Python 和 Pandas：将列组合成一个日期
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Python & Pandas: Combine columns into a date
提问by cqcn1991
In my dataframe, the time is separated in 3 columns: year, month, day, like this:
在 my 中dataframe，时间分为 3 列：year, month, day，如下所示：
How can I convert them into date, so I can do time series analysis?
如何将它们转换为date，以便进行时间序列分析？
I can do this:
我可以做这个：
df.apply(lambda x:'%s %s %s' % (x['year'],x['month'], x['day']),axis=1)
which gives:
这使：
1095 1954 1 1
1096 1954 1 2
1097 1954 1 3
1098 1954 1 4
1099 1954 1 5
1100 1954 1 6
1101 1954 1 7
1102 1954 1 8
1103 1954 1 9
1104 1954 1 10
1105 1954 1 11
1106 1954 1 12
1107 1954 1 13
But what follows?
但是接下来呢？
EDIT:This is what I end up with:
编辑：这就是我最终的结果：
from datetime import datetime
df['date']= df.apply(lambda x:datetime.strptime("{0} {1} {2}".format(x['year'],x['month'], x['day']), "%Y %m %d"),axis=1)
df.index= df['date']
采纳答案by FirebladeDan
Here's how to convert value to time:
以下是将值转换为时间的方法：
import datetime
df.apply(lambda x:datetime.strptime("{0} {1} {2} 00:00:00".format(x['year'],x['month'], x['day']), "%Y %m %d %H:%M:%S"),axis=1)
回答by Clément
It makes no sense to format a date to a string and immediately reparse it; use the datetimeconstructor instead:
将日期格式化为字符串并立即重新解析它是没有意义的；改用datetime构造函数：
df.apply(lambda x: datetime.date(x['year'], x['month'], x['day']), axis=1)
