From fdd07efa4d36da225b49656a546f6a02c0593b41 Mon Sep 17 00:00:00 2001
From: Rok Roskar <rokroskar@gmail.com>
Date: Fri, 24 Apr 2020 00:30:43 +0200
Subject: [PATCH] feat: add summary notebook

---
 README.md                        |  34 +-
 notebooks/datasets_summary.ipynb | 570 +++++++++++++++++++++++++++++++
 2 files changed, 593 insertions(+), 11 deletions(-)
 create mode 100644 notebooks/datasets_summary.ipynb

diff --git a/README.md b/README.md
index 3e172d41a..8e5302ca4 100644
--- a/README.md
+++ b/README.md
@@ -5,8 +5,13 @@ the spread and effect of COVID-19. The goal is to provide a central place where
 data, analysis, and discussion can be conducted and shared by a global community
 struggling to make sense of the current public health emergency.
 
-For each data source, we provide a simple summary notebook with interactive
-figures:
+The project includes simple tools that make it very easy to work with the
+heterogeneous case data from various sources using common code. For a simple
+example, have a look at the [Global and Regional COVID-19 summary
+notebook](https://renkulab.io/projects/covid-19/covid-19-public-data/files/blob/runs/datasets_summary.run.ipynb).
+
+For each data source, we provide a summary notebook with interactive figures
+that can be used as starting points for further exploration:
 
 * [Summary of global data from from JHU CSSE](https://renkulab.io/projects/covid-19/covid-19-public-data/files/blob/runs/Dashboard.run.ipynb)
 * [Global data from from ECDC](https://renkulab.io/projects/covid-19/covid-19-public-data/files/blob/runs/covid-19-ecdc.run.ipynb)
@@ -20,13 +25,19 @@ of all the data can be found in the table below.
 
 ## Getting started with the project
 
-The goal of this project is not to build yet another dashboard - instead, it
-provides a place for easy access to the relevant data for the purposes of
-analysis and collaboration. This project is envisioned to be hands-on; with a
-few clicks you can be analysing the latest data from around the globe.
+The project intends provides a place for easy access to the relevant data for
+the purposes of analysis and collaboration. It is envisioned to be hands-on;
+with a few clicks you can be analysing the latest data from around the globe. We
+hope that this will make it easier for domain specialists to team up with
+analysts and data scientists to tackle the open questions together.
+
+The simplest way to start is to simply browse the available notebooks and take a
+look at the provided visualizations. If you would like something to be changed
+or to include some different data, feel free to start a
+[discussion](https://renkulab.io/projects/covid-19/covid-19-public-data/collaboration/issues)!
 
-The simplest way to start is to make an account or logging in and forking the
-project. Then, [start an interactive
+When you are ready to get your hands on the data, start by making an account or
+logging in on https://renkulab.io and forking the project. Then, [start an interactive
 environment](https://renkulab.io/projects/covid-19/covid-19-public-data/environments/new)
 and use the hosted JupyterLab or RStudio to explore the data.
 
@@ -60,9 +71,10 @@ spain_df = converter.read_convert('./data/covid-19-spain')
 ```
 
 The resulting DataFrames have exactly the same structure so they can be used
-interchangably in any analysis or plotting code. See the [standardization
-notebook]('https://renkulab.io/projects/covid-19/covid-19-public-data/files/blob/notebooks/process/standardize_datasets.ipynb') for a more complete
-example.
+interchangably in any analysis or plotting code. See the [Global and Regional
+COVID-19 summary
+notebook](https://renkulab.io/projects/covid-19/covid-19-public-data/files/blob/runs/datasets_summary.run.ipynb)
+for a more complete example.
 
 ### Updating your branch or fork
 
diff --git a/notebooks/datasets_summary.ipynb b/notebooks/datasets_summary.ipynb
new file mode 100644
index 000000000..1d67f62a3
--- /dev/null
+++ b/notebooks/datasets_summary.ipynb
@@ -0,0 +1,570 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "version": "3.7.7-final"
+  },
+  "orig_nbformat": 2,
+  "file_extension": ".py",
+  "mimetype": "text/x-python",
+  "name": "python",
+  "npconvert_exporter": "python",
+  "pygments_lexer": "ipython3",
+  "version": 3,
+  "kernelspec": {
+   "name": "python37764bitvenvvenv814492364d964019a25eb1cf3dc3e99c",
+   "display_name": "Python 3.7.7 64-bit ('.venv': venv)"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import display, HTML\n",
+    "renkulab_credits = HTML(\"\"\"<p style=\"font-size: smaller\">Analysis and Visualization:\n",
+    "  <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">\n",
+    "  Covid-19 Public Data Collaboration Project @ renkulab.io\n",
+    "  </a>\n",
+    "  </p>\n",
+    "\"\"\")\n",
+    "ecdc_credits=HTML('''\n",
+    "<p style=\"font-size: smaller\">Data Sources: \n",
+    "  <a href=\"https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide\">ECDC</a>\n",
+    "</p>\n",
+    "'''\n",
+    ")\n",
+    "all_credits=HTML(\"\"\"\n",
+    "<p style=\"font-size: smaller\">Data Sources: \n",
+    "  <a href=\"https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide\">ECDC</a>, \n",
+    "  <a href=\"https://github.com/pcm-dpc/COVID-19\">Italian Civil Protection</a>, \n",
+    "  <a href=\"https://github.com/nytimes/covid-19-data\">The New York Times</a>, \n",
+    "  <a href=\"https://github.com/datadista/datasets\">Datadista</a>.\n",
+    "</p>\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Global and Regional Covid-19 Case Count Data\n",
+    "\n",
+    "In this project we collect data about the ongoing Covid-19 pandemic from a variety of sources. Some aggregate worldwide country-level statistics while others provide fine-grained data about regions of individual countries. \n",
+    "\n",
+    "Here we summarize some of the available data using the simple standardized views we have built for these datasets, which allow us to use the same analysis tools across the heterogeneous datasets. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard python imports - we will be using pandas for data wrangling and altair for visualization\n",
+    "from datetime import datetime\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import altair as alt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing our custom tools for the datasets in this repository. \n",
