Parse Date/Time/TimeZone triplets in samples and WQP

NEWS.md

@@ -1,3 +1,7 @@
+**05/07/2026:** Bumped the declared minimum Python version from **3.8** to **3.9** (`pyproject.toml`'s `requires-python` and the ruff target). This brings the manifest in line with what was already being tested — CI's matrix has long covered only 3.9, 3.13, and 3.14, the `waterdata` test module already skipped itself on Python < 3.10, and several modules already use 3.9-only stdlib (e.g. `zoneinfo`). Users on 3.8 will no longer be able to install the package; please upgrade.
+
+**05/07/2026:** `waterdata.get_samples()` and `wqp.get_results()` now append a derived `<prefix>DateTime` UTC column for every Date/Time/TimeZone triplet in the response (e.g. `Activity_StartDate` + `Activity_StartTime` + `Activity_StartTimeZone` → `Activity_StartDateTime`). Both the WQX3 (`<X>Date`/`<X>Time`/`<X>TimeZone`) and legacy WQP (`<X>Date`/`<X>Time/Time`/`<X>Time/TimeZoneCode`) shapes are recognized; abbreviations like EST/EDT/CST/PST resolve to a UTC `Timestamp`, unknown codes resolve to `NaT`, and the original triplet columns are preserved. Returned rows are also now sorted by `Activity_StartDateTime` (or the legacy `ActivityStartDateTime`) — the underlying APIs return rows in an unstable order. Mirrors R's `create_dateTime` and end-of-pipeline sort. Closes #266.
+
 **05/06/2026:** Each remaining active function in `dataretrieval.nwis` now emits a per-function `DeprecationWarning` naming the `waterdata` replacement to migrate to (visible the first time users call each getter). The `nwis` module is scheduled for removal on or after **2027-05-06**.
 
 **05/06/2026:** Added `waterdata.get_ratings(...)` — wraps the new Water Data STAC catalog (`api.waterdata.usgs.gov/stac/v0/search`) for USGS stage-discharge rating curves. Returns parsed `exsa` / `base` / `corr` rating tables as a dict of DataFrames keyed by feature ID, or just the list of available STAC features when `download_and_parse=False`. Mirrors R's `read_waterdata_ratings`.

dataretrieval/utils.py

@@ -94,6 +94,108 @@ def format_datetime(df, date_field, time_field, tz_field):
     return df
 
