DEPR: deprecate strings T, S, L, U, and N in offsets frequencies, resolution abbreviations, _attrname_to_abbrevs

asv_bench/benchmarks/arithmetic.py

@@ -262,7 +262,7 @@ class Timeseries:
     def setup(self, tz):
         N = 10**6
         halfway = (N // 2) - 1
-        self.s = Series(date_range("20010101", periods=N, freq="T", tz=tz))
+        self.s = Series(date_range("20010101", periods=N, freq="min", tz=tz))
         self.ts = self.s[halfway]
 
         self.s2 = Series(date_range("20010101", periods=N, freq="s", tz=tz))
@@ -460,7 +460,7 @@ class OffsetArrayArithmetic:
 
     def setup(self, offset):
         N = 10000
-        rng = date_range(start="1/1/2000", periods=N, freq="T")
+        rng = date_range(start="1/1/2000", periods=N, freq="min")
         self.rng = rng
         self.ser = Series(rng)
 
@@ -479,7 +479,7 @@ class ApplyIndex:
 
     def setup(self, offset):
         N = 10000
-        rng = date_range(start="1/1/2000", periods=N, freq="T")
+        rng = date_range(start="1/1/2000", periods=N, freq="min")
         self.rng = rng
 
     def time_apply_index(self, offset):

asv_bench/benchmarks/eval.py

@@ -44,7 +44,7 @@ class Query:
     def setup(self):
         N = 10**6
         halfway = (N // 2) - 1
-        index = pd.date_range("20010101", periods=N, freq="T")
+        index = pd.date_range("20010101", periods=N, freq="min")
         s = pd.Series(index)
         self.ts = s.iloc[halfway]
         self.df = pd.DataFrame({"a": np.random.randn(N), "dates": index}, index=index)

asv_bench/benchmarks/gil.py

@@ -178,7 +178,7 @@ def time_kth_smallest(self):
 class ParallelDatetimeFields:
     def setup(self):
         N = 10**6
-        self.dti = date_range("1900-01-01", periods=N, freq="T")
+        self.dti = date_range("1900-01-01", periods=N, freq="min")
         self.period = self.dti.to_period("D")
 
     def time_datetime_field_year(self):

asv_bench/benchmarks/index_cached_properties.py

@@ -25,14 +25,14 @@ def setup(self, index_type):
         N = 10**5
         if index_type == "MultiIndex":
             self.idx = pd.MultiIndex.from_product(
-                [pd.date_range("1/1/2000", freq="T", periods=N // 2), ["a", "b"]]
+                [pd.date_range("1/1/2000", freq="min", periods=N // 2), ["a", "b"]]
             )
         elif index_type == "DatetimeIndex":
-            self.idx = pd.date_range("1/1/2000", freq="T", periods=N)
+            self.idx = pd.date_range("1/1/2000", freq="min", periods=N)
         elif index_type == "Int64Index":
             self.idx = pd.Index(range(N), dtype="int64")
         elif index_type == "PeriodIndex":
-            self.idx = pd.period_range("1/1/2000", freq="T", periods=N)
+            self.idx = pd.period_range("1/1/2000", freq="min", periods=N)
         elif index_type == "RangeIndex":
             self.idx = pd.RangeIndex(start=0, stop=N)
         elif index_type == "IntervalIndex":

asv_bench/benchmarks/index_object.py

@@ -25,7 +25,7 @@ class SetOperations:
 
     def setup(self, index_structure, dtype, method):
         N = 10**5
-        dates_left = date_range("1/1/2000", periods=N, freq="T")
+        dates_left = date_range("1/1/2000", periods=N, freq="min")
         fmt = "%Y-%m-%d %H:%M:%S"
         date_str_left = Index(dates_left.strftime(fmt))
         int_left = Index(np.arange(N))

asv_bench/benchmarks/io/json.py

@@ -290,7 +290,7 @@ def time_float_longint_str_lines(self):
 class ToJSONMem:
     def setup_cache(self):
         df = DataFrame([[1]])
-        df2 = DataFrame(range(8), date_range("1/1/2000", periods=8, freq="T"))
+        df2 = DataFrame(range(8), date_range("1/1/2000", periods=8, freq="min"))
         frames = {"int": df, "float": df.astype(float), "datetime": df2}
 