+    "from covid_19_utils import helper, plotting\n",
+    "from covid_19_utils.converters import CaseConverter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = \"../data\"\n",
+    "atlas_path = \"../data/atlas\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Instantiating the CaseConverter to read in the data.\n",
+    "# It takes a path to the atlas dataset as an argument - the atlas holds various population and country metadata data. \n",
+    "converter = CaseConverter(atlas_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Global deaths attributed to Covid-19\n",
+    "\n",
+    "How deadly has the pandemic been over the past few months? Where has it proven to be the deadliest? We investigate the data collected by the [European Center for Disease Prevention and Control](https://www.ecdc.europa.eu/en) to gain some basic understanding of these questions. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read in the data from the European Center for Disease Prevention and Control\n",
+    "global_df = converter.read_convert(Path(data_path) / \"covid-19-ecdc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\"deceased_100k\": 26.895357643230486, \"deceased_daily\": 745, \"deceased\": 6499, \"positive_100k\": 835.328332147487, \"positive_daily\": 12644, \"positive\": 166686}, {\"date\": \"2020-03-17T00:00:00\", \"deceased_100k\": 33.89270093773523, \"deceased_daily\": 596, \"deceased\": 7095, \"positive_100k\": 916.4218832373579, \"positive_daily\": 12676, \"positive\": 179378}, {\"date\": \"2020-03-18T00:00:00\", \"deceased_100k\": 41.33940485104112, \"deceased_daily\": 789, \"deceased\": 7884, \"positive_100k\": 1002.0453384097254, \"positive_daily\": 14742, \"positive\": 194132}, {\"date\": \"2020-03-19T00:00:00\", \"deceased_100k\": 52.13768569150822, \"deceased_daily\": 951, \"deceased\": 8835, \"positive_100k\": 1198.8613004195056, \"positive_daily\": 18313, \"positive\": 212445}, {\"date\": \"2020-03-20T00:00:00\", \"deceased_100k\": 54.803276206731084, \"deceased_daily\": 1041, \"deceased\": 9876, \"positive_100k\": 1618.3383138444194, \"positive_daily\": 29223, \"positive\": 241672}, {\"date\": \"2020-03-21T00:00:00\", \"deceased_100k\": 59.35605195390208, \"deceased_daily\": 1366, \"deceased\": 11242, \"positive_100k\": 1905.3729257194093, \"positive_daily\": 28742, \"positive\": 270409}, {\"date\": \"2020-03-22T00:00:00\", \"deceased_100k\": 81.84219084525584, \"deceased_daily\": 1700, \"deceased\": 12942, \"positive_100k\": 2134.817794927858, \"positive_daily\": 34119, \"positive\": 304528}, {\"date\": \"2020-03-23T00:00:00\", \"deceased_100k\": 85.20559876654839, \"deceased_daily\": 1654, \"deceased\": 14596, \"positive_100k\": 2472.3566511749004, \"positive_daily\": 32999, \"positive\": 337527}, {\"date\": \"2020-03-24T00:00:00\", \"deceased_100k\": 89.3330274862667, \"deceased_daily\": 1760, \"deceased\": 16356, \"positive_100k\": 2809.5306759928503, \"positive_daily\": 39727, \"positive\": 377261}, {\"date\": \"2020-03-25T00:00:00\", \"deceased_100k\": 97.78487817530495, \"deceased_daily\": 2202, \"deceased\": 18558, \"positive_100k\": 3090.0887908593004, \"positive_daily\": 38919, \"positive\": 416175}, {\"date\": \"2020-03-26T00:00:00\", \"deceased_100k\": 103.94922436948765, \"deceased_daily\": 2428, \"deceased\": 20986, \"positive_100k\": 3514.950515554726, \"positive_daily\": 51209, \"positive\": 467391}, {\"date\": \"2020-03-27T00:00:00\", \"deceased_100k\": 115.88717657369988, \"deceased_daily\": 2681, \"deceased\": 23667, \"positive_100k\": 4430.578566742671, \"positive_daily\": 59742, \"positive\": 527133}, {\"date\": \"2020-03-28T00:00:00\", \"deceased_100k\": 125.15342317406983, \"deceased_daily\": 3319, \"deceased\": 26986, \"positive_100k\": 4879.04199751607, \"positive_daily\": 63965, \"positive\": 591098}, {\"date\": \"2020-03-29T00:00:00\", \"deceased_100k\": 138.28342498924474, \"deceased_daily\": 3459, \"deceased\": 30445, \"positive_100k\": 5365.526831107294, \"positive_daily\": 65119, \"positive\": 656217}, {\"date\": \"2020-03-30T00:00:00\", \"deceased_100k\": 156.54477643430639, \"deceased_daily\": 3124, \"deceased\": 33569, \"positive_100k\": 5737.248887019467, \"positive_daily\": 58502, \"positive\": 714719}, {\"date\": \"2020-03-31T00:00:00\", \"deceased_100k\": 170.07233539985168, \"deceased_daily\": 3700, \"deceased\": 37268, \"positive_100k\": 5950.109090560835, \"positive_daily\": 61791, \"positive\": 776418}, {\"date\": \"2020-04-01T00:00:00\", \"deceased_100k\": 192.47755045195905, \"deceased_daily\": 4628, \"deceased\": 41897, \"positive_100k\": 6320.406382544007, \"positive_daily\": 74407, \"positive\": 850912}, {\"date\": \"2020-04-02T00:00:00\", \"deceased_100k\": 211.9396675889544, \"deceased_daily\": 4980, \"deceased\": 46877, \"positive_100k\": 6810.140956941218, \"positive_daily\": 76877, \"positive\": 927794}, {\"date\": \"2020-04-03T00:00:00\", \"deceased_100k\": 242.01092092785726, \"deceased_daily\": 4693, \"deceased\": 51570, \"positive_100k\": 7320.388415408702, \"positive_daily\": 77586, \"positive\": 1005380}, {\"date\": \"2020-04-04T00:00:00\", \"deceased_100k\": 268.4565315487281, \"deceased_daily\": 6603, \"deceased\": 58173, \"positive_100k\": 7902.496754701269, \"positive_daily\": 81760, \"positive\": 1087140}, {\"date\": \"2020-04-05T00:00:00\", \"deceased_100k\": 292.3701265311368, \"deceased_daily\": 6093, \"deceased\": 64265, \"positive_100k\": 8340.401913526444, \"positive_daily\": 86479, \"positive\": 1173464}, {\"date\": \"2020-04-06T00:00:00\", \"deceased_100k\": 317.5052729694895, \"deceased_daily\": 4655, \"deceased\": 68921, \"positive_100k\": 8769.886641724202, \"positive_daily\": 71281, \"positive\": 1244900}, {\"date\": \"2020-04-07T00:00:00\", \"deceased_100k\": 339.74919494048675, \"deceased_daily\": 5137, \"deceased\": 74058, \"positive_100k\": 9151.297631479667, \"positive_daily\": 71390, \"positive\": 1316290}, {\"date\": \"2020-04-08T00:00:00\", \"deceased_100k\": 378.01687896792276, \"deceased_daily\": 7411, \"deceased\": 81469, \"positive_100k\": 9512.202394249607, \"positive_daily\": 74902, \"positive\": 1391192}, {\"date\": \"2020-04-09T00:00:00\", \"deceased_100k\": 397.8642020928085, \"deceased_daily\": 