"""
# Worldwide Covid-19 Vaccine Exploratory Analysis
"""
"""
**In this Exploratory Analysis, my goal is to find different trends in the vaccination among different countries**

The source for this dataset: https://www.nytimes.com/interactive/2021/world/covid-vaccinations-tracker.html

*Note that this analysis was done according to Vaccine Data of all Countries as on August 03, 2021*
"""
# Importing Libraries
import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
df = pd.read_csv('/kaggle/input/latest-worldwide-vaccine-data/Worldwide Vaccine Data.csv')
df.head()
df.info()
"""
As we can see we have 5 different columns,

1. Country - is our main column since our primary goal is to look at the vaccination trend across various Countries in the world

2. Doses administered per 100 people - For vaccines that require multiple doses, each individual dose is counted. As the same person may receive more than one dose, the number of doses per 100 people can be higher than 100.

3. Total doses administered - 
For vaccines that require multiple doses, each individual dose is counted. As the same person
may receive more than one dose, the number of doses can be higher than the number of
people in the population.

4. % of population vaccinated - means that % of the world population that has received at least one dose

5. % of population fully vaccinated - that have completed both doses for a given vaccine
"""
"""
# Cleaning up Data
"""
# Check for null values
df.isnull().sum().sum()
sns.heatmap(df.isnull())
df.isna().sum()
"""
There are 11 values that are missing, 'Doses administered per 100 people' and 'Total doses administered' each have one value missing which is not that bad but '% of population vaccinated' and '% of population fully vaccinated' have 3 and 6 values missing which is quite concerning. So it's safe to assume that these countries with missing values did not keep track of their vaccination and therefore are equivalent to getting no vacination at all.


We will use 0 to fill in these columns
"""
df.fillna(0, inplace=True)
df.isnull().sum().sum()
"""
Now we have successfully replaced all the null values with 0, now let's begin with our visualizations
"""
"""
# Visualizations
"""
sns.heatmap(df.corr(), annot=True)
"""
In this correlation above, we can see that 'Doses administered per 100 people' has the most correlation with '% of population vaccinated' followed by '% of population fully vaccinated'
"""
"""
# Top Vaccinated Countries
"""
"""
## 1. Let's see the % of population fully vaccinated
"""
df['% of population fully vaccinated'].max()
"""
Since the maximum percentage of a fully vaccinated country is 76, let's see how many countries are above the 70% mark
"""
max_full_vacc = df[df['% of population fully vaccinated'] > 70]

plt.figure(figsize=(10,5))
# make barplot
sns.barplot(x='Country', y="% of population fully vaccinated", data=max_full_vacc, order=max_full_vacc.sort_values('% of population fully vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population fully vaccinated", size=15)
plt.title("% of population fully vaccinated among all countrues", size=18)
plt.tight_layout()
"""
'Malta' is the most fully vaccinated country in the world with U.A.E at the second, these cross the 70% mark making them safer among other countries 

Also one can argue that with a population of about 515,000 over an area of 316 km2 (122 sq mi), Malta is the world's tenth smallest country in area, therefore it was easy to have more people be fully vaccinated but we should also consider the fact that it is the fourth most densely populated sovereign country so it is more likey for the virus to spread quickly. So they had to get most of their population vaccinated as quickly as possible.

But as of 8/11/21, there are 87 new COVID-19 cases according to: https://timesofmalta.com/articles/view/87-new-covid-19-cases-as-one-patient-dies.892786

U.A.E has even opened travels for countries without requiring people to vaccinate before travelling

**Now let's look at countries that are above the 50% mark**
"""
max_full_vacc = df[df['% of population fully vaccinated'] > 50]

plt.figure(figsize=(20,5))
# make barplot
sns.barplot(x='Country', y="% of population fully vaccinated", data=max_full_vacc, order=max_full_vacc.sort_values('% of population fully vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population fully vaccinated", size=15)
plt.title("% of population fully vaccinated among all countrues", size=18)
plt.tight_layout()
"""
Countries with small areas are more liable to have a higher percentage of being fully vaccinated as we can see from the graph above, there are a few exceptions but this shows that population is also a big factor in this analysis


