When passing DataFrame, dmatrices returns design matrix with zero rows using standardize in formula

When passing a pandas.DataFrame, dmatrices is returning a design matrix with no rows in at least two cases I could find.

Here's some minimal examples.

Case 1: All column values are the same

import pandas as pd
from patsy import dmatrices

df = pd.DataFrame({'a': [1, 1, 1], 'b': [0, 1, 0]})
formula = 'b ~ standardize(a)'
dmatrices(formula, data=df)

give

DesignMatrix with shape (0, 2)
  Intercept  standardize(a)
  Terms:
    'Intercept' (column 0)
    'standardize(a)' (column 1)

Case 2. Column values are different but contain np.nan

import pandas as pd
import numpy as np
from patsy import dmatrices

df = pd.DataFrame({'a': [2, 3, np.nan], 'b': [0, 1, 0]})
formula = 'b ~ standardize(a)'
dmatrices(formula, data=df)

gives the same

DesignMatrix with shape (0, 2)
  Intercept  standardize(a)
  Terms:
    'Intercept' (column 0)
    'standardize(a)' (column 1)

patsy version is the latest on conda, 0.5.1

Reproduce both cases from the issue using pandas, numpy, and patsy.dmatrices with standardize(a). Start at the dmatrices and standardize entry points and trace how constant values and NaN values affect row handling. Done means the examples no longer produce a design matrix with zero rows, with behavior covered for both cases.