#Scrape Tweets Data Using Python

[actosoluions]

How to Scrape Tweets Data by Location Using Python and snscrape?

In this blog, we will take a comprehensive look into scraping Python wrapper and its functionality and specifically focus on using it to search for tweets based on location. We will also delve into why the wrapper may not always perform as expected. Let's dive in

snscrape is a remarkable Python library that enables users to scrape tweets from Twitter without the need for personal API keys. With its lightning-fast performance, it can retrieve thousands of tweets within seconds. Moreover, snscrape offers powerful search capabilities, allowing for highly customizable queries. While the documentation for scraping tweets by location is currently limited, this blog aims to comprehensively introduce this topic. Let's delve into the details:

Introduction to Snscrape: Snscrape is a feature-rich Python library that simplifies scraping tweets from Twitter. Unlike traditional methods that require API keys, snscrape bypasses this requirement, making it accessible to users without prior authorization. Its speed and efficiency make it an ideal choice for various applications, from research and analysis to data collection.

The Power of Location-Based Tweet Scraping: Location-based tweet scraping allows users to filter tweets based on geographical coordinates or place names. This functionality is handy for conducting location-specific analyses, monitoring regional trends, or extracting data relevant to specific areas. By leveraging Snscrape's capabilities, users can gain valuable insights from tweets originating in their desired locations.

Exploring Snscrape's Location-Based Search Tools: Snscrape provides several powerful tools for conducting location-based tweet searches. Users can effectively narrow their search results to tweets from a particular location by utilizing specific parameters and syntax. This includes defining the search query, specifying the geographical coordinates or place names, setting search limits, and configuring the desired output format. Understanding and correctly using these tools is crucial for successful location-based tweet scraping.

Overcoming Documentation Gaps: While snscrape is a powerful library, its documentation on scraping tweets by location is currently limited. This article will provide a comprehensive introduction to the topic to bridge this gap, covering the necessary syntax, parameters, and strategies for effective location-based searches. Following the step-by-step guidelines, users can overcome the lack of documentation and successfully utilize snscrape for their location-specific scraping needs.

Best Practices and Tips: Alongside exploring Snscrape's location-based scraping capabilities, this article will also offer best practices and tips for maximizing the efficiency and reliability of your scraping tasks. This includes handling rate limits, implementing error-handling mechanisms, ensuring data consistency, and staying updated with any changes or updates in Snscrape's functionality.

Introduction of snscrape Using Python

In this blog, we’ll use tahe development version of snscrape that can be installed withpip install git+https://github.com/JustAnotherArchivist/snscrape.git

Note: this needs Python 3.8 or latest

Some familiarity of the Pandas module is needed.

We encourage you to explore and experiment with the various features of snscrape to better understand its capabilities. Additionally, you can refer to the mentioned article for more in-depth information on the subject. Later in this blog, we will delve deeper into the user field and its significance in tweet scraping. By gaining a deeper understanding of these concepts, you can harness the full potential of snscrape for your scraping tasks.

Advanced Search Features

In this code snippet, we define the search query as "pizza near:Los Angeles within:10km", which specifies that we want to search for tweets containing the word "pizza" near Los Angeles within a radius of 10 km. The TwitterSearchScraper object is created with the search query, and then we iterate over the retrieved tweets and print their content.

Feel free to adjust the search query and radius per your specific requirements.

For comparing results, we can utilize an inner merging on two DataFrames:common_rows = df_coord.merge(df_city, how='inner')

That returns 50 , for example, they both have the same rows.

What precisely is this place or location?

When determining the location of tweets on Twitter, there are two primary sources: the geo-tag associated with a specific tweet and the user's location mentioned in their profile. However, it's important to note that only a small percentage of tweets (approximately 1-2%) are geo-tagged, making it an unreliable metric for location-based searches. On the other hand, many users include a location in their profile, but it's worth noting that these locations can be arbitrary and inaccurate. Some users provide helpful information like "London, England," while others might use humorous or irrelevant descriptions like "My Parents' Basement."

Despite the limited availability and potential inaccuracies of geo-tagged tweets and user profile locations, Twitter employs algorithms as part of its advanced search functionality to interpret a user's location based on their profile. This means that when you look for tweets through coordinates or city names, the search results will include tweets geotagged from the location and tweets posted by users who have that location (or a location nearby) mentioned in their profile.

To illustrate the usage of location-based searching on Twitter, let's consider an example. Suppose we perform a search for tweets near "London." Here are two examples of tweets that were found using different methods:

The first tweet is geo-tagged, which means it contains specific geographic coordinates indicating its location. In this case, the tweet was found because of its geo-tag, regardless of whether the user has a location mentioned in their profile or not.

The following tweet isn’t geo-tagged, which means that it doesn't have explicit geographic coordinates associated with it. However, it was still included in the search results because a user has given a location in the profile that matches or is closely associated with London.

When performing a location-based search on Twitter, you can come across tweets that are either geo-tagged or have users with matching or relevant locations mentioned in their profiles. This allows for a more comprehensive search, capturing tweets from specific geographic locations and users who have declared their association with those locations.

Get Location From Scraped Tweets

If you're using snscrape to scrape tweets and want to extract the user's location from the scraped data, you can do so by following these steps. In the example below, we scrape 50 tweets within a 10km radius of Los Angeles, store the data in a DataFrame, and then create a new column to capture the user's location.

If It Doesn’t Work According to Your Expectations

The use of the near: and geocode: tags in Twitter's advanced search can sometimes yield inconsistent results, especially when searching for specific towns, villages, or countries. For instance, while searching for tweets nearby Lewisham, the results may show tweets from a completely different location, such as Hobart, Australia, which is over 17,000 km away.

To ensure more accurate results when scraping tweets by locations using snscrape, it is recommended to use the geocode tag having longitude & latitude coordinates, along with a specified radius, to narrow down the search area. This approach will provide more reliable and precise results based on the available data and features.

Conclusion

In conclusion, the snscrape Python module is a valuable tool for conducting specific and powerful searches on Twitter. Twitter has made significant efforts to convert user input locations into real places, enabling easy searching by name or coordinates. By leveraging its capabilities, users can extract relevant information from tweets based on various criteria.

For research, analysis, or other purposes, snscrape empowers users to extract valuable insights from Twitter data. Tweets serve as a valuable source of information. When combined with the capabilities of snscrape, even individuals with limited experience in Data Science or subject knowledge can undertake exciting projects.

Happy scrapping!

For more details, you can contact Actowiz Solutions anytime! Call us for all your mobile app scraping and web scraping services requirements.

sources :https://www.actowizsolutions.com/how-to-scrape-tweets-data-by-location-using-python-and-snscrape.php

[actowizsolutions0]

News Extract: Unlocking the Power of Media Data Collection

In today's fast-paced digital world, staying updated with the latest news is crucial. Whether you're a journalist, researcher, or business owner, having access to real-time media data can give you an edge. This is where news extract solutions come into play, enabling efficient web scraping of news sources for insightful analysis.

Why Extracting News Data Matters

News scraping allows businesses and individuals to automate the collection of news articles, headlines, and updates from multiple sources. This information is essential for:

Market Research: Understanding trends and shifts in the industry.

