{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# COVID-19 Interactive Analysis Dashboard"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is COVID-19?\n",
"\n",
"> Coronaviruses are a large family of viruses that may cause respiratory illnesses in humans ranging from common colds to more severe conditions such as Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory Syndrome (MERS).1\n",
"'Novel coronavirus' is a new, previously unidentified strain of coronavirus. The novel coronavirus involved in the current outbreak has been named SARS-CoV-2 by the World Health Organization (WHO). 3The disease it causes has been named “coronavirus disease 2019” (or “COVID-19”).`\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# importing libraries\n",
"\n",
"from __future__ import print_function\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"from IPython.core.display import display, HTML\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import plotly.express as px\n",
"#import folium\n",
"import plotly.graph_objects as go\n",
"import seaborn as sns\n",
"import ipywidgets as widgets"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# loading data right from the source:\n",
"death_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')\n",
"confirmed_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\n",
"recovered_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')\n",
"country_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_country.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# confirmed_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# recovered_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# death_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# country_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# data cleaning\n",
"\n",
"# renaming the df column names to lowercase\n",
"country_df.columns = map(str.lower, country_df.columns)\n",
"confirmed_df.columns = map(str.lower, confirmed_df.columns)\n",
"death_df.columns = map(str.lower, death_df.columns)\n",
"recovered_df.columns = map(str.lower, recovered_df.columns)\n",
"\n",
"# changing province/state to state and country/region to country\n",
"confirmed_df = confirmed_df.rename(columns={'province/state': 'state', 'country/region': 'country'})\n",
"recovered_df = confirmed_df.rename(columns={'province/state': 'state', 'country/region': 'country'})\n",
"death_df = death_df.rename(columns={'province/state': 'state', 'country/region': 'country'})\n",
"country_df = country_df.rename(columns={'country_region': 'country'})\n",
"# country_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# total number of confirmed, death and recovered cases\n",
"confirmed_total = int(country_df['confirmed'].sum())\n",
"deaths_total = int(country_df['deaths'].sum())\n",
"recovered_total = int(country_df['recovered'].sum())\n",
"active_total = int(country_df['active'].sum())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" Confirmed: 5593148 Deaths: 350509 Recovered: 2288034
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# displaying the total stats\n",
"\n",
"display(HTML(\"\" +\n",
" \" Confirmed: \" + str(confirmed_total) +\"\" +\n",
" \" Deaths: \" + str(deaths_total) + \"\"+\n",
" \" Recovered: \" + str(recovered_total) + \"\"+\n",
" \"
\")\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# COVID-19 Confirmed/Death/Recovered cases by countries\n",
"\n",
"## Enter number of countries you want the data for"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
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"model_id": "b040925074c84d618051a03094e191c2",
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"version_minor": 0
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"text/plain": [
"interactive(children=(Text(value='10', description='n'), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
},
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"text/plain": [
"VBox(children=(FigureWidget({\n",
" 'data': [], 'layout': {'template': '...'}\n",
"}),), layout=Layout(border='solid …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# sorting the values by confirmed descednding order\n",
"# country_df.sort_values('confirmed', ascending= False).head(10).style.background_gradient(cmap='copper')\n",
"fig = go.FigureWidget( layout=go.Layout() )\n",
