Skip to content

Latest commit

 

History

History
1639 lines (1441 loc) · 28.7 KB

README.md

File metadata and controls

1639 lines (1441 loc) · 28.7 KB

COVID-19-dataset-and-World-Happiness-Report-Analysis

I performed Data Analysis on COVID 19 dataset by John Hopkins University and World Happiness Report and found really interesting results. It shows that people living in developed countries are more prone to infection of the Corona Virus than people living in less developed countries.

Welcome to the Covid 19 Data Analysis Note book

Author : Poshan Pandey

Date : 6/5/2020

Let's import the modules

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
print("All modules imported!")
All modules imported!

Let's import the covid 19 datasets by John Hopkins University

https://github.com/CSSEGISandData/COVID-19

corona_dataset_csv = pd.read_csv("Datasets/time_series_covid19_confirmed_global.csv")
corona_dataset_csv.head()
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 5/26/20 5/27/20 5/28/20 5/29/20 5/30/20 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20
0 NaN Afghanistan 33.0000 65.0000 0 0 0 0 0 0 ... 11831 12456 13036 13659 14525 15205 15750 16509 17267 18054
1 NaN Albania 41.1533 20.1683 0 0 0 0 0 0 ... 1029 1050 1076 1099 1122 1137 1143 1164 1184 1197
2 NaN Algeria 28.0339 1.6596 0 0 0 0 0 0 ... 8697 8857 8997 9134 9267 9394 9513 9626 9733 9831
3 NaN Andorra 42.5063 1.5218 0 0 0 0 0 0 ... 763 763 763 764 764 764 765 844 851 852
4 NaN Angola -11.2027 17.8739 0 0 0 0 0 0 ... 70 71 74 81 84 86 86 86 86 86

5 rows × 139 columns

Checking the shape of the data

corona_dataset_csv.shape
(266, 139)

Deleting unnecessary columns

corona_dataset_csv.drop(["Lat", "Long"], axis = 1, inplace = True)
corona_dataset_csv.head(15)
Province/State Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/26/20 5/27/20 5/28/20 5/29/20 5/30/20 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20
0 NaN Afghanistan 0 0 0 0 0 0 0 0 ... 11831 12456 13036 13659 14525 15205 15750 16509 17267 18054
1 NaN Albania 0 0 0 0 0 0 0 0 ... 1029 1050 1076 1099 1122 1137 1143 1164 1184 1197
2 NaN Algeria 0 0 0 0 0 0 0 0 ... 8697 8857 8997 9134 9267 9394 9513 9626 9733 9831
3 NaN Andorra 0 0 0 0 0 0 0 0 ... 763 763 763 764 764 764 765 844 851 852
4 NaN Angola 0 0 0 0 0 0 0 0 ... 70 71 74 81 84 86 86 86 86 86
5 NaN Antigua and Barbuda 0 0 0 0 0 0 0 0 ... 25 25 25 25 25 26 26 26 26 26
6 NaN Argentina 0 0 0 0 0 0 0 0 ... 13228 13933 14702 15419 16214 16851 17415 18319 19268 20197
7 NaN Armenia 0 0 0 0 0 0 0 0 ... 7402 7774 8216 8676 8927 9282 9492 10009 10524 11221
8 Australian Capital Territory Australia 0 0 0 0 0 0 0 0 ... 107 107 107 107 107 107 107 107 107 107
9 New South Wales Australia 0 0 0 0 3 4 4 4 ... 3089 3090 3092 3092 3095 3098 3104 3104 3106 3110
10 Northern Territory Australia 0 0 0 0 0 0 0 0 ... 29 29 29 29 29 29 29 29 29 29
11 Queensland Australia 0 0 0 0 0 0 0 1 ... 1058 1058 1058 1058 1058 1058 1059 1059 1060 1060
12 South Australia Australia 0 0 0 0 0 0 0 0 ... 440 440 440 440 440 440 440 440 440 440
13 Tasmania Australia 0 0 0 0 0 0 0 0 ... 228 228 228 228 228 228 228 228 228 228
14 Victoria Australia 0 0 0 0 1 1 1 1 ... 1618 1628 1634 1645 1649 1653 1663 1670 1678 1681

