下面是使用pandas.DataFrame.loc并分配新df的方法

sport_pct = (df.iloc[:, 2:].sum() / df['Athlete'].sum() * 100).astype(int)
athlete_pct = int(df['Athlete'].sum() / len(df) * 100)

new_df = pd.DataFrame({
    'header 1': ['Athlete'] + list(sport_pct.index),
    '': [athlete_pct] + list(sport_pct.values)
})
print(new_df)

header 1    
0   Athlete  75
1  Baseball  33
2     Cheer  33
3  Football  33

# calculate total number of athletes
total_athletes = df['Athlete'].sum()

# calculate percentage of athletes for each sport
sport_percentages = df[['Baseball', 'Cheer', 
'Football']].sum().apply(lambda x: x/total_athletes * 100)

# calculate percentage of athletes in each group
group_percentages = df[['Baseball', 'Cheer', 'Football']].apply(lambda x: 
x/df['Athlete'].sum() * 100)

# concatenate the two results into one DataFrame
result = pd.concat([pd.DataFrame({'Athlete': [total_athletes]}), 
sport_percentages.to_frame().transpose(), group_percentages])

你可以试试groupby和agg，如下所示：

# group by 'Athlete' 
result = df.groupby('Athlete').agg(
    Total=('Athlete', 'size'),
    Baseball_Percentage=('Baseball', 'mean'),
    Cheer_Percentage=('Cheer', 'mean'),
    Football_Percentage=('Football', 'mean')
)

# calculate the percentage
result['Percentage_of_Total_Athletes'] = result['Total'] / result['Total'].sum() * 100