根据条件追加新行 python pandas
[英]appending new rows based on conditions python pandas
问题描述
Need to add new rows to current df, based on conditions.
需要根据条件向当前 df 添加新行。
Current df:当前df：
What I want to do is check the Sales Oder and if contains more than one order add new rows to df and update all columns remain as same as the current row but need to update Planned Shift Production(m) and Actual Shift Production(m) based on current row's AvgPlannedShiftProd and AvgActualShiftProd .我想要做的是检查销售订单，如果包含多个订单，则将新行添加到 df 并更新所有列与当前行保持相同，但需要更新计划班次生产（m）和实际班次生产（m）基于当前行的 AvgPlannedShiftProd 和 AvgActualShiftProd 。 End goal is to maintaining one Sale Order for each row.最终目标是为每一行维护一个销售订单。
df:东风：
T={'Date': {1195: '2021-05-24',
1196: '2021-05-24',
1197: '2021-05-25',
1198: '2021-05-25',
1199: '2021-05-26',
1200: '2021-05-26'},
'Machine No': {1195: 'M-1',
1196: 'M-1',
1197: 'M-1',
1198: 'M-1',
1199: 'M-1',
1200: 'M-1'},
'Shift': {1195: 'Day',
1196: 'Night',
1197: 'Day',
1198: 'Night',
1199: 'Day',
1200: 'Night'},
'Sales Order': {1195: 'Z21',
1196: 'A1',
1197: 'B1-B2-B3-B4-B5',
1198: 'B1-B2-B3-B4-B5',
1199: 'B1-B2-B3-B4-B5',
1200: 'B1-B2-B3-B4-B5'},
'Quality No': {1195: 'C100',
1196: 'C100',
1197: 'C100',
1198: 'C100',
1199: 'C100',
1200: 'C100'},
'Planned Shift Production (m)': {1195: 0,
1196: 0,
1197: 4240,
1198: 4232,
1199: 0,
1200: 0},
'Actual Shift Production (m)': {1195: 3611,
1196: 3384,
1197: 3097,
1198: 2989,
1199: 0,
1200: 0},
'NoOfSalesOrders': {1195: 1.0,
1196: 1.0,
1197: 5.0,
1198: 5.0,
1199: 5.0,
1200: 5.0},
'AvgPlannedShiftProd': {1195: 0.0,
1196: 0.0,
1197: 848.0,
1198: 846.4,
1199: 0.0,
1200: 0.0},
'AvgActualShiftProd': {1195: 3611.0,
1196: 3384.0,
1197: 619.4,
1198: 597.8,
1199: 0.0,
1200: 0.0}}
pd.DataFrame.from_dict(T)
Sample of Expected Output:预期 Output 样品：
Thnaks in advance !!!!!!!!!!提前谢谢！！！！！！！！！！
1 个解决方案
解决方案1
1 已采纳 2021-06-02 08:21:52
I hope I've understood your question right: you can split the Sales Order column, update "Planned Shift Production (m)" , "Actual Shift Production (m)" columns and .explode() :我希望我已经正确理解了您的问题：您可以拆分Sales Order列，更新"Planned Shift Production (m)" 、 "Actual Shift Production (m)"列和.explode() ：
df["Sales Order"] = df["Sales Order"].str.split("-")
df[["Planned Shift Production (m)", "Actual Shift Production (m)"]] = df[
["AvgPlannedShiftProd", "AvgActualShiftProd"]
]
print(df.explode("Sales Order"))
Prints:印刷：
Date Machine No Shift Sales Order Quality No Planned Shift Production (m) Actual Shift Production (m) NoOfSalesOrders AvgPlannedShiftProd AvgActualShiftProd
1195 2021-05-24 M-1 Day Z21 C100 0.0 3611.0 1.0 0.0 3611.0
1196 2021-05-24 M-1 Night A1 C100 0.0 3384.0 1.0 0.0 3384.0
1197 2021-05-25 M-1 Day B1 C100 848.0 619.4 5.0 848.0 619.4
1197 2021-05-25 M-1 Day B2 C100 848.0 619.4 5.0 848.0 619.4
1197 2021-05-25 M-1 Day B3 C100 848.0 619.4 5.0 848.0 619.4
1197 2021-05-25 M-1 Day B4 C100 848.0 619.4 5.0 848.0 619.4
1197 2021-05-25 M-1 Day B5 C100 848.0 619.4 5.0 848.0 619.4
1198 2021-05-25 M-1 Night B1 C100 846.4 597.8 5.0 846.4 597.8
1198 2021-05-25 M-1 Night B2 C100 846.4 597.8 5.0 846.4 597.8
1198 2021-05-25 M-1 Night B3 C100 846.4 597.8 5.0 846.4 597.8
1198 2021-05-25 M-1 Night B4 C100 846.4 597.8 5.0 846.4 597.8
1198 2021-05-25 M-1 Night B5 C100 846.4 597.8 5.0 846.4 597.8
1199 2021-05-26 M-1 Day B1 C100 0.0 0.0 5.0 0.0 0.0
1199 2021-05-26 M-1 Day B2 C100 0.0 0.0 5.0 0.0 0.0
1199 2021-05-26 M-1 Day B3 C100 0.0 0.0 5.0 0.0 0.0
1199 2021-05-26 M-1 Day B4 C100 0.0 0.0 5.0 0.0 0.0
1199 2021-05-26 M-1 Day B5 C100 0.0 0.0 5.0 0.0 0.0
1200 2021-05-26 M-1 Night B1 C100 0.0 0.0 5.0 0.0 0.0
1200 2021-05-26 M-1 Night B2 C100 0.0 0.0 5.0 0.0 0.0
1200 2021-05-26 M-1 Night B3 C100 0.0 0.0 5.0 0.0 0.0
1200 2021-05-26 M-1 Night B4 C100 0.0 0.0 5.0 0.0 0.0
1200 2021-05-26 M-1 Night B5 C100 0.0 0.0 5.0 0.0 0.0
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