Вопрос

I am trying to interpret a field as a date, change the date to represent the month the date appears in, offset the date by a month and then represent it as a date without a timestamps. I have ended up with this which looks and feels too unwieldy:

    df['DATE'].apply( lambda d: pd.to_datetime(pd.to_datetime(d).to_period('M').to_timestamp('M')\
                                      - np.timedelta64(1,'M')).date())

The timestamps are strings in this format:

    2012-09-01 00:00:00

Any ideas for a better way? Thanks.

Это было полезно?

Решение

Well, you can avoid the apply and do it vectorized (I think that makes it a bit nicer):

print df

                  date  x1
0  2010-01-01 00:00:00  10
1  2010-02-01 00:00:00  10
2  2010-03-01 00:00:00  10
3  2010-04-01 00:00:00  10
4  2010-04-01 00:00:00   5
5  2010-05-01 00:00:00   5

df['date'] = (pd.to_datetime(df['date']).values.astype('datetime64[M]')
              - np.timedelta64(1,'M'))
print df

        date  x1
0 2009-12-01  10
1 2010-01-01  10
2 2010-02-01  10
3 2010-03-01  10
4 2010-03-01   5
5 2010-04-01   5

Of course, the dates will still be datetime64[ns] since pandas always converts to that.

Edit: Suppose you wanted the end of the previous month instead of the beggining of the previous month:

df['date'] = (pd.to_datetime(df['date']).values.astype('datetime64[M]')
              - np.timedelta64(1,'D'))
print df

        date  x1
0 2009-11-30  10
1 2009-12-31  10
2 2010-01-31  10
3 2010-02-28  10
4 2010-02-28   5
5 2010-03-31   5

Edit: Jeff points out that a more pandonic way is to make date a DatetimeIndex and use a Date Offset. So something like:

df['date'] = pd.Index(df['date']).to_datetime() - pd.offsets.MonthBegin(1)
print df

        date  x1
0 2009-12-01  10
1 2010-01-01  10
2 2010-02-01  10
3 2010-03-01  10
4 2010-03-01   5
5 2010-04-01   5

Or month-ends:

df['date'] = pd.Index(df['date']).to_datetime() - pd.offsets.MonthEnd(1)
print df

        date  x1
0 2009-12-31  10
1 2010-01-31  10
2 2010-02-28  10 
3 2010-03-31  10
4 2010-03-31   5
5 2010-04-30   5
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