Getting most recent observation & date from several columns

Question

Take the following toy DataFrame:

data = np.arange(35, dtype=np.float32).reshape(7, 5)
data = pd.concat((
    pd.DataFrame(list('abcdefg'), columns=['field1']),
    pd.DataFrame(data, columns=['field2', '2014', '2015', '2016', '2017'])),
    axis=1)

data.iloc[1:4, 4:] = np.nan
data.iloc[4, 3:] = np.nan

print(data)
  field1  field2  2014  2015  2016  2017
0      a     0.0   1.0   2.0   3.0   4.0
1      b     5.0   6.0   7.0   NaN   NaN
2      c    10.0  11.0  12.0   NaN   NaN
3      d    15.0  16.0  17.0   NaN   NaN
4      e    20.0  21.0   NaN   NaN   NaN
5      f    25.0  26.0  27.0  28.0  29.0
6      g    30.0  31.0  32.0  33.0  34.0

I'd like to replace the "year" columns (2014-2017) with two fields: the most recent non-null observation, and the corresponding year of that observation. Assume field1 is a unique key. (I'm not looking to do any groupby ops, just 1 row per record.) I.e.:

  field1  field2   obs  date
0      a     0.0   4.0  2017
1      b     5.0   7.0  2015
2      c    10.0  12.0  2015
3      d    15.0  17.0  2015
4      e    20.0  21.0  2014
5      f    25.0  29.0  2017
6      g    30.0  34.0  2017

I've gotten this far:

pd.melt(data, id_vars=['field1', 'field2'], 
        value_vars=['2014', '2015', '2016', '2017'])\
    .dropna(subset=['value'])

   field1  field2 variable  value
0       a     0.0     2014    1.0
1       b     5.0     2014    6.0
2       c    10.0     2014   11.0
3       d    15.0     2014   16.0
4       e    20.0     2014   21.0
5       f    25.0     2014   26.0
6       g    30.0     2014   31.0
# ...

But am struggling with how to pivot back to desired format.

Answer 1

Maybe:

d2 = data.melt(id_vars=["field1", "field2"], var_name="date", value_name="obs").dropna(subset=["obs"])
d2["date"] = d2["date"].astype(int)
df = d2.loc[d2.groupby(["field1", "field2"])["date"].idxmax()]

which gives me

   field1  field2  date   obs
21      a     0.0  2017   4.0
8       b     5.0  2015   7.0
9       c    10.0  2015  12.0
10      d    15.0  2015  17.0
4       e    20.0  2014  21.0
26      f    25.0  2017  29.0
27      g    30.0  2017  34.0

Answer 2

what about the following apporach:

In [160]: df
Out[160]:
  field1  field2  2014  2015  2016  2017
0      a     0.0   1.0   2.0   3.0 -10.0
1      b     5.0   6.0   7.0   NaN   NaN
2      c    10.0  11.0  12.0   NaN   NaN
3      d    15.0  16.0  17.0   NaN   NaN
4      e    20.0  21.0   NaN   NaN   NaN
5      f    25.0  26.0  27.0  28.0  29.0
6      g    30.0  31.0  32.0  33.0  34.0

In [180]: df.groupby(lambda x: 'obs' if x.isdigit() else x, axis=1) \
     ...:   .last() \
     ...:   .assign(date=df.filter(regex='^\d{4}').loc[:, ::-1].notnull().idxmax(1))
Out[180]:
  field1  field2   obs  date
0      a     0.0 -10.0  2017
1      b     5.0   7.0  2015
2      c    10.0  12.0  2015
3      d    15.0  17.0  2015
4      e    20.0  21.0  2014
5      f    25.0  29.0  2017
6      g    30.0  34.0  2017