add lab-p7 and p7

lab-p7/README.md

+# Lab P7: Dictionaries
+
+In this lab, you will analyze water accessibility data using dictionaries.
+
+### Corrections/Clarifications
+
+None yet
+
+**Find any issues?** Report to us:
+
+- Ashwin Maran <amaran@wisc.edu>
+
+------------------------------
+
+## Learning Objectives
+
+In this lab, you will practice how to...
+* access and utilize data in CSV files,
+* deal with real-world datasets,
+* use dictionaries to organize data into key, value pairs.
+
+------------------------------
+## Segment 1: Setup
+
+Create a `lab-p7` directory and download the following files into the `lab-p7` directory.
+
+* `small_soccer_stars.csv`
+* `lab-p7.ipynb`
+* `public_tests.py`
+
+If you found your `.csv` file is downloaded as a `.txt` file (e.g. `small_soccer_stars.txt` instead of `small_soccer_stars.csv`), run `mv small_soccer_stars.txt small_soccer_stars.csv` from your Powershell/Terminal to change the extension of the file into `.csv` file manually. All the data that we need for P7 is stored in `small_soccer_stars.csv`
+
+To start, familiarize yourself with the dataset (`small_soccer_stars.csv`). Examine its contents using Microsoft Excel, Numbers (Mac) or any other spreadsheet viewing software.
+
+------------------------------
+
+## Segment 2: Using dictionaries to analyze the data
+
+You will be finishing the rest of your lab on `lab-p7.ipynb`. Run the command `jupyter notebook` from your Terminal/PowerShell window.
+Remember not to close this
+Terminal/PowerShell window while Jupyter is running, and open a new Terminal/PowerShell
+window if necessary.
+
+**Note**: For P7, you will be working on `p7.ipynb`, which is very similar to `lab-p7.ipynb`. We
+strongly recommend that you finish working on this notebook during the lab, so you can ask
+your TA/PM any questions about the notebook that may arise.
+
+**Note**: Unlike `p7.ipynb`, you do **not** have to submit `lab-p7.ipynb`. This notebook is solely
+for your practice and preparation for P7.
+
+------------------------------
+
+## Project 7
+
+You can now get started with [P7]((https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p7)). **You may use any helper functions created here in project P7**. Remember to only work with P7 with your partner from this point on. Have fun!