Competitor Analysis: Monitoring competitors’ media presence.

Brand Reputation Management: Keeping track of mentions across news sites.

Sentiment Analysis: Analyzing public opinion on key topics.

By leveraging news extract techniques, businesses can access and process large volumes of news data in real-time.

How News Scraping Works

Web scraping involves using automated tools to gather and structure information from online sources. A reliable news extraction service ensures data accuracy and freshness by:

Extracting news articles, titles, and timestamps.

Categorizing content based on topics, keywords, and sentiment.

Providing real-time or scheduled updates for seamless integration into reports.

The Best Tools for News Extracting

Various scraping solutions can help extract news efficiently, including custom-built scrapers and APIs. For instance, businesses looking for tailored solutions can benefit from web scraping services India to fetch region-specific media data.

Expanding Your Data Collection Horizons

Beyond news extraction, companies often need data from other platforms. Here are some additional scraping solutions:

Python scraping Twitter: Extract real-time tweets based on location and keywords.

Amazon reviews scraping: Gather customer feedback for product insights.

Flipkart scraper: Automate data collection from India's leading eCommerce platform.

Conclusion

Staying ahead in today’s digital landscape requires timely access to media data. A robust news extract solution helps businesses and researchers make data-driven decisions effortlessly. If you're looking for reliable news scraping services, explore Actowiz Solutions for customized web scraping solutions that fit your needs.

[drmikewatts]

Weekly Review 1 November 2024

Some interesting links that I Tweeted about in the last week (I also post these on Mastodon, Threads, Newsmast, and Bluesky):

I think this is the biggest reason to not use AI to generate important code or material-it's too easy for bad actors to inject malicious code into the model used: https://arstechnica.com/tech-policy/2024/10/bytedance-intern-fired-for-planting-malicious-code-in-ai-models/

Google's AI mediator, that helps guide people to agree: https://arstechnica.com/ai/2024/10/googles-deepmind-is-building-an-ai-to-keep-us-from-hating-each-other/

The quality of data being used to train AI is declining. Garbage in, garbage out: https://www.bigdatawire.com/2024/10/23/ai-has-a-data-problem-appen-report-says/

Like many other AI, this transcription tool hallucinates: https://techcrunch.com/2024/10/26/openais-whisper-transcription-tool-has-hallucination-issues-researchers-say/

It is going to take some time to sort out the legal issues around the scraping of content to train AI: https://www.theguardian.com/technology/2024/oct/25/unjust-threat-murdoch-and-artists-align-in-fight-over-ai-content-scraping

More ways AI will keep lawyers happy-who's responsible when an AI controlled vehicle crashes? https://dataconomy.com/2024/10/23/the-ethical-dilemmas-of-autonomous-cars-whos-responsible-in-a-crash/

Ten Python libraries you should be familiar with for working with data: https://www.kdnuggets.com/10-essential-python-libraries-for-data-science-in-2024

The last time I got a scam call I told them to talk to my d*ck and put the phone down the front of my trousers. I don't think that would work if it were an AI calling: https://www.theregister.com/2024/10/24/openai_realtime_api_phone_scam/

An AI that can write, and verify, code: https://techcrunch.com/2024/10/24/anthropics-ai-can-now-run-and-write-code/

Biased data produces biased AI models. This is as true for cybersecurity applications of AI as it is for anything else: https://www.datasciencecentral.com/why-ai-bias-is-a-cybersecurity-risk-and-how-to-address-it/

Do we really want an AI to be able to control the mouse on our computers? Maybe useful for people who have motor impairments or tremors: https://arstechnica.com/ai/2024/10/anthropic-publicly-releases-ai-tool-that-can-take-over-the-users-mouse-cursor/

AI company fires back at lawsuits over its scraping of content for training data: https://techcrunch.com/2024/10/24/they-wish-this-technology-didnt-exist-perplexity-responds-to-news-corps-lawsuit/

Did a chatbot AI really encourage a teenager to kill themselves? Time for guardrails: https://www.theguardian.com/technology/2024/oct/23/character-ai-chatbot-sewell-setzer-death

Using AI to enable a garden to talk back: https://www.theguardian.com/lifeandstyle/2024/oct/25/ai-powered-garden-chelsea-flower-show

The idea of multi-agent architectures has been around for decades. Will generative AI be able to coordinate different agents to perform useful tasks? https://www.informationweek.com/machine-learning-ai/10-reasons-why-multi-agent-architectures-will-supercharge-ai

It looks pretty obvious to me that some companies will try to use Microsoft's AI to replace workers, not augment them: https://dataconomy.com/2024/10/23/microsoft-rolls-out-virtual-employee-ai-agents-for-enterprises/

The US wants to use more AI, especially in national security: https://www.computerworld.com/article/3587124/white-house-tells-intelligence-agencies-use-more-ai.html

The AI Cisco is using for customer support: https://www.computerworld.com/article/3578806/ciscos-new-ai-agents-and-assistants-aim-to-ease-customer-service-headaches.html

If the AI chips only last three years, what happens to them after that? Can they be recycled, or is this another way AI can negatively impact the environment? https://www.extremetech.com/computing/data-center-ai-gpus-may-have-extremely-short-lifespans

AI generated material is a threat to us, especially its use in election interference: https://www.informationweek.com/cyber-resilience/ai-manipulation-threatens-the-bonds-of-our-digital-world

An approach to watermarking AI generated text: https://spectrum.ieee.org/watermark

Replacing journalists with AI is not a popular move: https://www.stuff.co.nz/world-news/360462671/polish-radio-station-replaces-journalists-ai-presenters

40 years later, Terminator continues to influence people's opinions of AI: https://arstechnica.com/ai/2024/10/40-years-later-the-terminator-still-shapes-our-view-of-ai/

Who needs AI safety? Not OpenAI: https://www.theregister.com/2024/10/25/open_ai_readiness_advisor_leaves/

[shalu620]

Discovering Python: Exciting Activities for Novice Programmers

Python, celebrated for its simplicity and adaptability, stands as a prime choice for aspiring programmers setting foot into the world of coding. With its intuitive syntax and expansive range of libraries and frameworks, Python presents boundless opportunities for exploration and innovation.

Considering the kind support of Learn Python Course in Hyderabad, Whatever your level of experience or reason for switching from another programming language, learning Python gets much more fun.

In this comprehensive guide, we'll explore a myriad of engaging activities tailored for beginners eager to delve into the realm of Python programming.

1. Crafting Simple Applications

Embark on your Python journey by crafting basic yet functional applications. From a rudimentary calculator to a to-do list app or even a text-based game, these projects serve as stepping stones for grasping fundamental programming concepts while refining your Python prowess.

2. Streamlining Mundane Tasks

Python's automation prowess revolutionizes the way we handle repetitive tasks. Develop scripts to automate tasks like file renaming, folder organization, or file downloads from the web. This not only saves time but also reinforces your grasp of Python essentials.

3. Delving into Data Analysis

Dive into the world of data analysis with Python's robust libraries such as Pandas and NumPy. Begin by manipulating datasets, performing elementary data analysis tasks, and visualizing results using libraries like Matplotlib or Seaborn.

