Python - Iterate over a list of attributes

Question

I have a feature in my data set that is a pandas timestamp object. It has (among many others) the following attributes: year, hour, dayofweek, month.

I can create ne

Answer 1

Don't use .apply here, pandas has various built-in utilities for handling datetime objects, use the dt attribute on the series objects:

In [11]: start = datetime(2011, 1, 1)
    ...: end = datetime(2012, 1, 1)
    ...:

In [12]: df = pd.DataFrame({'data':pd.date_range(start, end)})

In [13]: df.dtypes
Out[13]:
data    datetime64[ns]
dtype: object

In [14]: df['year'] = df.data.dt.year

In [15]: df['hour'] = df.data.dt.hour

In [16]: df['month'] = df.data.dt.month

In [17]: df['dayofweek'] = df.data.dt.dayofweek

In [18]: df.head()
Out[18]:
        data  year  hour  month  dayofweek
0 2011-01-01  2011     0      1          5
1 2011-01-02  2011     0      1          6
2 2011-01-03  2011     0      1          0
3 2011-01-04  2011     0      1          1
4 2011-01-05  2011     0      1          2

Or, dynamically as you wanted using getattr:

In [24]: df = pd.DataFrame({'data':pd.date_range(start, end)})

In [25]: nomtimes = ["year", "hour", "month", "dayofweek"]
    ...:

In [26]: df.head()
Out[26]:
        data
0 2011-01-01
1 2011-01-02
2 2011-01-03
3 2011-01-04
4 2011-01-05

In [27]: for t in nomtimes:
    ...:     df[t] = getattr(df.data.dt, t)
    ...:

In [28]: df.head()
Out[28]:
        data  year  hour  month  dayofweek
0 2011-01-01  2011     0      1          5
1 2011-01-02  2011     0      1          6
2 2011-01-03  2011     0      1          0
3 2011-01-04  2011     0      1          1
4 2011-01-05  2011     0      1          2

And if you must use a one-liner, go with:

In [30]: df = pd.DataFrame({'data':pd.date_range(start, end)})

In [31]: df.head()
Out[31]:
        data
0 2011-01-01
1 2011-01-02
2 2011-01-03
3 2011-01-04
4 2011-01-05

In [32]: df = df.assign(**{t:getattr(df.data.dt,t) for t in nomtimes})

In [33]: df.head()
Out[33]:
        data  dayofweek  hour  month  year
0 2011-01-01          5     0      1  2011
1 2011-01-02          6     0      1  2011
2 2011-01-03          0     0      1  2011
3 2011-01-04          1     0      1  2011
4 2011-01-05          2     0      1  2011