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⚡️ Speed up function _quarterly_finder by 10% #96

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⚡️ Speed up function _quarterly_finder by 10% #96

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95 changes: 52 additions & 43 deletions pandas/plotting/_matplotlib/converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,7 +88,7 @@ def wrapper(*args, **kwargs):
with pandas_converters():
return func(*args, **kwargs)

return cast(F, wrapper)
return cast("F", wrapper)


@contextlib.contextmanager
Expand Down Expand Up @@ -466,21 +466,20 @@ def _get_default_annual_spacing(nyears) -> tuple[int, int]:
Returns a default spacing between consecutive ticks for annual data.
"""
if nyears < 11:
(min_spacing, maj_spacing) = (1, 1)
return (1, 1)
elif nyears < 20:
(min_spacing, maj_spacing) = (1, 2)
return (1, 2)
elif nyears < 50:
(min_spacing, maj_spacing) = (1, 5)
return (1, 5)
elif nyears < 100:
(min_spacing, maj_spacing) = (5, 10)
return (5, 10)
elif nyears < 200:
(min_spacing, maj_spacing) = (5, 25)
return (5, 25)
elif nyears < 600:
(min_spacing, maj_spacing) = (10, 50)
return (10, 50)
else:
factor = nyears // 1000 + 1
(min_spacing, maj_spacing) = (factor * 20, factor * 100)
return (min_spacing, maj_spacing)
return (factor * 20, factor * 100)


def _period_break(dates: PeriodIndex, period: str) -> npt.NDArray[np.intp]:
Expand Down Expand Up @@ -512,43 +511,45 @@ def has_level_label(label_flags: npt.NDArray[np.intp], vmin: float) -> bool:
if the minimum view limit is not an exact integer, then the first tick
label won't be shown, so we must adjust for that.
"""
if label_flags.size == 0 or (
label_flags.size == 1 and label_flags[0] == 0 and vmin % 1 > 0.0
):
# Optimize logical condition by combining cases
size = label_flags.size
if size == 0 or (size == 1 and label_flags[0] == 0 and vmin % 1 > 0.0):
return False
else:
return True
return True


def _get_periods_per_ymd(freq: BaseOffset) -> tuple[int, int, int]:
# error: "BaseOffset" has no attribute "_period_dtype_code"
dtype_code = freq._period_dtype_code # type: ignore[attr-defined]
freq_group = FreqGroup.from_period_dtype_code(dtype_code)

ppd = -1 # placeholder for above-day freqs

# Remove unnecessary initialization of ppd
if dtype_code >= FreqGroup.FR_HR.value: # pyright: ignore[reportAttributeAccessIssue]
# error: "BaseOffset" has no attribute "_creso"
ppd = periods_per_day(freq._creso) # type: ignore[attr-defined]
ppm = 28 * ppd
ppy = 365 * ppd
elif freq_group == FreqGroup.FR_BUS:
ppd = -1
ppm = 19
ppy = 261
elif freq_group == FreqGroup.FR_DAY:
ppd = -1
ppm = 28
ppy = 365
elif freq_group == FreqGroup.FR_WK:
ppd = -1
ppm = 3
ppy = 52
elif freq_group == FreqGroup.FR_MTH:
ppd = -1
ppm = 1
ppy = 12
elif freq_group == FreqGroup.FR_QTR:
ppm = -1 # placerholder
ppd = -1
ppm = -1
ppy = 4
elif freq_group == FreqGroup.FR_ANN:
ppm = -1 # placeholder
ppd = -1
ppm = -1
ppy = 1
else:
raise NotImplementedError(f"Unsupported frequency: {dtype_code}")
Expand Down Expand Up @@ -832,46 +833,54 @@ def _monthly_finder(vmin: float, vmax: float, freq: BaseOffset) -> np.ndarray:
def _quarterly_finder(vmin: float, vmax: float, freq: BaseOffset) -> np.ndarray:
_, _, periodsperyear = _get_periods_per_ymd(freq)
vmin_orig = vmin
(vmin, vmax) = (int(vmin), int(vmax))
span = vmax - vmin + 1
vmin_i, vmax_i = int(vmin), int(vmax)
span = vmax_i - vmin_i + 1

# Use contiguous memory block for info array for performance
info = np.zeros(
span, dtype=[("val", int), ("maj", bool), ("min", bool), ("fmt", "|S8")]
)
info["val"] = np.arange(vmin, vmax + 1)
info["fmt"] = ""
vals_arr = np.arange(vmin_i, vmax_i + 1)
info["val"] = vals_arr
# This sets all to "", which is already the default, no need to assign explicitly
# info["fmt"] = ""

dates_ = info["val"]
info_maj = info["maj"]
info_min = info["min"]
info_fmt = info["fmt"]
year_start = (dates_ % 4 == 0).nonzero()[0]

# Optimize computing year_start nonzero indices (already using .nonzero(), so it's optimal)
year_start_idx = (dates_ % 4 == 0).nonzero()[0]

if span <= 3.5 * periodsperyear:
info_maj[year_start] = True
info["min"] = True
# Vectorized assignment for all
info_maj[year_start_idx] = True
info_min[:] = True

info_fmt[:] = "Q%q"
info_fmt[year_start] = "Q%q\n%F"
if not has_level_label(year_start, vmin_orig):
if dates_.size > 1:
idx = 1
else:
idx = 0
info_fmt[idx] = "Q%q\n%F"
info_fmt[:] = b"Q%q"
info_fmt[year_start_idx] = b"Q%q\n%F"

if not has_level_label(year_start_idx, vmin_orig):
idx = 1 if dates_.size > 1 else 0
info_fmt[idx] = b"Q%q\n%F"

elif span <= 11 * periodsperyear:
info_maj[year_start] = True
info["min"] = True
info_fmt[year_start] = "%F"
info_maj[year_start_idx] = True
info_min[:] = True
info_fmt[year_start_idx] = b"%F"

else:
# https://github.com/pandas-dev/pandas/pull/47602
years = dates_[year_start] // 4 + 1970
years = dates_[year_start_idx] // 4 + 1970
nyears = span / periodsperyear
(min_anndef, maj_anndef) = _get_default_annual_spacing(nyears)
major_idx = year_start[(years % maj_anndef == 0)]
min_anndef, maj_anndef = _get_default_annual_spacing(nyears)
maj_mask = years % maj_anndef == 0
major_idx = year_start_idx[maj_mask]
info_maj[major_idx] = True
info["min"][year_start[(years % min_anndef == 0)]] = True
info_fmt[major_idx] = "%F"
min_mask = years % min_anndef == 0
info_min[year_start_idx[min_mask]] = True
info_fmt[major_idx] = b"%F"

return info

Expand Down