def get_max_votes_info(group):
    # Find the row with the maximum votes
    max_row = group[group['Votes'] == group['Votes'].max()].iloc[0]
    # Return a tuple with the sum of the votes and the party and odd values for the row with the maximum votes
    return (group['Votes'].sum(), max_row['Party'], max_row['Odd'])

# Group the DataFrame by 'Election Yr.' and apply the custom function
df_grouped = df.groupby('Election Yr.').apply(get_max_votes_info)

# The resulting DataFrame has a multi-index, with the election year as the first level and the party and odd values as the second level
# You can reset the index to remove the multi-index if desired
df_result = df_grouped.reset_index()

# The resulting DataFrame should have three columns: 'Election Yr.', 'Votes', and 'Party', with the values you specified
print(df_result)

或尝试：

df_grouped = df.groupby('Election Yr.').agg({
    'Votes': 'sum',
    'Party': lambda x: x.loc[x.idxmax()],  # returns the value of 'Party' for the row with the maximum votes
    'Odd': 'first'  # returns the first value of 'Odd'
})

# The resulting DataFrame has a single index, with the election year as the only level
# You can reset the index to convert the index to a column if desired
df_result = df_grouped.reset_index()

# The resulting DataFrame should have three columns: 'Election Yr.', 'Votes', and 'Party', with the values you specified
print(df_result)

也可以使用它将max函数应用于'Votes'列，然后使用生成的索引选择'Party'列的相应值：

def get_max_votes_info(group):
    # Find the index of the row with the maximum votes
    max_index = group['Votes'].idxmax()
    # Return a tuple with the sum of the votes and the party and odd values for the row with the maximum votes
    return (group['Votes'].sum(), group.loc[max_index, 'Party'], group.loc[max_index, 'Odd'])

df_grouped = df.groupby('Election Yr.').apply(get_max_votes_info)
df_result = df_grouped.reset_index()

# The resulting DataFrame should have three columns: 'Election Yr.', 'Votes', and 'Party', with the values you specified
print(df_result)

您可以创建两个 Dataframe ：
1.每年投票总数和第一个奇数值
1.与年数最大值匹配的交易方（可以是多个）

df_sum_odd = df.groupby(['Election Yr.'], as_index=False).agg({'Votes':'sum', 'Odd':'first'})
df_party = df[df.groupby('Election Yr.').Votes.transform('max') == df.Votes][['Election Yr.', 'Party']]
df_sum_odd.merge(df_party, on = 'Election Yr.', how='right')

合并这两个变量后，您将收到以下结果：

Election Yr.    Votes   Odd Party
0   2000            240     aa  A
1   2000            240     aa  B
2   2005            240     ff  A
3   2005            240     ff  B

请注意，每个选举年有两行，因为有两个不同的政党与每年的最大选票值（50）相对应。