invegat/site_jobs.ipynb

## site_jobs.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "import os\n",
    "load_dotenv()\n",
    "bw_user = os.environ['bw_user']\n",
    "bw_password = os.environ['bw_password']\n",
    "tfn = 'technologies.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.keys import Keys\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "from selenium.common.exceptions import WebDriverException\n",
    "from bs4 import BeautifulSoup\n",
    "import io\n",
    "import pandas as pd\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "import time\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver = webdriver.chrome.webdriver.WebDriver('/usr/local/bin/chromedriver')\n",
    "driver.get(\"http://biglistofwebsites.com/\")\n",
    "# print(driver.title)\n",
    "assert \"Big Lists of Website\" in driver.title\n",
    "urls = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0x41,0x5B):\n",
    "  for j in range(0x41,0x5B):\n",
    "    search = f\"{chr(i)}{chr(j)}AA\"\n",
    "    elem = driver.find_element_by_css_selector(\"input[name='redirect']\")\n",
    "    elem.clear()\n",
    "    elem.send_keys(search)\n",
    "    elem.send_keys(Keys.RETURN)\n",
    "    #time.sleep(5)\n",
    "    source = driver.page_source\n",
    "    assert \"No results found.\" not in driver.page_source\n",
    "    html = BeautifulSoup(source, 'html.parser')\n",
    "    urls.extend([tx.text for tx in html.find_all('a', {'rel': 'nofollow'}, {'target': '_blank'})])\n",
    "    \n",
    "f = io.open('urls.csv', 'w')\n",
    "f.write(\",\".join(urls))\n",
    "f.close()\n",
    "print(len(urls))    \n",
    "driver.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class technologies_class:\n",
    "    def __init__(self, find_dups = False):\n",
    "        self.data = []\n",
    "        self.dups = []\n",
    "        self.find_dups = find_dups\n",
    "        try:\n",
    "          f = io.open(tfn, 'r')\n",
    "          source = f.read()\n",
    "          f.close()\n",
    "          for technology in source.split(','):\n",
    "            self.add(technology)\n",
    "        except IOError:\n",
    "            # print('IOError')\n",
    "            pass\n",
    "\n",
    "    def add(self, technology):\n",
    "        technology = technology.strip()      \n",
    "        try:\n",
    "            i = self.data.index(technology)\n",
    "        except ValueError:\n",
    "            pass\n",
    "        else:\n",
    "            if self.find_dups:\n",
    "                self.dups.append(technology)\n",
    "            return i\n",
    "        if technology[0] in ' \\t\\n':\n",
    "            print(f'space chars in technology |{technology}|')\n",
    "        self.data.append(technology)\n",
    "        return self.data.index(technology)\n",
    "\n",
    "    def technologies(self):\n",
    "        for t in self.data:\n",
    "            if t[0] in ' \\t\\n':\n",
    "                 print(f'space chars in technology |{t}|')\n",
    "            yield t\n",
    "    def print_dups(self):\n",
    "        print(f\"count: {len(self.dups)}\")\n",
    "        print(', '.join(self.dups))\n",
    "    def store(self):\n",
    "        f = io.open(tfn, 'w')\n",
    "        f.write(', '.join(self.data))\n",
    "        f.close()\n",
    "\n",
    "# technology.add('Google Analytics')\n",
    "# technology.store()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "technology = technologies_class()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "f = io.open('urls.csv', 'r')\n",
    "urls_ = f.read()\n",
    "f.close()\n",
    "urls = urls_.split(',')\n",
    "with_tech_url = 'https://pro.builtwith.com/'\n",
    "# with_tech_url = 'https://www.wappalyzer.com/'\n",
    "driver = webdriver.chrome.webdriver.WebDriver('/usr/local/bin/chromedriver')\n",
    "# dir(driver)\n",
    "wait = WebDriverWait(driver, 10)\n",
    "driver.get(with_tech_url)\n",
    "    #elem = driver.find_element_by_css_selector('input[placeholder=\"Website, Tech, Keyword\"]')\n",