6340, \"deceased\": 87809, \"positive_100k\": 10082.421757026395, \"positive_daily\": 84930, \"positive\": 1476122}, {\"date\": \"2020-04-10T00:00:00\", \"deceased_100k\": 421.95237122083824, \"deceased_daily\": 7223, \"deceased\": 95032, \"positive_100k\": 10597.53492742962, \"positive_daily\": 87039, \"positive\": 1563161}, {\"date\": \"2020-04-11T00:00:00\", \"deceased_100k\": 445.8324179307481, \"deceased_daily\": 7049, \"deceased\": 102081, \"positive_100k\": 11007.637390342274, \"positive_daily\": 89349, \"positive\": 1652510}, {\"date\": \"2020-04-12T00:00:00\", \"deceased_100k\": 467.65537760019873, \"deceased_daily\": 6104, \"deceased\": 108185, \"positive_100k\": 11391.94414299366, \"positive_daily\": 81707, \"positive\": 1734217}, {\"date\": \"2020-04-13T00:00:00\", \"deceased_100k\": 494.80787332010505, \"deceased_daily\": 5321, \"deceased\": 113506, \"positive_100k\": 11702.218636527557, \"positive_daily\": 72390, \"positive\": 1806607}, {\"date\": \"2020-04-14T00:00:00\", \"deceased_100k\": 510.4432800090097, \"deceased_daily\": 5344, \"deceased\": 118850, \"positive_100k\": 11953.947625125384, \"positive_daily\": 66386, \"positive\": 1872993}, {\"date\": \"2020-04-15T00:00:00\", \"deceased_100k\": 532.0111577554856, \"deceased_daily\": 7118, \"deceased\": 125968, \"positive_100k\": 12256.38033000318, \"positive_daily\": 75233, \"positive\": 1948226}, {\"date\": \"2020-04-16T00:00:00\", \"deceased_100k\": 558.8898483411101, \"deceased_daily\": 10345, \"deceased\": 136313, \"positive_100k\": 12681.841840775016, \"positive_daily\": 81008, \"positive\": 2029234}, {\"date\": \"2020-04-17T00:00:00\", \"deceased_100k\": 591.4719630569, \"deceased_daily\": 8507, \"deceased\": 144820, \"positive_100k\": 13135.758272832369, \"positive_daily\": 84339, \"positive\": 2113573}, {\"date\": \"2020-04-18T00:00:00\", \"deceased_100k\": 615.6720451088817, \"deceased_daily\": 8263, \"deceased\": 153083, \"positive_100k\": 13445.48112288265, \"positive_daily\": 83324, \"positive\": 2196897}, {\"date\": \"2020-04-19T00:00:00\", \"deceased_100k\": 635.5567727972367, \"deceased_daily\": 6421, \"deceased\": 159504, \"positive_100k\": 13809.158961723266, \"positive_daily\": 84121, \"positive\": 2281018}, {\"date\": \"2020-04-20T00:00:00\", \"deceased_100k\": 654.5443420600661, \"deceased_daily\": 5145, \"deceased\": 164649, \"positive_100k\": 14095.784712428986, \"positive_daily\": 74139, \"positive\": 2355157}, {\"date\": \"2020-04-21T00:00:00\", \"deceased_100k\": 674.4809259040974, \"deceased_daily\": 5203, \"deceased\": 169852, \"positive_100k\": 14438.969742471756, \"positive_daily\": 76037, \"positive\": 2431194}]}}, {\"mode\": \"vega-lite\"});\n</script>",
+      "text/plain": "alt.LayerChart(...)"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<IPython.core.display.HTML object>",
+      "text/html": "\n<p style=\"font-size: smaller\">Data Sources: \n  <a href=\"https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide\">ECDC</a>\n</p>\n"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<IPython.core.display.HTML object>",
+      "text/html": "<p style=\"font-size: smaller\">Analysis and Visualization:\n  <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">\n  Covid-19 Public Data Collaboration Project @ renkulab.io\n  </a>\n  </p>\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# Set a start date of Feb 1st and plot the daily deaths globally\n",
+    "start_date = datetime.fromisoformat('2020-02-01')\n",
+    "global_daily_totals = global_df.groupby('date').sum().reset_index()\n",
+    "\n",
+    "# basic chart\n",
+    "base = alt.Chart(global_daily_totals.loc[global_daily_totals.date >= start_date])\n",
+    "\n",
+    "chart = base.mark_line().encode(\n",
+    "    x=alt.X('date', title='Date'),\n",
+    "    y=alt.Y('deceased_daily', title='Daily deaths')\n",
+    ")\n",
+    "\n",
+    "# 7-day mean - we use the altair window function to compute this directly\n",
+    "running_mean = base.transform_window(\n",
+    "    mean_deceased='mean(deceased_daily)',\n",
+    "    frame=[-3, 3],\n",
+    ").mark_area(opacity=0.3, color='orange').encode(\n",
+    "    x='date',\n",
+    "    y='mean_deceased:Q'\n",
+    ")\n",
+    "\n",
+    "# combine the two charts together and set the size and title\n",
+    "combined = (\n",
+    "    chart +\n",
+    "    running_mean +\n",
+    "    running_mean.mark_line(color='orange')\n",
+    ").properties(\n",
+    "    width=600,\n",
+    "    height=350,\n",
+    "    title=\"Daily global deaths and 7-day mean\"\n",
+    ")\n",
+    "\n",
+    "display(combined)\n",
+    "display(ecdc_credits)\n",
+    "display(renkulab_credits)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Top ten countries by total number of deaths attributed to Covid-19"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sort countries by their total deaths \n",
+    "country_death_sort = global_df.groupby(\n",
+    "    \"country_label\"\n",
+    ")['deceased'].max().sort_values(\n",
+    "    ascending=False\n",
+    ").index.to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "                    Country  Deaths  Deaths per 100k  Positive cases  \\\n0  United States of America   42539        13.002211          787752   \n1                     Italy   24114        39.903174          181228   \n2                     Spain   20852        44.628268          200210   \n3                    France   20265        30.252028          114657   \n4            United Kingdom   16509        24.829674          124743   \n5                   Belgium    5828        51.024035           39983   \n6                      Iran    5209         6.367950           83505   \n7                     China    4636         0.332871           83849   \n8                   Germany    4598         5.544574          143457   \n9               Netherlands    3751        21.768883           33405   \n\n   Positive cases per 100k  \n0               240.779466  \n1               299.891035  \n2               428.497294  \n3               171.162438  \n4               187.614518  \n5               350.050446  \n6               102.084016  \n7                 6.020478  \n8               172.989985  \n9               193.865516  ",
