Create dictionary through text file

Use csv.DictReader. By giving the right data delimiter you get back a iteraror of dictionaries… and if the 1st line are given the columns’ name they will be the keys.

# ########################################
# this is used just to mimic a file object
from io import StringIO

data = """Year,Country,Coach,Captain
1930,Uruguay,Alberto Suppici,Jose Nasazzi
1934,Italy,Vittorio Pozzo,Gianpiero Combi
1938,Italy,Vittorio Pozzo,Giuseppe Meazza
1950,Uruguay,Juan Lopez,Obdulio Varela
1954,Germany,Sepp Herberger,Fritz Walter
1958,Brazil,Vicente Feola,Hilderaldo Bellini
1962,Brazil,Aymore Moreira,Mauro Ramos
1966,England,Alf Ramsey,Bobby Moore
1970,Brazil,Mario Zagallo,Carlos Alberto
1974,Germany,Helmut Schon,Franz Beckenbauer
1978,Argentina,Cesar Luis Menotti,Daniel Passarella
1982,Italy,Enzo Bearzot,Dino Zoff
1986,Argentina,Carlos Bilardo,Diego Maradona
1990,Germany,Franz Beckenbauer,Lothar Matth�us
1994,Brazil,Carlos Alberto Parreira,Dunga
1998,France,Aime Jacquet,Didier Deschamps
2002,Brazil,Luiz Felipe Scolari,Cafu
2006,Italy,Marcello Lippi,Fabio Cannavaro
2010,Spain,Vicente del Bosque,Iker Casillas
2014,Germany,Joachim Low,Philipp Lahm"""

f = StringIO(data)
# ################

import csv
from collections import defaultdict


# main part
dd = defaultdict(dict)
r = csv.DictReader(f, delimiter=',')
for line in r:
    if line['Country'] in dd:
        dd[line['Country']]['Wins'] += 1
    else:
        dd[line['Country']]['Wins'] = 1

    dd[line['Country']].setdefault("Years", []).append(line['Year'])


# alphabetic order 
dd_ordered = {c: dd[c] for c in sorted(dd)}

# check output
print(*dd_ordered.items(), sep='n')

This solution takes advantage of the pandas library’s dataframe functionality.

import pandas as pd

print(f'Country   Wins Years')
data = pd.read_csv('world_cup_champions.txt') # Create a pandas dataframe fron the data
bycountry = data.groupby('Country')  # Group the dataframe by country
for country, df in bycountry:  # For each (country) group
    years = [str(x[1]['Year']) for x in df.iterrows()] # Extract the years into a list
    print(f'{country:<10} {len(years)}  ', ', '.join(years))  # Print results

Which can be simplified to:

import pandas

print(f'Country   Wins Years')
for country, df in pandas.read_csv('world_cup_champions.txt').groupby('Country'):
    years = [str(x[1]['Year']) for x in df.iterrows()]
    print(f'{country:<10} {len(years)}  ', ', '.join(years))

Output:

Country   Wins Years
Argentina  2   1978, 1986
Brazil     5   1958, 1962, 1970, 1994, 2002
England    1   1966
France     1   1998
Germany    4   1954, 1974, 1990, 2014
Italy      4   1934, 1938, 1982, 2006
Spain      1   2010
Uruguay    2   1930, 1950

Suggestion 1: With pandas

If you’re allowed to use pandas, then you can just

# import pandas as pd
def main():
    inp_filepath = 'world_cup_champions.txt'

    df = pd.read_csv(inp_filepath) ## read from file 
    champions_df = df.groupby('Country').agg(
        Wins=('Year',len), Years=('Year',list), 
        Captains=('Captain', list), Coaches=('Coach', list) )

    champions = champions_df.to_dict('index')

    print(champions_df[['Wins']].assign(
        Years=champions_df['Years'].apply(lambda y: ', '.join(f'{i}' for i in y)) 
    ).sort_values('Wins', ascending=False).to_markdown(tablefmt='rst'))

^{[Use .sort_index() instead of .sort_values('Wins', ascending=False) to sort row by alphabetical order of countries instead of most wins.]}

champions would look like

{
  'Argentina': {'Wins': 2, 'Years': [1978, 1986], 'Captains': ['Daniel Passarella', 'Diego Maradona'], 'Coaches': ['Cesar Luis Menotti', 'Carlos Bilardo']},
  'Brazil': {'Wins': 5, 'Years': [1958, 1962, 1970, 1994, 2002], 'Captains': ['Hilderaldo Bellini', 'Mauro Ramos', 'Carlos Alberto', 'Dunga', 'Cafu'], 'Coaches': ['Vicente Feola', 'Aymore Moreira', 'Mario Zagallo', 'Carlos Alberto Parreira', 'Luiz Felipe Scolari']},
  'England': {'Wins': 1, 'Years': [1966], 'Captains': ['Bobby Moore'], 'Coaches': ['Alf Ramsey']},
  'France': {'Wins': 1, 'Years': [1998], 'Captains': ['Didier Deschamps'], 'Coaches': ['Aime Jacquet']},
  'Germany': {'Wins': 4, 'Years': [1954, 1974, 1990, 2014], 'Captains': ['Fritz Walter', 'Franz Beckenbauer', 'Lothar Matth�us', 'Philipp Lahm'], 'Coaches': ['Sepp Herberger', 'Helmut Schon', 'Franz Beckenbauer', 'Joachim Low']},
  'Italy': {'Wins': 4, 'Years': [1934, 1938, 1982, 2006], 'Captains': ['Gianpiero Combi', 'Giuseppe Meazza', 'Dino Zoff', 'Fabio Cannavaro'], 'Coaches': ['Vittorio Pozzo', 'Vittorio Pozzo', 'Enzo Bearzot', 'Marcello Lippi']},
  'Spain': {'Wins': 1, 'Years': [2010], 'Captains': ['Iker Casillas'], 'Coaches': ['Vicente del Bosque']},
  'Uruguay': {'Wins': 2, 'Years': [1930, 1950], 'Captains': ['Jose Nasazzi', 'Obdulio Varela'], 'Coaches': ['Alberto Suppici', 'Juan Lopez']}
}

