AcademicTrends/Task2 论文作者统计.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 任务说明\n",
    "\n",
    "- 任务主题：论文作者统计，统计所有论文作者出现评率Top10的姓名；\n",
    "- 任务内容：论文作者的统计、使用 **Pandas** 读取数据并使用字符串操作；\n",
    "- 任务成果：学习 **Pandas** 的字符串操作；"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据处理步骤\n",
    "\n",
    "在原始arxiv数据集中论文作者`authors`字段是一个字符串格式，其中每个作者使用逗号进行分隔分，所以我们我们首先需要完成以下步骤：\n",
    "\n",
    "- 使用逗号对作者进行切分；\n",
    "- 剔除单个作者中非常规的字符；\n",
    "\n",
    "具体操作可以参考以下例子："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "C. Bal\\\\'azs, E. L. Berger, P. M. Nadolsky, C.-P. Yuan\n",
    "\n",
    "# 切分为，其中\\\\为转义符\n",
    "\n",
    "C. Ba'lazs\n",
    "E. L. Berger\n",
    "P. M. Nadolsky\n",
    "C.-P. Yuan\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然在原始数据集中`authors_parsed`字段已经帮我们处理好了作者信息，可以直接使用该字段完成后续统计。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 字符串处理\n",
    "\n",
    "在Python中字符串是最常用的数据类型，可以使用引号('或\")来创建字符串。Python中所有的字符都使用字符串存储，可以使用方括号来截取字符串，如下实例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-02T07:19:04.356288Z",
     "start_time": "2021-01-02T07:19:04.347392Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var1[-10:]:  Datawhale!\n",
      "var2[1:5]:  Python \n"
     ]
    }
   ],
   "source": [
    "var1 = 'Hello Datawhale!'\n",
    "var2 = \"Python Everwhere!\"\n",
    " \n",
    "print(\"var1[-10:]: \", var1[-10:])\n",
    "print(\"var2[1:5]: \", var2[0:7])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同时在Python中还支持转义符：\n",
    "\n",
    "| \\(在行尾时) | 续行符     |\n",
    "| ----------- | ---------- |\n",
    "| \\\\          | 反斜杠符号 |\n",
    "| \\'          | 单引号     |\n",
    "| \\\"          | 双引号     |\n",
    "| \\n          | 换行       |\n",
    "| \\t          | 横向制表符 |\n",
    "| \\r          | 回车       |\n",
    "\n",
    "Python中还内置了很多内置函数，非常方便使用：\n",
    "\n",
    "| **方法**            | **描述**                                                     |\n",
    "| :------------------ | :----------------------------------------------------------- |\n",
    "| string.capitalize() | 把字符串的第一个字符大写                                     |\n",
    "| string.isalpha()    | 如果 string 至少有一个字符并且所有字符都是字母则返回 True,否则返回 False |\n",
    "| string.title()      | 返回\"标题化\"的 string,就是说所有单词都是以大写开始，其余字母均为小写(见 istitle()) |\n",
    "| string.upper()      | 转换 string 中的小写字母为大写                               |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 具体代码实现以及讲解"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-02T07:23:53.184385Z",
     "start_time": "2021-01-02T07:23:52.532581Z"
    }
   },
   "outputs": [],
   "source": [
    "# 导入所需的package\n",
    "import seaborn as sns #用于画图\n",
    "from bs4 import BeautifulSoup #用于爬取arxiv的数据\n",
    "import re #用于正则表达式，匹配字符串的模式\n",
    "import requests #用于网络连接，发送网络请求，使用域名获取对应信息\n",
    "import json #读取数据，我们的数据为json格式的\n",
    "import pandas as pd #数据处理，数据分析\n",
    "import matplotlib.pyplot as plt #画图工具"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-02T07:24:24.787957Z",
     "start_time": "2021-01-02T07:24:23.153747Z"
    }
   },
   "outputs": [],
   "source": [
    "def readArxivFile(path, columns=['id', 'submitter', 'authors', 'title', 'comments', 'journal-ref', 'doi',\n",
    "       'report-no', 'categories', 'license', 'abstract', 'versions',\n",
    "       'update_date', 'authors_parsed'], count=None):\n",
