Update dependencies

.pre-commit-config.yaml

@@ -7,7 +7,7 @@ repos:
     - id: trailing-whitespace
   repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
-  rev: v0.13.0
+  rev: v0.15.0
   hooks:
     # Run the linter.
     - id: ruff-check

.python-version

@@ -1 +1 @@
-3.10
+3.13

README.md

@@ -271,6 +271,8 @@ The first beta version of `market_prices` was released May 2022.
 
 Whilst the test suite is pretty comprehensive, there will inevitably be bugs. Please do raise an [issue](https://github.com/maread99/market_prices/issues) with any that you come across. Even better, offer a PR! Contributions welcome.
 
+The package is actively maintained to work with the latest versions of its dependencies, which include `numpy` and `pandas`. **Only limited consideration is given to backwards compatibility with earlier versions of dependencies** - no guarantees are offered in this regard. Of note, whilst the latest release works with pandas v3 and v2, support for pandas v1 was dropped some time ago.
+
 Please use [discussions](https://github.com/maread99/market_prices/discussions) to make any suggestions and offer general feedback.
 
 ## Disclaimers

pyproject.toml

@@ -52,7 +52,7 @@ classifiers = [
 ]
 
 dependencies = [
-    "exchange_calendars",
+    "exchange-calendars",
     "numpy",
     "pandas",
     "tzdata",
@@ -64,6 +64,7 @@ dependencies = [
 test = [
     "attrs",
     "hypothesis",
+    "pytz",  # required as stored hdf data includes pytz objects
     "pytest",
     "pytest-mock",
     "tables",

requirements.txt

@@ -1,5 +1,5 @@
 # This file was autogenerated by uv via the following command:
-#    uv export --format requirements-txt --no-emit-project --no-dev --no-hashes -o requirements.txt
+#    uv export --format requirements-txt --no-emit-project --no-hashes --no-dev -o requirements.txt
 beautifulsoup4==4.14.3
     # via yahooquery
 certifi==2026.1.4
@@ -14,7 +14,7 @@ colorama==0.4.6 ; sys_platform == 'win32'
     # via tqdm
 curl-cffi==0.14.0
     # via yahooquery
-exchange-calendars==4.12
+exchange-calendars==4.13.1
     # via market-prices
 idna==3.11
     # via requests
@@ -27,35 +27,40 @@ numpy==2.2.6 ; python_full_version < '3.11'
     #   exchange-calendars
     #   market-prices
     #   pandas
-numpy==2.4.1 ; python_full_version >= '3.11'
+numpy==2.4.2 ; python_full_version >= '3.11'
     # via
     #   exchange-calendars
     #   market-prices
     #   pandas
-pandas==2.3.3
+pandas==2.3.3 ; python_full_version < '3.11'
     # via
     #   exchange-calendars
     #   market-prices
     #   yahooquery
-pycparser==2.23 ; implementation_name != 'PyPy'
+pandas==3.0.0 ; python_full_version >= '3.11'
+    # via
+    #   exchange-calendars
+    #   market-prices
+    #   yahooquery
+pycparser==3.0 ; implementation_name != 'PyPy'
     # via cffi
 pyluach==2.3.0
     # via exchange-calendars
 python-dateutil==2.9.0.post0
     # via pandas
-pytz==2025.2
+pytz==2025.2 ; python_full_version < '3.11'
     # via pandas
 requests==2.32.5
     # via requests-futures
 requests-futures==1.0.2
     # via yahooquery
 six==1.17.0
     # via python-dateutil
-soupsieve==2.8.1
+soupsieve==2.8.3
     # via beautifulsoup4
 toolz==1.1.0
     # via exchange-calendars
-tqdm==4.67.1
+tqdm==4.67.3
     # via yahooquery
 typing-extensions==4.15.0
     # via beautifulsoup4

src/market_prices/helpers.py

@@ -211,7 +211,7 @@ def freq_format_error_msg() -> str:
         return (
             f'interval/frequency received as "{freq}", although must be defined'
             " as one or more consecutive digits followed by one or more"
-            ' consecutive letters. Valid examples: "30min", "3h", "1d", "3m".'
+            ' consecutive letters. Valid examples: "30min", "3h", "1D", "3m".'
         )
 
     unit = genutils.remove_digits(freq)
@@ -375,6 +375,14 @@ def resample(
     else:
         agg_f = agg_funcs(data)
 
