\n
Extract text from any webpage in just one click. Head over to Nanonets website scraper<\/a>, Add the URL and click “Scrape,” and download the webpage text as a file instantly. Try it for free now.<\/em><\/p>\n <\/p>\n <\/p>\n Let\u2019s take a look at the step-by-step process of using Python to scrape website data.<\/p>\n Step 1: Choose the Website and Webpage URL<\/strong><\/p>\n The first step is to select the website you want to scrape. For this particular tutorial, let\u2019s scrape https:\/\/www.imdb.com\/<\/a>. We will try to extract data on the top-rated movies on the website.<\/p>\n Step 2: Inspect the website<\/strong><\/p>\n Now the next step is to understand the website structure. Understand what the attributes of the elements that are of your interest are. Right-click on the website to select \u201cInspect\u201d. This will open the HTML code. Use the inspector tool to see the name of all the elements to use in the code.<\/p>\n Note these elements’ class names and ids as they will be used in the Python code.<\/p>\n Step 3: Installing the important libraries<\/strong><\/p>\n As discussed earlier, Python has several web scraping libraries. Today, we will use the following libraries:<\/p>\n Install the libraries using the following command<\/p>\n Step 4: Write the Python code<\/strong><\/p>\n Now, it\u2019s time to write the main python code. The code will perform the following steps:<\/p>\n Here’s the Python code to scrape the top-rated movies from IMDb:<\/p>\n Step 5: Exporting the extracted data<\/strong><\/p>\n Now, let\u2019s export the data as a CSV file. We will use the pandas library.<\/p>\n Step 6: Verify the extracted data<\/strong><\/p>\n Open the CSV file to verify that the data has been successfully scraped and stored.<\/p>\n We hope this tutorial will help you extract data from webpages easily. <\/p>\n Extract text from any webpage in just one click. Head over to Nanonets website scraper<\/a>, Add the URL and click “Scrape,” and download the webpage text as a file instantly. Try it for free now.<\/em><\/p>\n <\/p>\n <\/p>\n You can parse website text easily using BeautifulSoup or lxml. Here are the steps involved along with the code.<\/p>\n Here’s a code of how to parse text from a website using BeautifulSoup<\/strong>:<\/p>\n To scrape HTML forms using Python, you can use a library such as BeautifulSoup, lxml, or mechanize. Here are the general steps:<\/p>\n Here’s an example of how to scrape an HTML form using mechanize:<\/strong><\/p>\n Extract text from any webpage <\/a>in just one click. Head over to Nanonets website scraper, Add the URL and click “Scrape,” and download the webpage text as a file instantly. Try it for free now.<\/em><\/p>\n <\/p>\n <\/p>\n Let\u2019s compare all the python web scraping libraries. All of them have excellent community support, but they differ in ease of use and their use cases, as mentioned in the start of the blog. <\/p>\n <\/p>\n Library<\/span><\/p>\n<\/td>\n Ease of Use<\/span><\/p>\n<\/td>\n Performance<\/span><\/p>\n<\/td>\n Flexibility<\/span><\/p>\n<\/td>\n Community Support<\/span><\/p>\n<\/td>\n Legal\/Ethical Considerations<\/span><\/p>\n<\/td>\n<\/tr>\n BeautifulSoup<\/span><\/p>\n<\/td>\n Easy<\/span><\/p>\n<\/td>\n Moderate<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n Adhere to Terms of Use<\/span><\/p>\n<\/td>\n<\/tr>\n Scrapy<\/span><\/p>\n<\/td>\n Moderate<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n Adhere to Terms of Use<\/span><\/p>\n<\/td>\n<\/tr>\n Selenium<\/span><\/p>\n<\/td>\n Easy<\/span><\/p>\n<\/td>\n Moderate<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n Follow Best Practices<\/span><\/p>\n<\/td>\n<\/tr>\n Requests<\/span><\/p>\n<\/td>\n Easy<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n Adhere to Terms of Use<\/span><\/p>\n<\/td>\n<\/tr>\n PyQuery<\/span><\/p>\n<\/td>\n Easy<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n Adhere