The outbreak of Covid-19 resulted in a lot of restrictions which resulted in so many impacts on the global economy. Almost all the countries were impacted negatively by the rise in the cases of Covid-19. If you want to learn how to analyze the impacts of Covid-19 on the economy, this article is for you. In this article, I will take you through the task of Covid-19 Impacts Analysis using Python.
Covid-19 Impacts Analysis (Case Study)
The first wave of covid-19 impacted the global economy as the world was never ready for the pandemic. It resulted in a rise in cases, a rise in deaths, a rise in unemployment and a rise in poverty, resulting in an economic slowdown. Here, you are required to analyze the spread of Covid-19 cases and all the impacts of covid-19 on the economy.
The dataset we are using to analyze the impacts of covid-19 is downloaded from Kaggle. It contains data about:
- the country code
- name of all the countries
- date of the record
- Human development index of all the countries
- Daily covid-19 cases
- Daily deaths due to covid-19
- stringency index of the countries
- the population of the countries
- GDP per capita of the countries
You can download this dataset from here.
Covid-19 Impacts Analysis using Python
Let’s start the task of Covid-19 impacts analysis by importing the necessary Python libraries and the dataset:
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv("transformed_data.csv")
data2 = pd.read_csv("raw_data.csv")
print(data) CODE COUNTRY DATE HDI TC TD STI \
0 AFG Afghanistan 2019-12-31 0.498 0.000000 0.000000 0.000000
1 AFG Afghanistan 2020-01-01 0.498 0.000000 0.000000 0.000000
2 AFG Afghanistan 2020-01-02 0.498 0.000000 0.000000 0.000000
3 AFG Afghanistan 2020-01-03 0.498 0.000000 0.000000 0.000000
4 AFG Afghanistan 2020-01-04 0.498 0.000000 0.000000 0.000000
... ... ... ... ... ... ... ...
50413 ZWE Zimbabwe 2020-10-15 0.535 8.994048 5.442418 4.341855
50414 ZWE Zimbabwe 2020-10-16 0.535 8.996528 5.442418 4.341855
50415 ZWE Zimbabwe 2020-10-17 0.535 8.999496 5.442418 4.341855
50416 ZWE Zimbabwe 2020-10-18 0.535 9.000853 5.442418 4.341855
50417 ZWE Zimbabwe 2020-10-19 0.535 9.005405 5.442418 4.341855
POP GDPCAP
0 17.477233 7.497754
1 17.477233 7.497754
2 17.477233 7.497754
3 17.477233 7.497754
4 17.477233 7.497754
... ... ...
50413 16.514381 7.549491
50414 16.514381 7.549491
50415 16.514381 7.549491
50416 16.514381 7.549491
50417 16.514381 7.549491
[50418 rows x 9 columns]
The data we are using contains the data on covid-19 cases and their impact on GDP from December 31, 2019, to October 10, 2020.
Data Preparation
The dataset that we are using here contains two data files. One file contains raw data, and the other file contains transformed one. But we have to use both datasets for this task, as both of them contain equally important information in different columns. So let’s have a look at both the datasets one by one:
print(data.head())
CODE COUNTRY DATE HDI TC TD STI POP GDPCAP 0 AFG Afghanistan 2019-12-31 0.498 0.0 0.0 0.0 17.477233 7.497754 1 AFG Afghanistan 2020-01-01 0.498 0.0 0.0 0.0 17.477233 7.497754 2 AFG Afghanistan 2020-01-02 0.498 0.0 0.0 0.0 17.477233 7.497754 3 AFG Afghanistan 2020-01-03 0.498 0.0 0.0 0.0 17.477233 7.497754 4 AFG Afghanistan 2020-01-04 0.498 0.0 0.0 0.0 17.477233 7.497754
print(data2.head())
iso_code location date total_cases total_deaths \ 0 AFG Afghanistan 2019-12-31 0.0 0.0 1 AFG Afghanistan 2020-01-01 0.0 0.0 2 AFG Afghanistan 2020-01-02 0.0 0.0 3 AFG Afghanistan 2020-01-03 0.0 0.0 4 AFG Afghanistan 2020-01-04 0.0 0.0 stringency_index population gdp_per_capita human_development_index \ 0 0.0 38928341 1803.987 0.498 1 0.0 38928341 1803.987 0.498 2 0.0 38928341 1803.987 0.498 3 0.0 38928341 1803.987 0.498 4 0.0 38928341 1803.987 0.498 Unnamed: 9 Unnamed: 10 Unnamed: 11 Unnamed: 12 Unnamed: 13 0 #NUM! #NUM! #NUM! 17.477233 7.497754494 1 #NUM! #NUM! #NUM! 17.477233 7.497754494 2 #NUM! #NUM! #NUM! 17.477233 7.497754494 3 #NUM! #NUM! #NUM! 17.477233 7.497754494 4 #NUM! #NUM! #NUM! 17.477233 7.497754494
After having initial impressions of both datasets, I found that we have to combine both datasets by creating a new dataset. But before we create a new dataset, let’s have a look at how many samples of each country are present in the dataset:
data["COUNTRY"].value_counts()
Thailand 294
China 294
Norway 294
Afghanistan 294
United Arab Emirates 294
...
