{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

Visualizing the global spread of COVID-19

\n", "

Visualizing the spread of COVID-19 across the globe in the time period 22nd January to 23rd April

\n", "\n", "COVID-19 spread has become a global pandemic causing turmoil in the world.\n", "The aim of this project is to visualize the affected countries and spread of COVID-19 in the time period - January 22, 2020 to April 23, 2020, to get a sense of the following questions :-\n", "* How rapid was the spread of Corona-virus?\n", "* Which countries were among the first to be infected?\n", "
\n", "\n", "The dataset used for this project, is the Kaggle dataset - novel-corona-virus-2019

\n", "\n", "The dataset contains multiple files, but for our goal, we will use the covid_19_data.csv file.
\n", "This file contains Countrywise daily updates on the Confirmed, Deaths and Recoverd cases. A few columns :-
\n", "\n", " ObservationDate - Date on which data was recorded\n", " Country/Region - Country under observation\n", " Confirmed - No. of confirmed cases\n", " Deaths - No. of deaths\n", " Recovered - No. of Recovered\n", " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import plotly as py\n", "import plotly.express as px\n", "import plotly.graph_objs as go\n", "import matplotlib.animation as animate\n", "from IPython.display import HTML" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importing the dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SNoObservationDateProvince/StateCountry/RegionLast UpdateConfirmedDeathsRecovered
0101/22/2020AnhuiMainland China1/22/2020 17:001.00.00.0
1201/22/2020BeijingMainland China1/22/2020 17:0014.00.00.0
2301/22/2020ChongqingMainland China1/22/2020 17:006.00.00.0
3401/22/2020FujianMainland China1/22/2020 17:001.00.00.0
4501/22/2020GansuMainland China1/22/2020 17:000.00.00.0
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" ], "text/plain": [ " SNo ObservationDate Province/State Country/Region Last Update \\\n", "0 1 01/22/2020 Anhui Mainland China 1/22/2020 17:00 \n", "1 2 01/22/2020 Beijing Mainland China 1/22/2020 17:00 \n", "2 3 01/22/2020 Chongqing Mainland China 1/22/2020 17:00 \n", "3 4 01/22/2020 Fujian Mainland China 1/22/2020 17:00 \n", "4 5 01/22/2020 Gansu Mainland China 1/22/2020 17:00 \n", "\n", " Confirmed Deaths Recovered \n", "0 1.0 0.0 0.0 \n", "1 14.0 0.0 0.0 \n", "2 6.0 0.0 0.0 \n", "3 1.0 0.0 0.0 \n", "4 0.0 0.0 0.0 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('/home/hp/Downloads/Covid-19 Datasets/covid_19_data.csv')\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the dataset, to make our job easier, we can rename a few columns for easier understanding and usage
\n", "\n", " ObservationDate: Date\n", " Country/Region: Country\n", " Province/State: State\n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SNoDateStateCountryLast UpdateConfirmedDeathsRecovered
0101/22/2020AnhuiMainland China1/22/2020 17:001.00.00.0
1201/22/2020BeijingMainland China1/22/2020 17:0014.00.00.0
2301/22/2020ChongqingMainland China1/22/2020 17:006.00.00.0
3401/22/2020FujianMainland China1/22/2020 17:001.00.00.0
4501/22/2020GansuMainland China1/22/2020 17:000.00.00.0
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" ], "text/plain": [ " SNo Date State Country Last Update Confirmed \\\n", "0 1 01/22/2020 Anhui Mainland China 1/22/2020 17:00 1.0 \n", "1 2 01/22/2020 Beijing Mainland China 1/22/2020 17:00 14.0 \n", "2 3 01/22/2020 Chongqing Mainland China 1/22/2020 17:00 6.0 \n", "3 4 01/22/2020 Fujian Mainland China 1/22/2020 17:00 1.0 \n", "4 5 01/22/2020 Gansu Mainland China 1/22/2020 17:00 0.0 \n", "\n", " Deaths Recovered \n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.rename(columns={'ObservationDate':'Date','Country/Region':'Country','Province/State':'State'},inplace=True)\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now firstly, we will make a static choropleth using the `plotly` library, on the current number of confirmed cases i.e. as of April 23, 2020.
