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The Instagram Post scraper

chrisgreening profile image Chris Greening Updated on ・2 min read

In this series, I'm showing the available scraped data from all scrapers provided by instascrape. For this post, we're going to take a quick peak at what data points are scraped from an Instagram post when using the Post scraper.

GitHub logo chris-greening / instascrape

Powerful and flexible Instagram scraping library for Python, providing easy-to-use and expressive tools for accessing data programmatically

instascrape: powerful Instagram data scraping toolkit

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What is it?

instascrape is a lightweight Python package that provides an expressive and flexible API for scraping Instagram data. It is geared towards being a high-level building block on the data scientist's toolchain and can be seamlessly integrated and extended with industry standard tools for web scraping, data science, and analysis.

Key features

Here are a few of the things that instascrape does well:

  • Powerful, object-oriented scraping tools for profiles, posts, hashtags, reels, and IGTV
  • Scrapes HTML, BeautifulSoup, and JSON
  • Download content to your computer as png, jpg, mp4, and mp3
  • Dynamically retrieve HTML embed code for posts
  • Expressive and consistent API for concise and elegant code
  • Designed for seamless integration with Selenium, Pandas, and other industry standard tools for data collection and analysis
  • Lightweight; no boilerplate or configurations necessary
  • The only hard dependencies are Requests and…

The Post scraper scrapes 51 data points associated with an Instagram post.

Instance attribute names have been chosen to be semantic and easy to understand.

The data points

The best way to learn is by example so we'll take a look at an individual post from @google's scraped Instagram data.

All instascrape scrapers have a to_dict method that returns all data as a dictionary so we can see everything in one shot.

from instascrape import Post 
google_post = Post('')
{'csrf_token': 'UPIA1ETx3inJYcyZXJloxvfBcxbpZOHu',
 'viewer': None,
 'viewer_id': None,
 'country_code': 'US',
 'language_code': 'en',
 'locale': 'en_US',
 'device_id': '12345678-1234-1234-1234-123456789012',
 'browser_push_pub_key': 'BIBn3E_rWTci8Xn6P9Xj3btShT85Wdtne0LtwNUyRQ5XjFNkuTq9j4MPAVLvAFhXrUU1A9UxyxBA7YIOjqDIDHI',
 'key_id': '108',
 'public_key': 'ae57c2c94c4afb24eeda9cd759faeeb034086d0ebe60e90123baed5e38c3304f',
 'version': '10',
 'is_dev': False,
 'rollout_hash': 'b10813bd9030',
 'bundle_variant': 'es6',
 'frontend_dev': 'prod',
 'id': '2423598385863295176',
 'shortcode': 'CGiWeQjl4DI',
 'height': 1350,
 'width': 1080,
 'gating_info': None,
 'fact_check_overall_rating': None,
 'fact_check_information': None,
 'sensitivity_friction_info': None,
 'media_overlay_info': None,
 'media_preview': 'ACEq3DTDWKNRIUlcZLbj7jgH3961kk3qGGOf6dabdhWuOxSYqpHdeYdwO5D0PTjpnHtVsEEZHI9RVJisJiiloouI5/yAPWrlpHvjdT0ByPUE+n5VB15NXLN8Fl7Yz+VZN6GqRUjjMR2nIzwPf/61NVGhfb1BBwfb3HrV3yguZmOS33R2VfX6mgrvQ4AJx3/PH0qouys9f8yGru5DuPt+Roqvu9lopXLshS9OibYGdjhdpHuSemKio9PwqRl2OTfahjkYBH6/5/lUbXIhXHU449/enaodsIA4G4DArHh+brz9aaEyx9ob2op+0egop3FY/9k=',
 'display_url': '',
 'accessibility_caption': 'Photo by Google on October 19, 2020. Image may contain: outdoor.',
 'is_video': False,
 'tracking_token': 'eyJ2ZXJzaW9uIjo1LCJwYXlsb2FkIjp7ImlzX2FuYWx5dGljc190cmFja2VkIjp0cnVlLCJ1dWlkIjoiNGNkMjVjN2QxMzY3NDk0OWE5Y2VmNzA0ZWQ0YTc1NmYyNDIzNTk4Mzg1ODYzMjk1MTc2In0sInNpZ25hdHVyZSI6IiJ9',
 'tagged_users': [],
 'caption': 'Working from home can be ruff, but having #Dooglers around helps. 🐾 Tap the link in our bio to learn how Googlers are keeping our dog-friendly company culture going, even outside of the paw-ffice.',
 'caption_is_edited': False,
 'has_ranked_comments': False,
 'comments': 527,
 'comments_disabled': False,
 'commenting_disabled_for_viewer': False,
 'timestamp': 1603135463,
 'likes': 147553,
 'location': nan,
 'viewer_has_liked': False,
 'viewer_has_saved': False,
 'viewer_has_saved_to_collection': False,
 'viewer_in_photo_of_you': False,
 'viewer_can_reshare': True,
 'video_url': nan,
 'has_audio': nan,
 'video_view_count': nan,
 'username': 'google',
 'full_name': 'Google',
 'upload_date': datetime.datetime(2020, 10, 19, 15, 24, 23),
 'hashtags': ['Dooglers']}
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And there we have it! If you're interested in seeing instascrape in action, check out some of my other posts that explore practical examples:

In the next part of the series, we will be exploring what attributes are provided by the Hashtag scraper.

Discussion (1)

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villival profile image

you are amazing. Keep posting more posts ...