    "    # <a class=\"text-light\" href=\"//builtwith.com/login\">Log In</a>\n",
    "time.sleep(1)\n",
    "# elem = driver.find_element_by_css_selector(r'a[href=\"//builtwith.com/login\"]')\n",
    "# elem.click()\n",
    "print(driver.title)\n",
    "WebDriverWait(driver, 10).until(EC.title_contains(\"Log in\"))\n",
    "elem = wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR, \"input[type='email']\")))\n",
    "elem.send_keys(bw_user)\n",
    "# elem.send_keys(Keys.TAB) \n",
    "time.sleep(1)\n",
    "elem = wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR, \"input[type='password']\")))\n",
    "elem.send_keys(bw_password)\n",
    "# elem.send_keys(Keys.TAB)  \n",
    "time.sleep(1)\n",
    "elem = wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR, \"input[type='submit']\")))\n",
    "elem.click()\n",
    "rows = []\n",
    "for url in urls:\n",
    "#     if url in ['more info','collapse', 'Privacy Policy', 'Terms of Use' ]:\n",
    "#         continue\n",
    "  if url.find('.') > 0:\n",
    "    # print(url)\n",
    "    #elem = driver.find_element_by_css_selector('input[placeholder=\"Website, Tech, Keyword\"]')\n",
    "    elem = driver.find_element_by_css_selector(\"input[name='q']\")\n",
    "    elem.clear()\n",
    "    elem.send_keys(url)\n",
    "    elem.send_keys(Keys.RETURN)\n",
    "    assert \"No results found.\" not in driver.page_source\n",
    "    source = driver.page_source\n",
    "#     f = io.open(f'{url}.html', 'w')\n",
    "#     f.write(source)\n",
    "#     f.close()    \n",
    "    html = BeautifulSoup(source, 'html.parser')\n",
    "    h2as = []\n",
    "    for h2 in html.find_all('h2'):\n",
    "        try:\n",
    "            h2a = h2.find_next('a', {'class': 'text-dark'}).text\n",
    "        except:\n",
    "            pass\n",
    "        else:\n",
    "            h2as.append(h2a)\n",
    "    #print(f\"len h2as  {len(h2as)}\")\n",
    "    row = {'url': url}\n",
    "    for h2a in h2as:\n",
    "        i = technology.add(h2a)\n",
    "        if (i % 1000) == 0:\n",
    "          print(f\"i: {i}\")\n",
    "        # assert i < 10, \"for debugging\"\n",
    "        row[i] = 1\n",
    "    rows.append(row)\n",
    "#     if i > 10:\n",
    "#         break\n",
    "df = pd.DataFrame(rows)\n",
    "print(df.head())\n",
    "print(df.shape)\n",
    "df.to_pickle('sites.2.pkl') \n",
    "technology.store()\n",
    "try:\n",
    "  driver.close()\n",
    "except WebDriverException:\n",
    "  print('driver.close failed') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"text-dark\" href=\"https://trends.builtwith.com/analytics/Google-Analytics\">Google Analytics</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_pickle('sites.pkl')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df.url == 'hanatour.com']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "f = open(tfn, 'r')\n",
    "names_s = f.read()\n",
    "f.close()\n",
    "names = np.array(names_s.split(','))\n",
    "len(names)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {}\n",
    "for i,x in enumerate(names):\n",
    "  d[i] = x.strip()\n",
    "df.rename(index=str, columns=d, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df.columns.drop('url')] = df[df.columns.drop('url')].fillna(-1.0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-a418479287da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sites.pkl'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df.to_pickle('sites.pkl')"
   ]
  },
  {
   "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>url</th>\n",
       "      <th>Google Analytics</th>\n",
       "      <th>Matomo</th>\n",
       "      <th>Facebook Signal</th>\n",
       "      <th>Facebook Pixel</th>\n",
       "      <th>Facebook Conversion Tracking</th>\n",
       "      <th>Google Tag Manager</th>\n",
       "      <th>Korean</th>\n",
       "      <th>Cart Functionality</th>\n",
       "      <th>ASP.NET</th>\n",
       "      <th>...</th>\n",