**of these fully vaccinated countries, let's see the total doses administered**
"""
plt.figure(figsize=(20,5))
# make barplot
sns.barplot(x='Country', y="Total doses administered", data=max_full_vacc, order=max_full_vacc.sort_values('Total doses administered', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Total doses administered", size=15)
plt.title("Total doses administered for Top 50% of population fully vaccinated countrues", size=18)
plt.tight_layout()
"""
This graph confirms my speculation regarding the population factor, as we can see countries with high population like the UK have a higher number of total doses administered compare to Malta which has the highest % of population fully vaccinated, so it depends on the total population of the country as well

**Now Let's see this with Doses administered per 100 people**
"""
plt.figure(figsize=(20,5))
# make barplot
sns.barplot(x='Country', y="Doses administered per 100 people", data=max_full_vacc, order=max_full_vacc.sort_values('Doses administered per 100 people', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Doses administered per 100 people", size=15)
plt.title("Doses administered per 100 people for Top 50% of population fully vaccinated countrues", size=18)
plt.tight_layout()
"""
This is quite different than the total dose administered, although there is no trend here, all of them have a high rate but there's an anomaly with Hungary
"""
df[df['Country'] == 'Hungary']
"""
So this actually is one of our null values, so we'll just ignore it
"""
"""
## 2. Let's look at the % of population partially vaccinated among the top 50%
"""
max_par_vacc = df[df['% of population vaccinated'] > 50]

plt.figure(figsize=(35,10))
# make barplot
sns.barplot(x='Country', y="% of population vaccinated", data=max_par_vacc, order=max_par_vacc.sort_values('% of population vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population Partially vaccinated", size=15)
plt.title("% of population Partially vaccinated among all countrues", size=18)
plt.tight_layout()
"""
This confirms that the same countries that are fully vaccinated are also partially vaccinated as well, with an almost similar order

**Now let's look the Total doses administered**
"""
plt.figure(figsize=(35,10))
# make barplot
sns.barplot(x='Country', y="Total doses administered", data=max_par_vacc, order=max_par_vacc.sort_values('Total doses administered', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Total doses administered", size=15)
plt.title("Total doses administered for countries above 50% of population Partially vaccinated", size=18)
plt.tight_layout()
"""
So United States has the highest number of total doses administered among the most partially vaccinated countries

**Now let's see Doses administered per 100 people**
"""
plt.figure(figsize=(35,10))
# make barplot
sns.barplot(x='Country', y="Doses administered per 100 people", data=max_par_vacc, order=max_par_vacc.sort_values('Doses administered per 100 people', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Doses administered per 100 people", size=15)
plt.title("Doses administered per 100 people for 50% of population Partially vaccinated", size=18)
plt.tight_layout()
"""
This trend for partially vaccinated and fully vaccinated is the similar for Doses administered per 100 people
"""
"""
## 3. Let's look at the Total doses administered among the top 50% 
"""
highest_total_doses_administered = df['Total doses administered'].max()
mean_total_doses_administered = df['Total doses administered'].mean()

print("The maximum number for Total doses administered is:", highest_total_doses_administered)
print("The mean number for Total doses administered is:", highest_total_doses_administered)
total_doses = df[df['Total doses administered'] > mean_total_doses_administered]

plt.figure(figsize=(35,10))
# make barplot
sns.barplot(x='Country', y="Total doses administered", data=total_doses, order=total_doses.sort_values('Total doses administered', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Total doses administered", size=15)
plt.title("Total doses administered for countries above a 50% mark", size=18)
plt.tight_layout()
"""
China has the highest number of Total doses administered, that is at a very high peak, compare to India which comes in at the second number, there is a huge difference. This is due to the fact that China has produced Sinopharm and Sinovac, which have been approved by the World Health Organization (WHO) for emergency use. And based on this it should have a higher percentage of vaccinated population. It is not only using it for it's own population but also sending it to other countries as well.