+    # convert days to hours as Day is not considered a Tick frequency since pandas 3.0
+    if isinstance(rule, str):
+        n, unit = extract_freq_parts(rule)
+        if unit.upper() == "D":
+            rule = f"{24 * n}h"
+    if isinstance(rule, pd.offsets.Day):
+        rule = f"{24 * rule.n}h"
+
     resampler = resample_me.resample(rule, closed="left", label="left", origin=origin)
     resampled = resampler.agg(agg_f)
     resampled.columns = columns_

src/market_prices/prices/base.py

@@ -3337,7 +3337,7 @@ def get(  # noqa: C901, PLR0912
             p.get('10T', weeks=2) last two calendar weeks at ten minute
                 intervals.
             p.get(days=5) last five trading days at inferred interval.
-            p.get('1d', months=6) daily data for last six months.
+            p.get('1D', months=6) daily data for last six months.
             p.get('1h', months=3, end='2021-03-17 15:00') three months at
                 hourly intervals to defined datetime (timezone of 'end'
                 assumed as most common timezone of all symbols).
@@ -3514,7 +3514,7 @@ def get(  # noqa: C901, PLR0912
                     unit:
                         "min", "MIN", "T" or "t" for minutes
                         "h" or "H" for hours
-                        "d" or "D' for days
+                        "D' for days
                         'm' or "M" for months
 
             Examples:
@@ -3529,7 +3529,7 @@ def get(  # noqa: C901, PLR0912
                     timedelta(hours=3)
 
                 one day:
-                    "1d", "1D"
+                    "1D"
                     pd.Timedelta(1, "D"), pd.Timedelta(days=1)
                     timedelta(days=1), timedelta(1)
 
@@ -3538,8 +3538,8 @@ def get(  # noqa: C901, PLR0912
 
                 others:
                     "90min", "90MIN" - ninety minutes
-                    "5D", "5d" - five days
-                    "40D", "40d" - forty days
+                    "5D" - five days
+                    "40D" - forty days
                     "1M", "1m" - one month
                     pd.Timedelta(hours=3, minutes=30) - three and a half
                         hours

src/market_prices/prices/csv.py

@@ -33,8 +33,9 @@
 
 CSV_READ_DFLT_KWARGS: dict[str, Any] = {
     "header": 0,
-    "usecols": lambda x: x.lower()
-    in ["date", "open", "high", "low", "close", "volume"],
+    "usecols": lambda x: (
+        x.lower() in ["date", "open", "high", "low", "close", "volume"]
+    ),
     "index_col": "date",
     "parse_dates": ["date"],
 }
@@ -118,7 +119,7 @@ def _get_symbol_from_filename(name: str, symbols: list[str]) -> str | None:
             # at least two different symbols in filename
             return None
         symbol = part
-    return symbol if symbol else None
+    return symbol or None
 
 
 def _get_interval_from_filename(name: str) -> TDInterval | None:
@@ -1095,7 +1096,7 @@ def __init__(
         root = path
         self.PM_SUBSESSION_ORIGIN = pm_subsession_origin  # override class attr
         symbols_ = helpers.symbols_to_list(symbols)
-        delays = {symb: pd.Timedelta(0) for symb in symbols_}
+        delays = dict.fromkeys(symbols_, 0)
 
         csv_kwargs = _get_csv_read_kwargs(read_csv_kwargs)
         paths = self._csv_paths = get_csv_paths(root, symbols_)
@@ -1167,7 +1168,7 @@ def _compile_tables(
                 dfs.append(df_symb_)
             if not dfs:
                 continue
-            df = pd.concat(dfs, axis=1)
+            df = pd.concat(dfs, axis=1, sort=True)
             df = df.sort_index()
             df.columns = df.columns.set_names("symbol", level=0)
             dfs_by_intrvl[interval] = df

src/market_prices/prices/yahoo.py

@@ -814,7 +814,7 @@ def _tidy_yahoo(  # noqa: C901, PLR0912
             # for, at least, hong kong stocks include a single index after the close.
             params = dict(interval=interval, start=start, end=end)
             raise errors.PricesUnavailableFromSourceError(params, df)
-        df = pd.concat(sdfs, axis=1)
+        df = pd.concat(sdfs, axis=1, sort=True)
 
