Use of groupby in a function with Pandas

[Paulman]

Hello,

I'm totally stuck with a task on using groupby in a dataframe.

I have the following df in a csv file 'athletes.csv:

,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

The task is to call (and print) from a main function another function which takes three attributes:
- The dataframe df
- The age 15
- The mean value for all events (100m,200m,400m,800m,1500m)) for the age 15
The function should be grouped by gender and should reset the index.

The output should be like the below.
Input:
age_statistics(df,15,'mean')
Output:

 gender 100m 200m 400m 800m 1500m mean mean mean mean mean 0 F 8.990 27.920 54.790 119.890 249.210 1 M 10.695 22.545 49.340 113.735 229.250

I'm completely stuck with the function that groups the 15 years'old athletes in females and males and for each gender calculates the mean value for each event.

# function to groupby def age_statistics(df,age,mean): # no idea how to build it aggregated_dataframe = aggregated_dataframe.reset_index(drop=False) return aggregated_dataframe # main function def main(filename='athletes.csv'): df = pd.read_csv(filename, index_col=0) df['100m'] = df['100m'].astype(float) df['200m'] = df['200m'].astype(float) df['400m'] = df['400m'].astype(float) df['800m'] = df['800m'].astype(float) df['1500m'] = df['1500m'].astype(float) print(age_statistics(df,15,'mean')) # Do not edit this if __name__ == "__main__": main()

Anybody can help with that?