pandas :按日期中断日期时间间隔 [英] Pandas: break datetime intervals by day
问题描述
我有一个具有日期时间间隔的DataFrame，就像这样一个:
I have a DataFrame with datetime intervals, like this one:
id start_date end_date
1 1 2016-10-01 00:00:00 2016-10-01 03:00:00
2 1 2016-10-03 05:30:00 2016-10-03 06:30:00
3 2 2016-10-03 23:30:00 2016-10-04 01:00:00 # This line should be splitted
4 1 2016-10-04 05:00:00 2016-10-04 06:00:00
5 2 2016-10-04 05:50:00 2016-10-04 06:00:00
6 1 2016-10-05 18:50:00 2016-10-06 02:00:00 # This one too
....
我想分割"超过一天的时间间隔，以确保每一行都在同一天:
I'd like to "split" the intervals that cover more than one day, to ensure that each rows falls on the same day:
id start_date end_date
1 1 2016-10-01 00:00:00 2016-10-01 03:00:00
2 1 2016-10-03 05:30:00 2016-10-03 06:30:00
3 2 2016-10-03 23:30:00 2016-10-03 23:59:59 # Splitted
4 2 2016-10-04 00:00:00 2016-10-04 01:00:00 # Splitted
5 1 2016-10-04 05:00:00 2016-10-04 06:00:00
6 2 2016-10-04 05:50:00 2016-10-04 06:00:00
7 1 2016-10-05 18:50:00 2016-10-05 23:59:59 # Splitted
8 1 2016-10-06 00:00:00 2016-10-06 02:00:00 # Splitted
....
推荐答案
您可以使用 .dt访问器，以创建执行更新的位置的布尔索引，然后相应地进行调整:
You can use the .dt accessor to create a Boolean index of where to perform the updates, and then make the adjustments accordingly:
# Get the rows to split.
split_rows = (df['start_date'].dt.date != df['end_date'].dt.date)
# Get the new rows to append, adjusting the start_date to the next day.
new_rows = df[split_rows].copy()
new_rows['start_date'] = new_rows['start_date'].dt.date + pd.DateOffset(days=1)
# Adjust the end_date of the existing rows.
df.loc[split_rows, 'end_date'] = df.loc[split_rows, 'start_date'].dt.date + pd.DateOffset(days=1, seconds=-1)
# Append the new rows to the existing dataframe.
df = df.append(new_rows).sort_index().reset_index(drop=True)
以上过程假设start_date和end_date跨度之间的日期差只有一天.如果可能存在多天跨度，则可以将上述过程包装在while循环中:
The process above assumes that there will only be one day between difference in dates between start_date and end_date spans. If it's possible that there are multi-day spans, you can wrap the above process in a while loop:
# Get the rows to split.
split_rows = (df['start_date'].dt.date != df['end_date'].dt.date)
while split_rows.any():
# Get the new rows, adjusting the start_date to the next day.
new_rows = df[split_rows].copy()
new_rows['start_date'] = new_rows['start_date'].dt.date + pd.DateOffset(days=1)
# Adjust the end_date of the existing rows.
df.loc[split_rows, 'end_date'] = df.loc[split_rows, 'start_date'].dt.date + pd.DateOffset(days=1, seconds=-1)
# Append the new rows to the existing dataframe.
df = df.append(new_rows).sort_index().reset_index(drop=True)
# Get new rows to split (if the start_date to end_date span is more than 1 day).
split_rows = (df['start_date'].dt.date != df['end_date'].dt.date)
示例数据的结果输出:
id start_date end_date
0 1 2016-10-01 00:00:00 2016-10-01 03:00:00
1 1 2016-10-03 05:30:00 2016-10-03 06:30:00
2 2 2016-10-03 23:30:00 2016-10-03 23:59:59
3 2 2016-10-04 00:00:00 2016-10-04 01:00:00
4 1 2016-10-04 05:00:00 2016-10-04 06:00:00
5 2 2016-10-04 05:50:00 2016-10-04 06:00:00
6 1 2016-10-05 18:50:00 2016-10-05 23:59:59
7 1 2016-10-06 00:00:00 2016-10-06 02:00:00
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