def _compute_busday_offsets(announcement_dates):
"""
Compute expected business day offsets from a DataFrame of announcement
dates.
"""
# Column-vector of dates on which factor `compute` will be called.
raw_call_dates = announcement_dates.index.values.astype(
'datetime64[D]')[:, None]
# 2D array of dates containining expected nexg announcement.
raw_announce_dates = (
announcement_dates.values.astype('datetime64[D]'))
# Set NaTs to 0 temporarily because busday_count doesn't support NaT.
# We fill these entries with NaNs later.
whereNaT = raw_announce_dates == NaTD
raw_announce_dates[whereNaT] = make_datetime64D(0)
# The abs call here makes it so that we can use this function to
# compute offsets for both next and previous earnings (previous
# earnings offsets come back negative).
expected = abs(
np.busday_count(raw_call_dates, raw_announce_dates).astype(float))
expected[whereNaT] = np.nan
return pd.DataFrame(
data=expected,
columns=announcement_dates.columns,
index=announcement_dates.index,
)
def _compute_busday_offsets(announcement_dates):
"""
Compute expected business day offsets from a DataFrame of announcement
dates.
"""
# Column-vector of dates on which factor `compute` will be called.
raw_call_dates = announcement_dates.index.values.astype(
'datetime64[D]'
)[:, None]
# 2D array of dates containining expected nexg announcement.
raw_announce_dates = (
announcement_dates.values.astype('datetime64[D]')
)
# Set NaTs to 0 temporarily because busday_count doesn't support NaT.
# We fill these entries with NaNs later.
whereNaT = raw_announce_dates == NaTD
raw_announce_dates[whereNaT] = make_datetime64D(0)
# The abs call here makes it so that we can use this function to
# compute offsets for both next and previous earnings (previous
# earnings offsets come back negative).
expected = abs(np.busday_count(
raw_call_dates,
raw_announce_dates
).astype(float))
expected[whereNaT] = np.nan
return pd.DataFrame(
data=expected,
columns=announcement_dates.columns,
index=announcement_dates.index,
)