read_csv skiprows vs. read_excel
As I see `pandas.read_csv()` and `pandas.read_excel()` handle differently the `skiprows` argument. I have the same data in a CSV file and in an Excel file:

```
abc  def
  1   10
  2   11
  3   12
  4   13
  5   14
```

I want to use different column names when I read the data, so I specify the desired column names in `names` argument and I skip the first (header) row of my CSV & Excel files:
`pd.read_excel('test.xlsx', skiprows=0, names=['foo', 'bar'])` returns with my expected result:

```
   foo  bar
0    1   10
1    2   11
2    3   12
3    4   13
4    5   14
```

I get the same expected result with `pd.read_csv('test.csv', skiprows=1, names=['foo', 'bar'])`. But `pd.read_csv('test.csv', skiprows=0, names=['foo', 'bar'])` keeps the first (header) row of the input file:

```
   foo  bar
0  abc  def
1    1   10
2    2   11
3    3   12
4    4   13
5    5   14
```

Is this the expected behavior of `skiprows` or something is wrong at  `pandas.read_csv()`?