15 rows × 137 columns

Aggregating the data of all province/state of similar country

aggregated_corona_dataset = corona_dataset_csv.groupby("Country/Region").sum()
aggregated_corona_dataset.head(10)
1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20 1/31/20 ... 5/26/20 5/27/20 5/28/20 5/29/20 5/30/20 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20
Country/Region
Afghanistan 0 0 0 0 0 0 0 0 0 0 ... 11831 12456 13036 13659 14525 15205 15750 16509 17267 18054
Albania 0 0 0 0 0 0 0 0 0 0 ... 1029 1050 1076 1099 1122 1137 1143 1164 1184 1197
Algeria 0 0 0 0 0 0 0 0 0 0 ... 8697 8857 8997 9134 9267 9394 9513 9626 9733 9831
Andorra 0 0 0 0 0 0 0 0 0 0 ... 763 763 763 764 764 764 765 844 851 852
Angola 0 0 0 0 0 0 0 0 0 0 ... 70 71 74 81 84 86 86 86 86 86
Antigua and Barbuda 0 0 0 0 0 0 0 0 0 0 ... 25 25 25 25 25 26 26 26 26 26
Argentina 0 0 0 0 0 0 0 0 0 0 ... 13228 13933 14702 15419 16214 16851 17415 18319 19268 20197
Armenia 0 0 0 0 0 0 0 0 0 0 ... 7402 7774 8216 8676 8927 9282 9492 10009 10524 11221
Australia 0 0 0 0 4 5 5 6 9 9 ... 7139 7150 7165 7184 7192 7202 7221 7229 7240 7247
Austria 0 0 0 0 0 0 0 0 0 0 ... 16557 16591 16628 16655 16685 16731 16733 16759 16771 16805

10 rows × 135 columns

Visualizing the Corona Infection data of Nepal

aggregated_corona_dataset.loc["Nepal"].plot()
plt.title("Rate of Covid 19 Growth in Nepal")
plt.legend()
<matplotlib.legend.Legend at 0x21a2bd54e88>

png

Calculating derivative of above curve and finding the maximum infection rate

aggregated_corona_dataset.loc["Nepal"].diff().plot()
<matplotlib.axes._subplots.AxesSubplot at 0x21a2be86408>

png

aggregated_corona_dataset.loc["Nepal"].diff().max()
334.0

Finding maximum infection rate for all the countries and Adding it to new column in Dataframe

countries = list(aggregated_corona_dataset.index)
max_infection_rates = []
for c in countries:
    max_infection_rates.append(aggregated_corona_dataset.loc[c].diff().max())
aggregated_corona_dataset["max_infection_rates"] = max_infection_rates
aggregated_corona_dataset.head()
1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20 1/31/20 ... 5/27/20 5/28/20 5/29/20 5/30/20 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 max_infection_rates
Country/Region
Afghanistan 0 0 0 0 0 0 0 0 0 0 ... 12456 13036 13659 14525 15205 15750 16509 17267 18054 866.0
Albania 0 0 0 0 0 0 0 0 0 0 ... 1050 1076 1099 1122 1137 1143 1164 1184 1197 34.0
Algeria 0 0 0 0 0 0 0 0 0 0 ... 8857 8997 9134 9267 9394 9513 9626 9733 9831 199.0
Andorra 0 0 0 0 0 0 0 0 0 0 ... 763 763 764 764 764 765 844 851 852 79.0
Angola 0 0 0 0 0 0 0 0 0 0 ... 71 74 81 84 86 86 86 86 86 8.0

5 rows × 136 columns

Creating new dataframe with countries and maximum infection rate only

corona_data = pd.DataFrame(aggregated_corona_dataset["max_infection_rates"])
corona_data.head()
max_infection_rates
Country/Region
Afghanistan 866.0
Albania 34.0
Algeria 199.0
Andorra 79.0
Angola 8.0

