我是熊猫的新手，并且一直坚持计算。这是我的样本DF。我感兴趣的是获取每个组的最近收盘价行价。 （按日期，时间，option_type分组）
name date time open high low close option_type strike_price open_opn high_opn low_opn close_opn
0 NIFTY 20180903 09:16 11736.05 11736.10 11699.35 11720.15 CE 11800 104.95 109.45 104.50 107.25
1 NIFTY 20180903 09:16 11736.05 11736.10 11699.35 11720.15 CE 11700 160.00 164.25 159.90 161.60
2 NIFTY 20180903 09:16 11736.05 11736.10 11699.35 11720.15 CE 11600 223.20 229.10 223.20 226.30
0 NIFTY 20180903 09:16 11736.05 11736.10 11699.35 11720.15 PE 11800 166.05 166.95 163.55 165.95
1 NIFTY 20180903 09:16 11736.05 11736.10 11699.35 11720.15 PE 11600 88.80 89.45 86.45 89.00
2 NIFTY 20180903 09:16 11736.05 11736.10 11699.35 11720.15 PE 11700 122.35 122.65 119.40 122.30
这是我的预期输出。
name date time open high low close option_type strike_price open_opn high_opn low_opn close_opn
1 NIFTY 20180903 09:16 11736.05 11736.10 11699.35 11700.15 CE 11700 160.00 164.25 159.90 161.60
2 NIFTY 20180903 09:16 11736.05 11736.10 11699.35 11700.15 PE 11700 122.35 122.65 119.40 122.30
如果收盘价为11760，则预期o / p为。
0 NIFTY 20180903 09:16 11736.05 11736.10 11699.35 11720.15 CE 11800 104.95 109.45 104.50 107.25
0 NIFTY 20180903 09:16 11736.05 11736.10 11699.35 11720.15 PE 11800 166.05 166.95 163.55 165.95
请帮我。高度赞赏。
最佳答案
首先将sub与abs一起使用，然后通过sort_values与drop_duplicates将每个组的最小值获取：
df['diff'] = df['close'].sub(df['strike_price']).abs()
df = df.sort_values('diff').drop_duplicates(['date', 'time', 'option_type'])
print (df)
name date time open high low close option_type \
1 NIFTY 20180903 09:16 11736.05 11736.1 11699.35 11720.15 CE
2 NIFTY 20180903 09:16 11736.05 11736.1 11699.35 11720.15 PE
strike_price open_opn high_opn low_opn close_opn diff
1 11700 160.00 164.25 159.9 161.6 20.15
2 11700 122.35 122.65 119.4 122.3 20.15
DataFrameGroupBy.idxmin与loc的另一种解决方案：df = df.reset_index(drop=True)
df['diff'] = df['close'].sub(df['strike_price']).abs()
df = df.loc[df.groupby(['date', 'time', 'option_type'])['diff'].idxmin()]
print (df)
name date time open high low close option_type \
1 NIFTY 20180903 09:16 11736.05 11736.1 11699.35 11720.15 CE
5 NIFTY 20180903 09:16 11736.05 11736.1 11699.35 11720.15 PE
strike_price open_opn high_opn low_opn close_opn diff
1 11700 160.00 164.25 159.9 161.6 20.15
5 11700 122.35 122.65 119.4 122.3 20.15