Статья Автор: Захарова Дарья

Отчет. Критерий С.

with open('gym.csv', 'r') as f:
    d = {}
    names = []
    
    for line in f:
        vals = line.strip().split(',')
        
        if vals[0] == 'Age': 
            for val in vals:
                d[val] = []  
            names = vals  
        else:
            for i in range(len(vals)):
       
                if i not in {1, 9}:  
                    try:
                        d[names[i]].append(float(vals[i]))  
                    except ValueError: 
                        d[names[i]].append(None)
                else:
                    d[names[i]].append(vals[i])  
for name in names:
    if name in {'Gender', 'Workout_Type'}:
        continue    
    col = [x for x in d[name] if x is not None]  
    if len(col) == 0:
        print(f"Column {name} has no valid data.")
        continue

    print(f'Column: {name}')
    
    # Мин и Макс
    print(f'Min: {min(col)}')
    print(f'Max: {max(col)}')

    # Среднее значение
    mean = sum(col) / len(col)
    print(f'Mean: {mean}')

    # Медиана
    col.sort()
    median = (col[(len(col)-1)//2] + col[len(col)//2]) / 2
    print(f'Median: {median}')

    # Мода
    freqs = {}
    for val in col:
        freqs[val] = freqs.get(val, 0) + 1
    max_freq = max(freqs.values())
    modes = [k for k, v in freqs.items() if v == max_freq]
    print(f'Mode(s): {modes}')

    # Дисперсия
    variance = sum([(val - mean) ** 2 for val in col]) / (len(col) - 1)
    print(f'Variance: {variance}')
    
    # Стандартное отклонение
    stdev = variance ** 0.5
    print(f'Standard Deviation: {stdev}')

Прикрепленные файлы
gym.csv

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