Animated Covid 19 analysis using Python

Tue, 20 Jul 2021 00:00:00 +0000

Covid 19 analysis using Python

We use Python to animate the spread of covid around the world. Then we focus on a few countries and see how the impact of lockdown has affected the spread of covid in that country. We further see how the infection rates and death rates are correlated.

Importing modules

Task 1

import pandas as pd
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt
print('modules are imported')

modules are imported

Task 1.1:

Loading the Dataset

dataset_url = 'https://raw.githubusercontent.com/datasets/covid-19/main/data/countries-aggregated.csv'
fname = 'data/countries-aggregated.csv'
df = pd.read_csv(fname)

df_31May21 = df[df.Date == '2020-05-31']

df_31May21.head()

	Date	Country	Confirmed	Recovered	Deaths
130	2020-05-31	Afghanistan	15208	1328	258
658	2020-05-31	Albania	1137	872	33
1186	2020-05-31	Algeria	9394	5748	653
1714	2020-05-31	Andorra	764	694	51
2242	2020-05-31	Angola	86	18	4

Task 1.2:

let’s check the dataframe

df_31May21.head()

	Date	Country	Confirmed	Recovered	Deaths
130	2020-05-31	Afghanistan	15208	1328	258
658	2020-05-31	Albania	1137	872	33
1186	2020-05-31	Algeria	9394	5748	653
1714	2020-05-31	Andorra	764	694	51
2242	2020-05-31	Angola	86	18	4

df_31May21.tail()

	Date	Country	Confirmed	Recovered	Deaths
100450	2020-05-31	Vietnam	328	279	0
100978	2020-05-31	West Bank and Gaza	448	372	3
101506	2020-05-31	Yemen	323	14	80
102034	2020-05-31	Zambia	1057	779	7
102562	2020-05-31	Zimbabwe	178	29	4

let’s check the shape of the dataframe

df_31May21.shape

(195, 5)

df.shape

(102960, 5)

Task 2.1 :

let’s do some preprocessing

dfconf=df[df.Confirmed>0]

dfconf.head()

	Date	Country	Confirmed
33	2020-02-24	Afghanistan	1
34	2020-02-25	Afghanistan	1
35	2020-02-26	Afghanistan	1
36	2020-02-27	Afghanistan	1
37	2020-02-28	Afghanistan	1

dfconf.shape

(91970, 5)

dfconf[dfconf.Country=='Italy'].head(10)

	Date	Country	Confirmed
44889	2020-01-31	Italy	2
44890	2020-02-01	Italy	2
44891	2020-02-02	Italy	2
44892	2020-02-03	Italy	2
44893	2020-02-04	Italy	2
44894	2020-02-05	Italy	2
44895	2020-02-06	Italy	2
44896	2020-02-07	Italy	3
44897	2020-02-08	Italy	3
44898	2020-02-09	Italy	3

let’s see Global spread of Covid19

Code:

fig = px.choropleth(dfconf, locations='Country', locationmode='country names', color='Confirmed', animation_frame='Date')
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 30
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 5
fig.update_geos(projection_type="equirectangular", visible=True, resolution=50)
fig.update_layout(
title_text = 'Global Spread of Coronavirus',
title_x = 0.5,
geo=dict(
showframe = False,
showcoastlines = False,
))
#fig.show()
iplot(fig,show_link=False)
pio.write_json(fig,"file001.json",engine="json")
fig.write_html("plot001.html")

Chart 1: Global Spread of Covid over Time

Spread of Covid

Example of Infection rate in China:

Chart 2:

---

let’s see Global spread of Covid19

title: Chk Part 02

Let’s see Global deaths of Covid19

dfdeaths=df[df.Deaths>0]

dfdeaths.head()

	Date	Country	Confirmed	Recovered	Deaths
60	2020-03-22	Afghanistan	34	1	1
61	2020-03-23	Afghanistan	41	1	1
62	2020-03-24	Afghanistan	43	1	1
63	2020-03-25	Afghanistan	76	2	2
64	2020-03-26	Afghanistan	80	2	3

dfdeaths.shape

(81987, 5)