       "      <th>Piclens Lite</th>\n",
       "      <th>Vid.me</th>\n",
       "      <th>Global Layer</th>\n",
       "      <th>Klasik Framework</th>\n",
       "      <th>Resy</th>\n",
       "      <th>InfinityGrid</th>\n",
       "      <th>moofx</th>\n",
       "      <th>Meteor Slides</th>\n",
       "      <th>Calendly</th>\n",
       "      <th>At.js</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hanatour.com</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>digwebinterface.com</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>xfree.hu</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iplanetwork.com</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sdu.org.cn</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 10588 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   url  Google Analytics  Matomo  Facebook Signal  \\\n",
       "0         hanatour.com                 1       1                1   \n",
       "1  digwebinterface.com                 1      -1               -1   \n",
       "2             xfree.hu                -1      -1               -1   \n",
       "3      iplanetwork.com                 1      -1               -1   \n",
       "4           sdu.org.cn                -1      -1               -1   \n",
       "\n",
       "   Facebook Pixel  Facebook Conversion Tracking  Google Tag Manager  Korean  \\\n",
       "0               1                             1                   1       1   \n",
       "1              -1                            -1                  -1      -1   \n",
       "2              -1                            -1                  -1      -1   \n",
       "3              -1                            -1                  -1      -1   \n",
       "4              -1                            -1                  -1      -1   \n",
       "\n",
       "   Cart Functionality  ASP.NET  ...    Piclens Lite  Vid.me  Global Layer  \\\n",
       "0                   1        1  ...              -1      -1            -1   \n",
       "1                  -1       -1  ...              -1      -1            -1   \n",
       "2                  -1       -1  ...              -1      -1            -1   \n",
       "3                  -1       -1  ...              -1      -1            -1   \n",
       "4                  -1       -1  ...              -1      -1            -1   \n",
       "\n",
       "   Klasik Framework  Resy  InfinityGrid  moofx  Meteor Slides  Calendly  At.js  \n",
       "0                -1    -1            -1     -1             -1        -1     -1  \n",
       "1                -1    -1            -1     -1             -1        -1     -1  \n",
       "2                -1    -1            -1     -1             -1        -1     -1  \n",
       "3                -1    -1            -1     -1             -1        -1     -1  \n",
       "4                -1    -1            -1     -1             -1        -1     -1  \n",
       "\n",
       "[5 rows x 10588 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle('sites.pkl')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2659, 10588)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(['Neural Networks'], 'Neural')\n"
     ]
    }
   ],
   "source": [
    "def get_columns(key_, exact = False):\n",
    "  c = []\n",
    "  if type(key_) is not list:\n",
    "    key_ = [key_]\n",
    "  title = key_[0][0].upper() + key_[0][1:]\n",
    "  for key in key_:        \n",
    "    for column in df.columns:\n",
    "      if exact:\n",
    "        try:\n",
    "          assert(column == key),'no match'\n",
    "          key = key.lower()\n",
    "        except AssertionError:\n",
    "          continue\n",
    "      try:\n",
    "        index = column.lower().index(key)\n",
    "      except ValueError:\n",
    "        pass\n",
    "      else:\n",
    "        if column not in c:\n",
    "          c.append(column)\n",
    "  return (c, title)\n",
    "\n",
    "phpColumns = get_columns('php')\n",
