df.index.map with different size fails for Pandas > 0.22
#### Code Sample

```python
import pandas as pd

df = pd.DataFrame({'a': [0,1,2,3],
                   'b': ['a_1_bar', 'a_2_bar', 'b_1_bar', 'b_2_bar'],
                   'c': list('defg')})

df = df.set_index(['b','c'])
df.index.map(lambda x: tuple(x[0].split('_')))
```
#### Problem description

The code above works in Pandas 0.22 and lower, but fails since 0.23. This seems to be due to the fact that Pandas wants to preserve the names of the levels in the old index.

When the amount of levels in the new index is different compared to the old one, this fails with  a `ValueError` because of this mismatch.

`ValueError: Length of names must match number of levels in MultiIndex.`

#### Expected Output

Older version of pandas returned a new MultiIndex, without names for the levels.

```python
MultiIndex(levels=[['a', 'b'], ['1', '2'], ['bar']],
           labels=[[0, 0, 1, 1], [0, 1, 0, 1], [0, 0, 0, 0]])
```

I'm not sure whether this change was deliberate. If not, a workaround might be to only preserve the names if the new amount of levels matches the old one. And otherwise disregard the names, resulting in similar behavior as in the olders Pandas version.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.5.0
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: 0.11.2
IPython: 7.1.1
sphinx: 1.8.2
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