Chart 2: Global Deaths from Covid

Global Deaths from Covid
title: Chk Part 03

Let’s Visualize how intensive the Covid19 Transmission has been in each of the country

let’s start with an example:

df_china=df[df.Country == 'China']

import pandas as pd
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt
print('modules are imported')

modules are imported

df_china.head()

	Date	Country	Confirmed	Recovered	Deaths
19008	2020-01-22	China	548	28	17
19009	2020-01-23	China	643	30	18
19010	2020-01-24	China	920	36	26
19011	2020-01-25	China	1406	39	42
19012	2020-01-26	China	2075	49	56

let’s select the columns that we need

df_china=df_china[['Date','Confirmed']]

df_china.head()

	Date	Confirmed
19008	2020-01-22	548
19009	2020-01-23	643
19010	2020-01-24	920
19011	2020-01-25	1406
19012	2020-01-26	2075

calculating the first derivation of confrimed column

df_china['Infection Rate']=df_china['Confirmed'].diff()

df_china.head()

	Date	Confirmed	Infection Rate
19008	2020-01-22	548	NaN
19009	2020-01-23	643	95.0
19010	2020-01-24	920	277.0
19011	2020-01-25	1406	486.0
19012	2020-01-26	2075	669.0

#px.line(df_china, x='Date', y=['Confirmed', 'Infection Rate'])

df_china['Infection Rate'].max()

15136.0

Task 3.2:

Let’s Calculate Maximum infection rate for all of the countries

df.head()

	Date	Country
0	2020-01-22	Afghanistan
1	2020-01-23	Afghanistan
2	2020-01-24	Afghanistan
3	2020-01-25	Afghanistan
4	2020-01-26	Afghanistan

countries=list(df['Country'].unique())
#countries

countries=list(df['Country'].unique())

max_infection_rate=[]
for c in countries :
MIR = df[df.Country == c].Confirmed.diff().max()
max_infection_rate.append(MIR)
#print(max_infection_rate)

Task 3.3:

let’s create a new Dataframe

df_MIR=pd.DataFrame()
df_MIR['Country'] = countries
df_MIR['Max Infection Rate'] = max_infection_rate
df_MIR.head()

	Country	Max Infection Rate
0	Afghanistan	5168.0
1	Albania	1239.0
2	Algeria	1133.0
3	Andorra	299.0
4	Angola	405.0

Let’s plot the barchart : maximum infection rate of each country

#px.bar(df_MIR, x='Country', y='Max Infection Rate', color='Country', title='global maximum infection rate', log_y=True)

#log to increase the quALITY FOR low bars - changes scale for y axis

Task 4: Let’s See how National Lockdowns Impacts Covid19 transmission in Italy

COVID19 pandemic lockdown in Italy

On 9 March 2020, the government of Italy under Prime Minister Giuseppe Conte imposed a national quarantine, restricting the movement of the population except for necessity, work, and health circumstances, in response to the growing pandemic of COVID-19 in the country. source

italy_lockdown_start_date = '2020-03-09'
italy_lockdown_a_month_later = '2020-04-09'

df.head()

	Date	Country
0	2020-01-22	Afghanistan
1	2020-01-23	Afghanistan
2	2020-01-24	Afghanistan
3	2020-01-25	Afghanistan
4	2020-01-26	Afghanistan

let’s get data related to italy

df_italy=df[df.Country=='Italy']

lets check the dataframe

df_italy.head()

	Date	Country
44880	2020-01-22	Italy
44881	2020-01-23	Italy
44882	2020-01-24	Italy
44883	2020-01-25	Italy
44884	2020-01-26	Italy

let’s calculate the infection rate in Italy

df_italy['Infection Rate']=df_italy.Confirmed.diff()
df_italy.head()

/var/folders/43/4nqhk6qx3kxcwf85q5ncg9lm0000gn/T/ipykernel_74583/3001688291.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

	Date	Country	Infection Rate
44880	2020-01-22	Italy	NaN
44881	2020-01-23	Italy	0.0
44882	2020-01-24	Italy	0.0
44883	2020-01-25	Italy	0.0
44884	2020-01-26	Italy	0.0

ok! now let’s do the visualization

FigIt=px.line(df_italy, x='Date', y='Infection Rate', title="Before and After lockdown in Italy")
FigIt.show()

