incidunt/about-wordpress-plugin-deveploers.ipynb

## about-wordpress-plugin-deveploers.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先引入所有需要用的库\n",
    "\n",
    "#读取jsonl文件的库\n",
    "import jsonlines\n",
    "\n",
    "# 数据分析的库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 数据可视化的库\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54358, 63)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_names = []\n",
    "authors_nonames = []\n",
    "\n",
    "with jsonlines.open('../output.jsonl') as reader:\n",
    "    for obj in reader:\n",
    "        author = obj['author']\n",
    "        if author.strip()=='': # 对于没有作者名字的插件，有author这个key，但值为空\n",
    "            authors_nonames.append(author)\n",
    "        else:\n",
    "            authors_names.append(author)\n",
    "                \n",
    "len(authors_names), len(authors_nonames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用于图表的数据\n",
    "labels = 'with author name','without authot name'\n",
    "sizes = [len(authors_names), len(authors_nonames)]\n",
    " \n",
    "# 绘制图表\n",
    "plt.pie(sizes, labels=labels  )\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "author_profiles=[]\n",
    "\n",
    "with jsonlines.open('../output.jsonl') as reader:\n",
    "    for obj in reader:\n",
    "        author_profiles.append(str(obj['author_profile']).replace('https://profiles.wordpress.org/',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['brooksx',\n",
       "  'miunosoft',\n",
       "  'ouhinit',\n",
       "  'mdalby',\n",
       "  'ouhinit',\n",
       "  'ouhinit',\n",
       "  'ouhinit',\n",
       "  'primestrategy',\n",
       "  'scheeeli',\n",
       "  'ouhinit'],\n",
       " 54421)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_profiles[:10], len(author_profiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54421"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert output to new array, check length\n",
    "authors_array = np.asarray(author_profiles)\n",
    "len(authors_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>author_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>brooksx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>miunosoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ouhinit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mdalby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ouhinit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  author_name\n",
       "0     brooksx\n",
       "1   miunosoft\n",
       "2     ouhinit\n",
       "3      mdalby\n",
       "4     ouhinit"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert new array to dataframe\n",
    "df = pd.DataFrame(data=authors_array, columns=['author_name'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>author_name</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shawfactor</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>coffee2code</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>algoritmika</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yithemes</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>marcqueralt</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   author_name  counts\n",
       "0   shawfactor      93\n",
       "1  coffee2code      74\n",
       "2  algoritmika      69\n",
       "3     yithemes      61\n",
       "4  marcqueralt      54"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_count = df['author_name'].value_counts().rename_axis('author_name').reset_index(name='counts')\n",
    "author_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27289.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.994247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.867161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>93.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             counts\n",
       "count  27289.000000\n",
       "mean       1.994247\n",
       "std        2.867161\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        1.000000\n",
       "75%        2.000000\n",
       "max       93.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(y=author_count['counts'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn histogram\n",
    "g=sns.distplot(author_count['counts'],rug='True')\n",
    "\n",
    "# Set the `xscale`\n",
    "g.set(xscale=\"log\")\n",
    "\n",
    "# Add labels\n",
    "plt.title('distribution of plugins counts from same deveploper')\n",
    "plt.xlabel('counts')\n",
    "plt.ylabel('rate')\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "top10 = author_count[:10]\n",
    "plt.figure(figsize=(30,5))\n",
    "\n",
    "sns.barplot(top10.author_name, top10.counts)\n",
    "\n",
    "plt.title('Top 10 Plugin Developers in wordPress.org',fontsize=20)\n",
    "plt.ylabel('Number of Plugins', fontsize=16)\n",
    "plt.xlabel('author', fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面就是 wordPress.org 上发布的插件数量前10名的作者的链接\n",
    "\n",
    "|      name      |plugins|                                             profile                                              |                                                               website                                                                |\n",
    "|----------------|------:|--------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|\n",
    "|Peter Shaw      |     93| [https://profiles.wordpress.org/shawfactor](https://profiles.wordpress.org/shawfactor)           | [https://shawfactor.com/](https://shawfactor.com/)                                                                                   |\n",
    "|Scott Reilly    |     74| [https://profiles.wordpress.org/coffee2code](https://profiles.wordpress.org/coffee2code)         | [http://coffee2code.com/](http://coffee2code.com/)                                                                                   |\n",
    "|Algoritmika Ltd |     69| [https://profiles.wordpress.org/algoritmika](https://profiles.wordpress.org/algoritmika)         | [https://wpfactory.com](https://wpfactory.com)                                                                                       |\n",
    "|YITH            |     61| [https://profiles.wordpress.org/yithemes](https://profiles.wordpress.org/yithemes)               | [https://yithemes.com/](https://yithemes.com/)                                                                                       |\n",
    "|DeMomentSomTres |     54| [https://profiles.wordpress.org/marcqueralt](https://profiles.wordpress.org/marcqueralt)         | [http://DeMomentSomTres.com](http://DeMomentSomTres.com)                                                                             |\n",
    "|Gopi Ramasamy   |     54| [https://profiles.wordpress.org/gopiplus](https://profiles.wordpress.org/gopiplus)               | [http://www.gopiplus.com/work/2010/07/18/youtube-with-fancy-zoom/](http://www.gopiplus.com/work/2010/07/18/youtube-with-fancy-zoom/) |\n",
    "|Access Keys     |     53| [https://profiles.wordpress.org/access-keys](https://profiles.wordpress.org/access-keys)         | [https://access-keys.com](https://access-keys.com)                                                                                   |\n",
    "|WP OnlineSupport|     52| [https://profiles.wordpress.org/wponlinesupport](https://profiles.wordpress.org/wponlinesupport) | [https://www.wponlinesupport.com](https://www.wponlinesupport.com)                                                                   |\n",
    "|GamiPress       |     50| [https://profiles.wordpress.org/rubengc](https://profiles.wordpress.org/rubengc)                 | [https://gamipress.com/](https://gamipress.com/)                                                                                     |\n",
    "|BestWebSoft     |     48| [https://profiles.wordpress.org/bestwebsoft](https://profiles.wordpress.org/bestwebsoft)         | [https://bestwebsoft.com/](https://bestwebsoft.com/)                                                                                 |"
   ]
  }
 ],
 "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

## link.md

      
    Raw
  

              link.md
            
          
    用Python分析WordPress官网所有插件的开发者信息