CLN: replace unnecessary freq_to_period_freqstr with PeriodDtype constructor

pandas/_libs/tslibs/dtypes.pxd

@@ -11,7 +11,6 @@ cpdef int64_t periods_per_second(NPY_DATETIMEUNIT reso) except? -1
 cdef NPY_DATETIMEUNIT get_supported_reso(NPY_DATETIMEUNIT reso)
 cdef bint is_supported_unit(NPY_DATETIMEUNIT reso)
 
-cpdef freq_to_period_freqstr(freq_n, freq_name)
 cdef dict c_OFFSET_TO_PERIOD_FREQSTR
 cdef dict c_OFFSET_DEPR_FREQSTR
 cdef dict c_REVERSE_OFFSET_DEPR_FREQSTR

pandas/_libs/tslibs/dtypes.pyi

@@ -7,7 +7,6 @@ OFFSET_TO_PERIOD_FREQSTR: dict[str, str]
 def periods_per_day(reso: int = ...) -> int: ...
 def periods_per_second(reso: int) -> int: ...
 def abbrev_to_npy_unit(abbrev: str | None) -> int: ...
-def freq_to_period_freqstr(freq_n: int, freq_name: str) -> str: ...
 
 class PeriodDtypeBase:
     _dtype_code: int  # PeriodDtypeCode

pandas/_libs/tslibs/dtypes.pyx

@@ -334,15 +334,6 @@ cdef dict c_REVERSE_OFFSET_DEPR_FREQSTR = {
     v: k for k, v in c_OFFSET_DEPR_FREQSTR.items()
 }
 
-cpdef freq_to_period_freqstr(freq_n, freq_name):
-    if freq_n == 1:
-        freqstr = f"""{c_OFFSET_TO_PERIOD_FREQSTR.get(
-            freq_name, freq_name)}"""
-    else:
-        freqstr = f"""{freq_n}{c_OFFSET_TO_PERIOD_FREQSTR.get(
-            freq_name, freq_name)}"""
-    return freqstr
-
 # Map deprecated resolution abbreviations to correct resolution abbreviations
 cdef dict c_DEPR_ABBREVS = {
     "A": "Y",

pandas/_libs/tslibs/offsets.pyx

@@ -4709,7 +4709,7 @@ _lite_rule_alias = {
     "ns": "ns",
 }
 
-_dont_uppercase = _dont_uppercase = {"h", "bh", "cbh", "MS", "ms", "s"}
+_dont_uppercase = {"h", "bh", "cbh", "MS", "ms", "s"}
 
 
 INVALID_FREQ_ERR_MSG = "Invalid frequency: {0}"

pandas/_libs/tslibs/period.pyx

@@ -38,10 +38,7 @@ from libc.time cimport (
     tm,
 )
 
-from pandas._libs.tslibs.dtypes cimport (
-    c_OFFSET_TO_PERIOD_FREQSTR,
-    freq_to_period_freqstr,
-)
+from pandas._libs.tslibs.dtypes cimport c_OFFSET_TO_PERIOD_FREQSTR
 
 from pandas._libs.tslibs.np_datetime import OutOfBoundsDatetime
 
@@ -97,9 +94,6 @@ from pandas._libs.tslibs.dtypes cimport (
     attrname_to_abbrevs,
     freq_group_code_to_npy_unit,
 )
-
-from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
-
 from pandas._libs.tslibs.parsing cimport quarter_to_myear
 
 from pandas._libs.tslibs.parsing import parse_datetime_string_with_reso
@@ -1554,7 +1548,7 @@ def extract_ordinals(ndarray values, freq) -> np.ndarray:
         # if we don't raise here, we'll segfault later!
         raise TypeError("extract_ordinals values must be object-dtype")
 
-    freqstr = freq_to_period_freqstr(freq.n, freq.name)
+    freqstr = PeriodDtypeBase(freq._period_dtype_code, freq.n)._freqstr
 
     for i in range(n):
         # Analogous to: p = values[i]
@@ -1722,8 +1716,15 @@ cdef class PeriodMixin:
             condition = self.freq != other
 
         if condition:
-            freqstr = freq_to_period_freqstr(self.freq.n, self.freq.name)
-            other_freqstr = freq_to_period_freqstr(other.n, other.name)
+            freqstr = PeriodDtypeBase(
+                self.freq._period_dtype_code, self.freq.n
+            )._freqstr
+            if hasattr(other, "_period_dtype_code"):
+                other_freqstr = PeriodDtypeBase(
+                    other._period_dtype_code, other.n
+                )._freqstr
+            else:
+                other_freqstr = other.freqstr
             msg = DIFFERENT_FREQ.format(
                 cls=type(self).__name__,
                 own_freq=freqstr,
@@ -2479,7 +2480,7 @@ cdef class _Period(PeriodMixin):
         >>> pd.Period('2020-01', 'D').freqstr
         'D'
         """
-        freqstr = freq_to_period_freqstr(self.freq.n, self.freq.name)
+        freqstr = PeriodDtypeBase(self.freq._period_dtype_code, self.freq.n)._freqstr
         return freqstr
 