lab-p7/small_soccer_stars.csv

+ID,Name,Age,Nationality,Team,League,Value,Wage,Attacking,Movement,Defending,Goalkeeping,Overall rating,Position,Height,Preferred foot
+239085,E. Haaland,22,Norway,Manchester City,Premier League (England),€185M,€340K,78.6,83.6,38.0,10.4,91,ST,195cm,Left
+231747,K. Mbappé,24,France,Paris Saint Germain,Ligue 1 (France),€181.5M,€230K,83.0,92.4,30.7,8.4,91,ST,182cm,Right
+192985,K. De Bruyne,32,Belgium,Manchester City,Premier League (England),€103M,€350K,82.4,77.6,63.0,11.2,91,CM,181cm,Right
+202126,H. Kane,29,England,FC Bayern München,Bundesliga (Germany),€119.5M,€170K,88.0,74.0,43.3,10.8,90,ST,188cm,Right
+192119,T. Courtois,31,Belgium,Real Madrid,La Liga (Spain),€63M,€250K,17.2,58.0,18.0,86.6,90,GK,199cm,Left
+188545,R. Lewandowski,34,Poland,FC Barcelona,La Liga (Spain),€58M,€340K,86.6,80.8,32.0,10.2,90,ST,185cm,Right
+165153,K. Benzema,35,France,Al Ittihad,Pro League (Saudi Arabia),€51M,€95K,86.6,79.6,28.3,8.2,90,CF,185cm,Right
+158023,L. Messi,36,Argentina,Inter Miami,Major League Soccer (United States),€41M,€23K,81.8,87.0,26.3,10.8,90,CAM,169cm,Left
+239818,Rúben Dias,26,Portugal,Manchester City,Premier League (England),€106.5M,€250K,57.0,65.6,89.7,9.4,89,CB,187cm,Right
+238794,Vini Jr.,22,Brazil,Real Madrid,La Liga (Spain),€158.5M,€310K,73.8,90.8,25.0,7.2,89,LW,176cm,Right
+231866,Rodri,27,Spain,Manchester City,Premier League (England),€105.5M,€250K,71.2,66.8,84.3,9.8,89,CDM,191cm,Right
+209331,M. Salah,31,Egypt,Liverpool,Premier League (England),€85.5M,€260K,79.8,90.2,40.7,12.4,89,RW,175cm,Left
+203376,V. van Dijk,31,Netherlands,Liverpool,Premier League (England),€70.5M,€220K,63.0,70.0,89.0,11.6,89,CB,193cm,Right
+200145,Casemiro,31,Brazil,Manchester United,Premier League (England),€72M,€240K,74.6,67.4,89.0,13.4,89,CDM,185cm,Right
+190871,Neymar Jr,31,Brazil,Al Hilal,Pro League (Saudi Arabia),€85.5M,€115K,80.0,87.4,32.0,11.8,89,LW,175cm,Right
+212622,J. Kimmich,28,Germany,FC Bayern München,Bundesliga (Germany),€88M,€130K,77.6,79.6,81.3,12.0,88,CDM,177cm,Right
+212198,Bruno Fernandes,28,Portugal,Manchester United,Premier League (England),€92M,€260K,82.6,78.0,69.3,12.6,88,CAM,179cm,Right
+230621,G. Donnarumma,24,Italy,Paris Saint Germain,Ligue 1 (France),€85M,€90K,16.0,58.2,16.7,84.6,87,GK,196cm,Right
+228702,F. de Jong,26,Netherlands,FC Barcelona,La Liga (Spain),€103.5M,€240K,76.6,83.4,76.3,9.8,87,CM,181cm,Right
+222665,M. Ødegaard,24,Norway,Arsenal,Premier League (England),€109M,€170K,78.2,79.2,58.7,12.4,87,CAM,178cm,Left
+200104,H. Son,30,Korea Republic,Tottenham Hotspur,Premier League (England),€77M,€170K,80.2,83.4,38.0,10.6,87,LW,183cm,Right
+193080,De Gea,31,Spain,Manchester United,Premier League (England),€42M,€150K,20.6,56.4,19.0,82.2,87,GK,192cm,Right
+177003,L. Modrić,37,Croatia,Real Madrid,La Liga (Spain),€25M,€190K,76.0,82.8,71.7,10.4,87,CM,172cm,Right
+256790,J. Musiala,20,Germany,FC Bayern München,Bundesliga (Germany),€134.5M,€79K,69.4,88.2,65.0,8.4,86,CAM,184cm,Right