4. Harnessing Web Scraping Abilities

Explore Python's web scraping capabilities by extracting data from websites using libraries such as BeautifulSoup or Scrapy. Whether it's fetching news headlines, product prices, or weather forecasts, web scraping unlocks a realm of possibilities for data extraction and analysis.

5. Venturing into Web Development

Enter the domain of web development with Python by creating static websites using frameworks like Flask or Django. Commence with basic web pages and gradually incorporate more advanced features as your programming skills evolve. Enrolling in the Best Python Certification Online can help people realise Python's full potential and gain a deeper understanding of its complexities.

6. Experimenting with APIs

Leverage Python's versatility to interact with APIs offered by popular websites and services. From retrieving information to executing actions like posting tweets or fetching weather forecasts, experimenting with APIs introduces you to real-world data manipulation and interaction.

7. Engaging in Coding Challenges

Challenge yourself with coding problems on platforms like LeetCode, HackerRank, or CodeSignal. These platforms host a plethora of problems tailored for beginners, offering an excellent opportunity to hone problem-solving skills and elevate your coding proficiency.

8. Contributing to Open Source Projects

Immerse yourself in the open-source community by contributing to beginner-friendly projects on platforms like GitHub. Contributing to open-source projects not only allows you to apply your Python skills in real-world scenarios but also exposes you to collaborative development practices.

9. Participating in Python Meetups and Workshops

Join local Python user groups or partake in online meetups and workshops to connect with fellow enthusiasts and learn from seasoned developers. These events provide invaluable opportunities to expand your Python knowledge and network with industry professionals.

10. Diving into Python Books and Tutorials

Invest in beginner-friendly Python books and online tutorials to deepen your understanding of the language. With a plethora of resources available, ranging from comprehensive guides to hands-on tutorials, you'll find ample support to fuel your Python learning journey.

Embark on your Python voyage with zeal and curiosity, remembering that consistent practice and exploration are the keys to mastery. Whether you're building applications, analyzing data, or contributing to open-source projects, each endeavor brings you closer to becoming a proficient Python programmer. So roll up your sleeves, dive into these captivating activities, and let the adventure unfold!

[actowiz-123]

[actowiz1]

Scrape Tweets Data by Location Using Python and snscrap

In this blog, we will take a comprehensive look into scraping Python wrapper and its functionality and specifically focus on using it to search for tweets based on location. We will also delve into why the wrapper may not always perform as expected. Let's dive in

snscrape is a remarkable Python library that enables users to scrape tweets from Twitter without the need for personal API keys. With its lightning-fast performance, it can retrieve thousands of tweets within seconds. Moreover, snscrape offers powerful search capabilities, allowing for highly customizable queries. While the documentation for scraping tweets by location is currently limited, this blog aims to comprehensively introduce this topic. Let's delve into the details:

Introduction to Snscrape: Snscrape is a feature-rich Python library that simplifies scraping tweets from Twitter. Unlike traditional methods that require API keys, snscrape bypasses this requirement, making it accessible to users without prior authorization. Its speed and efficiency make it an ideal choice for various applications, from research and analysis to data collection.

The Power of Location-Based Tweet Scraping: Location-based tweet scraping allows users to filter tweets based on geographical coordinates or place names. This functionality is handy for conducting location-specific analyses, monitoring regional trends, or extracting data relevant to specific areas. By leveraging Snscrape's capabilities, users can gain valuable insights from tweets originating in their desired locations.

Exploring Snscrape's Location-Based Search Tools: Snscrape provides several powerful tools for conducting location-based tweet searches. Users can effectively narrow their search results to tweets from a particular location by utilizing specific parameters and syntax. This includes defining the search query, specifying the geographical coordinates or place names, setting search limits, and configuring the desired output format. Understanding and correctly using these tools is crucial for successful location-based tweet scraping.

Overcoming Documentation Gaps: While snscrape is a powerful library, its documentation on scraping tweets by location is currently limited. This article will provide a comprehensive introduction to the topic to bridge this gap, covering the necessary syntax, parameters, and strategies for effective location-based searches. Following the step-by-step guidelines, users can overcome the lack of documentation and successfully utilize snscrape for their location-specific scraping needs.

Best Practices and Tips: Alongside exploring Snscrape's location-based scraping capabilities, this article will also offer best practices and tips for maximizing the efficiency and reliability of your scraping tasks. This includes handling rate limits, implementing error-handling mechanisms, ensuring data consistency, and staying updated with any changes or updates in Snscrape's functionality.

Introduction of snscrape Using Python

In this blog, we’ll use tahe development version of snscrape that can be installed withpip install git+https://github.com/JustAnotherArchivist/snscrape.git

Note: this needs Python 3.8 or latest

Some familiarity of the Pandas module is needed.

We encourage you to explore and experiment with the various features of snscrape to better understand its capabilities. Additionally, you can refer to the mentioned article for more in-depth information on the subject. Later in this blog, we will delve deeper into the user field and its significance in tweet scraping. By gaining a deeper understanding of these concepts, you can harness the full potential of snscrape for your scraping tasks.

Advanced Search Features

In this code snippet, we define the search query as "pizza near:Los Angeles within:10km", which specifies that we want to search for tweets containing the word "pizza" near Los Angeles within a radius of 10 km. The TwitterSearchScraper object is created with the search query, and then we iterate over the retrieved tweets and print their content.

Feel free to adjust the search query and radius per your specific requirements.

For comparing results, we can utilize an inner merging on two DataFrames:common_rows = df_coord.merge(df_city, how='inner')

That returns 50 , for example, they both have the same rows.

What precisely is this place or location?

When determining the location of tweets on Twitter, there are two primary sources: the geo-tag associated with a specific tweet and the user's location mentioned in their profile. However, it's important to note that only a small percentage of tweets (approximately 1-2%) are geo-tagged, making it an unreliable metric for location-based searches. On the other hand, many users include a location in their profile, but it's worth noting that these locations can be arbitrary and inaccurate. Some users provide helpful information like "London, England," while others might use humorous or irrelevant descriptions like "My Parents' Basement."

Despite the limited availability and potential inaccuracies of geo-tagged tweets and user profile locations, Twitter employs algorithms as part of its advanced search functionality to interpret a user's location based on their profile. This means that when you look for tweets through coordinates or city names, the search results will include tweets geotagged from the location and tweets posted by users who have that location (or a location nearby) mentioned in their profile.

To illustrate the usage of location-based searching on Twitter, let's consider an example. Suppose we perform a search for tweets near "London." Here are two examples of tweets that were found using different methods:

The first tweet is geo-tagged, which means it contains specific geographic coordinates indicating its location. In this case, the tweet was found because of its geo-tag, regardless of whether the user has a location mentioned in their profile or not.

The following tweet isn’t geo-tagged, which means that it doesn't have explicit geographic coordinates associated with it. However, it was still included in the search results because a user has given a location in the profile that matches or is closely associated with London.

When performing a location-based search on Twitter, you can come across tweets that are either geo-tagged or have users with matching or relevant locations mentioned in their profiles. This allows for a more comprehensive search, capturing tweets from specific geographic locations and users who have declared their association with those locations.

