關(guān)聯(lián)上面是我使用 python 修改的 CSV 文件示例的鏈接，我需要添加一個(gè)時(shí)間列，如果前一行的日期匹配，則該時(shí)間列增加 1。如果日期更改，時(shí)間將從 8:00:00 重新開始此外，如果“PL Seq”從 G* 更改為 H*，時(shí)間也會(huì)從 8 重新開始。我覺得我的邏輯已經(jīng)很清晰了，只是寫起來有點(diǎn)困難。向 df 'Time' 添加一列，將第一個(gè) 'Time' 值設(shè)置為 8:00:00讀取 df 中的每一行如果日期值 = 上一行的日期值且 pl seq 值第一個(gè)字符 = 第一個(gè)字符，則將時(shí)間值設(shè)置為時(shí)間 +1否則將時(shí)間值設(shè)置為時(shí)間*請(qǐng)注意，我已經(jīng)有了更改訂單 # 的格式和目標(biāo)狀態(tài)日期的代碼當(dāng)前的MODELCHASS,Prod Date,PL SeqM742-021167,20200917,G0005M359-020535,20200917,G0010M742-022095,20200917,G0015M220-001083,20200918,G0400M742-022390,20200918,G0405M907-004747,20200918,H0090M934-005904,20200918,H0095預(yù)期的MODELCHASS,Prod Date,PL Seq,TimeM742 021167,2020-09-17T,G0005,8:00:00M359 020535,2020-09-17T,G0010,8:00:01M742 022095,2020-09-17T,G0015,8:00:02M220 001083,2020-09-18T,G0400,8:00:00M742 022390,2020-09-18T,G0405,8:00:01M907 004747,2020-09-18T,H0090,8:00:00M934 005904,2020-09-18T,H0095,8:00:01@Trenton 我們可以修改這個(gè)如果 H 訂單與 G 訂單的日期相同第 6 行中的當(dāng)前編輯MODELCHASS,Prod Date,PL SeqM742-021167,20200917,G0005M359-020535,20200917,G0010M742-022095,20200917,G0015M220-001083,20200918,G0400M742-022390,20200918,G0405M907-004747,20200917,H0090M934-005904,20200917,H0095預(yù)期編輯MODELCHASS,Prod Date,PL Seq,TimeM742 021167,2020-09-17T,G0005,8:00:00M359 020535,2020-09-17T,G0010,8:00:01M742 022095,2020-09-17T,G0015,8:00:02M220 001083,2020-09-18T,G0400,8:00:00M742 022390,2020-09-18T,G0405,8:00:01M907 004747,2020-09-17T,H0090,8:00:00M934 005904,2020-09-17T,H0095,8:00:01
1 回答
繁花如伊
TA貢獻(xiàn)2012條經(jīng)驗(yàn) 獲得超12個(gè)贊
將列轉(zhuǎn)換
'Prod Date'為日期時(shí)間對(duì)數(shù)據(jù)幀進(jìn)行排序
'Prod Date'，'PL Seq'因此'df'與加入的順序相同time_seq。答案的主要部分是創(chuàng)建一個(gè)包含和 的
DateRange列表.groupby.apply.groupby和Prod Date第一個(gè)元素'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)到目前為止，添加 8 小時(shí)的 Timedelta，以獲得
08:00:00對(duì)于每個(gè)組，使用 x 中的第一個(gè)值
start：x.values[0]的數(shù)量
periods是len[x]是
freq，'s'幾秒鐘。這將創(chuàng)建一個(gè)
DateRange，從中提取時(shí)間.time測(cè)試于
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
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