Question 4 (8 pts total): Male and female income by occupation. The dataset "bls.csv" contains the median weekly earnings of full-time wage and salary workers by detailed occupation and sex. The data is structured as comma-separated values (CSV). Each row has seven fields: Occupation: Job title as given from BLS. Industry summaries are given in ALL CAPS AIL_workers: Number of workers male and female, in thousands. AlLweekly: Median weekly income including male and female workers, in USD M workers: Number of male workers, in thousands. M_weekly: Median weekly income for male workers, in USD. . F workers: Number of female workers, in thousands. F_weekly: Median weekly income for female workers, in UsD. Part 1 (2 pts): Create a new dataframe that contains only the industry data (l.e., excluding the rows that are about occupations) and assign it to the variable industries Notes: In the Occupation column, industries are designated by upper case letters. Occupations (types of jobs) are mixed case. Use this distinction to help you answer subquestion a . You may find the df.loc function helpful In [ ]: # rour code here (you should end up with 23 rows in your dataframe) Question 4 (8 pts total): Male and female income by occupation. The dataset "bls.csv" contains the median weekly earnings of full-time wage and salary workers by detailed occupation and sex. The data is structured as comma-separated values (CSV). Each row has seven fields: Occupation: Job title as given from BLS. Industry summaries are given in ALL CAPS AIL_workers: Number of workers male and female, in thousands. AlLweekly: Median weekly income including male and female workers, in USD M workers: Number of male workers, in thousands. M_weekly: Median weekly income for male workers, in USD. . F workers: Number of female workers, in thousands. F_weekly: Median weekly income for female workers, in UsD. Part 1 (2 pts): Create a new dataframe that contains only the industry data (l.e., excluding the rows that are about occupations) and assign it to the variable industries Notes: In the Occupation column, industries are designated by upper case letters. Occupations (types of jobs) are mixed case. Use this distinction to help you answer subquestion a . You may find the df.loc function helpful In [ ]: # rour code here (you should end up with 23 rows in your dataframe)