Scrape Twitter with 5 Lines of Code
TLDRThe video tutorial demonstrates a method to efficiently scrape Twitter data in bulk using Python without the need for an API key. It introduces the 'SNS Scrape' package, which simplifies the process of extracting tweets based on searches or hashtags. The tutorial also covers the use of 'pandas' for data storage and 'TQDM' for progress tracking. By following the steps, users can collect and save tweet data as a CSV file, making it accessible for further analysis.
Takeaways
- 🌟 The video presents a method for scraping Twitter data in bulk without using the Twitter API, thus avoiding the limit of 100,000 requests per day.
- 🛠️ The Python package 'SNS Scrape' is introduced as the primary tool for extracting data from Twitter and other social networking sites.
- 💻 Requirements for using SNS Scrape include having Python 3.8 or higher and installing the package via pip.
- 📊 The tutorial also involves using 'pandas' for storing the scraped data as a data frame and 'TQDM' for a progress bar to track the scraping process.
- 🔍 A Twitter search scraper is created using SNS Scrape with a query, in this case, searching for tweets with the hashtag 'Python'.
- 📱 The script demonstrates how to extract specific information from tweets, such as the date, ID, content, username, like count, and retweet count.
- 🔗 The tutorial shows how to store the extracted tweet data in a list and then convert it into a pandas data frame for easier data manipulation.
- 📋 The data frame created can be saved as a CSV file, allowing for later analysis or use in other applications like Excel.
- 🚀 The video includes a demonstration of scaling up the process to extract and save 1,000 tweets, highlighting the efficiency and speed of the method.
- 📈 The addition of a progress bar using 'TQDM' is shown to be helpful when processing a large number of tweets, providing visual feedback on the progress.
- 🎓 The video serves as an educational resource for those interested in data analysis, social media data mining, or simply backing up personal Twitter data.
Q & A
What is the main topic of the video?
-The main topic of the video is about scraping Twitter data in bulk and storing it on a computer using Python.
What is the official way to pull Twitter data mentioned in the video?
-The official way to pull Twitter data mentioned in the video is using the Twitter API.
What is the limitation of using the Twitter API for data scraping?
-The limitation of using the Twitter API is that you are restricted to about 100,000 requests per day.
Which package does the video recommend for scraping Twitter data without using the API?
-The video recommends using the package called 'SNS Scrape' for scraping Twitter data without an API key.
What are the system requirements for using SNS Scrape?
-The system requirements for using SNS Scrape are having Python 3.8 or higher and using pip to install SNS Scrape.
How does the video demonstrate the scraping process?
-The video demonstrates the scraping process by creating a Twitter search scraper object, running a method to get items, and iterating over the results to extract data.
What kind of data can be extracted from a single tweet using the SNS Scrape package?
-Data that can be extracted from a single tweet includes the URL of the tweet, the date of the tweet, the content, reply count, like count, retweet count, and hashtags.
How does the video show storing the scraped Twitter data?
-The video shows storing the scraped Twitter data by creating a pandas data frame with the extracted information and then saving it as a CSV file.
What is the purpose of using TQDM in the video?
-The purpose of using TQDM in the video is to provide a progress bar that helps track the number of tweets pulled during the scraping process.
How many tweets does the video demonstrate extracting and saving as a CSV?
-The video demonstrates extracting and saving 1000 tweets as a CSV.
What is the significance of the progress bar in the scraping process?
-The significance of the progress bar in the scraping process is that it helps to visualize the progress and provides a user-friendly way to understand how many tweets have been scraped so far.
Outlines
🌐 Introduction to Scraping Twitter Data with Python
The paragraph introduces a method for scraping Twitter data in bulk and storing it on a computer using Python. It contrasts the official Twitter API, which is limited to 100,000 requests per day, with an alternative approach that allows for the extraction of millions of tweets without the need for an API key. The video will demonstrate how to use the 'SN Scrape' package for pulling data from social networking sites, including Twitter. The requirements for this method are Python 3.8 or higher and the installation of 'SN Scrape' via pip. The paragraph also mentions the use of 'pandas' for data storage and 'TQDM' for progress bar implementation, emphasizing the simplicity of using 'SN Scrape' for data extraction.
🔍 Extracting and Storing Twitter Data
This paragraph details the process of extracting Twitter data using the 'SN Scrape' package. It explains how to create a Twitter search scraper, specifying a search query, such as tweets with the hashtag 'Python'. The paragraph further discusses the structure of the data obtained from a single tweet, including the tweet's URL, date, content, and engagement metrics like reply count, like count, and retweet count. It also describes how to store selected tweet data in a list and iterate over multiple tweets to gather a larger dataset. The paragraph concludes with the demonstration of how to convert the list of tweet data into a pandas DataFrame for easier manipulation and mentions the option to save the data as a CSV file.
📊 Saving Tweets as CSV and Implementing a Progress Bar
The final paragraph focuses on saving the extracted tweet data as a CSV file and the addition of a progress bar for large-scale data extraction. It explains the process of saving 50 tweets as a CSV and provides a visual example of what the CSV file looks like. The paragraph then moves on to demonstrate how to use 'TQDM' to add a progress bar to the data extraction process, which is helpful when loading thousands of tweets. The example increases the number of tweets to 1000 and shows the progress bar in action, highlighting the speed and efficiency of the data extraction method. The video ends with a call to action for viewers to like and subscribe for more content.
Mindmap
Keywords
💡Twitter data
💡Python
💡SNS Scrape
💡Twitter API
💡Data analysis
💡CSV
💡Pandas
💡TQDM
💡Web scraping
💡Hashtag
💡Data frame
Highlights
The video presents a method for scraping Twitter data in bulk using Python, offering an alternative to the Twitter API which has a limit of 100,000 requests per day.
The package 'SNS Scrape' is introduced as a tool for pulling data from social networking sites without the need for an API key.
Python 3.8 or higher is required to use SNS Scrape, which can be installed via pip.
The tutorial also involves importing 'pandas' for data storage and 'TQDM' for progress bar implementation.
SNS Scrape's 'Twitter search scraper' is used to pull tweets based on a query, such as searching for tweets with the hashtag 'Python'.
The scraper object created with SNS Scrape can pull various tweet details like the URL, date, content, reply count, like count, and retweet count.
A demonstration is provided on how to extract specific tweet information such as the date, ID, content, username, like count, and retweet count.
The extracted tweet data can be stored as a list, which can then be converted into a pandas data frame for easier manipulation.
The tutorial shows how to limit the number of tweets pulled to a specific amount (e.g., 50 tweets) by using an enumeration and a conditional break.
The process of saving the data frame as a CSV file is demonstrated, allowing for data analysis or storage in other programs like Excel.
Adding a progress bar with 'TQDM' is discussed as a way to track the scraping process, especially useful for large-scale data collection.
The video includes a practical example of pulling 1000 tweets and saving them as a CSV, showcasing the speed and efficiency of the method.
The video concludes by encouraging viewers to like and subscribe, indicating the content is educational and intended for a wider audience.
The method shown in the video allows users to store their old tweets or perform analysis on collected tweet data.
The tutorial is practical and beginner-friendly, providing a step-by-step guide on how to use SNS Scrape for Twitter data scraping.
The use of 'TQDM' demonstrates how to enhance user experience by providing visual feedback during data scraping processes.
Transcripts
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