2023年3月31日 19:51:10go评论66阅读模式

英文:

python dataframe. Insert row based on various criteria

问题

以下是已翻译好的部分：

In the following dataframe, each row represents a part of a shift for a day.

import pandas as pd

df = pd.DataFrame()

df['number'] = '169', '169', '169', '169', '2129', '2129', '2129', '2129', '2129', '2129', '5645', '5645', '5645', '5645', '5645', '5645', '5645', '5645', '7480', '7480', '7480', '7480', '7480', '7480', '7480', '6713', '6713', '6713', '6713', '6713', '6713', '6713', '6713', '4004', '4004', '4004', '4004', '4004', '4004'
df['date'] = '2/03/2023', '4/03/2023', '4/03/2023', '5/03/2023', '2/03/2023', '2/03/2023', '2/03/2023', '4/03/2023', '4/03/2023', '5/03/2023', '27/02/2023', '27/02/2023', '27/02/2023', '28/02/2023', '3/03/2023', '3/03/2023', '3/03/2023', '3/03/2023', '1/03/2023', '2/03/2023', '3/03/2023', '4/03/2023', '4/03/2023', '5/03/2023', '5/03/2023', '2/03/2023', '2/03/2023', '2/03/2023', '4/03/2023', '4/03/2023', '4/03/2023', '4/03/2023', '5/03/2023', '28/02/2023', '1/03/2023', '2/03/2023', '3/03/2023', '4/03/2023', '4/03/2023'
df['start'] = '21:00:00', '21:00:00', '21:00:00', '21:00:00', '6:00:00', '6:00:00', '6:00:00', '14:30:00', '14:30:00', '14:30:00', '15:00:00', '15:00:00', '15:00:00', '21:00:00', '15:00:00', '15:00:00', '15:00:00', '15:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '14:30:00', '6:30:00', '6:30:00', '6:30:00', '6:30:00', '6:30:00', '6:30:00', '6:30:00', '14:30:00', '22:45:00', '22:45:00', '22:45:00', '22:45:00', '22:45:00', '22:45:00'
df['end'] = '7:00:00', '7:00:00', '7:00:00', '7:00:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '13:45:00', '13:45:00', '15:30:00', '15:15:00', '15:15:00', '13:30:00', '23:00:00', '22:30:00', '22:30:00', '22:30:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '16:15:00', '7:15:00', '7:15:00', '7:15:00', '7:15:00', '7:15:00', '7:15:00', '7:15:00', '7:15:00'
df['hours'] = '9.5', '2.5', '0.5', '6.5', '9.5', '10', '2', '2', '3.5', '2.5', '6', '10', '2', '3', '9.5', '9', '1', '2', '1.5', '1.5', '6.25', '6.25', '8', '1', '6.75', '6', '8', '10', '2', '3', '2', '8', '2', '1.5', '1.75', '8', '8', '8', '1.25', '6.75', '1.25', '4.75', '2'
df['day'] = 'Thursday', 'Saturday', 'Saturday', 'Saturday/Sunday', 'Sunday', 'Thursday', 'Thursday', 'Thursday', 'Saturday', 'Saturday', 'Sunday', 'Monday', 'Monday', 'Monday', 'Tuesday', 'Friday', 'Friday/Saturday', 'Friday', 'Friday', 'Friday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Saturday', 'Sunday', 'Sunday', 'Thursday', 'Thursday', 'Thursday', 'Saturday', 'Saturday', 'Saturday', 'Saturday', 'Sunday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Friday/Saturday', 'Saturday', 'Saturday/Sunday', 'Saturday'

print(df)

请注意，这只是一部分已翻译的内容。如果您需要更多翻译，请提供具体的部分。

英文:

In the following dataframe, each row represents a part of a shift for a day.

