lzh
1312 lines
163 KiB
Plaintext
1312 lines
163 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams['font.sans-serif']='SimHei'\n",
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"plt.rcParams['axes.unicode_minus']=False"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Title-题名</th>\n",
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" <th>Author-作者</th>\n",
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" <th>Organ-单位</th>\n",
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" <th>Source-文献来源</th>\n",
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" <th>Keyword-关键词</th>\n",
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" <th>Summary-摘要</th>\n",
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" <th>PubTime-发表时间</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>应用金融科技创新服务小微企业的途径与机制研究</td>\n",
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" <td>李慧;</td>\n",
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||
" <td>中国工商银行股份有限公司天津市分行;</td>\n",
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||
" <td>第三十四届中国（天津）2020’IT、网络、信息技术、电子、仪器仪表创新学术会议论文集</td>\n",
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||
" <td>融资困境;;金融科技;;小微企业</td>\n",
|
||
" <td>近些年来,人工智能、区块链、云计算和大数据成为了各行各业的焦点,传统金融业包括商业银行在内,...</td>\n",
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" <td>2020-08-17</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <th>1</th>\n",
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" <td>金融科技研究进展与评析</td>\n",
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" <td>鲁钊阳;张珂瑞;</td>\n",
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" <td>西南政法大学经济学院;</td>\n",
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" <td>金融理论与实践</td>\n",
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||
" <td>金融科技;;金融创新;;金融发展</td>\n",
|
||
" <td>从历史的角度来看,金融科技的产生是数百年来技术变革的必然产物,大数据、区块链、云计算、人工智...</td>\n",
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" <td>2020-08-12</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>2</th>\n",
|
||
" <td>打造科技创新引擎 赋能业务价值创造</td>\n",
|
||
" <td>陈满才;</td>\n",
|
||
" <td>中国工商银行金融科技部;</td>\n",
|
||
" <td>中国金融电脑</td>\n",
|
||
" <td>云计算平台;物联网;人工智能技术;普惠金融发展;服务实体经济;工作体制机制;产学研用;银行业...</td>\n",
|
||
" <td>当前,金融科技已逐渐从后台走向前台,成为推动金融转型升级的新引擎、金融服务实体经济的新途径、...</td>\n",
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" <td>2020-08-07</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>3</th>\n",
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" <td>新兴技术创新应用重塑金融服务 助力金融业数字化战略转型</td>\n",
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||
" <td>赵希同;</td>\n",
|
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" <td>中国银行信息科技部;</td>\n",
|
||
" <td>中国金融电脑</td>\n",
|
||
" <td>监管效能;新兴技术;区块链;差异化优势;容错纠错机制;数字化转型;创新应用;金融业;银行业;...</td>\n",
|
||
" <td>面对金融竞争的新常态,商业银行要顺势而为、及时转型,利用新兴技术撬动金融创新,构建以客户为中...</td>\n",
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" <td>2020-08-07</td>\n",
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" </tr>\n",
|
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" <tr>\n",
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" <th>4</th>\n",
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||
" <td>发力新技术创新,为金融服务注入新动能</td>\n",
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||
" <td>寇冠;</td>\n",
|
||
" <td>中信银行信息技术管理部;</td>\n",
|
||
" <td>中国金融电脑</td>\n",
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||
" <td>中信银行;技术服务平台;物联网;平台体系;科技创新工作;区块链;人工智能;供应链金融;数字货...</td>\n",
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" <td>中信银行秉承\"新技术驱动、价值导向\"的科技创新理念,兼顾互联网\"快\"和银行\"稳\"的特点,重点...</td>\n",
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" <td>2020-08-07</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
|
||
" Title-题名 Author-作者 Organ-单位 \\\n",
|
||
"0 应用金融科技创新服务小微企业的途径与机制研究 李慧; 中国工商银行股份有限公司天津市分行; \n",
|
||
"1 金融科技研究进展与评析 鲁钊阳;张珂瑞; 西南政法大学经济学院; \n",
|
||
"2 打造科技创新引擎 赋能业务价值创造 陈满才; 中国工商银行金融科技部; \n",
|
||
"3 新兴技术创新应用重塑金融服务 助力金融业数字化战略转型 赵希同; 中国银行信息科技部; \n",
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||
"4 发力新技术创新,为金融服务注入新动能 寇冠; 中信银行信息技术管理部; \n",
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||
"\n",
|
||
" Source-文献来源 \\\n",
|
||
"0 第三十四届中国（天津）2020’IT、网络、信息技术、电子、仪器仪表创新学术会议论文集 \n",
|
||
"1 金融理论与实践 \n",
|
||
"2 中国金融电脑 \n",
|
||
"3 中国金融电脑 \n",
|
||
"4 中国金融电脑 \n",
|
||
"\n",
|
||
" Keyword-关键词 \\\n",
|
||
"0 融资困境;;金融科技;;小微企业 \n",
|
||
"1 金融科技;;金融创新;;金融发展 \n",
|
||
"2 云计算平台;物联网;人工智能技术;普惠金融发展;服务实体经济;工作体制机制;产学研用;银行业... \n",
