lzh
631 lines
84 KiB
Plaintext
631 lines
84 KiB
Plaintext
{
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"cells": [
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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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"# 1、导入数据"
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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": 22,
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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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"import seaborn as sns\n",
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"plt.style.use('seaborn') #应用风格\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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{
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"data": {
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"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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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\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>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",
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" <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",
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" <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",
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" <td>陈满才;</td>\n",
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" <td>中国工商银行金融科技部;</td>\n",
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" <td>中国金融电脑</td>\n",
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" <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",
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" <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",
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" <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",
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" <td>中信银行信息技术管理部;</td>\n",
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" <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": [
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" Title-题名 Author-作者 Organ-单位 \\\n",
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"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",
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"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",
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"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",
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"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",
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" Source-文献来源 \\\n",
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"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",
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"1 金融理论与实践 \n",
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"2 中国金融电脑 \n",
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"3 中国金融电脑 \n",
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"4 中国金融电脑 \n",
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"\n",
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" Keyword-关键词 \\\n",
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"0 融资困境;;金融科技;;小微企业 \n",
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"1 金融科技;;金融创新;;金融发展 \n",
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"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",
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"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",
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"\n",
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" Summary-摘要 PubTime-发表时间 \n",
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"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",
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"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",
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"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",
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"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",
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"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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"## 2、作者计数统计"
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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": 25,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"根据作着排序结果如下：\n"
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]
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},
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{
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"data": {
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"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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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\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>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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" <th>数量</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>74</th>\n",
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" <td>金融科技创新服务小微企业融资研究综述与展望</td>\n",
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" <td>周雷;蔡佩瑶;刘婧;刘睿;</td>\n",
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" <td>苏州市职业大学商学院;上海远想会计师事务所;东南大学经济管理学院;</td>\n",
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" <td>市场周刊</td>\n",
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" <td>金融科技;;小微企业;;融资;;区块链;;金融创新</td>\n",
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" <td>随着区块链、人工智能、大数据等前沿技术与金融业的深度融合,金融科技迅速发展,为解决小微企业融...</td>\n",
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" <td>2020-04-01</td>\n",
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" <td>15</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>351</th>\n",
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" <td>金融科技创新支持小微企业融资案例研究</td>\n",
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" <td>周雷;刘睿;黄思涵;</td>\n",
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" <td>苏州市职业大学商学院;东南大学经济管理学院;</td>\n",
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" <td>Proceedings of 2019 International Conference o...</td>\n",
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" <td>金融科技;;小微企业;;融资;;互联网金融;;大数据</td>\n",
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" <td>金融科技的发展为解决小微企业融资这一\"世界性难题\"提供了新途径。作为国家开发银行的创新试点,...</td>\n",
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" <td>2019-05-26</td>\n",
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" <td>15</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>63</th>\n",
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" <td>金融科技服务小微企业融资案例研究——以国开行旗下“开鑫贷”平台为例</td>\n",
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" <td>周雷;刘睿;张璇;</td>\n",
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" <td>苏州市职业大学商学院;东南大学数学学院;</td>\n",
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" <td>金融理论与教学</td>\n",
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" <td>金融科技;;小微企业;;融资;;互联网金融;;大数据</td>\n",
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" <td>小微企业融资是金融服务实体经济的薄弱环节,而金融科技的发展为破解小微金融\"痛点\"提供了新途径...</td>\n",
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" <td>2020-04-25</td>\n",