Tajikistan 172
Comoros 171
Lesotho 158
Hong Kong 51
Solomon Islands 4
Name: COUNTRY, Length: 210, dtype: int64
So we don’t have an equal number of samples of each country in the dataset. Let’s have a look at the mode value:
data["COUNTRY"].value_counts().mode()
0 294 dtype: int64
So 294 is the mode value. We will need to use it for dividing the sum of all the samples related to the human development index, GDP per capita, and the population. Now let’s create a new dataset by combining the necessary columns from both the datasets:
# Aggregating the data
code = data["CODE"].unique().tolist()
country = data["COUNTRY"].unique().tolist()
hdi = []
tc = []
td = []
sti = []
population = data["POP"].unique().tolist()
gdp = []
for i in country:
hdi.append((data.loc[data["COUNTRY"] == i, "HDI"]).sum()/294)
tc.append((data2.loc[data2["location"] == i, "total_cases"]).sum())
td.append((data2.loc[data2["location"] == i, "total_deaths"]).sum())
sti.append((data.loc[data["COUNTRY"] == i, "STI"]).sum()/294)
population.append((data2.loc[data2["location"] == i, "population"]).sum()/294)
aggregated_data = pd.DataFrame(list(zip(code, country, hdi, tc, td, sti, population)),
columns = ["Country Code", "Country", "HDI",
"Total Cases", "Total Deaths",
"Stringency Index", "Population"])
print(aggregated_data.head())Country Code Country HDI Total Cases Total Deaths \ 0 AFG Afghanistan 0.498000 5126433.0 165875.0 1 ALB Albania 0.600765 1071951.0 31056.0 2 DZA Algeria 0.754000 4893999.0 206429.0 3 AND Andorra 0.659551 223576.0 9850.0 4 AGO Angola 0.418952 304005.0 11820.0 Stringency Index Population 0 3.049673 17.477233 1 3.005624 14.872537 2 3.195168 17.596309 3 2.677654 11.254996 4 2.965560 17.307957
I have not included the GDP per capita column yet. I didn’t find the correct figures for GDP per capita in the dataset. So it will be better to manually collect the data about the GDP per capita of the countries.
