Why is `pandas.read_csv` not the reciprocal of `df.to_csv`?
It seems strange to me that `pandas.read_csv` is not a direct reciprocal function to `df.to_csv`.  In this illustration, notice how when using all the default settings the original and final DataFrames differ by the "Unnamed" column.

```
In [1]: import pandas as pd

In [2]: orig_df = pd.DataFrame({'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}); orig_df
Out[2]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

[4 rows x 3 columns]

In [3]: orig_df.to_csv('test.csv')

In [4]: final_df = pd.read_csv('test.csv'); final_df
Out[4]: 
   Unnamed: 0  AAA  BBB  CCC
0           0    4   10  100
1           1    5   20   50
2           2    6   30  -30
3           3    7   40  -50

[4 rows x 4 columns]
```

It seems the default `read_csv` should instead be 

```
In [6]: final2_df = pd.read_csv('test.csv', index_col=0); final2_df
Out[7]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

[4 rows x 3 columns]
```

or the default `to_csv` should instead be

```
In [8]: df.to_csv('test2.csv', index=False)
```

which when read gives

```
In [9]: pd.read_csv('test2.csv')
Out[9]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50
```

[4 rows x 3 columns]

This inconsistency make using these functions unintuitive.  I do not really care which way it goes but I think the default behavior should be consistent with options for differences rather than different with options for consistency.
