Numpy vectorization messes up data type (2)

Question

I\'m having unwanted behaviour come out of np.vectorize, namely, it changes the datatype of the argument going into the original function. My original question

Answer 1

I think @rpanai answer on the original post is still the best. Here I share my tests:

def qualifies(dt, excluded_months = []):
    if dt.day < 5:
        return False
    if (dt + pd.tseries.offsets.MonthBegin(1) - dt).days < 5:
        return False
    if dt.month in excluded_months:
        return False
    return True

def new_qualifies(dt, excluded_months = []):
    dt = pd.Timestamp(dt)
    if dt.day < 5:
        return False
    if (dt + pd.tseries.offsets.MonthBegin(1) - dt).days < 5:
        return False
    if dt.month in excluded_months:
        return False
    return True

df = pd.DataFrame({'date': pd.date_range('2020-01-01', freq='7D', periods=12000)})

apply method:

%%timeit
df['qualifies1'] = df['date'].apply(lambda x: qualifies(x, [3, 8]))

385 ms ± 21.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

---

conversion method:

%%timeit
df['qualifies1'] = df['date'].apply(lambda x: new_qualifies(x, [3, 8]))

389 ms ± 12.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

---

vectorized code:

%%timeit
df['qualifies2'] =  np.logical_not((df['date'].dt.day<5).values | \
    ((df['date']+pd.tseries.offsets.MonthBegin(1)-df['date']).dt.days < 5).values |\
    (df['date'].dt.month.isin([3, 8])).values)

4.83 ms ± 117 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)