Skip to content

Commit

Permalink
updated conclusion
Browse files Browse the repository at this point in the history
  • Loading branch information
@srinivasseema
srinivasseema committed Jan 17, 2024
1 parent 830bd20 commit 9cd6554
Show file tree
Hide file tree
Showing 5 changed files with 16 additions and 103 deletions.
59 changes: 0 additions & 59 deletions examples/energy-data-analysis.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -724,65 +724,6 @@
"#print('MAPE of the forecasted data present in DataFrame:', mape)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'load'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32mc:\\Users\\seemasin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
"File \u001b[1;32mc:\\Users\\seemasin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mc:\\Users\\seemasin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\_libs\\index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
"File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 'load'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[36], line 5\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m 3\u001b[0m fig, axs \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m5\u001b[39m))\n\u001b[1;32m----> 5\u001b[0m groups \u001b[38;5;241m=\u001b[39m \u001b[43menergyDF\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mload\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mgroupby(pd\u001b[38;5;241m.\u001b[39mGrouper(freq\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m 7\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame()\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, group \u001b[38;5;129;01min\u001b[39;00m groups:\n",
"File \u001b[1;32mc:\\Users\\seemasin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 3760\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 3763\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
"File \u001b[1;32mc:\\Users\\seemasin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3657\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3658\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3659\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3660\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
"\u001b[1;31mKeyError\u001b[0m: 'load'"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"fig, axs = plt.subplots(1, 1, figsize=(8,5))\n",
"\n",
"groups = energyDF['total_load_actual'].groupby(pd.Grouper(freq='M'))\n",
"\n",
"df = pd.DataFrame()\n",
"\n",
"for name, group in groups:\n",
" df[name.month] = pd.Series(group.values)\n",
"\n",
"df.boxplot(ax=axs)\n",
"axs.set_xlabel('Month Year')\n",
"axs.set_ylabel('Energy Demanded MWh')\n",
"axs.set_title('Box plot month of year 2015-2018')\n",
"plt.subplots_adjust(hspace=0.5)\n",
"\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
Expand Down
4 changes: 4 additions & 0 deletions project/readme.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -43,3 +43,7 @@ This dataset contains yearly electrical consumption, generation data for europea

1. Explore Datasources [https://github.com/srinivasseema/made-template/issues/1]
2. Analyze data pipeline requirements


Slides Link
Video Link
12 changes: 12 additions & 0 deletions project/report.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -91215,6 +91215,18 @@
"\n",
" plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "2b5d0b40",
"metadata": {},
"source": [
"# Conclusion\n",
"\n",
"By harnessing the power of machine learning, this project will equip stakeholders with the knowledge and tools to navigate the evolving energy landscape and ensure energy security for all.\n",
"Proactively prepare for demand fluctuations caused by extreme weather events.\n",
"Optimize energy pricing strategies to ensure financial sustainability while maintaining affordability for consumers."
]
}
],
"metadata": {
Expand Down
44 changes: 0 additions & 44 deletions project/report.txt
View file

This file was deleted.

Binary file added project/slides.pdf
View file
Binary file not shown.

0 comments on commit 9cd6554

Please sign in to comment.