\n", "To achieve this, let us first group the data based on `Country` and `Date`, as there are recorded observations on the same day for a country. This will give us per day Stats for a country sorted by date in descending." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryDateSNoConfirmedDeathsRecovered
5350Mainland China01/22/2020535547.017.028.0
3691Hong Kong01/22/2020130.00.00.0
8762Thailand01/22/2020372.00.00.0
8630Taiwan01/22/2020291.00.00.0
4446Japan01/22/2020362.00.00.0
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" ], "text/plain": [ " Country Date SNo Confirmed Deaths Recovered\n", "5350 Mainland China 01/22/2020 535 547.0 17.0 28.0\n", "3691 Hong Kong 01/22/2020 13 0.0 0.0 0.0\n", "8762 Thailand 01/22/2020 37 2.0 0.0 0.0\n", "8630 Taiwan 01/22/2020 29 1.0 0.0 0.0\n", "4446 Japan 01/22/2020 36 2.0 0.0 0.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped = df.groupby(['Country','Date'])\n", "df_confirmed = grouped.sum().reset_index().sort_values(['Date'],ascending=False)\n", "df_confirmed.tail(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can observe that, there are Countries where there are 0 confirmed cases. We will have to remove these from our dataset" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df_confirmed = df_confirmed[df_confirmed.Confirmed > 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let us observe the distrbution in the `Confirmed` cases, before we start with the choropleth map" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.subplots(figsize=(24,8))\n", "plt.subplot(1,2,1)\n", "sns.distplot(df_confirmed.Confirmed)\n", "plt.subplot(1,2,2)\n", "sns.distplot(np.log1p(df_confirmed.Confirmed))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the first subplot, we can see how skewed our data is, due to this, when we color code the `Confirmed` cases most data points lesser than say 20k (confirmed cases) would be of the same color but in reality they should be distinct.
\n", "Thus taking the log `np.log10` of the data we can get better distictions in the Confirmed cases and visualize the spread of COVID-19 better.
\n", "We will call this `Affected_Factor` as it better describes the spread of confirmed cases on a Logrithmic scale." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df_confirmed['Affected_Factor'] = np.log10(df_confirmed.Confirmed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get the most recent recorded data, we will drop all duplicates based on `Country` and hence, by default retaining the most current recorded data points." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryDateSNoConfirmedDeathsRecoveredAffected_Factor
9083Turkey04/23/202017856101790.02491.018491.05.007705
6595Norway04/23/2020178117401.0194.032.03.869290
5194Luxembourg04/23/2020177863665.083.0728.03.564074
8947Togo04/23/20201785388.06.059.01.944483
6759Pakistan04/23/20201781311155.0237.02527.04.047470
\n", "
" ], "text/plain": [ " Country Date SNo Confirmed Deaths Recovered \\\n", "9083 Turkey 04/23/2020 17856 101790.0 2491.0 18491.0 \n", "6595 Norway 04/23/2020 17811 7401.0 194.0 32.0 \n", "5194 Luxembourg 04/23/2020 17786 3665.0 83.0 728.0 \n", "8947 Togo 04/23/2020 17853 88.0 6.0 59.0 \n", "6759 Pakistan 04/23/2020 17813 11155.0 237.0 2527.0 \n", "\n", " Affected_Factor \n", "9083 5.007705 \n", "6595 3.869290 \n", "5194 3.564074 \n", "8947 1.944483 \n", "6759 4.047470 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_confirmed.Country = df_confirmed.Country.str.strip()\n", "df_confirmed.drop_duplicates(subset=['Country'],inplace=True)\n", "df_confirmed.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally plotting the choropleth, on the `Country` as location and `Confirmed` as data to be color-coded." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { 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"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Confirmed cases as of April 23, 2020", "x": 0.5 } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure( \n", " data= go.Choropleth(\n", " locations= df_confirmed.Country, #Spatial coordinates\n", " locationmode= 'country names', #Form of the spatial coordinates above\n", " z = df_confirmed['Confirmed'], #Values to be color coded\n", " colorscale = 'RdBu', \n", " autocolorscale= False,\n", " reversescale= True,\n", " marker_line_color = 'darkgrey',\n", " marker_line_width = 0.1,\n", " colorbar_title= 'Confirmed Cases',\n", " )\n", ")\n", "\n", "fig.update_layout(\n", " title_text= 'Confirmed cases as of April 23, 2020',\n", " title_x= 0.5,\n", " geo= dict(\n", " showframe = False,\n", " showcoastlines = False,\n", " projection_type = 'equirectangular'\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Due to Github's inability to render the plot, the plot is saved as a `png` file as well" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As discussed above, we can see that for countries having cases between 0 and 50k have almost the same hue of blue and hence this is misleading as to the degree of spread of COVID-19.
