
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
# for better interactive visualization
import plotly.graph_objects as go
import plotly.express as px
from plotly.offline import iplot #to see graphs in offline mode

#to ignore the filter warnings
import warnings
warnings.filterwarnings('ignore')


files=os.listdir(r'C:\Users\Soham Das\Data-analysis-projects\Covid-19-Data-Analysis\Covid-19')
files
#we got many data sets so lets create the functions

['country_wise_latest.csv',
 'covid_19_clean_complete.csv',
 'day_wise.csv',
 'full_grouped.csv',
 'usa_country_wise.csv',
 'worldometer_data.csv']


# lets create a function to make our task simpler as we have to read data again & again 
def read_data(path,filename):
    return pd.read_csv(path+'/'+filename)


path=(r'C:\Users\Soham Das\Data-analysis-projects\Covid-19-Data-Analysis\Covid-19')
read_data(path,'worldometer_data.csv')
#this is our dataframe


#saving the dataframe as world_data
world_data=read_data(path,'worldometer_data.csv')
world_data.head()


day_wise=read_data(path,files[2])


group_data=read_data(path,files[3])


usa_data=read_data(path,files[4])


province_data=read_data(path,files[1])


day_wise.shape

(188, 12)


world_data.columns
#we get all the column names

Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region'],
      dtype='object')


#more the size of each block more will be the count
#we can also use piechart or a bargraph for the same purpose
columns=['TotalCases','TotalDeaths','TotalRecovered','ActiveCases']
for i in columns:
    fig=px.treemap(world_data[0:20],values=i,path=['Country/Region'],title="<b>TreeMap representation of different Countries w.r.t. their {}</b>".format(i))
    fig.show()
#for all the case USA's count is huge for all the cases


#whenever we hear the word 'trend' try to visualise using line plot
fig=px.line(day_wise,x="Date",y=["Confirmed","Deaths","Recovered","Active"],title="COVID cases-vs-date")
fig.show()
#we can say deathcount is low compared to the confirmed cases count
#the rate of recovery is moderate
#during the month of june we can see recovery count gets higher,
#than the active cases since the the old cases are recovering.


#we will call top 20 countries i.e [0:20]
pop_test_ratio=world_data.iloc[0:20]['Population']/world_data.iloc[0:20]['TotalTests']


pop_test_ratio

0       5.245489
1      16.106896
2      62.365033
3       4.911040
4      18.852446
5     122.115932
6      13.241331
7      10.866949
8      28.269105
9       6.618696
10     32.187237
11      3.877883
12      9.589865
13    107.484026
14    134.558952
15      8.514790
16     16.613857
17     56.934398
18      9.760649
19     16.353942
dtype: float64


fig=px.bar(world_data.iloc[0:20],color='Country/Region',y=pop_test_ratio,x='Country/Region',title="<b>Population vs Tests done</b>")
fig.show()
#Bangladesh has conducted tests for the highest percentage of it's population
#followed by Pakistan, Mexico and India


#BarPlot Representation of CoronaViruses Cases w.r.t Time
fig=px.bar(world_data.iloc[0:20],x='Country/Region',y=['Serious,Critical','TotalDeaths','TotalRecovered','ActiveCases','TotalCases'])


fig.update_layout({'title':"Coronavirus cases vs Time"})
fig.show()
#USA is the country which is most affected by COVID
#Brazil and India are also in critical situation


world_data.head()


world_data['Country/Region'].nunique()

209


fig=px.bar(world_data.iloc[0:20],y='Country/Region',x='TotalCases',color='TotalCases',text="TotalCases")
fig.update_layout(title_text="<b>Top 20 countries of Total confirmed cases</b>")
fig.show()


fig=px.bar(world_data.sort_values(by='TotalDeaths',ascending=False)[0:20],y='Country/Region',x='TotalDeaths',color='TotalDeaths',text="TotalDeaths")
fig.update_layout(title_text="<b>Top 20 countries of Total deaths</b>")
fig.show()


fig=px.bar(world_data.sort_values(by='ActiveCases',ascending=False)[0:20], y='Country/Region',x='ActiveCases',color='ActiveCases',text='ActiveCases')
fig.update_layout(title_text="<b>Top 20 countries of Total Active cases")
fig.show()


fig=px.bar(world_data.sort_values(by='TotalRecovered',ascending=False)[:20],y='Country/Region',x='TotalRecovered',color='TotalRecovered',text='TotalRecovered')
fig.update_layout(title_text="<b>Top 20 countries of Total Recovered")
fig.show()


world_data.columns

Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region'],
      dtype='object')


world_data[0:15]['Country/Region'].values

array(['USA', 'Brazil', 'India', 'Russia', 'South Africa', 'Mexico',
       'Peru', 'Chile', 'Colombia', 'Spain', 'Iran', 'UK', 'Saudi Arabia',
       'Pakistan', 'Bangladesh'], dtype=object)


labels=world_data[0:15]['Country/Region'].values
cases=['TotalCases','TotalDeaths','TotalRecovered','ActiveCases']
for i in cases:
    fig=px.pie(world_data[0:15],values=i,names=labels,hole=0.3,title=" {} Recordeded w.r.t. to WHO Region of 15 worst effected countries ".format(i))
    fig.show()


deaths_to_confirmed=((world_data['TotalDeaths']/world_data['TotalCases']))
fig = px.bar(world_data,x='Country/Region',y=deaths_to_confirmed)
fig.update_layout(title={'text':"Death to confirmed ratio of some  worst effected countries",'xanchor':'left'})
fig.show()

