Tukukkk

I have a pandas dataframe with two columns that are filled with pandas sets. I want to check that all values in one column are a subset of the other column. I thought the code below would work but it seems you cannot apply .issubset() to two series with sets.

Ex:

data = [[['one','orange','green'],['one','orange']],[['milk','honey'],['Clarke', 'honey']]]



df = pd.DataFrame(data, columns=['Column_1','Column_2'])



Are_all_column_2_values_valid = df.loc[:, 'Column_2'].apply(set).issubset(df.loc[:, 'Column_1'])



desired_output = pd.series([True,False])

All values in both sets will be strings.

Any help would greatly be appreciated!

First ensure you actually have series of sets:

df = df.apply(lambda x: x.apply(set))

Then use the syntactic sugar <= for set.issubset:

print(df['Column_2'] <= df['Column_1'])



0     True

1    False

dtype: bool

You can use a list comprehension like this:

>>> [set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1)]

[True, False]

Or as a Series:

>>> pd.Series(set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1))

0     True

1    False

dtype: bool