Excel 输出中的 Python Pandas 自定义时间格式
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Python Pandas custom time format in Excel output
提问by wrcobb
I have used pandas.groupbyto group a pandas DataFrame on two columns and calculate average and median times. My resulting dataset looks similar to this:
我曾经pandas.groupby在两列上对 Pandas DataFrame 进行分组并计算平均时间和中值时间。我得到的数据集看起来类似于：
Size Category Average Time Median Time
1 A 0.002056385 0.000310995
B 0.000310995
C 0.000310995
10 A 0.001852681
B 0.000310995
C 0.000310995
I would like to export this table to excel and format the Time Columns as a custom format in Excel like so (hh:mm:ss.000). In other words, I want to view the times as millisecond-level times. For example, 0.000310995 formatted in this fashion displays as 00:00:26.870 (26.870 seconds).
我想将此表导出到 excel 并将时间列格式化为 Excel 中的自定义格式（hh:mm:ss.000）。换句话说，我想将时间视为毫秒级时间。例如，以这种方式格式化的 0.000310995 显示为 00:00:26.870（26.870 秒）。
Does anyone have any insight on how to accomplish this feat?
有没有人对如何完成这一壮举有任何见解？
UPDATE:
更新：
I have gotten a bit closer by using to_datetime(df['Average Time'], unit='d'). My times are now formatted like 1970-01-01 00:02:57.638400in the DataFrame. However, when using to_excelto export to Excel they are formatted as 1970-01-01 00:02:58in the Excel output. At this point, I only need to drop the date portion and add millisecond precision to achieve my goal. Any thoughts?
我通过使用to_datetime(df['Average Time'], unit='d'). 我的时间现在像1970-01-01 00:02:57.638400在 DataFrame 中一样格式化。但是，当to_excel用于导出到 Excel 时，它们的格式1970-01-01 00:02:58与 Excel 输出中的格式相同。此时，我只需要删除日期部分并添加毫秒精度即可实现我的目标。有什么想法吗？
Thanks very much in advance for any help you can offer -
非常感谢您提供的任何帮助 -
回答by jmcnamara
You can use the datetime_formatparameter of ExcelWriterin Pandas:
您可以使用Pandas 中的datetime_format参数ExcelWriter：
import pandas as pd
from datetime import datetime
df = pd.DataFrame([datetime(2014, 9, 18, 12, 30, 5, 60000)])
writer = pd.ExcelWriter("time.xlsx", datetime_format='hh:mm:ss.000')
df.to_excel(writer, "Sheet1")
writer.close()
Which gives the following output:
这给出了以下输出：