**Based on countries that have highest Total doses administered (more than 50%) let's look at the % population fully vaccinated**
"""
plt.figure(figsize=(35,10))
# make barplot
sns.barplot(x='Country', y="% of population fully vaccinated", data=total_doses, order=total_doses.sort_values('% of population fully vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population fully vaccinated", size=15)
plt.title("% of population fully vaccinated for Total doses administered for countries above a 50% mark", size=18)
plt.tight_layout()
"""
As we can see that Chile has the highest percentage of population fully vaccinated while having the hightest number of Total doses administered. Now Malta and UAE are nowhere near to be found, since they do not have a high number of doses administered.

Over 60 per cent of Chileans are fully vaccinated, a higher proportion of the population than the United States, the majority with Sinovac’s CoronaVac.

**Based on countries that have highest Total doses administered (more than 50%) let's look at the % population partially vaccinated**
"""
plt.figure(figsize=(35,10))
# make barplot
sns.barplot(x='Country', y="% of population vaccinated", data=total_doses, order=total_doses.sort_values('% of population vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population partially vaccinated", size=15)
plt.title("% of population partially vaccinated for Total doses administered for countries above a 50% mark", size=18)
plt.tight_layout()
"""
This graph shows the same results as fully vaccinated

**Now let's compare it with Doses administered per 100 people**
"""
plt.figure(figsize=(35,10))
# make barplot
sns.barplot(x='Country', y="Doses administered per 100 people", data=total_doses, order=total_doses.sort_values('Doses administered per 100 people', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Doses administered per 100 people", size=15)
plt.title("Doses administered per 100 people for Total doses administered for countries above a 50% mark", size=18)
plt.tight_layout()
"""
A similar trend is seen but this time China makes it's way in at number 4
"""
"""
## 4. Let's look at the Doses administered per 100 people among the top 50%
"""
highest_per_100 = df['Doses administered per 100 people'].max()
mean_per_100 = df['Doses administered per 100 people'].mean()

print("The maximum number for Doses administered per 100 people is:", highest_per_100)
print("The mean number for Doses administered per 100 people is:", mean_per_100)
per_100 = df[df['Doses administered per 100 people'] > mean_per_100]

plt.figure(figsize=(70,10))
# make barplot
sns.barplot(x='Country', y="Doses administered per 100 people", data=per_100, order=per_100.sort_values('Doses administered per 100 people', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Doses administered per 100 people", size=15)
plt.title("Doses administered per 100 people for countries above a 50% mark", size=18)
plt.tight_layout()
"""
This is a similar trend like the % fully vaccinated countries
"""
"""
# Low Vaccinated Countries
"""
"""
## 5. Countries with null values
"""
"""
First let's look at the null values, which countires have 0% or missing data
"""
df[(df['% of population fully vaccinated'] == 0) | (df['% of population vaccinated'] == 0) | (df['Total doses administered'] == 0) | (df['Doses administered per 100 people'] == 0)]
"""
Serra Leone and Mainland China have no values in both fully or partially vaccinated, but I did some research and found that according to https://ourworldindata.org/covid-vaccinations?country=CHN 

China has 16% population fully vaccinated and 28% partially vacinated, now the reason our dataset might not have this value is because maybe (and this is just from my understanding) USA and other countries do not consider the Chinese vaccine even though WHO has given the green light to the two Chinese vaccines, made by Sinovac and Sinopharm. Or maybe China has not shared their stats related to how much of their population is vaccinated. 

What's important to note is that as we've seen above that China has more 'Doses administered per 100 people' which misleads us that 0% of their population is vaccinated and this is further confirmed by the 'Total doses administered' column where China has a higher number.