FigIt2=px.line(df_italy, x='Date', y='Infection Rate', title="Before and After lockdown in Italy")
FigIt2.add_shape(
dict(
type="line",
x0=italy_lockdown_start_date,
y0=0,
x1=italy_lockdown_start_date,
y1=df_italy['Infection Rate'].max(),
line=dict(color='red', width=2)
)
)
FigIt2.add_annotation(
dict(
x=italy_lockdown_start_date,
y=df_italy['Infection Rate'].max(),
text='Starting Date of Lockdown'
)
)
FigIt2.show()

FigIt3=px.line(df_italy, x='Date', y='Infection Rate', title="Before and After lockdown in Italy")
FigIt3.add_shape(
dict(
type="line",
x0=italy_lockdown_start_date,
y0=0,
x1=italy_lockdown_start_date,
y1=df_italy['Infection Rate'].max(),
line=dict(color='red', width=2)
)
)
FigIt3.add_annotation(
dict(
x=italy_lockdown_start_date,
y=df_italy['Infection Rate'].max(),
text='Starting Date of Lockdown'
)
)
FigIt3.add_shape(
dict(
type="line",
x0=italy_lockdown_a_month_later,
y0=0,
x1=italy_lockdown_a_month_later,
y1=df_italy['Infection Rate'].max(),
line=dict(color='red', width=2)
)
)
FigIt3.add_annotation(
dict(
x=italy_lockdown_a_month_later,
y=4000,
text='One month post Lockdown'
)
)
FigIt3.show()

---------------------------------------------------------------------------

FigIt2=px.line(df_italy, x='Date', y='Infection Rate', title="Before and After lockdown in Italy")
FigIt2.add_shape(
dict(
type="line",
x0=italy_lockdown_start_date,
y0=0,
x1=italy_lockdown_start_date,
y1=df_italy['Infection Rate'].max(),
line=dict(color='red', width=2)
)
)
FigIt2.add_annotation(
dict(
x=italy_lockdown_start_date,
y=df_italy['Infection Rate'].max(),
text='Starting Date of Lockdown'
)
)
FigIt2.show()

---------------------------------------------------------------------------

FigIt3=px.line(df_italy, x='Date', y='Infection Rate', title="Before and After lockdown in Italy")
FigIt3.add_shape(
dict(
type="line",
x0=italy_lockdown_start_date,
y0=0,
x1=italy_lockdown_start_date,
y1=df_italy['Infection Rate'].max(),
line=dict(color='red', width=2)
)
)
FigIt3.add_annotation(
dict(
x=italy_lockdown_start_date,
y=df_italy['Infection Rate'].max(),
text='Starting Date of Lockdown'
)
)
FigIt3.add_shape(
dict(
type="line",
x0=italy_lockdown_a_month_later,
y0=0,
x1=italy_lockdown_a_month_later,
y1=df_italy['Infection Rate'].max(),
line=dict(color='red', width=2)
)
)
FigIt3.add_annotation(
dict(
x=italy_lockdown_a_month_later,
y=4000,
text='One month post Lockdown'
)
)
FigIt3.show()

---------------------------------------------------------------------------

Before and After Lockdown

Task 5: Let’s See how National Lockdowns Impacts Covid19 active cases in Italy

df_italy.head()

	Date	Country	Infection Rate
44880	2020-01-22	Italy	NaN
44881	2020-01-23	Italy	0.0
44882	2020-01-24	Italy	0.0
44883	2020-01-25	Italy	0.0
44884	2020-01-26	Italy	0.0

let’s calculate number of active cases day by day

df_italy['Death Rate']=df_italy.Deaths.diff()

/var/folders/43/4nqhk6qx3kxcwf85q5ncg9lm0000gn/T/ipykernel_74583/834131105.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

let’s check the dataframe again

df_italy.head()

	Date	Country	Infection Rate	Death Rate
44880	2020-01-22	Italy	NaN	NaN
44881	2020-01-23	Italy	0.0	0.0
44882	2020-01-24	Italy	0.0	0.0
44883	2020-01-25	Italy	0.0	0.0
44884	2020-01-26	Italy	0.0	0.0

now let’s plot a line chart to compare COVID19 national lockdowns impacts on spread of the virus and number of active cases

figit4=px.line(df_italy, x='Date', y=['Infection Rate', 'Death Rate'])
figit4.show()