     def __repr__(self) -> str:

pandas/core/arrays/period.py

@@ -37,7 +37,6 @@
 from pandas._libs.tslibs.dtypes import (
     FreqGroup,
     PeriodDtypeBase,
-    freq_to_period_freqstr,
 )
 from pandas._libs.tslibs.fields import isleapyear_arr
 from pandas._libs.tslibs.offsets import (
@@ -323,7 +322,7 @@ def _from_datetime64(cls, data, freq, tz=None) -> Self:
         PeriodArray[freq]
         """
         if isinstance(freq, BaseOffset):
-            freq = freq_to_period_freqstr(freq.n, freq.name)
+            freq = PeriodDtype(freq)._freqstr
         data, freq = dt64arr_to_periodarr(data, freq, tz)
         dtype = PeriodDtype(freq)
         return cls(data, dtype=dtype)
@@ -397,7 +396,7 @@ def freq(self) -> BaseOffset:
 
     @property
     def freqstr(self) -> str:
-        return freq_to_period_freqstr(self.freq.n, self.freq.name)
+        return PeriodDtype(self.freq)._freqstr
 
     def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
         if dtype == "i8":
@@ -986,13 +985,15 @@ def raise_on_incompatible(left, right) -> IncompatibleFrequency:
     if isinstance(right, (np.ndarray, ABCTimedeltaArray)) or right is None:
         other_freq = None
     elif isinstance(right, BaseOffset):
-        other_freq = freq_to_period_freqstr(right.n, right.name)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            other_freq = PeriodDtype(right)._freqstr
     elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period)):
         other_freq = right.freqstr
     else:
         other_freq = delta_to_tick(Timedelta(right)).freqstr
 
-    own_freq = freq_to_period_freqstr(left.freq.n, left.freq.name)
+    own_freq = PeriodDtype(left.freq)._freqstr
     msg = DIFFERENT_FREQ.format(
         cls=type(left).__name__, own_freq=own_freq, other_freq=other_freq
     )

pandas/core/generic.py

@@ -34,7 +34,6 @@
     Timestamp,
     to_offset,
 )
-from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
 from pandas._typing import (
     AlignJoin,
     AnyArrayLike,
@@ -123,6 +122,7 @@
 from pandas.core.dtypes.dtypes import (
     DatetimeTZDtype,
     ExtensionDtype,
+    PeriodDtype,
 )
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
@@ -10228,9 +10228,9 @@ def _shift_with_freq(self, periods: int, axis: int, freq) -> Self:
             if freq != orig_freq:
                 assert orig_freq is not None  # for mypy
                 raise ValueError(
-                    f"Given freq {freq_to_period_freqstr(freq.n, freq.name)} "
+                    f"Given freq {PeriodDtype(freq)._freqstr} "
                     f"does not match PeriodIndex freq "
-                    f"{freq_to_period_freqstr(orig_freq.n, orig_freq.name)}"
+                    f"{PeriodDtype(orig_freq)._freqstr}"
                 )
             new_ax: Index = index.shift(periods)
         else:

pandas/core/indexes/datetimelike.py

@@ -28,7 +28,6 @@
     parsing,
     to_offset,
 )
-from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
 from pandas.compat.numpy import function as nv
 from pandas.errors import (
     InvalidIndexError,
@@ -45,7 +44,10 @@
     is_list_like,
 )
 from pandas.core.dtypes.concat import concat_compat
-from pandas.core.dtypes.dtypes import CategoricalDtype
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    PeriodDtype,
+)
 
 from pandas.core.arrays import (
     DatetimeArray,
@@ -113,7 +115,7 @@ def freqstr(self) -> str:
         if self._data.freqstr is not None and isinstance(
             self._data, (PeriodArray, PeriodIndex)
         ):
-            freq = freq_to_period_freqstr(self._data.freq.n, self._data.freq.name)
+            freq = PeriodDtype(self._data.freq)._freqstr
             return freq
         else:
             return self._data.freqstr  # type: ignore[return-value]

pandas/core/resample.py

@@ -25,7 +25,6 @@
     Timestamp,
     to_offset,
 )
-from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
 from pandas._typing import NDFrameT
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import (
@@ -38,7 +37,10 @@
     rewrite_warning,
 )
 
