For some reason, the simple last
operation is not working for my dataframe:
df
Out[57]:
month date value
0 2013-01-01 2013-01-25 0.0223
1 2013-01-01 2013-01-28 0.0006
2 2013-01-01 2013-01-29 0.0071
3 2013-01-01 2013-01-30 0.0062
4 2013-01-01 2013-01-31 0.0037
5 2013-02-01 2013-02-01 0.0151
6 2013-02-01 2013-02-04 0.012
7 2013-02-01 2013-02-05 0.0181
8 2013-02-01 2013-02-06 -0.0075
9 2013-02-01 2013-02-07 -0.0057
10 rows × 3 columns
df.groupby('month').last()
Out[58]:
date value
month
2013-01-01 2013-01-01 2013-01-01
2013-02-01 2013-02-01 2013-02-01
2 rows × 2 columns
df.dtypes
Out[59]:
month datetime64[ns]
date datetime64[ns]
value object
dtype: object
I am using pandas 13.1. Is this a new bug?
This is a bug in 0.13.1. Fixed in master/0.14 (releasing shortly). Also in 0.14 this will coerce the value column to float64
(you have it as object
for some reason; never a good thing with a float-like column).
Here's a work-around for 0.13.1 (the extra month column is also going away in 0.14).
In [14]: df.groupby('month').tail(1)
Out[14]:
month date value
month
2013-01-01 4 2013-01-01 2013-01-31 0.0037
2013-02-01 9 2013-02-01 2013-02-07 -0.0057
[2 rows x 3 columns]
Here's 0.14/master output
In [32]: df.groupby('month').last()
Out[32]:
date value
month
2013-01-01 2013-01-31 0.0037
2013-02-01 2013-02-07 -0.0057
In [33]: df.groupby('month').last().dtypes
Out[33]:
date datetime64[ns]
value float64
dtype: object
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