How to Scrape Instagram: An In-Depth Guide Using Python

With over 2 billion monthly active users across its family of apps, Instagram has grown into one of the largest and most influential social media platforms in the world.

The immense volume of data generated on Instagram – over 100 million photos and videos uploaded every day – makes it an absolute goldmine for data mining and web scraping. Whether you‘re a social media marketer, academic researcher, data scientist, or just a curious technologist – having the ability to extract and analyze public information from Instagram unlocks many possibilities.

However, because of Instagram‘s platform policies, scraping the site at scale requires more nuanced techniques than simple data extraction. In this comprehensive guide, you‘ll learn how to build flexible and resilient Instagram scrapers in Python capable of evading detection, avoiding bans, and extracting large amounts of public data.

Here‘s what I‘ll cover:

• What types of data can be scraped from Instagram and its value
• The legal and ethical guidelines to follow
• A comparison of tools and proxies for Instagram scraping
• Step-by-step guides to build scrapers with Requests and Selenium
• Expert tips for scraping smoothly at scale without getting blocked

Let‘s get started!

What Can You Scrape from Instagram and How Valuable Is It?

There is a diverse range of public data that can be legally scraped from Instagram profiles, posts, hashtags, stories, and comments including:

• User info – followers count, biography info, location
• Posts – captions, hashtags, mentions, likes, comments
• Hashtags – top/recent posts, user metadata
• Stories – views, mentions, locations, hashtags
• Comments – text, timestamps, author

This data can provide tremendous business value across many use cases:

• Social listening – Analyze brand mentions, hashtags, sentiments
• Influencer research – Discover influencers by engagement and audience
• Trend forecasting – Identify rising topics by hashtag volume
• Ad analytics – Track engagement on sponsored posts and stories
• Competitive benchmarking – Compare followings between competitor accounts
• Location analytics – Gauge popularity and foot traffic analysis
• Academic studies – Gather data for social science research

According to one estimate, the market for social media analytics is growing at over 20% annually and will reach nearly $7 billion by 2028. Having the ability to tap into Instagram data at scale can open up many possibilities.

Legal and Ethical Guidelines for Scraping Instagram

However, because Instagram and other social networks are vigilant about preventing scraping on their platforms, there are some crucial legal and ethical guidelines to keep in mind:

• Only scrape public data – Never attempt to access private profiles or locked account content. Avoid logging in with credentials or reverse engineering the Instagram app.

• Review the Terms of Use – Instagram‘s ToU prohibits unauthorized data collection and excessive automated scraping that may overload its systems. Understand acceptable use.

• Obtain consent where required – If scraping identifiable data, get consent. Be transparent how you‘ll use the data.

• Anonymize personal information – Avoid scraping emails, phone numbers, addresses, etc. that can identify individuals without permission.

• Don‘t spam or harass – It‘s unethical to scrape data for sending unsolicited communications or harassment.

• Limit volume – Scrape reasonable volumes that won‘t adversely impact Instagram‘s infrastructure. Use delays.

• Secure the data – Take measures to responsibly secure any scraped Instagram data in transit and storage.

While there is ambiguity around web scraping laws, following these principles will keep your scraping above-board and ethical. When in doubt, consult an attorney.

Technical Tools You‘ll Need for Scraping Instagram

The main tools you‘ll need to start scraping Instagram are:

Python

Python is the most popular language for writing scraping programs because of its simplicity and powerful libraries like Requests, BeautifulSoup, Selenium, etc. I recommend Python 3.6 or higher.

Requests

A Python library that allows you to send HTTP requests and easily scrape API data or HTML pages. Great for basic scraping.

BeautifulSoup

Used to parse and extract data out of HTML and XML pages using Python. Helps identify and extract elements.

Selenium

An automation framework for controlling browsers like Chrome and Firefox via Python code. Necessary for dynamic scraping.

WebDrivers

Like ChromeDriver for controlling the Chrome browser using Selenium. Set up the driver for your chosen browser.

Proxies

Essential for distributing requests across multiple IPs and avoiding getting blocked by Instagram.

There are also many helpful Python modules like time, random, json, etc. that are useful for building robust scrapers.

I recommend installing Python and creating a virtual environment to install the packages:

# Install virtualenv 
pip install virtualenv

# Create virtual env 
virtualenv myenv 

# Activate virtual env
source myenv/bin/activate

# Install packages
pip install requests selenium beautifulsoup4

This will setup a nice isolated environment for your project.

Now let‘s look at proxies…

A Comparison of Different Proxy Services for Instagram Scraping

Proxies play an indispensable role in scraping Instagram smoothly at scale. By routing your requests through a large pool of proxies, you can:

• Avoid getting banned by distributing requests across multiple IPs
• Overcome regional restrictions by using proxies from different geographic areas
• Scrape from different locations to get region-specific results
• Rotate IPs to mimic natural human browsing behavior

However, there are a few aspects to consider when choosing a proxy provider:

Residential vs Data Center Proxies

Residential proxies originate from regular consumer devices like phones and laptops. This makes them great for mimicking real users. But they may have slightly slower speeds.

