Exam Objectives $$ Read and write files in a comma separated values format. $$ Replace missing values in DataFrame columns with specified "neutral" values. $$ Remove rows from a DataFrame that are either irrelevant or may interfere wilt later analysis. Add a new, calculated column to a DataFrame. $$ Group rows based on value$($s$)$ of specified column$($s$).$ Aggregate data within groups for use in analysis. Provided to You $1.$ These instructions $2.$ A "fresh" data set $($Exam$03$Input.csv$)3.$ Images of results at specific "checkpoints" in the work $4.$ A grading rubric Also Available to You Please make full use of the demonstration code provided to you as well as links to W$3$ Schools tutorials. Code you have worked on through the semester should also be useful to you. You are welcome to research online Python and Pandas documentation. Please be aware, though, that any code you find on a forum, discussion board, or any other website will need modification in order to work with the provided data and produce the desired results. Please Note!! IF YOU USE CODE COLLECTED OUTSIDE CLASS, you are required to document it FULLY. That is$,$ "parse" the code as I have for you throughout the semester. Your documentation must be detailed and specific enough to make it entirely clear to me that you fully understand the code you have used. Any code using a technique not covered in class without such $($very$,$ very detailed$)$ documentation earns a score of zero $(0)$ for that task. Exam Tasks I very strongly recommend approaching the tasks in the order in which presented. These instructions include samples of what the data should look like at different points in developing your script; these samples assume that tasks are executed in the order presented. $1.$ Read in the provided data file $($Exam$03$Input$-$csv$)$ to create a DataFrame object. $2.$ "Scrub" the data to prepare it for analysis: a$.$ Replace missing values in the Fees column with the value of zero $(0).$ b$.$ Remove all rows representing canceled sections$/$classes$.3.$ Add a calculated column named Total Fees to the DataFrame: a$.$ Add the column to the end of the DataFrame b$.$ Calculate the value of the new column as: Enrolled Fees $4.$ Group the data based on Semester and Course values. Based on this grouping, sum both the Enrolled and $($new$)$ Total Fees columns. $5.$ Write the results of the previous step to a comma separated values file; name the file Exam$030$utput.csv$.$ Notes for Your Script $$ Add comments above the code for each task specifying the task number. o Including comments for sub$-$tasks would be VERY helpful. $$ There are no specific naming requirements for your script. Your Submission You will attach your executable Python script $($with extension py$)$ to the Blackboard assignment. $$ Do not submit Exam$030$utput.csv$.$ When I execute your code in order to evaluate it$,$ it will create a "fresh" copy of your output. I will use that output to grade your submission. I will neither download nor open any output file submitted. Coding Checkpoints Please find output reflecting the desired results after completing each of the required tasks below. PLEASE NOTE: You are not required to include output statements in your submitted script. You are welcome to do so in order to verify that your process is working correctly. Alternately, you may choose to use the Variable Explorer in Anaconda for that purpose. Task $01$ Task $01$ Results DataFrame shape: $(1784,24)$ Task $02$ Including only selected columns, first $15$ rows: Task $02$ Results Semester Status Fees $202130$ OPEN $201\mathrm{.}0202130$ OPEN $30\mathrm{.}0202130$ OPEN $0\mathrm{.}0202130$ OPEN $105\mathrm{.}0202130$ OPEN $0\mathrm{.}0202130$ OPEN $105\mathrm{.}0202130$ OPEN $55\mathrm{.}0202130$ OPEN $0\mathrm{.}0202130$ OPEN $105\mathrm{.}0202130$ OPEN $0\mathrm{.}0202130$ OPEN $30\mathrm{.}0202130$ OPEN $30\mathrm{.}0202130$ OPEN $30\mathrm{.}0202130$ OPEN $0\mathrm{.}015202130$ OPEN $201\mathrm{.}0$ DataFrame shape AFTER removing rows: $(1700-24)$ CARMISTETAAAAA DAEUS $0$ b $7910111214$ Task $03.$ Depending on your approach, you may see a Python message suggesting using the loc method and referring to "caveats" in the documentation. Provided you achieve the desired results, you may ignore those messages. Task $03$ Results Enrolled Fees Total Fees $26934\mathrm{.}0134201\mathrm{.}0752250\mathrm{.}0390\mathrm{.}09\mathrm{.}910395\mathrm{.}00\mathrm{.}039...335\mathrm{.}0335\mathrm{.}0335\mathrm{.}0335\mathrm{.}04440-0$ DAEMY. $1779178017811782178330\mathrm{.}0.0\mathrm{.}0105\mathrm{.}00\mathrm{.}0...335\mathrm{.}0335\mathrm{.}0335\mathrm{.}01335\mathrm{.}024185\mathrm{.}01$ LLLT $.$ Task $04$ Results Total Fees Semester Course $2017101403112470\mathrm{.}020313920\mathrm{.}0203311270\mathrm{.}020413280\mathrm{.}02440\mathrm{.}02043...2021307311129\mathrm{.}00\mathrm{.}0731273132322\mathrm{.}073150\mathrm{.}073160\mathrm{.}0[71$b rows x $2$ columns$]$ Task $04$ Results Semester Course Enrolled Total Fees $20171014039781124709839202017102031201710203398112702017102041328020171020432440423642017103003201710352820171030632017103073$ Task $05$ EEDE Enrolled $97898986261$ S$61$ LULO$02919644635630...$ RETE $1680$ Functional Requirements Code executes properly without errors, abends, or unexpected messages Required Tasks $01.$ Read in provided data file; instantiate DataFrame Correct input file referenced Correct Pandas funct