How You Can Scrape Instagram Followers Information with Python

Which Type of Information Can You Extract from Instagram?

What data do you scrape from Instagram Accounts, like Phone Numbers or Email Addresses?

All the information will assist you in further monitoring your datasets. For example, if you need to search for influencers amongst your followers, you can sort your database by the number of followers. The method you quickly see in Instagram clients is general and could be used to spread the word about your brand.

How Can You Find Phone Numbers and Email Address from Instagram Followers?

Some users rarely know about this, but only 30-40% of Instagram handlers have accurate information on their Instagram accounts.

Some users wisely add their information, while others need to.

In the below-given points, you will come to know how to find your Email Address and mobile numbers on Instagram: -

How Rapidly Can You Acquire Data from Instagram Followers?

Many data providers gather Instagram client information and can rapidly extract anyone's Instagram follower's data.

Before letting you know how you can do this yourself, I want to explain why it is sometimes good to outsource the extracting quickly.

There are many Instagram email scraper tools available in the marketplace so that you can validate data upon extracting, and they provide advanced aiming options like age, gender, location, and interest.

Why do You Require Additional Targeting?

Extracting Instagram supporters is targeted. You may finish up with a bundle of users outside your company.

And if you are thinking of extracting emails for email marketing, then the impact will be harmful to your campaigns, i.e., actual bounce rates.

Why Do You Need to Clean Scraped Data?

Web-Extracting means you contain a considerable amount of waste in your net. Below are some examples:

You can use Instagram email lists for emailing or for Facebook ads.

The more emails you generate and fake emails you catch, your business domain will likely become spam. The regular emails will be measured as spam and need to grasp the anticipated destination.

If you import the listing as Common Viewers, Facebook will help you recognize that many contacts are fake, which might permanently ban you.

That's why with Instagram scraper, we also confirm the emails and provide additional aims like age, gender, emails, or location based on keywords in the client's profile.

How Can You Extract Instagram Follower Data for Free?

If you have less than 1000 supporters, you can scrape data physically. To see extra details on a profile, you must visit all clients with the Instagram app, i.e., for a mobile user only.

Gather the information by copy-pasting it on a worksheet.

We only suggest physically visiting up to 1k user profiles (you can't do that with your profile) because your account will get postponed.

Building an Instagram Follower Scraper with Python

Python is the finest way to extract anything.

Below are two simple tricks for Scraping Instagram follower data using Python.

Scrape Instagram Followers (Users Information) List

If you need to extract usernames and followers, you can apply the below-given code:

from datetime import datetime
from modules import compare
from modules import file_io
from modules import stats
 
from modules.scraper import Scraper
from modules.utils import ask_input, ask_multiple_option
 
 
groups = ['followers', 'following']
 
# Ask for input
target = ask_input('Enter the target username: ')
group = ask_multiple_option(options = groups + ['both']);
print('\nEnter your Instagram credentials')
username = ask_input('Username: ')
password = ask_input(is_password = True)
 
def scrape(group):
    differs = False
    scraper = Scraper(target)
    startTime = datetime.now()
 
    scraper.authenticate(username, password)
    users = scraper.get_users(group, verbose=True)
    scraper.close()
 
    last_users = file_io.read_last(target, group)
    if last_users:
        differs = bool(compare.get_diffs(users, last_users))
 
    if (differs or not last_users):
        file_io.store(target, group, users)
    # Stats
    stats.numbers(len(users), scraper.expected_number)
    if (differs): stats.diff(users, last_users)
    print('Took ' + str(datetime.now() - startTime))
 
if (group == 'both'):
    for group in groups:
        scrape(group)
else:
    scrape(group)

It is tough to scrape the Instagram followers accounts of specific followers.

Analyzing user profiles with a massive follower base will take much work to extract with this technique. For a minor account, it must do the work.

Here's all needed to perform this:

Want to scrape Instagram followers data?

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Scraping Followers Information (Category, Email)

If you only require to extract listings of an Instagram user profile that trail a particular account, then you can do this with the initial part. If you need data on those users, you must extract extra information, such as mobile numbers, categories, email addresses, particular profiles, etc.

You will need an Instagram account to log in through the mobile API. Here are some samples of how you can browse it.

from requests import get
from bs4 import BeautifulSoup
import sys
import re
import json
from datetime import datetime
 
URL = 'https://www.instagram.com'
 
def get_data(username):
    url = '%s/%s/' % (URL, username)
    page = get(url, timeout=5)
    soup = BeautifulSoup(page.content, 'html.parser')
    data = soup.find_all('meta', attrs={'property': 'og:description'})
    photo = soup.find_all('meta', attrs={'property': 'og:image'})
    text = data[0].get('content').split()
    retext = re.findall(
    jsontext = json.loads(retext)['entry_data']['ProfilePage'][0]
    print(jsontext)
    media_likes = 0
    media_comments = 0
    media_views = 0
    media_count = 0
    media_videos = 0
    media_list = []
    for media in jsontext['graphql']['user']['edge_owner_to_timeline_media']['edges']:
        media_count += 1
        media_likes += media['node']['edge_liked_by']['count']
        media_comments += media['node']['edge_media_to_comment']['count']
        if media['node']['is_video']:
            media_videos += 1
            media_views += media['node']['video_view_count']
            media_list.append({
                'id': '2042452950535483363',
                'img_url': media['node']['thumbnail_src'],
                'date_posted': datetime.fromtimestamp(media['node']['taken_at_timestamp']),
                'likes': media['node']['edge_liked_by']['count']  
            })
        else:
            media_list.append({
                'id': '2042452950535483363',
                'img_url': media['node']['thumbnail_src'],
                'date_posted': datetime.fromtimestamp(media['node']['taken_at_timestamp']),
                'likes': media['node']['edge_liked_by']['count'],
            })
    media_video_like_engagement = 0
    media_video_comment_engagement = 0
    if media_videos != 0:
        media_video_like_engagement = (
            ig_tv_likes / ig_tv_videos) / (ig_tv_views / ig_tv_videos)
 
    return....

For the complete code, you can click on the below-given link.

If you are in trouble, you can upload the listing of the user profile you previously got with API and develop it with an email address if you are in trouble.

Catch Instagram Follower Information

If you need the capability of using Python but need to export a listing of your supporters (information) you can utilize, then you can contact Web Screen Scraping. The maximum number of followers you can download is about 50k. If you aim for less than 500 followers accounts, you can export the listings without cost.

If you want to Scrape Instagram Follower's Information using Python, you can contact Web Screen Scraping for all your queries.

Blocking and Login Requirement

Although Instagram started limiting public access to its public data, scraping Instagram appears to be simple. It frequently limited users to a few requests per hour and demanded logins for additional requests.

Let's use Web Screen Scraping API, which can get around all of these obstacles, to get around this.

Which has several robust features that will enable us to go over Instagram's blocking:

It will use the Web Screen Scraping-SDK Python library and the Web Screen Scraping anti-scraping protection bypass feature. Let us first use pip to install the Web Screen Scraping-SDK:

We have to swap out HTTP queries for Web Screen Scraping-SDK requests in our Instagram web scraper to use Web Screen Scraping API. Let's look at the complete scraper code with the integration of Web Screen Scraping.

Complete Scraper Code

The last piece of Instagram scraper code discussed in this guide is here. Our scraper explains how to identify Instagram users and posts as well as how to extract data from Instagram profiles and posts:

Web Screen Scraping complete scraper code

To avoid Instagram's login requirement in the case above, we used ScrapFly's Anti Bot Protection Bypass function. We only needed to change a few lines of code to make this possible, and then anyone could browse any Instagram page without signing in!