As all other countries except China in the column above are African, these countries do not have enough facilities to push the vaccination process forward, so are still far behind most countries

Serria Leone has no track of their population partially vaccinated but yet manages to get 2.9 doses administered per 100 people

"Sierra Leone has today received the second batch of 96,000 doses of the AstraZeneca-Oxford COVID-19 vaccine donated by France and delivered by UNICEF through the COVAX Facility" according to this news site: https://www.africanews.com/2021/08/09/coronavirus-sierra-leone-welcomes-the-arrival-of-covid-19-vaccines-shipped-via-covax-facility/
. As of now there is an increase in the number of COVID cases over there, and this is due to less availability of vaccinations accross the country

Hungary as we've seen before has no values for doses but still shows a higher % of vaccinated population.

Pakistan shows 0% partially vaccinated but 3% fully vaccinated population, according to this post:
https://www.business-standard.com/article/current-affairs/only-3-5-of-pakistan-s-population-vaccinated-against-covid-so-far-report-121070700081_1.html
There is a 14% partially vaccinated population in Pakistan
"""
"""
## 6. Lowest fully vaccinated Countries
"""
low_full_vacc = df[(df['% of population fully vaccinated'] > 0) & (df['% of population fully vaccinated'] < 1)]

plt.figure(figsize=(30,5))
# make barplot
sns.barplot(x='Country', y="% of population fully vaccinated", data=low_full_vacc, order=low_full_vacc.sort_values('% of population fully vaccinated', ascending = True).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population fully vaccinated", size=15)
plt.title("Lowest % of population fully vaccinated among all countrues", size=18)
plt.tight_layout()
"""
It is quite clear that most countries with low vaccination percentage are the ones that have a poor economy, these mostly include African Countries

**Among them, let's see how many are more partially vaccnated**
"""
plt.figure(figsize=(30,5))
# make barplot
sns.barplot(x='Country', y="% of population vaccinated", data=low_full_vacc, order=low_full_vacc.sort_values('% of population vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population partially vaccinated", size=15)
plt.title("Lowest % of population partially vaccinated among all countrues", size=18)
plt.tight_layout()
"""
Brunei, Oman and India can be seen as more partially vaccinated while having a low fully vaccnated status

**Now lets see which among them have high total doses**
"""
plt.figure(figsize=(35,5))
# make barplot
sns.barplot(x='Country', y="Total doses administered", data=low_full_vacc, order=low_full_vacc.sort_values('Total doses administered', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Total doses administered", size=15)
plt.title("Highest Total doses administered among lowest fully vaccinated countries", size=18)
plt.tight_layout()
"""
Vietnam and Nigera have more doses administered while being lowest fully vaccinated countries

**Now let's look at the Doses administered per 100 people**
"""
plt.figure(figsize=(35,5))
# make barplot
sns.barplot(x='Country', y="Doses administered per 100 people", data=low_full_vacc, order=low_full_vacc.sort_values('Doses administered per 100 people', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Doses administered per 100 people", size=15)
plt.title("Highest Doses administered per 100 people among lowest fully vaccinated countries", size=18)
plt.tight_layout()
"""
Vanatau and Tajakistan come in along with Vietnam to make more doses per 100 people
"""
"""
## 7. Lowest Partially vaccinated Countries
"""
low_par_vacc = df[(df['% of population vaccinated'] > 0) & (df['% of population vaccinated'] < 10)]

plt.figure(figsize=(50,5))
# make barplot
sns.barplot(x='Country', y="% of population vaccinated", data=low_par_vacc, order=low_par_vacc.sort_values('% of population vaccinated', ascending = True).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population partially vaccinated", size=15)
plt.title("Lowest % of population partially vaccinated among all countrues", size=18)
plt.tight_layout()
"""
The bottom 10% Lowest Partially vaccinated Country are mostly African

