You can use the sample method*:

In [11]: df = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8]], columns=["A", "B"])

In [12]: df.sample(2)
Out[12]:
   A  B
0  1  2
2  5  6

In [13]: df.sample(2)
Out[13]:
   A  B
3  7  8
0  1  2

*On one of the section DataFrames.

Note: If you have a larger sample size that the size of the DataFrame this will raise an error unless you sample with replacement.

In [14]: df.sample(5)
ValueError: Cannot take a larger sample than population when 'replace=False'

In [15]: df.sample(5, replace=True)
Out[15]:
   A  B
0  1  2
1  3  4
2  5  6
3  7  8
1  3  4

I think what you want is a little bit more complex than what DataFrame.sample provides out of the box. A random sample satisfying each of your conditions could be generated (respectively) like this:

1. Filter for women only, and randomly sample n/2, then do the same for men, and then pool them
2. Filter for under 40s, randomly sample n/2, then do the same for over-40s and then combine them. (Though note that this does not guarantee a median of exactly 40.)

If you want to combine the two constraints, you might need to sample 4 times - women under 40, men under 40, etc. But this is the general idea.

Code for sampling would look like:

df.loc[df.age < 40, 'subject_id'].sample(n/2)
df.loc[df.gender == 'F', 'subject_id'].sample(n/2)