Performance issue when replacing a sting column with integer
#### Code Sample

```python
import numpy as np
import pandas as pd
from string import ascii_uppercase

df = pd.DataFrame(columns=list(ascii_uppercase),
                  data=pd.util.testing.rands_array(10, (10 ** 6, 26)))
arr = np.random.randint(1, 10, 10 ** 6)
```
```python
%%time
df['I'] = arr
# Wall time: 2.46 s
```
```python
%%time
df['J'] = arr.astype(object)
# Wall time: 69 ms
```
#### Problem description

For some reason, replacing string columns with integers in large dataframes seems extremely slow.

#### Expected Output
Same for both cases, e.g. `Wall time: 69 ms`

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.4
pytest: 3.5.1
pip: 19.0.2
setuptools: 39.1.0
Cython: 0.28.2
numpy: 1.15.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 3.0.0
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
