BUG: DataFrame.apply(raw=True) applies function twice on first row/column
- [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

```python
In [1]: import pandas as pd
   ...:
   ...: df = pd.DataFrame({'a': [1,2,3]})
   ...:
   ...: def mul2(x):
   ...:     print('Hello')
   ...:     return x*2
   ...:
   ...: print('Apply:')
   ...: df.apply(mul2)
   ...:
   ...: print('\nApply Raw:')
   ...: df.apply(mul2, raw=True)

Apply:
Hello

Apply Raw:
Hello
Hello
```

#### Problem description

Because of an implementation detail, the applied function is applied twice on the first row/column,
This results in side effects happening twice.

Other than the `apply_raw` code path, this issues should be fixed by https://github.com/pandas-dev/pandas/pull/34183.



#### Expected Output
```python
Apply:
Hello

Apply Raw:
Hello
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : 07edc871f5f220c4e4835447cdcf61d64d5a93ab
python           : 3.7.5.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.5.0
Version          : Darwin Kernel Version 19.5.0: Thu Apr 30 18:25:59 PDT 2020; root:xnu-6153.121.1~7/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.0.dev0+1706.g07edc871f.dirty
numpy            : 1.18.3
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 19.2.3
setuptools       : 41.2.0
Cython           : 0.29.17
pytest           : 5.4.1
hypothesis       : 5.11.0
sphinx           : 3.0.3
blosc            : 1.9.1
feather          : None
xlsxwriter       : 1.2.8
lxml.etree       : 4.5.0
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.14.0
pandas_datareader: None
bs4              : 4.9.0
bottleneck       : 1.3.2
fastparquet      : 0.3.3
gcsfs            : None
matplotlib       : 3.2.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : 0.17.0
pytables         : None
pyxlsb           : None
s3fs             : 0.4.2
scipy            : 1.4.1
sqlalchemy       : 1.3.16
tables           : 3.6.1
tabulate         : 0.8.7
xarray           : 0.15.1
xlrd             : 1.2.0
xlwt             : 1.3.0
numba            : 0.49.0

</details>
