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IIabm/LoadFirm_20230213-1.ipynb

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Firm.csv
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{
"cells": [
{
"cell_type": "code",
AmendFirm
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"execution_count": 12,
Firm.csv
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"metadata": {},
"outputs": [
LoadFirm
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{
"name": "stderr",
"output_type": "stream",
"text": [
AmendFirm
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"C:\\Users\\25759\\AppData\\Local\\Temp\\ipykernel_20892\\1462409850.py:27: SettingWithCopyWarning: \n",
LoadFirm
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"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" Firm_attr['Product_Code'] = firm_product\n"
]
},
AmendFirm
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{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"['2.1.4.1', '2.1.4.2']\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"['2.1.4.1', '2.1.4.2']\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.4.1', '2.1.4.2']\n",
"[]\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
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"[]\n",
"[]\n",
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"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.4.1', '2.1.4.2']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
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"[]\n",
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"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"['1.3.2.1']\n",
"['1.3.4.1', '1.3.4.2', '1.3.4.3']\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"[]\n",
"[]\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
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"[]\n",
"[]\n",
"[]\n",
"[]\n",
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"[]\n",
"[]\n",
"[]\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"[]\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"[]\n",
"[]\n",
"['1.3.4.1', '1.3.4.2', '1.3.4.3']\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"['2.1.4.1', '2.1.4.2']\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"['1.3.4.1', '1.3.4.2', '1.3.4.3']\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"['1.3.4.1', '1.3.4.2', '1.3.4.3']\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"['2.1.4.1', '2.1.4.2']\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"['2.1.4.1', '2.1.4.2']\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.1', '2.1.2', '2.1.3', '2.1.4']\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"['2.1.4.1', '2.1.4.2']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"[]\n",
"[]\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['1.3.5.1']\n",
"[]\n",
"['2.1.4.1', '2.1.4.2']\n",
"[]\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"['2.1.4.1', '2.1.4.2']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"[]\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['1.3.2.1']\n",
"['1.3.4.1', '1.3.4.2', '1.3.4.3']\n",
"['1.3.5.1']\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['1.3.2.1']\n",
"[]\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"[]\n",
"[]\n",
"['2.1.4.1', '2.1.4.2']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5']\n",
"['2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7']\n",
"[]\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"['1.3.5.1']\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.4.1', '2.1.4.2']\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n",
"[]\n",
"['2.1.4.1', '2.1.4.2']\n",
"[]\n",
"['2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4']\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"[]\n",
"['2.3.1', '2.3.2', '2.3.3']\n",
"[]\n"
]
},
Firm.csv
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{
"data": {
LoadFirm
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"image/png": "iVBORw0KGgoAAAANSUhEUgAACyIAAAsQCAYAAABS037aAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAC4jAAAuIwF4pT92AAEAAElEQVR4nOzdd3iV9f3/8dd99slOSAgQpoggG1EQFfdWXMWtLXZ8a6e1orW1+rN2OGtrW611FbXWarG2uHCBoAwVhbAEkRnCCNnj5Mz7/v1xwoGQebJOEp6P6+KCc9+f+3O/T668EU9e530My7IsAQAAAAAAAAAAAAAAAAAAAEAcbIkuAAAAAAAAAAAAAAAAAAAAAEDPQxAZAAAAAAAAAAAAAAAAAAAAQNwIIgMAAAAAAAAAAAAAAAAAAACIG0FkAAAAAAAAAAAAAAAAAAAAAHEjiAwAAAAAAAAAAAAAAAAAAAAgbgSRAQAAAAAAAAAAAAAAAAAAAMSNIDIAAAAAAAAAAAAAAAAAAACAuBFEBgAAAAAAAAAAAAAAAAAAABA3gsgAAAAAAAAAAAAAAAAAAAAA4kYQGQAAAAAAAAAAAAAAAAAAAEDcCCIDAAAAAAAAAAAAAAAAAAAAiBtBZAAAAAAAAAAAAAAAAAAAAABxI4gMAAAAAAAAAAAAAAAAAAAAIG4EkQEAAAAAAAAAAAAAAAAAAADEjSAyAAAAAAAAAAAAAAAAAAAAgLgRRAYAAAAAAAAAAAAAAAAAAAAQN4LIAAAAAAAAAAAAAAAAAAAAAOJGEBkAAAAAAAAAAAAAAAAAAABA3AgiAwAAAAAAAAAAAAAAAAAAAIgbQWQAAAAAAAAAAAAAAAAAAAAAcSOIDAAAAAAAAAAAAAAAAAAAACBuBJEBAAAAAAAAAAAAAAAAAAAAxI0gMgAAAAAAAAAAAAAAAAAAAIC4EUQGAAAAAAAAAAAAAAAAAAAAEDeCyAAAAAAAAAAAAAAAAAAAAADiRhAZAAAAAAAAAAAAAAAAAAAAQNwIIgMAAAAAAAAAAAAAAAAAAACIm6Orb1heXq5FixbFHg8aNEhut7urywAAAAAAAAAAAAAAAAAAAAB6tEAgoIKCgtjjU045RRkZGV12/y4PIi9atEiXXHJJV98WAAAAAAAAAAAAAAAAAAAA6NX++9//6uKLL+6y+9m67E4AAAAAAAAAAAAAAAAAAAAAeg2CyAAAAAAAAAAAAAAAAAAAAADi5ujqGw4aNKje4//+97868sgju7oMAAAAAAAAAAAAAAAAAAAAoEf76quvdMkll8QeH5rT7WxdHkR2u931Hh955JEaM2ZMV5cBAAAAAAAAAAAAAAAAAAAA9CqH5nQ7m61L7wYAAAAAAAAAAAAAAAAAAACgVyCIDAAAAAAAAAAAAAAAAAAAACBuBJEBAAAAAAAAAAAAAAAAAAAAxI0gMgAAAAAAAAAAAAAAAAAAAIC4EUQGAAAAAAAAAAAAAAAAAAAAEDeCyAAAAAAAAAAAAAAAAAAAAADiRhAZAAAAAAAAAAAAAAAAAAAAQNwIIgMAAAAAAAAAAAAAAAAAAACIG0FkAAAAAAAAAAAAAAAAAAAAAHEjiAwAAAAAAAAAAAAAAAAAAAAgbgSRAQAAAAAAAAAAAAAAAAAAAMSNIDIAAAAAAAAAAAAAAAAAAACAuBFEBgAAAAAAAAAAAAAAAAAAABA3gsgAAAAAAAAAAAAAAAAAAAAA4kYQGQAAAAAAAAAAAAAAAAAAAEDcCCIDAAAAAAAAAAAAAAAAAAAAiBtBZAAAAAAAAAAAAAAAAAAAAABxI4gMAAAAAAAAAAAAAAAAAAAAIG4EkQEAAAAAAAAAAAAAAAAAAADEjSAyAAAAAAAAAAAAAAAAAAAAgLgRRAYAAAAAAAAAAAAAAAAAAAAQN4LIAAAAAAAAAAAAAAAAAAAAAOJGEBkAAAAAAAAAAAAAAAAAAABA3AgiAwAAAAAAAAAAAAAAAAAAAIgbQWQAAAAAAAAAAAAAAAAAAAAAcSOIDAAAAAAAAAAAAAAAAAAAACBuBJEBAAAAAAAAAAAAAAAAAAAAxI0gMgAAAAAAAAAAAAAAAAAAAIC4EUQGAAAAAAAAAAAAAAAAAAAAEDeCyAAAAAAAAAAAAAAAAAAAAADiRhAZAAAAAAAAAAAAAAAAAAAAQNwIIgMAAAAAAAAAAAAAAAAAAACIG0FkAAAAAAAAAAAAAAAAAAAAAHEjiAwAAAAAAAAAAAAAAAAAAAAgbgSRAQAAAAAAAAAAAAAAAAAAAMSNIDIAAAAAAAAAAAAAAAAAAACAuBFEBgAAAAAAAAAAAAAAAAAAABA3gsgAAAAAAAAAAAAAAAAAAAAA4kYQGQAAAAAAAAAAAAAAAAAAAEDcCCIDAAAAAAAAAAAAAAAAAAAAiBtBZAAAAAAAAAAAAAAAAAAAAABxI4gMAAAAAAAAAAAAAAAAAAAAIG4EkQEAAAAAAAAAAAAAAAAAAADEjSAyAAAAAAAAAAAAAAAAAAAAgLgRRAYAAAAAAAAAAAAAAAAAAAAQN4LIAAAAAAAAAAAAAAAAAAAAAOJGEBkAAAAAAAAAAAAAAAAAAABA3AgiAwAAAAAAAAAAAAAAAAAAAIgbQWQAAAAAAAAAAAAAAAAAAAAAcSOIDAAAAAAAAAAAAAAAAAAAACBuBJEBAAAAAAAAAAAAAAAAAAAAxI0gMgAAAAAAAAAAAAAAAAAAAIC4EUQGAAAAAAAAAAAAAAAAAAAAEDeCyAAAAAAAAAAAAAAAAAAAAADiRhAZAAAAAAAAAAAAAAAAAAAAQNwIIgMAAAAAAAAAAAAAAAAAAACIG0FkAAAAAAAAAAAAAAAAAAAAAHEjiAwAAAAAAAAAAAAAAAAAAAAgbgSRAQAAAAAAAAAAAAAAAAAAAMSNIDIAAAAAAAAAAAAAAAAAAACAuBFEBgAAAAAAAAAAAAAAAAAAABA3gsgAAAAAAAAAAAAAAAAAAAAA4kYQGQAAAAAAAAAAAAAAAAAAAEDcCCIDAAAAAAAAAAAAAAAAAAAAiBtBZAAAAAAAAAAAAAAAAAAAAABxI4gMAAAAAAAAAAAAAAAAAAAAIG4EkQEAAAAAAAAAAAAAAAAAAADEjSAyAAAAAAAAAAAAAAAAAAAAgLgRRAYAAAAAAAAAAAAAAAAAAAAQN4LIAAAAAAAAAAAAAAAAAAAAAOJGEBkAAAAAAAAAAAAAAAAAAABA3AgiAwAAAAAAAAAAAAAAAAAAAIgbQWQAAAAAAAAAAAAAAAAAAAAAcSOIDAAAAAAAAAAAAAAAAAAAACBuBJEBAAAAAAAAAAAAAAAAAAAAxI0gMgAAAAAAAAAAAAAAAAAAAIC4EUQGAAAAAAAAAAAAAAAAAAAAEDeCyAAAAAAAAAAAAAAAAAAAAADiRhAZAAAAAAAAAAAAAAAAAAAAQNwIIgMAAAAAAAAAAAAAAAAAAACIG0FkAAAAAAAAAAAAAAAAAAAAAHEjiAwAAAAAAAAAAAAAAAAAAAAgbgSRAQAAAAAAAAAAAAAAAAAAAMSNIDIAAAAAAAAAAAAAAAAAAACAuDkSXQAAAAAAAADQmSzL0p5Kv9YUVmj9rkqV+0LyhyMKhEwFI6ZcdpvcTps8DrsykpwaPSBN4/LS1S/NI8MwEl0+0C3QRwAAAAAAAACAxhBEBgAAAAAAQK9SVOXXqoJyrS2s0Jq6X8XVwbj3yU5xaVxeusblpWtsXromDspQ31RPJ1QMdD/0EQAAAAAAAACgNQgiAwAAAAAAoMeLmJYWbCjS88u3a/GmfR2yZ3F1UAs37tPCjQf2O3lEjq4/fohOH9VXdhtTXtG70EcAAAAAAAAAgHgRRAYAAAAAAECPta8qoJdXFOifn+xQYXltp99v8aZ9Wrxpn/IyvLpmymBdcewg5aS6O/2+QGeijwAAAAAAAAAAbUUQGQAAAAAAAD1OfkG5nl6yVW+t3a1QxOry+xeW1+rBdzbqj+9/qfPG9te3ThymCYMyurwOoD3oIwAAAAAAAABAexFEBgAAAAAAQI/hC4b1wPyNmrNsW6JLkSSFIpbm5e/SvPxdmnXCUN12zkg
Firm.csv
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"text/plain": [
LoadFirm
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"<Figure size 3600x3600 with 1 Axes>"
Firm.csv
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]
},
"metadata": {},
LoadFirm
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"output_type": "display_data"
Firm.csv
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}
],
"source": [
"import pandas as pd\n",
LoadFirm
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"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.rcParams['font.sans-serif'] = 'SimHei'\n",
Firm.csv
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"\n",
AmendFirm
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"# init graph bom\n",
"BomNodes = pd.read_csv('BomNodes.csv', index_col=0)\n",
"BomNodes.set_index('Code', inplace=True)\n",
"BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)\n",
"BomCateNet.fillna(0, inplace=True)\n",
"\n",
"G_bom = nx.from_pandas_adjacency(BomCateNet, create_using=nx.MultiDiGraph())\n",
"\n",
"bom_labels_dict = {}\n",
"for code in G_bom.nodes:\n",
" bom_labels_dict[code] = BomNodes.loc[code].to_dict()\n",
"nx.set_node_attributes(G_bom, bom_labels_dict)\n",
"\n",
"# init graph firm\n",
"Firm = pd.read_csv(\"Firm_amended.csv\")\n",
Firm.csv
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"Firm.fillna(0, inplace=True)\n",
LoadFirm
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"Firm_attr = Firm[[\"Code\",\"Name\",\"Type_Region\"]]\n",
"firm_product = []\n",
"for _, row in Firm.loc[:,'1':].iterrows():\n",
" firm_product.append(row[row==1].index.to_list())\n",
"Firm_attr['Product_Code'] = firm_product\n",
"Firm_attr.set_index('Code')\n",
"\n",
AmendFirm
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"G_Firm =nx.MultiDiGraph()\n",
"G_Firm.add_nodes_from(Firm[\"Code\"])\n",
LoadFirm
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"\n",
AmendFirm
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"firm_labels_dict = {}\n",
"for code in G_Firm.nodes:\n",
" firm_labels_dict[code] = Firm_attr.loc[code].to_dict()\n",
"nx.set_node_attributes(G_Firm, firm_labels_dict)\n",
LoadFirm
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"\n",
AmendFirm
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"# add edge to G_firm according to G_bom\n",
"product_codes = nx.get_node_attributes(G_Firm, 'Product_Code')\n",
"for node in nx.nodes(G_Firm):\n",
" # print(product_codes[node])\n",
" for product_code in product_codes[node]:\n",
" # print(product_code)\n",
" print(list(G_bom.neighbors(product_code)))\n",
"\n",
"# print graph\n",
"pos = nx.nx_agraph.graphviz_layout(G_Firm, prog=\"twopi\", args=\"\")\n",
"node_labels = nx.get_node_attributes(G_Firm, 'Name')\n",
LoadFirm
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"plt.figure(figsize=(12, 12), dpi=300)\n",
AmendFirm
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"nx.draw_networkx_nodes(G_Firm, pos)\n",
"nx.draw_networkx_edges(G_Firm, pos)\n",
"nx.draw_networkx_labels(G_Firm, pos, labels = node_labels, font_size=6)\n",
LoadFirm
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"plt.show()"
Firm.csv
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]
},
AmendFirm
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{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NodeView(('1', '1.1', '1.1.1', '1.1.2', '1.1.3', '1.2', '1.2.1', '1.2.2', '1.2.3', '1.3', '1.3.1', '1.3.1.1', '1.3.1.2', '1.3.1.3', '1.3.1.4', '1.3.1.5', '1.3.1.6', '1.3.1.7', '1.3.2', '1.3.2.1', '1.3.3', '1.3.3.1', '1.3.3.2', '1.3.3.3', '1.3.3.4', '1.3.3.5', '1.3.3.6', '1.3.3.7', '1.3.4', '1.3.4.1', '1.3.4.2', '1.3.4.3', '1.3.5', '1.3.5.1', '1.4', '1.4.1', '1.4.1.1', '1.4.1.2', '1.4.1.3', '1.4.1.4', '1.4.1.5', '1.4.2', '1.4.2.1', '1.4.2.2', '1.4.2.3', '1.4.2.4', '1.4.2.5', '1.4.2.6', '1.4.2.7', '1.4.3', '1.4.3.1', '1.4.3.2', '1.4.3.3', '1.4.3.4', '1.4.3.5', '1.4.3.6', '1.4.4', '1.4.4.1', '1.4.4.2', '1.4.4.3', '1.4.4.4', '1.4.4.5', '1.4.5', '1.4.5.1', '1.4.5.2', '1.4.5.3', '1.4.5.4', '1.4.5.5', '1.4.5.6', '1.4.5.7', '1.4.5.8', '1.4.5.9', '2', '2.1', '2.1.1', '2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5', '2.1.2', '2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4', '2.1.3', '2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7', '2.1.4', '2.1.4.1', '2.1.4.1.1', '2.1.4.1.2', '2.1.4.1.3', '2.1.4.1.4', '2.1.4.2', '2.1.4.2.1', '2.1.4.2.2', '2.2', '2.3', '2.3.1', '2.3.2', '2.3.3'))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"G_bom.nodes()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['1.1', '1.2', '1.3', '1.4', '2']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(G_bom.neighbors('1'))"
]
},
Firm.csv
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "bcdafc093860683ffb58d6956591562b7f8ed5d58147d17d71a5d4d6605a08df"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}