BUG: pandas.groupby.apply does not work with floating point indexed dataframe since 1.0.0
- [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.

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

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
import pandas as pd  # pandas==1.0.4, but this issue exists since 1.0.0
import numpy as np  # numpy==1.16.3

toy_df = pd.DataFrame(
    {
        'group': np.random.randint(0, 5, 10), 
        'value': np.random.random(10)
    }, 
    index=np.random.random(10),
)

# fails with floating point index
toy_df.groupby('group').apply(sum)
toy_df.groupby('group').apply(lambda x: x['value'].sum())

# with integer index then it works:
toy_df.index = np.random.randint(0, 5, 10)
toy_df.groupby('group').apply(lambda x: x['value'].sum())
toy_df.groupby('group').apply(sum)
```

#### Problem description

[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]

I found this peculiar issue with the new version of pandas. When I have floating point index in a pandas DataFrame, then I cannot perform any `groupby.apply` operation on it. It will always give the below error:

```python
ValueError: index must be monotonic increasing or decreasing
During handling of the above exception, another exception occurred:
...
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Float64HashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Float64HashTable.get_item()
KeyError: 0.0
```

When I used `ipdb` to trace inside the error, I found that the following line caused the problem:
```python
> pandas/core/groupby/ops.py(930)fast_apply()
    929 
--> 930         sdata = self._get_sorted_data()
    931         return libreduction.apply_frame_axis0(sdata, f, names, starts, ends)
```

after this line, `sdata` still does not have a sorted index, and then the next line raises the `ValueError: index must be monotonic increasing or decreasing`.

I tested that this error exists in `pandas==1.0.4` and `pandas==1.0.0` but does not exist in `pandas==0.25.1`.

#### Expected Output

Expected output from groupby apply should be the same in the example with either floating point index or integer index.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.0.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.14.173-137.229.amzn2.x86_64
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.4
numpy            : 1.16.3
pytz             : 2020.1
dateutil         : 2.8.0
pip              : 20.0.2
setuptools       : 47.1.1.post20200604
Cython           : 0.29.2
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.15.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.17.1
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : 0.4.0
scipy            : 1.4.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
