Given a sample dataframe df as:

   a  b
0  1  2
1  2  3
2  3  4
3  4  5

what you want is:

df['a'] = df['a'].apply(lambda x: x + 1)

that returns:

   a  b
0  2  2
1  3  3
2  4  4
3  5  5

You need mask:

sample['PR'] = sample['PR'].mask(sample['PR'] < 90, np.nan)

Another solution with loc and boolean indexing:

sample.loc[sample['PR'] < 90, 'PR'] = np.nan

Sample:

import pandas as pd
import numpy as np

sample = pd.DataFrame({'PR':[10,100,40] })
print (sample)
    PR
0   10
1  100
2   40

sample['PR'] = sample['PR'].mask(sample['PR'] < 90, np.nan)
print (sample)
      PR
0    NaN
1  100.0
2    NaN

sample.loc[sample['PR'] < 90, 'PR'] = np.nan
print (sample)
      PR
0    NaN
1  100.0
2    NaN

EDIT:

Solution with apply:

sample['PR'] = sample['PR'].apply(lambda x: np.nan if x < 90 else x)

Timings len(df)=300k:

sample = pd.concat([sample]*100000).reset_index(drop=True)

In [853]: %timeit sample['PR'].apply(lambda x: np.nan if x < 90 else x)
10 loops, best of 3: 102 ms per loop

In [854]: %timeit sample['PR'].mask(sample['PR'] < 90, np.nan)
The slowest run took 4.28 times longer than the fastest. This could mean that an intermediate result is being cached.
100 loops, best of 3: 3.71 ms per loop

You need to call apply on the dataframe, with axis=1, instead of on the B column:

>>> df.apply(lambda x: x['B']+3 if x['B']<x['C'] else x['B'], axis=1)
0     8
1     8
2    10
dtype: int64

But, a much more efficient (faster) way would be to do this:

>>> df['B'] + df['B'].lt(df['C']) * 3
0     8
1     8
2    10
dtype: int64