**Comparing with % fully vaccinated**
"""
plt.figure(figsize=(50,5))
# make barplot
sns.barplot(x='Country', y="% of population fully vaccinated", data=low_par_vacc, order=low_par_vacc.sort_values('% of population fully vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population fully vaccinated", size=15)
plt.title("Lowest % of population fully vaccinated among all countrues", size=18)
plt.tight_layout()
"""
More fully vaccinated among the lowest partially vaccinated are Botswana, Ukraine, Georgia and Jamaica with higher numbers

**Comparing with Total Doses**
"""
plt.figure(figsize=(50,5))
# make barplot
sns.barplot(x='Country', y="Total doses administered", data=low_par_vacc, order=low_par_vacc.sort_values('Total doses administered', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Total doses administered", size=15)
plt.title("Highest Total doses administered for lowest % of population partially vaccinated among all countrues", size=18)
plt.tight_layout()
"""
Bangladesh and Iran seem to have more doses administered

**Comparing with doses per 100 people**
"""
plt.figure(figsize=(50,5))
# make barplot
sns.barplot(x='Country', y="Doses administered per 100 people", data=low_par_vacc, order=low_par_vacc.sort_values('Doses administered per 100 people', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Doses administered per 100 people", size=15)
plt.title("Highest Doses administered per 100 people for lowest % of population partially vaccinated among all countrues", size=18)
plt.tight_layout()
"""
Almost the same result but with Georgia coming in at the second place
"""
"""
## 8. Lowest Total doses administered
"""
remove_zero = df[df['Total doses administered'] == 0].index
df.drop(remove_zero, inplace=True)
mean_dose = df['Total doses administered'].mean()
min_dose = df['Total doses administered'].min()

print("Lowest dose administered:", min_dose)
print("Average dose administered:", mean_dose)
print("Difference:", mean_dose - min_dose)
"""
A huge difference between the average and the lowest dose, means more countries are below average, so we'll look at the countries with the lowest starting from 50,000 doses administered
"""
low_dose = df[df['Total doses administered'] < 50000] 

plt.figure(figsize=(50,5))
# make barplot
sns.barplot(x='Country', y="Total doses administered", data=low_dose, order=low_dose.sort_values('Total doses administered', ascending = True).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Total doses administered", size=15)
plt.title("Lowest Total doses administered among all countrues", size=18)
plt.tight_layout()
"""
These are mostly very small countries, more like Islands

**Now let's compare it with % fully vaccincated**
"""
plt.figure(figsize=(30,5))
# make barplot
sns.barplot(x='Country', y="% of population fully vaccinated", data=low_dose, order=low_dose.sort_values('% of population fully vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population fully vaccinated", size=15)
plt.title("Lowest % of population fully vaccinated per Total doses administered", size=18)
plt.tight_layout()
"""
Among the lowest number of doses administered, Saint Lucia has the highest % fully vaccnated

**Now let's see it with partially vaccinated**
"""
plt.figure(figsize=(50,5))
# make barplot
sns.barplot(x='Country', y="% of population vaccinated", data=low_dose, order=low_dose.sort_values('% of population vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population partially vaccinated", size=15)
plt.title("Lowest % of population partially vaccinated per Total doses administered", size=18)
plt.tight_layout()
"""
Samoa has more partially vaccinated population among the lowest doses administered

**Now let's look at Doses administered per 100 people**
"""
plt.figure(figsize=(20,5))
# make barplot
sns.barplot(x='Country', y="Doses administered per 100 people", data=low_dose, order=low_dose.sort_values('Doses administered per 100 people', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Doses administered per 100 people", size=15)
plt.title("Doses administered per 100 people for Total doses administered", size=18)
plt.tight_layout()
"""
Again the results are similar
"""
"""
## 9. Lowest Doses administered per 100 people
"""
per100_min = df['Doses administered per 100 people'].min()
per100_mean = df['Doses administered per 100 people'].mean()

print("Minimum Dose administered per 100 people:", per100_min)
print("Mean Doses administered per 100 people:", per100_mean)
low_per_100 = df[df['Doses administered per 100 people'] < 1]

plt.figure(figsize=(20,5))
# make barplot
sns.barplot(x='Country', y="Doses administered per 100 people", data=low_per_100, order=low_per_100.sort_values('Doses administered per 100 people', ascending = True).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Doses administered per 100 people", size=15)
plt.title("Lowest Doses administered per 100 people", size=18)
plt.tight_layout()
"""
These countries are the lowest 1% with Congo at 0.1