\n", "To rectify this, we will use the new column we created above `Affected_Factor` for the plotting. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "autocolorscale": false, "colorbar": { "title": { "text": "Confirmed Cases (Log scale)" } }, "colorscale": [ [ 0, "rgb(103,0,31)" ], [ 0.1, "rgb(178,24,43)" ], [ 0.2, "rgb(214,96,77)" ], [ 0.3, "rgb(244,165,130)" ], [ 0.4, "rgb(253,219,199)" ], [ 0.5, "rgb(247,247,247)" ], [ 0.6, "rgb(209,229,240)" ], [ 0.7, "rgb(146,197,222)" ], [ 0.8, "rgb(67,147,195)" ], [ 0.9, "rgb(33,102,172)" ], [ 1, "rgb(5,48,97)" ] ], "hovertext": [ 101790, 7401, 3665, 88, 11155, 981, 12368, 2463, 1235, 1398, 15, 138, 368, 45, 3435, 1981, 437, 479, 501, 81, 384, 686, 862, 869170, 35921, 7775, 10511, 1154, 87026, 46, 50036, 6661, 2284, 663, 74, 425, 10, 76, 16, 795, 1300, 153129, 15, 8022, 174, 76, 1366, 13, 9, 50, 316, 1097, 328, 213024, 320, 1523, 3568, 13930, 19, 186, 723, 1716, 5, 631, 480, 4561, 11, 2399, 1035, 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"Guatemala", "Costa Rica", "Guinea", "US", "Netherlands", "Indonesia", "Poland", "Ghana", "Iran", "Mozambique", "Brazil", "Australia", "Hungary", "Albania", "Uganda", "Georgia", "Suriname", "Zambia", "Central African Republic", "Cyprus", "North Macedonia", "Germany", "Saint Lucia", "Belarus", "Sudan", "Barbados", "Slovenia", "Saint Vincent and the Grenadines", "MS Zaandam", "Guinea-Bissau", "Montenegro", "Bulgaria", "Somalia", "Spain", "Kenya", "Armenia", "Morocco", "Saudi Arabia", "Laos", "Congo (Brazzaville)", "Andorra", "Oman", "South Sudan", "Kyrgyzstan", "West Bank and Gaza", "Colombia", "Burundi", "Kuwait", "Hong Kong", "South Korea", "South Africa", "Chile", "Cabo Verde", "Iceland", "Honduras", "Latvia", "Chad", "Sao Tome and Principe", "Libya", "Philippines", "Vietnam", "India", "Kazakhstan", "Guyana", "Algeria", "Burkina Faso", "Liberia", "Bahrain", "Haiti", "Tunisia", "Portugal", "Holy See", "Nepal", "Papua New Guinea", "Lebanon", "Uruguay", "Trinidad and Tobago", "Congo 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\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure( \n", " data= go.Choropleth(\n", " locations= df_confirmed.Country, #Spatial coordinates\n", " locationmode= 'country names', #Form of the spatial coordinates above\n", " z = df_confirmed['Affected_Factor'], #Values to be color coded\n", " colorscale = 'RdBu', \n", " autocolorscale= False,\n", " hovertext=df_confirmed.Confirmed,\n", " reversescale= True,\n", " marker_line_color = 'darkgrey',\n", " marker_line_width = 0.1,\n", " colorbar_title= 'Confirmed Cases (Log scale)',\n", " )\n", ")\n", "\n", "fig.update_layout(\n", " title_text= 'COVID-19 Spread as of April 23, 2020',\n", " title_x= 0.5,\n", " geo= dict(\n", " showframe = False,\n", " showcoastlines = False,\n", " projection_type = 'equirectangular'\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Due to Github's inability to render the plot, the plot is saved as a `png` file as well" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot, can differentiate between countries and `Affected_Factor` acts as a good proxy to show degree of spread of COVID-19 in the world
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Next let us now try to animate the choropleth, to show the spread of COVID-19 in the time period - January 22, 2020 to April 23, 2020.
