Add new column based on boolean values in a different column

You can create a dict and call map:

In [176]:

temp = {True:'senior', False:'Non-senior'}
snr['refined_seniority'] = snr['seniority'].map(temp)
snr
Out[176]:
  name seniority refined_seniority
0    A     False        Non-senior
1    B     False        Non-senior
2    C     False        Non-senior
3    D      True            senior
4    E     False        Non-senior

As user @Jeff has pointed out using map or apply should be a last resort if a vectorised solution can be applied.

Or use numpy where

In [178]:

snr['refined_seniority'] = np.where(snr['seniority'] == True, 'senior', 'Non-senior')
snr
Out[178]:
  name seniority refined_seniority
0    A     False        Non-senior
1    B     False        Non-senior
2    C     False        Non-senior
3    D      True            senior
4    E     False        Non-senior

If you modifed your function to this then it would work:

In [187]:

def refine_seniority(contact):
    if contact == True:
        return 'senior'
    else:
        return 'Non-senior'

snr['refined_seniority'] = snr['seniority'].apply(refine_seniority)
snr
Out[187]:
  name seniority refined_seniority
0    A     False        Non-senior
1    B     False        Non-senior
2    C     False        Non-senior
3    D      True            senior
4    E     False        Non-senior

What you wrote is incorrect, you are calling apply on the df but the column as a label does not exist, see below:

In [193]:

def refine_seniority(contact):
    print(contact)


snr['refined_seniority'] = snr.apply(refine_seniority)

0    A
1    B
2    C
3    D
4    E
Name: name, dtype: object
0    False
1    False
2    False
3     True
4    False
Name: seniority, dtype: object

Here you can see that it outputs 2 pandas series, there is no key value for 'seniority' hence the error.