    "reduxColumns = get_columns('redux')\n",
    "reactColumns = get_columns('react')\n",
    "jqueryColumns = get_columns('jquery')\n",
    "facebookColumns = get_columns('facebook')\n",
    "neuralColumns = get_columns('neural')\n",
    "print(neuralColumns)\n",
    "wordpressColumns = get_columns('wordpress')\n",
    "twitterColumns = get_columns('twitter')\n",
    "angularColumns = get_columns('angular')\n",
    "vueColumns =  get_columns(['Vue','Vuetify','vuex','Revue'],True)\n",
    "typescriptColumns = get_columns('typescript')\n",
    "railsColumns = get_columns('rails')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def phpColumn(row):\n",
    "  for php in phpColumns:\n",
    "    if row[php] == 1:\n",
    "      return 1\n",
    "  return -1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['job'] = df.apply(lambda row: phpColumn(row),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.url[df.job == 1].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_dups = technologies_class(find_dups=True)\n",
    "test_dups.print_dups()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|Matomo|\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(123)\n",
    "ratings = [(t.strip(), random.uniform(0.2,0.8)) for t in test_dups.technologies() ]\n",
    "print(f\"|{ratings[1][0]}|\")\n",
    "\n",
    "f = io.open('ratings.csv', 'w')\n",
    "f.write(','.join(f\"({r[0].strip()},{r[1]})\" for r in ratings))\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_dict = {}\n",
    "for r in ratings:\n",
    "    rating_dict[r[0]] = r[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2659, 10588)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confidence intervals!\n",
    "# Similar to hypothesis testing, but centered at sample mean\n",
    "# Better than reporting the \"point estimate\" (sample mean)\n",
    "# Why? Because point estimates aren't always perfect\n",
    "\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "def confidence_interval(data, confidence=0.95):\n",
    "  \"\"\"\n",
    "  Calculate a confidence interval around a sample mean for given data.\n",
    "  Using t-distribution and two-tailed test, default 95% confidence. \n",
    "  \n",
    "  Arguments:\n",
    "    data - iterable (list or numpy array) of sample observations\n",
    "    confidence - level of confidence for the interval\n",
    "  \n",
    "  Returns:\n",
    "    tuple of (mean, lower bound, upper bound)\n",
    "  \"\"\"\n",
    "  data = np.array(data)\n",
    "  mean = np.mean(data)\n",
    "  n = len(data)\n",
    "  stderr = stats.sem(data)\n",
    "  interval = stderr * stats.t.ppf((1 + confidence) / 2., n - 1)\n",
    "  return (mean, mean - interval, mean + interval)\n",
    "\n",
    "def report_confidence_interval(confidence_interval):\n",
    "  \"\"\"\n",
    "  Return a string with a pretty report of a confidence interval.\n",
    "  \n",
    "  Arguments:\n",
    "    confidence_interval - tuple of (mean, lower bound, upper bound)\n",
    "  \n",
    "  Returns:\n",
    "    None, but prints to screen the report\n",
    "  \"\"\"\n",
    "  #print('Mean: {}'.format(confidence_interval[0]))\n",
    "  #print('Lower bound: {}'.format(confidence_interval[1]))\n",
    "  #print('Upper bound: {}'.format(confidence_interval[2]))\n",
    "  s = \"our mean lies in the interval ]{:.2}, {:.2}[\".format(\n",
    "      confidence_interval[1], confidence_interval[2])\n",
    "  s = f\"our mean {confidence_interval[0]:.2f} lies in the interval {confidence_interval[1]:.2f} - {confidence_interval[2]:.2f}\"\n",
    "  return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def notdecreasing(x):\n",
    "    dx = np.diff(x)\n",
    "    return np.all(dx >= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "minLen = 30\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "half_mean_height = 0.01\n",
    "\n",
    "\n",
    "def printChart(c, data):\n",
    "\n",
    "    df = pd.DataFrame.from_dict(data)\n",
    "    # print(df.head())\n",
    "    n = len(data)\n",