\n", "We will use the same dataset, with similar modifications, this time we will group the data by `Date` and `Country` rather the other way round, as we want aggregated data on per-day basis. We will consider countries having cases more than 0 at any point in time. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "grouped = df[df.Confirmed > 0].groupby(['Date','Country'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateCountrySNoConfirmedDeathsRecovered
001/22/2020Japan362.00.00.0
101/22/2020Macau211.00.00.0
201/22/2020Mainland China373547.017.028.0
301/22/2020South Korea381.00.00.0
401/22/2020Taiwan291.00.00.0
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" ], "text/plain": [ " Date Country SNo Confirmed Deaths Recovered\n", "0 01/22/2020 Japan 36 2.0 0.0 0.0\n", "1 01/22/2020 Macau 21 1.0 0.0 0.0\n", "2 01/22/2020 Mainland China 373 547.0 17.0 28.0\n", "3 01/22/2020 South Korea 38 1.0 0.0 0.0\n", "4 01/22/2020 Taiwan 29 1.0 0.0 0.0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_date_series = grouped.sum().reset_index()\n", "df_date_series.Country = df_date_series.Country.str.strip()\n", "df_date_series.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a similar fashion we will calculate the proxy - `Affected_Factor` for the data points to better understand the spread (explained above)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateCountrySNoConfirmedDeathsRecoveredAffected_Factor
001/22/2020Japan362.00.00.00.301030
101/22/2020Macau211.00.00.00.000000
201/22/2020Mainland China373547.017.028.02.737987
301/22/2020South Korea381.00.00.00.000000
401/22/2020Taiwan291.00.00.00.000000
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" ], "text/plain": [ " Date Country SNo Confirmed Deaths Recovered \\\n", "0 01/22/2020 Japan 36 2.0 0.0 0.0 \n", "1 01/22/2020 Macau 21 1.0 0.0 0.0 \n", "2 01/22/2020 Mainland China 373 547.0 17.0 28.0 \n", "3 01/22/2020 South Korea 38 1.0 0.0 0.0 \n", "4 01/22/2020 Taiwan 29 1.0 0.0 0.0 \n", "\n", " Affected_Factor \n", "0 0.301030 \n", "1 0.000000 \n", "2 2.737987 \n", "3 0.000000 \n", "4 0.000000 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_date_series['Affected_Factor'] = np.log10(df_date_series.Confirmed)\n", "df_date_series.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the `plotly.express` module, we can plot an animated choropleth map. Using `Country` as location, `Affected_Factor` as values to be color-coded and `Date` as the time-series upon which animation will be built." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "customdata": [ [ 2, 0, 0 ], [ 1, 0, 0 ], [ 547, 28, 17 ], [ 1, 0, 0 ], [ 1, 0, 0 ], [ 2, 0, 0 ], [ 1, 0, 0 ] ], "geo": "geo", "hovertemplate": "%{hovertext}

Date=01/22/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=01/22/2020
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Date=01/23/2020
Confirmed=%{customdata[0]}
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Date=01/24/2020
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Date=01/25/2020
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Date=01/26/2020
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Date=01/27/2020
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Date=01/28/2020
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Date=01/29/2020
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Date=01/30/2020
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Date=01/31/2020
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Date=02/01/2020
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Date=02/02/2020
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Date=02/03/2020
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Date=02/04/2020
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Date=02/05/2020
Confirmed=%{customdata[0]}
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Deaths=%{customdata[2]}
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Date=02/06/2020
Confirmed=%{customdata[0]}
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Deaths=%{customdata[2]}
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Date=02/07/2020
Confirmed=%{customdata[0]}
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Date=02/08/2020
Confirmed=%{customdata[0]}
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Date=02/09/2020
Confirmed=%{customdata[0]}
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Date=02/10/2020
Confirmed=%{customdata[0]}
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Date=02/11/2020
Confirmed=%{customdata[0]}
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Deaths=%{customdata[2]}
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Date=02/12/2020
Confirmed=%{customdata[0]}
