Faster implementation of pandas apply function

Question

I have a pandas dataFrame in which I would like to check if one column is contained in another.

Suppose:

df = DataFrame({'A': ['some text here', 'another text', 'and this'], 
                'B': ['some', 'somethin', 'this']})

I would like to check if df.B[0] is in df.A[0], df.B[1] is in df.A[1] etc.

Current approach

I have the following apply function implementation

df.apply(lambda x: x[1] in x[0], axis=1)

result is a Series of [True, False, True]

which is fine, but for my dataFrame shape (it is in the millions) it takes quite long.
Is there a better (i.e. faster) implamentation?

Unsuccesfull approach

I tried the pandas.Series.str.contains approach, but it can only take a string for the pattern.

df['A'].str.contains(df['B'], regex=False)

Answer 1

Use np.vectorize - bypasses the apply overhead, so should be a bit faster.

v = np.vectorize(lambda x, y: y in x)

v(df.A, df.B)
array([ True, False,  True], dtype=bool)

---

Here's a timings comparison -

df = pd.concat([df] * 10000)

%timeit df.apply(lambda x: x[1] in x[0], axis=1)
1 loop, best of 3: 1.32 s per loop

%timeit v(df.A, df.B)
100 loops, best of 3: 5.55 ms per loop

# Psidom's answer
%timeit [b in a for a, b in zip(df.A, df.B)]
100 loops, best of 3: 3.34 ms per loop

Both are pretty competitive options!

Edit, adding timings for Wen's and Max's answers -

# Wen's answer
%timeit df.A.replace(dict(zip(df.B.tolist(),[np.nan]*len(df))),regex=True).isnull()
10 loops, best of 3: 49.1 ms per loop

# MaxU's answer
%timeit df['A'].str.split(expand=True).eq(df['B'], axis=0).any(1)
10 loops, best of 3: 87.8 ms per loop