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||
"3 监管效能;新兴技术;区块链;差异化优势;容错纠错机制;数字化转型;创新应用;金融业;银行业;... \n",
|
||
"4 中信银行;技术服务平台;物联网;平台体系;科技创新工作;区块链;人工智能;供应链金融;数字货... \n",
|
||
"\n",
|
||
" Summary-摘要 PubTime-发表时间 \n",
|
||
"0 近些年来,人工智能、区块链、云计算和大数据成为了各行各业的焦点,传统金融业包括商业银行在内,... 2020-08-17 \n",
|
||
"1 从历史的角度来看,金融科技的产生是数百年来技术变革的必然产物,大数据、区块链、云计算、人工智... 2020-08-12 \n",
|
||
"2 当前,金融科技已逐渐从后台走向前台,成为推动金融转型升级的新引擎、金融服务实体经济的新途径、... 2020-08-07 \n",
|
||
"3 面对金融竞争的新常态,商业银行要顺势而为、及时转型,利用新兴技术撬动金融创新,构建以客户为中... 2020-08-07 \n",
|
||
"4 中信银行秉承\"新技术驱动、价值导向\"的科技创新理念,兼顾互联网\"快\"和银行\"稳\"的特点,重点... 2020-08-07 "
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df=pd.read_csv('CNKI.csv',encoding='gbk')\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 作者合作网络"
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]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"authors=[]\n",
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||
"keywords={}\n",
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"\n",
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"for keys in df['Author-作者']:\n",
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||
" key_list=keys.split(';')\n",
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" for key in key_list:\n",
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" if key=='':\n",
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||
" continue\n",
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||
" if key in keywords:\n",
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||
" keywords[key]+=1\n",
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||
" else:\n",
|
||
" keywords[key]=1\n",
|
||
" if key not in authors:\n",
|
||
" authors.append(key)\n"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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" <th></th>\n",
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" <th>李慧</th>\n",
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" <th>鲁钊阳</th>\n",
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" <th>张珂瑞</th>\n",
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" <th>陈满才</th>\n",
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" <th>赵希同</th>\n",
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" <th>寇冠</th>\n",
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" <th>史晨阳</th>\n",
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" <th>吴华晖</th>\n",
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" <th>卢河英</th>\n",
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" <th>薛洪言</th>\n",
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" <th>...</th>\n",
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" <th>葛秀楠</th>\n",
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" <th>综合凤凰科技和中新网的报道整理</th>\n",
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" <th>刘曙峰</th>\n",
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" <th>于跃</th>\n",
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" <th>舒文琼</th>\n",
|
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" <th>韩倩倩</th>\n",
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" <th>秦谊</th>\n",
|
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" <th>许高攀</th>\n",
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" <th>曾文华</th>\n",
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" <th>吴学谋</th>\n",
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"<p>5 rows × 1168 columns</p>\n",
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"</div>"
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],
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"text/plain": [
|
||
" 李慧 鲁钊阳 张珂瑞 陈满才 赵希同 寇冠 史晨阳 吴华晖 卢河英 薛洪言 ... 葛秀楠 \\\n",
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"李慧 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 \n",
|
||
"鲁钊阳 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 \n",
|
||
"张珂瑞 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 \n",
|
||
"陈满才 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 \n",
|
||
"赵希同 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 \n",
|
||
"\n",
|
||
" 综合凤凰科技和中新网的报道整理 刘曙峰 于跃 舒文琼 韩倩倩 秦谊 许高攀 曾文华 吴学谋 \n",
|
||
"李慧 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"鲁钊阳 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"张珂瑞 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"陈满才 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"赵希同 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
"[5 rows x 1168 columns]"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"l=len(authors)\n",