 
+# (time-suffix, tz-suffix) pairs that follow a "<prefix>Date" column.
+_TIME_TZ_SUFFIXES = (
+    # WQX3 / Samples, e.g.
+    #   Activity_StartDate / Activity_StartTime / Activity_StartTimeZone
+    ("Time", "TimeZone"),
+    # Legacy WQP (slash-separated), e.g.
+    #   ActivityStartDate / ActivityStartTime/Time / ActivityStartTime/TimeZoneCode
+    ("Time/Time", "Time/TimeZoneCode"),
+)
+
+
+def _build_utc_datetime(
+    date_series: pd.Series, time_series: pd.Series, tz_series: pd.Series
+) -> pd.Series:
+    """Combine date + time + tz-abbreviation columns into a UTC pandas Series.
+
+    Unknown timezone codes (and rows missing any of the three values) yield
+    ``NaT``. The input columns are not mutated.
+    """
+    offsets = tz_series.map(tz)
+    combined = (
+        date_series.astype("string")
+        + " "
+        + time_series.astype("string")
+        + " "
+        + offsets.astype("string")
+    )
+    return pd.to_datetime(
+        combined, format="%Y-%m-%d %H:%M:%S %z", utc=True, errors="coerce"
+    )
+
+
+def _attach_datetime_columns(df: pd.DataFrame) -> pd.DataFrame:
+    """Add ``<prefix>DateTime`` UTC columns for any Date/Time/TimeZone triplets
+    and sort the frame by the activity-start datetime.
+
+    Detects two naming patterns that appear in USGS Samples and Water Quality
+    Portal CSV responses:
+
+    * **WQX3** — ``<prefix>Date``, ``<prefix>Time``, ``<prefix>TimeZone``
+    * **Legacy WQP** — ``<prefix>Date``, ``<prefix>Time/Time``,
+      ``<prefix>Time/TimeZoneCode``
+
+    For every triplet present, a new ``<prefix>DateTime`` column is appended
+    holding a UTC ``Timestamp`` (offsets resolved via
+    :data:`dataretrieval.codes.tz`). The original Date/Time/TimeZone columns
+    are left intact, and an existing ``<prefix>DateTime`` column is never
+    overwritten.
+
+    Rows are sorted (and the index reset) by the canonical activity-start
+    datetime when present — ``Activity_StartDateTime`` (WQX3) or
+    ``ActivityStartDateTime`` (legacy WQP) — falling back to the first
+    detected ``*Date`` column. Mirrors R ``dataRetrieval``'s
+    end-of-pipeline sort in ``importWQP.R``.
+
+    Parameters
+    ----------
+    df : ``pandas.DataFrame``
+        DataFrame returned from a Samples or WQP CSV endpoint.
+
+    Returns
+    -------
+    df : ``pandas.DataFrame``
+        A new DataFrame with derivable ``<prefix>DateTime`` columns appended
+        and rows sorted by the activity-start datetime (if any date column
+        was detected).
+    """
+    columns = set(df.columns)
+    new_columns = {}
+    first_date_col = None
+    for col in df.columns:
+        if not col.endswith("Date"):
+            continue
+        if first_date_col is None:
+            first_date_col = col
+        prefix = col.removesuffix("Date")
+        target = prefix + "DateTime"
+        if target in columns or target in new_columns:
+            continue
+        for time_suffix, tz_suffix in _TIME_TZ_SUFFIXES:
+            time_col = prefix + time_suffix
+            tz_col = prefix + tz_suffix
+            if time_col in columns and tz_col in columns:
+                new_columns[target] = _build_utc_datetime(
+                    df[col], df[time_col], df[tz_col]
+                )
+                break
+    if new_columns:
+        # Concat in one shot — per-column assignment on a wide CSV-derived
+        # frame triggers pandas' fragmentation PerformanceWarning.
+        df = pd.concat([df, pd.DataFrame(new_columns, index=df.index)], axis=1)
+    if "Activity_StartDateTime" in df.columns:
+        sort_key = "Activity_StartDateTime"
+    elif "ActivityStartDateTime" in df.columns:
+        sort_key = "ActivityStartDateTime"
+    else:
+        sort_key = first_date_col
+    if sort_key is not None:
+        df = df.sort_values(by=sort_key, ignore_index=True)
+    return df
+
+
 class BaseMetadata:
     """Base class for metadata.
 

dataretrieval/waterdata/api.py

@@ -16,7 +16,7 @@
 import requests
 from requests.models import PreparedRequest
 
-from dataretrieval.utils import BaseMetadata, to_str
+from dataretrieval.utils import BaseMetadata, _attach_datetime_columns, to_str
 from dataretrieval.waterdata.filters import FILTER_LANG
 from dataretrieval.waterdata.types import (
     CODE_SERVICES,
@@ -2266,7 +2266,15 @@ def get_samples(
     Returns
     -------
     df : ``pandas.DataFrame``
-        Formatted data returned from the API query.
+        Formatted data returned from the API query. For each
+        ``<prefix>Date`` / ``<prefix>Time`` / ``<prefix>TimeZone`` triplet in
+        the response (e.g. ``Activity_StartDate``, ``Activity_StartTime``,
+        ``Activity_StartTimeZone``), an additional ``<prefix>DateTime`` column
+        is appended holding a UTC ``Timestamp`` derived from the three. The
+        original Date/Time/TimeZone columns are left intact; rows whose
+        timezone abbreviation is not recognized resolve to ``NaT``. Rows are
+        sorted by ``Activity_StartDateTime`` when present (the API's default
+        order is unstable).
     md : :obj:`dataretrieval.utils.Metadata`
         Custom ``dataretrieval`` metadata object pertaining to the query.
 
@@ -2323,6 +2331,7 @@ def get_samples(
     response.raise_for_status()
 
     df = pd.read_csv(StringIO(response.text), delimiter=",")
+    df = _attach_datetime_columns(df)
 
     return df, BaseMetadata(response)
 

dataretrieval/waterdata/ratings.py

@@ -14,7 +14,8 @@
 
 import logging
 import os
-from typing import Any, Iterable, Literal, get_args
+from collections.abc import Iterable
+from typing import Any, Literal, get_args
 
 import pandas as pd
 import requests

dataretrieval/waterdata/utils.py

@@ -6,10 +6,10 @@
 import re
 from datetime import datetime
 from typing import Any, get_args
+from zoneinfo import ZoneInfo
 
 import pandas as pd
 import requests
-from zoneinfo import ZoneInfo
 
 from dataretrieval import __version__
 from dataretrieval.utils import BaseMetadata