As we have so many countries in this data, it will not be easy to manually collect the data about the GDP per capita of all the countries. So let’s select a subsample from this dataset. To create a subsample from this dataset, I will be selecting the top 10 countries with the highest number of covid-19 cases. It will be a perfect sample to study the economic impacts of covid-19. So let’s sort the data according to the total cases of Covid-19:
# Sorting Data According to Total Cases data = aggregated_data.sort_values(by=["Total Cases"], ascending=False) print(data.head())
Country Code Country HDI Total Cases Total Deaths \
200 USA United States 0.92400 746014098.0 26477574.0
27 BRA Brazil 0.75900 425704517.0 14340567.0
90 IND India 0.64000 407771615.0 7247327.0
157 RUS Russia 0.81600 132888951.0 2131571.0
150 PER Peru 0.59949 74882695.0 3020038.0
Stringency Index Population
200 3.350949 19.617637
27 3.136028 19.174732
90 3.610552 21.045353
157 3.380088 18.798668
150 3.430126 17.311165
Now here’s how we can select the top 10 countries with the highest number of cases:
# Top 10 Countries with Highest Covid Cases data = data.head(10) print(data)
Country Code Country HDI Total Cases Total Deaths \
200 USA United States 0.924000 746014098.0 26477574.0
27 BRA Brazil 0.759000 425704517.0 14340567.0
90 IND India 0.640000 407771615.0 7247327.0
157 RUS Russia 0.816000 132888951.0 2131571.0
150 PER Peru 0.599490 74882695.0 3020038.0
125 MEX Mexico 0.774000 74347548.0 7295850.0
178 ESP Spain 0.887969 73717676.0 5510624.0
175 ZAF South Africa 0.608653 63027659.0 1357682.0
42 COL Colombia 0.581847 60543682.0 1936134.0
199 GBR United Kingdom 0.922000 59475032.0 7249573.0
Stringency Index Population
200 3.350949 19.617637
27 3.136028 19.174732
90 3.610552 21.045353
157 3.380088 18.798668
150 3.430126 17.311165
125 3.019289 18.674802
178 3.393922 17.660427
175 3.364333 17.898266
42 3.357923 17.745037
199 3.353883 18.033340
Now I will add two more columns (GDP per capita before Covid-19, GDP per capita during Covid-19) to this dataset:
data["GDP Before Covid"] = [65279.53, 8897.49, 2100.75,
11497.65, 7027.61, 9946.03,
29564.74, 6001.40, 6424.98, 42354.41]
data["GDP During Covid"] = [63543.58, 6796.84, 1900.71,
10126.72, 6126.87, 8346.70,
27057.16, 5090.72, 5332.77, 40284.64]
print(data) Country Code Country HDI Total Cases Total Deaths \
200 USA United States 0.924000 746014098.0 26477574.0
27 BRA Brazil 0.759000 425704517.0 14340567.0
90 IND India 0.640000 407771615.0 7247327.0
157 RUS Russia 0.816000 132888951.0 2131571.0
150 PER Peru 0.599490 74882695.0 3020038.0
125 MEX Mexico 0.774000 74347548.0 7295850.0
178 ESP Spain 0.887969 73717676.0 5510624.0
175 ZAF South Africa 0.608653 63027659.0 1357682.0
42 COL Colombia 0.581847 60543682.0 1936134.0
199 GBR United Kingdom 0.922000 59475032.0 7249573.0
Stringency Index Population GDP Before Covid GDP During Covid
200 3.350949 19.617637 65279.53 63543.58
27 3.136028 19.174732 8897.49 6796.84
90 3.610552 21.045353 2100.75 1900.71
157 3.380088 18.798668 11497.65 10126.72
150 3.430126 17.311165 7027.61 6126.87
125 3.019289 18.674802 9946.03 8346.70
178 3.393922 17.660427 29564.74 27057.16
175 3.364333 17.898266 6001.40 5090.72
42 3.357923 17.745037 6424.98 5332.77
199 3.353883 18.033340 42354.41 40284.64
Note: The data about the GDP per capita is collected manually.