import pandas as pd
df = pd.DataFrame()
df[&#39;number&#39;] = &#39;169&#39;, &#39;169&#39;, &#39;169&#39;, &#39;169&#39;, &#39;2129&#39;, &#39;2129&#39;, &#39;2129&#39;, &#39;2129&#39;, &#39;2129&#39;, &#39;2129&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;,&#39;4004&#39;,   &#39;4004&#39;, &#39;4004&#39;, &#39;4004&#39;, &#39;4004&#39;, &#39;4004&#39;
df[&#39;date&#39;] =&#39;2/03/2023&#39; ,&#39;4/03/2023&#39;,   &#39;4/03/2023&#39;,&#39;5/03/2023&#39;,    &#39;2/03/2023&#39;,    &#39;2/03/2023&#39;,    &#39;2/03/2023&#39; ,&#39;4/03/2023&#39;,   &#39;4/03/2023&#39;,    &#39;5/03/2023&#39;,    &#39;27/02/2023&#39;,   &#39;27/02/2023&#39;,   &#39;27/02/2023&#39;,   &#39;28/02/2023&#39;,   &#39;3/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;1/03/2023&#39;,    &#39;2/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;4/03/2023&#39;,    &#39;4/03/2023&#39;,    &#39;5/03/2023&#39;,    &#39;5/03/2023&#39;,&#39;2/03/2023&#39;,    &#39;2/03/2023&#39;,    &#39;2/03/2023&#39; ,&#39;4/03/2023&#39;,   &#39;4/03/2023&#39;,    &#39;4/03/2023&#39;,    &#39;4/03/2023&#39; ,&#39;5/03/2023&#39;,&#39;28/02/2023&#39;,  &#39;1/03/2023&#39;,    &#39;2/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;4/03/2023&#39;,    &#39;4/03/2023&#39;
df[&#39;start&#39;] = &#39;21:00:00&#39;    ,&#39;21:00:00&#39;,    &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;6:00:00&#39;,  &#39;6:00:00&#39;,  &#39;6:00:00&#39;,  &#39;14:30:00&#39;, &#39;14:30:00&#39;, &#39;14:30:00&#39;, &#39;15:00:00&#39;, &#39;15:00:00&#39;, &#39;15:00:00&#39;, &#39;21:00:00&#39;, &#39;15:00:00&#39;, &#39;15:00:00&#39;, &#39;15:00:00&#39;, &#39;15:00:00&#39;, &#39;7:00:00&#39;,  &#39;7:00:00&#39;,&#39;7:00:00&#39; ,&#39;7:00:00&#39;, &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;14:30:00&#39;, &#39;6:30:00&#39;,  &#39;6:30:00&#39;,  &#39;6:30:00&#39;,  &#39;6:30:00&#39;,&#39;6:30:00&#39;,    &#39;6:30:00&#39;,  &#39;6:30:00&#39;,&#39;14:30:00&#39;,&#39;22:45:00&#39;,    &#39;22:45:00&#39;, &#39;22:45:00&#39;, &#39;22:45:00&#39;, &#39;22:45:00&#39;, &#39;22:45:00&#39;
df[&#39;end&#39;] = &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;13:45:00&#39;, &#39;13:45:00&#39;  ,&#39;15:30:00&#39;,    &#39;15:15:00&#39;, &#39;15:15:00&#39;, &#39;13:30:00&#39;, &#39;23:00:00&#39;, &#39;22:30:00&#39;, &#39;22:30:00&#39;, &#39;22:30:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;  ,&#39;16:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;
df[&#39;hours&#39;] = &#39;9.5&#39;, &#39;2.5&#39;, &#39;7&#39;, &#39;9.5&#39;, &#39;10&#39;, &#39;2&#39;, &#39;2&#39;, &#39;3.5&#39;, &#39;2.5&#39;, &#39;6&#39;, &#39;10&#39;, &#39;2&#39;, &#39;3&#39;, &#39;9.5&#39;, &#39;10&#39;, &#39;2&#39;, &#39;1.5&#39;, &#39;1.5&#39;, &#39;6.25&#39;, &#39;6.25&#39;, &#39;8&#39;, &#39;1&#39;, &#39;6.75&#39;, &#39;6&#39;, &#39;8&#39;, &#39;10&#39;, &#39;2&#39;, &#39;3&#39;, &#39;2&#39;, &#39;8&#39;, &#39;2&#39;, &#39;1.5&#39;, &#39;1.75&#39;, &#39;8&#39;, &#39;8&#39;, &#39;8&#39;, &#39;8&#39;, &#39;6&#39;, &#39;2&#39;
df[&#39;day&#39;] = &#39;Thursday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Sunday&#39;, &#39;Thursday&#39;, &#39;Thursday&#39;, &#39;Thursday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Sunday&#39;, &#39;Monday&#39;, &#39;Monday&#39;, &#39;Monday&#39;, &#39;Tuesday&#39;, &#39;Friday&#39;, &#39;Friday&#39;, &#39;Friday&#39;, &#39;Friday&#39;, &#39;Wednesday&#39;, &#39;Thursday&#39;, &#39;Friday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Sunday&#39;, &#39;Sunday&#39;, &#39;Thursday&#39;, &#39;Thursday&#39;, &#39;Thursday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Sunday&#39;,&#39;Tuesday&#39;, &#39;Wednesday&#39;, &#39;Thursday&#39;, &#39;Friday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;
print(df)

