BUG: Fix nanvar for large float16 arrays
Summary of bug:

Above a certain length, calling `.std()` and `.var()` on a Series or DataFrame fails for float types. The is easily shown with float16 in the following example:

```python
>>> import pandas as pd
>>> import numpy as np

>>> zeros = 32760
>>> ones = 32759
>>> pd.Series([0] * zeros + [1] * ones).astype("float16").var()
0.25

>>> zeros = 32760
>>> ones = 32760
>>> pd.Series([0] * zeros + [1] * ones).astype("float16").var()
0.0
```

What is happening here is that both `count` and `d` overflow to `inf` as 65519 is the largest integer float16 can represent. Example:

```python
>>> import numpy as np
>>> np.float16(65519)
65500.0
>>> np.float16(65520)
inf
```

This makes the variance go to 0.0. Since the `avg` calculation on line 927 of this file casts to float64 anyway, it seems like there is no downside to not casting `count` and `d`. It will also improve accuracy in cases where the overflow does not happen.






INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.8.2.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 20.3.0
Version          : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_GB.UTF-8
LOCALE           : en_GB.UTF-8

pandas           : 1.2.3
numpy            : 1.20.1

- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry

Seems like the unit test failures are related to matplotlib not this change so I do not think this logic is tested.

If someone agrees this is a useful fix, I am happy to make an issue, add tests and make a whatsnew entry. > we have almost 0 float16 support, what exactly are you trying to fix?

@jreback I do agree with you float16 is very niche, but since this isn't a huge change and is something that is support in numpy (see below) I think it's worth changing.

The problem is that once you have a DataFrame/Series longer than 65519 entries, it will count those entries and cast it to a float16. This will fail as it can't be represented so will be `inf` and since you are dividing by this, it will cause the standard deviation and variance to be 0.0, which is incorrect and a bug. Note it does not occur using numpy and it definitely is not intended/expected for users:

```python
>>> import numpy as np
>>> zeros = 32760
>>> ones = 32760
>>> np.array([0] * zeros + [1] * ones).astype("float16").var()
0.2498
```

It is also not an issue with `astype` in Pandas, I've isolated it to this line of code. To clarify the bug in one line: 

Any Series or DataFrame with type float16 that has more than 65519 entries will unexpectedly return 0.0 variance and standard deviation, no matter their values and not in accordance with numpy. This pull request is stale because it has been open for thirty days with no activity. Please update or respond to this comment if you're still interested in working on this. closing this as stale ping if you would like to address and can reopen