`r`

is a numpy (rec)array. So `r["dt"] >= startdate`

is also a (boolean) array. For numpy arrays the `&`

operation returns the elementwise-and of the two boolean arrays.

The NumPy developers felt there was no one commonly understood way to evaluate an array in boolean context: it could mean `True`

if *any* element is `True`

, or it could mean `True`

if *all* elements are `True`

, or `True`

if the array has non-zero length, just to name three possibilities.

Since different users might have different needs and different assumptions, the NumPy developers refused to guess and instead decided to raise a ValueError whenever one tries to evaluate an array in boolean context. Applying `and`

to two numpy arrays causes the two arrays to be evaluated in boolean context (by calling `__bool__`

in Python3 or `__nonzero__`

in Python2).

Your original code

mask = ((r["dt"] >= startdate) & (r["dt"] <= enddate)) selected = r[mask]

looks correct. However, if you do want `and`

, then instead of `a and b`

use `(a-b).any()`

or `(a-b).all()`

.