Maximum occurrences in a list

Question

I have a data frame like the one below.

import pandas as pd

data = {'Date': ['2022/09/01', '2022/09/02', '2022/09/03', '2022/09/04', '2022/09/05','2022/09/01', '2022/09/02', '2022/09/03', '2022/09/04', '2022/09/05','2022/09/01', '2022/09/02', '2022/09/03', '2022/09/04', '2022/09/05'],
        'Runner': ['Runner A', 'Runner A', 'Runner A', 'Runner A', 'Runner A','Runner B', 'Runner B', 'Runner B', 'Runner B', 'Runner B','Runner C', 'Runner C', 'Runner C', 'Runner C', 'Runner C'],
        'Training Time': ['less than 1 hour', 'less than 1 hour', 'less than 1 hour', 'less than 1 hour', '1 hour to 2 hour','less than 1 hour', '1 hour to 2 hour', 'less than 1 hour', '1 hour to 2 hour', '2 hour to 3 hour', '1 hour to 2 hour ', '2 hour to 3 hour' ,'1 hour to 2 hour ', '2 hour to 3 hour', '2 hour to 3 hour']
        }

df = pd.DataFrame(data)

I have counted the occurrence for each runner using the below code

s = df.groupby(['Runner','Training Time']).size()
s

The result is like below.
Each Runner

And when I use below code to get the max occurence.

df = s.loc[s.groupby(level=0).idxmax()].reset_index().drop(0,axis=1)
df

Result
Max Occurrence

The problem is on Runner B. It should show "1 hour to 2 hour" and "less than 1 hour". But now it only shows ""1 hour to 2 hour"

How can I fix the issue? Thanks.
Expected Result

Asked By: PJL

||

Source

Answer 1

thsi is the best i could come up with … its kind of gross

def agg_most_common(vals):
    print("vals")
    matches = []
    for i in collections.Counter(vals).most_common():
        if not matches or matches[0][1] == i[1]:
            matches.append(i)
        else:
            break
    return [x[0] for x in matches]

print(df.groupby('Runner')['Training Time'].agg(agg_most_common))

Answered By: Joran Beasley

Answer 2

import pandas as pd

data = {'Date': ['2022/09/01', '2022/09/02', '2022/09/03', '2022/09/04', '2022/09/05','2022/09/01', '2022/09/02', '2022/09/03', '2022/09/04', '2022/09/05','2022/09/01', '2022/09/02', '2022/09/03', '2022/09/04', '2022/09/05'],
        'Runner': ['Runner A', 'Runner A', 'Runner A', 'Runner A', 'Runner A','Runner B', 'Runner B', 'Runner B', 'Runner B', 'Runner B','Runner C', 'Runner C', 'Runner C', 'Runner C', 'Runner C'],
        'Training Time': ['less than 1 hour', 'less than 1 hour', 'less than 1 hour', 'less than 1 hour', '1 hour to 2 hour','less than 1 hour', '1 hour to 2 hour', 'less than 1 hour', '1 hour to 2 hour', '2 hour to 3 hour', '1 hour to 2 hour ', '2 hour to 3 hour' ,'1 hour to 2 hour ', '2 hour to 3 hour', '2 hour to 3 hour']
        }

df = pd.DataFrame(data)
s = df.groupby(['Runner', 'Training Time'], as_index=False).size()
s.columns = ['Runner', 'Training Time', 'Size']

r = s.groupby(['Runner'], as_index=False)['Size'].max()

df_list = []
for index, row in r.iterrows():
    temp_df = s[(s['Runner'] == row['Runner']) & (s['Size'] == row['Size'])]
    df_list.append(temp_df)

df_report = pd.concat(df_list)
print(df_report)
    
df_report.to_csv('report.csv', index = False)

Answered By: Ashish Jain

Maximum occurrences in a list

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