SIMPLE EXPLORATORY DATA ANALYSIS & DATA CLEANSING:

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For the dataframe, please enter the functions and show the output to do each of the following. Create a separate text block and code block to answer each question. If you use the same function to answer more than one question, just re-enter the function call in the corresponding text/code block.

Print the number of rows & columns in the dataframe.

Print the data type for each column of the dataframe.

Print out number of unique values in each column.

Please print the unique values for any 3 columns.

Show the values for all columns and rows in the dataframe.

Show the number of null values in each column.

After reviewing the data, do any columns appear to have the wrong datatype? If yes, write the python code to change the column to have the correct data type. Please note, "string" data will be shown as "object" data type in a pandas dataframe.

After reviewing the data, do any columns appear to be meaningless, essentially empty, redundant, or irrelevant? If yes, write the python code to remove those columns from your dataframe.

Display the common descriptive statistics for each numeric column in the dataframe.

What is the overall average, median, and mode total revenue?

What is the median total revenue by sector?

What is the median total revenue by class?

What is the median total revenue by sector and class?