map(lambda x: x.encode()) on a dataframe column produces address of result instead of the actual result
#### Code Sample, a copy-pastable example if possible
Using pandas 1.0.1 and python 3.7
Using a text file consisting of words separated by carriage returns named sampleTEXT.txt
```
import base64
import numpy as np
import pandas as pd
words = pd.read_table("sampleTEXT.txt",names=['word'],header=None)
words.head()
---      word
---------------
---0    difference
---1    where
---2    mc
---3    is
---4    the
---
---
words['words_encoded'] = map(lambda x: x.encode('base64','strict'), words['word'])
print (words)
```
#### Problem description
output is this
```
               word                   words_encoded
0         difference              <map object at 0x7fac57615310>
1              where               <map object at 0x7fac57615310>
2                 mc                 <map object at 0x7fac57615310>
3                 is                   <map object at 0x7fac57615310>
4                the                  <map object at 0x7fac57615310>
...              ...                             ...
999995  distribution          <map object at 0x7fac57615310>
999996            in               <map object at 0x7fac57615310>
999997      scenario           <map object at 0x7fac57615310>
999998          less              <map object at 0x7fac57615310>
999999          land              <map object at 0x7fac57615310>

[1000000 rows x 2 columns]
```

#### Expected Output

I expect the ACTUAL base64 encoded words in the second column


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

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.6.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.3.0-28-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.1
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 45.2.0.post20200209
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.1
IPython          : 7.12.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.3
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None
</details>