to Terms of Use<\/span><\/p>\n<\/td>\n<\/tr>\n LXML<\/span><\/p>\n<\/td>\n Moderate<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n Adhere to Terms of Use<\/span><\/p>\n<\/td>\n<\/tr>\n MechanicalSoup<\/span><\/p>\n<\/td>\n Easy<\/span><\/p>\n<\/td>\n Moderate<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n Adhere to Terms of Use<\/span><\/p>\n<\/td>\n<\/tr>\n BeautifulSoup4<\/span><\/p>\n<\/td>\n Easy<\/span><\/p>\n<\/td>\n Moderate<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n Adhere to Terms of Use<\/span><\/p>\n<\/td>\n<\/tr>\n PySpider<\/span><\/p>\n<\/td>\n Easy<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n High<\/span><\/p>\n<\/td>\n Adhere to Terms of Use<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n <\/p>\n Python is an excellent option for scraping website data in real-time. Another alternative is to use automated website scraping tools l<\/a>ike Nanonets. You can use the free website-to-text tool<\/a>. But, if you need to automate web scraping for larger projects, you can contact Nanonets.<\/p>\n <\/p>\n <\/p>\n Extract text from any webpage in just one click. Head over to Nanonets website scraper, Add the URL and click “Scrape,” and download the webpage text as a file instantly. Try it for free now.<\/em><\/p>\n <\/p>\n <\/p>\n![](\"https:\/\/wordpress-1016567-4521551.cloudwaysapps.com\/wp-content\/uploads\/2023\/03\/web-scraping-with-python-tutorial.png\")
<\/figure>\n
\nHow to scrape data from websites using python?<\/h2>\n
![](\"https:\/\/wordpress-1016567-4521551.cloudwaysapps.com\/wp-content\/uploads\/2023\/03\/web-scraping-with-python-tutorial-1.png\")
<\/figure>\n\n
pip install requests beautifulsoup4 pandas time<\/code><\/pre>\n\n
import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport time\n# URL of the website to scrape\nurl = \"https:\/\/www.imdb.com\/chart\/top\"\n# Send an HTTP GET request to the website\nresponse = requests.get(url)\n# Parse the HTML code using BeautifulSoup\nsoup = BeautifulSoup(response.content, 'html.parser')\n# Extract the relevant information from the HTML code\nmovies = []\nfor row in soup.select('tbody.lister-list tr'):\ntitle = row.find('td', class_='titleColumn').find('a').get_text()\nyear = row.find('td', class_='titleColumn').find('span', class_='secondaryInfo').get_text()[1:-1]\nrating = row.find('td', class_='ratingColumn imdbRating').find('strong').get_text()\nmovies.append([title, year, rating])\n# Store the information in a pandas dataframe\ndf = pd.DataFrame(movies, columns=['Title', 'Year', 'Rating'])\n# Add a delay between requests to avoid overwhelming the website with requests\ntime.sleep(1)<\/code><\/pre>\n# Export the data to a CSV file\ndf.to_csv('top-rated-movies.csv', index=False)<\/code><\/pre>\n
\n<\/figure>\n
\nHow to parse text from website?<\/h2>\n
\n
import requests\nfrom bs4 import BeautifulSoup\n# Send an HTTP request to the URL of the webpage you want to access\nresponse = requests.get(\"https:\/\/www.example.com\")\n# Parse the HTML content using BeautifulSoup\nsoup = BeautifulSoup(response.content, \"html.parser\")\n# Extract the text content of the webpage\ntext = soup.get_text()\nprint(text)<\/code><\/pre>\nHow to scrape HTML forms using Python?<\/h2>\n
\n
\nimport mechanize\n# Create a mechanize browser object\nbrowser = mechanize.Browser()\n# Send an HTTP request to the URL of the webpage with the form you want to scrape\nbrowser.open(\"https:\/\/www.example.com\/form\")\n# Select the form to scrape\nbrowser.select_form(nr=0)\n# Extract the input fields and their corresponding values\nfor control in browser.form.controls:\nprint(control.name, control.value)\n# Submit the form\nbrowser.submit()<\/code><\/pre>\n
\n<\/figure>\n
\nComparing all Python web scraping libraries<\/h2>\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Conclusion<\/h2>\n
<\/figure>\nFAQs<\/h2>\n
How to use HTML parser for web scraping using Python?<\/h4>\n