    "    plt.figure(figsize=(max(n / 5, 5), 5))\n",
    "    data_order = [\n",
    "        'min_score','below_ci', 'ci_half_l', 'mean', 'ci_half_h', 'max_score'\n",
    "    ]\n",
    "    for _,r in df.iterrows():\n",
    "      # assert(notdecreasing([r['min_score'],r['low'],r['mean_low'],r['mean_high'],r['high'],r['max_score'] ])), \"decreasing\"\n",
    "      assert(r.max_score <= 1),\"max_score > 1\"\n",
    "    colors = [None, \"b\",\"g\",'black','g', \"gold\"]\n",
    "    #   values = np.array([[d[name] for name in data_order] for d in data])\n",
    "    order = np.array(data_order)\n",
    "    ind = np.arange(n)  # the x locations for the groups\n",
    "    bottom = np.array(df['min_score'])\n",
    "    width = 0.4\n",
    "    name_dict = {\n",
    "        'below_ci': 'Low Score',\n",
    "        'ci_half_l': 'Confidence Interval',\n",
    "        'mean': 'Confidence Interval Mean',\n",
    "        'ci_half_h': None,\n",
    "        'max_score': 'High Score'\n",
    "    }\n",
    "    \n",
    "    for name, color in zip(data_order[1:], colors[1:]):\n",
    "        value = np.array(df[name])\n",
    "        assert(value.max() <= 1), \"value > 1\"\n",
    "        assert(value.min() >= 0), \"value < 0\"\n",
    "        # print(f\"bottom: {bottom[0]} value: {value[0]}\")\n",
    "        # p1 = plt.bar(ind, value, width, color=color, bottom = bottom, label=name_dict[name], alpha=(1 if name == 'mean_low' else 0.5))\n",
    "        p1 = plt.bar(\n",
    "            ind,\n",
    "            value,\n",
    "            width,\n",
    "            color=color,\n",
    "            bottom=bottom,\n",
    "            label=name_dict[name])\n",
    "        bottom += value\n",
    "        # print(f'name: {name} color: {color} value: {value}')\n",
    "    #plt.bar(ind,np.array(df.mean_high),width, color='black', bottom=np.array(df.mean_low), label = 'Mean')\n",
    "\n",
    "\n",
    "#   plt.yticks(bottoms+0.4, [\"data %d\" % (t+1) for t in bottoms])\n",
    "    plt.legend(loc=\"best\", bbox_to_anchor=(1.0, 1.00))\n",
    "    plt.ylabel('Scores')\n",
    "    plt.title(f\"Companies with the {c} library\")\n",
    "    plt.xticks(\n",
    "        ind,\n",
    "        np.array(df['url']),\n",
    "        rotation=('vertical' if n > 2 else 'horizontal'))\n",
    "    #   start, end = plt.yaxis\n",
    "    #   plt.yaxis.set_major_locator(ticker.MultipleLocator((end - start) / 10))\n",
    "    axes = plt.gca()\n",
    "    axes.set_ylim([0, 1])\n",
    "    plt.yticks(np.arange(0, 1, .1))\n",
    "    #   plt.subplots_adjust(right=0.85)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Php company taritaworld.fi 95% confidence interval our mean 0.60 lies in the interval 0.45 - 0.75\n",
      "for library Php company weathersphere.com 95% confidence interval our mean 0.53 lies in the interval 0.38 - 0.67\n",
      "for library Php company xfree.hu 95% confidence interval our mean 0.51 lies in the interval 0.33 - 0.68\n",
      "for library Php company digwebinterface.com 95% confidence interval our mean 0.50 lies in the interval 0.45 - 0.55\n",
      "for library Php company rotapan.com 95% confidence interval our mean 0.49 lies in the interval 0.33 - 0.64\n",
      "for library Php company ioaa2015.org 95% confidence interval our mean 0.49 lies in the interval 0.32 - 0.66\n",
      "for library Php company dar-ul-hikmah.org 95% confidence interval our mean 0.49 lies in the interval 0.35 - 0.62\n",
      "for library Php company emaa-musique.com 95% confidence interval our mean 0.45 lies in the interval 0.31 - 0.59\n",
      "for library Php company northernilanglersassoc.com 95% confidence interval our mean 0.42 lies in the interval 0.34 - 0.51\n",
      "for library Php company basketamericano.com 95% confidence interval our mean 0.38 lies in the interval 0.24 - 0.52\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Redux company pgaafly.com 95% confidence interval our mean 0.59 lies in the interval 0.47 - 0.72\n",
