PERF: Vectorized string operations are slower than for-loops
- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest version of pandas.

- [X] (optional) I have confirmed this bug exists on the master branch of pandas.

---


#### Code Sample, a copy-pastable example

**In [1]:**
```python
import pandas as pd
import numpy as np
print(pd.__version__)

non_padded = pd.Series(np.random.randint(100, 99999, size=10000))

def for_loop(series):
    return pd.Series([str(zipcode).zfill(5) for zipcode in series])

def vectorized(series):
    return series.astype(str).str.zfill(5)

%timeit for_loop(non_padded)
%timeit vectorized(non_padded)

```

**Out [1]:**
```
0.25.1
3.32 ms ± 44.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
7.18 ms ± 60.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```

#### Problem description

In most cases, using a `for-loop` in pandas is much slower than its vectorized equivalent. However, the above operations takes over twice as long when using vectorization. I have replicated this issue on MacOS & Ubuntu.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.1
numpy            : 1.17.2
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 20.0.2
setuptools       : 41.4.0
Cython           : 0.29.13
pytest           : 5.2.1
hypothesis       : None
sphinx           : 2.2.0
blosc            : None
feather          : None
xlsxwriter       : 1.2.1
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.8.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 3.0.0
pandas_gbq       : None
pyarrow          : 0.15.1
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.9
tables           : 3.5.2
xarray           : 0.14.0
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.1

</details>