+      "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</th>\n      <th>Deaths</th>\n      <th>Deaths per 100k</th>\n      <th>Positive cases</th>\n      <th>Positive cases per 100k</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>United States of America</td>\n      <td>42539</td>\n      <td>13.002211</td>\n      <td>787752</td>\n      <td>240.779466</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Italy</td>\n      <td>24114</td>\n      <td>39.903174</td>\n      <td>181228</td>\n      <td>299.891035</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Spain</td>\n      <td>20852</td>\n      <td>44.628268</td>\n      <td>200210</td>\n      <td>428.497294</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>France</td>\n      <td>20265</td>\n      <td>30.252028</td>\n      <td>114657</td>\n      <td>171.162438</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>United Kingdom</td>\n      <td>16509</td>\n      <td>24.829674</td>\n      <td>124743</td>\n      <td>187.614518</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Belgium</td>\n      <td>5828</td>\n      <td>51.024035</td>\n      <td>39983</td>\n      <td>350.050446</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Iran</td>\n      <td>5209</td>\n      <td>6.367950</td>\n      <td>83505</td>\n      <td>102.084016</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>China</td>\n      <td>4636</td>\n      <td>0.332871</td>\n      <td>83849</td>\n      <td>6.020478</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Germany</td>\n      <td>4598</td>\n      <td>5.544574</td>\n      <td>143457</td>\n      <td>172.989985</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Netherlands</td>\n      <td>3751</td>\n      <td>21.768883</td>\n      <td>33405</td>\n      <td>193.865516</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "# define some column translations for nicer display and print the top ten\n",
+    "columns = [\"deceased\", \"deceased_100k\", \"positive\", \"positive_100k\"]\n",
+    "pretty_print_columns = {\n",
+    "    \"deceased\": \"Deaths\",\n",
+    "    \"deceased_daily\": \"Daily deaths\",\n",
+    "    \"deceased_100k\": \"Deaths per 100k\",\n",
+    "    \"positive\": \"Positive cases\", \n",
+    "    \"positive_daily\": \"Daily positive cases\",\n",
+    "    \"positive_100k\": \"Positive cases per 100k\",\n",
+    "    \"country_label\": \"Country\",\n",
+    "    \"county_state\": \"County, State\",\n",
+    "    \"region_label\": \"Region\"\n",
+    "    }\n",
+    "\n",
+    "# global data sorted by cumulative deaths\n",
+    "global_sorted = global_df.set_index([\"country_label\"]).loc[country_death_sort]\n",
+    "\n",
+    "# display a table of cumulative data for the top ten countries by total deaths\n",
+    "global_sorted.loc[\n",
+    "    global_sorted.date == global_sorted.date.max()\n",
+    "][columns].reset_index().rename(\n",
+    "    columns=pretty_print_columns\n",
+    ")[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Growth of deaths in hardest-hit regions\n",
+    "\n",
+    "For the three top countries in the list above we have regional datasets - in each of those, specific regions have contributed disproportionately to the death counts in their respective countries. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read in the regional data for the U.S., Italy and Spain\n",
+    "us_df = converter.read_convert(Path(data_path) / \"covid-19-us-nyt\")\n",
+    "spain_df = converter.read_convert(Path(data_path) / \"covid-19-spain\")\n",
+    "italy_df = converter.read_convert(Path(data_path) / \"covid-19-italy/dpc-covid19-ita-regioni.csv\")\n",
+    "\n",
+    "# In the U.S. there are counties with the same name in different states - create a unique key\n",
+    "us_df['county_state'] = us_df.apply(lambda row: f\"{row['admin2_label']}, {row['region_label']}\", axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In each country, find the three most affected regions. \n",
+    "# Note that in these data we have two levels of sub-regions. \n",
+    "# For the U.S. data this means that \"regions\" are \"states\" and \"admin_2\" refers to \"counties\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we define a simple function to return the top five \"keys\" based on a particular \"field\"\n",
+    "\n",
+    "def get_top5(df,key=\"region_label\",field=\"deceased\"):\n",
+    "    \"\"\"Return the top 5 keys\"\"\"\n",
+    "    return df.groupby(key).last().sort_values(by=field, ascending=False).reset_index().iloc[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<IPython.core.display.HTML object>",
+      "text/html": "\n        <h3>\n        Top five U.S. counties and regions in Spain and Italy \n        by cummulative deaths attributed to Covid-19.\n        </h3>\n        "
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<IPython.core.display.HTML object>",
+      "text/html": "\n        <h4>United States</h4>\n        <p style=\"font-size: smaller\">(based on data from Apr. 20, 2020)</p>\n        <table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>County, State</th>\n      <th>Deaths</th>\n      <th>Deaths per 100k</th>\n      <th>Positive cases</th>\n      <th>Positive cases per 100k</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>New York City, New York</td>\n      <td>10009</td>\n      <td>120.057811</td>\n      <td>136816</td>\n      <td>1641.105952</td>\n    </tr>\n    <tr>\n      <td>Nassau, New York</td>\n      <td>1638</td>\n      <td>120.714204</td>\n      <td>30677</td>\n      <td>2260.775106</td>\n    </tr>\n    <tr>\n      <td>Wayne, Michigan</td>\n      <td>1148</td>\n      <td>65.624637</td>\n      <td>13912</td>\n      <td>795.269996</td>\n    </tr>\n    <tr>\n      <td>Cook, Illinois</td>\n      <td>915</td>\n      <td>17.766187</td>\n      <td>22101</td>\n      <td>429.126216</td>\n    </tr>\n    <tr>\n      <td>Suffolk, New York</td>\n      <td>887</td>\n      <td>60.070391</td>\n      <td>27662</td>\n      <td>1873.356445</td>\n    </tr>\n  </tbody>\n</table>\n\n        <h4>Spain</h4>\n        <p style=\"font-size: smaller\">(based on data from Apr. 21, 2020)</p>\n        <table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>Region</th>\n      <th>Deaths</th>\n      <th>Deaths per 100k</th>\n      <th>Positive cases</th>\n      <th>Positive cases per 100k</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>Madrid</td>\n      <td>7460.0</td>\n      <td>111.979242</td>\n      <td>57997</td>\n      <td>870.571060</td>\n    </tr>\n    <tr>\n      <td>Cataluña</td>\n      <td>4152.0</td>\n      <td>54.491807</td>\n      <td>43112</td>\n      <td>565.811850</td>\n    </tr>\n    <tr>\n      <td>Castilla-La Mancha</td>\n      <td>2075.0</td>\n      <td>98.512532</td>\n      <td>17045</td>\n      <td>809.227040</td>\n    </tr>\n    <tr>\n      <td>Castilla y León</td>\n      <td>1521.0</td>\n      <td>62.885177</td>\n      <td>16259</td>\n      <td>672.222282</td>\n    </tr>\n    <tr>\n      <td>País Vasco</td>\n      <td>1103.0</td>\n      <td>50.883261</td>\n      <td>12810</td>\n      <td>590.947024</td>\n    </tr>\n  </tbody>\n</table>\n\n        <h4>Italy</h4>\n        <p style=\"font-size: smaller\">(based on data from Apr. 21, 2020)</p>\n        <table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>Region</th>\n      <th>Deaths</th>\n      <th>Deaths per 100k</th>\n      <th>Positive cases</th>\n      <th>Positive cases per 100k</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>Lombardy</td>\n      <td>12579</td>\n      <td>124.946685</td>\n      <td>67931</td>\n      <td>674.755803</td>\n    </tr>\n    <tr>\n      <td>Emilia-Romagna</td>\n      <td>3147</td>\n      <td>70.568813</td>\n      <td>23092</td>\n      <td>517.818569</td>\n    </tr>\n    <tr>\n      <td>Piedmont</td>\n      <td>2485</td>\n      <td>57.042434</td>\n      <td>21955</td>\n      <td>503.970475</td>\n    </tr>\n    <tr>\n      <td>Veneto</td>\n      <td>1154</td>\n      <td>23.422826</td>\n      <td>16404</td>\n      <td>332.953237</td>\n    </tr>\n    <tr>\n      <td>Liguria</td>\n      <td>990</td>\n      <td>63.844606</td>\n      <td>6764</td>\n      <td>436.206986</td>\n    </tr>\n  </tbody>\n</table>\n        "
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# get the top 5 U.S. counties\n",
+    "top5_us = get_top5(us_df, key=\"county_state\")\n",
+    "top5_spain = get_top5(spain_df)\n",
+    "top5_italy = get_top5(italy_df)\n",
+    "date = top5_spain.iloc[0].date.strftime(\"%b. %d, %Y\")\n",
+    "\n",
+    "# we will use HTML renderings of these DataFrames to make them look nice on the final notebook\n",
+    "top5_us_html = top5_us[\n",
+    "    ['county_state']+columns\n",
+    "].rename(\n",
+    "    columns=pretty_print_columns\n",
+    ").to_html(\n",
+    "    index=False\n",
+    ")\n",
+    "\n",
+    "top5_spain_html = top5_spain[\n",
+    "    ['region_label']+columns\n",
+    "].rename(\n",
+    "    columns=pretty_print_columns\n",
+    ").to_html(\n",
+    "    index=False\n",
+    ")\n",
+    "\n",
+    "top5_italy_html = top5_italy[\n",
+    "    ['region_label']+columns\n",
+    "].rename(\n",
+    "    columns=pretty_print_columns\n",
+    ").to_html(\n",
+    "    index=False\n",
+    ")\n",
+    "\n",
+    "# display some generic text - we use HTML display for consistency with the rest of the notebook\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "        <h3>\n",
+    "        Top five U.S. counties and regions in Spain and Italy \n",
+    "        by cummulative deaths attributed to Covid-19.\n",
+    "        </h3>\n",
+    "        \"\"\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        f\"\"\"\n",
+    "        <h4>United States</h4>\n",
+    "        <p style=\"font-size: smaller\">(based on data from {top5_us.iloc[0].date.strftime(\"%b. %d, %Y\")})</p>\n",
+    "        {top5_us_html}\n",
+    "\n",
+    "        <h4>Spain</h4>\n",
+    "        <p style=\"font-size: smaller\">(based on data from {top5_spain.iloc[0].date.strftime(\"%b. %d, %Y\")})</p>\n",
+    "        {top5_spain_html}\n",
+    "\n",
+    "        <h4>Italy</h4>\n",
+    "        <p style=\"font-size: smaller\">(based on data from {top5_italy.iloc[0].date.strftime(\"%b. %d, %Y\")})</p>\n",
+    "        {top5_italy_html}\n",
+    "        \"\"\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    ""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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See the javascript console for the full traceback.`));\n    }\n\n    if(typeof define === \"function\" && define.amd) {\n      requirejs.config({paths});\n      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n    } else if (typeof vegaEmbed === \"function\") {\n      displayChart(vegaEmbed);\n    } else {\n      loadScript(\"vega\")\n        .then(() => loadScript(\"vega-lite\"))\n        .then(() => loadScript(\"vega-embed\"))\n        .catch(showError)\n        .then(() => displayChart(vegaEmbed));\n    }\n  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"title\": {\"anchor\": \"start\"}}, \"data\": {\"name\": \"data-e04cb19009fa635f6381fe8cbe26ccec\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"plot_key\", \"legend\": {\"title\": \"Region\"}}, \"opacity\": {\"condition\": {\"value\": 1, \"selection\": \"selector026\"}, \"value\": 0.2}, \"tooltip\": 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\"deceased_100k\": 4.384168442999112, \"deceased_daily\": \"\", \"deceased\": 216.0, \"positive_100k\": 120.72700879147554, \"positive_daily\": \"\", \"positive\": 5948, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 1343.2199037999778, \"tested\": 66178, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-03-25T17:00:00\", \"deceased_100k\": 5.236645640248939, \"deceased_daily\": \"\", \"deceased\": 258.0, \"positive_100k\": 130.75376439722353, \"positive_daily\": \"\", \"positive\": 6442, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 1438.5958645113337, \"tested\": 70877, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-03-26T17:00:00\", \"deceased_100k\": 5.82526084787382, \"deceased_daily\": \"\", \"deceased\": 287.0, \"positive_100k\": 140.7602229268465, \"positive_daily\": \"\", \"positive\": 6935, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 1618.8744946535473, \"tested\": 79759, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-03-27T17:00:00\", \"deceased_100k\": 6.352984827123715, \"deceased_daily\": \"\", \"deceased\": 313.0, \"positive_100k\": 152.1671797090942, \"positive_daily\": \"\", \"positive\": 7497, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 1697.3835851050312, \"tested\": 83627, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-03-28T17:00:00\", \"deceased_100k\": 