熊猫数据框中的for循环

Question

我在使用 pandas 数据框的“for 循环”时遇到问题，希望有人能提供帮助。

我在 csv 文件中有以下数据框：

,forename,surname,gender,age,100m,200m,400m,800m,1500m
0,Migdalia,Parrish,F,18,11.08,29.0,59.41,122.05,259.11
1,Valerie,Lee,F,10,17.23,46.0,100.02,232.64,480.95
2,John,Debnam,M,17,10.81,25.89,50.6,110.29,232.39
3,Roy,Miller,M,10,19.18,46.74,95.32,201.14,430.27
4,Aida,Aumiller,F,11,15.3,41.83,81.06,189.03,394.9
5,Marcia,Brown,F,19,11.13,24.62,57.59,119.13,256.37
6,Harry,Knows,M,16,12.39,25.94,49.67,106.56,237.14
7,Barry,Lennon,M,14,11.15,23.56,46.46,110.89,230.49
8,Lilia,Armstrong,F,13,8.84,25.09,59.54,128.95,258.47
9,Johnny,Casey,M,15,9.65,22.67,49.46,112.85,233.87
10,Donald,Taylor,M,15,11.74,22.42,49.22,114.62,224.63
11,Martha,Woods,F,14,9.01,24.34,55.25,118.8,254.87
12,Diane,Lauria,F,15,8.99,27.92,54.79,119.89,249.21
13,Yvonne,Pumphrey,F,16,8.84,27.29,57.63,123.13,247.41
14,Betty,Stephenson,F,14,11.04,28.73,59.05,126.29,256.44
15,Lilia,Armstrong,F,12,11.31,34.43,74.28,150.05,321.07

我必须创建一个调用另一个函数的主函数，该函数使用“for 循环”检索每个年龄 (10,11,12,13,14,15,16) 时间最快的运动员特定的性别（例如“F”）和距离（例如“100m”）。

例如：

Input:
fastest_athletes = find_fastest_athletes(df,"100m","F",[10,11,12,13,14,15,16])
Output:
{
10: {’forename’: 'Valerie’, 'surname’: 'Lee’, 'time’: '17.23’},
11: {’forename’: 'Aida’, 'surname’: 'Aumiller’, 'time’: '15.3’},
12: {’forename’: 'Lilia’, 'surname’: 'Armstrong’, 'time’: '11.31’},
13: {’forename’: 'Lilia’, 'surname’: 'Armstrong’, 'time’: '8.84’},
14: {’forename’: 'Martha’, 'surname’: 'Woods’, 'time’: '9.01’},
15: {’forename’: 'Diane’, 'surname’: 'Lauria’, 'time’: '8.99’},
16: {’forename’: 'Yvonne’, 'surname’: 'Pumphrey’, 'time’: '8.84’}
}

我做了以下代码：

# Function with the for loop
def find_fastest_athletes(df,distance,gender,ages):
  for age in range(10,16):
    fastest_athletes = df[(df["gender"] == gender) & (df["age"] == age)]
    fastest_athletes_sorted = fastest_athletes.sort_values(distance,ascending=True)
    fastest_athletes_value = fastest_athletes_sorted.iloc[[0]][["forename","surname","100m"]]
    athletes_data = fastest_athletes_value.to_string(index=False, header=False).split('  ')
    athletes_data_dict = {
        'forename': athletes_data[0].strip(),
        'surname': athletes_data[1],
        'time': float(athletes_data[2])
        }
  return athletes_data_dict
  
# Main function
def main(filename='athletes.csv'):
    df = pd.read_csv(filename, index_col=0)
    df['100m'] = df['100m'].astype(float)
    print(find_fastest_athletes(df,'100m','F',[10,11,12,13,14,15,16]))
    return
   
if __name__ == "__main__":
  main()  

通过我的编码，我得到的输出只有上一个年龄段（16 岁）的最快运动员，而不是每个年龄段（10、11、12、13、14、15、16）的所有最快运动员，为什么那个？

另外如何在每行的开头添加年龄？

Answer 1

最简单的答案是，当您想要的只是一个分组时，您不应该执行复杂的功能。我不知道为什么你的代码不起作用。

# Melt into a long-form dataframe
df_long = df.melt(id_vars=['forename', 'surname', 'gender', 'age'], var_name='event', value_name='time')

# Get the indices for maximum times in each gender/age/event and then use those indices to get the original rows
df_fastest = df_long.iloc[df_long.groupby(['gender', 'age', 'event'])['time'].idxmax(), :]

对于一个事件：

>>> df_fastest.loc[df_fastest['event'] == '100m', :]

    forename    surname gender  age event   time
1   Valerie Lee F   10  100m    17.23
4   Aida    Aumiller    F   11  100m    15.30
15  Lilia   Armstrong   F   12  100m    11.31
...

Answer 2

我认为您的代码仅返回上一个时代最快的运动员的原因 是因为您一直在覆盖变量 athletes_data_dict-

 def find_fastest_athletes(df,distance,gender,ages):
   tmp = []
   for age in ages:
     fastest_athletes = df.loc[(df.gender == gender) & (df.age == age)]

     fastest_athletes_sorted = fastest_athletes.sort_values(distance,ascending=True)

     fastest_athletes_value = fastest_athletes_sorted.iloc[[0]][["forename","surname","100m"]]
     athletes_data = fastest_athletes_value.to_string(index=False, header=False).split('  ')
     athletes_data_dict = {
    'forename': athletes_data[0].strip(),
    'surname': athletes_data[1],
    'time': float(athletes_data[2])
    }
    tmp.append(athletes_data_dict)
return tmp

我还更改了数据框的切片以及循环中的范围。 所以我用一个列表来存储所有的值，对我来说效果很好。