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Deaths=%{customdata[2]}
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Date=02/13/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/14/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/15/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/16/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/17/2020
Confirmed=%{customdata[0]}
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Deaths=%{customdata[2]}
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Date=02/18/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/19/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/20/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
Country=%{location}
Affected_Factor=%{z}", "hovertext": [ "Australia", "Belgium", "Cambodia", "Canada", "Egypt", "Finland", "France", "Germany", "Hong Kong", "India", "Iran", "Italy", "Japan", "Macau", "Mainland China", "Malaysia", "Nepal", "Others", "Philippines", "Russia", "Singapore", "South Korea", "Spain", "Sri Lanka", "Sweden", "Taiwan", "Thailand", "UK", "US", "United Arab Emirates", "Vietnam" ], "locationmode": "country names", "locations": [ "Australia", "Belgium", "Cambodia", "Canada", "Egypt", "Finland", "France", "Germany", "Hong Kong", "India", "Iran", "Italy", "Japan", "Macau", "Mainland China", "Malaysia", "Nepal", "Others", "Philippines", "Russia", "Singapore", "South Korea", "Spain", "Sri Lanka", "Sweden", "Taiwan", "Thailand", "UK", "US", "United Arab Emirates", "Vietnam" ], "name": "", "type": "choropleth", "z": [ 1.1760912590556813, 0, 0, 0.9030899869919435, 0, 0, 1.0791812460476249, 1.2041199826559248, 1.8325089127062364, 0.47712125471966244, 0.6989700043360189, 0.47712125471966244, 1.9731278535996986, 1, 4.875055472760003, 1.3424226808222062, 0, 2.8020892578817325, 0.47712125471966244, 0.3010299956639812, 1.9242792860618816, 2.0170333392987803, 0.3010299956639812, 0, 0, 1.380211241711606, 1.5440680443502757, 0.9542425094393249, 1.1760912590556813, 0.9542425094393249, 1.2041199826559248 ] } ], "name": "02/20/2020" }, { "data": [ { "coloraxis": "coloraxis", "customdata": [ [ 19, 11, 0 ], [ 1, 1, 0 ], [ 1, 1, 0 ], [ 9, 3, 0 ], [ 1, 0, 0 ], [ 1, 1, 0 ], [ 12, 4, 1 ], [ 16, 14, 0 ], [ 68, 5, 2 ], [ 3, 3, 0 ], [ 18, 0, 4 ], [ 1, 0, 0 ], [ 20, 0, 1 ], [ 105, 22, 1 ], [ 1, 0, 0 ], [ 10, 6, 0 ], [ 75472, 18693, 2236 ], [ 22, 15, 0 ], [ 1, 1, 0 ], [ 634, 1, 2 ], [ 3, 1, 1 ], [ 2, 2, 0 ], [ 85, 37, 0 ], [ 204, 16, 2 ], [ 2, 2, 0 ], [ 1, 1, 0 ], [ 1, 0, 0 ], [ 26, 2, 1 ], [ 35, 17, 0 ], [ 9, 8, 0 ], [ 35, 5, 0 ], [ 9, 4, 0 ], [ 16, 14, 0 ] ], "geo": "geo", "hovertemplate": "%{hovertext}

Date=02/21/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/22/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/23/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/24/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/25/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/26/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/27/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/28/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=02/29/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=03/01/2020
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Date=03/02/2020
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Confirmed=%{customdata[0]}
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Date=03/04/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
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Date=03/14/2020
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Date=03/20/2020
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Date=03/21/2020
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Date=03/24/2020
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Date=03/27/2020
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Date=03/30/2020
Confirmed=%{customdata[0]}
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Date=04/01/2020
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Date=04/02/2020
Confirmed=%{customdata[0]}
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Date=04/03/2020
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Date=04/04/2020
Confirmed=%{customdata[0]}
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Date=04/05/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
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Date=04/06/2020
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Date=04/08/2020
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Date=04/09/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=04/12/2020
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Date=04/13/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=04/14/2020