dataretrieval/wqp.py

@@ -17,7 +17,7 @@
 
 import pandas as pd
 
-from .utils import BaseMetadata, query
+from .utils import BaseMetadata, _attach_datetime_columns, query
 
 if TYPE_CHECKING:
     from pandas import DataFrame
@@ -101,7 +101,14 @@ def get_results(
     Returns
     -------
     df : ``pandas.DataFrame``
-        Formatted data returned from the API query.
+        Formatted data returned from the API query. For each
+        ``<prefix>Date`` / ``<prefix>Time`` / ``<prefix>TimeZone`` triplet in
+        the response (legacy WQP uses ``<prefix>Time/Time`` and
+        ``<prefix>Time/TimeZoneCode``), an additional ``<prefix>DateTime``
+        column is appended holding a UTC ``Timestamp``. Original triplet
+        columns are preserved; unrecognized timezone codes yield ``NaT``.
+        Rows are sorted by ``ActivityStartDateTime`` (or ``Activity_StartDateTime``
+        for WQX3 responses) when present.
     md : :obj:`dataretrieval.utils.Metadata`
         Custom ``dataretrieval`` metadata object pertaining to the query.
 
@@ -147,6 +154,7 @@ def get_results(
     response = query(url, kwargs, delimiter=";", ssl_check=ssl_check)
 
     df = pd.read_csv(StringIO(response.text), delimiter=",", low_memory=False)
+    df = _attach_datetime_columns(df)
     return df, WQP_Metadata(response)
 
 

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 name = "dataretrieval"
 description = "Discover and retrieve water data from U.S. federal hydrologic web services."
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 keywords = ["USGS", "water data"]
 license = "CC0-1.0"
 license-files = ["LICENSE.md"]
@@ -63,7 +63,7 @@ repository = "https://github.com/DOI-USGS/dataretrieval-python.git"
 write_to = "dataretrieval/_version.py"
 
 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 extend-exclude = ["demos"]
 
 [tool.ruff.lint]

tests/utils_test.py

@@ -97,3 +97,61 @@ def test_to_str_custom_delimiter(self):
 
     def test_to_str_non_iterable(self):
         assert utils.to_str(123) is None
+
+
+class Test_attach_datetime_columns:
+    """Tests of _attach_datetime_columns, which derives <prefix>DateTime UTC
+    columns from Date/Time/TimeZone triplets in Samples and WQP CSVs."""
+
+    def test_wqx3_triplet_resolves_to_utc(self):
+        df = pd.DataFrame(
+            {
+                "Activity_StartDate": ["2024-01-09", "2024-02-15"],
+                "Activity_StartTime": ["10:00:00", "14:30:00"],
+                "Activity_StartTimeZone": ["PST", "EST"],
+            }
+        )
+        df = utils._attach_datetime_columns(df)
+        assert df["Activity_StartDateTime"][0] == pd.Timestamp(
+            "2024-01-09 18:00:00", tz="UTC"
+        )
+        assert df["Activity_StartDateTime"][1] == pd.Timestamp(
+            "2024-02-15 19:30:00", tz="UTC"
+        )
+        assert df["Activity_StartTimeZone"].tolist() == ["PST", "EST"]
+
+    def test_legacy_wqp_triplet_resolves_to_utc(self):
+        df = pd.DataFrame(
+            {
+                "ActivityStartDate": ["2024-01-09"],
+                "ActivityStartTime/Time": ["10:00:00"],
+                "ActivityStartTime/TimeZoneCode": ["PST"],
+            }
+        )
+        df = utils._attach_datetime_columns(df)
+        assert df["ActivityStartDateTime"][0] == pd.Timestamp(
+            "2024-01-09 18:00:00", tz="UTC"
+        )
+
+    def test_unknown_timezone_is_NaT(self):
+        df = pd.DataFrame(
+            {
+                "Activity_StartDate": ["2024-01-09"],
+                "Activity_StartTime": ["10:00:00"],
+                "Activity_StartTimeZone": ["BOGUS"],
+            }
+        )
+        df = utils._attach_datetime_columns(df)
+        assert df["Activity_StartDateTime"].isna().all()
+
+    def test_existing_datetime_column_not_overwritten(self):
+        df = pd.DataFrame(
+            {
+                "Activity_StartDate": ["2024-01-09"],
+                "Activity_StartTime": ["10:00:00"],
+                "Activity_StartTimeZone": ["PST"],
+                "Activity_StartDateTime": ["preexisting"],
+            }
+        )
+        df = utils._attach_datetime_columns(df)
+        assert df["Activity_StartDateTime"].tolist() == ["preexisting"]

tests/waterdata_filters_test.py

@@ -190,14 +190,18 @@ def fake_walk_pages(*_args, **_kwargs):
         frame = pd.DataFrame({"id": [f"chunk-{idx}"], "value": [idx]})
         return frame, _fake_response()
 