and the printed output would be

=========  ======  ============================
Country      Wins  Years
=========  ======  ============================
Brazil          5  1958, 1962, 1970, 1994, 2002
Germany         4  1954, 1974, 1990, 2014
Italy           4  1934, 1938, 1982, 2006
Argentina       2  1978, 1986
Uruguay         2  1930, 1950
England         1  1966
France          1  1998
Spain           1  2010
=========  ======  ============================

---

---

Suggestion 2: Without pandas

def createDict(file_lines):
    finalDict = {}
    rKeys = ['Years', 'Coaches', 'Captains']
    fData = [l.strip().split(',') for l in file_lines[1:]]

    for year,country,coach,captain in fData:
        finalDict.setdefault(country, {'Wins':0, **{k:[] for k in rKeys}})
        finalDict[country]['Wins'] += 1
        for k,v in zip(rKeys,[year,coach,captain]): 
            finalDict[country][k].append(v)
    return finalDict

def printChamps(unsorted_dict, idx='Country',idxLen=10,ctKey='Wins',cLen=4,**lenRef):
    if not lenRef: 
        lenRef = {'Years':30} ## {'Years':30,'Captains':70,'Coaches':50} ## 
    
    vals_n_lens = [(idx,idxLen), (ctKey,cLen), *lenRef.items()]
    print("  ".join(f"{k:{l}}" for k,l in vals_n_lens))
    print("  ".join(f"{'='*len(k):{l}}" for k,l in vals_n_lens))

    for c, rDict in sorted(unsorted_dict.items()):
        vals_n_lens = [(c,idxLen), (rDict[ctKey],cLen)]
        vals_n_lens += [(', '.join(rDict.get(k,[])), l) for k, l in lenRef.items()]
        print("  ".join(f"{k:<{l}}" for k,l in vals_n_lens))

def main():
    inp_filepath = 'world_cup_champions.txt'
    with open(inp_filepath, 'r') as f: 
        champions = createDict(f.readlines())
    printChamps(champions)

champions would look the same as in the pandas solution, but the printed output would look a bit closer to the desired output depicted in your question:

Country     Wins  Years                         
=======     ====  =====                         
Argentina   2     1978, 1986                    
Brazil      5     1958, 1962, 1970, 1994, 2002  
England     1     1966                          
France      1     1998                          
Germany     4     1954, 1974, 1990, 2014        
Italy       4     1934, 1938, 1982, 2006        
Spain       1     2010                          
Uruguay     2     1930, 1950                    

---

If you

• either changed the default lenRef in printChamps to {'Years':30,'Captains':70,'Coaches':50} (currently commented out),
• or called printChamps from main like printChamps(champions, Years=30,Captains=70,Coaches=50),

then the printed output would look like

Country     Wins  Years                           Captains                                                                Coaches                                           
=======     ====  =====                           ========                                                                =======                                           
Argentina   2     1978, 1986                      Daniel Passarella, Diego Maradona                                       Cesar Luis Menotti, Carlos Bilardo                
Brazil      5     1958, 1962, 1970, 1994, 2002    Hilderaldo Bellini, Mauro Ramos, Carlos Alberto, Dunga, Cafu            Vicente Feola, Aymore Moreira, Mario Zagallo, Carlos Alberto Parreira, Luiz Felipe Scolari
England     1     1966                            Bobby Moore                                                             Alf Ramsey                                        
France      1     1998                            Didier Deschamps                                                        Aime Jacquet                                      
Germany     4     1954, 1974, 1990, 2014          Fritz Walter, Franz Beckenbauer, Lothar Matth�us, Philipp Lahm          Sepp Herberger, Helmut Schon, Franz Beckenbauer, Joachim Low
Italy       4     1934, 1938, 1982, 2006          Gianpiero Combi, Giuseppe Meazza, Dino Zoff, Fabio Cannavaro            Vittorio Pozzo, Vittorio Pozzo, Enzo Bearzot, Marcello Lippi
Spain       1     2010                            Iker Casillas                                                           Vicente del Bosque                                
Uruguay     2     1930, 1950                      Jose Nasazzi, Obdulio Varela                                            Alberto Suppici, Juan Lopez