**Let's see which one has more fully vaccinated percentage**
"""
plt.figure(figsize=(20,5))
# make barplot
sns.barplot(x='Country', y="% of population fully vaccinated", data=low_per_100, order=low_per_100.sort_values('% of population fully vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population fully vaccinated", size=15)
plt.title("% of population fully vaccinated for Lowest Doses administered per 100 people", size=18)
plt.tight_layout()
"""
These  lie almost on a similar number

**Let's see which one has more partially vaccinated percentage**
"""
plt.figure(figsize=(20,5))
# make barplot
sns.barplot(x='Country', y="% of population vaccinated", data=low_per_100, order=low_per_100.sort_values('% of population vaccinated', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("% of population partially vaccinated", size=15)
plt.title("% of population partially vaccinated for Lowest Doses administered per 100 people", size=18)
plt.tight_layout()
"""
Syria is more partially vaccinated among them 

**Let's see which one has more Total doses administered**
"""
plt.figure(figsize=(20,5))
# make barplot
sns.barplot(x='Country', y="Total doses administered", data=low_per_100, order=low_per_100.sort_values('Total doses administered', ascending = False).Country)
# set labels
plt.xlabel("Countries", size=15)
plt.ylabel("Total doses administered", size=15)
plt.title("Total doses administered for Lowest Doses administered per 100 people", size=18)
plt.tight_layout()
"""
Again Syria takes the lead
"""
"""
# Summary of Analysis
"""
"""
**1. Top Fully vaccinated Countries**
Highest rate: 76%
Highest Fully vaccinated Countries: Malta and U.A.E 
These were above the 70% rate
Among the top 50% there were mostly countries with smaller area and a good economy
Total doses administered among them were high for countries with a larger population like Germany and U.K 
More doses per 100 people were again higher for countries with low area and population like U.A.E

Note: United States, the manufacturer of the two most trusted vaccinations agaisnt COVID-19, Pfizer and Moderna, is nowhere to be seen in this category.

**2. Top Partially Vaccinated**
Highest rate: 80%
Almost the same countries that were fully vaccinated are also the top partially vaccnated as well, this time United States also makes it way in the top 50%
Among these partially vaccinated, United States has the highest number of doses administered

**3. Top Total doses administered**
Highest number: 1688683000
China has this highest number and the difference between the second highest country is quite large
Among these, Chile has more fully and aprtially vaccinated population and more total doses administered

**4. Top Doses administered per 100 people**
The maximum number for Doses administered per 100 people is: 172
This trend is quite similar to that of Top fully vaccinated countries

**5. Lowest fully vaccinated countries**
Lowest Rate: 0.1% (Haiti)
Most countries are African with poor economy
Among these, Vanuatu is more partially vaccnated
Vietnam has more Total doses administered

**6. Lowest Partially vaccinated Countries**
Congo is at the bottom
Among them, Bostwana, Ukraine and Georgia are more fully vaccinated
Bangladesh has more total doses administered

**7. Lowest Total doses administered**
Lowest dose administered: 6576
Haiti is again the at the bottom
Tonga being more fully and partailly vaccinated

**8. Lowest Doses administered per 100 people**
Minimum Dose administered per 100 people: 0.1
Congo is at the bottom, as well as Haiti
They almost have a similar fully vaccinated percentage
but Syria is more partially vaccinated and with more total doses administered

"""