for each row if a cell in ‘day’ column is either Friday, Saturday or Sunday AND the 'start' column is before midnight AND the 'end' column is after midnight, the row that has the balance of 'hours' that pass through midnight (lets call this row 'old row') I need to do the following. By 'pass through midnight' an example I would refer to is number '169' starts 21:00 on Saturday; as the first row for Saturday was 2.5 hours (ie ends 23:30) it is the second row regarding Saturday for 7 hours which 'passes through midnight'.

insert a row (lets call this row 'new row') directly underneath 'old row'
'old row' 'hours' data needs to be changed to the remaining that the entire shift for the day has to reach midnight. (e.g. for 'Aberash' out of the 7 hours only 0.5 hours is required to reach midnight (as previous row ended 23:30)
The new row receives the balance of 'hours' (i.e.. 7 hours - 0.5 hours = 6.5 hours)
The new row 'day' column data is changed to the following:

i) If 'old row' was Friday new row is Friday/Saturday

ii) If 'old row' was Saturday new row is Saturday/Sunday

iii) If 'old row' was Sunday new row is Sunday/Monday

Other data not discussed in 'new row' should be same 'old row'.

Below is what the dataframe should look like after the above

import pandas as pd
df = pd.DataFrame()
df[&#39;number&#39;] = &#39;169&#39;, &#39;169&#39;, &#39;169&#39;, &#39;169&#39;, &#39;169&#39;, &#39;2129&#39;, &#39;2129&#39;, &#39;2129&#39;, &#39;2129&#39;, &#39;2129&#39;, &#39;2129&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;5645&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;7480&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;, &#39;6713&#39;,&#39;4004&#39;,    &#39;4004&#39;,&#39;4004&#39;,  &#39;4004&#39;, &#39;4004&#39;, &#39;4004&#39;, &#39;4004&#39;, &#39;4004&#39;
df[&#39;date&#39;] = &#39;2/03/2023&#39;,&#39;4/03/2023&#39;,&#39;4/03/2023&#39;,   &#39;4/03/2023&#39;,&#39;5/03/2023&#39;,    &#39;2/03/2023&#39;,    &#39;2/03/2023&#39;,    &#39;2/03/2023&#39; ,&#39;4/03/2023&#39;,   &#39;4/03/2023&#39;,    &#39;5/03/2023&#39;,    &#39;27/02/2023&#39;,   &#39;27/02/2023&#39;,   &#39;27/02/2023&#39;,   &#39;28/02/2023&#39;,   &#39;3/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;1/03/2023&#39;,    &#39;2/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;4/03/2023&#39;,    &#39;4/03/2023&#39;,    &#39;5/03/2023&#39;,    &#39;5/03/2023&#39;,&#39;2/03/2023&#39;,    &#39;2/03/2023&#39;,    &#39;2/03/2023&#39; ,&#39;4/03/2023&#39;,   &#39;4/03/2023&#39;,    &#39;4/03/2023&#39;,    &#39;4/03/2023&#39; ,&#39;5/03/2023&#39;,&#39;28/02/2023&#39;,  &#39;1/03/2023&#39;,    &#39;2/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;3/03/2023&#39;,    &#39;4/03/2023&#39;,    &#39;4/03/2023&#39;,    &#39;4/03/2023&#39;