Get Location From Scraped Tweets

If you're using snscrape to scrape tweets and want to extract the user's location from the scraped data, you can do so by following these steps. In the example below, we scrape 50 tweets within a 10km radius of Los Angeles, store the data in a DataFrame, and then create a new column to capture the user's location.

If It Doesn’t Work According to Your Expectations

The use of the near: and geocode: tags in Twitter's advanced search can sometimes yield inconsistent results, especially when searching for specific towns, villages, or countries. For instance, while searching for tweets nearby Lewisham, the results may show tweets from a completely different location, such as Hobart, Australia, which is over 17,000 km away.

To ensure more accurate results when scraping tweets by locations using snscrape, it is recommended to use the geocode tag having longitude & latitude coordinates, along with a specified radius, to narrow down the search area. This approach will provide more reliable and precise results based on the available data and features.

Conclusion

In conclusion, the snscrape Python module is a valuable tool for conducting specific and powerful searches on Twitter. Twitter has made significant efforts to convert user input locations into real places, enabling easy searching by name or coordinates. By leveraging its capabilities, users can extract relevant information from tweets based on various criteria.

For research, analysis, or other purposes, snscrape empowers users to extract valuable insights from Twitter data. Tweets serve as a valuable source of information. When combined with the capabilities of snscrape, even individuals with limited experience in Data Science or subject knowledge can undertake exciting projects.

Happy scrapping!

For more details, you can contact Actowiz Solutions anytime! Call us for all your mobile app scraping and web scraping services requirements.

know more https://www.actowizsolutions.com/scrape-tweets-data-by-location-python-snscrape.php

[iwebdatascrape]

2023 Guide: How To Scrape Social Media Data Using Python?

Social Media Data scraping services, also popularly considered data scraping, collects data from social media platforms. It automatically collects information from websites using specialized tools or software called social media scraper.

Several platforms like Facebook, Twitter, Instagram, LinkedIn, and others provide APIs that enable developers to retrieve data from their platforms. This data is, however, limited. On the other hand, data collection from Instagram, Twitter, Facebook, etc., helps scrape Social Media Data Using Python by pretending human interaction and navigating several web pages.

The general steps involved in Social Media data extraction are:

Data Collection: You must first identify the target platform and the specific data for extraction. It includes user profiles, posts, comments, likes, followers, and other important information.

Crawling & Parsing: The process of crawling web pages is to find and extract the desired data. Parsing involves the extraction and structuring of the relevant data from the HTML or JSON content.

Data Storage & Analysis: After the scraping procedure, the extracted data is in a structured format, including CSV or JSON format. This data is then helpful for analysis and processing and used for various purposes, like market research, sentiment analysis, or customer insights.

List of Data Fields

Below is the list of the data fields found when performing social media data mining.

User information, including profiles, usernames, bio, location, profile picture, URL, number of followers, number of followings, etc.

Posts information, including content, shares, comments, timestamp, photos, URLs, videos, hashtags, etc.

Comments, including content, username, timestamp, etc.

Likes and reactions

Followers and following, including profile information, usernames, counts, etc.

Hashtags and mentions in the posts.

Images, videos, and audio

Likes, comments, shares, tweets, views, etc.

Importance of Web Scraping Social Media Data

Although, there are several benefits of web scraping social media data. But, here, we have emphasized the major ones.

Market Research: Social media platforms contain valuable information about users' preferences, opinions, and behavior. Use Social Media Data scraping services to collect this information to develop effective marketing strategies and make informed business decisions. Scrape social media data to gain insights into consumer trends, identify target audiences, and analyze competitors' movements.

Sentiment Analysis: Sentiment analysis of social media data helps judge products, brands, or events. By collecting data from social media platforms, businesses can monitor customer satisfaction, track potential issues, and address those issues promptly.

Identify Influencer: Social media influencers are a significant part of marketing campaigns. Use social media data extraction services to collect data on influential individuals in specific industries. Analyze their content, engagement rate, etc., to find suitable influencers to enjoy collaboration.

Customer Details: Social media web scraping python can collect data on customers, their interests, behavior, etc. Use this data to enhance customer offerings.

Trend Monitoring: Stay updated with the trending topics and stay trendy to engage with your target audience.

Scrape Social Media Data Using Python

To extract social media data using Python, have Python, pip, BeautifulSoup, and Selenium installed.

Let's start with Facebook scraping.

Scrape Facebook Using Python

While starting with the Facebook data collection, the role of Facebook data scraping services is to first look into the page you want to parse. Decide which data you want to scrape, which path you want the bot to take, and the specific data selectors you require. The entire process is achieved with Facebook Pages scraper.

Scrape Twitter Using Python

Here, we will scrape all the Tweets text using the data-testid=tweetText attribute.

Scrape Instagram Using Python

Conclusion

In this article, we have elaborated on the methods of scraping social media data using Python. We have emphasized Facebook, Twitter, and Instagram data scraping and how to use the scraped data for sentiment analysis, market research, and analyzing trends.

For more information, get in touch with iWeb Data Scraping now! You can also reach us for all your web scraping service and mobile app data scraping requirements.

[zephiris]

Eh all programming languages are good for certain use cases (aside from Java - Kotlin is better for android and Go is better for anything else).

Python is good at quick and dirty automation that just needs to get done. It’s very friendly to use and won’t pout at you when you ask it do something. Also once you learn to navigate pandas+numpy combined with Jupyter Notebooks it gets wayyyy faster and easier to use for data wrangling.

For example, I recently used Python to scrape hundreds of thousands of tweets via snscrape without having to use twitter’s API. Once I downloaded all the tweets it took me about 30 minutes to then do some basic analysis/labeling/sorting on said tweets.

Yes pip is terrible. Yes Python has only a hint of types (typescript style type hinting arrived in 3.something). Yes pickle creates so many vulnerabilities. Yes performant Python is basically C in a trench coat.

All that said, there’s a reason Python is many people’s first typed programming language and why I continue to use it whenever I have some data I have to fetch, transform, and analyze or whenever I’m just starting to explore a new field of computer science.

Writing Python is basically like writing pseudo code so I love it for anything that I just need to code up and run once or twice for either a proof of concept before moving to a more “serious” language or just discard the program is for my one-time personal use only.

No one should ever have to maintain more than 1k lines of Python but I will still occasionally write that much Python simply because it lets me explore high level techniques without worrying about being perfectly precise.

Python is not for production but instead for messing around. Python is that goofy ahh language that everyone likes because it doesn���t mind when you affectionately mess with it. Python is the adorable sidekick that makes programming fun again and for that I adore it

Java is a trash language that should burn in the parts of hell where hitler is

Rust on the other hand is a bratty lil language that should burn in the parts of hell where queers party

[philipholt]

Looking back on Software Development in 2020 and forward to 2021

I think we can all agree 2020 sucked. Hopefully 2021 will be better.

I've been a remote worker for 13 years by choice but in 2020 I HAD TO DO IT because, well, most programmers and tech workers did. I wrote about how Remote work != Quarantine Work while our whole division and then the whole company moved back home! We were a fairly remote-friendly company before but I have to admit I didn't always think my coworkers had really deep empathy for the remote...until they, too, were forced to be remote.

