Add new column based on boolean values in a different column

Question

I'm trying to add a new column to a DataFrame based on the boolean values in another column.

Given a DataFrame like this:

snr = DataFrame({ 'name': ['A', 'B', 'C', 'D', 'E'],  'seniority': [False, False, False, True, False] })

The furthest I've come so far is this:

def refine_seniority(contact):
    contact['refined_seniority'] = 'Senior' if contact['seniority'] else 'Non-Senior'

snr.apply(refine_seniority)

yet I'm getting this error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-208-0694ebf79a50> in <module>()
      2     contact['refined_seniority'] = 'Senior' if contact['seniority'] else 'Non-Senior'
      3 
----> 4 snr.apply(refine_seniority)
      5 
      6 snr

/usr/lib/python2.7/dist-packages/pandas/core/frame.pyc in apply(self, func, axis, broadcast, raw, args, **kwds )
   4414                     return self._apply_raw(f, axis)
   4415                 else:
-> 4416                     return self._apply_standard(f, axis)
   4417             else:
   4418                 return self._apply_broadcast(f, axis)

/usr/lib/python2.7/dist-packages/pandas/core/frame.pyc in _apply_standard(self, func, axis, ignore_failures)
   4489                     # no k defined yet
   4490                     pass
-> 4491                 raise e
   4492 
   4493 

KeyError: ('seniority', u'occurred at index name')

Feels like I'm missing some fundamental understanding on DataFrames, but I'm stuck.

What's the proper way to add a new column based on boolean values in a different column?

Answer 1

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.

Answer 2

snr['refine_seniority']= snr['seniority'].map({True:'senior', False:'Non-senior'})