BUG:pandas use all() or any() get wrong result
- [ ] I have checked that this issue has not already been reported.
yes
- [ ] I have confirmed this bug exists on the latest version of pandas.
yes
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
yes
---

**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
print(data['shift1'].values.all() > 0,data['shift1'].values)
print(data['shift1'].values.any() > 0,data['shift1'].values[0],data['shift1'].values[1],-3680.0>0)
```

#### Problem description
True [-3680. -3680.]
True -3680.0 -3680.0 False
The value of series is clearly less than 0, but using all () and any () gives the wrong result

#### Expected Output
True [-3680. -3680.]
True -3680.0 -3680.0 False

#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.7.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 1.0.3
numpy            : 1.18.4
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 19.3.1
setuptools       : 39.1.0
<details>

[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]

</details>
