If you have to use the "apply" variant, the code should be:

df['product_AH'] = df.apply(lambda row: row.Age * row.Height, axis=1)

The parameter to the function applied is the whole row.

But much quicker solution is:

df['product_AH'] = df.Age * df.Height

(1.43 ms, compared to 5.08 ms for the "apply" variant).

This way computation is performed using vectorization, whereas apply refers to each row separately, applies the function to it, then assembles all results and saves them in the target column, which is considerably slower.

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