PART $2$: Coding $($PYTHON$!)$

SPECIFICATION

Description: This program uses the covid$\_$comorbidities$\_$USsummary.csv data to print totals and create a bar and pie charts showing the number of people who died because of comorbidities in COVID$-19$ cases. It will also explore correlations between the different variables.

Input:

covid$\_$comorbidities$\_$USsummary.csv

Output:

$-$ Bar chart with classes of conditions. The $\backslash ($ x $\backslash )-$axis will be the class conditions, $\backslash ($ y $\backslash )-$axis the related number of deaths

$-$ Pie chart on the age class, showing the distribution of deaths per age group $($sample below $-$ NOT the actual graph$)$

$-$ Print the comorbidity with the highest number of "COVID$-19$ Deaths" for the population of less than $35$ years of age and the percentage of this comorbidity for the same population.

Procedure:

Import the required libraries.

Read the data file into a pandas data structure.

Remove records with "Age Group" equal to either 'Not stated' or 'All Ages'.

Remove records with "Condition" equal to COVID$-19.$

Remove the columns "Condition Group", "ICD$10\_$codes", "Number of Mentions".

Create the charts, as subplots.

Subplot $221($left$)$: bar chart

bar plot

x$=[$ list of conditions $]$

y$=$ values for each one of the groups. Name y axis as "Count"

do not overlap labels

PLEASE NOTE: the number of conditions can be high. You can have all of them in the

chart OR only the top $5.$ There is no penalty or extra points either ways

Subplot $222($right$)$: pie chart. This is with the same y data as the bar chart

pie plot

label the wedges using the age categories

Add percentage values as second label to your pie chart

Insert a proper title to the main plot

Print:

the comorbidity with the highest number of deaths for the population of less than $35$ years of

age;

the percentage of the total deaths this comorbidity represents for the total deaths in the

same population.

Run a correlation analysis to determine relationships between variables. Either the original CSV file

or a pandas from the steps above can be used. Please keep in mind that correlation analysis is

working on numerical variables only. For this assignment, you can consider only the variables that

are numerical, without converting the categorical into numerical.

Write a $2+$ pages doc$/$pdf report containing:

your own interpretation of your analysis $($graph included$)$;

As non$-$mandatory requirement, a comparison of your findings with an analysis of COVID$-19$

comorbidities from ChatGPT or Bard$/$Gemini$.$ There is no penalty or extra points for not

doing$/$doing this type of comparison