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Fifa19.csv edit pass on P7 Oct 15, 2019
README.md p7 readme Oct 18, 2019
excel.md
preview.csv preview.csv Oct 15, 2019
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README.md

Project 7: Fédération Internationale de Football Association

Corrections

  • Oct 18: test.py is updated. Please re-download it.

Intro

Let's Play Fifa19, Python style! In this project, you will get more practice with lists and start using dictionaries. Start by downloading test.py and Fifa19.csv (which was adapted from this dataset). This dataset is too large to preview on GitHub (>17K rows), but you can view the raw version or using a program such as Excel. You can also preview an example with 100 rows here. For this project, you'll create a new main.ipynb and answer questions in the usual format.

The Data

Try to familarize yourself with the data before starting the analysis. We have players belonging to a wide range of nationalities and clubs in Fifa19. As you can see the data includes their weekly wages, in Euros (yes, wages are per week!), net worth of the player (in Euros) and the performace rating (score out of 100). For instance, the player named "Neymar" is associated with Brazil, is signed up by club "Paris Saint-Germain", and is paid a weekly wage of '€290K' (290000 Euros).

To ingest the data to your notebook, paste the following in an early cell:

import csv

fifa_file = open('Fifa19.csv', encoding='utf-8')
file_reader = csv.reader(fifa_file)
player_data = list(file_reader)
fifa_file.close()
header = player_data[0]
player_data = player_data[1:]
for row in player_data:
    for idx in [2,4]:
        row[idx] = float(row[idx])

Consider peeking at the first few rows:

print(header)
for row in player_data[:5]:
    print(row)

It's up to you to write any functions that will make it more convenient to access this data.

Let's Start!

Question 1: what is the name of the oldest player?

Question 2: what is the name of the highest-paid player?

If two players are paid the same, break the tie in favor of whoever appears first in the dataset.

Question 3: what is the name of the highest value player?

Question 4: what club is that player(in above q3) in?


Complete the following function in your notebook:

def get_column(col_name):
    pass # replace this

The function extracts an entire column from player_data to a list, which it returns. For example, imagine player_data contained this:

[
    ["a", "b", "c"],
    ["d", "e", "f"],
    ["g", "h", "i"]
]

And header contains this:

["X", "Y", "Z"],

Then column "X" is ["a", "d", "g"], column "Y" is ["b", "e", "h"], and column "Z" is ["c", "f", "i"]. A call to get_column("Y") should therefore return ["b", "e", "h"], and so on.


Question 5: what are the first five nationalities listed in the dataset?

Use get_column, then take a slice from the list that is returned to you.

Question 6: which five names are alphabetically first in the dataset?

By alphabetically, we mean according to Python (e.g., it is true that "B" < "a"), so don't use the lower method or anything.

Don't deduplicate names in this output in the case that multiple players have the same name.

Question 7: what is the average value?

Question 8: what is the average age?


Define a function player_count that takes a parameter, country, and counts the number of players belonging to that country. This function will be useful for the questions that follow.


Question 9: how many players have Portugal as their nationality?

Question 10: how many players have Brazil as their nationality?

Question 11: which country has the most players participating in FIFA19?

The player_count function can be useful here.

Hint 1: Make sure you aren't calling player_count more times than necessary. If you're not careful, the code will be very slow to execute!

Hint 2: Mutiple ways to implement your code. Try to find the best way!


Define a function player_to_dict that takes a parameter, player_id, and returns a dict containing all the information about the player that matches. Find the player row by matching player_id to the Id column in the data.


Question 12: what are the stats for the player with Id equal to 20801?

Use your player_to_dict function. The output should be a dictionary like this:

{'Id': '20801',
 'Name': 'Cristiano Ronaldo',
 'Age': 33.0,
 'Nationality': 'Portugal',
 'Overall': 94.0,
 'Club': 'Juventus',
 'Value': '€77M',
 'Wage': '€405K',
 'Preferred Foot': 'Right',
 'Jersey Number': '7',
 'Height': "6'2",
 'Weight': '183lbs'}

Question 13: what are the stats for the player with Id equal to 190871?

Question 14: what are the stats for the player with Id equal to 158023?

Question 15: what are the stats for the player with Id equal to 192985?

Question 16: how many players are there per nationality?

Answer in the form of a dictionary where the key is the nationality and the value is the number of players for that nationality.

{'Argentina': 937,
 'Portugal': 322,
 'Brazil': 827,
 'Spain': 1072,
 'Belgium': 260,
 ...
}

Question 17: how many players for each Jersey Number?

Jersey Number should be a string (don't convert it). In 60 cases, the Jersey Number is the empty string. Your dictionary should include this.


For the following questions, your lab work will be especially useful.

Question 18: what is the average player score (represented by the overall column) per Jersey Number?

Answer with a dict, mapping Jersey Number to average score.

Question 19: which Jersey Number has highest average overall?

Question 20: which club has highest average wage?

You can’t perform that action at this time.