My table looks like this(df):

"category" is str, and product_in_cat is list type. I have a list:product=[A,B,G] I want to get a final [dict(str:list)] looks like:

[{cat1:[A,B]},{cat2:[G]}]

I think I can use below code:

list1=[]
for inde,row in df.iterrows():
        list1.append.({row['category']:row['product_in_cat'] in product})
        

I know this part is not correct,row['product_in_cat'] in product but I am not sure how to filter out the list column base on the given "product" list. Please help, and thank you in advance!

CodePudding user response：

You can use np.intersect1d to find the common part of two lists:

import numpy as np


df_ = df['product_in_cat'].apply(lambda x: np.intersect1d(x, product).tolist())
l = [{k: v} for k, v in zip(df['category'], df_)]

print(l)

[{'cat1': ['A', 'B']}, {'cat2': ['G']}]

CodePudding user response：

You can use convert each list in the column to a set and use intersection with the external product list:

import pandas as pd

lst = ['A','B','G']
data = {'category':['cat 1','cat 2'],
        'product_in_cat': [ ['A','B','C'] ,['E','F','G']]} 
df = pd.DataFrame(data)
 
dict(zip(df['category'],df['product_in_cat'].apply(lambda x: set(x).intersection(lst))))

#output
{'cat 1': {'A', 'B'}, 'cat 2': {'G'}}