Pandera - validation based on multiple columns

I have created a Pandera validation schema for a Pandas dataframe with ~150 columns, like the first two rows in the schema below. The single column validation is working, but how can I combine two or more columns for validation? I found two related questions here and here, but I still don't manage to build a valid schema. Row nr 3 (0.0/1.0) is not valid:

   preg  nr_preg
   1.0      2.0
   0.0      NaN
   0.0      1.0
   NaN      NaN


import pandas as pd
import numpy as np
import pandera as pa

df = pd.DataFrame({'preg': [1, 0, 0, np.nan], 'nr_preg': [2, np.nan, 1, np.nan]})

schema = pa.DataFrameSchema({
    'preg': pa.Column(float, pa.Check.isin([1, 0]), nullable=True),
    'nr_preg': pa.Column(float, pa.Check.in_range(1, 10), nullable=True),
    # ...
    # not working:
    # if preg=0 -> nr_preg must be NaN
    'preg': pa.Column(float, pa.Check(lambda s: s['preg'] == 0 & s['nr_preg'].isnull() == False), nullable=True)
})