Slow performance with plotly chart builder

Hello all,
I need to use plotly as the backend of a microservice who generates charts dynamically.
Unfortunately, after a little benchmarking, I found that the plotly.express framework is very slow (around 5 secs to generate a chart from 500 lines dataset).

Here is the script I use to generate a scatter matrix:

import sys
import os
import traceback
import json
import time
sys.path.append('c:\\statwolf\\python\packages\Lib\site-packages')
input = json.loads('{\"file\":\"/tmp/data.tsv",\"color\":\"club_country\",\"dimensions\":[\"nolo\",\"tolo\",\"yolo\"]}')
def action():
    def run():
        import plotly.express as px
        from pandas import read_csv
    
        color = None if input['color'] == "" else input['color']
    
        first = time.time()
        d = read_csv(input['file'], sep='\t')
        second = time.time()
        fig = px.scatter_matrix(d, dimensions=input['dimensions'], color=color)
        third = time.time()
        j = fig.to_json()
        fourth = time.time()
    
        print('read: ' + str(second - first))
        print('plot: ' + str(third - second))
        print('json: ' + str(fourth - third))
        return j
    
    import time
    
    for i in range(0, 3):
        start = time.time()
        result = run()
        end = time.time()
    
        print('iteration: ' + str(i) + '\ntime: ' + str(end - start))
    
    return result
    
result = None
try:
    result = { 'outcome': action() }
except Exception as e:
    traceback.print_exc()
    result = { 'error': str(e) }
resultDir = os.path.dirname(os.path.realpath(__file__))
resultFile = open(resultDir + '/result.json', 'w')
json.dump(result, resultFile)
resultFile.close()

from the dataset:
https://www.dropbox.com/s/cm9i3pfv10exbba/data.tsv?dl=1

and this is the report with timing:
https://www.dropbox.com/s/l2x3jqzea4i4xqw/report.txt?dl=1

Now:

1. Is there any tweak I can implement to improve performances?
2. Do you plan to focus on speed for the following releases?

Beginnen Sie damit, das bereitgestellte Benchmark-Skript erneut auszuführen und den verlinkten Timing-Bericht zu prüfen, wobei Sie die Laufzeiten von read_csv, plotly.express.scatter_matrix und fig.to_json getrennt betrachten. Verfolgen Sie den Einstiegspunkt von scatter-matrix sowie vorhandene Performance-Tests oder Benchmarks. Für den Abschluss müsste ein reproduzierbarer Engpass identifiziert und eine messbare Verbesserung vereinbart werden; das Issue definiert jedoch kein Abnahmekriterium.