BUG: select_dtypes return unexpected results when assigning a column by subtraction from original ones
## ENV

- Mac OSX Sierra 10.12.6
- pandas version: 0.20.3

## Code Example and Error  

After assigning `test` column by subtracting from two ones in `cf` dataframe, it could not return `test` column result by `select_dtypes(include=['int64'])` . Repeat same command again, it return the index. But for  original `df`, it is fine no matter how many times running  `select_dtypes(include=['int64'])`.( I do not show it because it seems redundant. )
 
```
In [1]: import numpy as np
   ...: import pandas as pd
   ...: from sklearn.datasets import load_iris
   ...:

In [2]: iris = load_iris()
   ...: df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
   ...:                      columns= iris['feature_names'] + ['target'])
   ...:
   ...: df.columns = ['sl','sw','pl','sw','target']
   ...:

In [3]: cf = df.copy()
   ...:
   ...: cf = cf.astype('int64')
   ...:
   ...:
   ...: cf['test'] = cf.eval('sl-pl')
   ...:

In [4]: cf.select_dtypes(include=['int64'])
Out[4]:
     sl  sw  pl  sw  target
0     5   3   1   0       0
1     4   3   1   0       0
2     4   3   1   0       0
3     4   3   1   0       0
4     5   3   1   0       0
5     5   3   1   0       0
6     4   3   1   0       0
7     5   3   1   0       0
8     4   2   1   0       0
9     4   3   1   0       0
10    5   3   1   0       0
11    4   3   1   0       0
12    4   3   1   0       0
13    4   3   1   0       0
14    5   4   1   0       0
15    5   4   1   0       0
16    5   3   1   0       0
17    5   3   1   0       0
18    5   3   1   0       0
19    5   3   1   0       0
20    5   3   1   0       0
21    5   3   1   0       0
22    4   3   1   0       0
23    5   3   1   0       0
24    4   3   1   0       0
25    5   3   1   0       0
26    5   3   1   0       0
27    5   3   1   0       0
28    5   3   1   0       0
29    4   3   1   0       0
..   ..  ..  ..  ..     ...
120   6   3   5   2       2
121   5   2   4   2       2
122   7   2   6   2       2
123   6   2   4   1       2
124   6   3   5   2       2
125   7   3   6   1       2
126   6   2   4   1       2
127   6   3   4   1       2
128   6   2   5   2       2
129   7   3   5   1       2
130   7   2   6   1       2
131   7   3   6   2       2
132   6   2   5   2       2
133   6   2   5   1       2
134   6   2   5   1       2
135   7   3   6   2       2
136   6   3   5   2       2
137   6   3   5   1       2
138   6   3   4   1       2
139   6   3   5   2       2
140   6   3   5   2       2
141   6   3   5   2       2
142   5   2   5   1       2
143   6   3   5   2       2
144   6   3   5   2       2
145   6   3   5   2       2
146   6   2   5   1       2
147   6   3   5   2       2
148   6   3   5   2       2
149   5   3   5   1       2

[150 rows x 5 columns]

In [5]: cf.select_dtypes(include=['int64'])
Out[5]:
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]

[150 rows x 0 columns]
```
## Expected Output 

It can return new columns after assigning columns no matter how many times running `select_dtype(include=['int64'])`

## pd.show_versions()

<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: zh_CN.UTF-8
LOCALE: zh_CN.UTF-8

pandas: 0.20.3
pytest: None
pip: 9.0.1
setuptools: 36.5.0
Cython: None
numpy: 1.13.1
scipy: 0.19.0
xarray: None
IPython: 6.2.1
sphinx: None
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.0.0
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None
```
</details>