DOC: when accessed from inside a column-wise `df.apply(..., axis=1)`, row.index refers to df.columns, and row.name refers to df.index value
DOC BUG: clarification for the function/lambda called inside [apply()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html?highlight=apply#pandas.DataFrame.apply)
When accessed from inside a column-wise `df.apply(lambda row:..., axis=1)`, things get counterintuitive:

- **row** is now a (horizontal) Series...
- **row.index** is now the column names from df.columns
- **row.name** is the (individual) row-index value of that row in the parent df

#### Code Sample, a copy-pastable example if possible

```python
df = pd.DataFrame({'x':[1,2,3], 'y':[4,5,6]})
df.apply(lambda row: row.index, axis=1)
0    Index(['x', 'y'], dtype='object')
1    Index(['x', 'y'], dtype='object')
2    Index(['x', 'y'], dtype='object')
```
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.2
</details>