|
||
"arr=np.zeros((l,l))\n",
|
||
"co_author_arr=pd.DataFrame(arr,index=authors,columns=authors)\n",
|
||
"co_author_arr.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100.0%"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"leg=len(authors)\n",
|
||
"count=0\n",
|
||
"for index,row in co_author_arr.iterrows():\n",
|
||
" for name in row.index:\n",
|
||
" if name!=index:\n",
|
||
" row[name]+=1\n",
|
||
" row[name]+=co_author_arr.loc[name,index]\n",
|
||
" count+=1\n",
|
||
" print('\\r{}%'.format(count/leg*100),end='')\n",
|
||
" "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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|
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"\n",
|
||
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
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" text-align: right;\n",
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||
" }\n",
|
||
"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>李慧</th>\n",
|
||
" <th>鲁钊阳</th>\n",
|
||
" <th>张珂瑞</th>\n",
|
||
" <th>陈满才</th>\n",
|
||
" <th>赵希同</th>\n",
|
||
" <th>寇冠</th>\n",
|
||
" <th>史晨阳</th>\n",
|
||
" <th>吴华晖</th>\n",
|
||
" <th>卢河英</th>\n",
|
||
" <th>薛洪言</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>葛秀楠</th>\n",
|
||
" <th>综合凤凰科技和中新网的报道整理</th>\n",
|
||
" <th>刘曙峰</th>\n",
|
||
" <th>于跃</th>\n",
|
||
" <th>舒文琼</th>\n",
|
||
" <th>韩倩倩</th>\n",
|
||
" <th>秦谊</th>\n",
|
||
" <th>许高攀</th>\n",
|
||
" <th>曾文华</th>\n",
|
||
" <th>吴学谋</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>李慧</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>鲁钊阳</th>\n",
|
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" <td>2.0</td>\n",
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" <td>0.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
|
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" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>张珂瑞</th>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2.0</td>\n",
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" <td>0.0</td>\n",
|
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>...</td>\n",
|
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|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>陈满才</th>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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|
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" <td>1.0</td>\n",
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|
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|
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" <td>1.0</td>\n",
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|
||
" <td>1.0</td>\n",
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" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>赵希同</th>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2.0</td>\n",
|
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|
||
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|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
||
" <td>...</td>\n",
|
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" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 1168 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 李慧 鲁钊阳 张珂瑞 陈满才 赵希同 寇冠 史晨阳 吴华晖 卢河英 薛洪言 ... 葛秀楠 \\\n",
|
||
"李慧 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 \n",
|
||
"鲁钊阳 2.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 \n",
|
||
"张珂瑞 2.0 2.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 \n",
|
||
"陈满才 2.0 2.0 2.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 \n",
|
||
"赵希同 2.0 2.0 2.0 2.0 0.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 \n",
|
||
"\n",
|
||
" 综合凤凰科技和中新网的报道整理 刘曙峰 于跃 舒文琼 韩倩倩 秦谊 许高攀 曾文华 吴学谋 \n",
|
||
"李慧 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n",
|
||
"鲁钊阳 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n",
|
||
"张珂瑞 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n",
|
||
"陈满才 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n",
|
||
"赵希同 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 \n",
|
||
"\n",
|
||
"[5 rows x 1168 columns]"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"co_author_arr.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 建立聚类表格"
|
||
]
|
||
},
|
||
{
|
||
"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>融资困境</th>\n",
|
||
" <th>金融科技</th>\n",
|
||
" <th>小微企业</th>\n",
|
||
" <th>金融创新</th>\n",
|
||
" <th>金融发展</th>\n",
|
||
" <th>云计算平台</th>\n",
|
||
" <th>物联网</th>\n",
