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DataScience Programs like real-time examples will be discussed here
Covid-19 Analysis and Prediction
What is Data Science?
Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
COVID-19 - Analysis, Viz, Prediction & Comparisons
Corona Virus
Coronaviruses are zoonotic viruses (means transmitted between animals and people).
Symptoms include from fever, cough, respiratory symptoms, and breathing difficulties.
In severe cases, it can cause pneumonia, severe acute respiratory syndrome (SARS), kidney failure and even death. Coronaviruses are also asymptomatic, means a person can be a carrier for the infection but experiences no symptoms
Novel coronavirus (nCoV)
A novel coronavirus (nCoV) is a new strain that has not been previously identified in humans.
COVID-19 (Corona Virus Disease 2019)
Caused by a SARS-COV-2 coronavirus.
First identified in Wuhan, Hubei, China. Earliest reported symptoms reported in November 2019.
The first cases were linked to contact with the Huanan Seafood Wholesale Market, which sold live animals.
On 30 January the WHO declared the outbreak to be a Public Health Emergency of International Concern
for the installation executing this data science program try to use an online notebook to have a great experience and performance of execution, result as well show below the page itself
Libraries
to install for requiring to run data science analytics and charts
Installation methods
# install calmap calling this built-in module
! pip install calmap
#requre libraries we are going user for this program import
# essential libraries importing to work with as well
import json
import random
from urllib.request import urlopen
# storing and anaysis
import numpy as np
import pandas as pd
# visualization for data we are importing these libraries
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objs as go
import plotly.figure_factory as ff
import calmap
import folium
# color pallette for coluring for beautification
cnf = '#393e46' # confirmed - grey
dth = '#ff2e63' # death - red
rec = '#21bf73' # recovered - cyan
act = '#fe9801' # active case - yellow
# converter for conversation
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
# hide warnings if any warning messages
import warnings
warnings.filterwarnings('ignore')
# html embedding to visualize data in HTML format like charts and tables
from IPython.display import Javascript
from IPython.core.display import display
from IPython.core.display import HTML
Dataset to use
# list files
# !ls ../input/corona-virus-report
# importing datasets
full_table = pd.read_csv('../input/corona-virus-report/covid_19_clean_complete.csv', parse_dates=['Date'])
full_table.head()
Processing for data steps
Cleaning Data
# cases to prepare few i am prepared
cases = ['Confirmed', 'Deaths', 'Recovered', 'Active']
# Active Case = confirmed - deaths - recovered
full_table['Active'] = full_table['Confirmed'] - full_table['Deaths'] - full_table['Recovered']
# replacing Mainland china with just China
full_table['Country/Region'] = full_table['Country/Region'].replace('Mainland China', 'China')
# filling missing values
full_table[['Province/State']] = full_table[['Province/State']].fillna('')
full_table[cases] = full_table[cases].fillna(0)
Derived Tables from previous
# cases in the ships
ship = full_table[full_table['Province/State'].str.contains('Grand Princess')|full_table['Country/Region'].str.contains('Cruise Ship')]
# china and the row
china = full_table[full_table['Country/Region']=='China']
row = full_table[full_table['Country/Region']!='China']
# latest
full_latest = full_table[full_table['Date'] == max(full_table['Date'])].reset_index()
china_latest = full_latest[full_latest['Country/Region']=='China']
row_latest = full_latest[full_latest['Country/Region']!='China']
# latest condensed
full_latest_grouped = full_latest.groupby('Country/Region')['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()
china_latest_grouped = china_latest.groupby('Province/State')['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()
row_latest_grouped = row_latest.groupby('Country/Region')['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()
Latest Data as representing from analytic reports
Latest Complete Data
temp = full_table.groupby(['Country/Region', 'Province/State'])['Confirmed', 'Deaths', 'Recovered', 'Active'].max()
# temp.style.background_gradient(cmap='Reds')
Latest Condensed Data
temp = full_table.groupby('Date')['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()
temp = temp[temp['Date']==max(temp['Date'])].reset_index(drop=True)
temp.style.background_gradient(cmap='Pastel1')
tm = temp.melt(id_vars="Date", value_vars=['Active', 'Deaths', 'Recovered'])
fig = px.treemap(tm, path=["variable"], values="value", height=400, width=600, color_discrete_sequence=[rec, act, dth])
fig.show()
Country-wise Data as you may get from online for testing purpose
In each country
temp_f = full_latest_grouped.sort_values(by='Confirmed', ascending=False)
temp_f = temp_f.reset_index(drop=True)
temp_f.style.background_gradient(cmap='Reds')
Countries with deaths reported
temp_flg = temp_f[temp_f['Deaths']>0][['Country/Region', 'Deaths']]
temp_flg.sort_values('Deaths', ascending=False).reset_index(drop=True).style.background_gradient(cmap='Reds')
Countries with no cases recovered
temp = temp_f[temp_f['Recovered']==0][['Country/Region', 'Confirmed', 'Deaths', 'Recovered']]
temp.reset_index(drop=True).style.background_gradient(cmap='Reds')
Countries with all cases died
temp = row_latest_grouped[row_latest_grouped['Confirmed']== row_latest_grouped['Deaths']]
temp = temp[['Country/Region', 'Confirmed', 'Deaths']]
temp = temp.sort_values('Confirmed', ascending=False)
temp = temp.reset_index(drop=True)
temp.style.background_gradient(cmap='Reds')
Maps to identify the place effected
Across the world
# World wide
m = folium.Map(location=[0, 0], tiles='cartodbpositron', min_zoom=1, max_zoom=4, zoom_start=1)
for i in range(0, len(full_latest)):
folium.Circle(
location=[full_latest.iloc[i]['Lat'], full_latest.iloc[i]['Long']],
color='crimson',
tooltip = '
Country : '+str(full_latest.iloc[i]['Country/Region'])+
'
Province : '+str(full_latest.iloc[i]['Province/State'])+
'
Confirmed : '+str(full_latest.iloc[i]['Confirmed'])+
'
Deaths : '+str(full_latest.iloc[i]['Deaths'])+
'
Recovered : '+str(full_latest.iloc[i]['Recovered']),
radius=int(full_latest.iloc[i]['Confirmed'])**1.1).add_to(m)
# Confirmed
fig = px.choropleth(full_latest_grouped, locations="Country/Region",
locationmode='country names', color="Confirmed",
hover_name="Country/Region", range_color=[1,7000],
color_continuous_scale="aggrnyl",
title='Countries with Confirmed Cases')
fig.update(layout_coloraxis_showscale=False)
fig.show()
Note: this is the tutorial is for education purpose of data science program for covid-19 treat as same