Python 将 Pandas 系列转换为 DataFrame 中的 DateTime
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Convert Pandas Series to DateTime in a DataFrame
提问by 1EnemyLeft
I have a Pandas DataFrame as below
我有一个如下所示的 Pandas DataFrame
ReviewID ID Type TimeReviewed
205 76032930 51936827 ReportID 2015-01-15 00:05:27.513000
232 76032930 51936854 ReportID 2015-01-15 00:06:46.703000
233 76032930 51936855 ReportID 2015-01-15 00:06:56.707000
413 76032930 51937035 ReportID 2015-01-15 00:14:24.957000
565 76032930 51937188 ReportID 2015-01-15 00:23:07.220000
>>> type(df)
<class 'pandas.core.frame.DataFrame'>
TimeReviewed is a series type
TimeReviewed 是一个系列类型
>>> type(df.TimeReviewed)
<class 'pandas.core.series.Series'>
I've tried below, but it still doesn't change the Series type
我在下面尝试过，但它仍然没有改变系列类型
import pandas as pd
review = pd.to_datetime(pd.Series(df.TimeReviewed))
>>> type(review)
<class 'pandas.core.series.Series'>
How can I change the df.TimeReviewed to DateTime type and pull out year, month, day, hour, min, sec separately? I'm kinda new to python, thanks for your help.
如何将 df.TimeReviewed 更改为 DateTime 类型并分别提取年、月、日、小时、分钟、秒？我对 python 有点陌生，谢谢你的帮助。
采纳答案by DSM
You can't: DataFramecolumns are Series, by definition. That said, if you make the dtype(the type of all the elements) datetime-like, then you can access the quantities you want via the .dtaccessor (docs):
您不能：根据定义，DataFrame列是Series。也就是说，如果您使dtype（所有元素的类型）类似日期时间，那么您可以通过访问.dt器（docs）访问您想要的数量：
>>> df["TimeReviewed"] = pd.to_datetime(df["TimeReviewed"])
>>> df["TimeReviewed"]
205 76032930 2015-01-24 00:05:27.513000
232 76032930 2015-01-24 00:06:46.703000
233 76032930 2015-01-24 00:06:56.707000
413 76032930 2015-01-24 00:14:24.957000
565 76032930 2015-01-24 00:23:07.220000
Name: TimeReviewed, dtype: datetime64[ns]
>>> df["TimeReviewed"].dt
<pandas.tseries.common.DatetimeProperties object at 0xb10da60c>
>>> df["TimeReviewed"].dt.year
205 76032930 2015
232 76032930 2015
233 76032930 2015
413 76032930 2015
565 76032930 2015
dtype: int64
>>> df["TimeReviewed"].dt.month
205 76032930 1
232 76032930 1
233 76032930 1
413 76032930 1
565 76032930 1
dtype: int64
>>> df["TimeReviewed"].dt.minute
205 76032930 5
232 76032930 6
233 76032930 6
413 76032930 14
565 76032930 23
dtype: int64
If you're stuck using an older version of pandas, you can always access the various elements manually (again, after converting it to a datetime-dtyped Series). It'll be slower, but sometimes that isn't an issue:
如果您坚持使用旧版本的pandas，您始终可以手动访问各种元素（再次将其转换为日期时间类型的系列之后）。它会更慢，但有时这不是问题：
>>> df["TimeReviewed"].apply(lambda x: x.year)
205 76032930 2015
232 76032930 2015
233 76032930 2015
413 76032930 2015
565 76032930 2015
Name: TimeReviewed, dtype: int64
回答by CodeFarmer
Some handy script:
一些方便的脚本：
hour = df['assess_time'].dt.hour.values[0]
回答by Shashwat Yadav
df=pd.read_csv("filename.csv" , parse_dates=["<column name>"])
type(df.<column name>)
example: if you want to convert day which is initially a string to a Timestamp in Pandas
示例：如果您想将最初是字符串的日期转换为 Pandas 中的时间戳
df=pd.read_csv("weather_data2.csv" , parse_dates=["day"])
type(df.day)
The output will be pandas.tslib.Timestamp
输出将是 pandas.tslib.Timestamp