---------------------------------------------------------------------------

Absolute Death Rates and Infection Rates Before and After Lockdown - not easily comparable

df_italy['N Infection Rate']=df_italy['Infection Rate']/df_italy['Infection Rate'].max()
df_italy['N Death Rate']=df_italy['Death Rate']/df_italy['Death Rate'].max()

/var/folders/43/4nqhk6qx3kxcwf85q5ncg9lm0000gn/T/ipykernel_74583/3675118474.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/var/folders/43/4nqhk6qx3kxcwf85q5ncg9lm0000gn/T/ipykernel_74583/3675118474.py:2: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

figf= px.line(df_italy, x='Date', y=['N Infection Rate', 'N Death Rate'])
figf.show()

---------------------------------------------------------------------------

figf1= px.line(df_italy, x='Date', y=['N Infection Rate', 'N Death Rate'], title="Infection Rate and Death rate pre and post lockdown")
figf1.add_shape(
dict(
type="line",
x0=italy_lockdown_start_date,
y0=0,
x1=italy_lockdown_start_date,
y1=df_italy['N Infection Rate'].max(),
line=dict(color='yellow', width=2)
)
)
figf1.add_annotation(
dict(
x=italy_lockdown_start_date,
y=df_italy['N Infection Rate'].max(),
text='Starting Date of Lockdown'
)
)
figf1.add_shape(
dict(
type="line",
x0=italy_lockdown_a_month_later,
y0=0,
x1=italy_lockdown_a_month_later,
y1=df_italy['N Infection Rate'].max(),
line=dict(color='yellow', width=2)
)
)
figf1.add_annotation(
dict(
x=italy_lockdown_a_month_later,
y=0,
text='One month post Lockdown'
)
)
figf1.show()

---------------------------------------------------------------------------

Relative Infection Rates and Death Rates Before and After Lockdown

Posts | Saif Sayeed, Syed

Animated Covid 19 analysis using Python

Covid 19 analysis using Python

Importing modules

Task 1

Task 1.1:

Loading the Dataset

Task 1.2:

let’s check the dataframe

let’s check the shape of the dataframe

Task 2.1 :

let’s do some preprocessing

let’s see Global spread of Covid19

Code:

Chart 1: Global Spread of Covid over Time

Chart 2:

let’s see Global spread of Covid19

title: Chk Part 02

Let’s see Global deaths of Covid19

Chart 2: Global Deaths from Covid

Global Deaths from Covid
title: Chk Part 03

Let’s Visualize how intensive the Covid19 Transmission has been in each of the country

Task 3.2:

Let’s Calculate Maximum infection rate for all of the countries

Task 3.3:

let’s create a new Dataframe

Let’s plot the barchart : maximum infection rate of each country

Task 4: Let’s See how National Lockdowns Impacts Covid19 transmission in Italy

COVID19 pandemic lockdown in Italy

Task 5: Let’s See how National Lockdowns Impacts Covid19 active cases in Italy

Relevance of Public Health for Businesses

Posts | Saif Sayeed, Syed

Animated Covid 19 analysis using Python

Covid 19 analysis using Python

Importing modules

Task 1

Task 1.1:

Loading the Dataset

Task 1.2:

let’s check the dataframe

let’s check the shape of the dataframe

Task 2.1 :

let’s do some preprocessing

let’s see data related to a country for example Italy

let’s see Global spread of Covid19

Code:

Chart 1: Global Spread of Covid over Time

Chart 2:

let’s see Global spread of Covid19

title: Chk Part 02

Let’s see Global deaths of Covid19

Chart 2: Global Deaths from Covid

Global Deaths from Covid title: Chk Part 03

Let’s Visualize how intensive the Covid19 Transmission has been in each of the country

Task 3.2:

Let’s Calculate Maximum infection rate for all of the countries

Task 3.3:

let’s create a new Dataframe

Let’s plot the barchart : maximum infection rate of each country

Task 4: Let’s See how National Lockdowns Impacts Covid19 transmission in Italy

COVID19 pandemic lockdown in Italy

Task 5: Let’s See how National Lockdowns Impacts Covid19 active cases in Italy

Relevance of Public Health for Businesses

Global Deaths from Covid
title: Chk Part 03