Bump pandas and redis requirements

pyproject.toml

@@ -27,13 +27,13 @@ requires-python = ">=3.11"
 dependencies = [
   "psycopg>=3.1.10,<4.0",
   "sqlalchemy>=2.0.8,<3.0",
-  "pandas>=2.2,<3.0",
+  "pandas>=3.0.0,<4.0",
   "pint>=0.23.0",
   "argon2_cffi>=23.1.0",
   "alembic>=1.8.0,<2.0",
   "click>=8.1.3,<9.0",
   "celery>=5.3.1,<6.0",
-  "redis>=4.3.4,<5.0",
+  "redis>=7.4.0,<8.0",
   "requests>=2.28.2",
 ]
 

requirements/install-min.in

@@ -1,10 +1,10 @@
 psycopg==3.1.10
 sqlalchemy==2.0.8
-pandas==2.2
+pandas==3.0.0
 argon2_cffi==23.1.0
 alembic==1.8.0
 click==8.1.3
 celery==5.3.1
-redis==4.3.4
+redis==7.4.0
 pint==0.23.0
 requests==2.28.2

requirements/install-min.txt

@@ -37,8 +37,6 @@ click-plugins==1.1.1.2
     # via celery
 click-repl==0.3.0
     # via celery
-deprecated==1.3.1
-    # via redis
 greenlet==3.4.0
     # via sqlalchemy
 idna==3.11
@@ -52,10 +50,8 @@ markupsafe==3.0.3
 numpy==1.26.4
     # via pandas
 packaging==26.1
-    # via
-    #   kombu
-    #   redis
-pandas==2.2.0
+    # via kombu
+pandas==3.0.0
     # via -r requirements/install-min.in
 pint==0.23
     # via -r requirements/install-min.in
@@ -69,9 +65,7 @@ python-dateutil==2.9.0.post0
     # via
     #   celery
     #   pandas
-pytz==2026.1.post1
-    # via pandas
-redis==4.3.4
+redis==7.4.0
     # via -r requirements/install-min.in
 requests==2.28.2
     # via -r requirements/install-min.in
@@ -90,7 +84,6 @@ tzdata==2026.1
     # via
     #   celery
     #   kombu
-    #   pandas
 urllib3==1.26.20
     # via requests
 vine==5.1.0
@@ -100,5 +93,3 @@ vine==5.1.0
     #   kombu
 wcwidth==0.6.0
     # via prompt-toolkit
-wrapt==2.1.2
-    # via deprecated

requirements/install.txt

@@ -55,7 +55,7 @@ numpy==2.4.4
     # via pandas
 packaging==26.1
     # via kombu
-pandas==2.3.3
+pandas==3.0.2
     # via bemserver-core (pyproject.toml)
 pint==0.25.3
     # via bemserver-core (pyproject.toml)
@@ -71,9 +71,7 @@ python-dateutil==2.9.0.post0
     # via
     #   celery
     #   pandas
-pytz==2026.1.post1
-    # via pandas
-redis==4.6.0
+redis==7.4.0
     # via bemserver-core (pyproject.toml)
 requests==2.33.1
     # via bemserver-core (pyproject.toml)
@@ -92,9 +90,7 @@ typing-extensions==4.15.0
     #   psycopg
     #   sqlalchemy
 tzdata==2026.1
-    # via
-    #   kombu
-    #   pandas
+    # via kombu
 tzlocal==5.3.1
     # via celery
 urllib3==2.6.3

src/bemserver_core/__init__.py

@@ -2,8 +2,6 @@
 
 import os
 
-import pandas as pd
-
 from bemserver_core import (
     common,
     database,
@@ -18,9 +16,6 @@
 from bemserver_core.exceptions import BEMServerCoreSettingsError
 from bemserver_core.process.weather import wdp
 
-# Set pandas future flags to silence deprecation warnings
-pd.set_option("future.no_silent_downcasting", True)
-
 
 class BEMServerCore:
     def __init__(self):

src/bemserver_core/input_output/timeseries_data_io.py

@@ -388,8 +388,10 @@ def get_timeseries_data(
             data, columns=("timestamp", "id", "name", "value")
         ).set_index("timestamp")
         data_df["value"] = data_df["value"].astype(float)
-        data_df.index = pd.DatetimeIndex(data_df.index, tz="UTC").tz_convert(
-            ZoneInfo(timezone)
+        data_df.index = (
+            pd.DatetimeIndex(data_df.index, tz="UTC")
+            .as_unit("us")
+            .tz_convert(ZoneInfo(timezone))
         )
 
         data_df = data_df.pivot(columns=col_label, values="value")
@@ -482,7 +484,9 @@ def get_timeseries_buckets_data(
         )
 
         if not timeseries:
-            return pd.DataFrame({}, index=complete_idx)
+            ret_df = pd.DataFrame({}, index=complete_idx)
+            ret_df.columns.name = col_label
+            return ret_df
 