+253163,R. Araujo,24,Uruguay,FC Barcelona,La Liga (Spain),€93M,€175K,62.6,70.2,86.0,10.6,86,CB,188cm,Right
+252371,J. Bellingham,20,England,Real Madrid,La Liga (Spain),€100.5M,€175K,74.8,79.8,77.7,9.6,86,CM,186cm,Right
+251854,Pedri,20,Spain,FC Barcelona,La Liga (Spain),€105M,€165K,66.8,84.4,70.0,9.2,86,CM,174cm,Right
+246669,B. Saka,21,England,Arsenal,Premier League (England),€99M,€150K,74.2,85.2,60.7,10.0,86,RW,178cm,Left
+235243,M. de Ligt,23,Netherlands,FC Bayern München,Bundesliga (Germany),€83M,€84K,62.2,66.4,86.7,11.2,86,CB,189cm,Right
+232411,C. Nkunku,25,France,Chelsea,Premier League (England),€86.5M,€185K,76.6,85.2,60.0,8.6,86,CAM,175cm,Right
+231443,O. Dembélé,26,France,Paris Saint Germain,Ligue 1 (France),€80M,€150K,72.0,87.6,35.0,9.8,86,RW,178cm,Left
+215914,N. Kanté,32,France,Al Ittihad,Pro League (Saudi Arabia),€45M,€73K,64.4,79.4,87.7,10.8,86,CDM,168cm,Right
+211110,P. Dybala,29,Argentina,Roma,Serie A (Italy),€68M,€130K,79.8,83.4,37.3,5.2,86,CAM,177cm,Left
+208722,S. Mané,31,Senegal,Al Nassr,Pro League (Saudi Arabia),€56.5M,€80K,80.0,86.4,40.7,11.2,86,LM,174cm,Right
+204485,R. Mahrez,32,Algeria,Al Ahli Jeddah,Pro League (Saudi Arabia),€54M,€72K,75.2,84.4,32.7,10.8,86,RM,179cm,Left
+199556,M. Verratti,30,Italy,Paris Saint Germain,Ligue 1 (France),€65M,€135K,70.6,76.6,81.3,12.8,86,CM,165cm,Right
+186942,İ. Gündoğan,32,Germany,FC Barcelona,La Liga (Spain),€53.5M,€220K,74.4,73.0,73.0,9.6,86,CM,180cm,Right
+182521,T. Kroos,33,Germany,Real Madrid,La Liga (Spain),€42M,€240K,79.2,65.0,67.0,10.2,86,CM,183cm,Right
+20801,Cristiano Ronaldo,38,Portugal,Al Nassr,Pro League (Saudi Arabia),€23M,€66K,81.8,75.4,26.7,11.6,86,ST,187cm,Right
+237692,P. Foden,23,England,Manchester City,Premier League (England),€81.5M,€180K,70.2,85.2,55.3,10.4,85,CAM,171cm,Left
+268965,Zhang Yujun,19,China PR,Hebei,Super League (China PR),€100K,€1K,41.4,55.8,43.0,10.8,46,CM,176cm,Right
+268963,Zhang Jiahui,19,China PR,Hebei,Super League (China PR),€100K,€900,41.2,54.0,43.7,10.0,46,CM,182cm,Right
+268280,L. Hebbelmann,22,Germany,Meppen,3. Liga (Germany),€60K,€500,35.6,38.4,44.3,10.4,46,CDM,181cm,Right
+262760,N. Logue,22,Republic of Ireland,Finn Harps,Premier Division (Republic of Ireland),€100K,€500,40.0,49.8,43.3,7.4,46,CAM,178cm,Right
+258802,B. Singh,22,India,Jamshedpur,Indian Super League (India),€110K,€500,41.4,58.2,22.7,10.8,46,ST,172cm,Right
+269032,Chen Zeshi,16,China PR,Shandong Taishan,Super League (China PR),€100K,€500,41.0,60.4,39.7,9.6,45,RM,180cm,Right
+269038,Zhang Wenxuan,16,China PR,Guangzhou,Super League (China PR),€110K,€500,37.2,61.0,42.7,11.6,44,CB,175cm,Right
+268279,J. Looschen,24,Germany,Meppen,3. Liga (Germany),€60K,€500,38.0,36.6,23.7,9.8,44,CAM,178cm,Right
+213799,H. McFadden,19,Republic of Ireland,Sligo Rovers,Premier Division (Republic of Ireland),€20K,€500,29.4,45.0,46.3,11.6,44,CB,182cm,Right
+183162,J. Yates,19,England,Rotherham United,Championship (England),€0,€0,26.2,34.0,14.0,13.8,40,ST,170cm,Right

