从 Pandas 日期时间列中分别提取月份和年份 [英] Extracting just Month and Year separately from Pandas Datetime column

问题描述

我有一个数据框 df，包含以下列:

I have a Dataframe, df, with the following column:

df['ArrivalDate'] =
...
936   2012-12-31
938   2012-12-29
965   2012-12-31
966   2012-12-31
967   2012-12-31
968   2012-12-31
969   2012-12-31
970   2012-12-29
971   2012-12-31
972   2012-12-29
973   2012-12-29
...

列的元素是pandas.tslib.Timestamp.

The elements of the column are pandas.tslib.Timestamp.

我只想包括年和月.我以为会有简单的方法来做到这一点，但我想不通.

I want to just include the year and month. I thought there would be simple way to do it, but I can't figure it out.

这是我尝试过的:

df['ArrivalDate'].resample('M', how = 'mean')

我收到以下错误:

Only valid with DatetimeIndex or PeriodIndex 

然后我尝试了:

df['ArrivalDate'].apply(lambda(x):x[:-2])

我收到以下错误:

'Timestamp' object has no attribute '__getitem__' 

有什么建议吗?

我有点想通了.

df.index = df['ArrivalDate']

然后，我可以使用索引对另一列重新采样.

Then, I can resample another column using the index.

但我仍然想要一种重新配置整个列的方法.有什么想法吗?

But I'd still like a method for reconfiguring the entire column. Any ideas?

推荐答案

如果您希望新列分别显示年和月，您可以这样做:

If you want new columns showing year and month separately you can do this:

df['year'] = pd.DatetimeIndex(df['ArrivalDate']).year
df['month'] = pd.DatetimeIndex(df['ArrivalDate']).month

或...

df['year'] = df['ArrivalDate'].dt.year
df['month'] = df['ArrivalDate'].dt.month

然后您可以将它们组合起来或照原样使用它们.

Then you can combine them or work with them just as they are.

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