7.347541557248513, \"deceased_daily\": \"\", \"deceased\": 362.0, \"positive_100k\": 160.9558136712174, \"positive_daily\": \"\", \"positive\": 7930, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 1814.1526640521324, \"tested\": 89380, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-03-29T17:00:00\", \"deceased_100k\": 7.956453840998389, \"deceased_daily\": \"\", \"deceased\": 392.0, \"positive_100k\": 169.64296225271565, \"positive_daily\": \"\", \"positive\": 8358, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 1923.8380634316106, \"tested\": 94784, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-03-30T17:00:00\", \"deceased_100k\": 8.382692439623304, \"deceased_daily\": \"\", \"deceased\": 413.0, \"positive_100k\": 177.07169211446413, \"positive_daily\": \"\", \"positive\": 8724, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 2028.5100850082144, \"tested\": 99941, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-03-31T17:00:00\", \"deceased_100k\": 9.68170531162304, \"deceased_daily\": \"\", \"deceased\": 477.0, \"positive_100k\": 185.81973192433736, \"positive_daily\": \"\", \"positive\": 9155, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 2156.3207733673134, \"tested\": 106238, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-01T17:00:00\", \"deceased_100k\": 10.12824098637295, \"deceased_daily\": \"\", \"deceased\": 499.0, \"positive_100k\": 195.35935770308546, \"positive_daily\": \"\", \"positive\": 9625, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 2288.4141447887864, \"tested\": 112746, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-02T17:00:00\", \"deceased_100k\": 10.798044498497815, \"deceased_daily\": \"\", \"deceased\": 532.0, \"positive_100k\": 205.2237366998334, \"positive_daily\": \"\", \"positive\": 10111, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 2442.1441993595054, \"tested\": 120320, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-03T17:00:00\", \"deceased_100k\": 11.60992754349765, \"deceased_daily\": \"\", \"deceased\": 572.0, \"positive_100k\": 212.388604571957, \"positive_daily\": \"\", \"positive\": 10464, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 2567.37715905073, \"tested\": 126490, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-04T17:00:00\", \"deceased_100k\": 12.320325207872505, \"deceased_daily\": \"\", \"deceased\": 607.0, \"positive_100k\": 219.6955519769555, \"positive_daily\": \"\", \"positive\": 10824, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 2705.3769796245774, \"tested\": 133289, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-05T17:00:00\", \"deceased_100k\": 12.807455034872405, \"deceased_daily\": \"\", \"deceased\": 631.0, \"positive_100k\": 227.85497657920388, \"positive_daily\": \"\", \"positive\": 11226, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 2860.060996773171, \"tested\": 140910, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-06T17:00:00\", \"deceased_100k\": 13.436664394747279, \"deceased_daily\": \"\", \"deceased\": 662.0, \"positive_100k\": 235.2025181364524, \"positive_daily\": \"\", \"positive\": 11588, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 2969.2186721733988, \"tested\": 146288, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-07T17:00:00\", \"deceased_100k\": 14.106467906872144, \"deceased_daily\": \"\", \"deceased\": 695.0, \"positive_100k\": 242.04263279057596, \"positive_daily\": \"\", \"positive\": 11925, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 3116.453662384119, \"tested\": 153542, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-08T17:00:00\", \"deceased_100k\": 14.938648027996976, \"deceased_daily\": \"\", \"deceased\": 736.0, \"positive_100k\": 251.88671471119898, \"positive_daily\": \"\", \"positive\": 12410, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 3313.436786177204, \"tested\": 163247, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-09T17:00:00\", \"deceased_100k\": 15.344589550496892, \"deceased_daily\": \"\", \"deceased\": 756.0, \"positive_100k\": 262.50208552457184, \"positive_daily\": \"\", \"positive\": 12933, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 3480.055484087295, \"tested\": 171456, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-10T17:00:00\", \"deceased_100k\": 16.095581367121742, \"deceased_daily\": \"\", \"deceased\": 793.0, \"positive_100k\": 272.40705867356985, \"positive_daily\": \"\", \"positive\": 13421, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 3667.6816557867573, \"tested\": 180700, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-11T17:00:00\", \"deceased_100k\": 16.866870259871586, \"deceased_daily\": \"\", \"deceased\": 831.0, \"positive_100k\": 279.4501440889434, \"positive_daily\": \"\", \"positive\": 13768, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 3874.955397175215, \"tested\": 190912, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-12T17:00:00\", \"deceased_100k\": 17.37429716299648, \"deceased_daily\": \"\", \"deceased\": 856.0, \"positive_100k\": 285.7219406115671, \"positive_daily\": \"\", \"positive\": 14077, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 4027.7923803964345, \"tested\": 198442, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-13T17:00:00\", \"deceased_100k\": 17.902021142246372, \"deceased_daily\": \"\", \"deceased\": 882.0, \"positive_100k\": 289.2536318573164, \"positive_daily\": \"\", \"positive\": 14251, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 4121.86932823579, \"tested\": 203077, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-14T17:00:00\", \"deceased_100k\": 18.389150969246277, \"deceased_daily\": \"\", \"deceased\": 906.0, \"positive_100k\": 292.92740263594067, \"positive_daily\": \"\", \"positive\": 14432, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 4239.612666836892, \"tested\": 208878, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-15T17:00:00\", \"deceased_100k\": 19.079251557496136, \"deceased_daily\": \"\", \"deceased\": 940.0, \"positive_100k\": 296.8244412519399, \"positive_daily\": \"\", \"positive\": 14624, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 4391.1506371861105, \"tested\": 216344, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-16T17:00:00\", \"deceased_100k\": 