      "for library Redux company petshopvelmu.fi 95% confidence interval our mean 0.54 lies in the interval 0.43 - 0.65\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library React company deco27.com 95% confidence interval our mean 0.68 lies in the interval 0.57 - 0.79\n",
      "for library React company ahaaglobal.com 95% confidence interval our mean 0.56 lies in the interval 0.39 - 0.73\n",
      "for library React company quranonline.net 95% confidence interval our mean 0.56 lies in the interval 0.38 - 0.74\n",
      "for library React company syaasports.com 95% confidence interval our mean 0.55 lies in the interval 0.34 - 0.76\n",
      "for library React company ahbabullah.com 95% confidence interval our mean 0.53 lies in the interval 0.32 - 0.74\n",
      "for library React company qt2alumni.com 95% confidence interval our mean 0.53 lies in the interval 0.38 - 0.67\n",
      "for library React company liantopainthorses.com 95% confidence interval our mean 0.47 lies in the interval 0.35 - 0.59\n",
      "for library React company blubberblog.org 95% confidence interval our mean 0.45 lies in the interval 0.14 - 0.77\n",
      "for library React company typhoon2000.ph 95% confidence interval our mean 0.45 lies in the interval 0.30 - 0.60\n",
      "for library React company egaa-gym.dk 95% confidence interval our mean 0.41 lies in the interval 0.08 - 0.74\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Rails company sea-weather.com 95% confidence interval our mean 0.55 lies in the interval 0.42 - 0.67\n",
      "for library Rails company sp-koti.fi 95% confidence interval our mean 0.51 lies in the interval 0.32 - 0.71\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Jquery company lamk.fi 95% confidence interval our mean 0.61 lies in the interval 0.51 - 0.71\n",
      "for library Jquery company acaaa.org 95% confidence interval our mean 0.60 lies in the interval 0.52 - 0.68\n",
      "for library Jquery company moore-exposure.com 95% confidence interval our mean 0.57 lies in the interval 0.46 - 0.68\n",
      "for library Jquery company pinguinstorello.com 95% confidence interval our mean 0.57 lies in the interval 0.43 - 0.71\n",
      "for library Jquery company gold-city-nutrition.de 95% confidence interval our mean 0.55 lies in the interval 0.44 - 0.65\n",
      "for library Jquery company inaa.org 95% confidence interval our mean 0.55 lies in the interval 0.42 - 0.67\n",
      "for library Jquery company lightupcaps.com 95% confidence interval our mean 0.54 lies in the interval 0.41 - 0.67\n",
      "for library Jquery company michclay.com 95% confidence interval our mean 0.51 lies in the interval 0.37 - 0.65\n",
      "for library Jquery company iplanetwork.com 95% confidence interval our mean 0.46 lies in the interval 0.41 - 0.50\n",
      "for library Jquery company hanatour.com 95% confidence interval our mean 0.44 lies in the interval 0.38 - 0.50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Facebook company brianmmorgan.com 95% confidence interval our mean 0.61 lies in the interval 0.52 - 0.71\n",
      "for library Facebook company infosystem.biz 95% confidence interval our mean 0.58 lies in the interval 0.43 - 0.73\n",
      "for library Facebook company climaespacial.net 95% confidence interval our mean 0.58 lies in the interval 0.42 - 0.73\n",
      "for library Facebook company topatruck.com 95% confidence interval our mean 0.54 lies in the interval 0.38 - 0.69\n",
      "for library Facebook company imuscle.de 95% confidence interval our mean 0.51 lies in the interval 0.39 - 0.64\n",
      "for library Facebook company digwebinterface.com 95% confidence interval our mean 0.50 lies in the interval 0.45 - 0.55\n",
      "for library Facebook company phaa.com 95% confidence interval our mean 0.50 lies in the interval 0.36 - 0.64\n",
      "for library Facebook company sapokka.fi 95% confidence interval our mean 0.48 lies in the interval 0.34 - 0.63\n",
      "for library Facebook company piaa.asn.au 95% confidence interval our mean 0.46 lies in the interval 0.36 - 0.56\n",