Data center proxies offer blazing fast speeds but are easier to detect as scrapers. A mix of both proxy types is ideal.

Location Diversity

Choose proxy providers with geographically diverse IP pools across countries, cities, and ISPs. This allows for geo-targeting and better distribution.

Bandwidth Limits

Some proxies have monthly bandwidth limits. For heavy usage, look for unlimited plans or those with 100+ GB monthly limits.

SOCKS5 vs HTTP(S) Protocols

Make sure the provider offers both SOCKS5 and HTTPS proxy types for full flexibility.

Authentication Mechanisms

Supports all methods like username/password auth, IP authorization, etc. This enables integrating proxies easily into code.

Reliability & Uptime

High proxy uptime and reliability is crucial for long scraping jobs. Look for providers with 95%+ uptime guarantees.

API Access

For dynamically rotating proxies in your scrapers, API access to the proxy pool is very convenient.

Some of the best proxy services that meet the above criteria are Bright Data, Oxylabs, Smartproxy, Soax, and GeoSurf. For Instagram scraping, I recommend Bright Data and Smartproxy for the optimal combination of proxy types, locations, and API integration.

Building a Simple Instagram Scraper with Python Requests

Now that we‘ve covered the basics, let‘s start coding! I‘ll first show you how to build a simple Instagram profile scraper with Python Requests.

We‘ll be scraping the following public data points:

• Profile name
• Follower count
• Number of posts
• Biography info
• Profile pic URL

Import Libraries

We‘ll import the Requests library for sending HTTP requests along with JSON for parsing API responses:

import requests 
import json

Time and Random will be useful for pacing requests and randomizing headers:

import time
import random

Define Headers

We need to set request headers that mimic a real browser visit. A key one is User-Agent – we can randomly rotate multiple user agents:

user_agents = [
  ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36‘,
  ‘Mozilla/5.0 (iPhone; CPU iPhone OS 12_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148‘  
]

headers = {
  ‘User-Agent‘: random.choice(user_agents)
}

Initialize Proxy

Let‘s use Bright Data‘s Instagram proxy pool to avoid getting blocked:

from brightdata.foundation import connect

# Connect to proxy API
proxy_api = connect(YOUR_KEY)

instagram_pool = proxy_api.instagram_proxy()
print(instagram_pool) # Retrieve proxy

proxies = {
  ‘http‘: ‘http://‘ + instagram_pool,  
  ‘https‘: ‘https://‘ + instagram_pool
}

We now have an anonymous Instagram proxy ready!

Make Request

We‘ll define a function to handle making the GET request to an Instagram profile URL:

def scrape_profile(url):

  try:
    response = requests.get(url, 
                            headers=headers,
                            proxies=proxies)

  except:
    print(‘Request Failed‘)

  else:
    # Success
    print(‘Request Succeeded‘)
    return response

Now we can call it to scrape a profile:

url = ‘https://www.instagram.com/selenagomez/‘
result = scrape_profile(url)

On success, result will contain the HTML of the profile page to parse.

Parse Data

Next, we can use Python‘s built-in JSON library to parse the HTML and extract info:

html = result.text

data = json.loads(html)

username = data[‘entry_data‘][‘ProfilePage‘][0][‘graphql‘][‘user‘][‘username‘]
followers = data[‘entry_data‘][‘ProfilePage‘][0][‘graphql‘][‘user‘][‘edge_followed_by‘][‘count‘]
posts = data[‘entry_data‘][‘ProfilePage‘][0][‘graphql‘][‘user‘][‘edge_owner_to_timeline_media‘][‘count‘]
bio = data[‘entry_data‘][‘ProfilePage‘][0][‘graphql‘][‘user‘][‘biography‘]
pic_url = data[‘entry_data‘][‘ProfilePage‘][0][‘graphql‘][‘user‘][‘profile_pic_url_hd‘]

print(f"{username} has {followers} followers") 
print(f"Bio: {bio}")

And we have successfully scraped key profile data! With minor modifications, this technique can be adapted to scrape any public Instagram page.

Next, let‘s look at a more advanced scraping approach using Selenium.

Building a Dynamic Instagram Scraper with Python and Selenium

While Requests is great for simple scraping, to extract data from dynamic pages and emulate complex user actions, we need a real browser. This is where Selenium comes in.

Some examples of dynamic scraping include:

• Scrolling through feeds to load content
• Expanding comments sections
• Clicking buttons to reveal more data
• Handling pagination on hashtag/location pages

Let‘s see how to build an Instagram hashtags scraper with Selenium in Python.

