[FIXED] How to add a time sequence column, based on groups

Issue

link

Above is a link to an example of a CSV file I am modifying using python, I need to add a time column that increases by 1 if the date from the previous row matches.

If the date Changes the time would start back over at 8:00:00

additionally if the 'PL Seq' changes from G* to H* the time would also start back over at 8.

I think I have the logic down, just having a hard time writing it.

add a column to the df 'Time' set the first 'Time' value to 8:00:00

read each row in the df

If date value = date value of previous row and pl seq value first character = first character set time value to time +1

else set time value to time

*Just a note I already have the code to change the format of the order #'s and the Dates of the Goal state

Current

MODELCHASS,Prod Date,PL Seq
M742-021167,20200917,G0005
M359-020535,20200917,G0010
M742-022095,20200917,G0015
M220-001083,20200918,G0400
M742-022390,20200918,G0405
M907-004747,20200918,H0090
M934-005904,20200918,H0095

Expected

MODELCHASS,Prod Date,PL Seq,Time
M742 021167,2020-09-17T,G0005,8:00:00
M359 020535,2020-09-17T,G0010,8:00:01
M742 022095,2020-09-17T,G0015,8:00:02
M220 001083,2020-09-18T,G0400,8:00:00
M742 022390,2020-09-18T,G0405,8:00:01
M907 004747,2020-09-18T,H0090,8:00:00
M934 005904,2020-09-18T,H0095,8:00:01

@Trenton Can we modify this If H orders have the same date as G orders for example

Current Edit in Line 6

MODELCHASS,Prod Date,PL Seq
M742-021167,20200917,G0005
M359-020535,20200917,G0010
M742-022095,20200917,G0015
M220-001083,20200918,G0400
M742-022390,20200918,G0405
M907-004747,20200917,H0090
M934-005904,20200917,H0095

Expected Edit

MODELCHASS,Prod Date,PL Seq,Time
M742 021167,2020-09-17T,G0005,8:00:00
M359 020535,2020-09-17T,G0010,8:00:01
M742 022095,2020-09-17T,G0015,8:00:02
M220 001083,2020-09-18T,G0400,8:00:00
M742 022390,2020-09-18T,G0405,8:00:01
M907 004747,2020-09-17T,H0090,8:00:00
M934 005904,2020-09-17T,H0095,8:00:01

Solution

• Convert the 'Prod Date' column to a datetime
• Sort the dataframe by 'Prod Date' and 'PL Seq' so 'df' will be in the same order as time_seq for joining.
• The main part of the answer is to create a DateRange list with the .groupby and .apply
• .groupby the Prod Date and the first element of 'PL Seq'
  • df.groupby(['Prod Date', df['PL Seq'].str[0]])
• .apply(lambda x: (pd.date_range(start=x.values[0] + pd.Timedelta(hours=8), periods=len(x), freq='s')).time)
  • For each group, use the first value in x as start: x.values[0]
    • To this date, add an 8 hour Timedelta, to get 08:00:00
  • The number of periods is len[x]
  • the freq is 's', for seconds.
  • This creates a DateRange, from which the time is extracted with .time
• Tested in python 3.10, pandas 1.4.3

import pandas as pd

# setup test dataframe
data = {'MODELCHASS': ['M742-021167', 'M359-020535', 'M742-022095', 'M220-001083', 'M742-022390', 'M907-004747', 'M934-005904'],
        'Prod Date': [20200917, 20200917, 20200917, 20200918, 20200918, 20200918, 20200918],
        'PL Seq': ['G0005', 'G0010', 'G0015', 'G0400', 'G0405', 'H0090', 'H0095']}

df = pd.DataFrame(data)

# convert Prod Date to a datetime column
df['Prod Date'] = pd.to_datetime(df['Prod Date'], format='%Y%m%d')

# sort the dataframe by values so the order will correspond to the groupby order
df = df.sort_values(['Prod Date', 'PL Seq']).reset_index(drop=True)

# groupby Prod Date and the first character of PL Seq
# create a DateRange sequence for each group
# reshape the dataframe
time_seq = (df.groupby(['Prod Date', df['PL Seq'].str[0]])['Prod Date']
            .apply(lambda x: (pd.date_range(start=x.values[0] + pd.Timedelta(hours=8), periods=len(x), freq='s')).time)
            .reset_index(name='time_seq')
            .explode('time_seq', ignore_index=True))

# join the time_seq column to df
df_new = df.join(time_seq.time_seq)

# display(df_new)
    MODELCHASS  Prod Date PL Seq  time_seq
0  M742-021167 2020-09-17  G0005  08:00:00
1  M359-020535 2020-09-17  G0010  08:00:01
2  M742-022095 2020-09-17  G0015  08:00:02
3  M220-001083 2020-09-18  G0400  08:00:00
4  M742-022390 2020-09-18  G0405  08:00:01
5  M907-004747 2020-09-18  H0090  08:00:00
6  M934-005904 2020-09-18  H0095  08:00:01

Answered By - Trenton McKinney