename)\n",
- " df = df.set_index(['Province/State', 'Country/Region', 'Lat', 'Long'])\n",
- " df.columns = pd.to_datetime(df.columns)\n",
- " return df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "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": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "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": 5,
- "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>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": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "current_totals_df = current_region_totals_df(frames_map)\n",
- "current_totals_df[current_totals_df['confirmed'] > 100]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Read in World Bank data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "import zipfile\n",
- "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": "markdown",
- "metadata": {},
- "source": [
- "There is 2018 pop data for all countries/regions except Eritrea"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
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- " }\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": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pop_df[pd.isna(pop_df['2018'])]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Fix the country/region names that differ between the World Bank population data and the JHU CSSE data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "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')['2018']\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": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- " vertical-align: middle;\n",
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- " }\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": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "current_totals_df[current_totals_df.index.isin(data_pop_ser.index) == False]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Compute rates per 100,000 for regions with more than 100 cases"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- "</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>Italy</th>\n",
- " <td>16.794282</td>\n",
- " <td>1.044161</td>\n",
- " <td>1.198055</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Republic of Korea</th>\n",
- " <td>14.550136</td>\n",
- " <td>0.104580</td>\n",
- " <td>0.478355</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Iran (Islamic Republic of)</th>\n",
- " <td>9.831264</td>\n",
- " <td>0.355745</td>\n",
- " <td>3.338620</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Norway</th>\n",
- " <td>7.526810</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.018817</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Bahrain</th>\n",
- " <td>7.008874</td>\n",
- " <td>0.000000</td>\n",
- " <td>1.401775</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Mainland China</th>\n",
- " <td>5.798468</td>\n",
- " <td>0.225169</td>\n",
- " <td>4.315697</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Switzerland</th>\n",
- " <td>5.765250</td>\n",
- " <td>0.035226</td>\n",
- " <td>0.035226</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Denmark</th>\n",
- " <td>4.519231</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.017249</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Spain</th>\n",
- " <td>3.627705</td>\n",
- " <td>0.074908</td>\n",
- " <td>0.068488</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Sweden</th>\n",
- " <td>3.486143</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.009820</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Singapore</th>\n",
- " <td>2.837546</td>\n",
- " <td>0.000000</td>\n",
- " <td>1.383303</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>France</th>\n",
- " <td>2.663194</td>\n",
- " <td>0.049263</td>\n",
- " <td>0.017914</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Belgium</th>\n",
- " <td>2.337580</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.008755</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Netherlands</th>\n",
- " <td>2.216932</td>\n",
- " <td>0.023214</td>\n",
- " <td>0.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Austria</th>\n",
- " <td>2.057186</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.045213</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Germany</th>\n",
- " <td>1.756947</td>\n",
- " <td>0.002412</td>\n",
- " <td>0.021706</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Hong Kong SAR</th>\n",
- " <td>1.610522</td>\n",
- " <td>0.040263</td>\n",
- " <td>0.872366</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>UK</th>\n",
- " <td>0.574531</td>\n",
- " <td>0.009024</td>\n",
- " <td>0.027072</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>US</th>\n",
- " <td>0.510442</td>\n",
- " <td>0.017117</td>\n",
- " <td>0.004585</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Japan</th>\n",
- " <td>0.459183</td>\n",
- " <td>0.007903</td>\n",
- " <td>0.079824</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Australia</th>\n",
- " <td>0.428131</td>\n",
- " <td>0.012004</td>\n",
- " <td>0.084026</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Malaysia</th>\n",
- " <td>0.409153</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.076121</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " confirmed deaths recovered\n",
- "Italy 16.794282 1.044161 1.198055\n",
- "Republic of Korea 14.550136 0.104580 0.478355\n",
- "Iran (Islamic Republic of) 9.831264 0.355745 3.338620\n",
- "Norway 7.526810 0.000000 0.018817\n",
- "Bahrain 7.008874 0.000000 1.401775\n",
- "Mainland China 5.798468 0.225169 4.315697\n",
- "Switzerland 5.765250 0.035226 0.035226\n",
- "Denmark 4.519231 0.000000 0.017249\n",
- "Spain 3.627705 0.074908 0.068488\n",
- "Sweden 3.486143 0.000000 0.009820\n",
- "Singapore 2.837546 0.000000 1.383303\n",
- "France 2.663194 0.049263 0.017914\n",
- "Belgium 2.337580 0.000000 0.008755\n",
- "Netherlands 2.216932 0.023214 0.000000\n",
- "Austria 2.057186 0.000000 0.045213\n",
- "Germany 1.756947 0.002412 0.021706\n",
- "Hong Kong SAR 1.610522 0.040263 0.872366\n",
- "UK 0.574531 0.009024 0.027072\n",
- "US 0.510442 0.017117 0.004585\n",
- "Japan 0.459183 0.007903 0.079824\n",
- "Australia 0.428131 0.012004 0.084026\n",
- "Malaysia 0.409153 0.000000 0.076121"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "current_per_100000_df = current_totals_df[current_totals_df['confirmed'] > 100]\n",
- "current_per_100000_df = current_per_100000_df.div(data_pop_ser, 'index').mul(100000).dropna()\n",
- "current_per_100000_df.sort_values('confirmed', ascending=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/openzh-covid-19-example.ipynb b/notebooks/examples/openzh-covid-19-example.ipynb
similarity index 100%
rename from notebooks/openzh-covid-19-example.ipynb
rename to notebooks/examples/openzh-covid-19-example.ipynb
diff --git a/notebooks/CompileGeoData.ipynb b/notebooks/process/CompileGeoData.ipynb
similarity index 100%
rename from notebooks/CompileGeoData.ipynb
rename to notebooks/process/CompileGeoData.ipynb
diff --git a/notebooks/process/README.md b/notebooks/process/README.md
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+# notebook/processing
+
+Notebooks used to process data.
\ No newline at end of file
diff --git a/notebooks/ToRates.ipynb b/notebooks/process/ToRates.ipynb
similarity index 100%
rename from notebooks/ToRates.ipynb
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