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" <td>15</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>188</th>\n",
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" <td>综合金融服务体系服务实体经济高质量发展研究——以苏州市小微企业数字征信实验区为例</td>\n",
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" <td>周雷;刘睿;金吉鸿;</td>\n",
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" <td>苏州市职业大学商学院;苏州市地方金融监督管理局;</td>\n",
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" <td>征信</td>\n",
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" <td>金融服务平台;;实体经济;;高质量发展;;金融科技;;数字征信;;小微企业</td>\n",
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" <td>为破解民营小微企业融资的信息不对称难题,服务实体经济高质量发展,江苏省苏州市搭建了综合金融服...</td>\n",
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" <td>2019-11-18</td>\n",
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" <td>15</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>850</th>\n",
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" <td>金融科技带动银行网点智能化</td>\n",
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" <td>本刊编辑部;</td>\n",
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" <td>时代金融</td>\n",
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" <td>时代金融</td>\n",
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" <td>银行网点;影像识别;</td>\n",
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" <td>金融科技发展迅猛,国内银行越来越多地用更好的金融基础设施为更多用户提供高效率的服务。依托人工...</td>\n",
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" <td>2017-08-10</td>\n",
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" <td>12</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 \\\n",
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"74 金融科技创新服务小微企业融资研究综述与展望 周雷;蔡佩瑶;刘婧;刘睿; \n",
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"351 金融科技创新支持小微企业融资案例研究 周雷;刘睿;黄思涵; \n",
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"63 金融科技服务小微企业融资案例研究——以国开行旗下“开鑫贷”平台为例 周雷;刘睿;张璇; \n",
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"188 综合金融服务体系服务实体经济高质量发展研究——以苏州市小微企业数字征信实验区为例 周雷;刘睿;金吉鸿; \n",
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"850 金融科技带动银行网点智能化 本刊编辑部; \n",
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"\n",
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" Organ-单位 \\\n",
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"74 苏州市职业大学商学院;上海远想会计师事务所;东南大学经济管理学院; \n",
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"351 苏州市职业大学商学院;东南大学经济管理学院; \n",
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"63 苏州市职业大学商学院;东南大学数学学院; \n",
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"188 苏州市职业大学商学院;苏州市地方金融监督管理局; \n",
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"850 时代金融 \n",
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"\n",
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" Source-文献来源 \\\n",
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"74 市场周刊 \n",
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"351 Proceedings of 2019 International Conference o... \n",
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"63 金融理论与教学 \n",
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"188 征信 \n",
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"850 时代金融 \n",
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"\n",
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" Keyword-关键词 \\\n",
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"74 金融科技;;小微企业;;融资;;区块链;;金融创新 \n",
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"351 金融科技;;小微企业;;融资;;互联网金融;;大数据 \n",
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"63 金融科技;;小微企业;;融资;;互联网金融;;大数据 \n",
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"188 金融服务平台;;实体经济;;高质量发展;;金融科技;;数字征信;;小微企业 \n",
|
|
"850 银行网点;影像识别; \n",
|
|
"\n",
|
|
" Summary-摘要 PubTime-发表时间 数量 \n",
|
|
