collecting-data-using-webscraping.ipynb

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   "source": [
    "**Web-Scraping-Lab**\n",
    "\n",
    "scrape data of programming languages and the associated average salary from a URL"
   ]
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   "source": [
    "import the modules needed"
   ]
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   "source": [
    "from bs4 import BeautifulSoup # this module helps in web scrapping.\n",
    "import requests  # this module helps us to download a web page\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "#this url contains the data you need to scrape\n",
    "url = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/labs/datasets/Programming_Languages.html\""
   ]
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   "source": [
    "data = requests.get(url).text"
   ]
  },
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   "source": [
    "soup=BeautifulSoup(data, \"html5lib\")"
   ]
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     "data": {
      "text/plain": [
       "[['Python', '$114,383'],\n",
       " ['Java', '$101,013'],\n",
       " ['R', '$92,037'],\n",
       " ['Javascript', '$110,981'],\n",
       " ['Swift', '$130,801'],\n",
       " ['C++', '$113,865'],\n",
       " ['C#', '$88,726'],\n",
       " ['PHP', '$84,727'],\n",
       " ['SQL', '$84,793'],\n",
       " ['Go', '$94,082']]"
      ]
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   "source": [
    "table=soup.find('table')\n",
    "def get_data(table):\n",
    "    language_salary_list = []\n",
    "    for row in table.find_all('tr'):\n",
    "        cols=row.find_all('td')\n",
    "        language_name = cols[1].getText()\n",
    "        annual_average_salary = cols[3].getText()\n",
    "    #     print(\"{}:{}\".format(language_name, annual_average_salary))\n",
    "        language_salary_list.append([language_name, annual_average_salary])\n",
    "    language_salary_list.remove(['Language', 'Average Annual Salary'])\n",
    "    return language_salary_list\n",
    "\n",
    "data=get_data(table)\n",
    "data"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Avg Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Python</td>\n",
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       "      <th>4</th>\n",
       "      <td>Swift</td>\n",
       "      <td>$130,801</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C++</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C#</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PHP</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SQL</td>\n",
       "      <td>$84,793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Go</td>\n",
       "      <td>$94,082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "     Language Avg Salary\n",
       "0      Python   $114,383\n",
       "1        Java   $101,013\n",
       "2           R    $92,037\n",
       "3  Javascript   $110,981\n",
       "4       Swift   $130,801\n",
       "5         C++   $113,865\n",
       "6          C#    $88,726\n",
       "7         PHP    $84,727\n",
       "8         SQL    $84,793\n",
       "9          Go    $94,082"
      ]
     },
     "execution_count": 6,
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   "source": [
    "df = pd.DataFrame(data, columns=['Language', 'Avg Salary'])\n",
    "df"
   ]
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   "source": [
    "Q1. Which language are developers paid the most according to the output of the web scraping lab?"
   ]
  },
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      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Avg Salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>4</th>\n",
       "      <td>Swift</td>\n",
       "      <td>130801</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python</td>\n",
       "      <td>114383</td>\n",
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       "      <td>C++</td>\n",
       "      <td>113865</td>\n",
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       "      <th>3</th>\n",
       "      <td>Javascript</td>\n",
       "      <td>110981</td>\n",
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       "      <th>1</th>\n",
       "      <td>Java</td>\n",
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       "      <td>R</td>\n",
       "      <td>92037</td>\n",
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       "      <th>6</th>\n",
       "      <td>C#</td>\n",
       "      <td>88726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SQL</td>\n",
       "      <td>84793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PHP</td>\n",
       "      <td>84727</td>\n",
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      "text/plain": [
       "     Language  Avg Salary\n",
       "4       Swift      130801\n",
       "0      Python      114383\n",
       "5         C++      113865\n",
       "3  Javascript      110981\n",
       "1        Java      101013\n",
       "9          Go       94082\n",
       "2           R       92037\n",
       "6          C#       88726\n",
       "8         SQL       84793\n",
       "7         PHP       84727"
      ]
     },
     "execution_count": 7,
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   ],
   "source": [
    "q1 = df.copy()\n",
    "q1['Avg Salary'] = q1['Avg Salary'].str.replace('$', '').str.replace(',', '').astype(int)\n",
    "q1.sort_values('Avg Salary', ascending = False)"
   ]
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    "tags": []
   },
   "source": [
    "Q2. Code segments to scrape images: soup.find_all(“img”)"
   ]
  }
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