超出范围的纳秒级时间戳 [英] Out of bounds nanosecond timestamp

问题描述

我在Googlespeedsheet中有一个变量['date_hiring']，格式为

I have a variable ['date_hiring'] in Googlespeedsheet in format like

16.01.2016

我在Python中将其导入，该变量具有对象类型.我尝试转换为日期时间

I import it in Python, the variable has an object type. I try to convert to datetime

from datetime import datetime
data['date_hiring'] = pd.to_datetime(data['date_hiring'])

我得到

OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 16-01-06 00:00:00

我从此 pandas知道偏移前滚后加上一个月的偏移量即

由于熊猫以纳秒分辨率表示时间戳，因此 可以使用64位整数表示的时间跨度仅限于 大约584年

Since pandas represents timestamps in nanosecond resolution, the timespan that can be represented using a 64-bit integer is limited to approximately 584 years

但是在Googlespeedsheet中的原始数据中，我没有类似"16.01.06"的数据

but in original data in the Googlespeedsheet i have no data like '16.01.06'

就像'16 .06.2006'

Just like '16.06.2006'

所以问题出在转换

如何改进它?

推荐答案

根据文档， dayfirst 字段默认为false:

According to the documentation, the dayfirst field defaults to false:

dayfirst:布尔值，默认为False

dayfirst : boolean, default False

因此，它必须确定那里有一个错误的日期，并试图将其解释为一天中的某个时间.

So it must have decided that there was a malformed date there and tried to interpret it as a time-of-day.

但是即使那样，它可能也不认为16点是几小时或几分钟，因此它尝试将其转换为秒.但是还有一个额外的小数点，所以它放弃了，说我不喜欢小数秒. (或者类似的东西.)

But even then it probably didn't think that 16 point anything could be hours or minutes, so it tried to convert it as seconds. But there is a extra decimal point so it gave up and said I don't like the fractional seconds. (Or something like that.)

我认为您可以通过提供明确的格式字符串或至少设置 dayfirst 来解决此问题.

I think you can fix it by giving an explicit format string or at least setting dayfirst.

这篇关于超出范围的纳秒级时间戳的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！