"74 随着区块链、人工智能、大数据等前沿技术与金融业的深度融合,金融科技迅速发展,为解决小微企业融... 2020-04-01 15 \n",
|
|
"351 金融科技的发展为解决小微企业融资这一\"世界性难题\"提供了新途径。作为国家开发银行的创新试点,... 2019-05-26 15 \n",
|
|
"63 小微企业融资是金融服务实体经济的薄弱环节,而金融科技的发展为破解小微金融\"痛点\"提供了新途径... 2020-04-25 15 \n",
|
|
"188 为破解民营小微企业融资的信息不对称难题,服务实体经济高质量发展,江苏省苏州市搭建了综合金融服... 2019-11-18 15 \n",
|
|
"850 金融科技发展迅猛,国内银行越来越多地用更好的金融基础设施为更多用户提供高效率的服务。依托人工... 2017-08-10 12 "
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 576x396 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"keywords={}\n",
|
|
"exponet=[]#每篇文章各个关键词出现次数之和，排序依据\n",
|
|
"for keys in df['Author-作者']:\n",
|
|
" key_list=keys.split(';')\n",
|
|
" for key in key_list:\n",
|
|
" if key=='':\n",
|
|
" continue\n",
|
|
" if key in keywords:\n",
|
|
" keywords[key]+=1\n",
|
|
" else:\n",
|
|
" keywords[key]=1\n",
|
|
"\n",
|
|
"keywords=dict(sorted(keywords.items(),key=lambda x:x[1],reverse=True)[:10])\n",
|
|
"plt.title('发表文章最多作者Top10')\n",
|
|
"plt.bar(keywords.keys(),keywords.values())\n",
|
|
"plt.xticks(rotation=30)\n",
|
|
"#排序DataFrame\n",
|
|
"for keys in df['Author-作者']:\n",
|
|
" score=0\n",
|
|
" for key in keys.split(';'):\n",
|
|
" try:\n",
|
|
" score+=keywords[key]\n",
|
|
" except:\n",
|
|
" pass\n",
|
|
" exponet.append(score)\n",
|
|
"df['数量']=exponet\n",
|
|
"df=df.sort_values(by='数量',ascending=False)\n",
|
|
"print(\"根据作着排序结果如下：\")\n",
|
|
"df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 3、发表时间统计"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"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>Title-题名</th>\n",
|
|
" <th>Author-作者</th>\n",
|
|
" <th>Organ-单位</th>\n",
|
|
" <th>Source-文献来源</th>\n",
|
|
" <th>Keyword-关键词</th>\n",
|
|
" <th>Summary-摘要</th>\n",
|
|
" <th>PubTime-发表时间</th>\n",
|
|
" <th>数量</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <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",
|
|
" <td>李慧;</td>\n",
|
|
" <td>中国工商银行股份有限公司天津市分行;</td>\n",
|
|
" <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",
|
|
" <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",
|
|
" <td>2020-08-17</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>金融科技研究进展与评析</td>\n",
|
|
" <td>鲁钊阳;张珂瑞;</td>\n",
|
|
" <td>西南政法大学经济学院;</td>\n",
|
|
" <td>金融理论与实践</td>\n",
|
|
" <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",
|
|
" <td>2020-08-12</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <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",
|
|
" <td>赵希同;</td>\n",
|
|
" <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",
|
|
" <td>2020-08-07</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>发力新技术创新,为金融服务注入新动能</td>\n",
|
|
" <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",
|
|
" <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",
|
|
" <td>2020-08-07</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>聚焦科技创新 智造数字光大</td>\n",
|
|
" <td>史晨阳;</td>\n",
|
|
" <td>中国光大银行信息科技部;</td>\n",
|
|
" <td>中国金融电脑</td>\n",
|
|
" <td>光大银行;科技创新机制;云计算平台;智能服务;研发平台;金融科技创新;分布式架构;数字化转型...</td>\n",
|
|
" <td>逆水行舟,不进则退。面对新时代复杂的内外部经济形势,光大银行将坚定战略发展定力,稳中求进,积...</td>\n",
|
|
" <td>2020-08-07</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
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"</div>"
|
|
],
|
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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",
|
|
"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",
|
|
"5 聚焦科技创新 智造数字光大 史晨阳; 中国光大银行信息科技部; \n",
|
|
"\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",
|
|
"3 中国金融电脑 \n",
|
|
"4 中国金融电脑 \n",
|
|
"5 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",
|
|
"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",
|
|
"5 光大银行;科技创新机制;云计算平台;智能服务;研发平台;金融科技创新;分布式架构;数字化转型... \n",
|
|
"\n",
|
|
