[英]Applying a function on specific Dataframe rows with datetime index as arguments
[英]Dataframe applying function to rows with specific condition
這是我的數據框示例:
id DPT_DATE TRANCHE_NO TRAIN_NO J_X RES_HOLD_IND
0 2017-04-01 330.0 1234.0 -1.0 100.0
1 2017-04-01 330.0 1234.0 0.0 80.0
2 2017-04-02 331.0 1235.0 -1.0 91.0
3 2017-04-02 331.0 1235.0 0.0 83.0
4 2017-04-03 332.0 1236.0 -1.0 92.0
5 2017-04-03 332.0 1236.0 0.0 81.0
6 2017-04-04 333.0 1237.0 -1.0 87.0
7 2017-04-04 333.0 1237.0 0.0 70.0
8 2017-04-05 334.0 1238.0 -1.0 93.0
9 2017-04-05 334.0 1238.0 0.0 90.0
10 2017-04-06 335.0 1239.0 -1.0 89.0
11 2017-04-06 335.0 1239.0 0.0 85.0
12 2017-04-07 336.0 1240.0 -1.0 82.0
13 2017-04-07 336.0 1240.0 0.0 76.0
這是火車預訂的數據框,DPT_DATE =出發日期TRAIN_NO =火車數量J_X =出發前的天(J_X = 0.0表示出發的日期,J_X = -1表示出發的日期),RES_HOLD_IND是預訂保留天
我想創建一個新列,因此對於每個DPT_DATE和TRAIN_NO,我都會在當天J_X = -1的情況下給我RES_HOLD_IND
示例(我想要這個):
id DPT_DATE TRANCHE_NO TRAIN_NO J_X RES_HOLD_IND RES_J-1
0 2017-04-01 330.0 1234.0 -1.0 100.0 100.0
1 2017-04-01 330.0 1234.0 0.0 80.0 100.0
2 2017-04-02 331.0 1235.0 -1.0 91.0 91.0
3 2017-04-02 331.0 1235.0 0.0 83.0 91.0
4 2017-04-03 332.0 1236.0 -1.0 92.0 92.0
5 2017-04-03 332.0 1236.0 0.0 81.0 92.0
6 2017-04-04 333.0 1237.0 -1.0 87.0 87.0
7 2017-04-04 333.0 1237.0 0.0 70.0 87.0
謝謝您的幫助!
我想你需要先過濾器boolean indexing
或query
,然后groupby
與DataFrameGroupBy.ffill
什么工作不錯,如果總是-1
值在每組第一行:
df['RES_J-1'] = df.query('J_X == -1')['RES_HOLD_IND']
#alternative
#df['RES_J-1'] = df.loc[df['J_X'] == -1, 'RES_HOLD_IND']
df['RES_J-1'] = df.groupby(['DPT_DATE','TRAIN_NO'])['RES_J-1'].ffill()
print (df)
DPT_DATE TRANCHE_NO TRAIN_NO J_X RES_HOLD_IND RES_J-1
0 2017-04-01 330.0 1234.0 -1.0 100.0 100.0
1 2017-04-01 330.0 1234.0 0.0 80.0 100.0
2 2017-04-02 331.0 1235.0 -1.0 91.0 91.0
3 2017-04-02 331.0 1235.0 0.0 83.0 91.0
4 2017-04-03 332.0 1236.0 -1.0 92.0 92.0
5 2017-04-03 332.0 1236.0 0.0 81.0 92.0
6 2017-04-04 333.0 1237.0 -1.0 87.0 87.0
7 2017-04-04 333.0 1237.0 0.0 70.0 87.0
8 2017-04-05 334.0 1238.0 -1.0 93.0 93.0
9 2017-04-05 334.0 1238.0 0.0 90.0 93.0
10 2017-04-06 335.0 1239.0 -1.0 89.0 89.0
11 2017-04-06 335.0 1239.0 0.0 85.0 89.0
12 2017-04-07 336.0 1240.0 -1.0 82.0 82.0
13 2017-04-07 336.0 1240.0 0.0 76.0 82.0
如果-1
在每個組中僅一個,但並非總是第一次使用:
df['RES_J-1'] = df.groupby(['DPT_DATE','TRAIN_NO'])['RES_J-1']
.apply(lambda x: x.ffill().bfill())
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