{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# COVID-19 by Country #\n", "\n", "Data comes from https://data.gov.hk" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import requests\n", "import datetime\n", "from itertools import product\n", "\n", "from ipywidgets import widgets\n", "from IPython.display import display\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import geopandas as gpd\n", "\n", "from lets_plot import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'load_lets_plot_js' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mC:\\Temp/ipykernel_9612/2399501846.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mload_lets_plot_js\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'load_lets_plot_js' is not defined" ] } ], "source": [ "load_lets_plot_js()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "API_URL = 'https://api.data.gov.hk/v1/historical-archive'\n", "DATA_IN_CHINA_URL = 'http://www.chp.gov.hk/files/misc/latest_situation_of_reported_cases_mainland_china_eng.csv'\n", "DATA_OUTSIDE_CHINA_URL = 'http://www.chp.gov.hk/files/misc/countries_areas_outside_mainland_china_have_reported_cases_eng.csv'\n", "START_DATE = pd.Timestamp(2020, 1, 14)\n", "END_DATE = pd.Timestamp.today()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def player_widget(plots, *, fps=1):\n", " interval = max(1, int(1000 / fps))\n", " player = widgets.Play(min=0, max=len(plots) - 1, step=1, value=0, interval=interval)\n", " slider = widgets.IntSlider(min=0, max=len(plots) - 1, step=1, value=0)\n", " widgets.jslink((player, 'value'), (slider, 'value'))\n", " widget = widgets.HBox([player, slider])\n", " iout = widgets.interactive_output(lambda n, m: display(plots[n]), {'n': slider, 'm': player})\n", " return display(widget, iout)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def load_data(data_url):\n", " DEFAULT_TIMESTAMP = '20200302-0916'\n", " response = requests.get('%s/list-file-versions' % API_URL, params=dict(\n", " url=data_url,\n", " start=START_DATE.strftime('%Y%m%d'),\n", " end=END_DATE.strftime('%Y%m%d'),\n", " ))\n", " time = response.json()['timestamps'][-1] if response.status_code == 200 else DEFAULT_TIMESTAMP\n", " return pd.read_csv('{api_url}/get-file?url={data_url}&time={time}'.format(\n", " api_url=API_URL,\n", " data_url=data_url,\n", " time=time,\n", " ))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def ffill_columns(df, *, columns=[]):\n", " def get_ffill_cb():\n", " last_max_value = 0\n", " def ffill_cb(value):\n", " nonlocal last_max_value\n", " if not np.isnan(value) and value > last_max_value:\n", " last_max_value = value\n", " return last_max_value\n", " return ffill_cb\n", " for column in columns:\n", " df[column] = df[column].apply(get_ffill_cb())\n", " return df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def fix_country_name(name):\n", " SUBSTITUTION = {\n", " 'Korea': 'South Korea',\n", " 'United States': 'United States of America',\n", " 'The Philippines': 'Philippines',\n", " 'North Macedonia': 'Macedonia',\n", " 'Geogia': 'Georgia',\n", " 'Holland': 'Netherlands',\n", " 'Germany ': 'Germany',\n", " 'United Kingdom and Northern Ireland': 'United Kingdom',\n", " }\n", " return SUBSTITUTION[name] if name in SUBSTITUTION.keys() else name" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def simplify_geoms(world_gdf, *, tolerance=.5):\n", " DANGEROUS_GEOMS = ['South Africa']\n", " stable_gdf = world_gdf[world_gdf.name.isin(DANGEROUS_GEOMS)].copy()\n", " changeable_gdf = world_gdf[~world_gdf.name.isin(DANGEROUS_GEOMS)].copy()\n", " changeable_gdf.geometry = changeable_gdf.geometry.simplify(tolerance)\n", " return pd.concat([stable_gdf, changeable_gdf])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Prepare the gdf with simplified country polygons\n", "world_gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))[['name', 'geometry']]\n", "world_gdf = simplify_geoms(world_gdf.copy())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Prepare the df with disease data, combined from two tables\n", "columns_sub = {'As of date': 'date', 'Number of cases': 'cases', 'Number of death cases': 'deaths'}\n", "china_df = pd.DataFrame(load_data(DATA_IN_CHINA_URL))\n", "china_df = china_df.rename(columns=columns_sub)[columns_sub.values()]\n", "china_df['country'] = 'China'\n", "china_df.date = pd.to_datetime(china_df.date, dayfirst=True, errors='coerce')\n", "\n", "columns_sub = {'As of date': 'date', 'Other countries/areas': 'country', 'Number of cases/confirmed cases': 'cases', 'Number of deaths among confirmed cases': 'deaths'}\n", "world_df = pd.DataFrame(load_data(DATA_OUTSIDE_CHINA_URL))\n", "world_df = world_df.rename(columns=columns_sub)[columns_sub.values()]\n", "world_df.date = pd.to_datetime(world_df.date, dayfirst=True, errors='coerce')\n", "world_df.country = world_df.country.apply(fix_country_name)\n", "\n", "df = pd.concat([china_df, world_df], sort=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# In disease data select only the countries that are in world\n", "df = df[df.country.isin(world_gdf.name.unique())]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Add missing pairs (date, country) to dataframe (for filling gaps in the time scale)\n", "index_tuples = product(pd.date_range(START_DATE, END_DATE), world_gdf.name.unique())\n", "multi_index = pd.MultiIndex.from_tuples(index_tuples, names=['date', 'country'])\n", "df = df.groupby(['date', 'country']).max().sort_index().reindex(multi_index).reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Fix empty and incorrect 'cases' and 'deaths'\n", "df = pd.concat([\n", " ffill_columns(df[df.country == country].copy(), columns=['cases', 'deaths'])\n", " for country in df.country.unique()\n", "]).reset_index(drop=True)\n", "df.cases = df.cases.astype(int)\n", "df.deaths = df.deaths.astype(int)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df['cases_log'] = np.log(df.cases + 1)\n", "cases_limit = df.cases_log.max()\n", "\n", "p = ggplot() + \\\n", " theme(legend_position='none', axis_title='blank', axis_text='blank', axis_ticks='blank', axis_line='blank') + \\\n", " ggsize(800, 600)\n", "\n", "plots = []\n", "for current_date in pd.date_range(START_DATE, END_DATE):\n", " current_gdf = world_gdf.merge(df[df.date == current_date], left_on='name', right_on='country')[['country', 'cases_log', 'geometry']]\n", " current_gdf = current_gdf[current_gdf.cases_log > 0]\n", " plots.append(\n", " p + \\\n", " geom_polygon(aes(fill='cases_log', color='country'), data=current_gdf, size=.01) + \\\n", " scale_color_gradient(name='Country') + \\\n", " scale_fill_gradient(name='Cases Count(log10)', low='white', high='red', limits=[0, cases_limit]) + \\\n", " geom_polygon(data=world_gdf, color='gray', fill='white', size=.5, alpha=0) + \\\n", " ggtitle('COVID-19 on %s' % current_date.strftime('%m/%d/%Y'))\n", " )" ] }, { "cell_type": 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