      "for library Facebook company hanatour.com 95% confidence interval our mean 0.44 lies in the interval 0.38 - 0.50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Neural company fitnesschampionsbypam.com 95% confidence interval our mean 0.39 lies in the interval 0.29 - 0.49\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Wordpress company kens-cmeas.com 95% confidence interval our mean 0.58 lies in the interval 0.43 - 0.73\n",
      "for library Wordpress company govreports.com.au 95% confidence interval our mean 0.57 lies in the interval 0.45 - 0.70\n",
      "for library Wordpress company maaa.vn 95% confidence interval our mean 0.57 lies in the interval 0.44 - 0.69\n",
      "for library Wordpress company nesec.org 95% confidence interval our mean 0.55 lies in the interval 0.37 - 0.73\n",
      "for library Wordpress company smaaa.org 95% confidence interval our mean 0.55 lies in the interval 0.47 - 0.63\n",
      "for library Wordpress company pilotsguide.com 95% confidence interval our mean 0.54 lies in the interval 0.41 - 0.67\n",
      "for library Wordpress company nocodexgenocide.com 95% confidence interval our mean 0.53 lies in the interval 0.41 - 0.66\n",
      "for library Wordpress company aiaa-aviation.org 95% confidence interval our mean 0.53 lies in the interval 0.40 - 0.65\n",
      "for library Wordpress company geofaq.ru 95% confidence interval our mean 0.53 lies in the interval 0.38 - 0.67\n",
      "for library Wordpress company rtaa.org.au 95% confidence interval our mean 0.53 lies in the interval 0.37 - 0.68\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Twitter company petersoninstitute.org 95% confidence interval our mean 0.64 lies in the interval 0.51 - 0.77\n",
      "for library Twitter company mymsaa.org 95% confidence interval our mean 0.61 lies in the interval 0.45 - 0.77\n",
      "for library Twitter company acaaa.org 95% confidence interval our mean 0.60 lies in the interval 0.52 - 0.68\n",
      "for library Twitter company poppanat.fi 95% confidence interval our mean 0.55 lies in the interval 0.39 - 0.72\n",
      "for library Twitter company thedistricteight.org 95% confidence interval our mean 0.53 lies in the interval 0.38 - 0.68\n",
      "for library Twitter company vipstaginghomemgt.com 95% confidence interval our mean 0.52 lies in the interval 0.40 - 0.64\n",
      "for library Twitter company pdq-estates.co.uk 95% confidence interval our mean 0.51 lies in the interval 0.33 - 0.69\n",
      "for library Twitter company kingbody.ru 95% confidence interval our mean 0.50 lies in the interval 0.37 - 0.63\n",
      "for library Twitter company theplayoffs.com.br 95% confidence interval our mean 0.48 lies in the interval 0.29 - 0.67\n",
      "for library Twitter company iplanetwork.com 95% confidence interval our mean 0.46 lies in the interval 0.41 - 0.50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Angular company ankankanfan.tumblr.com 95% confidence interval our mean 0.62 lies in the interval 0.34 - 0.89\n",
      "for library Angular company rivalsfanstore.com 95% confidence interval our mean 0.61 lies in the interval 0.25 - 0.98\n",
      "for library Angular company pilatesbyteresa.com 95% confidence interval our mean 0.61 lies in the interval 0.47 - 0.76\n",
      "for library Angular company wdw.at 95% confidence interval our mean 0.56 lies in the interval 0.28 - 0.84\n",
      "for library Angular company gtaaquaria.com 95% confidence interval our mean 0.53 lies in the interval 0.35 - 0.71\n",
      "for library Angular company apnapakdramas.com 95% confidence interval our mean 0.53 lies in the interval 0.27 - 0.79\n",
      "for library Angular company streetroad.com 95% confidence interval our mean 0.52 lies in the interval 0.39 - 0.65\n",
      "for library Angular company ncaagymnews.weebly.com 95% confidence interval our mean 0.45 lies in the interval 0.24 - 0.65\n",
