fix: rm unused cell outputs

Nixtla · Nov 4, 2024 · 57d1043 · 57d1043
1 parent 898080b
commit 57d1043
Showing 1 changed file with 2 additions and 22 deletions.
diff --git a/nbs/docs/use-cases/3_electricity_demand.ipynb b/nbs/docs/use-cases/3_electricity_demand.ipynb
@@ -510,18 +510,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ubuntu/projects/nixtla/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "2024-11-04 02:25:49,888\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
-      "2024-11-04 02:25:50,287\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from neuralforecast.core import NeuralForecast\n",
     "from neuralforecast.models import NHITS"
@@ -542,16 +531,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_356865/3927391461.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
-      "  train_df = df.groupby('unique_id').apply(lambda group: group.iloc[:-96]).reset_index(drop=True)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df = df.groupby('unique_id').apply(lambda group: group.iloc[:-96]).reset_index(drop=True)"
    ]