This case study explores the use of both tuples and lists in Python to analyze monthly sales data for a company

over the course of a year. Each element of the tuple represents sales for a specific month $($in thousands of

dollars$).$ We will perform various data analysis operations to compute total sales, average sales, identify the

highest and lowest sales months, count months with sales exceeding a threshold, and check if sales fell below a

certain threshold.

Data Representation

The monthly sales data for $12$ months is represented as a $1$D tuple. The sales values are in thousands of dollars,

and the tuple is as follows:

monthly sales $=(45,52,39,62,48,53,47,60,49,55,50,59)$

Additionally, we will create a list of months, which will allow us to associate the sales data with each month

for better readability.

months $=[$J anuary$$

$,$

$$ F ebruary$$

$$ M arch$$

$,$

$,$

$$ April$$

$,$

$$ M ay$$

$$ J une$$

$,$

$,$

$$ J uly$$

$,$

$$ August$$

$,$

$$September$,$October$,$November$,$December$]$

Questions and Analysis

Question $1$

What is the total sales for the year?

Question $2$

What is the average monthly sales?

Question $3$

What were the highest and lowest sales months?

Question $5$

Did sales ever fall below $$40$k$?$

Expected Output

$$ Total Sales for the Year: $600$k

$$ Average Monthly Sales: $50\mathrm{.}00$k

$$ Highest Sales in a Month: $62$k $($Month: April$)$

$$ Lowest Sales in a Month: $39$k $($Month: March$)$

$$ Months with Sales Above $50$k: $[$February$,$April$,$June$,$August$,$October$,$November$,$

$$December$]$

$$ Did Sales Ever Fall Below $40$k$?$ Yes $($Month: March$)$ How do I code this into python?