Optimize table updates

**Is your feature request related to a problem? Please describe.**
Updating a table with more than 64 columns and 1000 rows of data is time consuming. Currently, it takes about 20-25 seconds. I improved it a bit by converting pandas dataframe to numpy array. If it is possible to optimize the 2D array data in a parallel way, there is a hope that the loading speed can be reduced to one tenth of the level.

**Describe the solution you'd like**
It would be nice to be able to separate the data update from the way each cell is generated. I've used commercial components in my work, and I think that approach is good.

```
data = [from optimized io routine]
table.data_source = data
```

Or, I'd like to see examples using parallel libraries such as multiprocessing, joblib, dask, ray, etc. I've tried a few things, and here's what happened: I tried the following in a function that I pass as an argument to joblib's parallel(), but only the row is generated and nothing is displayed inside the actual cell.

```
with table_row():
[list comprehension]
```

**Describe alternatives you've considered**
None

**Additional context**
None