Last week on the podcast, I got to speak with Amanda Silver. She's a CVP in the Microsoft Developer Division who has been coding and thinking deeply about coding for many years. She's leading the creation of tools like Visual Studio, Visual Code, Live Share, Code Spaces, IntelliCode, and other collaborative productivity products. She's always thinking about what coding will look like in 1, 5, and even 10+ years.

We talked about her thoughts on moving the division remote and whether it would slow us down. Would it change how we develop software? What about when everyone comes back? After talking to her about her thoughts on 2020 and where she thinks we're heading, I got to thinking myself and wanted to put those thoughts down.

2020 broke everything, and developers like to fix things

Somewhere in the spring as we started into lockdown, developers started making sites. Sites to track COVID, GitHub projects with scripts to scrape data and analyze it. Javascripters started making D3.js visualizations and codepen users started building on top of them. Bots on twitter would tweet out updates and parse new data.

When there's a problem - especially a scary or untenable one - developers run towards the challenge. Necessity breeds invention and 2020 was definitely a year where we were collectively reminded there was a bunch of stuff that was always possible, but we needed a push. Cameras and mics were upgraded, ring lights were purchased, home networks got fancier, and everyone who could called their ISP and got an upgraded plan. We could have done all this before, but why? Remote work happened for the first time in 2020, and I say that having worked remotely forever.

We HAVE to collaborate remotely now

Back in 2010 I spoke to PhDs at Microsoft Research about how people feel when they are remote and what they can do to be more connected. Ten years! Folks thought it was pretty "out there" but I sure needed my virtual cubicle buddy this year.

2020 accelerated what was possible with remote collaboration. I spent hours coding with Live Share, pushing text and coding context over the wire, not a ridiculous 4k worth of pixels. Having two cursors (mine and my friends) - or even 10! - in one Visual Studio seemed like magic. Even more magic is me pressing F5 and my coworker hitting their localhost and seeing our app running! We needed tech like this more than ever in 2020.

I heard one story where a company sent everyone home but folks had disparate desktops and laptops so they set up 100s of Virtual Desktops over a weekend so everyone was able to log into secure work systems from their home machines.

For us, since we use Github and Azure DevOps here in DeviDiv, our collaboration model is asynchronous and distributed whether we are in the office or not. Can you imagine everyone working remotely while using a locking source control system in 2020? I feel bad for those who are in that predicament.

Can something be BETTER remotely?

Many of us miss being in the same room with co-workers, and we will be together again one day, but are there some things that the constraint of being remote can make better? In the podcast episode Amanda said that our new hire bootcamp was so much better remotely!

She said (paraphrasing a bit):

We have a bootcamp for anybody who's newly started on the team. They actually fly out for two weeks. And the first week is introduction and the second week is our customer driven workshop. And our customer driven workshop is basically this really intense team project where you break up into groups of five to six people, and you're given a business assignment like - how could we double the number of Python developers using Visual Studio Code.

You're basically doing like stickies on the wall the entire week - that's how you collaborate. I've been so amazed that that has transitioned to be remote first. And it's better. It's better. That was a brainstorming process that I thought was only possible in person it's better.

When we moved remote, we had to essentially reboot the way that we thought about our meeting culture to actually make it much more inclusive. And if we go from 40 to 50% of the people participating to just 2 people participating, that's a huge, not only degradation, but you're wasting people's time. Right?

Now if we can actually take six people who've never met each other before and get them to work super collaboratively on a new problem area that they've never worked on before. It's incredible. And the thing that's also really awesome about it is they are forced by nature of the fact that this is remote to actually create it as digital content. Whereas in the beginning they would literally walk us through sticky notes on the wall and they had fantastic ideas, but it was really kind of somewhat unorganized and, and it was hard to be able to see and, and retain and share out afterwards what these incredible ideas were that they came up with.

But when remotely starts with this digital format by necessity because everyone is remote first, we actually now have all of these things archived. We can come back to them, we can go back and actually see, you know, what was the genesis of the thought and, and pursue a lot of these things that we really weren't being able to pursue previously.

Constraints breed innovation!

It was nice to be reminded that People are People

2020 normalized being a person. Having a boss welcome a sad child to sit with them during a meeting reminded me that, what, my boss is a person? With a life and kids? Having meetings while going for walks, talking about treadmill desks, and video called parties with family, and OMG when will this be over is the most horrible team building exercise ever.

It's forced us to rethink our group's culture, how our interpersonal dynamics work, how many meetings we have (let's have less), and it's given everyone the joy of somewhat flexible hours. We talk more now about 'is everyone in this meeting being heard?' than ever before. We use the "hand raising" tool in Teams to make sure all voices get a chance to speak.

If 2020 hadn’t happened, we may not have made these important leaps forward. MAYBE this would have happened by 2025 or 2030 but COVID was the pivot point that forced the issue.

Here's some other blog posts that are both reflecting on our last year and hopeful for the coming year:

Software Development in 2021 and Beyond by Amanda Silver

4 Open Source Lessons for 2021 by Sarah Novotny

Low-code Trends: Why Low-Code Will Be Big In Your 2021 Tech Strategy by Dona Sarkar

PODCAST: Living through 2020 as a Remote Developer

Sponsor: Looking for free secure coding training but don’t know where to turn? Check out Veracode Security Labs Community Edition to start hacking and patching real apps online. Try it today.

© 2020 Scott Hanselman. All rights reserved.

      Looking back on Software Development in 2020 and forward to 2021 published first on http://7elementswd.tumblr.com/

[buysgreys]

Best language for webscraper

Read the second part where we send out the tweets and tag our ISP for slow internet speed. Now just run your app and let’s see what you get! For a good guide on hosting a bot on heroku please check out this great article. Once all of this has been completed we can host our bot on AWS or my personal recommendation heroku. Once you receive the email confirming your API keys be sure to copy them into your function so they function as expected. To learn what these tokens actually do I would recommend that you check out the tweepy documentation. The consumer_key, consumer_secret, access_token and access_token_secret are all API keys provided to us by twitter and should be some long, unreadable string. The tweet function that we wrote will take one argument of ��top post’ which is what we figured out in the scrape section. There’s a lot going on here so let’s slowly go through it. set_access_token ( access_token, access_token_secret ) api = tweepy. OAuthHandler ( consumer_key, consumer_secret ) auth. Features The reason why Python is a preferred language to use for web scraping is that Scrapy and Beautiful Soup are two of the most widely employed frameworks based on Python. It is a complete product because it can handle almost all processes related to data extraction smoothly. strip () tweet ( top_post ) def tweet ( top_post ): consumer_key = "#" consumer_secret = "#" access_token = "#" access_token_secret = "#" auth = tweepy. Python is the most popular language for web scraping. find ( "h2", class_ = "crayons-story_title" ). find_all ( class_ = "crayons-story_indention" ) top_post = posts. i only know python but it has many ways selenium, scrapy, beautifulsoup, all very easy to learn. To note, the biggest constraint in these things is usually network speed. find ( class_ = "articles-list crayons-layout_content" ) posts = home. Speed doesn't matter much for this kind of application, but ease of programming does - so Python and Beautiful Soup are the places to start. Uses RemoteTable gem internally.From bs4 import BeautifulSoup import tweepy import requests def scrape (): page = requests. Goutte project web site: First release dateĭownload, unpack from a ZIP/TAR/GZ/BZ2 archive, parse, correct, convert units and import Google Spreadsheets, XLS, ODS, XML, CSV, HTML, etc. It provides a nice API to crawl websites and extract data from the HTML/XML responses. Guzzle project web site: Programming language With reference to web scraping languages, this is popularly used for such a process. Python Python is one of the most common coding languages. Remember, HTML is the file type used to display all the textual information on a webpage. In return, the scraper gets the requested information in HTML format. It simplifies how you interact with other sites and takes away all your worries.īuzz is a lightweight PHP 5.3 library for issuing HTTP requests. Answer (1 of 6): Top 5 web scraping languages for web scraping 1. The first simple step in any web scraping program (also called a scraper) is to request the target website for the contents of a specific URL. Requests for PHP is a humble HTTP request library. Urllib2 extensible library for opening URLs It is designed to conform to the WHATWG HTML specification, as is implemented by all major web browsers. Html5lib is a pure-python library for parsing HTML. Httpv://httpv://httpv://First release date One of them is Python But Python remains the most preferred choice of businesses to scrape content from website because of the ease of use, a large collection. Stateful programmatic web browsing in Python, after Andy Lester’s Perl module WWW::Mechanize. Httpv://httpv://httpv://httpv://Last release date An open source and collaborative framework for extracting the data you need from websites.