|
||
" <th>人工智能技术</th>\n",
|
||
" <th>普惠金融发展</th>\n",
|
||
" <th>服务实体经济</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>金融危机</th>\n",
|
||
" <th>危机根源</th>\n",
|
||
" <th>第四次科技革命</th>\n",
|
||
" <th>泛系资源</th>\n",
|
||
" <th>泛通</th>\n",
|
||
" <th>交通</th>\n",
|
||
" <th>通信</th>\n",
|
||
" <th>网络</th>\n",
|
||
" <th>泛系数学</th>\n",
|
||
" <th>泛系史学</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Title-题名</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>应用金融科技创新服务小微企业的途径与机制研究</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>金融科技研究进展与评析</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>打造科技创新引擎 赋能业务价值创造</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
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|
||
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|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
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],
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"text/plain": [
|
||
" 融资困境 金融科技 小微企业 金融创新 金融发展 云计算平台 物联网 人工智能技术 \\\n",
|
||
"Title-题名 \n",
|
||
"应用金融科技创新服务小微企业的途径与机制研究 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"金融科技研究进展与评析 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"打造科技创新引擎 赋能业务价值创造 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"新兴技术创新应用重塑金融服务 助力金融业数字化战略转型 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"发力新技术创新,为金融服务注入新动能 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
" 普惠金融发展 服务实体经济 ... 金融危机 危机根源 第四次科技革命 泛系资源 \\\n",
|
||
"Title-题名 ... \n",
|
||
"应用金融科技创新服务小微企业的途径与机制研究 0.0 0.0 ... 0.0 0.0 0.0 0.0 \n",
|
||
"金融科技研究进展与评析 0.0 0.0 ... 0.0 0.0 0.0 0.0 \n",
|
||
"打造科技创新引擎 赋能业务价值创造 0.0 0.0 ... 0.0 0.0 0.0 0.0 \n",
|
||
"新兴技术创新应用重塑金融服务 助力金融业数字化战略转型 0.0 0.0 ... 0.0 0.0 0.0 0.0 \n",
|
||
"发力新技术创新,为金融服务注入新动能 0.0 0.0 ... 0.0 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
" 泛通 交通 通信 网络 泛系数学 泛系史学 \n",
|
||
"Title-题名 \n",
|
||
"应用金融科技创新服务小微企业的途径与机制研究 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
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|
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"新兴技术创新应用重塑金融服务 助力金融业数字化战略转型 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"发力新技术创新,为金融服务注入新动能 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
"[5 rows x 2021 columns]"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"keywords=[]\n",
|
||
"for keys in df['Keyword-关键词']:\n",
|
||
" key_list=keys.split(';')\n",
|
||
" for key in key_list:\n",
|
||
" if key=='':\n",
|
||
" continue\n",
|
||
" if key not in keywords:\n",
|
||
" keywords.append(key)\n",
|
||
" \n",
|
||
"title=df['Title-题名']\n",
|
||
"arr=np.zeros((len(title),len(keywords)))\n",
|
||
"tit_key=pd.DataFrame(arr,index=title,columns=keywords)\n",
|
||
"tit_key.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
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"data": {
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|
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|
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|
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|
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|
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" <th>泛系史学</th>\n",
|
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|
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|
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|
||
"</table>\n",
|
||
"<p>5 rows × 2021 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 融资困境 金融科技 小微企业 金融创新 金融发展 云计算平台 物联网 人工智能技术 \\\n",
|
||
"Title-题名 \n",
|
||
"应用金融科技创新服务小微企业的途径与机制研究 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
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|
||
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|
||
"新兴技术创新应用重塑金融服务 助力金融业数字化战略转型 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 \n",
|
||
"发力新技术创新,为金融服务注入新动能 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 \n",
|
||
"\n",
|
||
" 普惠金融发展 服务实体经济 ... 金融危机 危机根源 第四次科技革命 泛系资源 \\\n",
|
||
"Title-题名 ... \n",
|
||
"应用金融科技创新服务小微企业的途径与机制研究 0.0 0.0 ... 0.0 0.0 0.0 0.0 \n",
|
||
"金融科技研究进展与评析 0.0 0.0 ... 0.0 0.0 0.0 0.0 \n",
|
||
"打造科技创新引擎 赋能业务价值创造 1.0 1.0 ... 0.0 0.0 0.0 0.0 \n",
|
||
"新兴技术创新应用重塑金融服务 助力金融业数字化战略转型 0.0 0.0 ... 0.0 0.0 0.0 0.0 \n",
|
||
"发力新技术创新,为金融服务注入新动能 0.0 0.0 ... 0.0 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
" 泛通 交通 通信 网络 泛系数学 泛系史学 \n",
|
||
"Title-题名 \n",
|
||
