[Python] Visualization of ranking models based on time executions

I'm working on reproducing the following plot for my own case:

so, let's say I already have my results ready and in the form of data frames as below:

import pandas as pd

# Data
data1 = {
'Models': ['LinearRegression', 'Random Forest', 'XGBoost', 'MLPRegressor', 'SVR', 'CatBoostRegressor', 'LGBMRegressor'],
'MAE': [4.906, 2.739, 2.826, 5.234, 5.061, 2.454, 2.76]
'Runtime [ms]': [0:00:43.387145, 0:28:11.761681, 0:03:58.883474, 0:01:44.252276 , 0:04:52.754769, 0:19:36.925169, 0:04:51.223103]
}
data2 = {
'Models': ['LinearRegression', 'Random Forest', 'XGBoost', 'MLPRegressor', 'SVR', 'CatBoostRegressor', 'LGBMRegressor'],
'MAE': [4.575, 2.345, 2.129, 4.414, 4.353, 2.281, 2.511]
'Runtime [ms]': [0:00:45.055854, 0:10:55.468473, 0:01:01.575033 , 0:00:31.231719 , 0:02:12.258870, 0:08:16.526615 , 0:15:25.084937]
}
data3 = {
'Models': ['LinearRegression', 'Random Forest', 'XGBoost', 'MLPRegressor', 'SVR', 'CatBoostRegressor', 'LGBMRegressor'],
'MAE': [4.575, 2.345, 2.129, 4.414, 4.353, 2.281, 2.511]
'Runtime [ms]': [0:00:40.055854, 0:11:55.468473, 0:01:03.575033 , 0:00:29.231719 , 0:02:02.258870, 0:07:16.526615 , 0:13:25.084937]
}


df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
df3 = pd.DataFrame(data3)


So now I can calculate the ranking by computing absolute\relative frequencies in the final ranked table in the expected output.

and get something like this to rank top models, but I will lose Runtime [ms]:

Top models(sorted) count freq(%)
LinearRegression,MLPRegressor,SVR,LGBMRegresso... 2 66.666667
MLPRegressor,SVR,LinearRegression,XGBoost,LGBM... 1 33.333333


How can I get close to this plot?

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