         # At this stage, date_trunc can only aggregate by 1 x unit.
         # For a N x width bucket size, the remaining aggregation is
@@ -513,7 +517,9 @@ def get_timeseries_buckets_data(
             data, columns=("timestamp", "id", "name", "value")
         ).set_index("timestamp")
 
-        data_df.index = pd.DatetimeIndex(data_df.index, tz="UTC").tz_convert(tz_info)
+        data_df.index = (
+            pd.DatetimeIndex(data_df.index, tz="UTC").as_unit("us").tz_convert(tz_info)
+        )
 
         # Pivot table to get timeseries in columns
         data_df = data_df.pivot(values="value", columns=col_label).fillna(fill_value)
@@ -652,6 +658,7 @@ def delete(cls, start_dt, end_dt, timeseries, data_state):
 
 def to_utc_index(index):
     """Create UTC datetime index from timezone aware datetime list"""
+    # https://github.com/pandas-dev/pandas/issues/54995
 
     try:
         # Cast to series so that output is series, with an apply method
@@ -669,7 +676,7 @@ def to_utc_index(index):
     except TypeError as exc:
         raise TimeseriesDataIODatetimeError("Invalid or TZ-naive timestamp") from exc
 
-    return pd.DatetimeIndex(index, name="timestamp")
+    return pd.DatetimeIndex(index, name="timestamp").as_unit("us")
 
 
 class TimeseriesDataCSVIO(TimeseriesDataIO, BaseCSVIO):

src/bemserver_core/process/cleanup.py

@@ -37,10 +37,10 @@ def cleanup(
     ts_mins = Timeseries.get_property_for_many_timeseries(timeseries_ids, "Min")
     ts_maxs = Timeseries.get_property_for_many_timeseries(timeseries_ids, "Max")
 
-    for ts_id, (_, col) in zip(timeseries_ids, data_df.items(), strict=True):
+    for ts_id in timeseries_ids:
         if (ts_min := ts_mins[ts_id]) is not None:
-            col.loc[col < float(ts_min)] = np.nan
+            data_df.loc[data_df[ts_id] < float(ts_min), ts_id] = np.nan
         if (ts_max := ts_maxs[ts_id]) is not None:
-            col.loc[col > float(ts_max)] = np.nan
+            data_df.loc[data_df[ts_id] > float(ts_max), ts_id] = np.nan
 
     return data_df

src/bemserver_core/process/energy_power.py

@@ -137,7 +137,7 @@ def energyindex2power(
     power_s = power_s.resample(pd_freq, closed="left", label="left").agg("mean")
 
     # Convert to desired unit
-    convert_from = ureg.validate_unit(index_ts.unit_symbol) / ureg.validate_unit("ns")
+    convert_from = ureg.validate_unit(index_ts.unit_symbol) / ureg.validate_unit("us")
     power_s = pd.Series(
         ureg.convert(power_s.values, convert_from, convert_to),
         index=power_s.index,

src/bemserver_core/process/weather.py

@@ -111,7 +111,7 @@ def get_weather_data(
                 index=pd.DatetimeIndex(
                     pd.to_datetime(timestamps, utc=True, unit="s"),
                     name="timestamp",
-                ),
+                ).as_unit("us"),
                 data=ret_data["data"],
                 # Strip (unit) from column names to facilitate selection
                 columns=[str(col).split()[0] for col in ret_data["columns"]],

tests/common/test_units.py

@@ -5,6 +5,7 @@
 import pytest
 
 import pandas as pd
+from pandas.testing import assert_frame_equal
 
 from bemserver_core import BEMServerCore
 from bemserver_core.common import ureg
@@ -66,7 +67,7 @@ def test_ureg_convert_dimensionality_error(self):
     def test_ureg_convert_df(self):
         data_df = pd.DataFrame({"id": [0, 1, 2]})
         ureg.convert_df(data_df, {"id": "km"}, {"id": "m"})
-        assert data_df.equals(1000.0 * pd.DataFrame({"id": [0, 1, 2]}))
+        assert_frame_equal(data_df, (1000.0 * pd.DataFrame({"id": [0, 1, 2]})))
 
     def test_ureg_convert_df_undefined_unit(self):
         data_df = pd.DataFrame({"id": [0, 1, 2]})