      "for library Angular company ecaa.jp 95% confidence interval our mean 0.44 lies in the interval 0.31 - 0.58\n",
      "for library Angular company djaa.com 95% confidence interval our mean 0.43 lies in the interval 0.16 - 0.69\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Vue company modelyf1.cz 95% confidence interval our mean 0.57 lies in the interval 0.41 - 0.73\n",
      "for library Vue company aquarelando.com 95% confidence interval our mean 0.52 lies in the interval 0.31 - 0.73\n",
      "for library Vue company wstemp01.com 95% confidence interval our mean 0.51 lies in the interval 0.41 - 0.60\n",
      "for library Vue company mjaa.org.au 95% confidence interval our mean 0.45 lies in the interval 0.14 - 0.76\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for library Typescript company spool.com.ua 95% confidence interval our mean 0.50 lies in the interval 0.38 - 0.62\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from functools import reduce\n",
    "df.drop_duplicates('url', inplace=True)\n",
    "\n",
    "\n",
    "def conjunction(conditions):\n",
    "    return reduce(np.logical_or, conditions)\n",
    "\n",
    "\n",
    "for columns, title in [\n",
    "        phpColumns, reduxColumns, reactColumns, railsColumns, jqueryColumns, facebookColumns,\n",
    "        neuralColumns, wordpressColumns, twitterColumns, angularColumns,\n",
    "        vueColumns, typescriptColumns\n",
    "]:\n",
    "    dd = df[conjunction([df[c] == 1 for c in columns])]\n",
    "    scores = [{\n",
    "        'idx':\n",
    "        idx,\n",
    "        'url':\n",
    "        r.url,\n",
    "        'score':\n",
    "        reduce((lambda s, c: s + (rating_dict[c.strip()] if r[c] == 1 else 0)),\n",
    "               df.columns[1:], 0)\n",
    "    } for idx, r in dd.iterrows()]\n",
    "    scores = sorted(scores, key=lambda d: d['score'], reverse=True)[0:10]\n",
    "    rows = df[df.url.isin([r['url'] for r in scores])]\n",
    "    if len(scores) != len(rows):\n",
    "        print(len(scores), len(rows))\n",
    "        print(\",\".join([r['url'] for r in scores]))\n",
    "        print(\",\".join([r.url for _, r in rows.iterrows()]))\n",
    "    cis = []\n",
    "    for _,r in rows.iterrows():\n",
    "        ratings = np.array([])\n",
    "        for c in df.columns[1:]:\n",
    "            if r[c] == 1:\n",
    "                ratings = np.append(ratings,rating_dict[c.strip()]) \n",
    "                assert(rating_dict[c.strip()] <= 1), \"random.random > 1\"\n",
    "        assert(ratings.max() <= 1),\"random.random > 1?\"\n",
    "        assert(ratings.min() >= 0),\"random.random < 0\"\n",
    "        ci = confidence_interval(ratings)\n",
    "\n",
    "        min_score = ratings.min()\n",
    "        below_ci = max(ci[1] - min_score,0)\n",
    "        ci_l = (ci[2] - ci[1])\n",
    "        ci_half_l = ci[0] - ci[1] - half_mean_height\n",
    "        ci_half_h = ci_l - ci_half_l\n",
    "        mean = 2 * half_mean_height\n",
    "        max_score = max(ratings.max() - ci[2], 0)        \n",
    "        cis.append({\n",
    "            'ci': ci,\n",
    "            title: title,\n",
    "            'url': r.url,\n",
    "            'min_score': min_score,\n",
    "            'below_ci': below_ci,\n",
    "            'ci_half_l': ci_half_l,  \n",
    "            'mean': mean,\n",
    "            'ci_half_h': ci_half_h,\n",
    "            'max_score': max_score \n",
    "        })\n",
    "    if len(cis) > 0:\n",
    "      cis = sorted(cis, key=lambda v: v['ci'][0], reverse=True)\n",
    "      for _ci in cis:\n",
    "#         assert(_ci['max_score'] <= 1),\"max_score > 1\"\n",
    "        # assert((_ci['max_score'] <= 1) or (_ci['max_score'] == _ci['high'])),\"max_score > 1\"\n",
    "        print(f\"for library {title} company {_ci['url']} 95% confidence interval {report_confidence_interval(_ci['ci'])}\")\n",
    "      printChart(title,cis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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