"def highlight_col(x):\n",
" r = 'background-color: red'\n",
" y = 'background-color: purple'\n",
" g = 'background-color: grey'\n",
" df1 = pd.DataFrame('', index=x.index, columns=x.columns)\n",
" df1.iloc[:, 4] = y\n",
" df1.iloc[:, 5] = r\n",
" df1.iloc[:, 6] = g\n",
" \n",
" return df1\n",
"\n",
"def show_latest_cases(n):\n",
" n = int(n)\n",
" return country_df.sort_values('deaths', ascending= False).head(n).style.apply(highlight_col, axis=None)#'confirmed'\n",
"\n",
"interact(show_latest_cases, n='10')\n",
"\n",
"ipywLayout = widgets.Layout(border='solid 2px green')\n",
"ipywLayout.display='none' # uncomment this, run cell again - then the graph/figure disappears\n",
"widgets.VBox([fig], layout=ipywLayout)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"sorted_country_df = country_df.sort_values('confirmed', ascending= False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Slide to check for the worst hit countries"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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"interactive(children=(IntSlider(value=10, description='n', max=30, min=-10), Output()), _dom_classes=('widget-…"
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"text/plain": [
"VBox(children=(FigureWidget({\n",
" 'data': [], 'layout': {'template': '...'}\n",
"}),), layout=Layout(border='solid …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# # plotting the 20 worst hit countries\n",
"\n",
"def bubble_chart(n):\n",
" fig = px.scatter(sorted_country_df.head(n), x=\"country\", y=\"confirmed\", size=\"confirmed\", color=\"country\",\n",
" hover_name=\"country\", size_max=60)\n",
" fig.update_layout(\n",
" title=str(n) +\" Worst hit countries\",\n",
" xaxis_title=\"Countries\",\n",
" yaxis_title=\"Confirmed Cases\",\n",
" width = 700\n",
" )\n",
" fig.show();\n",
"\n",
"interact(bubble_chart, n=10)\n",
"\n",
"ipywLayout = widgets.Layout(border='solid 2px green')\n",
"ipywLayout.display='none'\n",
"widgets.VBox([fig], layout=ipywLayout)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"def plot_cases_of_a_country(country):\n",
" labels = ['confirmed', 'deaths']\n",
" colors = ['blue', 'red']\n",
" mode_size = [6, 8]\n",
" line_size = [4, 5]\n",
" \n",
" df_list = [confirmed_df, death_df]\n",
" \n",
" fig = go.Figure();\n",
" \n",
" for i, df in enumerate(df_list):\n",
" if country == 'World' or country == 'world':\n",
" x_data = np.array(list(df.iloc[:, 20:].columns))\n",
" y_data = np.sum(np.asarray(df.iloc[:,4:]),axis = 0)\n",
" \n",
" else: \n",
" x_data = np.array(list(df.iloc[:, 20:].columns))\n",
" y_data = np.sum(np.asarray(df[df['country'] == country].iloc[:,20:]),axis = 0)\n",
" \n",
" fig.add_trace(go.Scatter(x=x_data, y=y_data, mode='lines+markers',\n",
" name=labels[i],\n",
" line=dict(color=colors[i], width=line_size[i]),\n",
" connectgaps=True,\n",
" text = \"Total \" + str(labels[i]) +\": \"+ str(y_data[-1])\n",
" ));\n",
" \n",
" fig.update_layout(\n",
" title=\"COVID 19 cases of \" + country,\n",
" xaxis_title='Date',\n",
" yaxis_title='No. of Confirmed Cases',\n",
" margin=dict(l=20, r=20, t=40, b=20),\n",
" paper_bgcolor=\"lightgrey\",\n",
" width = 800,\n",
" \n",
" );\n",
" \n",
" fig.update_yaxes(type=\"linear\")\n",
" fig.show();\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check the details of your country or the World\n",
"\n",
"* Enter the name of your country(in capitalized format(e.g. Italy)) and world for total cases"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
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"text/plain": [
"interactive(children=(Text(value='Brazil', description='country'), Output()), _dom_classes=('widget-interact',…"
]
},
"metadata": {},
"output_type": "display_data"
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"}),), layout=Layout(border='solid …"
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interact(plot_cases_of_a_country, country='Brazil') #'World'\n",
"\n",
"ipywLayout = widgets.Layout(border='solid 2px green')\n",
"ipywLayout.display='none' # uncomment this, run cell again - then the graph/figure disappears\n",
"widgets.VBox([fig], layout=ipywLayout)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 10 worst hit countries - Confirmed cases"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
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