Orange canvas is a good, high-performance data mining app.

installing it using pip:
python -m pip install PyQt5 PyQtWebEngine orange3
( need latest python 3, like 3.7+ )
( April 2020, python3.8 easy fix: if there is an error with pyqtgraph\ , need to replace in this file .clock() by .perf_counter()
running it:
python -m Orange.canvas

Orange is very easy to use and reasonably responsive.

But it is a bit limited with data preprocessing,
but Here I present a workaround:

from import table_from_frame,table_to_frame
df= table_to_frame(in_data)
#here you go
out_data = table_from_frame(df)

Now I can take a function of temporizing data series, like the following and convert it to Orange with ease

def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataY.append(dataset[i + look_back, 0])
return numpy.array(dataX), numpy.array(dataY)

The result is, like this:

from import table_from_frame,table_to_frame
import numpy as np
import pandas as pd

df= table_to_frame(in_data)

for i in range(len(dataset)-look_back):
samples_titles=[ "p"+str(i+1) for i in range(look_back)] 
df = pd.DataFrame(data=numpy_samples, columns=samples_titles)
out_data = table_from_frame(df)