根据条件 python pandas 向 dataframe 添加新行

Question

Need to add new rows to dataframe based on condition.

Current dataframe:

In this dataframe there are 4 columns. what i want to do ischeck the 'Time' column and check the nearest value for 12PM mid night in every night shift and add two new row as 11:59:59 and 00:00:01 with same values as the that nearest datapoint.

For examle: Closest value(to 12PM) for 03-01 Night is 21:46:54. so need to add two rows,

W25     03-01 Night    RUNNING    23:59:59
W25     03-01 Night    RUNNING    00:00:01

so final expected dataframe should be like this:

Sample data:

data={'Machine': {0: 'W5', 343: 'W5', 344: 'W5', 586: 'W5', 587: 'W5'}, 'State': {0: 'start', 343: 'STOPPED', 344: 'RUNNING', 586: 'STOPPED', 587: 'MAINT'}, 'Day-Shift': {0: '03-01 Night', 343: '03-01 Night', 344: '03-01 Night', 586: '03-01 Night', 587: '03-01 Night'}, 'Time': {0: Timestamp('2021-03-01 21:00:00'), 343: Timestamp('2021-03-01 22:16:54'), 344: Timestamp('2021-03-01 23:16:54'), 586: Timestamp('2021-03-01 23:48:45'), 587: Timestamp('2021-03-02 02:28:54')}}

Really appreciate your support !!!!!

Answer 1

you can use idxmax() to find the max record per day, then create a datetime object.

df1 = df.loc[df.groupby([df['Time'].dt.normalize()])['Time'].idxmax()]
df1 = pd.concat([df1] * 2)

df1['Time'] = pd.to_datetime((df1['Time'].dt.normalize().astype(str) + [' 23:59:59', ' 00:00:01']))

---

print(df1)

    Machine  State  Day-Shift                Time
587     W25  MAINT  03-01 Day 2021-03-01 23:59:59
587     W25  MAINT  03-01 Day 2021-03-01 00:00:01

---

df = pd.concat([df,df1]).sort_index().reset_index(drop=True)


  Machine    State  Day-Shift                Time
0     W25    start  03-01 Day 2021-03-01 07:00:00
1     W25  STOPPED  03-01 Day 2021-03-01 07:16:54
2     W25  RUNNING  03-01 Day 2021-03-01 07:16:54
3     W25  STOPPED  03-01 Day 2021-03-01 07:28:45
4     W25    MAINT  03-01 Day 2021-03-01 07:28:54
5     W25    MAINT  03-01 Day 2021-03-01 23:59:59
6     W25    MAINT  03-01 Day 2021-03-01 00:00:01