BUG: could be a problem related to dropna()
#### Code Sample

```python
import pandas as pd
import numpy as np

df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
                   "toy": [np.nan, 'Batmobile', 'Bullwhip'],
                   "born": [pd.NaT, pd.Timestamp("1940-04-25"),
                            pd.NaT]})
#df
#        born      name        toy
#0        NaT    Alfred        NaN
#1 1940-04-25    Batman  Batmobile
#2        NaT  Catwoman   Bullwhip

df.dropna(inplace=True)
#df 
#        born    name        toy 
#1 1940-04-25  Batman  Batmobile

# A. 
dat_df = pd.DataFrame(data=np.zeros(1), columns=['Score'])
dat_df['SID'] = df['name']
#dat_df 
#   Score  SID 
#0    0.0  NaN 

# B.
dat_df = pd.DataFrame(data=np.zeros(1), columns=['Score'])
dat_df['SID'] = df['name'].values
#dat_df 
#   Score     SID
#0    0.0  Batman 

```
#### Problem description
Have a look at the code in block A and B. In A, df['name'] was used, while in B, df['name'].values was used. Apperantly, output of A was not correct, where it seems that the first term in the original df (before applying dropna()) was assign to the new dat_df. 
I tested this issue with version 0.18.1, 0.25.1, and 1.0.1. 

