Changing the value of column based on conditions: Pandas

I have the following information.

Let’s say I have the following list.

my_list = [2,3,4,5]

My dataframe is as follows:

df

Col1      Value
[2,3,6]    Hot
[7]        Mild
[10,11]    Cool
[5,9]      Cool
[2,5,6]    Mild

I would like to check if one of the value from the list my_list exist in the column Col1. If it exist, change the value in Value column to Mild in the corresponding row. But if the Value is Hot keep as it is.

I would like to see something like below.

Col1      Value
[2,3,6]    Hot
[7]        Mild
[10,11]    Cool
[5,9]      Mild
[2,5,6]    Mild

I am just looking for a simple script that can iterate and check in every row and change a value in another column the corresponding row.

I have tried this.

df["Value"] =(d["Value"].apply(lambda x: "Mild" if  len(set(x["Col1"]).intersection(my_list)) >0 
                                            and (x["Value"] != 'Hot') else x["Value"],axis=1) )                           

Can any one help on this?

>Solution :

The approach is almost identical to your previous question, just change one condition:

match = df['Col1'].explode().isin(my_list).groupby(level=0).any()
df['Value'] = df['Value'].mask(match & df['Value'].ne('Hot'), 'Mild')

output:

        Col1 Value
0  [2, 3, 6]   Hot
1        [7]  Mild
2   [10, 11]  Cool
3     [5, 9]  Mild
4  [2, 5, 6]  Mild