-    with mock.patch(
-        "dataretrieval.waterdata.utils._construct_api_requests",
-        side_effect=fake_construct_api_requests,
-    ), mock.patch(
-        "dataretrieval.waterdata.utils._walk_pages", side_effect=fake_walk_pages
-    ), mock.patch(
-        "dataretrieval.waterdata.filters._effective_filter_budget",
-        return_value=_CQL_FILTER_CHUNK_LEN,
+    with (
+        mock.patch(
+            "dataretrieval.waterdata.utils._construct_api_requests",
+            side_effect=fake_construct_api_requests,
+        ),
+        mock.patch(
+            "dataretrieval.waterdata.utils._walk_pages", side_effect=fake_walk_pages
+        ),
+        mock.patch(
+            "dataretrieval.waterdata.filters._effective_filter_budget",
+            return_value=_CQL_FILTER_CHUNK_LEN,
+        ),
     ):
         df, _ = get_continuous(
             monitoring_location_id="USGS-07374525",
@@ -239,14 +243,18 @@ def fake_walk_pages(*_args, **_kwargs):
         frame = pd.DataFrame({"id": ["shared-feature"], "value": [1]})
         return frame, _fake_response()
 
-    with mock.patch(
-        "dataretrieval.waterdata.utils._construct_api_requests",
-        return_value=_fake_prepared_request(),
-    ), mock.patch(
-        "dataretrieval.waterdata.utils._walk_pages", side_effect=fake_walk_pages
-    ), mock.patch(
-        "dataretrieval.waterdata.filters._effective_filter_budget",
-        return_value=_CQL_FILTER_CHUNK_LEN,
+    with (
+        mock.patch(
+            "dataretrieval.waterdata.utils._construct_api_requests",
+            return_value=_fake_prepared_request(),
+        ),
+        mock.patch(
+            "dataretrieval.waterdata.utils._walk_pages", side_effect=fake_walk_pages
+        ),
+        mock.patch(
+            "dataretrieval.waterdata.filters._effective_filter_budget",
+            return_value=_CQL_FILTER_CHUNK_LEN,
+        ),
     ):
         df, _ = get_continuous(
             monitoring_location_id="USGS-07374525",
@@ -293,14 +301,18 @@ def fake_walk_pages(*_args, **_kwargs):
             )
         return frame, _fake_response()
 
-    with mock.patch(
-        "dataretrieval.waterdata.utils._construct_api_requests",
-        return_value=_fake_prepared_request(),
-    ), mock.patch(
-        "dataretrieval.waterdata.utils._walk_pages", side_effect=fake_walk_pages
-    ), mock.patch(
-        "dataretrieval.waterdata.filters._effective_filter_budget",
-        return_value=_CQL_FILTER_CHUNK_LEN,
+    with (
+        mock.patch(
+            "dataretrieval.waterdata.utils._construct_api_requests",
+            return_value=_fake_prepared_request(),
+        ),
+        mock.patch(
+            "dataretrieval.waterdata.utils._walk_pages", side_effect=fake_walk_pages
+        ),
+        mock.patch(
+            "dataretrieval.waterdata.filters._effective_filter_budget",
+            return_value=_CQL_FILTER_CHUNK_LEN,
+        ),
     ):
         df, _ = get_continuous(
             monitoring_location_id="USGS-07374525",
@@ -434,14 +446,17 @@ def fake_construct_api_requests(**kwargs):
         sent_filters.append(kwargs.get("filter"))
         return _fake_prepared_request()
 
-    with mock.patch(
-        "dataretrieval.waterdata.utils._construct_api_requests",
-        side_effect=fake_construct_api_requests,
-    ), mock.patch(
-        "dataretrieval.waterdata.utils._walk_pages",
-        return_value=(
-            pd.DataFrame({"id": ["row-1"], "value": [1]}),
-            _fake_response(),
+    with (
+        mock.patch(
+            "dataretrieval.waterdata.utils._construct_api_requests",
+            side_effect=fake_construct_api_requests,
+        ),
+        mock.patch(
+            "dataretrieval.waterdata.utils._walk_pages",
+            return_value=(
+                pd.DataFrame({"id": ["row-1"], "value": [1]}),
+                _fake_response(),
+            ),
         ),
     ):
         get_continuous(