Analyzing the Spread of Covid-19
Now let’s start by analyzing the spread of covid-19 in all the countries with the highest number of covid-19 cases. I will first have a look at all the countries with the highest number of covid-19 cases:
figure = px.bar(data, y='Total Cases', x='Country',
title="Countries with Highest Covid Cases")
figure.show()
We can see that the USA is comparatively having a very high number of covid-19 cases as compared to Brazil and India in the second and third positions. Now let’s have a look at the total number of deaths among the countries with the highest number of covid-19 cases:
figure = px.bar(data, y='Total Deaths', x='Country',
title="Countries with Highest Deaths")
figure.show()
Just like the total number of covid-19 cases, the USA is leading in the deaths, with Brazil and India in the second and third positions. One thing to notice here is that the death rate in India, Russia, and South Africa is comparatively low according to the total number of cases. Now let’s compare the total number of cases and total deaths in all these countries:
fig = go.Figure()
fig.add_trace(go.Bar(
x=data["Country"],
y=data["Total Cases"],
name='Total Cases',
marker_color='indianred'
))
fig.add_trace(go.Bar(
x=data["Country"],
y=data["Total Deaths"],
name='Total Deaths',
marker_color='lightsalmon'
))
fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.show()
Now let’s have a look at the percentage of total deaths and total cases among all the countries with the highest number of covid-19 cases:
# Percentage of Total Cases and Deaths
cases = data["Total Cases"].sum()
deceased = data["Total Deaths"].sum()
labels = ["Total Cases", "Total Deaths"]
values = [cases, deceased]
fig = px.pie(data, values=values, names=labels,
title='Percentage of Total Cases and Deaths', hole=0.5)
fig.show()
Below is how you can calculate the death rate of Covid-19 cases:
death_rate = (data["Total Deaths"].sum() / data["Total Cases"].sum()) * 100
print("Death Rate = ", death_rate)Death Rate = 3.6144212045653767
Another important column in this dataset is the stringency index. It is a composite measure of response indicators, including school closures, workplace closures, and travel bans. It shows how strictly countries are following these measures to control the spread of covid-19:
fig = px.bar(data, x='Country', y='Total Cases',
hover_data=['Population', 'Total Deaths'],
color='Stringency Index', height=400,
title= "Stringency Index during Covid-19")
fig.show()
Here we can see that India is performing well in the stringency index during the outbreak of covid-19.
Analyzing Covid-19 Impacts on Economy
Now let’s move to analyze the impacts of covid-19 on the economy. Here the GDP per capita is the primary factor for analyzing the economic slowdowns caused due to the outbreak of covid-19. Let’s have a look at the GDP per capita before the outbreak of covid-19 among the countries with the highest number of covid-19 cases:
fig = px.bar(data, x='Country', y='Total Cases',
hover_data=['Population', 'Total Deaths'],
color='GDP Before Covid', height=400,
title="GDP Per Capita Before Covid-19")
fig.show()
Now let’s have a look at the GDP per capita during the rise in the cases of covid-19:
fig = px.bar(data, x='Country', y='Total Cases',
hover_data=['Population', 'Total Deaths'],
color='GDP During Covid', height=400,
title="GDP Per Capita During Covid-19")
fig.show()
Now let’s compare the GDP per capita before covid-19 and during covid-19 to have a look at the impact of covid-19 on the GDP per capita:
fig = go.Figure()
fig.add_trace(go.Bar(
x=data["Country"],
y=data["GDP Before Covid"],
name='GDP Per Capita Before Covid-19',
marker_color='indianred'
))
fig.add_trace(go.Bar(
x=data["Country"],
y=data["GDP During Covid"],
name='GDP Per Capita During Covid-19',
marker_color='lightsalmon'
))
fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.show()
You can see a drop in GDP per capita in all the countries with the highest number of covid-19 cases.
One other important economic factor is Human Development Index. It is a statistic composite index of life expectancy, education, and per capita indicators. Let’s have a look at how many countries were spending their budget on the human development:
fig = px.bar(data, x='Country', y='Total Cases',
hover_data=['Population', 'Total Deaths'],
color='HDI', height=400,
title="Human Development Index during Covid-19")
fig.show()
So this is how we can analyze the spread of Covid-19 and its impact on the economy.
Conclusion
In this task, we studied the spread of covid-19 among the countries and its impact on the global economy. We saw that the outbreak of covid-19 resulted in the highest number of covid-19 cases and deaths in the united states. One major reason behind this is the stringency index of the United States. It is comparatively low according to the population. We also analyzed how the GDP per capita of every country was affected during the outbreak of covid-19. I hope you liked this article on Covid-19 impacts analysis using Python. Feel free to ask valuable questions in the comments section below.
From where and how did you added “GDP Before Covid” and “GDP Before Covid” columns?
Please explain it briefly
The data about GDP has been collected manually from the trusted sources like wikipedia, and official government websites of the countries.
Hello Aman,
I have been working on my data analysis skill and I must confess that what you have up here is the best material.
Thanks for being studious.
keep visiting 😀