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Date=04/15/2020
Confirmed=%{customdata[0]}
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Date=04/16/2020
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Date=04/17/2020
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Date=04/18/2020
Confirmed=%{customdata[0]}
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Date=04/19/2020
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Date=04/20/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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Date=04/21/2020
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Date=04/22/2020
Confirmed=%{customdata[0]}
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Date=04/23/2020
Confirmed=%{customdata[0]}
Recovered=%{customdata[1]}
Deaths=%{customdata[2]}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.choropleth(\n", " df_date_series, #Dataframe\n", " locations= 'Country', #Spatial coordinates, can give Lat and Lon in separate params\n", " locationmode= 'country names', #Type of spatial coordinates\n", " color= 'Affected_Factor', #Values to be color coded\n", " hover_name= 'Country', #Text to be displayed in Bold upon hover\n", " hover_data= ['Confirmed','Recovered','Deaths'], #Extra text to be displayed in Hover tip\n", " animation_frame= 'Date', #Data for animation, time-series data\n", " color_continuous_scale= px.colors.sequential.RdBu_r\n", ")\n", "\n", "fig.update_layout(\n", " title_text = 'Global spread in Coronavirus over time (January 22 - April 23)',\n", " title_x = 0.5,\n", " geo= dict(\n", " showframe= False,\n", " showcoastlines= False,\n", " projection_type = 'equirectangular'\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Due to Github's inability to render the plot, the plot is saved as a `png` file as well" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe the animation plot above showing the spread of COVID-19 in the time period chosen. Using `Affected_Factor` we can see the degree of spread over time distinctly.\n", "\n", "In the choropleth maps above we can clearly see the general spread across the globe, but when it comes to head to head comparision, our `Affected Factor` doesnt really help. Keeping thi problem in mind, we are going to make a racing bar plot to get a head to head (in terms of country) idea of the spread." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First let us make a normal Bar plot for the 10 most affected countries as of April 23, 2020. This will give us a sense of how to style the racing bar plot later on." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Country')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "top_10_affected = df_confirmed.sort_values('Confirmed',ascending=False).iloc[:10]\n", "top_10_affected = top_10_affected[['Country','Confirmed']].set_index('Country')\n", "fig,ax = plt.subplots(figsize=(16,10))\n", "ax.barh(top_10_affected.index,top_10_affected['Confirmed'])\n", "plt.xlabel('Number of cases')\n", "plt.ylabel('Country')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at this, first thing is to have the widest bar on top rather than the bottom. Next thing is to assign colours to each country, make it look more vivid." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'US': '#adb0ff',\n", " 'Spain': '#ffb3ff',\n", " 'Italy': '#90d595',\n", " 'France': '#e48381',\n", " 'Germany': '#aafbff',\n", " 'UK': '#f7bb5f',\n", " 'Turkey': '#eafb50',\n", " 'Iran': '#009999',\n", " 'Mainland China': '#fb6b19',\n", " 'Russia': '#1fb1fb'}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colors = dict(zip(\n", " top_10_affected.index,\n", " ['#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50','#009999','#fb6b19','#1fb1fb']\n", "))\n", "colors" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Country')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "top_10_affected = top_10_affected[::-1]\n", "fig,ax = plt.subplots(figsize=(16,10))\n", "ax.barh(top_10_affected.index,top_10_affected['Confirmed'],color=[colors[x] for x in top_10_affected.index])\n", "plt.xlabel('Number of cases')\n", "plt.ylabel('Country')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For every bar, we also need to display the information of the `Confirmed` cases." