Docs: Does pandas `.apply()` twice on first row, or not?
#### Code Sample

```python
import pandas as pd
df = pd.DataFrame({'a':list('abcdef')})                                                   
L = []
def fcn(x): 
    global L
    L.append(x) 
df.a.apply(fcn)
print(L)
# ['a', 'b', 'c', 'd', 'e', 'f']

L=[]
df.apply(lambda x: fcn(x.a), axis=1) 
print(L)
# ['a', 'b', 'c', 'd', 'e', 'f']
```

#### Problem description

According to the current `pd.DataFrame.apply()` docs: "In the current implementation apply calls func twice on the first column/row to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first column/row."

#### Expected Output

```
['a', 'a', 'b', 'c', 'd', 'e', 'f']
['a', 'a', 'b', 'c', 'd', 'e', 'f']
```

#### Output of ``pd.show_versions()``
<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.4
scipy: 1.3.0
pyarrow: 0.11.1
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.0
bs4: None
html5lib: None
sqlalchemy: 1.2.5
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

</details>