" Summary-摘要 PubTime-发表时间 数量 \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 0 \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 0 \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 0 \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 0 \n",
|
|
"5 逆水行舟,不进则退。面对新时代复杂的内外部经济形势,光大银行将坚定战略发展定力,稳中求进,积... 2020-08-07 0 "
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"#将发表时间列转时间序列\n",
|
|
"df=df.sort_values(by='PubTime-发表时间',ascending=False)\n",
|
|
"df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 4、绘制折线图"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 86,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0,0.5,'文章累计数')"
|
|
]
|
|
},
|
|
"execution_count": 86,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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D8P4arHhguNTluAyGOBERubTsG1X4JOUq+gQqsf6ROCjkjC47doKIiFxWeY0euw5egtxHhg1Lx3rtoi6dYYgTEZFLMpqt2HngIm4bLFj5sxEYGq2RuiSXwxAnIiKXIwgC/vllHn4sa8DsCf0w24tXZesKQ5yIiFzO/525ifTsUgztF4zEeSOkLsdlMcSJiMil5BZUY/83V6EJUGL90rHwVTCqOsPOEBGRy6isNeBvBy9BJgPWL41DSBAnsnWFIU5ERC7BZLbir59eRIPejMR5wzG8fx+pS3J5DHEiIpKcIAjYezQfBbfqET8uCnMmciKbIxjiREQkuW/OFuHkpVuIiQrCqgdHQObFdya7EwxxIiKSVP6P1fj46ysI9vfFhqVj4auQS12S22CIExGRZKrqGieyAcCTj8Shb7CfxBW5F4Y4ERFJwmyxYuenl1CnMyNh7jCMHBgidUlux6EQLyws7PSx6urqXiuGiIi8gyAISDp2GddL6jAjLhIP3NNf6pLckkMhvmXLFvzv//4vvv76a1y7dg2CIAAArFYrnnzySWRnZzu1SCIi8iwpWcX47kIJBkUE4bGHRnIiWw8pHNnJx8cHfn5+uHbtGk6cOIErV66gX79+0Ov1iI+PR2xsrLPrJCIiD3H1Zi3+3/9dRqDaFxuWxUHpy4lsPdVliL/xxhsYOnQo9Ho9li5dCkEQkJeXh9TUVHz++efw8fHB7373O7FqJSIiN1ddb8TOTy9CEBonsoVp1FKX5Na6HE6Pj49HdXU1oqKiMH/+fEyYMAH/+Mc/MHr0aHz66afYvXs3XnjhBZhMJrHqJSIiN2Wx2vDOwYuovW3Cr+4fitGDOJHtbnUZ4gUFBbjnnnsgk8mQnJyMhx56CH369EFxcTFu376NzMxMTJo0Cenp6WLVS0REbur/fXUF14rqMH1MBH42ZYDU5XiELofTp02bhvT0dNTW1uLtt9/GhAkTkJiYiHnz5uHIkSOorKzE4cOHoVA4dGqdiIi81PHzxUg5V4QB4YFYvWAUJ7L1ki7Td/369VCr1bh+/TpMJhMyMjLQt29fDBw4ENu2bcOjjz6Kzz77DI888ohY9RIRkZu5VlyLD4/lI8BPgaeWjYWKE9l6TZfD6QcOHMDmzZsREBAApVKJXbt2oaSkBFlZWdi6dSteeuklHDlyBEajsd1z6+vrsXbtWjz++OPYsGEDTCYTtm7dioSEBLzzzjvN+3W0jYiIPENtgxHvfHoJVpuAdQ/HQduHE9l6U5ch/t577wEAIiIi8NJLL2H9+vV4+OGHERsbiw0bNmDatGmYN28eTpw40e65hw8fxpo1a/D+++8jLCwMR44cgc1mw759+1BYWIgbN27g2LFj7bYREZFnaJzIdgnV9Ub8YvZQxMb0lbokjyMT7Cu3dOHWrVuIjIzEhQsXMGbMGOTk5GDcuHEAAJ1OB39//y6fv3HjRjQ0NGD16tWYPXs2Pv/8cxgMBuTm5mLmzJmtti1fvrzL17JYrFBwcXwiIpf37oEL+CztOu4b3w/PrZrM8+BO4NCMtMjISFitVly9ehXjxo1rDnAA3Qb4uXPnUFtbi+joaERERAAANBoNioqKoNPp2m3rTnW1zpGS3ZpWG4Ty8nqpy3B57JNj2CfHsE/du5MefXehBJ+lXUe0NgArHxiGiooGJ1fnOnr7Z0mrDer0MYdvgCKXy5GcnHxHb1xTU4NXX30Vr732Gvz9/WEwGAA0Hr3bbLYOtxERkXu7XlKHvUfz4a9qnMjmp+QVTM5yR3cxu5OhEJPJhKeffhqbN29GdHQ04uLikJmZCQDIy8vrdBsREbmvutsm7Pz0IqxWG55YEouIkK5Ha+nudPnn0XPPPQelUgmg8Y4zBQUF+OMf/9hqH7PZjNdff73dc5OTk5GTk4Ndu3Zh165dWLZsGQ4dOoSysjIcP34c+/fvh0wmQ2JiYqttRETknixWG/528BKq6oxYNmsIxg0Nlbokj9dliK9YsQK+vr7N31+6dAkJCQmt9ulsydXExEQkJia22jZ37lykpaVh7dq1CApqHONPSkpqt42IiNzPJ99eQ35hDe4ZocWiewdJXY5X6DLEJ06c2Or7gIAAxMXF9fjNNBoNFi5c2O02IiJyL99fuoX/O1OIqFB/PL5oNGeii8Thc+JWqxUWiwVA4xD6m2++2fw9ERF5r4Jb9fjgyzyoVXL8dvk4qFWcyCaWbkM8Pz8fH330EXx8fPDSSy8BAHx9fREeHo5nnnkGDlxmTkREHqpeZ8JfD1yE2WLDbxbHIrIvJ7KJqcs/l1JTU/HKK69g69atqKysxJo1a5pnkAuCgLq6Ouzfv7/deXIiIvJ8VpsNuw5lo7LOgEfiYzBhWJjUJXmdLkO8rq4Oe/fuRf/+/VFRUYEZM2Zgx44dYtVGREQu7F8pPyC3oBoThoXh5/cNlrocr9RliC9evLj5az8/P8yZM8fZ9RARkRs4lVuKL0/9iMi+/lj78zHw4UQ2STg8sS0wMLD5lqOc0EZE5L0Kyxrw/pFcqJRyPLVsLPz9OJFNKt12/pNPPoGvry/MZjN++ctfAgDWrVuHzZs34+TJk+jbty+WLl3q9EKJiEh6DXoz/nrgAkxmGzYsHYt+YQFSl+TVug3x9957D3FxccjOzkZ4eDhu376NgoICDB48GM888wz27dsnRp1ERCQxq03A7sPZKK8x4OczBuOekVqpS/J63Q6nR0RE4N5770V4eDj27NmDmpoa9O3bF2fOnMHy5csRGBgoRp1ERCSxj77MxaXrVRg3NBSPxMdIXQ7BgSNxmUzW/M+vfvUrmEwm+Pj4ID4+HjNnzhSjRiIiklBVnQFfn72JL9J/RHiIGk8sHgMfH05kcwXdhvjFixdRWFiI2tpaqNVq7N27F8HBwVi8eDFkMhliYmLw1ltviVErERGJxCYIyLlehW/PFSHragUEAQgOUDZNZPPt/gVIFN2GeFxcHJYsWYLDhw/jo48+QkBAAAoKCrBz507ExMQ0r+JGRETur0FvxncXSpByrghlNXoAwKCIINw/KRqLZg5FfZ1e4gqpJYeH0wEgMjISMTEx2L17N5KSkjBgwAD813/9l9OLJCIi5xEEAT8U1+Hbc0U4lVsGi9UGX4UP7hsbifsn9kdMVBBkMhn8VArUS10stdJtiBcVFSElJQUlJSXYvHkzUlNTcd9992HcuHH46KOPsHjxYkRERIhRKxER9SKjyYr0nFv49mwRfixrAABEhKgxZ2I07hsbhUA1h81dnUzo5g4mn332GRQKBaxWKxYtWgQAOHjwIMLDw6HX65Gbm4unnnpKlGIBoLzc8/8O1GqDvOJz3i32yTHsk2O8qU9FFbeRcrYIJ7NLoDda4SOTYcLwMNw/MRqjB4d0uvqaN/XobvR2n7TaoE4f6/ZI/Oc//3m7bfaV2wDg/vvv72FZREQkFovVhrOXy/Ht2SLkF9YAADSBSvxs8gDMGt8PfYP9JK6QesKhtfIsFguSk5OxYsWKVtvfe+89/OY3v3FKYUREdPcqaw1IPV+E4+dLUHfbBAAYPSgE90+MxoThYVDIHV59m1xQlyFuNpuxfft2PP/88/j8888xduxY6PV6CIKAAQMG4Ouvv2aIExG5GJsgIPt6Fb49W4Tz1xovD1OrFPjZ5AGYM7EfokK5VKqn6DLEfX19ce3aNej1eigUCrz77rs4e/YsBg8ejAULFkCpVIpVJxERdaNeZ8J3F0uQeq74p8vDIoMwd2I0po6JgMpXLnGF1Nu6HU43mUxYsmQJBg8ejCFDhqC+vh4rV65EaWmpGPUREVEXBEHAteI6fHu2CKfzfro8LH5sFO6fFI2YqGCpSyQn6jbEVSoVDhw4gE2bNrVagtVgMMBqtaK6uhohISFi1EpERE0MJgvSc0qR0ubysPsnRmMGLw/zGl2G+MaNGwEAffr0AdD4F5/93++++y70ej0WL16M7777zsllEhER0PHlYfeM0GLOpGiMHtT55WHkmboM8eeffx4vvPACjEZjq+0ymQxPP/00jh49ir179zq1QCIib2e/POybs0W43HR5WJ+my8NmT4hGSJBK4gpJKl2GeFRUFMxmM9avX99q+VUiInK+yloDUrKKcOICLw+jjnV7Tlwul+Ott97CU089hXPnziE7OxvPP/88Nm3aJEZ9RERep+62CR98kdd8eZg/Lw+jTnQZ4haLBf3794darYbFYkFSUlKrxw8fPuzU4oiIvNHhtOvIulrReHnYpGhMHc3Lw6hjXYa4QqHAtm3b8MUXX2DhwoXtHl+4cCFKSkoQFRXltAKJiLyJxWrD6bwyBPv74sXH7oHch0Pm1DmHfjp27NiBRx99tN32WbNm4be//W2vF0VE5K1yblSjXmfGlNERDHDqlkM/IZ2tzPb9999j2rRpvVoQEZE3S8+5BQCYHstbPFP3HApxk8mExYsXY/v27fjxxx+btx88eBC//OUvu3xuRUUFEhMTAQClpaWYNWsWVq1ahVWrVqGqqgoAsHXrViQkJOCdd97p6ecgInJ7RpMV5y5XILyPGkO40ho5wKG7mKnVauzduxcpKSnYvHkzJk6ciFGjRmHYsGEYPHhwp8+rra3Fc889B72+cQ3f8+fPY926dc2hDgDHjh2DzWbDvn378Pzzz+PGjRtdviYRkac6d6UcRrMV08ZE8JJeckiXIf7FF19g3759EAQBwcHBWLJkCZYsWYI///nPeOWVV7pdqU0ul2PHjh1Yv349ACArKwvp6en45JNPMHPmTPz+97/HqVOnsGDBAgBAfHw8MjMzuwzxkBB/KBSeP0uzq5vA00/YJ8ewT46Ruk9nr2YDABbOHCJ5LZ1x1bpcjVh96jTEDQYDMjIysH37dqxbtw5A43Krn376KbKysrB8+XIcOHAAq1ev7vTFAwMDW30/a9YsrF+/Hmq1Gv/xH/+BvLw86HQ6REQ0nvvRaDQoKirqsuDqap3DH85dabVBKC+vl7oMl8c+OYZ9cozUfarXmXAuvwyDIoOgksEl/5tJ3SN30dt96uoPgk5D3M/PD3/6058ANJ4Tf+GFF5Cbm4u5c+fio48+gp+fHxITE7FgwQKEh4c7VMikSZOaJ8mNGTMGBQUF8Pf3h8FgAADodDrYbDZHPxcRkcc4nVcGq03AvWM4oY0c59DENpVKhaVLl+KTTz7BU089BbVaDZlMhl/96ldITk52+M1+/etfo6ysDHq9HmlpaRg+fDji4uKQmZkJAMjLy0N0dHTPPgkRkRtLzy6FDMBUhjjdAYcmtgmCgMmTJ7fb/sADD2DlypXN57y7s2HDBjz22GPw9fXFihUrMGTIEISHhyMxMRFlZWU4fvw49u/ff2efgIjIzZXX6HG1qBajB4WgTyBvZkKOcyjE7efE2woODsZ7773X7fPty7VOnz4dX375ZavHAgMDkZSUhLS0NKxduxZBQZw0QUTeJT2nFACvDac751CIP/TQQ50+FhkZeddFaDSaDpd1JSLydIIgID37FhRyH9wzwrH5RUR2XNOPiEhChWUNKKnUYcKwUPj7OXRcRdSMIU5EJKH0bPtQ+t2PapL3YYgTEUnEZhOQkVsKf5UCY4eESl0OuSGGOBGRRPILa1Bdb8TkUVr4KvjrmO4cf2qIiCSSYb9j2RgOpVPPMMSJiCRgtthwJq8cIUEqjBjYR+pyyE0xxImIJHDhWiV0RgumjYmAD+9YRj3EECcikkB681A6F3ihnmOIExGJTGew4PzVSvQLC8CA8MDun0DUCYY4EZHIMi+XwWK1YfqYCMg4lE53gSFORCSy5gVeOJROd4khTkQkoup6I/IKqjGsvwZhfdRSl0NujiFORCSiU7mlEMCjcOodDHEiIhGlZ5dC7iPDlFG8YxndPYY4EZFISipvo6C0HrExfRHkr5S6HPIADHEiIpH8dMcyDqVT72CIExGJQBAEpOfcgspXjonDtFKXQx6CIU5EJIIfiutQXmPAxBFhUCnlUpdDHoIhTkQkgp+uDecdy6j3MMSJiJzMarPhVF4pgvx9ERsTInU55EEY4kRETpZzoxr1OjOmjoqA3Ie/dqn38KeJiMjJ0rOb7ljGWenUyxjiREROZDRZcfZyBbR9/DCkX7DU5ZCHYYgTETkvEazbAAATv0lEQVTRuavlMJqtmDYmkncso17HECcicqKMplnp93IonZyAIU5E5CT1OhMuXa/CoIggRIUGSF0OeSCGOBGRk5zJK4PVJnBCGzkNQ5yIyEm+zymFDMDU0Qxxcg6GOBGRE1TU6HH1Zi1GDQpBSJBK6nLIQzk9xCsqKpCYmAgAMJvNWLduHVasWIHk5OROtxERubuMXPsyqzwKJ+dxaojX1tbiueeeg16vBwB8+OGHiI2Nxccff4yjR4+ioaGhw21ERO5MEASkZ5dCIffBPSPDpS6HPJjCmS8ul8uxY8cOrF+/HgCQkZGBZ555BgAwZcoUXLp0qcNt06dP7/Q1Q0L8oVB4/h2AtNogqUtwC+yTY9gnx/RWn64X16Ko4jZmjIvCoAGetVY6f5YcI1afnBrigYGBrb7X6/WIiGgcWtJoNKisrOxwW1eqq3XOKdaFaLVBKC+vl7oMl8c+OYZ9ckxv9unIdz8AACYODfWo3vNnyTG93aeu/iAQdWKbv78/DAYDAECn08Fms3W4jYjIXdkEARk5pVCrFBg3NFTqcsjDiRrisbGxyMzMBADk5eUhOjq6w21ERO7qSmENquuNmDxSC18vOPVH0nLqcHpbS5cuxRNPPIEzZ87g6tWrGD9+PCIiItptIyJyV983LbM6PTZS4krIG4hyJJ6UlAQAiI6Oxvvvv49Jkybhgw8+gFwu73AbEZE7MltsOJNXhpAgFUYO6CN1OeQFRD0SB4CIiAgsXLiw221ERO7m4g+V0BktmDk+Cj4+vGMZOR9XbCMi6iXp2bcAANPHcCidxMEQJyLqBXqjBVlXKxEV6o+BEYHdP4GoFzDEiYh6QWZ+OSxWG6bHRkIm41A6iYMhTkTUC9Jz7EPpXCudxMMQJyK6SzUNRuQWVGNodDC0fdRSl0NehCFORHSXTuWUQhA4oY3ExxAnIrpL6Tml8JHJMGU071hG4mKIExHdhVtVOty4VY+4IX0R7K+UuhzyMgxxIqK78NO14ZzQRuJjiBMR9ZAgCEjPLoXS1wcThodJXQ55IYY4EVEP/VBSh7IaPSYN18JPKfoq1kQMcSKinspovmMZh9JJGgxxIqIesNpsOJVbikC1L8YM7it1OeSlGOJERD2Qe6MadTozpo4Oh0LOX6UkDf7kERH1wPf2oXQu8EISYogTEd0ho9mKs1fKEabxw9DoYKnLIS/GECciukNZVypgNFkxPTaCdywjSTHEiYjuUEYOh9LJNTDEiYjuQIPejIs/VGJgRCD6hQVIXQ55OYY4EdEdOJ1XBqtN4FE4uQSGOBHRHUjPvgUZgGlcK51cAEOciMhBFbV6XLlZi5ED+yAkSCV1OUQMcSIiRzVPaIvlUDq5BoY4EZGD0nNKoZDLMHmkVupSiAAwxImIHFJY1oCi8tsYNzQM/n6+UpdDBIAhTkTkkPTsWwCA6ZzQRi6EIU5E1A2bICAjtxRqlQLjh4VKXQ5RM4Y4EVE3rhTWoKrOiHtGauGrkEtdDlEzhjgRUTfSm2al38uhdHIxCjHfzGKxYN68eRgwYAAA4MUXX8TRo0eRmpqKcePG4eWXXxazHCKiblmsNpzJK4MmUImRA0OkLoeoFVGPxPPz87Fo0SIkJSUhKSkJZrMZmZmZSE5ORmhoKE6ePClmOURE3bp4rRK3DRZMGx0BHx/esYxci6hH4llZWUhJSUFGRgZGjBiBmJgYPPjgg5DJZIiPj8fx48cxY8aMLl8jJMQfCi84J6XVBkldgltgnxzDPjmmoz6d/SIPALAgfgj7CP4sOUqsPoka4mPHjsWePXsQHh6OZ599FkajETExMQAAjUaDioqKbl+julrn7DIlp9UGoby8XuoyXB775Bj2yTEd9UlvtOBU9i1EhfojWOnj9X3kz5JjertPXf1BIGqIjxo1CkqlEgAQFxcHi8UCo9EIANDpdBAEQcxyiIi6dPZyOcwWG6aPiYBMxqF0cj2inhPfsmUL8vLyYLVa8dVXX0Gn0yEzMxMAkJeXh+joaDHLISLqkn2BF96xjFyVqEfiGzZswObNmwEAc+fOxfr165GYmIht27bhxIkT+Pvf/y5mOUREnaptMCKnoBpD+wUjPMRf6nKIOiRqiI8YMQL//ve/W2374IMPkJKSgtWrVzdfekZEJLWM3DIIAu9YRq5N1BDviJ+fH+bPny91GURErWTk3IKPTIYpo8KlLoWoU1yxjYiojdIqHa6X1CM2pi+CA5RSl0PUKYY4EVEb3/OOZeQmGOJERC0IgoD0nFIofX0wcUSY1OUQdYkhTkTUwvWSepRV6zFxuBZ+SsmnDRF1iSFORNRCeg6H0sl9MMSJiJpYbTacyi1DoNoXsTF9pS6HqFsMcSKiJrkF1ai7bcKUUeFQyPnrkVwff0qJiJqkZ5cCAKbHciid3ANDnIgIgMFkQeblcoRp/DAsWiN1OUQOYYgTEQE4nVMKo8mKabxjGbkRhjgREYDUszcBcK10ci8McSLyeg16MzLzSjEgPBDRYQFSl0PkMIY4EXm9M3llsFgFTmgjt8MQJyKvl559CzIZMG00Q5zcC9cUJCKPZbHaYDBZoTdaoDdaYDBZoTNaYDBaoG/afttgxuWbtRg7NAx9g/2kLpnojjDEicjlWG0tw9faFMCWpgC2Qm9qsb1FIOtbfG0wWmCy2Bx+z3lTBzrxExE5B0OciHqNzSbAYA9Yk6U5hFsFsNHS/JihzX72r01mx8O3JaXCB34qBdQqBfoGqaBWKeCnlMNfpWjaLodapYBaqYCfqmm7UoEgf1+MHRmBioqGXu4IkXMxxIkINkGAsYOj2eYhaIOlXTh3dARsNFl79P6+Ch+olXL4qRToE6iCWilvCmBFUwA3ha9K0byff1NA27f7KeV3tVQqrw0nd8QQJ3Jj9vBtfa639VCzj0KOimpdu6HmtkHdEwq5rPnINjhA2Xxkq1Z1HLRq+2NKBdR+iuaw5jrlRD3DECeSgCAIMJqtXQw1W1sEctsjYGurfYQevL/cpyl8VXKE91E3DjUr5U3B2nqoueUQtLrlUbFSAV8Fw5dISgxxojsgCAJMFlvHs507DNoOhqCbvhd6kL4+MllzqIZp/JqHlrsaao6KCIJRb2oKZwX8VY3Dzhw+JnJ/DHHyCoIgwGyxtR5CNlqgazoS7mqoWd9yH6MVth6kr0yG5iPbvsEqqFUBzedxOw7gFkfALR5TKu48fLXaIJSX199xzUTk+hji5PLMFmv72c5GCxQFNSiraGg/BN32UqOmf1ttPQhfoPnotU+QClGhiuZh6LZDzc1D0C3O/dofU/ryyJeIeh9DnJzGYrW1Gmpuf51vB0PQbc4DG0wWWKw9Dd/GoNUEKBHRV91qONlP2XoIunmGs0refO5XrZJD5Stn+BKRy2KIUzv2Va5+CuCuh5rtC3G0PVq2WHt2ra99iDnI3xcRIT9Nump7rjc8LBAWk6X1eeGmx1RKOXwYvkTk4RjiHsRqs7WY1dzy+t02s50NLY5+O1iG0nwHq1y1pPKVQ62SI1DtizCNutOFNVqf6239mJ9SDh8fx8KX53qJyNsxxF2AzSa0GlIubzChpLSuw/PArc/1Nk3MagrnHq9y5esDddOiGqHBqhZDzfIWQ9Dth5rbBrCj4UtERL2DIX4XGpeY7Oxc709DzXpT+/PALZ9jNPdsoY3mJSaVcoS0WGKy+Si3Rei2DmBFiyFoOeQ+vNaXiMgdeXWIC4KAW1U63DZ0dK6346Hmltt7usSkQu7TOLlKpUCfAFXz8LJ9qDk0xB+C1fbT0pNtJl/1xhKTRETk/lwixLdu3Ypr165h9uzZWL9+vWjve/RUIfZ/e9Xh/e2rXPmrFIgIUbcaWu7osqKOLkXyc2CVK57rJSIiR0ge4seOHYPNZsO+ffvw/PPP48aNGxg8eLAo7z12aCiq6gzwbTon3N15YC4xSURErkQmCD1Z/LH3bNu2DTNnzsTs2bPx+eefw2AwYPny5Z3ub7FYoVDIRayQiIjINUl+JK7T6RAREQEA0Gg0KCoq6nL/6mqdGGVJisPpjmGfHMM+OYZ96h575Jje7pNWG9TpY5KPD/v7+8NgMABoDHSbrWeXSREREXkbyUM8Li4OmZmZAIC8vDxER0dLXBEREZF7kHw4fd68eUhMTERZWRmOHz+O/fv3S10SERGRW5D8SDwwMBBJSUkYP3489u7di6Cgzsf+iYiI6CeSH4kDjRPaFi5cKHUZREREbkXyI3EiIiLqGYY4ERGRm2KIExERuSmGOBERkZtiiBMREbkpyddOJyIiop7hkTgREZGbYogTERG5KYY4ERGRm2KIExERuSmGOBERkZtiiBMREbkphjgREZGbYogTERG5KYa4yOrr67F27Vo8/vjj2LBhA0wmE7Zu3YqEhAS88847zftVVFQgMTGx+Xuz2Yx169ZhxYoVSE5OlqJ00fS0R8XFxVi1ahUee+wx/PGPf4Snr2PU0z7ZXb58GWvWrBGzZEncbZ/WrVuH3NxcMUuWRE/7VFhYiNWrV+Phhx/Gzp07pShdNI70qKN9AHTYy97AEBfZ4cOHsWbNGrz//vsICwvDkSNHYLPZsG/fPhQWFuLGjRuora3Fc889B71e3/y8Dz/8ELGxsfj4449x9OhRNDQ0SPgpnKunPdq3bx/+9Kc/Ye/evSgpKUF+fr6En8L5etonABAEAa+//josFotE1Yvnbvp0+PBhDBgwAKNHj5aoevHcze+mjRs34tChQ/juu+9QVVUl4adwLkd61HafEydO4NixY+326y0McZGtXLkS9913HwCguroahw8fxoIFCwAA8fHxyMzMhFwux44dOxAYGNj8vIyMjOb9pkyZgkuXLolfvEh62qNNmzZh6NChAICamhqEhISIX7yIetonAPjXv/6FadOmiV6zFHrap5qaGmzfvh0ajQbp6emS1C6mnvapT58+yM/PR0VFBUwmE4KCgiSpXwyO9KjtPqGhoTh16lS7/XoLQ1wi586dQ21tLSIjIxEREQEA0Gg0qKysRGBgYLv/EfR6fbv9PN2d9sjuyJEjGD58ePNzPN2d9sn+y+fxxx+XolzJ3GmfPvjgA8yfPx8JCQk4dOgQvv76aynKFt2d9mnmzJk4f/48kpKSMH36dCgUCinKFlVXPWq7z4QJE6DT6Zz2+5shLoGamhq8+uqreO211+Dv7w+DwQAA0Ol0sNlsHT7H0f08RU96BDSen/vHP/6BrVu3ilWqpHrSpzfeeAObN2+Gr6+vmKVKqid9ys3NRWJiIrRaLebPn49Tp06JWbIketKn3bt34/XXX8emTZtgNBqRlpYmZsmic6RHLfcBnPv7myEuMpPJhKeffhqbN29GdHQ04uLimodW8vLyEB0d3eHzYmNjHdrPE/S0R7W1tfj973+P1157zaOH9Ox62qfTp0/jL3/5C1atWoXc3Fy8+eabYpYtup72aeDAgbh58yYA4NKlS+jXr59oNUuhp326efMmSkpKYDQakZ2dDZlMJmbZonKkR233AeBwL3vC88c9XExycjJycnKwa9cu7Nq1C8uWLcOhQ4dQVlaG48ePY//+/R0+b+nSpXjiiSdw5swZXL16FePHjxe5cvH0tEe7d+9GSUkJtm3bBgD47W9/i6lTp4pZuqh62qejR482f71q1Sps2rRJrJIl0dM+rV27Fi+++CL+9re/Qa1W4+233xa5cnH1tE8bN27EqlWrUFVVhfvvvx/Tp08XuXLxONKjtvs8+uijmDdvHhITE7vtZU/wfuIuoLa2FmlpaZgyZQq0Wm2n+5WWliIzMxMzZ870iiPNlhztkbdjnxzDPjmGfeqeoz1yVi8Z4kRERG6K58SJiIjcFEOciIjITTHEiahZVlZWt6sBmkwmj7/EkchdMMSJCEDjxJv//M//RHFxcZf7HTp0CG+88QYA4MSJEygtLcU///lPfPPNN2KUSUQt8BIzIgIA7Nq1C76+vti+fTsEQYDZbIZSqYTFYoHRaMTHH38MoDHE7YtYvPnmm9i5cydiY2Nx4MABzJ07V8qPQOR1GOJEhNzcXHz55Zf48ssvERgYiDNnziA5ORmvv/56q/1OnjwJi8WCgQMH4vz58+jfvz+ioqIQGRmJV199FRUVFQgLC5PoUxB5Hw6nE3m5mpoaPP3009iyZUu7G6XYj8jNZjNMJhO2b98OX19fWK1W/Pd//zc2btwIAJDJZHjmmWfwyiuvePwtYIlcCa8TJ/JyZrMZaWlpyM3NxRdffAGtVov6+nqUlJRg+PDhsFqtWLJkCaxWK4qLi5GZmYnBgwcjKysLSqUScrm8+bUMBgMSEhKwcuVKCT8RkfdgiBMRAOC9995DeHg4Hn744Q6H0202G+rr6/HUU09h/fr1mDx5Mv7nf/4HM2fOxIwZM7Bx40Y88cQTGDZsGPz8/CT8JETeg+fEibzcgQMHsH//ftTV1cHPzw+HDx9uPhL/9a9/DaDxCHvOnDlISEgAANx7770AgIqKCoSHhwNovPQsLCyMAU4kIoY4kZdbtmwZli1b1mpbZxPb6urqmr82mUy4fPkyhg4dCgAwGo1QqVTOL5iImnFiGxG1IwhChwu6WCwWWK1WAMBf/vIXLF68GEBjoBcXF3vdjXmIpMYQJ6J2TCYTzGZzu+0WiwVmsxnffPMNqqursWbNGly9ehXz58/HwoULoVBwcI9ITJzYRkQ9IggCZDKZ1GUQeTUeiRNRjzDAiaTHECciInJTDHEiIiI3xRAnIiJyUwxxIiIiN/X/AZOl0hZyhBTdAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<Figure size 576x396 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"dates=pd.to_datetime(df['PubTime-发表时间'])\n",
|
|
"year_num=dates.map(lambda x:x.year).value_counts()\n",
|
|
"year_num.index=year_num.index.sort_values(ascending=False)\n",
|
|
"year_num.plot()\n",
|
|
"plt.xlabel('年份')\n",
|
|
"plt.ylabel('文章累计数')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 85,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0,0.5,'文章累计数')"
|
|
]
|
|
},
|
|
"execution_count": 85,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 576x396 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 73,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"2009 360\n",
|
|
"2016 239\n",
|
|
"2017 178\n",
|
|
"2018 158\n",
|
|
"2019 21\n",
|
|
"2020 2\n",
|
|
"Name: PubTime-发表时间, dtype: int64"
|
|
]
|
|
},
|
|
"execution_count": 73,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 119,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"status:0,1,2\n",
|
|
"begin_time:1563084688\n",
|
|
"end_time:1563766460\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"s='status=0,1,2&begin_time=1563084688&end_time=1563766460'\n",
|
|
"for i in s.split('&'):\n",
|
|
" print(i.split('=')[0]+':'+i.split('=')[1])"
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|