Populate a new column in a pandas data frame with a binary value if another columns value is in a list or set

Question

I have a pandas dataframe, and I want to create a new column with values 'in list' or 'not in list', based on whether an entry in the first column is in a list. To illustrate I have a toy example below. I have a solution which works, however it seems very cumbersome and not very pythonic. I do also get a SettingWithCopyWarning. Is there a better or more recommended way to achieve this in python?

#creating a toy dataframe with one column
df = pd.DataFrame({'col_1': [1,2,3,4,6]})

#the list we want to check if any value in col_1 is in 
list_ = [2,3,3,3]

#creating a new empty column
df['col_2'] = None

    col_1   col_2
0   1   None
1   2   None
2   3   None
3   4   None
4   6   None

My solution is to loop through the first column and populate the second

for index, i in enumerate(df['col_1']):
    if i in list_:
        df['col_2'].iloc[index] = 'in list'
    else:
        df['col_2'].iloc[index] = 'not in list'

    col_1   col_2
0   1   not in list
1   2   in list
2   3   in list
3   4   not in list
4   6   not in list

Which produces the correct result, but I would like to learn a more pythonic way of achieving this.

Mayank Porwal · Accepted Answer · 2020-12-16 18:45:25Z

1

Use Series.isin with Series.map:

In [1197]: df['col_2'] = df.col_1.isin(list_).map({False: 'not in list', True: 'in list'})

In [1198]: df
Out[1198]: 
   col_1        col_2
0      1  not in list
1      2      in list
2      3      in list
3      4  not in list
4      6  not in list

answered Dec 16, 2020 at 18:45

Mayank Porwal

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