Use contains for boolean mask and then numpy.where:

m = df['a'].str.contains('foo') & (df['b'] == 'bar')
print (m)
0     True
1    False
2    False
dtype: bool

df['new'] = np.where(m, 'yes', 'no')
print (df)
        a       b    c  new
0     foo     bar  baz  yes
1     bar     foo  baz   no
2  foobar  barfoo  baz   no

Or if need alo check column b for substrings:

m = df['a'].str.contains('foo') & df['b'].str.contains('bar')
df['new'] = np.where(m, 'yes', 'no')
print (df)
        a       b    c  new
0     foo     bar  baz  yes
1     bar     foo  baz   no
2  foobar  barfoo  baz  yes

If need custom function, what should be slowier in bigger DataFrame:

def somefunction (row):
    if 'foo' in row['a'] and row['b'] == 'bar':
        return 'yes'
    return 'no'

print (df.apply(somefunction, axis=1))
0    yes
1     no
2     no
dtype: object

def somefunction (row):
    if 'foo' in row['a']  and  'bar' in row['b']:
        return 'yes'
    return 'no'

print (df.apply(somefunction, axis=1))
0    yes
1     no
2    yes
dtype: object

Timings:

df = pd.concat([df]*1000).reset_index(drop=True)

def somefunction (row):
    if 'foo' in row['a'] and row['b'] == 'bar':
        return 'yes'
    return 'no'

In [269]: %timeit df['new'] = df.apply(somefunction, axis=1)
10 loops, best of 3: 60.7 ms per loop

In [270]: %timeit df['new1'] = np.where(df['a'].str.contains('foo') & (df['b'] == 'bar'), 'yes', 'no')
100 loops, best of 3: 3.25 ms per loop

df = pd.concat([df]*10000).reset_index(drop=True)

def somefunction (row):
    if 'foo' in row['a'] and row['b'] == 'bar':
        return 'yes'
    return 'no'

In [272]: %timeit df['new'] = df.apply(somefunction, axis=1)
1 loop, best of 3: 614 ms per loop

In [273]: %timeit df['new1'] = np.where(df['a'].str.contains('foo') & (df['b'] == 'bar'), 'yes', 'no')
10 loops, best of 3: 23.5 ms per loop

• startswith and in, return a Boolean.
• The in operator is a test of membership.
• This can be performed with a list-comprehension or filter.
• Using a list-comprehension, with in, is the fastest implementation tested.
• If case is not an issue, consider mapping all the words to lowercase.
  • l = list(map(str.lower, l)).
• Tested with python 3.11.0

filter:

• Using filter creates a filter object, so list() is used to show all the matching values in a list.

l = ['ones', 'twos', 'threes']
wanted = 'three'

# using startswith
result = list(filter(lambda x: x.startswith(wanted), l))

# using in
result = list(filter(lambda x: wanted in x, l))

print(result)
[out]:
['threes']

list-comprehension

l = ['ones', 'twos', 'threes']
wanted = 'three'

# using startswith
result = [v for v in l if v.startswith(wanted)]

# using in
result = [v for v in l if wanted in v]

print(result)
[out]:
['threes']

Which implementation is faster?

• Tested in Jupyter Lab using the words corpus from nltk v3.7, which has 236736 words
• Words with 'three'
  • ['three', 'threefold', 'threefolded', 'threefoldedness', 'threefoldly', 'threefoldness', 'threeling', 'threeness', 'threepence', 'threepenny', 'threepennyworth', 'threescore', 'threesome']

from nltk.corpus import words

%timeit list(filter(lambda x: x.startswith(wanted), words.words()))
%timeit list(filter(lambda x: wanted in x, words.words()))
%timeit [v for v in words.words() if v.startswith(wanted)]
%timeit [v for v in words.words() if wanted in v]

%timeit results

62.8 ms ± 816 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
53.8 ms ± 982 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
56.9 ms ± 1.33 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
47.5 ms ± 1.04 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)