#hovering the mouse over the longest bar we find It's yemen
#yemen has highest Death:Confirmed ratio


deaths_to_recovered=((world_data['TotalDeaths']/world_data['TotalRecovered']))
fig = px.bar(world_data,x='Country/Region',y=deaths_to_recovered)
fig.update_layout(title={'text':"Death to recovered ratio of some  worst effected countries",'xanchor':'left'})
fig.show()
#we can see recovery rate of Yemen and Belgium is quite high with respect to death count


tests_to_confirmed=((world_data['TotalTests']/world_data['TotalCases']))
fig = px.bar(world_data,x='Country/Region',y=tests_to_confirmed)
fig.update_layout(title={'text':"Tests to confirmed ratio of some  worst effected countries",'xanchor':'left'})
fig.show()
#We would find Laos is where highest no. of people getting tested is COVID +ve


world_data['Serious,Critical']/world_data['TotalDeaths']

0      0.112381
1      0.084323
2      0.214804
3      0.157470
4      0.056122
         ...   
204         NaN
205         NaN
206         NaN
207         NaN
208         NaN
Length: 209, dtype: float64


serious_to_death=((world_data['Serious,Critical']/world_data['TotalDeaths']))
fig = px.bar(world_data,x='Country/Region',y=serious_to_death)
fig.update_layout(title={'text':"serious to Death ratio of some worst effected countries",'xanchor':'left'})
fig.show()
#we can conclude that most of the people having serious COVID condition have a high chance of death
#We can find namibia at the top
#but there are many countries with serious fatal conditions


group_data.head()


#for creating subplots in plotly
from plotly.subplots import make_subplots  
import plotly.graph_objects as go


def country_visualization(group_data,country):   
    #we will accept the country name
    data=group_data[group_data['Country/Region']==country]
    df=data.loc[:,['Date','Confirmed','Deaths','Recovered','Active']]
    #calling functions which makes subplots
    #then we need to mention the features needed to be visualised
    fig = make_subplots(rows=1, cols=4,subplot_titles=("Confirmed", "Active", "Recovered",'Deaths'))
    #Confirmed
    fig.add_trace(
        go.Scatter(name="Confirmed",x=df['Date'],y=df['Confirmed']),
        row=1, col=1
    )
    
    #Active
    fig.add_trace(
        go.Scatter(name="Active",x=df['Date'],y=df['Active']),
        row=1, col=2
    )
    
    #Recovered
    fig.add_trace(
        go.Scatter(name="Recovered",x=df['Date'],y=df['Recovered']),
        row=1, col=3
    )
    
    #Deaths
    fig.add_trace(
        go.Scatter(name="Deaths",x=df['Date'],y=df['Deaths']),
        row=1, col=4
    )

    fig.update_layout(height=600, width=1000, title_text="Date Vs Recorded Cases of {}".format(country))
    fig.show()


country_visualization(group_data,'India')
#all the graphs are almost of same trend for india


country_visualization(group_data,'US')
#for US we see the a slight change in death Curve and active curve trend

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia
3	Russia	Europe	1.459409e+08	871894	NaN	14606.0	NaN	676357.0	NaN	180931.0	2300.0	5974.0	100.0	29716907.0	203623.0	Europe
4	South Africa	Africa	5.938157e+07	538184	NaN	9604.0	NaN	387316.0	NaN	141264.0	539.0	9063.0	162.0	3149807.0	53044.0	Africa

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia
3	Russia	Europe	1.459409e+08	871894	NaN	14606.0	NaN	676357.0	NaN	180931.0	2300.0	5974.0	100.0	29716907.0	203623.0	Europe
4	South Africa	Africa	5.938157e+07	538184	NaN	9604.0	NaN	387316.0	NaN	141264.0	539.0	9063.0	162.0	3149807.0	53044.0	Africa

	Date	Country/Region	WHO Region
0	2020-01-22	Afghanistan	Eastern Mediterranean
1	2020-01-22	Albania	Europe
2	2020-01-22	Algeria	Africa
3	2020-01-22	Andorra	Europe
4	2020-01-22	Angola	Africa

Soham Das¶

Covid 19 Data Analysis¶

1)Data Preparation¶

2)Which Country has maximum Total cases, Deaths, Recovered & active cases¶

Lets create TreeMap Representation of our data¶

3)what is the trend of Confirmed Deaths Recovered Active cases¶

4)Population : Tests Done ratio¶

5)20 countries that are badly affected by corona¶

6)Top 20 countries of Total Confirmed Cases, Total Recovered Cases, Total Deaths,Total Active Cases¶

6.1)Top 20 countries of Total Confirmed cases¶

6.2)Top 20 countries of Total deaths¶

6.3)Top 20 countries of Total active cases¶

6.4)Top 20 countries of Total Recoveries¶

7)Pie Chart Representation of stats of worst affected countries¶

Pie Charts in Donut Shape¶

8)Deaths:Confirmed ratio¶

9)Deaths to recovered ratio¶

10)Tests to Confirmed Ratio¶

11)Serious to Deaths Ratio¶

12)Visualize Confirmed, Active, Recovered , Deaths Cases(entire statistics ) of a particular country¶