wide_to_long with NaNs in i argument
#### Code and output:

```python
import numpy as np
import pandas as pd
df1 = pd.DataFrame({'id' : ['a', np.NaN, 'c'],
                   'A_1999' : [1, 2, 3],
                   'A_2000' : [5, 6, 7]})

df1
    id  A_1999  A_2000
0    a       1       5
1  NaN       2       6
2    c       3       7

df2 = pd.wide_to_long(df1,
                      stubnames = ['A'],
                      sep = '_',
                      i = 'id',
                      j = 'year').reset_index()

df2
  id  year  A
0  a  1999  1
1  a  1999  2
2  c  1999  3
3  a  2000  5
4  a  2000  6
5  c  2000  7
```
#### Problem description

df1 has one NaN in the id column. When applying `wide_to_long` to df1, `wide_to_long` seems to fill the NaNs from df1 with the preceding value from the df1 dataframe (namely, **a**).

Why might this be a problem: instances of `id == 'a'` in df1 are not the same as in df2 (because the NaNs from df1 have been replaced by **a** in df2):

```
df1.loc[df1['id'] == 'a',]

  id  A_1999  A_2000
0  a       1       5
```

versus:

```python
df2.loc[df2['id'] == 'a',]

  id  year  A
0  a  1999  1
1  a  1999  2
3  a  2000  5
4  a  2000  6
```

#### Expected Output

I would expect `wide_to_long` to either

1.  throw an error that the column that is passed to its `i` argument contains NaNs, or
2. preserve the NaN case in the second dataframe (not sure whether this is desirable), i.e.:

```python
    id  year  A
0    a  1999  1
1  NaN  1999  2
2    c  1999  3
3    a  2000  5
4  NaN  2000  6
5    c  2000  7
```


#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : None

pandas           : 1.0.1
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 45.2.0.post20200210
Cython           : 0.29.15
pytest           : 5.3.5
hypothesis       : 5.4.1
sphinx           : 1.8.5
blosc            : None
feather          : None
xlsxwriter       : 1.2.7
lxml.etree       : 4.5.0
html5lib         : 1.0.1
pymysql          : None
psycopg2         : 2.8.3 (dt dec pq3 ext lo64)
jinja2           : 2.11.1
IPython          : 7.12.0
pandas_datareader: None
bs4              : 4.8.2
bottleneck       : 1.3.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.0
matplotlib       : 3.1.3
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.3.5
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.13
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.7
numba            : 0.43.1
</details>