         if empty_sdfs:
             # add symbols for which prices unavailable over requested period

src/market_prices/pt.py

@@ -1244,7 +1244,7 @@ def _downsample_cbdays(
             "\nNB. Downsampling will downsample to a frequency defined in"
             " CustomBusinessDay when either `pdfreq` is passed as a"
             " CustomBusinessDay (or multiple of) or when the table has a"
-            ' CustomBusinessDay frequency and `pdfreq` is passed with unit "d".'
+            ' CustomBusinessDay frequency and `pdfreq` is passed with unit "D".'
         )
 
         if not isinstance(calendar, xcals.ExchangeCalendar):
@@ -1261,7 +1261,7 @@ def _downsample_cbdays(
 
         # Passing 'end' to the origin argument of DataFrame.resample has no
         # effect when passing rule as a CustomBusinessDay.
-        origin = "start"
+        origin = "start_day"
 
         # To ensure every indice, INCLUDING the last indice, comprises x
         # CustomBusinessDays it's necessary to curtail the start of the dataframe
@@ -1286,8 +1286,8 @@ def _downsample_cbdays(
         resampled.index.right.freq = pdfreq
         return resampled
 
-    def _downsample_days(self, pdfreq: str | pd.offsets.BaseOffset) -> pd.DataFrame:
-        """Downsample to a frequency with unit "d"."""
+    def _downsample_days(self, pdfreq: str | pd.offsets.Day) -> pd.DataFrame:
+        """Downsample to a frequency with unit "D"."""
         df = self.prices
         pd_offset = pd.tseries.frequencies.to_offset(pdfreq)
 
@@ -1329,7 +1329,9 @@ def _downsample_months(
                 if not pre_table_sessions.empty:
                     start_ds = pd_offset.rollforward(start_table)
                     df = df[start_ds:]
-        resampled = helpers.resample(df, pdfreq, origin="start", nominal_start=start_ds)
+        resampled = helpers.resample(
+            df, pdfreq, origin="start_day", nominal_start=start_ds
+        )
         resampled.index = pdutils.get_interval_index(resampled.index, pdfreq)
 
         if drop_incomplete_last_indice:
@@ -1359,13 +1361,13 @@ def downsample(
         pdfreq
             Downsample frequency as CustomBusinessDay or any valid input to
             pd.tseries.frequencies.to_offset. Examples:
-                '2d', '3d', '5d', '10d', 'MS', 'QS'
+                '2D', '3D', '5D', '10D', 'MS', 'QS'
                 pd.offsets.Day(5), pd.offsets.Day(15)
                 3 * calendar.day (where calendar is an instance of
                     xcals.ExchangeCalendar and hence calendar.day is an
                     instance of CustomBusinessDay).
 
-            If `pdfreq` has unit 'd', for example "5d", and table has a
+            If `pdfreq` has unit 'D', for example "5D", and table has a
             frequency in CustomBusinessDay then will assume required
             frequency is the table's CustomBusinessDay frequency.
 
@@ -1440,7 +1442,7 @@ def downsample(
                 f"Received `pdfreq` as {pdfreq} although must be either of"
                 " type pd.offsets.CustomBusinessDay or acceptable input to"
                 " pd.tseries.frequencies.to_offset that describes a"
-                ' frequency greater than one day. For example "2d", "5d"'
+                ' frequency greater than one day. For example "2D", "5D"'
                 ' "QS" etc.'
             )
             raise ValueError(msg) from None
@@ -1449,7 +1451,7 @@ def downsample(
             freqstr: str = offset.freqstr
 
         value, unit = helpers.extract_freq_parts(freqstr)
-        if unit.lower() == "d":
+        if unit.upper() == "D":
             if isinstance(self.freq, pd.offsets.CustomBusinessDay):
                 pdfreq = self.freq.base * value
                 return self._downsample_cbdays(pdfreq, calendar)
@@ -1459,7 +1461,7 @@ def downsample(
         ext = ["t", "T", "H", "S"]  # for pandas pre 2.2 compatibility
         if unit in invalid_units + ext:
             raise ValueError(
-                "Cannot downsample to a `pdfreq` with a unit more precise than 'd'."
+                "Cannot downsample to a `pdfreq` with a unit more precise than 'D'."
             )
 
         return self._downsample_months(freqstr, calendar, drop_incomplete_last_indice)