lzh
1409 lines
168 KiB
Plaintext
1409 lines
168 KiB
Plaintext
{
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{
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"cell_type": "code",
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"execution_count": 1,
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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": 2,
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"metadata": {},
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"outputs": [
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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",
|
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" <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",
|
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" <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",
|
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" <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",
|
||
" <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",
|
||
" <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": [
|
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" 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",
|
||
"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",
|
||
"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": 2,
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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": 21,
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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",
|
||
"exponet=[]#每篇文章各个关键词出现次数之和，排序依据\n",
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||
"for keys in df['Author-作者']:\n",
|
||
" key_list=keys.split(';')\n",
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||
" for key in key_list:\n",
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" if key=='':\n",
|
||
" continue\n",
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||
" if key in keywords:\n",
|
||
" keywords[key]+=1\n",
|
||
" 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",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"\n",
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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>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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" </tr>\n",
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" <th>李慧</th>\n",
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"</table>\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",
|
||
"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": 23,
|
||
"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": 32,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" 100.00%"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"total=l*l\n",
|
||
"for i in range(l):\n",
|
||
" for j in range(l):\n",
|
||
" if i!=j:\n",
|
||
" co_author_arr.iloc[i,j]+=1\n",
|
||
" co_author_arr.iloc[i,j]+=co_author_arr.iloc[j,i]\n",
|
||
" print('\\r {:.2f}%'.format((i*l+j)/total*100),end='')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 101,
|
||
"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>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>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>鲁钊阳</th>\n",
|
||
" <td>54.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>张珂瑞</th>\n",
|
||
" <td>54.0</td>\n",
|
||
" <td>54.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>陈满才</th>\n",
|
||
" <td>54.0</td>\n",
|
||
" <td>54.0</td>\n",
|
||
" <td>54.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>赵希同</th>\n",
|
||
" <td>54.0</td>\n",
|
||
" <td>54.0</td>\n",
|
||
" <td>54.0</td>\n",
|
||
" <td>54.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>4.0</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 33.0 33.0 33.0 33.0 33.0 33.0 33.0 33.0 33.0 ... 4.0 \n",
|
||
"鲁钊阳 54.0 0.0 33.0 33.0 33.0 33.0 33.0 33.0 33.0 33.0 ... 4.0 \n",
|
||
"张珂瑞 54.0 54.0 0.0 33.0 33.0 33.0 33.0 33.0 33.0 33.0 ... 4.0 \n",
|
||
"陈满才 54.0 54.0 54.0 0.0 33.0 33.0 33.0 33.0 33.0 33.0 ... 4.0 \n",
|
||
"赵希同 54.0 54.0 54.0 54.0 0.0 33.0 33.0 33.0 33.0 33.0 ... 4.0 \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 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 \n",
|
||
"鲁钊阳 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 \n",
|
||
"张珂瑞 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 \n",
|
||
"陈满才 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 \n",
|
||
"赵希同 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 \n",
|
||
"\n",
|
||
"[5 rows x 1168 columns]"
|
||
]
|
||
},
|
||
"execution_count": 101,
|
||
"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": 37,
|
||
"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",
|
||
" <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>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",
|
||
" <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",
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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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>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>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</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>金融科技研究进展与评析</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</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",
|
||
" <td>0.0</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>...</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>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</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>1.0</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>发力新技术创新,为金融服务注入新动能</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>1.0</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",
|
||
" </tbody>\n",
|
||
"</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",
|
||
"金融科技研究进展与评析 0.0 1.0 0.0 1.0 1.0 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 1.0 1.0 1.0 \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 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": 64,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"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": 76,
|
||
"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": 100,
|
||
"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"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|