[bananainter]

Motorola flash tool for android

MOTOROLA FLASH TOOL FOR ANDROID HOW TO

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Delete All BookMarks On Chrome at Once To delete all the google chrome bookmarks at once, you need to follow these steps: Bij BMW Tweaks kunt u uw CIC, NBT of NBT2 navigatie laten updaten naar de meest recente versie.

MOTOROLA FLASH TOOL FOR ANDROID HOW TO

If you are wondering how to delete all bookmarks on chrome in one click then here is how you can do this. So you need to delete them in together with one click.xHP Flashtool is the worlds only solution for your 6/8-Speed Automatic or 7-Speed DCT Transmission! Choose from pre-defined maps, or. Network Interface Card: A network interface controller (NIC) (also known as a network interface card, network adapter) is an electronic device that connects a computer to a computer network/ Modern NIC usually comes up with speed of 1-10Gbps. Basic Request Lets cover some basic terminologies before we dig into Receive Side Scaling and Receive Packet Steering. Xef (Deprecated) Even this api is still working, it has been deprecated in favor of the new REST one See Xef Catalog section one instead.

MOTOROLA FLASH TOOL FOR ANDROID FREE

Feel free to navigate through the docs and tell us if you feel that something can be improved. This need, along with the desire to own and manage my own data spurred. While Google would certainly offer better search results for most of the queries that we were interested in, they no longer offer a cheap and convenient way of creating custom search engines.

A research project I spent time working on during my master’s required me to scrape, index and rerank a largish number of websites.

If any of the index patterns listed there have no existing indexes in Elasticsearch, then the page will not respond all to you left clicking on those patterns, making it impossible to highlight the pattern you want to delete. In Kibana, go to Management->Index Patterns.

I saw this issue with ElasticStack 5.3.0.

GoToConnect comes packed with over 100 features across cloud VoIP and web, audio and video conferencing. Scroll down until you find the cover that matches the book that you wish to delete Set your view to "Large Icons" so you can see the pictures of the covers of your books 16. Duplicate Sweeper can delete duplicate files, photos, music and more.

MOTOROLA FLASH TOOL FOR ANDROID PC

Find and remove duplicate files on your PC or Mac. Both have the same engine inside (Truth is that CLI tool is just the program that uses the library under the hood). It is also available as a library for developers and as a CLI for terminal-based use cases.

What is CURL ? CURL is a tool for data transfer.

Today were reviewing Xdelete after 6 months of use on the 335! I'll be answering some questions and going over some new features from a recent update! Enjoy. As I noticed how incredible slow and ineffecient managing my woocommerce store was, I decided to build a very simple woocommerce store manager using Django. Hi everyone, In my free time I'm working on setting up a small wordpress + woocommerce webshop. DataWrangler xcopy - xdelete? Pixelab_Datman: 12/19/01 10:23 AM: Rather than trying to devise a convoluted batch file, I suggest the use of XXCOPY which is designed for common What is a wide ip?¶ A wide IP maps a fully-qualified domain name (FQDN) to one or more pools of virtual servers that host the content of a domain.

The latest Tweets from Sathyasarathi Python coder, Chatterbox, Anorexic, Linux Geek.

xDelete lets you take control of your BMW xDrive System! Worth the price just if you want a bit of fun or to solve an xdrive fault.$ oc exec -it elasticsearch-cdm-xxxx-1-yyyy-zzzz -n openshift-logging bash bash-4.2$ health Tue Nov 10 06:19: epoch timestamp cluster status node.total node.data shards pri relo init unassign pending_tasks max_task_wait_time active_shards_percent XDelete. My car is completely different and a whole new level of fun.

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apk dosyaları kurulmasına izin, o zaman güvenle RollingAPK üzerinde mevcut tüm Android Uygulamaları ve Oyunları yükleyebilirsiniz! When I finally installed xDelete on my e92 335i xdrive it worked like a charm. apk Uygulamasını yüklemek için: Cihazınızda Ayarlar menüsüne gidin ve bilinmeyen kaynaklardan. Eğer bazı kolay talimat yapmalıyım cihazınızda Bx xd Android.

MOTOROLA FLASH TOOL FOR ANDROID APK

Preamble Back in the windows 3.1, and for a long time there after, one of the powerful tools shipped with windows was xdel.exe!Then it stopped being included, I guess because you can now do the same 'function' using (a) the built-in 'del', with lots of enhanced abilities, or (b) in the GUI windows explorer.Android Uygulama - Bx xd APK üzerinde indirmek için kullanılabilir.

[worthwebscraping]

Significance of Twitter Scraping

Twitter is one of the most popular and established social media platforms. It allows the straightforward download of unlimited tweets. Using plugins and effective tools, one can fetch important data from Twitter’s database. The platform has vast content that can be useful for users. Various business firms scrape internal data for knowing the nature of twitter’s audience. Twitter reflects the actual image of any account, which helps in scraping data. The market trends can be easily understood and analyzed with the help of Twitter data.

Following are some impeccable advantages of Twitter data scraping-

Detecting Exclusive Market Trends

Twitter is the new way in the market for increasing business prospects. Scraping the tweets, likes, and shares will help in engaging with the audience. The data helps in understanding the customer’s behavior and studying the market trends. A business person can enhance their product type by taking the help of Twitter data. It helps in designing the product and making it more effective. Data scraping from Twitter helps in gathering sales leads, tracking online presence, and market research. Also Instagram data scraping helps lot for analysis purpose.

Customers Engagement

Twitter data presents an honest look to customers about the brand name. Businesses can gain a lot of customers by knowing about their choices by scraping data from Twitter. The factors like customer’s experience and perception about the product can improve by collecting twitter’s data. Business people can manage the audience list from Twitter and further use it in promoting the brand.

