select column in pandas dataframe whose nested list value matches a given value

Question

I have a data frame like this:

>>> MCQ_DATA['q45']
10    [13, 14]
11    [13, 14]
12    [13, 12]
13    [13, 12]
14     [9, 12]
15      [2, 6]
16         [2]
17        [16]
18    [13, 12]
19    [13, 11]

So all values for column 'q45' are lists. I want to create a boolean filter for rows that contain 13, like:

>>> [MCQ_DATA['q45'] == 13]
10    True
11    True
12    True
13    True
14    False
15    False
16    False
17    False
18    True
19    True

Already tried these:

• [MCQ_DATA['q45'] == 13] returns false for everything.

• [MCQ_DATA['q45'].isin([13])] returns TypeError. Also, this looks for values against a list. I want to check a nested list in a dataframe for a value. (from Use a list of values to select rows from a pandas dataframe)

• df.loc[df['q45'] == 13] returns an empty dataframe, because none of the values are 13. (From Select rows from a DataFrame based on values in a column in pandas)

• df['q45'].apply(lambda sublist: 13 in sublist)finally, this worked. But the source says this is not an efficient way to do it: Operating on tuples held within Pandas DataFrame column)

I've after looking on SO for the prerequisite half-hour, if the last way is not the right way, what IS the right way?

Further testing found this also works. I would say it is the best approach. Uses pandas framework and easily readable:

• df['q45'].dropna().apply({13}.issubset) -- I'm guessing this is faster if I do that on a large scale, but maybe someone knows. (I needed the .dropna() because nan gives an error.)