-from pandas.core.dtypes.dtypes import ArrowDtype
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    PeriodDtype,
+)
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
     ABCSeries,
@@ -2633,7 +2635,7 @@ def asfreq(
 
         if isinstance(freq, BaseOffset):
             if hasattr(freq, "_period_dtype_code"):
-                freq = freq_to_period_freqstr(freq.n, freq.name)
+                freq = PeriodDtype(freq)._freqstr
 
         new_obj = obj.copy()
         new_obj.index = obj.index.asfreq(freq, how=how)

pandas/io/json/_table_schema.py

@@ -15,7 +15,6 @@
 from pandas._libs import lib
 from pandas._libs.json import ujson_loads
 from pandas._libs.tslibs import timezones
-from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
 from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.base import _registry as registry
@@ -212,8 +211,7 @@ def convert_json_field_to_pandas_type(field) -> str | CategoricalDtype:
         elif field.get("freq"):
             # GH#9586 rename frequency M to ME for offsets
             offset = to_offset(field["freq"])
-            freq_n, freq_name = offset.n, offset.name
-            freq = freq_to_period_freqstr(freq_n, freq_name)
+            freq = PeriodDtype(offset)._freqstr
             # GH#47747 using datetime over period to minimize the change surface
             return f"period[{freq}]"
         else:

pandas/tests/arrays/test_datetimelike.py

@@ -11,7 +11,9 @@
     OutOfBoundsDatetime,
     Timestamp,
 )
-from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
+from pandas._libs.tslibs import to_offset
+
+from pandas.core.dtypes.dtypes import PeriodDtype
 
 import pandas as pd
 from pandas import (
@@ -51,7 +53,7 @@ def period_index(freqstr):
         warnings.filterwarnings(
             "ignore", message="Period with BDay freq", category=FutureWarning
         )
-        freqstr = freq_to_period_freqstr(1, freqstr)
+        freqstr = PeriodDtype(to_offset(freqstr))._freqstr
         pi = pd.period_range(start=Timestamp("2000-01-01"), periods=100, freq=freqstr)
     return pi
 
@@ -761,7 +763,7 @@ def test_to_period(self, datetime_index, freqstr):
         dti = datetime_index
         arr = dti._data
 
-        freqstr = freq_to_period_freqstr(1, freqstr)
+        freqstr = PeriodDtype(to_offset(freqstr))._freqstr
         expected = dti.to_period(freq=freqstr)
         result = arr.to_period(freq=freqstr)
         assert isinstance(result, PeriodArray)

pandas/tests/plotting/test_datetimelike.py

@@ -6,6 +6,7 @@
     timedelta,
 )
 import pickle
+import re
 
 import numpy as np
 import pytest
@@ -14,7 +15,8 @@
     BaseOffset,
     to_offset,
 )
-from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
+
+from pandas.core.dtypes.dtypes import PeriodDtype
 
 from pandas import (
     DataFrame,
@@ -238,7 +240,8 @@ def test_line_plot_period_mlt_frame(self, frqncy):
             index=idx,
             columns=["A", "B", "C"],
         )
-        freq = freq_to_period_freqstr(1, df.index.freq.rule_code)
+        freq = re.split(r"\d", PeriodDtype(df.index.freq)._freqstr)[-1]
+
         freq = df.index.asfreq(freq).freq
         _check_plot_works(df.plot, freq)
 
@@ -253,7 +256,7 @@ def test_line_plot_datetime_frame(self, freq):
             index=idx,
             columns=["A", "B", "C"],
         )
-        freq = freq_to_period_freqstr(1, df.index.freq.rule_code)
+        freq = re.split(r"\d", PeriodDtype(df.index.freq)._freqstr)[-1]
         freq = df.index.to_period(freq).freq
         _check_plot_works(df.plot, freq)
 

pandas/tseries/frequencies.py

@@ -19,10 +19,7 @@
     MONTHS,
     int_to_weekday,
 )
-from pandas._libs.tslibs.dtypes import (
-    OFFSET_TO_PERIOD_FREQSTR,
-    freq_to_period_freqstr,
-)
+from pandas._libs.tslibs.dtypes import OFFSET_TO_PERIOD_FREQSTR
 from pandas._libs.tslibs.fields import (
     build_field_sarray,
     month_position_check,
@@ -559,7 +556,7 @@ def _maybe_coerce_freq(code) -> str:
     """
     assert code is not None
     if isinstance(code, DateOffset):
-        code = freq_to_period_freqstr(1, code.name)
+        code = PeriodDtype(to_offset(code.name))._freqstr
     if code in {"h", "min", "s", "ms", "us", "ns"}:
         return code
     else: