This could actually be done quickly and intuitively using linear algebra.

So consider your player as label binarized array (can be done with MultiLabelBinarizer) so you would expect an array of size (2060590, 39) containing 0 an 1, rearrange the columns similar as how you order the your player table (or the other-way around which ever is easier), basically such that first column of your new matrix correspond to the same player on the player table. Finally just apply matrix multiplication, and done.

This is an example using generated sample, but hopefully you get the idea of doing this.

import numpy as np
import pandas as pd
from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()

sample_active = pd.Series([[100,50,150,200],
                           [100,50,150],
                           [100,50],
                           [100]])
sample_df = pd.DataFrame()

sample_df['id'] = ['fadfsadsa', 'dsafsadf', 'dfsafsda', 'dasfasdfsaf']
sample_df['active'] = sample_active
## sample_df should look close to your original df

classes = [50,100,150,200]

player_df = pd.DataFrame({cl : np.random.uniform(0,1,size=5) for cl in classes}).T
player_df.columns = ['A','B','C','D','E'] 

sample_transformed = mlb.fit_transform(sample_active.values) ##apply multilabel binarizer

output = sample_transformed.dot(player_df.loc[mlb.classes_]) ##matrix multiply and get your required answer, use loc so the order will be similar as your binarized matrix.


new_df = pd.concat([sample_df['id'], pd.DataFrame(output)], axis = 1)
new_df.columns = ['id'] + list(player_df.columns)

This could actually be done quickly and intuitively using linear algebra.

So consider your player as label binarized array (can be done with MultiLabelBinarizer) so you would expect an array of size (2060590, 39) containing 0 an 1, rearrange the columns similar as how you order the your player table (or the other-way around which ever is easier), basically such that first column of your new matrix correspond to the same player on the player table. Finally just apply matrix multiplication, and done.

This is an example using generated sample, but hopefully you get the idea of doing this.

import numpy as np
import pandas as pd
from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()

sample_active = pd.Series([[100,50,150,200],
                           [100,50,150],
                           [100,50],
                           [100]])
sample_df = pd.DataFrame()

sample_df['id'] = ['fadfsadsa', 'dsafsadf', 'dfsafsda', 'dasfasdfsaf']
sample_df['active'] = sample_active
## sample_df should look close to your original df

classes = [50,100,150,200]

player_df = pd.DataFrame({cl : np.random.uniform(0,1,size=5) for cl in classes}).T
player_df.columns = ['A','B','C','D','E'] 

sample_transformed = mlb.fit_transform(sample_active.values) ##apply multilabel binarizer

output = sample_transformed.dot(player_df.loc[mlb.classes_]) ##matrix multiply and get your required answer, use loc so the order will be similar as your binarized matrix.


new_df = pd.concat([sample_df['id'], pd.DataFrame(output)], axis = 1)
new_df.columns = ['id'] + list(player_df.columns)

For your case I think this should work :

mlb = MultiLabelBinarizer()
active_transformed = mlb.fit_transform(uuid_df['Active'])
output = active_transformed.dot(points_table_df.loc[mlb.classes_])
df = pd.concat([uuid_df[['UUID']], output], axis = 1)
df.columns = ['UUID'] + list(points_table_df.columns)

Try it!

This could actually be done quickly and intuitively using linear algebra.

So consider your player as label binarized array (can be done with LabelBinarizer) so you would expect an array of size (2060590, 39) containing 0 an 1, rearrange the columns similar as how you order the your player table (or the other-way around which ever is easier), basically such that first column of your new matrix correspond to the same player on the player table. Finally just apply matrix multiplication, and done.

This could actually be done quickly and intuitively using linear algebra.

So consider your player as label binarized array (can be done with MultiLabelBinarizer) so you would expect an array of size (2060590, 39) containing 0 an 1, rearrange the columns similar as how you order the your player table (or the other-way around which ever is easier), basically such that first column of your new matrix correspond to the same player on the player table. Finally just apply matrix multiplication, and done.

This is an example using generated sample, but hopefully you get the idea of doing this.

import numpy as np
import pandas as pd
from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()

sample_active = pd.Series([[100,50,150,200],
                           [100,50,150],
                           [100,50],
                           [100]])
sample_df = pd.DataFrame()

sample_df['id'] = ['fadfsadsa', 'dsafsadf', 'dfsafsda', 'dasfasdfsaf']
sample_df['active'] = sample_active
## sample_df should look close to your original df

classes = [50,100,150,200]

player_df = pd.DataFrame({cl : np.random.uniform(0,1,size=5) for cl in classes}).T
player_df.columns = ['A','B','C','D','E'] 

sample_transformed = mlb.fit_transform(sample_active.values) ##apply multilabel binarizer

output = sample_transformed.dot(player_df.loc[mlb.classes_]) ##matrix multiply and get your required answer, use loc so the order will be similar as your binarized matrix.


new_df = pd.concat([sample_df['id'], pd.DataFrame(output)], axis = 1)
new_df.columns = ['id'] + list(player_df.columns)