BUG: pandas styling doesn't display for all rows in large dataframes
- [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.

# Update 2

- The issue seems to be Google Chrome and Microsoft Edge
- Jupyter Lab in Firefox correctly displays all of the styled rows and correctly renders an output HTML file.
- The question is then, what are the relevant settings in Chrome and Edge, or why doesn't this work in Chrome or Edge?

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

```python
import pandas as pd
import numpy as np
from faker import Faker  # conda install -c conda-forge faker or pip install Faker

# for fake names
fake = Faker()

# test data
np.random.seed(365)
rows = 11000

# change 36 or 158 to test where the rows stop appearing
vals = {f'val{i}': np.random.randint(1, 11, size=(rows)) for i in range(1, 36)}
data = {'name': np.random.choice([fake.unique.name() for i in range(158)], size=rows),
        'cat': np.random.randint(1, 4, size=(rows))}
data.update(vals)

df = pd.DataFrame(data)

# used to create the mask for the background color
mean = df.groupby('cat').mean().round(2)

# calculate the mean for each name and cat
cat_mean = df.groupby(['name', 'cat']).mean()


def color(x):
    """Function to apply background color"""
    c1 = 'background-color: green'
    c = '' 
    # compare columns
    mask1 = x.gt(mean)
    # DataFrame with same index and columns names as original filled empty strings
    df1 =  pd.DataFrame(c, index=x.index, columns=x.columns)
    # modify values of df1 column by boolean mask
    df1.iloc[mask1] = c1
    display(df1)

    return df1

# displays the notebook in Jupyter
cat_mean.style.apply(color, axis=None)

# In PyCharm saving rendered styler to file
cm = cat_mean.style.apply(color, axis=None).set_precision(3).render()

with open('cm_test.html', 'w') as f:
    f.write(cm)
```

#### Problem description

- Given a large dataframe, in this case `474 rows x 35 columns`, the applied styling does not correctly display for all rows in Jupyter or if saving the file to HTML.
  - [![enter image description here][1]][1]
- If the number of rows or columns increases beyond this size, then more rows aren't displayed properly
- We can see from the styling map, that the rows are correctly mapped with a background color, but it isn't displayed.
  - [![enter image description here][2]][2]
- If the number of rows or columns is reduced, then all of the rows display the correct styling.
- `jupyterlab v3.0.11` and `pandas v1.2.3`
- In `PyCharm 2021.1 (Professional Edition) Build #PY-211.6693.115, built on April 6, 2021` saving the redendered styler to a file has the same result, so this isn't just an issue with Jupyter.
- This issue is reproducible on two different systems that I have tried.
- If the shape is reduced to `471 rows × 35 columns` or `474 rows × 34 columns`, then all rows correctly display the highlighting.
- Associated Stack Overflow [question][3].

#### Workaround
##### Split the DataFrame
- It's dissatisfying, but splitting the DataFrame, and applying the style will work, since if reduces the overall size.

```python
cat_mean.iloc[:237, :].style.apply(color, axis=None)
cat_mean.iloc[237:, :].style.apply(color, axis=None)
```

##### Save to Excel
- All rows are displayed correctly with the highlight color when saving to Excel

```python
test = cat_mean.style.apply(color, axis=None)
test.to_excel('test.xlsx', engine='openpyxl')
```

#### Expected Output

- All rows should display highlighting.

#### Output1 of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.8.8.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United States.1252

pandas           : 1.2.3
numpy            : 1.19.2
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 52.0.0.post20210125
Cython           : 0.29.22
pytest           : 6.2.3
hypothesis       : None
sphinx           : 3.5.3
blosc            : None
feather          : None
xlsxwriter       : 1.3.8
lxml.etree       : 4.6.3
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.22.0
pandas_datareader: 0.9.0
bs4              : 4.9.3
bottleneck       : 1.3.2
fsspec           : 0.9.0
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.4
numexpr          : 2.7.3
odfpy            : None
openpyxl         : 3.0.7
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.2
sqlalchemy       : 1.4.5
tables           : 3.6.1
tabulate         : 0.8.9
xarray           : None
xlrd             : 2.0.1
xlwt             : 1.3.0
numba            : 0.53.1

</details>

# Update 1

- The following options produced the same results as the original attempts
  - Standard python console creating HTML file
  - PyCharm creating HTML file
  - Jupyter Lab displaying styled dataframe in a cell

#### Output2 of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : 2cb96529396d93b46abab7bbc73a208e708c642e
python           : 3.9.2.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United States.1252

pandas           : 1.2.4
numpy            : 1.20.0
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 52.0.0.post20210125
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.22.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>


  [1]: https://i.stack.imgur.com/iaq8J.png
  [2]: https://i.stack.imgur.com/3D0gy.png
  [3]: https://stackoverflow.com/q/67065785/7758804