    "    '''\n",
    "    定义读取文件的函数\n",
    "        path: 文件路径\n",
    "        columns: 需要选择的列\n",
    "        count: 读取行数\n",
    "    '''\n",
    "    \n",
    "    data  = []\n",
    "    with open(path, 'r') as f: \n",
    "        for idx, line in enumerate(f): \n",
    "            if idx == count:\n",
    "                break\n",
    "                \n",
    "            d = json.loads(line)\n",
    "            d = {col : d[col] for col in columns}\n",
    "            data.append(d)\n",
    "\n",
    "    data = pd.DataFrame(data)\n",
    "    return data\n",
    "\n",
    "data = readArxivFile('arxiv-metadata-oai-snapshot.json', \n",
    "                     ['id', 'authors', 'categories', 'authors_parsed'],\n",
    "                    100000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了方便处理数据，我们只选择了三个字段进行读取。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据统计\n",
    "\n",
    "接下来我们将完成以下统计操作：\n",
    "\n",
    "- 统计所有作者姓名出现频率的Top10；\n",
    "- 统计所有作者姓（姓名最后一个单词）的出现频率的Top10；\n",
    "- 统计所有作者姓第一个字符的评率；\n",
    "\n",
    "为了节约计算时间，下面选择部分类别下的论文进行处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-02T07:24:24.816940Z",
     "start_time": "2021-01-02T07:24:24.789818Z"
    }
   },
   "outputs": [],
   "source": [
    "# 选择类别为cs.CV下面的论文\n",
    "data2 = data[data['categories'].apply(lambda x: 'cs.CV' in x)]\n",
    "\n",
    "# 拼接所有作者\n",
    "all_authors = sum(data2['authors_parsed'], [])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "处理完成后`all_authors`变成了所有一个list，其中每个元素为一个作者的姓名。我们首先来完成姓名频率的统计。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-02T07:24:25.929001Z",
     "start_time": "2021-01-02T07:24:25.809119Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Count')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 拼接所有的作者\n",
    "authors_names = [' '.join(x) for x in all_authors]\n",
    "authors_names = pd.DataFrame(authors_names)\n",
    "\n",
    "# 根据作者频率绘制直方图\n",
    "plt.figure(figsize=(10, 6))\n",
    "authors_names[0].value_counts().head(10).plot(kind='barh')\n",
    "\n",
    "# 修改图配置\n",
    "names = authors_names[0].value_counts().index.values[:10]\n",
    "_ = plt.yticks(range(0, len(names)), names)\n",
    "plt.ylabel('Author')\n",
    "plt.xlabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-02T07:24:08.468797Z",
     "start_time": "2021-01-02T07:24:08.458964Z"
    }
   },
   "source": [
    "接下来统计姓名姓，也就是`authors_parsed`字段中作者第一个单词："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-02T07:24:42.314923Z",
     "start_time": "2021-01-02T07:24:42.199767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Count')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "authors_lastnames = [x[0] for x in all_authors]\n",
    "authors_lastnames = pd.DataFrame(authors_lastnames)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "authors_lastnames[0].value_counts().head(10).plot(kind='barh')\n",
    "\n",
    "names = authors_lastnames[0].value_counts().index.values[:10]\n",
    "_ = plt.yticks(range(0, len(names)), names)\n",
    "plt.ylabel('Author')\n",
    "plt.xlabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制得到的结果，从结果看出这些都是华人或者中国姓氏~\n",
    "\n",
    "\n",
    "统计所有作者姓第一个字符的评率，这个流程与上述的类似，同学们可以自行尝试。\n"
   ]
  },
  {
   "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": "3.6.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}