Strange behavior with median
Consider the following dataframe:
```
       b           c     d     e  f     g     h
0   6.25  2018-04-01  True   NaN  7  54.0  64.0
1  32.50  2018-04-01  True   NaN  7  54.0  64.0
2  16.75  2018-04-01  True   NaN  7  54.0  64.0
3  29.25  2018-04-01  True   NaN  7  54.0  64.0
4  21.75  2018-04-01  True   NaN  7  54.0  64.0
5  21.75  2018-04-01  True  True  7  54.0  64.0
6   7.75  2018-04-01  True  True  7  54.0  64.0
7  23.25  2018-04-01  True  True  7  54.0  64.0
8  12.25  2018-04-01  True  True  7  54.0  64.0
9  30.50  2018-04-01  True   NaN  7  54.0  64.0
```
(copy and paste and use `df = pd.read_clipboard()` to create the dataframe)

Finding the medians initially works with no problem:

```python
df.median()

b    21.75
d     1.00
e     1.00
f     7.00
g    54.00
h    64.00
dtype: float64
```
However, if a column is dropped and then the `median` is found, the median for column `e` disappears:

```python
new_df = df.drop(columns=['b'])
new_df.median()

d     1.0
f     7.0
g    54.0
h    64.0
dtype: float64
```

This behavior is a little unexpected and finding the median for column e by itself still works:

```python
new_df['e'].median()
1.0
```

Using `skipna=False` does not make a difference:
```python
new_df.median(skipna=False)

d     1.0
f     7.0
g    54.0
h    64.0
dtype: float64
```

(it does for the original dataframe):

```python
df.median(skipna=False)

b    21.75
d     1.00
e      NaN
f     7.00
g    54.00
h    64.00
dtype: float64
```

The datatype of column `e` is `object` in both `df` and `new_df` and the only difference between the two dataframes is `new_df` does not have column `b`. Adding the column back into `new_df` does not resolve the issue. This only occurs when the first column `b` is dropped. It does not occur if column `e` is a float or integer datatype.


This behavior is present in both `pandas==0.22.0` and `pandas==0.24.1`

I also created a [Stack Overflow Question](https://stackoverflow.com/questions/54755354/strange-behavior-with-pandas-median) based on this issue.