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anova
2023-07-08 16:23:44 +08:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>自变量</th>\n",
" <th>level</th>\n",
" <th>企业产品中断累计次数</th>\n",
" <th>企业产品中断最大传导次数</th>\n",
" <th>企业产品退出市场数量</th>\n",
" <th>网络恢复用时</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>单一供应商重要性</td>\n",
" <td>低</td>\n",
" <td>1.915</td>\n",
" <td>0.7324</td>\n",
" <td>0.6919</td>\n",
" <td>2.111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>单一供应商重要性</td>\n",
" <td>中</td>\n",
" <td>2.219</td>\n",
" <td>0.9477</td>\n",
" <td>0.7478</td>\n",
" <td>2.242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>单一供应商重要性</td>\n",
" <td>高</td>\n",
" <td>2.708</td>\n",
" <td>1.1902</td>\n",
" <td>0.8713</td>\n",
" <td>2.469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>可重构性</td>\n",
" <td>低</td>\n",
" <td>2.367</td>\n",
" <td>1.0258</td>\n",
" <td>0.8611</td>\n",
" <td>2.515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>可重构性</td>\n",
" <td>中</td>\n",
" <td>2.228</td>\n",
" <td>0.9255</td>\n",
" <td>0.7274</td>\n",
" <td>2.195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>可重构性</td>\n",
" <td>高</td>\n",
" <td>2.247</td>\n",
" <td>0.9191</td>\n",
" <td>0.7226</td>\n",
" <td>2.111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>多供应商策略</td>\n",
" <td>单供应商</td>\n",
" <td>2.523</td>\n",
" <td>1.1081</td>\n",
" <td>0.8349</td>\n",
" <td>2.402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>多供应商策略</td>\n",
" <td>双供应商</td>\n",
" <td>2.253</td>\n",
" <td>0.9342</td>\n",
" <td>0.7568</td>\n",
" <td>2.230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>多供应商策略</td>\n",
" <td>三供应商</td>\n",
" <td>2.066</td>\n",
" <td>0.8281</td>\n",
" <td>0.7193</td>\n",
" <td>2.189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>对规模较大企业的倾向</td>\n",
" <td>不倾向</td>\n",
" <td>2.393</td>\n",
" <td>1.0197</td>\n",
" <td>0.7709</td>\n",
" <td>2.334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>对规模较大企业的倾向</td>\n",
" <td>倾向</td>\n",
" <td>2.168</td>\n",
" <td>0.8939</td>\n",
" <td>0.7698</td>\n",
" <td>2.214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>额外产能</td>\n",
" <td>低</td>\n",
" <td>2.322</td>\n",
" <td>0.9941</td>\n",
" <td>0.8553</td>\n",
" <td>2.442</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>额外产能</td>\n",
" <td>中</td>\n",
" <td>2.323</td>\n",
" <td>0.9772</td>\n",
" <td>0.7891</td>\n",
" <td>2.330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>额外产能</td>\n",
" <td>高</td>\n",
" <td>2.197</td>\n",
" <td>0.8991</td>\n",
" <td>0.6667</td>\n",
" <td>2.050</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 自变量 level 企业产品中断累计次数 企业产品中断最大传导次数 企业产品退出市场数量 网络恢复用时\n",
"8 单一供应商重要性 低 1.915 0.7324 0.6919 2.111\n",
"9 单一供应商重要性 中 2.219 0.9477 0.7478 2.242\n",
"10 单一供应商重要性 高 2.708 1.1902 0.8713 2.469\n",
"5 可重构性 低 2.367 1.0258 0.8611 2.515\n",
"6 可重构性 中 2.228 0.9255 0.7274 2.195\n",
"7 可重构性 高 2.247 0.9191 0.7226 2.111\n",
"13 多供应商策略 单供应商 2.523 1.1081 0.8349 2.402\n",
"12 多供应商策略 双供应商 2.253 0.9342 0.7568 2.230\n",
"11 多供应商策略 三供应商 2.066 0.8281 0.7193 2.189\n",
"1 对规模较大企业的倾向 不倾向 2.393 1.0197 0.7709 2.334\n",
"0 对规模较大企业的倾向 倾向 2.168 0.8939 0.7698 2.214\n",
"4 额外产能 低 2.322 0.9941 0.8553 2.442\n",
"3 额外产能 中 2.323 0.9772 0.7891 2.330\n",
"2 额外产能 高 2.197 0.8991 0.6667 2.050"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"config = {\"figure.dpi\": 300,\n",
" \"font.family\": 'serif',\n",
" \"font.serif\": ['SimSun']}\n",
"df = pd.read_csv('analysis/anova_visualization.csv', encoding='utf-8-sig')\n",
"df['sort_index'] = df['level'].map({'不倾向':0,\n",
" '倾向':1,\n",
" '低':0,\n",
" '中':1,\n",
" '高':2,\n",
" '单供应商':0,\n",
" '双供应商':1,\n",
" '三供应商':2})\n",
"df.sort_values(['自变量', 'sort_index'], inplace=True)\n",
"df.drop(columns='sort_index', inplace=True)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '对规模较大企业的倾向')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_name = '对规模较大企业的倾向'\n",
"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\")\n",
"ax.set_title(x_name)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '额外产能')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_name = '额外产能'\n",
"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\")\n",
"ax.set_title(x_name)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '可重构性')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_name = '可重构性'\n",
"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\")\n",
"ax.set_title(x_name)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '单一供应商重要性')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_name = '单一供应商重要性'\n",
"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\")\n",
"ax.set_title(x_name)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '多供应商策略')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1920x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_name = '多供应商策略'\n",
"df_x = df.loc[df['自变量']==x_name, 'level':].set_index('level').stack().reset_index().rename(columns={'level':'水平','level_1':'响应变量',0:'均值'})\n",
"sns.set_theme(style=\"whitegrid\", rc=config)\n",
"ax = sns.pointplot(data=df_x, x=\"水平\", y=\"均值\", hue=\"响应变量\")\n",
"ax.set_title(x_name)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "iiabm_py3.8.8",
"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
},
"nbformat": 4,
"nbformat_minor": 2
}