Importing the World Happiness Report dataset

happiness_report_csv = pd.read_csv("Datasets/worldwide_happiness_report.csv")
happiness_report_csv.head()
Overall rank Country or region Score GDP per capita Social support Healthy life expectancy Freedom to make life choices Generosity Perceptions of corruption
0 1 Finland 7.769 1.340 1.587 0.986 0.596 0.153 0.393
1 2 Denmark 7.600 1.383 1.573 0.996 0.592 0.252 0.410
2 3 Norway 7.554 1.488 1.582 1.028 0.603 0.271 0.341
3 4 Iceland 7.494 1.380 1.624 1.026 0.591 0.354 0.118
4 5 Netherlands 7.488 1.396 1.522 0.999 0.557 0.322 0.298

Deleting the unnecessary columns and changing indices to Country or region

useless_cols = ["Overall rank", "Score", "Generosity", "Perceptions of corruption"]
happiness_report_csv.drop(useless_cols, axis = 1, inplace = True)
happiness_report_csv.set_index("Country or region", inplace= True)
happiness_report_csv.head()
GDP per capita Social support Healthy life expectancy Freedom to make life choices
Country or region
Finland 1.340 1.587 0.986 0.596
Denmark 1.383 1.573 0.996 0.592
Norway 1.488 1.582 1.028 0.603
Iceland 1.380 1.624 1.026 0.591
Netherlands 1.396 1.522 0.999 0.557

Comparing Number of countries in Happiness and Covid 19 datasets

corona_data.shape
(188, 1)

happiness_report_csv.shape
(156, 4)

Number of countries in Corina dataset is more than World Happiness Report Dataset

So, We have to join them ussing Inner join

final_data = corona_data.join(happiness_report_csv, how = "inner")
final_data.head()
max_infection_rates GDP per capita Social support Healthy life expectancy Freedom to make life choices
Afghanistan 866.0 0.350 0.517 0.361 0.000
Albania 34.0 0.947 0.848 0.874 0.383
Algeria 199.0 1.002 1.160 0.785 0.086
Argentina 949.0 1.092 1.432 0.881 0.471
Armenia 697.0 0.850 1.055 0.815 0.283

Calculating Correleation Matrix for the final Data

final_data.corr()
max_infection_rates GDP per capita Social support Healthy life expectancy Freedom to make life choices
max_infection_rates 1.000000 0.207071 0.158977 0.218118 0.071825
GDP per capita 0.207071 1.000000 0.757521 0.859431 0.394799
Social support 0.158977 0.757521 1.000000 0.751632 0.456317
Healthy life expectancy 0.218118 0.859431 0.751632 1.000000 0.423146
Freedom to make life choices 0.071825 0.394799 0.456317 0.423146 1.000000

Visualizing our final result

Plotting GDP vs Maximum Infection Rate

x = final_data["GDP per capita"]
y = final_data["max_infection_rates"]
sns.regplot(x,np.log(y)).set_title("Relationship Between Corona Infection Rate and GDP per Capita")
Text(0.5, 1.0, 'Relationship Between Corona Infection Rate and GDP per Capita')

png

Plotting Social support vs Maximum Infection Rate

x = final_data["Social support"]
y = final_data["max_infection_rates"]
sns.regplot(x,np.log(y)).set_title("Relationship Between Corona Infection Rate and Social Support")
Text(0.5, 1.0, 'Relationship Between Corona Infection Rate and Social Support')

png

Plotting Social support vs Health Life Expectancy

x = final_data["Healthy life expectancy"]
y = final_data["max_infection_rates"]
sns.regplot(x,np.log(y)).set_title("Relationship Between Corona Infection Rate and Health Life Expectancy")
Text(0.5, 1.0, 'Relationship Between Corona Infection Rate and Health Life Expectancy')

png

Plotting Social support vs Freedom to make life choices

x = final_data["Freedom to make life choices"]
y = final_data["max_infection_rates"]
sns.regplot(x,np.log(y)).set_title("Relationship Between Corona Infection Rate and Freedom to make life choices")
Text(0.5, 1.0, 'Relationship Between Corona Infection Rate and Freedom to make life choices')

png

------------------------------------The End--------------------------------