         return frames

asv_bench/benchmarks/join_merge.py

@@ -212,7 +212,7 @@ class JoinNonUnique:
     # outer join of non-unique
     # GH 6329
     def setup(self):
-        date_index = date_range("01-Jan-2013", "23-Jan-2013", freq="T")
+        date_index = date_range("01-Jan-2013", "23-Jan-2013", freq="min")
         daily_dates = date_index.to_period("D").to_timestamp("S", "S")
         self.fracofday = date_index.values - daily_dates.values
         self.fracofday = self.fracofday.astype("timedelta64[ns]")
@@ -338,7 +338,7 @@ class MergeDatetime:
     def setup(self, units, tz):
         unit_left, unit_right = units
         N = 10_000
-        keys = Series(date_range("2012-01-01", freq="T", periods=N, tz=tz))
+        keys = Series(date_range("2012-01-01", freq="min", periods=N, tz=tz))
         self.left = DataFrame(
             {
                 "key": keys.sample(N * 10, replace=True).dt.as_unit(unit_left),

asv_bench/benchmarks/sparse.py

@@ -22,7 +22,7 @@ class SparseSeriesToFrame:
     def setup(self):
         K = 50
         N = 50001
-        rng = date_range("1/1/2000", periods=N, freq="T")
+        rng = date_range("1/1/2000", periods=N, freq="min")
         self.series = {}
         for i in range(1, K):
             data = np.random.randn(N)[:-i]

asv_bench/benchmarks/timeseries.py

@@ -116,7 +116,7 @@ def time_infer_freq(self, freq):
 class TimeDatetimeConverter:
     def setup(self):
         N = 100000
-        self.rng = date_range(start="1/1/2000", periods=N, freq="T")
+        self.rng = date_range(start="1/1/2000", periods=N, freq="min")
 
     def time_convert(self):
         DatetimeConverter.convert(self.rng, None, None)
@@ -129,9 +129,9 @@ class Iteration:
     def setup(self, time_index):
         N = 10**6
         if time_index is timedelta_range:
-            self.idx = time_index(start=0, freq="T", periods=N)
+            self.idx = time_index(start=0, freq="min", periods=N)
         else:
-            self.idx = time_index(start="20140101", freq="T", periods=N)
+            self.idx = time_index(start="20140101", freq="min", periods=N)
         self.exit = 10000
 
     def time_iter(self, time_index):
@@ -149,7 +149,7 @@ class ResampleDataFrame:
     param_names = ["method"]
 
     def setup(self, method):
-        rng = date_range(start="20130101", periods=100000, freq="50L")
+        rng = date_range(start="20130101", periods=100000, freq="50ms")
         df = DataFrame(np.random.randn(100000, 2), index=rng)
         self.resample = getattr(df.resample("1s"), method)
 
@@ -163,8 +163,8 @@ class ResampleSeries:
 
     def setup(self, index, freq, method):
         indexes = {
-            "period": period_range(start="1/1/2000", end="1/1/2001", freq="T"),
-            "datetime": date_range(start="1/1/2000", end="1/1/2001", freq="T"),
+            "period": period_range(start="1/1/2000", end="1/1/2001", freq="min"),
+            "datetime": date_range(start="1/1/2000", end="1/1/2001", freq="min"),
         }
         idx = indexes[index]
         ts = Series(np.random.randn(len(idx)), index=idx)
@@ -270,7 +270,7 @@ class DatetimeAccessor:
 
     def setup(self, tz):
         N = 100000
-        self.series = Series(date_range(start="1/1/2000", periods=N, freq="T", tz=tz))
+        self.series = Series(date_range(start="1/1/2000", periods=N, freq="min", tz=tz))
 
     def time_dt_accessor(self, tz):
         self.series.dt

asv_bench/benchmarks/tslibs/timestamp.py

@@ -136,10 +136,10 @@ def time_to_julian_date(self, tz):
         self.ts.to_julian_date()
 
     def time_floor(self, tz):
-        self.ts.floor("5T")
+        self.ts.floor("5min")
 
     def time_ceil(self, tz):
-        self.ts.ceil("5T")
+        self.ts.ceil("5min")
 
 
 class TimestampAcrossDst:

pandas/_libs/tslibs/dtypes.pxd

@@ -11,6 +11,7 @@ cpdef int64_t periods_per_second(NPY_DATETIMEUNIT reso) except? -1
 cpdef NPY_DATETIMEUNIT get_supported_reso(NPY_DATETIMEUNIT reso)
 cpdef bint is_supported_unit(NPY_DATETIMEUNIT reso)
 