p7/README.md

+# Project 7 (P7): Soccer Stars
+
+
+## Corrections and clarifications:
+
+* None yet.
+
+**Find any issues?** Report to us:
+- Yuheng Wu <wu459@wisc.edu>
+- Takis Chytas <chytas@wisc.edu>
+- Shutong Wu <shutong.wu@wisc.edu>
+- Cathy Cao <ccao35@wisc.edu>
+- Aboli Pai <apai7@wisc.edu>
+- David Parra <deparra@wisc.edu>
+
+## Instructions:
+
+This project will focus on **utilizing lists** and **managing dictionaries**. To start, download [`p7.ipynb`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p7/p7.ipynb), [`public_tests.py`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p7/public_tests.py), and [`soccer_stars.csv`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p7/soccer_stars.csv).
+
+**Important Note:** If it takes too long to load the file `soccer_stars.csv` on GitLab, you can directly download the file from this link: [`soccer_stars.csv`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/raw/main/p7/soccer_stars.csv). You will need to **Right Click**, and click on the **Save as...** button to save the file through this method.
+
+
+**Note:** Please go through [Lab-P7](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/lab-p7) before you start the project. The lab contains some very important information that will be necessary for you to finish the project.
+
+You will work on `p7.ipynb` and hand it in. You should follow the provided directions for each question. Questions have **specific** directions on what **to do** and what **not to do**.
+
+After you've downloaded the file to your `p7` directory, open a terminal window and use `cd` to navigate to that directory. To make sure you're in the correct directory in the terminal, type `pwd`. To make sure you've downloaded the notebook file, type `ls` to ensure that `p7.ipynb`, `public_tests.py`, and `soccer_stars.csv` are listed. Then run the command `jupyter notebook` to start Jupyter, and get started on the project!
+
+**IMPORTANT**: You should **NOT** terminate/close the session where you run the above command. If you need to use any other Terminal/PowerShell commands, open a new window instead. Keep constantly saving your notebook file, by either clicking the "Save and Checkpoint" button (floppy disk) or using the appropriate keyboard shortcut.
+
+------------------------------
+
+## IMPORTANT Submission instructions:
+- Review the [Grading Rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p7/rubric.md), to ensure that you don't lose points during code review.
+- Login to [Gradescope](https://www.gradescope.com/) and upload the zip file into the P7 assignment.
+- If you completed the project with a **partner**, make sure to **add their name** by clicking "Add Group Member"
+in Gradescope when uploading the P7 zip file.
+
+   <img src="images/add_group_member.png" width="400">
+
+   **Warning:** You will have to add your partner on Gradescope even if you have filled out this information in your `p7.ipynb` notebook.
+
+- It is **your responsibility** to make sure that your project clears auto-grader tests on the Gradescope test system. Otter test results should be available within forty minutes after your submission (usually within ten minutes). **Ignore** the `-/100.00` that is displayed to the right. You should be able to see both PASS / FAIL results for the 20 test cases, which is accessible via Gradescope Dashboard (as in the image below):
+
+    <img src="images/gradescope.png" width="400">
+
+- You can view your **final score** at the **end of the page**. If you pass all tests, then you will receive **full points** for the project. Otherwise, you can see your final score in the **summary** section of the test results (as in the image below):
+
+   <img src="images/summary.png" width="400">
+
+   If you want more details on why you lost points on a particular test, you can scroll up to find more details about the test.

p7/images/README.md

+# Images
+
+Images from p7 are stored here.

p7/rubric.md

+# Project 7 (P7) grading rubric
+
+## Code reviews
+
+- The Gradescope autograder will make deductions based on the rubric provided below.
+- To ensure that you don't lose any points, you must review the rubric and make sure that you have followed the instructions provided in the project correctly.
+
+## Rubric
+
+### General guidelines:
+
+- Did not save the notebook file prior to running the cell containing "export". We cannot see your output if you do not save before generating the zip file. This deduction will become stricter for future projects. (-3)
+- Used concepts/modules such as pandas not covered in class yet - built-in functions that you have been introduced to can be used. (-3)
+- Import statements are not all placed at the top of the notebook. (-1)
+- Hardcoded answers. (all points allotted for that question)
+
+### Question specific guidelines:
+
+- `format_euros` (3)
+	- function logic is incorrect when the input ends with `"K"` (-1)
+	- function logic is incorrect when the input ends with `"M"` (-1)
+	- function logic is incorrect when the input does not end with `"K"` or `"M"` (-1)
+
+- `cell` (5)
+	- function does not typecast the correct columns to `int` or `float` as expected (-1)
+	- function does not format the `Height` column correctly (-1)
+	- function does not use `format_euros` to format the relevant columns (-1)
+	- function typecasts based on the column index and not the `col_name` (-1)
+	- variables `csv_data`, `csv_header`, and `csv_rows` are not defined as expected (-1)
+
+- `players` (5)
+	- data structure is not defined correctly (-2)
+	- logic used to define data structure is incorrect (-2)
+	- `cell` function is not used to read data (-1)
+
+- q1 (2)
+	- `players` data structure is not used to read data (-2)
+
+- q2 (4)
+	- incorrect logic is used to find the player with the highest `Value`(-1)
+	- incorrect logic is used to find the statistics of the player with the highest `Value`(-1)
+	- `players` data structure is not used to read data (-1)
+
+- q3 (4)
+	- incorrect logic is used to find the player with the highest `Wage`(-1)
+	- incorrect logic is used to find the `Nationality` of the player with the highest `Wage`(-1)
+	- `players` data structure is not used to read data (-1)
+
+- q4 (3)
+  - the player with the highest `Wage` is recomputed (-1)
+	- incorrect logic is used to find the `Position` of the player with the highest `Wage`(-1)
+	- `players` data structure is not used to read data (-1)
+
+- q5 (3)
+	- incorrect logic is used to answer (-1)
+	- teams whose `League` is not exactly as required are added to the list (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q6 (4)
+	- incorrect logic is used to answer (-1)
+	- the keys of `preferred_foot_count` are hardcoded (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q7 (5)
+	- incorrect logic is used to answer (-2)
+	- the keys of `preferred_foot_avg_overall` are hardcoded (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q8 (4)
+	- incorrect logic is used to answer (-1)
+	- the keys of `positions_count` are hardcoded (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q9 (5)
+	- incorrect logic is used to answer (-2)
+	- the keys of `positions_avg_age` are hardcoded (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q10 (5)
+	- incorrect logic is used to answer (-2)
+	- the keys of `positions_avg_height` are hardcoded (-1)
+	- `players` data structure is not used to read data (-1)
+
+- `average_stat_by_position` (4)
+	- function logic is incorrect (-2)
+	- function only works for certain numerical columns (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q11 (2)
+	- `average_stat_by_position` function is not used to answer (-2)
+
+- q12 (2)
+	- `average_stat_by_position` function is not used to answer (-2)
+
+- q13 (4)
+	- incorrect logic is used to answer (-2)
+	- `average_stat_by_position` function is not used to answer (-1)
+
+- `best_player_of_team_at_position` (5)
+	- function logic is incorrect when there is a unique best player of the `team` at `position` (-2)
+	- function logic is incorrect when there are multiple players tied for best player of the `team` at `position` (-1)
+	- function logic is incorrect when there are no players of the `team` at `position` (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q14 (2)
+	- `best_player_of_team_at_position` function is not used to answer (-2)
+
+- q15 (3)
+	- `best_player_of_team_at_position` function is not used to answer (-2)
+	- `players` data structure is not used to read data (-1)
+
+- `best_starting_players_of` (5)
+	- function logic is incorrect (-2)
+	- all positions are looped through instead of just the unique positions (-1)
+	- `best_player_of_team_at_position` function is not used to answer (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q16 (2)
+	- `best_starting_players_of` function is not used to answer (-2)
+
+- q17 (4)
+	- incorrect logic is used to answer (-1)
+	- `best_starting_players_of` function is not used to answer (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q18 (5)
+	- incorrect logic is used to answer (-2)
+  - `best_starting_players_of` function is not used to find the best starting players (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q19 (6)
+	- incorrect logic is used to answer (-2)
+  - best starting players of a single team is computed more than once (-1)
+	- the list of unique teams in the league is recomputed (-1)
+	- `best_starting_players_of` function is not used to answer (-1)
+	- `players` data structure is not used to read data (-1)
+
+- q20 (4)
+	- incorrect logic is used to answer (-2)
+	- `avg_attacking_prem_league` is not used to answer (-1)