The employees.csv file contains information about employees of a company with following information: First Name', 'Gender', 'Start Date', 'Last Login Time', 'Salary', 'Bonus $\%\text{'},$ 'Senior Management', 'Team' $1.$ Read the csv file and provide summary of the DataFrame by printing the object. $2.$ Fill all the null values in Gender column with No Gender and 'Senior Management' column with Dont Know. $3.$ Using the fillna $(0),$ fillna $()$ with method ffill and interpolate $()$ function to fill the missing values in 'Salary', 'Bonus $\%\text{'}$ columns and calculate the average $($mean$)$ salary and bonus for each method. $4.$ Filter the rows to only show employee with Salaries $>100$K$\mathrm{.}5.$ Print the names of male and female employees with highest and lowest salaries. $6.$ For each Team, calculate the minimum and maximum salaries. $7.$ Find the maximum and average salary of Male and Female as different groups. $8.$ For each combination of Team and Gender, calculate the mean salary. $9.$ Check the number of rows and columns and drop those rows if 'any' values are missing in a row. $10.$ Find all the employees whose bonuses are in the range of $>=5\%$ and $<10\%\mathrm{.}11.$ Find the total salary of each employee by considering both base salary and bonus and add it as an extra column Total Salary at the end. $12.$ Sort the dataset with 'Start Date' and replace it as the index. $13.$ Find the employees who joined in the years between $2000-2010.14.$ Use apply$()$ to find the difference between Max and Min Salary and Max and Min Bonus.