Competition Tracking

The data of other competing companies are scraped periodically. Twitter data helps in staying up to date with the competitor’s strategies. Various policies are designed with data for maintaining customers engagement. Every move of fellow firms can be tracked by scraping their data. Scraping competitor’s data can help in supporting infrastructure, adequate management, and successful marketing initiatives.

Brand Analyzation

Twitter helps in building the brand image with the help of data. Sentimental analysis and text analysis helps in deriving high-quality information. The brand image expands the reach of the business. The companies can use Twitter data in understanding the needs of customers and working on them. Mentions and shares can help in balancing the nature of the target audience.

Here are some best Twitter scraper tools:

Scrape storm- It is an API powdered identification system and allows public data for scraping.

Octoparse- It is a cloud-based platform that allows vast Twitter scraping and allows automatic processing of tasks.

Wrapping Up

Data scraping from Twitter can help business officials in getting incredible data and file analysis. The business comes to know about the individual’s interests and views. The content from Twitter helps in understanding the nature of different groups of individuals. Twitter API has an average limit of taking only 3200(last) tweets. The data helps in collecting customer’s feedback. It also makes it easy to manage the audience’s opinions about a particular product, service, or idea. Therefore, using Twitter in your lead generation process can generate high-quality traffic more efficiently. More better understanding download our scraped data of twitter data scraping.

Apart for data scraping services, our tutorial channel for learning scraping, once look at python tutorial for How to Scrape Tweets from Twitter using Python.

[actowiz-123]

How to Scrape Tweets Data by Location Using Python and snscrape?

In this blog, we will take a comprehensive look into scraping Python wrapper and its functionality and specifically focus on using it to search for tweets based on location. We will also delve into why the wrapper may not always perform as expected. Let's dive in

snscrape is a remarkable Python library that enables users to scrape tweets from Twitter without the need for personal API keys. With its lightning-fast performance, it can retrieve thousands of tweets within seconds. Moreover, snscrape offers powerful search capabilities, allowing for highly customizable queries. While the documentation for scraping tweets by location is currently limited, this blog aims to comprehensively introduce this topic. Let's delve into the details:

Introduction to Snscrape: Snscrape is a feature-rich Python library that simplifies scraping tweets from Twitter. Unlike traditional methods that require API keys, snscrape bypasses this requirement, making it accessible to users without prior authorization. Its speed and efficiency make it an ideal choice for various applications, from research and analysis to data collection.

The Power of Location-Based Tweet Scraping: Location-based tweet scraping allows users to filter tweets based on geographical coordinates or place names. This functionality is handy for conducting location-specific analyses, monitoring regional trends, or extracting data relevant to specific areas. By leveraging Snscrape's capabilities, users can gain valuable insights from tweets originating in their desired locations.

Exploring Snscrape's Location-Based Search Tools: Snscrape provides several powerful tools for conducting location-based tweet searches. Users can effectively narrow their search results to tweets from a particular location by utilizing specific parameters and syntax. This includes defining the search query, specifying the geographical coordinates or place names, setting search limits, and configuring the desired output format. Understanding and correctly using these tools is crucial for successful location-based tweet scraping.

Overcoming Documentation Gaps: While snscrape is a powerful library, its documentation on scraping tweets by location is currently limited. This article will provide a comprehensive introduction to the topic to bridge this gap, covering the necessary syntax, parameters, and strategies for effective location-based searches. Following the step-by-step guidelines, users can overcome the lack of documentation and successfully utilize snscrape for their location-specific scraping needs.

Best Practices and Tips: Alongside exploring Snscrape's location-based scraping capabilities, this article will also offer best practices and tips for maximizing the efficiency and reliability of your scraping tasks. This includes handling rate limits, implementing error-handling mechanisms, ensuring data consistency, and staying updated with any changes or updates in Snscrape's functionality.

Introduction of snscrape Using Python

In this blog, we’ll use tahe development version of snscrape that can be installed withpip install git+https://github.com/JustAnotherArchivist/snscrape.git

Note: this needs Python 3.8 or latest

Some familiarity of the Pandas module is needed.

We encourage you to explore and experiment with the various features of snscrape to better understand its capabilities. Additionally, you can refer to the mentioned article for more in-depth information on the subject. Later in this blog, we will delve deeper into the user field and its significance in tweet scraping. By gaining a deeper understanding of these concepts, you can harness the full potential of snscrape for your scraping tasks.

Advanced Search Features

In this code snippet, we define the search query as "pizza near:Los Angeles within:10km", which specifies that we want to search for tweets containing the word "pizza" near Los Angeles within a radius of 10 km. The TwitterSearchScraper object is created with the search query, and then we iterate over the retrieved tweets and print their content.

Feel free to adjust the search query and radius per your specific requirements.

For comparing results, we can utilize an inner merging on two DataFrames:common_rows = df_coord.merge(df_city, how='inner')

That returns 50 , for example, they both have the same rows.

What precisely is this place or location?

When determining the location of tweets on Twitter, there are two primary sources: the geo-tag associated with a specific tweet and the user's location mentioned in their profile. However, it's important to note that only a small percentage of tweets (approximately 1-2%) are geo-tagged, making it an unreliable metric for location-based searches. On the other hand, many users include a location in their profile, but it's worth noting that these locations can be arbitrary and inaccurate. Some users provide helpful information like "London, England," while others might use humorous or irrelevant descriptions like "My Parents' Basement."

Despite the limited availability and potential inaccuracies of geo-tagged tweets and user profile locations, Twitter employs algorithms as part of its advanced search functionality to interpret a user's location based on their profile. This means that when you look for tweets through coordinates or city names, the search results will include tweets geotagged from the location and tweets posted by users who have that location (or a location nearby) mentioned in their profile.

To illustrate the usage of location-based searching on Twitter, let's consider an example. Suppose we perform a search for tweets near "London." Here are two examples of tweets that were found using different methods:

The first tweet is geo-tagged, which means it contains specific geographic coordinates indicating its location. In this case, the tweet was found because of its geo-tag, regardless of whether the user has a location mentioned in their profile or not.

The following tweet isn’t geo-tagged, which means that it doesn't have explicit geographic coordinates associated with it. However, it was still included in the search results because a user has given a location in the profile that matches or is closely associated with London.

When performing a location-based search on Twitter, you can come across tweets that are either geo-tagged or have users with matching or relevant locations mentioned in their profiles. This allows for a more comprehensive search, capturing tweets from specific geographic locations and users who have declared their association with those locations.

Get Location From Scraped Tweets

If you're using snscrape to scrape tweets and want to extract the user's location from the scraped data, you can do so by following these steps. In the example below, we scrape 50 tweets within a 10km radius of Los Angeles, store the data in a DataFrame, and then create a new column to capture the user's location.

If It Doesn’t Work According to Your Expectations

The use of the near: and geocode: tags in Twitter's advanced search can sometimes yield inconsistent results, especially when searching for specific towns, villages, or countries. For instance, while searching for tweets nearby Lewisham, the results may show tweets from a completely different location, such as Hobart, Australia, which is over 17,000 km away.

