The document discusses analyzing data related to Covid19 and its effects on social phenomena. It outlines collecting data on total cases, deaths, recoveries, suicides, divorces, unemployment and population for different countries. The methodology includes collecting, analyzing and finding relationships in the data. The analysis finds no relationship between social phenomena studied and Covid19, but finds the number of infections proportional to deaths and recoveries as expected. It concludes that Covid19 did not significantly affect social issues worldwide.

1. Covid19 and its effect on some social phenomena
May 30, 2020
1 Capstone Project
1.0.1 Applied Data Science Capstone by IBM/Coursera
1.1 Table of contents
• Section ??
• Section 1.3
• Section 1.4
• Section 1.5
• Section ??
• Section 1.7
1.2 Introduction: Business Problem
In this project, we will try to collect data for the countries of the world to analyze
and understand the relationship between them and to understand the causes to avoid
the risks for the future.
1.3 Data
Our problem is an increase in the number of deaths, in some social and economic
phenomena that appeared with Sofid 19 For this, the suffering that we will need will
be related to the number of infections and the number of cases that have suffered from
these phenomena.
Country
Total Cases
Total Deaths
Total of Recovered
Total of Suicides
Total of Divorce
total of Population
ChomageAvant
ChomageApres
1

2. 1.4 Methodology
In this project, we will direct our efforts at the beginning to collect useful data in order to get good
results because wrong data can only give us wrong results. In the first step, we collected the
required data.
The second step will be our analysis of the data we collected and the analysis of each column on
this in order to have a good idea of the data that we will use
In the third step we will try to find out the possible relationship between the columns and try
to use more than one program to verify the results
1.5 Code
Let’s perform some basic explanatory data analysis and derive some additional info from our raw
data. First let’s count the number of restaurants in every area candidate:
[14]: import pandas as pd # import library to read data into dataframe
import numpy as np # import numpy library
!conda install -c conda-forge folium=0.5.0 --yes
import folium
import matplotlib.pyplot as plt
Collecting package metadata (current_repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.7.10
latest version: 4.8.3
Please update conda by running
$ conda update -n base conda
# All requested packages already installed.
[15]: dataset = pd.read_csv("covid19.csv")
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058:
DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or
set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
[16]: print(dataset.dtypes)
2

3. Country object
Latitude float64
Longitude float64
TotalCases float64
TotalDeaths float64
TotalRecovered float64
ActiveCases float64
SeriousCritical float64
TotalnTests float64
TotalSuicides float64
TotDivorce float64
Population float64
ChomageAvant float64
ChomageApres float64
dtype: object
[17]: dataset.head(3)
[17]: Country Latitude Longitude TotalCases TotalDeaths TotalRecovered
0 USA 37.090240 -95.712891 1555133.0 92198.0 359137.0
1 Argentina -38.416097 -63.616672 8371.0 382.0 2625.0
2 Bangladesh 23.684994 90.356331 276505.0 27563.0 192253.0
ActiveCases SeriousCritical TotalnTests TotalSuicides TotDivorce
0 1103798.0 16868.0 12350393.0 1001.0 6615.0
1 5364.0 154.0 105829.0 520.0 615.0
2 56689.0 1208.0 3037840.0 122.0 520.0
Population ChomageAvant ChomageApres
0 330774664.0 11429792.0 1429793.0
1 45146322.0 1009794.0 1429794.0
2 46752605.0 5329797.0 5606026.0
[18]: dataset.shape
[18]: (1048573, 14)
[19]: # To find the correlation among
# the columns using pearson method
dataset.corr(method ='pearson')
[19]: Latitude Longitude TotalCases TotalDeaths TotalRecovered
Latitude 1.000000 0.042665 0.077525 0.045510 0.082350
Longitude 0.042665 1.000000 -0.118664 -0.121212 -0.111525
TotalCases 0.077525 -0.118664 1.000000 0.913665 0.916726
TotalDeaths 0.045510 -0.121212 0.913665 1.000000 0.879314
TotalRecovered 0.082350 -0.111525 0.916726 0.879314 1.000000
3

