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.

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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()

Task 1.2:

let’s check the dataframe

df_31May21.head()

df_31May21.tail()

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()

dfconf.shape

(91970, 5)

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

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

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

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()

dfdeaths.shape

(81987, 5)

Chart 2: 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()

let’s select the columns that we need

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

df_china.head()

calculating the first derivation of confrimed column

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

df_china.head()

#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()

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()

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()

let’s get data related to italy

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

lets check the dataframe

df_italy.head()

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

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()

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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()

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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()

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

df_italy.head()

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()

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()

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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()

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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()

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