To ensure more accurate results when scraping tweets by locations using snscrape, it is recommended to use the geocode tag having longitude & latitude coordinates, along with a specified radius, to narrow down the search area. This approach will provide more reliable and precise results based on the available data and features.

Conclusion

In conclusion, the snscrape Python module is a valuable tool for conducting specific and powerful searches on Twitter. Twitter has made significant efforts to convert user input locations into real places, enabling easy searching by name or coordinates. By leveraging its capabilities, users can extract relevant information from tweets based on various criteria.

For research, analysis, or other purposes, snscrape empowers users to extract valuable insights from Twitter data. Tweets serve as a valuable source of information. When combined with the capabilities of snscrape, even individuals with limited experience in Data Science or subject knowledge can undertake exciting projects.

Happy scrapping!

For more details, you can contact Actowiz Solutions anytime! Call us for all your mobile app scraping and web scraping services requirements.

[actowiz1]

In this blog, we will take a comprehensive look into scraping Python wrapper and its functionality and specifically focus on using it to search for tweets based on location.

know more https://www.actowizsolutions.com/scrape-tweets-data-by-location-python-snscrape.php

[suzanneshannon]

Looking back on Software Development in 2020 and forward to 2021

I think we can all agree 2020 sucked. Hopefully 2021 will be better.

I've been a remote worker for 13 years by choice but in 2020 I HAD TO DO IT because, well, most programmers and tech workers did. I wrote about how Remote work != Quarantine Work while our whole division and then the whole company moved back home! We were a fairly remote-friendly company before but I have to admit I didn't always think my coworkers had really deep empathy for the remote...until they, too, were forced to be remote.

Last week on the podcast, I got to speak with Amanda Silver. She's a CVP in the Microsoft Developer Division who has been coding and thinking deeply about coding for many years. She's leading the creation of tools like Visual Studio, Visual Code, Live Share, Code Spaces, IntelliCode, and other collaborative productivity products. She's always thinking about what coding will look like in 1, 5, and even 10+ years.

We talked about her thoughts on moving the division remote and whether it would slow us down. Would it change how we develop software? What about when everyone comes back? After talking to her about her thoughts on 2020 and where she thinks we're heading, I got to thinking myself and wanted to put those thoughts down.

2020 broke everything, and developers like to fix things

Somewhere in the spring as we started into lockdown, developers started making sites. Sites to track COVID, GitHub projects with scripts to scrape data and analyze it. Javascripters started making D3.js visualizations and codepen users started building on top of them. Bots on twitter would tweet out updates and parse new data.

When there's a problem - especially a scary or untenable one - developers run towards the challenge. Necessity breeds invention and 2020 was definitely a year where we were collectively reminded there was a bunch of stuff that was always possible, but we needed a push. Cameras and mics were upgraded, ring lights were purchased, home networks got fancier, and everyone who could called their ISP and got an upgraded plan. We could have done all this before, but why? Remote work happened for the first time in 2020, and I say that having worked remotely forever.

We HAVE to collaborate remotely now

Back in 2010 I spoke to PhDs at Microsoft Research about how people feel when they are remote and what they can do to be more connected. Ten years! Folks thought it was pretty "out there" but I sure needed my virtual cubicle buddy this year.

2020 accelerated what was possible with remote collaboration. I spent hours coding with Live Share, pushing text and coding context over the wire, not a ridiculous 4k worth of pixels. Having two cursors (mine and my friends) - or even 10! - in one Visual Studio seemed like magic. Even more magic is me pressing F5 and my coworker hitting their localhost and seeing our app running! We needed tech like this more than ever in 2020.

I heard one story where a company sent everyone home but folks had disparate desktops and laptops so they set up 100s of Virtual Desktops over a weekend so everyone was able to log into secure work systems from their home machines.

For us, since we use Github and Azure DevOps here in DeviDiv, our collaboration model is asynchronous and distributed whether we are in the office or not. Can you imagine everyone working remotely while using a locking source control system in 2020? I feel bad for those who are in that predicament.

Can something be BETTER remotely?

Many of us miss being in the same room with co-workers, and we will be together again one day, but are there some things that the constraint of being remote can make better? In the podcast episode Amanda said that our new hire bootcamp was so much better remotely!

She said (paraphrasing a bit):

We have a bootcamp for anybody who's newly started on the team. They actually fly out for two weeks. And the first week is introduction and the second week is our customer driven workshop. And our customer driven workshop is basically this really intense team project where you break up into groups of five to six people, and you're given a business assignment like - how could we double the number of Python developers using Visual Studio Code.

You're basically doing like stickies on the wall the entire week - that's how you collaborate. I've been so amazed that that has transitioned to be remote first. And it's better. It's better. That was a brainstorming process that I thought was only possible in person it's better.

When we moved remote, we had to essentially reboot the way that we thought about our meeting culture to actually make it much more inclusive. And if we go from 40 to 50% of the people participating to just 2 people participating, that's a huge, not only degradation, but you're wasting people's time. Right?

Now if we can actually take six people who've never met each other before and get them to work super collaboratively on a new problem area that they've never worked on before. It's incredible. And the thing that's also really awesome about it is they are forced by nature of the fact that this is remote to actually create it as digital content. Whereas in the beginning they would literally walk us through sticky notes on the wall and they had fantastic ideas, but it was really kind of somewhat unorganized and, and it was hard to be able to see and, and retain and share out afterwards what these incredible ideas were that they came up with.

But when remotely starts with this digital format by necessity because everyone is remote first, we actually now have all of these things archived. We can come back to them, we can go back and actually see, you know, what was the genesis of the thought and, and pursue a lot of these things that we really weren't being able to pursue previously.

Constraints breed innovation!

It was nice to be reminded that People are People

2020 normalized being a person. Having a boss welcome a sad child to sit with them during a meeting reminded me that, what, my boss is a person? With a life and kids? Having meetings while going for walks, talking about treadmill desks, and video called parties with family, and OMG when will this be over is the most horrible team building exercise ever.

It's forced us to rethink our group's culture, how our interpersonal dynamics work, how many meetings we have (let's have less), and it's given everyone the joy of somewhat flexible hours. We talk more now about 'is everyone in this meeting being heard?' than ever before. We use the "hand raising" tool in Teams to make sure all voices get a chance to speak.

If 2020 hadn’t happened, we may not have made these important leaps forward. MAYBE this would have happened by 2025 or 2030 but COVID was the pivot point that forced the issue.

Here's some other blog posts that are both reflecting on our last year and hopeful for the coming year:

Software Development in 2021 and Beyond by Amanda Silver

4 Open Source Lessons for 2021 by Sarah Novotny

Low-code Trends: Why Low-Code Will Be Big In Your 2021 Tech Strategy by Dona Sarkar

PODCAST: Living through 2020 as a Remote Developer

Sponsor: Looking for free secure coding training but don’t know where to turn? Check out Veracode Security Labs Community Edition to start hacking and patching real apps online. Try it today.

© 2020 Scott Hanselman. All rights reserved.

      Looking back on Software Development in 2020 and forward to 2021 published first on https://deskbysnafu.tumblr.com/