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(figsize=(16,10))\n", "ax.barh(top_10_affected.index,top_10_affected['Confirmed'],color=[colors[x] for x in top_10_affected.index])\n", "plt.xlabel('Number of cases')\n", "plt.ylabel('Country')\n", "plt.yticks([])\n", "for i,(cases,country) in enumerate(zip(top_10_affected.Confirmed,top_10_affected.index)):\n", " ax.text(cases,i,country+\" \",ha='right')\n", " ax.text(cases,i,cases,ha='left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks like a good starter plot, now to animate this over time, we first need a function that will create a barplot similar to the above for every frame (date). This function will be later used and called repeatedly for each frame. Before we go there, we will have to create a mapping for each unique country to a color." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Japan': '#adb0ff',\n", " 'Macau': '#ffb3ff',\n", " 'Mainland China': '#90d595',\n", " 'South Korea': '#e48381',\n", " 'Taiwan': '#f7bb5f',\n", " 'Thailand': '#fb6b19',\n", " 'US': '#1fb1fb',\n", " 'Hong Kong': '#adb0ff',\n", " 'Singapore': '#ffb3ff',\n", " 'Vietnam': '#90d595',\n", " 'France': '#e48381',\n", " 'Australia': '#f7bb5f',\n", " 'Malaysia': '#fb6b19',\n", " 'Nepal': '#1fb1fb',\n", " 'Canada': '#adb0ff',\n", " 'Cambodia': '#ffb3ff',\n", " 'Ivory Coast': '#90d595',\n", " 'Sri Lanka': '#e48381',\n", " 'Germany': '#f7bb5f',\n", " 'Finland': '#fb6b19',\n", " 'United Arab Emirates': '#1fb1fb',\n", " 'India': '#adb0ff',\n", " 'Philippines': '#ffb3ff',\n", " 'Italy': '#90d595',\n", " 'Russia': '#e48381',\n", " 'Sweden': '#f7bb5f',\n", " 'UK': '#fb6b19',\n", " 'Spain': '#1fb1fb',\n", " 'Belgium': '#adb0ff',\n", " 'Others': '#ffb3ff',\n", " 'Egypt': '#90d595',\n", " 'Iran': '#e48381',\n", " 'Israel': '#f7bb5f',\n", " 'Lebanon': '#fb6b19',\n", " 'Afghanistan': '#1fb1fb',\n", " 'Bahrain': '#adb0ff',\n", " 'Iraq': '#ffb3ff',\n", " 'Kuwait': '#90d595',\n", " 'Oman': '#e48381',\n", " 'Algeria': '#f7bb5f',\n", " 'Austria': '#fb6b19',\n", " 'Croatia': '#1fb1fb',\n", " 'Switzerland': '#adb0ff',\n", " 'Brazil': '#ffb3ff',\n", " 'Georgia': '#90d595',\n", " 'Greece': '#e48381',\n", " 'North Macedonia': '#f7bb5f',\n", " 'Norway': '#fb6b19',\n", " 'Pakistan': '#1fb1fb',\n", " 'Romania': '#adb0ff',\n", " 'Denmark': '#ffb3ff',\n", " 'Estonia': '#90d595',\n", " 'Netherlands': '#e48381',\n", " 'San Marino': '#f7bb5f',\n", " 'Azerbaijan': '#fb6b19',\n", " 'Belarus': '#1fb1fb',\n", " 'Iceland': '#adb0ff',\n", " 'Lithuania': '#ffb3ff',\n", " 'Mexico': '#90d595',\n", " 'New Zealand': '#e48381',\n", " 'Nigeria': '#f7bb5f',\n", " 'North Ireland': '#fb6b19',\n", " 'Ireland': '#1fb1fb',\n", " 'Luxembourg': '#adb0ff',\n", " 'Monaco': '#ffb3ff',\n", " 'Qatar': '#90d595',\n", " 'Armenia': '#e48381',\n", " 'Czech Republic': '#f7bb5f',\n", " 'Dominican Republic': '#fb6b19',\n", " 'Ecuador': '#1fb1fb',\n", " 'Andorra': '#adb0ff',\n", " 'Indonesia': '#ffb3ff',\n", " 'Latvia': '#90d595',\n", " 'Morocco': '#e48381',\n", " 'Portugal': '#f7bb5f',\n", " 'Saudi Arabia': '#fb6b19',\n", " 'Senegal': '#1fb1fb',\n", " 'Argentina': '#adb0ff',\n", " 'Chile': '#ffb3ff',\n", " 'Jordan': '#90d595',\n", " 'Ukraine': '#e48381',\n", " 'Faroe Islands': '#f7bb5f',\n", " 'Gibraltar': '#fb6b19',\n", " 'Hungary': '#1fb1fb',\n", " 'Liechtenstein': '#adb0ff',\n", " 'Poland': '#ffb3ff',\n", " 'Saint Barthelemy': '#90d595',\n", " 'Tunisia': '#e48381',\n", " 'Bosnia and Herzegovina': '#f7bb5f',\n", " 'Palestine': '#fb6b19',\n", " 'Slovenia': '#1fb1fb',\n", " 'South Africa': '#adb0ff',\n", " 'Bhutan': '#ffb3ff',\n", " 'Cameroon': '#90d595',\n", " 'Colombia': '#e48381',\n", " 'Costa Rica': '#f7bb5f',\n", " 'Peru': '#fb6b19',\n", " 'Serbia': '#1fb1fb',\n", " 'Slovakia': '#adb0ff',\n", " 'Togo': '#ffb3ff',\n", " 'Vatican City': '#90d595',\n", " 'French Guiana': '#e48381',\n", " 'Malta': '#f7bb5f',\n", " 'Martinique': '#fb6b19',\n", " 'Bangladesh': '#1fb1fb',\n", " 'Bulgaria': '#adb0ff',\n", " 'Maldives': '#ffb3ff',\n", " 'Moldova': '#90d595',\n", " 'Paraguay': '#e48381',\n", " 'Republic of Ireland': '#f7bb5f',\n", " 'Albania': '#fb6b19',\n", " 'Brunei': '#1fb1fb',\n", " 'Cyprus': '#adb0ff',\n", " 'St. Martin': '#ffb3ff',\n", " \"('St. Martin',)\": '#90d595',\n", " 'Burkina Faso': '#e48381',\n", " 'Channel Islands': '#f7bb5f',\n", " 'Holy See': '#fb6b19',\n", " 'Mongolia': '#1fb1fb',\n", " 'Panama': '#adb0ff',\n", " 'occupied Palestinian territory': '#ffb3ff',\n", " 'Bolivia': '#90d595',\n", " 'Congo (Kinshasa)': '#e48381',\n", " 'Honduras': '#f7bb5f',\n", " 'Jamaica': '#fb6b19',\n", " 'Reunion': '#1fb1fb',\n", " 'Turkey': '#adb0ff',\n", " 'Cuba': '#ffb3ff',\n", " 'Guyana': '#90d595',\n", " 'Antigua and Barbuda': '#e48381',\n", " 'Aruba': '#f7bb5f',\n", " 'Cayman Islands': '#fb6b19',\n", " 'Ethiopia': '#1fb1fb',\n", " 'Guadeloupe': '#adb0ff',\n", " 'Guinea': '#ffb3ff',\n", " 'Kazakhstan': '#90d595',\n", " 'Kenya': '#e48381',\n", " 'Sudan': '#f7bb5f',\n", " 'Curacao': '#fb6b19',\n", " 'Eswatini': '#1fb1fb',\n", " 'Gabon': '#adb0ff',\n", " 'Ghana': '#ffb3ff',\n", " 'Guatemala': '#90d595',\n", " 'Guernsey': '#e48381',\n", " 