From 57d1043188d98a80e3e27f8f1a8521d866cf7dd9 Mon Sep 17 00:00:00 2001 From: FedericoGarza Date: Mon, 4 Nov 2024 02:30:47 +0000 Subject: [PATCH] fix: rm unused cell outputs --- nbs/docs/use-cases/3_electricity_demand.ipynb | 24 ++----------------- 1 file changed, 2 insertions(+), 22 deletions(-) diff --git a/nbs/docs/use-cases/3_electricity_demand.ipynb b/nbs/docs/use-cases/3_electricity_demand.ipynb index 94314e45..3d43dc2d 100644 --- 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)" ]