19.911431678620968, \"deceased_daily\": \"\", \"deceased\": 981.0, \"positive_100k\": 304.2531711136884, \"positive_daily\": \"\", \"positive\": 14990, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 4557.688146791702, \"tested\": 224549, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-17T17:00:00\", \"deceased_100k\": 20.82480010424578, \"deceased_daily\": \"\", \"deceased\": 1026.0, \"positive_100k\": 312.04724834568685, \"positive_daily\": \"\", \"positive\": 15374, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 4804.764454461278, \"tested\": 236722, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-18T17:00:00\", \"deceased_100k\": 21.494603616370647, \"deceased_daily\": \"\", \"deceased\": 1059.0, \"positive_100k\": 318.50171855343547, \"positive_daily\": \"\", \"positive\": 15692, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 5020.055540919108, \"tested\": 247329, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-19T17:00:00\", \"deceased_100k\": 22.06292174787053, \"deceased_daily\": \"\", \"deceased\": 1087.0, \"positive_100k\": 323.4339080518095, \"positive_daily\": \"\", \"positive\": 15935, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 5191.931181545573, \"tested\": 255797, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-20T17:00:00\", \"deceased_100k\": 22.570348650995427, \"deceased_daily\": \"\", \"deceased\": 1112.0, \"positive_100k\": 327.3309466678087, \"positive_daily\": \"\", \"positive\": 16127, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 5293.680424160178, \"tested\": 260810, \"county_state\": null, \"plot_key\": \"Veneto\"}, {\"admin2_label\": \"\", \"admin2\": \"\", \"country_label\": \"Italy\", \"country\": \"ITA\", \"date\": \"2020-04-21T17:00:00\", \"deceased_100k\": 23.422825848245257, \"deceased_daily\": \"\", \"deceased\": 1154.0, \"positive_100k\": 332.95323675443257, \"positive_daily\": \"\", \"positive\": 16404, \"region_iso\": \"IT-34\", \"region_label\": \"Veneto\", \"tested_100k\": 5441.016899751523, \"tested\": 268069, \"county_state\": null, \"plot_key\": \"Veneto\"}]}}, {\"mode\": \"vega-lite\"});\n</script>",
+      "text/plain": "alt.Chart(...)"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<IPython.core.display.HTML object>",
+      "text/html": "\n<p style=\"font-size: smaller\">Data Sources: \n  <a href=\"https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide\">ECDC</a>, \n  <a href=\"https://github.com/pcm-dpc/COVID-19\">Italian Civil Protection</a>, \n  <a href=\"https://github.com/nytimes/covid-19-data\">The New York Times</a>, \n  <a href=\"https://github.com/datadista/datasets\">Datadista</a>.\n</p>\n"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<IPython.core.display.HTML object>",
+      "text/html": "<p style=\"font-size: smaller\">Analysis and Visualization:\n  <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">\n  Covid-19 Public Data Collaboration Project @ renkulab.io\n  </a>\n  </p>\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# generate a simple plots comparing the per-capita cumulative deaths\n",
+    "\n",
+    "base = alt.Chart(combined_df[combined_df.plot_key.isin(top5_regions)])\n",
+    "deaths_100k = plotting.generate_region_chart(\n",
+    "    base, \n",
+    "    column='deceased_100k', \n",
+    "    region_column='plot_key',\n",
+    "    ytitle='Deaths per 100k population', \n",
+    "    legend_title='Region',\n",
+    "    tooltip_title='Deaths/100k'\n",
+    ").properties(\n",
+    "    height=450,\n",
+    "    width=600,\n",
+    "    title='Total deaths per 100k population in the top 5 regions from the U.S., Spain and Italy'\n",
+    ").configure_title(\n",
+    "    anchor='start'\n",
+    ")\n",
+    "display(deaths_100k)\n",
+    "display(all_credits)\n",
+    "display(renkulab_credits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/html": "\n<div id=\"altair-viz-e738a4d576e848fbb09af8ea18057591\"></div>\n<script type=\"text/javascript\">\n  (function(spec, embedOpt){\n    const outputDiv = document.getElementById(\"altair-viz-e738a4d576e848fbb09af8ea18057591\");\n    const paths = {\n      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.2?noext\",\n      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n    };\n\n    function loadScript(lib) {\n      return new Promise(function(resolve, reject) {\n        var s = document.createElement('script');\n        s.src = paths[lib];\n        s.async = true;\n        s.onload = () => resolve(paths[lib]);\n        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n        document.getElementsByTagName(\"head\")[0].appendChild(s);\n      });\n    }\n\n    function showError(err) {\n      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n      throw err;\n    }\n\n    function displayChart(vegaEmbed) {\n      vegaEmbed(outputDiv, spec, embedOpt)\n        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n    }\n\n    if(typeof define === \"function\" && define.amd) {\n      requirejs.config({paths});\n      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n    } else if (typeof vegaEmbed === \"function\") {\n      displayChart(vegaEmbed);\n    } else {\n      loadScript(\"vega\")\n        .then(() => loadScript(\"vega-lite\"))\n        .then(() => loadScript(\"vega-embed\"))\n        .catch(showError)\n        .then(() => displayChart(vegaEmbed));\n    }\n  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"title\": {\"anchor\": \"start\"}}, \"data\": {\"name\": \"data-01ab1c1d95140bb1dea53f0665566d45\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"plot_key\", \"legend\": {\"title\": \"Region\"}}, \"opacity\": {\"condition\": {\"value\": 1, \"selection\": \"selector030\"}, \"value\": 0.2}, \"tooltip\": 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\"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-07T00:00:00\", \"deceased_100k\": 22.98005594100185, \"deceased_daily\": \"\", \"deceased\": 402.0, \"positive_100k\": 517.0512586725416, \"positive_daily\": \"\", \"positive\": 9045, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 7}, {\"index\": 7138, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-08T00:00:00\", \"deceased_100k\": 25.49528594449459, \"deceased_daily\": \"\", \"deceased\": 446.0, \"positive_100k\": 550.263727582298, \"positive_daily\": \"\", \"positive\": 