'Jersey': '#f7bb5f',\n", " 'Mauritania': '#fb6b19',\n", " 'Namibia': '#1fb1fb',\n", " 'Rwanda': '#adb0ff',\n", " 'Saint Lucia': '#ffb3ff',\n", " 'Saint Vincent and the Grenadines': '#90d595',\n", " 'Seychelles': '#e48381',\n", " 'Suriname': '#f7bb5f',\n", " 'Trinidad and Tobago': '#fb6b19',\n", " 'Uruguay': '#1fb1fb',\n", " 'Venezuela': '#adb0ff',\n", " 'Central African Republic': '#ffb3ff',\n", " 'Congo (Brazzaville)': '#90d595',\n", " 'Equatorial Guinea': '#e48381',\n", " 'Kosovo': '#f7bb5f',\n", " 'Uzbekistan': '#fb6b19',\n", " 'Benin': '#1fb1fb',\n", " 'Greenland': '#adb0ff',\n", " 'Guam': '#ffb3ff',\n", " 'Liberia': '#90d595',\n", " 'Mayotte': '#e48381',\n", " 'Puerto Rico': '#f7bb5f',\n", " 'Republic of the Congo': '#fb6b19',\n", " 'Somalia': '#1fb1fb',\n", " 'Tanzania': '#adb0ff',\n", " 'The Bahamas': '#ffb3ff',\n", " 'Barbados': '#90d595',\n", " 'Montenegro': '#e48381',\n", " 'The Gambia': '#f7bb5f',\n", " 'Djibouti': '#fb6b19',\n", " 'Gambia, The': '#1fb1fb',\n", " 'Kyrgyzstan': '#adb0ff',\n", " 'Mauritius': '#ffb3ff',\n", " 'Zambia': '#90d595',\n", " 'Bahamas, The': '#e48381',\n", " 'Chad': '#f7bb5f',\n", " 'El Salvador': '#fb6b19',\n", " 'Fiji': '#1fb1fb',\n", " 'Nicaragua': '#adb0ff',\n", " 'Angola': '#ffb3ff',\n", " 'Cabo Verde': '#90d595',\n", " 'Haiti': '#e48381',\n", " 'Madagascar': '#f7bb5f',\n", " 'Niger': '#fb6b19',\n", " 'Papua New Guinea': '#1fb1fb',\n", " 'Zimbabwe': '#adb0ff',\n", " 'Cape Verde': '#ffb3ff',\n", " 'East Timor': '#90d595',\n", " 'Eritrea': '#e48381',\n", " 'Uganda': '#f7bb5f',\n", " 'Bahamas': '#fb6b19',\n", " 'Dominica': '#1fb1fb',\n", " 'Gambia': '#adb0ff',\n", " 'Grenada': '#ffb3ff',\n", " 'Mozambique': '#90d595',\n", " 'Syria': '#e48381',\n", " 'Timor-Leste': '#f7bb5f',\n", " 'Belize': '#fb6b19',\n", " 'Laos': '#1fb1fb',\n", " 'Libya': '#adb0ff',\n", " 'Diamond Princess': '#ffb3ff',\n", " 'Guinea-Bissau': '#90d595',\n", " 'Mali': '#e48381',\n", " 'Saint Kitts and Nevis': '#f7bb5f',\n", " 'West Bank and Gaza': '#fb6b19',\n", " 'Burma': '#1fb1fb',\n", " 'MS Zaandam': '#adb0ff',\n", " 'Botswana': '#ffb3ff',\n", " 'Burundi': '#90d595',\n", " 'Sierra Leone': '#e48381',\n", " 'Malawi': '#f7bb5f',\n", " 'South Sudan': '#fb6b19',\n", " 'Western Sahara': '#1fb1fb'}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colors = dict(zip(\n", " df_date_series.Country.unique(),\n", " ['#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#f7bb5f','#fb6b19','#1fb1fb'] * 31\n", "))\n", "colors" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def plot_barh(date):\n", " data = df_date_series[df_date_series.Date.eq(date)].sort_values(\n", " 'Confirmed',\n", " ascending=False\n", " ).head(10) #Taking top 10 most countries with cases for each date\n", " data = data[::-1] #Reversing it, so we have highest value on top\n", " \n", " ax.clear() #Clear all axes before proceeding, as same subplot used for every frame\n", " \n", " ax.barh( #Making the horizontal bar plot for 10 countries of a particular date\n", " data.Country,\n", " data.Confirmed,\n", " color= [colors[c] for c in data.Country]\n", " )\n", " \n", " max_val = data.Confirmed.max() #At any given time, the right bound of the x-axis\n", " tot_bars = len(data)\n", " \n", " for i,(cases,country,deaths) in enumerate(zip(data.Confirmed,data.Country,data.Deaths)):\n", " ax.text(cases,i,country+\" \",ha='right') #Adding Country informations\n", " ax.text(cases,i-0.15,deaths,ha='left') #Adding Death count\n", " ax.text(cases,i+0.15,cases,ha='left') #Adding Confirmed cases count\n", " \n", " \n", " ax.set_yticks([])\n", " ax.set_xticks([])\n", " \n", " ax.set_title('Number of COVID-19 cases per country',fontsize=18)\n", " ax.spines['top'].set_visible(False)\n", " ax.spines['right'].set_visible(False)\n", " ax.spines['bottom'].set_visible(False)\n", " \n", " ax.text(max_val,tot_bars/2,date,fontsize=16) #Displaying date for which this frame is create\n", " \n", "#plt.box(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have out function ready, we are going to give it to the `matplotlib.animation` module with the frames as `Date` to build the animation for us." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(figsize=(16,10)) #Set figure for plot\n", "animator = animate.FuncAnimation(fig,plot_barh,frames=df_date_series.Date.unique()) #Building animation\n", "HTML(animator.to_jshtml()) #Displaying animation in JsHTML format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The racing bar plot gives a good head to head count of number of cases and deaths." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This concludes the project of Visualizing COVID-19 Spread." ] } ], "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }