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{
"cells": [
{
"cell_type": "code",
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"execution_count": 207,
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"metadata": {},
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"C:\\Users\\25759\\AppData\\Local\\Temp\\ipykernel_3908\\296948596.py:42: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
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" Firm_copy.insert(Firm_copy.columns.get_loc('Revenue'),'Revenue_Log', series_Revenue_Log)\n",
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"C:\\Users\\25759\\AppData\\Local\\Temp\\ipykernel_3908\\296948596.py:44: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
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" Firm_copy.insert(Firm_copy.columns.get_loc('Num_Employ'),'Num_Employ_Log', series_Num_Employ_Log)\n"
]
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"1\n"
]
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}
],
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"source": [
"import pandas as pd\n",
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"import numpy as np\n",
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"import math\n",
"import statsmodels.formula.api as smf\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
"plt.rcParams['font.sans-serif'] = 'SimHei'\n",
"\n",
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"# init graph bom\n",
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"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 = nx.from_pandas_adjacency(BomCateNet, create_using=nx.MultiDiGraph())\n",
"\n",
"labels_dict = {}\n",
"for code in G.nodes:\n",
" labels_dict[code] = BomNodes.loc[code].to_dict()\n",
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"nx.set_node_attributes(G, labels_dict)\n",
"\n",
"# load firm\n",
"Firm = pd.read_csv(\"Firm.csv\")\n",
"Firm_copy = Firm.copy()\n",
"\n",
"# get log revenue\n",
"Firm_copy['Revenue_Log'] = Firm_copy['Revenue'].map(math.log)\n",
"Firm_copy['Num_Employ_Log'] = Firm_copy['Num_Employ'].map(math.log)\n",
"data_ols = Firm_copy[Firm_copy[['Revenue_Log', 'Num_Employ_Log']].notnull().all(axis=1)][['Revenue_Log', 'Num_Employ_Log']]\n",
"\n",
"ols_model = smf.ols('Revenue_Log ~ Num_Employ_Log', data=data_ols)\n",
"ols_results = ols_model.fit()\n",
"b = ols_results.params.Intercept\n",
"a = ols_results.params.Num_Employ_Log\n",
"# sns.regplot(x='Num_Employ_Log',y='Revenue_Log',data=data_ols[data_ols.notnull().all(axis=1)])\n",
"\n",
"Firm_copy.loc[Firm_copy['Revenue_Log'].isnull(), 'Revenue_Log'] = Firm_copy[Firm_copy['Revenue_Log'].isnull()]['Num_Employ_Log'].map(lambda x: a*x + b)\n",
"series_Revenue_Log = Firm_copy.pop('Revenue_Log')\n",
"Firm_copy.insert(Firm_copy.columns.get_loc('Revenue'),'Revenue_Log', series_Revenue_Log)\n",
"series_Num_Employ_Log = Firm_copy.pop('Num_Employ_Log')\n",
"Firm_copy.insert(Firm_copy.columns.get_loc('Num_Employ'),'Num_Employ_Log', series_Num_Employ_Log)\n",
"\n",
"# lift firm from neighboring tier\n",
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"node_list = list(nx.bfs_tree(G, '1'))\n",
"node_list.reverse()\n",
"for node in node_list[:-1]:\n",
" if G.out_degree(node) > 0 and sum(Firm_copy[node]==1) == 0:\n",
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" list_neighbors = list(G.neighbors(node))\n",
" firm_list = Firm_copy.index[(Firm_copy[list_neighbors]==1).all(axis=1)].to_list()\n",
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" if firm_list: # there exist firm that produces all components\n",
" # lift firm with size above average in firm_list\n",
" average_size = Firm_copy.loc[firm_list, 'Revenue_Log'].mean()\n",
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" firm_selected_list = Firm_copy.loc[firm_list].loc[Firm_copy.loc[firm_list, 'Revenue_Log'] > average_size].index.to_list()\n",
" Firm_copy.loc[firm_selected_list, node] = 1\n",
" Firm_copy.loc[firm_selected_list, list_neighbors] = np.nan\n",
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" else: # select top 15% firm in terms of size\n",
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" firm_list = Firm_copy.index[(Firm_copy[list_neighbors]==1).any(axis=1)].to_list()\n",
" firm_df = Firm_copy.loc[firm_list].sort_values('Revenue_Log', ascending=False)\n",
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" num_firm_selected = round(firm_df.shape[0] * 0.15)\n",
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" firm_selected_list = firm_df.index[0: num_firm_selected].to_list()\n",
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" Firm_copy.loc[firm_selected_list, node] = 1\n",
" Firm_copy.loc[firm_selected_list, list_neighbors] = np.nan\n",
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"\n",
"# output\n",
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"Firm_copy.to_csv('Firm_amended.csv', index=False, encoding='utf-8-sig')"
]
},
{
"cell_type": "code",
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"execution_count": 211,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 3600x3600 with 1 Axes>"
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]
},
"metadata": {},
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"output_type": "display_data"
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}
],
"source": [
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"# visualization\n",
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"firm_num_dict = {}\n",
"for node in nx.nodes(G):\n",
" firm_num_dict[node]= sum(Firm_copy[node]==1)\n",
"nx.set_node_attributes(G, firm_num_dict, name=\"Num_Firm\")\n",
"\n",
"pos = nx.nx_agraph.graphviz_layout(G, prog=\"twopi\", args=\"\")\n",
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"dict_num_firm = nx.get_node_attributes(G, 'Num_Firm')\n",
"dict_node_name = nx.get_node_attributes(G, 'Name')\n",
"node_labels = {}\n",
"for node in nx.nodes(G):\n",
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" node_labels[node] = f\"{node} {str(dict_node_name[node])} {str(dict_num_firm[node])}\"\n",
" # node_labels[node] = f\"{str(dict_num_firm[node])}\"\n",
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"plt.figure(figsize=(12, 12), dpi=300)\n",
"nx.draw_networkx_nodes(G, pos)\n",
"nx.draw_networkx_edges(G, pos)\n",
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"nx.draw_networkx_labels(G, pos, labels = node_labels, font_size=4)\n",
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"plt.show()"
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]
},
{
"cell_type": "code",
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"execution_count": 215,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[99]\n"
]
}
],
"source": [
"node = '2.1'\n",
"if G.out_degree(node) > 0 and sum(Firm_copy[node]==1) == 0:\n",
" list_neighbors = list(G.neighbors(node))\n",
" firm_list = Firm_copy.index[(Firm_copy[list_neighbors]==1).all(axis=1)].to_list()\n",
" print(firm_list)\n",
" if firm_list: # there exist firm that produces all components\n",
" # lift firm with size above average in firm_list\n",
" average_size = Firm_copy.loc[firm_list, 'Revenue_Log'].mean()\n",
" firm_selected_list = Firm_copy.loc[firm_list].loc[Firm_copy.loc[firm_list, 'Revenue_Log'] > average_size].index.to_list()\n",
" Firm_copy.loc[firm_selected_list, node] = 1\n",
" Firm_copy.loc[firm_selected_list, list_neighbors] = np.nan\n",
" else: # select top 15% firm in terms of size\n",
" firm_list = Firm_copy.index[(Firm_copy[list_neighbors]==1).any(axis=1)].to_list()\n",
" firm_df = Firm_copy.loc[firm_list].sort_values('Revenue_Log', ascending=False)\n",
" num_firm_selected = round(firm_df.shape[0] * 0.2)\n",
" print(num_firm_selected)\n",
" firm_selected_list = firm_df.index[0: num_firm_selected].to_list()\n",
" Firm_copy.loc[firm_selected_list, node] = 1\n",
" Firm_copy.loc[firm_selected_list, list_neighbors] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 209,
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"metadata": {},
"outputs": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
"0 7393.0\n",
"1 171.0\n",
"2 113.0\n",
"3 24.0\n",
"4 242.0\n",
" ... \n",
"165 60.0\n",
"166 292.0\n",
"167 13371.0\n",
"168 5057.0\n",
"169 5173.0\n",
"Name: Num_Employ, Length: 129, dtype: float64\n"
]
},
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{
"data": {
"text/plain": [
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"<seaborn.axisgrid.FacetGrid at 0x1dd9f878790>"
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]
},
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"execution_count": 209,
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"metadata": {},
"output_type": "execute_result"
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},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
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}
],
"source": [
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"import seaborn as sns\n",
"data = Firm_copy[Firm_copy['Num_Employ'] > 0]['Num_Employ']\n",
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"print(data)\n",
"sns.displot(data)"
]
},
{
"cell_type": "code",
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"execution_count": 210,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1.089000e+10\n",
"1 1.380000e+08\n",
"6 1.590000e+08\n",
"11 6.750000e+08\n",
"13 2.470000e+09\n",
" ... \n",
"160 6.894667e+09\n",
"162 5.880760e+07\n",
"167 4.110000e+10\n",
"168 4.519000e+09\n",
"169 3.470400e+10\n",
"Name: Revenue, Length: 110, dtype: float64\n"
]
},
{
"data": {
"text/plain": [
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"<seaborn.axisgrid.FacetGrid at 0x1dda1ef1ca0>"
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]
},
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"execution_count": 210,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"data = Firm_copy[Firm_copy['Revenue'] > 0]['Revenue']\n",
"print(data)\n",
"sns.displot(data)"
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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
}
