strings not properly detected despite correct dtype in read_csv
Hello there!

I am working with text data, and I read my data in using

```
full_list =[]

for myfile in all_files:
    print("processing " + myfile)
    news = pd.read_csv(myfile, usecols = ['FULL_TIMESTAMP', 'HEADLINE'], dtype = {'HEADLINE' : str})
    full_list.append(news)
   
data_full = pd.concat(full_list)
```

As you see, I make sure that my headline variable is a `str`. However, when I type

`collapsed = data_full.groupby('day').HEADLINE.agg(lambda x: '| '.join(x))`

I get :


```
  File "<ipython-input-1-8ce0197f52ac>", line 34, in <module>
    collapsed =data_full.groupby('day').HEADLINE.agg(lambda x: '| '.join(x))

  File "C:\Users\me\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\groupby.py", line 2668, in aggregate
    result = self._aggregate_named(func_or_funcs, *args, **kwargs)

  File "C:\Users\me\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\groupby.py", line 2786, in _aggregate_named
    output = func(group, *args, **kwargs)

  File "<ipython-input-1-8ce0197f52ac>", line 34, in <lambda>
    collapsed = data_full.groupby('day').HEADLINE.agg(lambda x: '| '.join(x))

TypeError: sequence item 21: expected string, float found
```

To fix the problem, I need first to type

`data_full['HEADLINE'] = data_full['HEADLINE'].astype(str)`

Is that expected? I thought specifying the `dtypes` in `read_csv` was the most robust solution to have consistent types in the data? Still using Pandas 19.2.

Thanks!