9626, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 8}, {\"index\": 7139, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-09T00:00:00\", \"deceased_100k\": 28.810816403644107, \"deceased_daily\": \"\", \"deceased\": 504.0, \"positive_100k\": 576.9594642102778, \"positive_daily\": \"\", \"positive\": 10093, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 9}, {\"index\": 7140, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-10T00:00:00\", \"deceased_100k\": 34.813069821069966, \"deceased_daily\": \"\", \"deceased\": 609.0, \"positive_100k\": 602.4547501547725, \"positive_daily\": \"\", \"positive\": 10539, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 10}, {\"index\": 7141, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-11T00:00:00\", \"deceased_100k\": 37.27113550630151, \"deceased_daily\": \"\", \"deceased\": 652.0, \"positive_100k\": 626.0064492783862, \"positive_daily\": \"\", \"positive\": 10951, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 11}, {\"index\": 7142, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-12T00:00:00\", \"deceased_100k\": 40.24368005588384, \"deceased_daily\": \"\", \"deceased\": 704.0, \"positive_100k\": 638.1824490680216, \"positive_daily\": \"\", \"positive\": 11164, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 12}, {\"index\": 7143, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-13T00:00:00\", \"deceased_100k\": 43.44488187851096, \"deceased_daily\": \"\", \"deceased\": 760.0, \"positive_100k\": 665.8499791064418, \"positive_daily\": \"\", \"positive\": 11648, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 13}, {\"index\": 7144, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-14T00:00:00\", \"deceased_100k\": 46.87474097418288, \"deceased_daily\": \"\", \"deceased\": 820.0, \"positive_100k\": 697.919161650974, \"positive_daily\": \"\", \"positive\": 12209, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 14}, {\"index\": 7145, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-15T00:00:00\", \"deceased_100k\": 50.533257342899596, \"deceased_daily\": \"\", \"deceased\": 884.0, \"positive_100k\": 717.0692082684757, \"positive_daily\": \"\", \"positive\": 12544, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 15}, {\"index\": 7146, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-16T00:00:00\", \"deceased_100k\": 56.07819621423586, \"deceased_daily\": 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\"2020-04-18T00:00:00\", \"deceased_100k\": 61.16582053948253, \"deceased_daily\": \"\", \"deceased\": 1070.0, \"positive_100k\": 770.0605312966068, \"positive_daily\": \"\", \"positive\": 13471, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 18}, {\"index\": 7149, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-19T00:00:00\", \"deceased_100k\": 63.96687213428127, \"deceased_daily\": \"\", \"deceased\": 1119.0, \"positive_100k\": 782.6938456323317, \"positive_daily\": \"\", \"positive\": 13692, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 19}, {\"index\": 7150, \"admin2_label\": \"Wayne\", \"admin2\": \"\", \"country_label\": \"United States of America\", \"country\": \"USA\", \"date\": \"2020-04-20T00:00:00\", \"deceased_100k\": 65.62463736385602, \"deceased_daily\": \"\", \"deceased\": 1148.0, \"positive_100k\": 795.2699956497954, \"positive_daily\": \"\", \"positive\": 13912, \"region_iso\": \"\", \"region_label\": \"Michigan\", \"tested_100k\": null, \"tested\": null, \"county_state\": \"Wayne, Michigan\", \"plot_key\": \"Wayne, Michigan\", \"sinceDay0\": 20}]}}, {\"mode\": \"vega-lite\"});\n</script>",
+      "text/plain": "alt.Chart(...)"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<IPython.core.display.HTML object>",
+      "text/html": "\n<p style=\"font-size: smaller\">Data Sources: \n  <a href=\"https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide\">ECDC</a>, \n  <a href=\"https://github.com/pcm-dpc/COVID-19\">Italian Civil Protection</a>, \n  <a href=\"https://github.com/nytimes/covid-19-data\">The New York Times</a>, \n  <a href=\"https://github.com/datadista/datasets\">Datadista</a>.\n</p>\n"
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<IPython.core.display.HTML object>",
+      "text/html": "<p style=\"font-size: smaller\">Analysis and Visualization:\n  <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">\n  Covid-19 Public Data Collaboration Project @ renkulab.io\n  </a>\n  </p>\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "# Now for a slightly more complicated plot, we make use of some of the helper functions\n",
+    "\n",
+    "# Making a plot of the evolution of the death count since the 10th death in each region\n",
+    "since_10th_death = helper.make_since_df(\n",
+    "    combined_df[combined_df.plot_key.isin(top5_regions)], \n",
+    "    'deceased', \n",
+    "    'plot_key'\n",
+    ")\n",
+    "\n",
+    "base = alt.Chart(\n",
+    "    since_10th_death, \n",
+    "    title=\"Top 5 regions from each country: total deaths since 10th death\"\n",
+    ").encode(\n",
+    "    alt.Y(scale=alt.Scale(type='log'))\n",
+    ")\n",
+    "\n",
+    "line_chart = plotting.make_region_since_chart(\n",
+    "    base, \n",
+    "    'deceased', \n",
+    "    'sinceDay0', \n",
+    "    'plot_key', \n",
+    "    'Days since 10th death',\n",
+    "    'Cumulative deaths',\n",
+    "    'Cases',\n",
+    "    'Region'\n",
+    ").properties(\n",
+    "    width=450,\n",
+    "    height=450\n",
+    ").configure_title(\n",
+    "    anchor='start'\n",
+    ")\n",
+    "display(line_chart)\n",
+    "display(all_credits)\n",
+    "display(renkulab_credits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#\n",
+    "# Here we will create a combined DataFrame for plotting all three datasets at once\n",
+    "#\n",
+    "\n",
+    "# create a common key to use for selecting regions\n",
+    "us_df['plot_key'] = us_df['county_state']\n",
+    "spain_df['plot_key'] = spain_df['region_label']\n",
+    "italy_df['plot_key'] = italy_df['region_label']\n",
+    "\n",
+    "# pull the top-5 region names into a list\n",
+    "top5_regions = top5_us.county_state.to_list() + top5_spain.region_label.to_list() + top5_italy.region_label.to_list()\n",
+    "combined_df = pd.concat([us_df, spain_df, italy_df])"
+   ]
+  }
+ ]
+}
\ No newline at end of file
-- 
GitLab