合并 Pandas DataFrame DateTime 列
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Combine Pandas DataFrame DateTime Columns
提问by Santiago Munez
Supposely I have dataframes as below:
假设我有如下数据框：
Year Month Day
2003 1 8
2003 2 7
How to combine the Year, Month, and Day in the newly defined column in the dataframe as such the dataframe would be:
如何在数据框中新定义的列中组合年、月和日，这样数据框将是：
Year Month Day Date
2003 1 8 2003-1-8
2003 2 7 2003-2-7
Any idea on this?
对此有什么想法吗？
I am using pandas python dataframe
我正在使用 Pandas python 数据框
Thanks!
谢谢！
回答by Viktor Kerkez
>>> from datetime import datetime
>>> df['Date'] = df.apply(lambda row: datetime(
row['Year'], row['Month'], row['Day']), axis=1)
>>> df
Year Month Day Date
0 2003 1 8 2003-01-08 00:00:00
1 2003 2 7 2003-02-07 00:00:00
Update 2020-03-12:The answer from sacul is better and faster:
2020-03-12 更新：来自 sacul 的答案更好更快：
%%timeit
df.apply(lambda row: datetime(
row['Year'], row['Month'], row['Day']), axis=1)
2.53 s ± 169 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# use below, above is slow!!!
%%timeit
pd.to_datetime(df[['Year','Month','Day']])
14.4 ms ± 3.37 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
回答by sacuL
Better use pd.to_datetime:
更好地使用pd.to_datetime：
df['Date'] = pd.to_datetime(df[['Year','Month','Day']])
>>> df
Year Month Day Date
0 2003 1 8 2003-01-08
1 2003 2 7 2003-02-07
