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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

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