To start performing each task, import the Numpy and pandas library and give it an appropriate alias name. Find the version of the pandas package on your system.

In performing the tasks $,$ consider the sales data and advertising expenses from January to December:

Month AdExp Sales

Jan $165000$

Feb $20\mathrm{.}714000$

Mar $26\mathrm{.}536000$

Apr $30\mathrm{.}640000$

May $32\mathrm{.}349000$

Jun $29\mathrm{.}57000$

Jul $28\mathrm{.}352000$

Aug $31\mathrm{.}365000$

Sep $32\mathrm{.}217000$

Oct $26\mathrm{.}45000$

Nov $23\mathrm{.}417000$

Dec $16\mathrm{.}41000$

Task $1$

Create a dictionary of the sales data using $$Month$$ as index.

Task $2$

Create a pandas series using the sales dictionary created above.

i$)$ Print all the months from the series.

ii$)$ Print all the sales value from the series.

iii$)$ Print the sales for the last $6$ months $($Jul$-$Dec$).$

iv$)$ Change the sales for Jun to $11000$ in the series object.

v$)$ Display all months when sales was above $30000.$

vi$)$ Find the average sales for the $12$ months.

vii$)$ There has been additional sale of $2500,4800$ and $3900$ in the month of Aug, Sep and Oct respectively. Create a series of this additional sales for the mentioned months. Find the total sales across the months considering the additional sales.

viii$)$ Display the months with minimum and maximum sales $($consider the additional sales given in $$vii$.$

Task $3$

Create a numpy array using the original data given above for AdExp and Sales.

Task $4$

Using the numpy array in Task $3,$ create a dataframe with months as index and the given column names above.

i$)$ Select and display the AdExp and Sales values for Mar, Apr and May.

ii$)$ Select and display AdExp for Nov.

iii$)$ Select and display Sales for Feb.

iv$)$ Create a new column "Net$\_$Sales" which is the difference of Sales and AdExp $($Sales $-$AdExp$).$

v$)$ Find the average value of "AdExp", "Sales" and "Net$\_$Sales".

vi$)$ Find the sum of Sales for the $12$ months.

vii$)$ Get the min, max, median, standard deviation, $25$ percentile value, $75$ percentile value and the average of "AdExp", "Sales" and "Net$\_$Sales" using a single function.

viii$)$ Sort the data by Sales in descending order and display.

ix$)$ Include the index as a column in the dataframe. Change the column name to "Month". Hint: Use the reset$\_$index$()$ and rename$()$ function.

x$)$ Write the data into a csv file "Sale.csv$".$ Exclude the index while writing into the file.