BUG: read_excel blows the memory when using openpyxl engine
---

**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
# Your code here
Python 3.9.1 | packaged by conda-forge | (default, Dec  9 2020, 01:07:47) 
[Clang 11.0.0 ] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.__version__
'1.2.0'
>>> df=pd.read_excel('full_data.xlsx')

```

#### Problem description

I am not quite sure how to describe the bug, the code just got stuck when I run `pd.read_excel('full_data.xlsx')`. I found this line cost a significant amount of memories (almost `14G` but the `.xlsx` file is just `9MB`).

I speculate it is result from `read_excel` now leverage `openpyxl` as default engine in `python3.9`. Loading this file in `python3.8` works fine.

```
>>> from openpyxl import load_workbook
>>> wb=load_workbook('full_data.xlsx')
>>> df=pd.DataFrame(wb['Sheet1'].values)
```

The above codes also leads to the same issue.
