Which departments are the two highest in terms of total spending on salaries?

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please see attached file
What are the main expense types in the data set, and what is the total amount of spending for each
of the main expense types over the entire period of the record?
Please break this down by the categories listed in the ’Expense Type’ column, and look at the actual
(not recommended or approved) amounts.
2. Which departments are the two highest in terms of total spending on salaries?
3. What fraction of total spending on salaries by the city is represented by each of the two top departments?
4. For each individual year, what are the mean recommended, approved, and actual total amounts for
dental insurance (as indicated in the Expense Category column)?
Draw a line plot showing how the actual, approved, and recommended amounts for F.I.C.A taxes (in
the expense category column) increased year-to-year.
Hint: you may use the df.replace(0.0, np.NAN) method in pandas to replace the zero approved and
actual amounts in 2020 and 2021 with NaNs, so that the line plot does not show zero for those years
(but you will not lose points for plotting the zero values.)
6. Bonus! 15 pts. Draw a box plot describing the distribution of recommended budget sizes for salaries between the
different departments, in each year. Each box in the plot should represent a single fiscal year, and
describe the distribution, among departments, of the total amount recommended with Expense Type
’Salaries’, in that year. You may want to zoom the plot in (adjust the limits of the ’y’ axis) on the box
to emphasize inter-annual differences. This will remove some of the outliers from the visible portion of
the plot, which is ok.
Please submit:
a. A single .py file containing all the code you used for the assignment (and nothing else please!). Please
comments your code to indicate what each section is for, and in particular where the solution to each
of the questions listed below begins.
b. A screenshot of the output of your code in a python terminal.
c. Graphics files of any plots (.png, .jpg, etc.).

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