+cdef dict c_DEPR_ABBREVS
 cdef dict attrname_to_abbrevs
 cdef dict npy_unit_to_attrname
 cdef dict attrname_to_npy_unit

pandas/_libs/tslibs/dtypes.pyi

@@ -4,6 +4,7 @@ from enum import Enum
 #  are imported in tests.
 _attrname_to_abbrevs: dict[str, str]
 _period_code_map: dict[str, int]
+DEPR_ABBREVS: dict[str, str]
 
 def periods_per_day(reso: int) -> int: ...
 def periods_per_second(reso: int) -> int: ...

pandas/_libs/tslibs/dtypes.pyx

@@ -1,6 +1,9 @@
 # period frequency constants corresponding to scikits timeseries
 # originals
 from enum import Enum
+import warnings
+
+from pandas.util._exceptions import find_stack_level
 
 from pandas._libs.tslibs.np_datetime cimport (
     NPY_DATETIMEUNIT,
@@ -141,11 +144,11 @@ _period_code_map = {
     "B": PeriodDtypeCode.B,        # Business days
     "D": PeriodDtypeCode.D,        # Daily
     "H": PeriodDtypeCode.H,        # Hourly
-    "T": PeriodDtypeCode.T,        # Minutely
+    "min": PeriodDtypeCode.T,        # Minutely
     "S": PeriodDtypeCode.S,        # Secondly
-    "L": PeriodDtypeCode.L,       # Millisecondly
-    "U": PeriodDtypeCode.U,       # Microsecondly
-    "N": PeriodDtypeCode.N,       # Nanosecondly
+    "ms": PeriodDtypeCode.L,       # Millisecondly
+    "us": PeriodDtypeCode.U,       # Microsecondly
+    "ns": PeriodDtypeCode.N,       # Nanosecondly
 }
 
 _reverse_period_code_map = {
@@ -174,15 +177,28 @@ _attrname_to_abbrevs = {
     "month": "M",
     "day": "D",
     "hour": "H",
-    "minute": "T",
+    "minute": "min",
     "second": "S",
-    "millisecond": "L",
-    "microsecond": "U",
-    "nanosecond": "N",
+    "millisecond": "ms",
+    "microsecond": "us",
+    "nanosecond": "ns",
 }
 cdef dict attrname_to_abbrevs = _attrname_to_abbrevs
 cdef dict _abbrev_to_attrnames = {v: k for k, v in attrname_to_abbrevs.items()}
 
+# Map deprecated resolution abbreviations to correct resolution abbreviations
+DEPR_ABBREVS: dict[str, str]= {
+    "T": "min",
+    "t": "min",
+    "L": "ms",
+    "l": "ms",
+    "U": "us",
+    "u": "us",
+    "N": "ns",
+    "n": "ns",
+}
+cdef dict c_DEPR_ABBREVS = DEPR_ABBREVS
+
 
 class FreqGroup(Enum):
     # Mirrors c_FreqGroup in the .pxd file
@@ -273,6 +289,14 @@ class Resolution(Enum):
         True
         """
         try:
+            if freq in DEPR_ABBREVS:
+                warnings.warn(
+                    f"Code freq={freq} is deprecated "
+                    "and will be removed in a future version.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                freq = freq.replace("T", "min").replace("L", "ms")
             attr_name = _abbrev_to_attrnames[freq]
         except KeyError:
             # For quarterly and yearly resolutions, we need to chop off
@@ -283,6 +307,14 @@ class Resolution(Enum):
             if split_freq[1] not in _month_names:
                 # i.e. we want e.g. "Q-DEC", not "Q-INVALID"
                 raise
+            if split_freq[0] in {"T", "L"}:
+                warnings.warn(
+                    f"Code freq={split_freq[0]} is deprecated "
+                    "and will be removed in a future version.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+                split_freq[0] = split_freq[0].replace("T", "min").replace("L", "ms")
             attr_name = _abbrev_to_attrnames[split_freq[0]]
 
         return cls.from_attrname(attr_name)

pandas/_libs/tslibs/nattype.pyx

@@ -996,23 +996,23 @@ timedelta}, default 'raise'
         >>> ts.round(freq='H') # hour
         Timestamp('2020-03-14 16:00:00')
 