df[&#39;start&#39;] = &#39;21:00:00&#39;,&#39;21:00:00&#39;,&#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;6:00:00&#39;,  &#39;6:00:00&#39;,  &#39;6:00:00&#39;,  &#39;14:30:00&#39;, &#39;14:30:00&#39;, &#39;14:30:00&#39;, &#39;15:00:00&#39;, &#39;15:00:00&#39;, &#39;15:00:00&#39;, &#39;21:00:00&#39;, &#39;15:00:00&#39;, &#39;15:00:00&#39;, &#39;15:00:00&#39;, &#39;15:00:00&#39;,&#39;15:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,&#39;7:00:00&#39; ,&#39;7:00:00&#39;, &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;14:30:00&#39;, &#39;6:30:00&#39;,  &#39;6:30:00&#39;,  &#39;6:30:00&#39;,  &#39;6:30:00&#39;,&#39;6:30:00&#39;,    &#39;6:30:00&#39;,  &#39;6:30:00&#39;,&#39;14:30:00&#39;,&#39;22:45:00&#39;,    &#39;22:45:00&#39;, &#39;22:45:00&#39;, &#39;22:45:00&#39;, &#39;22:45:00&#39;, &#39;22:45:00&#39;, &#39;22:45:00&#39;, &#39;22:45:00&#39;
df[&#39;end&#39;] = &#39;7:00:00&#39;,  &#39;7:00:00&#39;,&#39;7:00:00&#39;,    &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;7:00:00&#39;,  &#39;13:45:00&#39;, &#39;13:45:00&#39;  ,&#39;15:30:00&#39;,    &#39;15:15:00&#39;, &#39;15:15:00&#39;, &#39;13:30:00&#39;, &#39;23:00:00&#39;, &#39;22:30:00&#39;, &#39;22:30:00&#39;, &#39;22:30:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;, &#39;21:00:00&#39;  ,&#39;16:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;,&#39;7:15:00&#39;
df[&#39;hours&#39;] = &#39;9.5&#39;, &#39;2.5&#39;, &#39;0.5&#39;, &#39;6.5&#39;, &#39;9.5&#39;, &#39;10&#39;, &#39;2&#39;, &#39;2&#39;, &#39;3.5&#39;, &#39;2.5&#39;, &#39;6&#39;, &#39;10&#39;, &#39;2&#39;, &#39;3&#39;, &#39;9.5&#39;, &#39;9&#39;, &#39;1&#39;, &#39;2&#39;, &#39;1.5&#39;, &#39;1.5&#39;, &#39;6.25&#39;, &#39;6.25&#39;, &#39;8&#39;, &#39;1&#39;, &#39;6.75&#39;, &#39;6&#39;, &#39;8&#39;, &#39;10&#39;, &#39;2&#39;, &#39;3&#39;, &#39;2&#39;, &#39;8&#39;, &#39;2&#39;, &#39;1.5&#39;, &#39;1.75&#39;, &#39;8&#39;, &#39;8&#39;, &#39;8&#39;, &#39;1.25&#39;, &#39;6.75&#39;, &#39;1.25&#39;, &#39;4.75&#39;, &#39;2&#39;
df[&#39;day&#39;] = &#39;Thursday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Saturday/Sunday&#39;, &#39;Sunday&#39;, &#39;Thursday&#39;, &#39;Thursday&#39;, &#39;Thursday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Sunday&#39;, &#39;Monday&#39;, &#39;Monday&#39;, &#39;Monday&#39;, &#39;Tuesday&#39;, &#39;Friday&#39;, &#39;Friday/Saturday&#39;, &#39;Friday&#39;, &#39;Friday&#39;, &#39;Friday&#39;, &#39;Wednesday&#39;, &#39;Thursday&#39;, &#39;Friday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Sunday&#39;, &#39;Sunday&#39;, &#39;Thursday&#39;, &#39;Thursday&#39;, &#39;Thursday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Saturday&#39;, &#39;Sunday&#39;, &#39;Tuesday&#39;, &#39;Wednesday&#39;, &#39;Thursday&#39;, &#39;Friday&#39;, &#39;Friday/Saturday&#39;, &#39;Saturday&#39;, &#39;Saturday/Sunday&#39;, &#39;Saturday&#39;
print(df)

答案1

得分: 1

这个解决方案很丑陋、慢，总体上难以管理。
编写它让我感到非常愤怒。

这是为什么选择正确的数据结构能够导致更清晰代码的明证。不管怎样，这是代码：

import pandas as pd
from datetime import timedelta

df = pd.DataFrame()

df['number'] = '169', '169', '169', '169', '2129', '2129', '2129', '2129', '2129', '2129', '5645', '5645', '5645', '5645', '5645', '5645', '5645', '5645', '7480', '7480', '7480', '7480', '7480', '7480', '7480', '6713', '6713', '6713', '6713', '6713', '6713', '6713', '6713', '4004', '4004', '4004', '4004', '4004', '4004'
df['date'] = '2/03/2023', '4/03/2023', '4/03/2023', '5/03/2023', '2/03/2023', '2/03/2023', '2/03/2023', '4/03/2023', '4/03/2023', '5/03/2023', '27/02/2023', '27/02/2023', '27/02/2023', '28/02/2023', '3/03/2023', '3/03/2023', '3/03/2023', '3/03/2023', '1/03/2023', '2/03/2023', '3/03/2023', '4/03/2023', '4/03/2023', '5/03/2023', '5/03/2023', '2/03/2023', '2/03/2023', '2/03/2023', '4/03/2023', '4/03/2023', '4/03/2023', '4/03/2023', '5/03/2023', '28/02/2023', '1/03/2023', '2/03/2023', '3/03/2023', '4/03/2023', '4/03/2023'
df['start'] = '21:00:00', '21:00:00', '21:00:00', '21:00:00', '6:00:00', '6:00:00', '6:00:00', '14:30:00', '14:30:00', '14:30:00', '15:00:00', '15:00:00', '15:00:00', '21:00:00', '15:00:00', '15:00:00', '15:00:00', '15:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '14:30:00', '6:30:00', '6:30:00', '6:30:00', '6:30:00', '6:30:00', '6:30:00', '6:30:00', '14:30:00', '22:45:00', '22:45:00', '22:45:00', '22:45:00', '22:45:00', '22:45:00'
df['end'] = '7:00:00', '7:00:00', '7:00:00', '7:00:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '13:45:00', '13:45:00', '15:30:00', '15:15:00', '15:15:00', '13:30:00', '23:00:00', '22:30:00', '22:30:00', '22:30:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '16:15:00', '7:15:00', '7:15:00', '7:15:00', '7:15:00', '7:15:00', '7:15:00'
df['hours'] = '9.5', '2.5', '7', '9.5', '10', '2', '2', '3.5', '2.5', '6', '10', '2', '3', '9.5', '10', '2', '1.5', '1.5', '6.25', '6.25', '8', '1', '6.75', '6', '8', '10', '2', '3', '2', '8', '2', '1.5', '1.75', '8', '8', '8', '8', '6', '2'
df['day'] = 'Thursday', 'Saturday', 'Saturday', 'Sunday', 'Thursday', 'Thursday', 'Thursday', 'Saturday', 'Saturday', 'Sunday', 'Monday', 'Monday', 'Monday', 'Tuesday', 'Friday', 'Friday', 'Friday', 'Friday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Saturday', 'Sunday', 'Sunday', 'Thursday', 'Thursday', 'Thursday', 'Saturday', 'Saturday', 'Saturday', 'Saturday', 'Sunday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Saturday'

# 转换为更合理的数据类型
df['date'] = pd.to_datetime(df['date'], dayfirst=True)
df['hours'] = df['hours'].astype(float)

# df_okay 将存储无问题的班次
# 任何在周五、周六和周日之外的班次不是我们关心的
df_okay = df[~df['day'].isin(['Friday', 'Saturday', 'Sunday'])].copy()

df = df[df['day'].isin(['Friday', 'Saturday', 'Sunday'])].copy()

# 让我们使用日期时间对象来比

<details>
<summary>英文:</summary>

This solution is ugly, slow, and generally unmanageable. 
Writing it made me genuinely angry.

This is a testament to why choosing the right data structures leads to cleaner code. Anyway, here it is:

import pandas as pd
from datetime import timedelta

df = pd.DataFrame()

df['number'] = '169', '169', '169', '169', '2129', '2129', '2129', '2129', '2129', '2129', '5645', '5645', '5645', '5645', '5645', '5645', '5645', '5645', '7480', '7480', '7480', '7480', '7480', '7480', '7480', '6713', '6713', '6713', '6713', '6713', '6713', '6713', '6713','4004', '4004', '4004', '4004', '4004', '4004'
df['date'] ='2/03/2023' ,'4/03/2023', '4/03/2023','5/03/2023', '2/03/2023', '2/03/2023', '2/03/2023' ,'4/03/2023', '4/03/2023', '5/03/2023', '27/02/2023', '27/02/2023', '27/02/2023', '28/02/2023', '3/03/2023', '3/03/2023', '3/03/2023', '3/03/2023', '1/03/2023', '2/03/2023', '3/03/2023', '4/03/2023', '4/03/2023', '5/03/2023', '5/03/2023','2/03/2023', '2/03/2023', '2/03/2023' ,'4/03/2023', '4/03/2023', '4/03/2023', '4/03/2023' ,'5/03/2023','28/02/2023', '1/03/2023', '2/03/2023', '3/03/2023', '4/03/2023', '4/03/2023'
df['start'] = '21:00:00' ,'21:00:00', '21:00:00', '21:00:00', '6:00:00', '6:00:00', '6:00:00', '14:30:00', '14:30:00', '14:30:00', '15:00:00', '15:00:00', '15:00:00', '21:00:00', '15:00:00', '15:00:00', '15:00:00', '15:00:00', '7:00:00', '7:00:00','7:00:00' ,'7:00:00', '7:00:00', '7:00:00', '14:30:00', '6:30:00', '6:30:00', '6:30:00', '6:30:00','6:30:00', '6:30:00', '6:30:00','14:30:00','22:45:00', '22:45:00', '22:45:00', '22:45:00', '22:45:00', '22:45:00'
df['end'] = '7:00:00', '7:00:00', '7:00:00', '7:00:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '7:00:00', '13:45:00', '13:45:00' ,'15:30:00', '15:15:00', '15:15:00', '13:30:00', '23:00:00', '22:30:00', '22:30:00', '22:30:00', '21:00:00', '21:00:00', '21:00:00', '21:00:00' ,'16:15:00','7:15:00','7:15:00','7:15:00','7:15:00','7:15:00','7:15:00'
df['hours'] = '9.5', '2.5', '7', '9.5', '10', '2', '2', '3.5', '2.5', '6', '10', '2', '3', '9.5', '10', '2', '1.5', '1.5', '6.25', '6.25', '8', '1', '6.75', '6', '8', '10', '2', '3', '2', '8', '2', '1.5', '1.75', '8', '8', '8', '8', '6', '2'
df['day'] = 'Thursday', 'Saturday', 'Saturday', 'Sunday', 'Thursday', 'Thursday', 'Thursday', 'Saturday', 'Saturday', 'Sunday', 'Monday', 'Monday', 'Monday', 'Tuesday', 'Friday', 'Friday', 'Friday', 'Friday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Saturday', 'Sunday', 'Sunday', 'Thursday', 'Thursday', 'Thursday', 'Saturday', 'Saturday', 'Saturday', 'Saturday', 'Sunday','Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Saturday'

converting to more sensible data types

df['date'] = pd.to_datetime(df['date'], dayfirst=True)
df['hours'] = df['hours'].astype(float)

df_okay will be the sink for the non-problematic shifts

any shift outside of Friday, Saturday and Sunday aren't our concern

df_okay = df[~df['day'].isin(['Friday', 'Saturday','Sunday'])].copy()

df = df[df['day'].isin(['Friday', 'Saturday','Sunday'])].copy()

let's use datetime objects to compare dates and calculate durations

df['start_dt'] = pd.to_datetime(df['date'].dt.strftime('%Y-%m-%d ') + df['start'])
df['end_dt'] = pd.to_datetime(df['date'].dt.strftime('%Y-%m-%d ') + df['end'])

let's add a day for the end datetime of shifts which go over midnight

df.loc[df['start_dt']>df['end_dt'], 'end_dt'] += timedelta(1)

any shift which doesn't go over midnight can't be problematic

so it goes in the sink

df_okay = pd.concat((df_okay, df[df['start_dt'].dt.date == df['end_dt'].dt.date]))

these shifts go over midnight so they're at risk

df = df[df['start_dt'].dt.date != df['end_dt'].dt.date]

let's calculate the total number of hours for each `number` during the shift

df_problem_shifts = df[['number', 'start_dt', 'end_dt', 'hours']].groupby(['number','start_dt', 'end_dt']).sum().reset_index().rename(columns={'hours':'total_hours'})

let's calculate the number of available hours between the beginning of the shift and midnight

df_problem_shifts['to_midnight'] = (pd.to_datetime(df_problem_shifts['start_dt'].dt.date + timedelta(days=1)) - df_problem_shifts['start_dt'] ).dt.total_seconds()/3600

these shifts have a sum of `hours` which is more than the available hours until midnight

df_problem_shifts = df_problem_shifts[df_problem_shifts['to_midnight'] < df_problem_shifts['total_hours']]

simple dictionnary which will be used to replace `day` with `day/day+1`

days = ['Friday', 'Saturday', 'Sunday', 'Monday']
days = {day: f"{day}/{day2}" for (day, day2) in zip(days, days[1:])}
new_rows = []
for i,row in df_problem_shifts.iterrows():
df_hours = df.loc[(df.number == row.number) & (df.start_dt == row.start_dt) & (df.end_dt == row.end_dt)]
available_hours = row.to_midnight
for j,row2 in df_hours.iterrows():
if available_hours == 0:
df.loc[j,'day'] = days[df.loc[j, 'day']]
else:
available_hours -= row2.hours
if available_hours < 0:
df.loc[j, 'hours'] = row2.hours + available_hours
row2.day = days[row2.day]
row2.hours = - available_hours
available_hours = 0
new_rows.append(row2)
new_rows = pd.DataFrame(new_rows)

at this point df contains the shift rows for all

shifts which had to be modified

df = pd.concat((df, new_rows))

putting back all the shifts which were

previously declared non-problematic

df = pd.concat((df_okay,df)).drop(['start_dt', 'end_dt'], axis=1)
df.sort_values(['number', 'date'])


</details>

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Python DataFrame。根据各种标准插入行。

问题

答案1

converting to more sensible data types

df_okay will be the sink for the non-problematic shifts

any shift outside of Friday, Saturday and Sunday aren't our concern

let's use datetime objects to compare dates and calculate durations

let's add a day for the end datetime of shifts which go over midnight

any shift which doesn't go over midnight can't be problematic

so it goes in the sink

these shifts go over midnight so they're at risk

let's calculate the total number of hours for each `number` during the shift

let's calculate the number of available hours between the beginning of the shift and midnight

these shifts have a sum of `hours` which is more than the available hours until midnight

simple dictionnary which will be used to replace `day` with `day/day+1`

at this point df contains the shift rows for all

shifts which had to be modified

putting back all the shifts which were

previously declared non-problematic

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发表评论

问题

答案1

converting to more sensible data types

df_okay will be the sink for the non-problematic shifts

any shift outside of Friday, Saturday and Sunday aren't our concern

let's use datetime objects to compare dates and calculate durations

let's add a day for the end datetime of shifts which go over midnight

any shift which doesn't go over midnight can't be problematic

so it goes in the sink

these shifts go over midnight so they're at risk

let's calculate the total number of hours for each number during the shift

let's calculate the number of available hours between the beginning of the shift and midnight

these shifts have a sum of hours which is more than the available hours until midnight

simple dictionnary which will be used to replace day with day/day+1

at this point df contains the shift rows for all

shifts which had to be modified

putting back all the shifts which were

previously declared non-problematic

发表评论

let's calculate the total number of hours for each `number` during the shift

these shifts have a sum of `hours` which is more than the available hours until midnight

simple dictionnary which will be used to replace `day` with `day/day+1`