使用 pandas.to_datetime 时只保留日期部分 [英] Keep only date part when using pandas.to_datetime
问题描述
我使用 pandas.to_datetime 来解析我的数据中的日期.Pandas 默认用 datetime64[ns] 表示日期，即使这些日期都是每天.我想知道是否有一种优雅/聪明的方式将日期转换为 datetime.date 或 datetime64[D] 这样，当我将数据写入 CSV 时，日期没有附加 00:00:00.我知道我可以逐个元素地手动转换类型:
I use pandas.to_datetime to parse the dates in my data. Pandas by default represents the dates with datetime64[ns] even though the dates are all daily only.
I wonder whether there is an elegant/clever way to convert the dates to datetime.date or datetime64[D] so that, when I write the data to CSV, the dates are not appended with 00:00:00. I know I can convert the type manually element-by-element:
[dt.to_datetime().date() for dt in df.dates]
但这真的很慢，因为我有很多行，这有点违背了使用 pandas.to_datetime 的目的.有没有办法一次转换整列的 dtype ?或者，pandas.to_datetime 是否支持精度规范，以便我可以在处理日常数据时摆脱时间部分?
But this is really slow since I have many rows and it sort of defeats the purpose of using pandas.to_datetime. Is there a way to convert the dtype of the entire column at once? Or alternatively, does pandas.to_datetime support a precision specification so that I can get rid of the time part while working with daily data?
推荐答案
从 0.15.0 版本开始，这现在可以使用 .dt 只访问日期组件:
Since version 0.15.0 this can now be easily done using .dt to access just the date component:
df['just_date'] = df['dates'].dt.date
上面返回一个 datetime.date dtype，如果你想要一个 datetime64 那么你可以 normalize午夜，因此它将所有值设置为 00:00:00:
The above returns a datetime.date dtype, if you want to have a datetime64 then you can just normalize the time component to midnight so it sets all the values to 00:00:00:
df['normalised_date'] = df['dates'].dt.normalize()
这将 dtype 保持为 datetime64，但显示仅显示 date 值.
This keeps the dtype as datetime64, but the display shows just the date value.
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