"应用金融科技创新服务小微企业的途径与机制研究 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"金融科技研究进展与评析 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"打造科技创新引擎 赋能业务价值创造 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"新兴技术创新应用重塑金融服务 助力金融业数字化战略转型 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"发力新技术创新,为金融服务注入新动能 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
"[5 rows x 2021 columns]"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df=df.set_index('Title-题名')\n",
|
||
"for index,row in tit_key.iterrows():\n",
|
||
" kw=df.loc[index,'Keyword-关键词']\n",
|
||
" try:\n",
|
||
" keys=kw.split(';')\n",
|
||
" for k in keys:\n",
|
||
" if k in keywords:\n",
|
||
" row[k]=1\n",
|
||
" except:\n",
|
||
" pass\n",
|
||
"\n",
|
||
"tit_key.head() "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 使用轮廓系数评估"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"正在构建模型:100.0%"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 720x432 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 720x432 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 720x432 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.cluster import KMeans \n",
|
||
"from sklearn.metrics import silhouette_score\n",
|
||
"from sklearn.metrics import calinski_harabaz_score\n",
|
||
"\n",
|
||
"\n",
|
||
"#评价指标\n",
|
||
"silhouettteScore = []\n",
|
||
"CH_score=[]\n",
|
||
"SSE=[]\n",
|
||
"for i in range(2,30):\n",
|
||
" ##构建并训练模型\n",
|
||
" print('\\r正在构建模型:{}%'.format((i-1)/28*100),end='')\n",
|
||
" kmeans = KMeans(n_clusters = i,random_state=123).fit(tit_key)\n",
|
||
" score = silhouette_score(tit_key,kmeans.labels_)\n",
|
||
" ch=calinski_harabaz_score(tit_key,kmeans.labels_)\n",
|
||
" \n",
|
||
" silhouettteScore.append(score)\n",
|
||
" CH_score.append(ch)\n",
|
||
" SSE.append(kmeans.inertia_)\n",
|
||
"def plot_one(scores,title):\n",
|
||
" plt.figure(figsize=(10,6))\n",
|
||
" plt.plot(range(2,30),scores,linewidth=1.5, linestyle=\"-\")\n",
|
||
" plt.title(title)\n",
|
||
"plot_one(silhouettteScore,'轮廓系数')\n",
|
||
"plot_one(CH_score,'CH')\n",
|
||
"plot_one(SSE,'SSE')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"- 由上图选取k=16作为评价指标"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 数据降维并可视化"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x720 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.manifold import TSNE\n",
|
||
"\n",
|
||
"\n",
|
||
"kmeans=KMeans(n_clusters=16,random_state=123).fit(tit_key) #构建并训练模型\n",
|
||
"\n",
|
||
"tsne = TSNE(n_components=2,init='random',random_state=177).fit(tit_key)\n",
|
||
"result=pd.DataFrame(tsne.embedding_) ##将原始数据转换为DataFrame\n",
|
||
"result['labels'] = kmeans.labels_ ##将聚类结果存储进df数据表\n",
|
||
" \n",
|
||
"\n",
|
||
"# ## 绘制图形\n",
|
||
"plt.figure(figsize=(12,10))\n",
|
||
"for i in range(17):\n",
|
||
" temp=result[result['labels']==i]\n",
|
||
" plt.scatter(temp[0],temp[1])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Scalar value for argument 'color' is not numeric\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"#Python中实现视频流中的人脸检测\n",
|
||
"import cv2\n",
|
||
"cap = cv2.VideoCapture(0)\n",
|
||
"face = cv2.CascadeClassifier('E:\\\\anaconda3\\\\Lib\\\\site-packages\\\\cv2\\\\data\\\\haarcascade_frontalface_default.xml')\n",
|
||
"eye = cv2.CascadeClassifier('E:\\\\anaconda3\\\\Lib\\\\site-packages\\\\cv2\\\\data\\\\haarcascade_eye.xml')\n",
|
||
"smile = cv2.CascadeClassifier('E:\\\\anaconda3\\\\Lib\\\\site-packages\\\\cv2\\\\data\\\\haarcascade_smile.xml')\n",
|
||
"while(1):\n",
|
||
" ret,frame = cap.read()\n",
|
||
" gray = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)\n",
|
||
" faces = face.detectMultiScale(gray,1.1,3,0,(200,100))\n",
|
||
" for (x,y,w,h) in faces:\n",
|
||
" img = cv2.rectangle(frame,(x,y),(x+w,y+h),(255,255,0),2)\n",
|
||
" gray_roi = gray[y:y+h,x:x+h]\n",
|
||
" eyes = eye.detectMultiScale(gray_roi,1.02,3,0,(50,50))\n",
|
||
" for (ex,ey,ew,eh) in eyes:\n",
|
||
" cv2.rectangle(img,(x+ex,y+ey),(x+ex+ew,y+ey+eh),(0,255,0),2)\n",
|
||
" smiles = smile.detectMultiScale(gray,1.1,3,0,(100,100))\n",
|
||
" for (sx,sy,sw,sh) in smiles:\n",
|
||
" cv2.rectangle(frame,(sx,sy),(sx+sw,sy+sh),(0,255,255),2)\n",
|
||
" cv2.imshow('摄像头',frame)\n",
|
||
" try:\n",
|
||
" cv2.rectangle(frame,'呵呵','r')\n",
|
||
" except Exception as e :\n",
|
||
" print(e)\n",
|
||
" break\n",
|
||
" if cv2.waitKey(1) & 0xFF == ord('q'):\n",
|
||
" break\n",
|
||
"cap.release()\n",
|
||
"cv2.destroyAllWindows()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"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": "3.6.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|