Install Selenium

First, we need to install Selenium. I recommend doing this in a virtual environment:

pip install selenium

We also need WebDriver to control the browser. For Chrome:

pip install chromedriver-autoinstaller

Import Selenium, WebDriver, and other modules:

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait 
import time

WebDriverWait will be useful for waiting for page elements to load.

Launch Headless Chrome

We‘ll launch Chrome in headless mode so no browser GUI pops up:

options = webdriver.ChromeOptions() 
options.headless=True

driver = webdriver.Chrome(options=options)

Handle Proxies

Just like with Requests, proxies are crucial to distribute requests and avoid blocks.

We can configure Selenium to use Bright Data‘s Instagram proxy pool like so:

from brightdata.foundation import connect 

proxy_api = connect(YOUR_KEY)
instagram_pool = proxy_api.instagram_proxy()

prox = Proxy()
prox.proxy_type = ProxyType.MANUAL
prox.http_proxy = ‘http://‘ + instagram_pool 
prox.ssl_proxy = ‘http://‘ + instagram_pool

capabilities = webdriver.DesiredCapabilities.CHROME
prox.add_to_capabilities(capabilities)

driver = webdriver.Chrome(desired_capabilities=capabilities)

Now Selenium will route traffic through our rotating Instagram proxies.

Scrape Hashtag Page

Let‘s define a function to scrape data from an Instagram hashtag page:

def scrape_hashtag(hashtag):

  url = f‘https://www.instagram.com/explore/tags/{hashtag}/‘

  driver.get(url)
  time.sleep(5)

Once loaded, we can scroll through the page to load posts:

  posts = driver.find_element(By.CLASS_NAME, ‘_aagw‘)

  for i in range(0, 5):
    driver.execute_script("arguments[0].scrollTop = arguments[0].scrollHeight", posts)
    time.sleep(5)

Next, let‘s extract info like the post captions:

  captions = []

  posts = driver.find_elements(By.CLASS_NAME, ‘KL4Bh‘)

  for post in posts:
    caption = post.find_element(By.CLASS_NAME, ‘C4VMK‘).text
    captions.append(caption)

  print(captions)

And that‘s it! We can call the function to scrape any hashtag:

scrape_hashtag(‘instagramprogramming‘) 

This script collects the first few post captions for a given hashtag. Much more data like likes, comments, author details etc. can also be extracted in a similar manner by identifying the relevant HTML elements.

Expert Tips for Avoiding Detection When Scraping Instagram

Now that you know how to build an Instagram scraper, here are some pro tips to scrape smoothly at scale:

Use a proxy rotation pattern

Simply cycling through proxies randomly is easy to detect. Use a pattern like residential proxies from US cities for 10 mins, then switch to datacenter proxies in Europe for 5 mins.

Vary actions: scrolling, delays, clicks etc.

Avoid performing the exact same actions predictably. Introduce small variations in scroll depth, waits, element selection etc.

Monitor IP quality for blocks

Frequently check if your Instagram proxies are getting blocked and refresh them accordingly. Services like Bright Data report live blacklisting stats.

Manage sessions and cookies

Maintaining proper sessions and cookies avoids having to frequently solve captchas that pop up on new logins.

Try different browser profiles

Experiment with Chrome, Firefox, and Edge profiles in Selenium with different extensions, settings, etc.

Solve captchas automatically

If captchas do appear, use a service like Anti-Captcha to automatically solve them so your scraper can keep running.

Scrape across geographic locations

Instagram serves some content specific to countries and regions. Use residential proxies from different cities/countries.

Practice responsible scraping

Whatever your use case, make sure to implement delays, handle errors and throttling properly, and not overload Instagram servers.

Following these tips and best practices will enable you to extract large volumes of Instagram data without disruptions or getting blocked. Please remember to always scrape ethically!

Concluding Thoughts

In summary, here are the key steps we covered in this guide to building robust Instagram scrapers:

• Use Proxies – Rotate IPs through different proxy servers to distribute requests

• Vary Patterns – Introduce randomness in headers, actions, delays, etc.

• Handle Dynamics – Use Selenium and headless Chrome to render full pages

• Maintain Sessions – Keep cookies and logins to avoid captchas

• Scrape Responsibly – Implement delays, limits, and handle errors properly

• Check Terms of Use – Understand and comply with Instagram‘s data policies

• Anonymize Data – Avoid collecting personally identifiable information

• Secure Data – Store and transmit scraped data securely

Following these principles will enable you to tap into the wealth of public data on Instagram while respecting the platform‘s boundaries. As Instagram continues to evolve its policies and detection methods, constantly experiment and learn to stay on top of scraping best practices.

I hope this guide provided you a comprehensive overview of how to scrape Instagram with Python and equippped you with the skills to start extracting valuable data. Happy and ethical scraping!