support timezones for pandas in fill_gaps (#85)

nbs/preprocessing.ipynb

@@ -72,6 +72,8 @@
     "        else:\n",
     "            if isinstance(freq, str):\n",
     "                # this raises a nice error message if it isn't a valid datetime\n",
+    "                if isinstance(bound, pd.Timestamp) and bound.tz is not None:\n",
+    "                    bound = bound.tz_localize(None)\n",
     "                val = np.datetime64(bound)\n",
     "            else:\n",
     "                val = bound\n",
@@ -203,8 +205,13 @@
     "            # such as MS = 'Month Start' -> 'M', YS = 'Year Start' -> 'Y'\n",
     "            freq = freq[0]\n",
     "        delta: Union[np.timedelta64, int] = np.timedelta64(n, freq)\n",
+    "        tz = df[time_col].dt.tz\n",
+    "        if tz is not None:\n",
+    "            df = df.copy(deep=False)\n",
+    "            df[time_col] = df[time_col].dt.tz_localize(None)\n",
     "    else:\n",
     "        delta = freq\n",
+    "        tz = None\n",
     "    times_by_id = df.groupby(id_col, observed=True)[time_col].agg(['min', 'max'])\n",
     "    starts = _determine_bound(start, freq, times_by_id, 'min')\n",
     "    ends = _determine_bound(end, freq, times_by_id, 'max') + delta\n",
@@ -228,6 +235,8 @@
     "            times += offset.base\n",
     "    idx = pd.MultiIndex.from_arrays([uids, times], names=[id_col, time_col])\n",
     "    res = df.set_index([id_col, time_col]).reindex(idx).reset_index()\n",
+    "    if tz is not None:\n",
+    "        res[time_col] = res[time_col].dt.tz_localize(tz, ambiguous='infer')\n",
     "    extra_cols = df.columns.drop([id_col, time_col]).tolist()\n",
     "    if extra_cols:\n",
     "        check_col = extra_cols[0]\n",
@@ -252,7 +261,7 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/utilsforecast/blob/main/utilsforecast/preprocessing.py#L56){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/utilsforecast/blob/main/utilsforecast/preprocessing.py#L58){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### fill_gaps\n",
        "\n",
@@ -278,7 +287,7 @@
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/utilsforecast/blob/main/utilsforecast/preprocessing.py#L56){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/utilsforecast/blob/main/utilsforecast/preprocessing.py#L58){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### fill_gaps\n",
        "\n",
@@ -1598,7 +1607,7 @@
     "    # inferred frequency is the expected\n",
     "    first_serie = filled[filled['unique_id'] == 1]\n",
     "    if isinstance(freq, str):\n",
-    "        inferred_freq = pd.infer_freq(first_serie['ds'])\n",
+    "        inferred_freq = pd.infer_freq(first_serie['ds'].dt.tz_localize(None))\n",
     "        assert inferred_freq == pd.tseries.frequencies.to_offset(freq)\n",
     "    else:\n",
     "        assert all(first_serie['ds'].diff().value_counts().index == [freq])\n",
@@ -1628,7 +1637,7 @@
     "        assert max_dates[0] == expected_end\n",
     "\n",
     "n_periods = 100\n",
-    "freqs = ['YE', 'YS', 'ME', 'MS', 'W', 'W-TUE', 'D', 's', 'ms', 1, 2, '20D', '30s', '2YE', '3YS', '30min', 'B', '1h', 'QS-OCT', 'QE']\n",
+    "freqs = ['YE', 'YS', 'ME', 'MS', 'W', 'W-TUE', 'D', 's', 'ms', 1, 2, '20D', '30s', '2YE', '3YS', '30min', 'B', '1h', 'QS-NOV', 'QE']\n",
     "try:\n",
     "    pd.tseries.frequencies.to_offset('YE')\n",
     "except ValueError:\n",
@@ -1640,8 +1649,9 @@
     "        for f in freqs if isinstance(f, str)\n",
     "    ]\n",
     "for freq in freqs:\n",
-    "    if isinstance(freq, (pd.offsets.BaseOffset, str)):         \n",
+    "    if isinstance(freq, (pd.offsets.BaseOffset, str)):\n",
     "        dates = pd.date_range('1900-01-01', periods=n_periods, freq=freq)\n",
+    "        dates = dates.tz_localize('Europe/Berlin')\n",
     "        offset = pd.tseries.frequencies.to_offset(freq)\n",
     "    else:\n",
     "        dates = np.arange(0, freq * n_periods, freq, dtype=np.int64)\n",

settings.ini

@@ -1,7 +1,7 @@
 [DEFAULT]
 repo = utilsforecast
 lib_name = utilsforecast
-version = 0.1.10
+version = 0.1.11
 min_python = 3.8
 license = apache2
 black_formatting = True

utilsforecast/__init__.py

@@ -1 +1 @@
-__version__ = "0.1.10"
+__version__ = "0.1.11"

utilsforecast/preprocessing.py

@@ -28,6 +28,8 @@ def _determine_bound(bound, freq, times_by_id, agg) -> np.ndarray:
         else:
             if isinstance(freq, str):
                 # this raises a nice error message if it isn't a valid datetime
+                if isinstance(bound, pd.Timestamp) and bound.tz is not None:
+                    bound = bound.tz_localize(None)
                 val = np.datetime64(bound)
             else:
                 val = bound
@@ -149,8 +151,13 @@ def fill_gaps(
             # such as MS = 'Month Start' -> 'M', YS = 'Year Start' -> 'Y'
             freq = freq[0]
         delta: Union[np.timedelta64, int] = np.timedelta64(n, freq)
+        tz = df[time_col].dt.tz
+        if tz is not None:
+            df = df.copy(deep=False)
+            df[time_col] = df[time_col].dt.tz_localize(None)
     else:
         delta = freq
+        tz = None
     times_by_id = df.groupby(id_col, observed=True)[time_col].agg(["min", "max"])
     starts = _determine_bound(start, freq, times_by_id, "min")
     ends = _determine_bound(end, freq, times_by_id, "max") + delta
@@ -172,6 +179,8 @@ def fill_gaps(
             times += offset.base
     idx = pd.MultiIndex.from_arrays([uids, times], names=[id_col, time_col])
     res = df.set_index([id_col, time_col]).reindex(idx).reset_index()
+    if tz is not None:
+        res[time_col] = res[time_col].dt.tz_localize(tz, ambiguous="infer")
     extra_cols = df.columns.drop([id_col, time_col]).tolist()
     if extra_cols:
         check_col = extra_cols[0]