-        >>> ts.round(freq='T') # minute
+        >>> ts.round(freq='min') # minute
         Timestamp('2020-03-14 15:33:00')
 
         >>> ts.round(freq='S') # seconds
         Timestamp('2020-03-14 15:32:52')
 
-        >>> ts.round(freq='L') # milliseconds
+        >>> ts.round(freq='ms') # milliseconds
         Timestamp('2020-03-14 15:32:52.193000')
 
-        ``freq`` can also be a multiple of a single unit, like '5T' (i.e.  5 minutes):
+        ``freq`` can also be a multiple of a single unit, like '5min' (i.e.  5 minutes):
 
-        >>> ts.round(freq='5T')
+        >>> ts.round(freq='5min')
         Timestamp('2020-03-14 15:35:00')
 
-        or a combination of multiple units, like '1H30T' (i.e. 1 hour and 30 minutes):
+        or a combination of multiple units, like '1H30min' (i.e. 1 hour and 30 minutes):
 
-        >>> ts.round(freq='1H30T')
+        >>> ts.round(freq='1H30min')
         Timestamp('2020-03-14 15:00:00')
 
         Analogous for ``pd.NaT``:
@@ -1085,7 +1085,7 @@ timedelta}, default 'raise'
         >>> ts.floor(freq='H') # hour
         Timestamp('2020-03-14 15:00:00')
 
-        >>> ts.floor(freq='T') # minute
+        >>> ts.floor(freq='min') # minute
         Timestamp('2020-03-14 15:32:00')
 
         >>> ts.floor(freq='S') # seconds
@@ -1094,14 +1094,14 @@ timedelta}, default 'raise'
         >>> ts.floor(freq='N') # nanoseconds
         Timestamp('2020-03-14 15:32:52.192548651')
 
-        ``freq`` can also be a multiple of a single unit, like '5T' (i.e.  5 minutes):
+        ``freq`` can also be a multiple of a single unit, like '5min' (i.e.  5 minutes):
 
-        >>> ts.floor(freq='5T')
+        >>> ts.floor(freq='5min')
         Timestamp('2020-03-14 15:30:00')
 
-        or a combination of multiple units, like '1H30T' (i.e. 1 hour and 30 minutes):
+        or a combination of multiple units, like '1H30min' (i.e. 1 hour and 30 minutes):
 
-        >>> ts.floor(freq='1H30T')
+        >>> ts.floor(freq='1H30min')
         Timestamp('2020-03-14 15:00:00')
 
         Analogous for ``pd.NaT``:
@@ -1174,7 +1174,7 @@ timedelta}, default 'raise'
         >>> ts.ceil(freq='H') # hour
         Timestamp('2020-03-14 16:00:00')
 
-        >>> ts.ceil(freq='T') # minute
+        >>> ts.ceil(freq='min') # minute
         Timestamp('2020-03-14 15:33:00')
 
         >>> ts.ceil(freq='S') # seconds
@@ -1183,14 +1183,14 @@ timedelta}, default 'raise'
         >>> ts.ceil(freq='U') # microseconds
         Timestamp('2020-03-14 15:32:52.192549')
 
-        ``freq`` can also be a multiple of a single unit, like '5T' (i.e.  5 minutes):
+        ``freq`` can also be a multiple of a single unit, like '5min' (i.e.  5 minutes):
 
-        >>> ts.ceil(freq='5T')
+        >>> ts.ceil(freq='5min')
         Timestamp('2020-03-14 15:35:00')
 
-        or a combination of multiple units, like '1H30T' (i.e. 1 hour and 30 minutes):
+        or a combination of multiple units, like '1H30min' (i.e. 1 hour and 30 minutes):
 
-        >>> ts.ceil(freq='1H30T')
+        >>> ts.ceil(freq='1H30min')
         Timestamp('2020-03-14 16:30:00')
 
         Analogous for ``pd.NaT``: