How to keep column names when converting from pandas to numpy

Question

According to this post, I should be able to access the names of columns in an ndarray as a.dtype.names

Howevever, if I convert a pandas DataFrame to an ndarray with df.a

Answer 1

Pandas dataframe also has a handy to_records method. Demo:

X = pd.DataFrame(dict(age=[40., 50., 60.], 
                      sys_blood_pressure=[140.,150.,160.]))
m = X.to_records(index=False)
print repr(m)

Returns:

rec.array([(40.0, 140.0), (50.0, 150.0), (60.0, 160.0)], 
          dtype=[('age', '

This is a "record array", which is an ndarray subclass that allows field access using attributes, e.g. m.age in addition to m['age'].

You can pass this to a cython function as a regular float array by constructing a view:

m_float = m.view(float).reshape(m.shape + (-1,))
print repr(m_float)

Which gives:

rec.array([[  40.,  140.],
           [  50.,  150.],
           [  60.,  160.]], 
          dtype=float64)

Note in order for this to work, the original Dataframe must have a float dtype for every column. To make sure use m = X.astype(float, copy=False).to_records(index=False).