4. ActiveCases 0.058711 -0.127603 0.969123 0.917324 0.840746
SeriousCritical 0.090577 -0.137823 0.925652 0.862173 0.849204
TotalnTests 0.074428 -0.093833 0.914447 0.847369 0.844584
TotalSuicides 0.020706 -0.109554 0.506676 0.501775 0.485221
TotDivorce 0.062851 -0.074299 0.236482 0.187101 0.317395
Population 0.057728 -0.071265 0.237910 0.189295 0.320625
ChomageAvant 0.025095 -0.077159 0.125502 0.099432 0.257755
ChomageApres 0.022373 -0.070818 0.077284 0.056962 0.217904
ActiveCases SeriousCritical TotalnTests TotalSuicides
Latitude 0.058711 0.090577 0.074428 0.020706
Longitude -0.127603 -0.137823 -0.093833 -0.109554
TotalCases 0.969123 0.925652 0.914447 0.506676
TotalDeaths 0.917324 0.862173 0.847369 0.501775
TotalRecovered 0.840746 0.849204 0.844584 0.485221
ActiveCases 1.000000 0.929876 0.837759 0.459290
SeriousCritical 0.929876 1.000000 0.769787 0.439402
TotalnTests 0.837759 0.769787 1.000000 0.641682
TotalSuicides 0.459290 0.439402 0.641682 1.000000
TotDivorce 0.188688 0.229280 0.346318 0.593624
Population 0.188786 0.229294 0.347775 0.594046
ChomageAvant 0.065720 0.095368 0.235404 0.367431
ChomageApres 0.013918 0.049084 0.114001 0.347208
TotDivorce Population ChomageAvant ChomageApres
Latitude 0.062851 0.057728 0.025095 0.022373
Longitude -0.074299 -0.071265 -0.077159 -0.070818
TotalCases 0.236482 0.237910 0.125502 0.077284
TotalDeaths 0.187101 0.189295 0.099432 0.056962
TotalRecovered 0.317395 0.320625 0.257755 0.217904
ActiveCases 0.188688 0.188786 0.065720 0.013918
SeriousCritical 0.229280 0.229294 0.095368 0.049084
TotalnTests 0.346318 0.347775 0.235404 0.114001
TotalSuicides 0.593624 0.594046 0.367431 0.347208
TotDivorce 1.000000 0.999135 0.744975 0.738443
Population 0.999135 1.000000 0.745265 0.738783
ChomageAvant 0.744975 0.745265 1.000000 0.998578
ChomageApres 0.738443 0.738783 0.998578 1.000000
[20]: dataset.corr(method ='kendall')
[20]: Latitude Longitude TotalCases TotalDeaths TotalRecovered
Latitude 1.000000 -0.002608 0.043826 0.059894 0.015944
Longitude -0.002608 1.000000 -0.097654 -0.046629 -0.104866
TotalCases 0.043826 -0.097654 1.000000 0.710343 0.756996
TotalDeaths 0.059894 -0.046629 0.710343 1.000000 0.624133
TotalRecovered 0.015944 -0.104866 0.756996 0.624133 1.000000
4

5. ActiveCases 0.032781 -0.063536 0.748626 0.618563 0.598033
SeriousCritical 0.064721 -0.057530 0.607061 0.628207 0.521221
TotalnTests 0.079205 -0.077840 0.570199 0.498728 0.592082
TotalSuicides -0.019768 -0.128530 0.293936 0.076026 0.215060
TotDivorce 0.070009 -0.022108 0.481206 0.456143 0.419283
Population 0.067034 -0.006568 0.465475 0.445378 0.405245
ChomageAvant 0.079011 -0.007534 0.435592 0.418533 0.384043
ChomageApres 0.073119 -0.001256 0.432460 0.416487 0.379766
ActiveCases SeriousCritical TotalnTests TotalSuicides
Latitude 0.032781 0.064721 0.079205 -0.019768
Longitude -0.063536 -0.057530 -0.077840 -0.128530
TotalCases 0.748626 0.607061 0.570199 0.293936
TotalDeaths 0.618563 0.628207 0.498728 0.076026
TotalRecovered 0.598033 0.521221 0.592082 0.215060
ActiveCases 1.000000 0.571016 0.474536 0.222289
SeriousCritical 0.571016 1.000000 0.405944 0.167889
TotalnTests 0.474536 0.405944 1.000000 0.224171
TotalSuicides 0.222289 0.167889 0.224171 1.000000
TotDivorce 0.478735 0.365303 0.438588 0.128268
Population 0.467144 0.367007 0.427841 0.125523
ChomageAvant 0.417807 0.315703 0.399128 0.087978
ChomageApres 0.414237 0.312655 0.394130 0.086858
TotDivorce Population ChomageAvant ChomageApres
Latitude 0.070009 0.067034 0.079011 0.073119
Longitude -0.022108 -0.006568 -0.007534 -0.001256
TotalCases 0.481206 0.465475 0.435592 0.432460
TotalDeaths 0.456143 0.445378 0.418533 0.416487
TotalRecovered 0.419283 0.405245 0.384043 0.379766
ActiveCases 0.478735 0.467144 0.417807 0.414237
SeriousCritical 0.365303 0.367007 0.315703 0.312655
TotalnTests 0.438588 0.427841 0.399128 0.394130
TotalSuicides 0.128268 0.125523 0.087978 0.086858
TotDivorce 1.000000 0.962099 0.780031 0.775722
Population 0.962099 1.000000 0.785925 0.781186
ChomageAvant 0.780031 0.785925 1.000000 0.986497
ChomageApres 0.775722 0.781186 0.986497 1.000000
[21]: # Seaborn visualization library
import seaborn as sns
# Create the default pairplot
sns.pairplot(dataset)
[21]: <seaborn.axisgrid.PairGrid at 0x7f3a784f6438>
5

6. [22]: #correlation matrix
corrmat = dataset.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(corrmat, vmax=.8, square=True);
6

7. 1.6 Analysis Results
Through our analysis of my results, we find that there is no relationship between the studied social
phenomena and the virus covid 19, while, as expected, the number of infections in each country is
proportional to the number of deaths and the number of those recovering.
1.7 Conclusion
By analyzing the results of the data, we conclude that the Safid 19 virus has not affected much
social issues in the world, contrary to what it wants to promote some parties.
7