Rusty  Shanahan

Rusty Shanahan


Why Third-Party Security Adoption Must Get Better

Major exploits such as the Target and Equifax hacks made headlines a few years ago. But these infamous attacks have not necessarily served as a wake up call for many, if not most, organizations. They lack the security tools, processes and culture required to properly protect their data, Chenxi Wang, Ph.D., managing general partner of Rain Capital, said.

“Everybody read about those headlines, but translating that into the work [organizations] do today, I think there’s still a gap,” Wang said. “As security industry professionals — myself included — we need to reach out more to the adjacent community and especially with dev these days. I mean software is eating the world and dev is the one driving software, so we need to work with dev to make it happen.”

In this edition of [The New Stack Makers ] podcast hosted by Alex Williams, founder and publisher of The New Stack, Wang spoke about these and other third-party security trends. The podcast was recorded in anticipation of [The State of Cloud Native Security] virtual summit to take place on June 24.

The New Stack Makers · Chenxi Wang, Ph.D. Why Third-Party Security Adoption Has to Get Better

Like the Target and Equifax breaches, government-sponsored attacks also make headlines. According the U.S Justice Department, for example, a North Korean team of hackers allegedly orchestrated the [WannaCry exploit] for ransomware.

Regardless of their origins, security policies, and the resulting tools adopted, must all take into account that the vast majority of attacks are financially motivated. “By and large hacking follows a trail of money,” Wang said. “They want to hit places and systems where there is the most valuable data.”

Data stored on the cloud is often seen as a potential goldmine among those seeking vulnerabilities for access to sensitive and often compromising data to broker or exploit as organizations increasingly make the shift to the cloud. At the same time, DevOps and DevSecOps teams often lack the toolsets they need to lock down what is, for many organizations, a new universe. Difficulties in monitoring cloud logs is one example of challenges they face when trying to manage security on cloud services.

“I can’t tell you how many security professionals have been telling me they’re drowning in those cloud logs and they need something to help them to aggregate and make sense of and to analyze and visualize them,” Wang said. “I think that’s one gap that the cloud service providers are not providing today.”

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The tools DevOps and DevSecOps teams require must also be able to encompass DevOps’ increased reliance and adoption of open source tools — and that can pose challenges.

#devops #security #tools #podcast #sponsored #the new stack makers

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Why Third-Party Security Adoption Must Get Better

Migrating From Jira Server: Guide, Pros, And Cons

February 15, 2022 marked a significant milestone in Atlassian’s Server EOL (End Of Life) roadmap. This was not the final step. We still have two major milestones ahead of us: end of new app sales in Feb 2023, and end of support in Feb 2024. In simpler words, businesses still have enough time to migrate their Jira Server to one of the two available products – Atlassian Cloud or Atlassian DC. But the clock is ticking. 

Jira Cloud VS Data Center

If we were to go by Atlassian numbers, 95% of their new customers choose cloud. 

“About 80% of Fortune 500 companies have an Atlassian Cloud license. More than 90% of new customers choose cloud first.” – Daniel Scott, Product Marketing Director, Tempo

So that’s settled, right? We are migrating from Server to Cloud? And what about the solution fewer people talk about yet many users rely on – Jira DC? 

Both are viable options and your choice will depend greatly on the needs of your business, your available resources, and operational processes. 

Let’s start by taking a look at the functionality offered by Atlassian Cloud and Atlassian DC.

FeatureAtlassian CloudAtlassian Data Center
Product PlansMultiple plansOne plan
BillingMonthly and annualAnnual only
Pricing modelPer user or tieredTiered only
SupportVarying support levels depending on your plan: Enterprise support coverage is equivalent to Atlassian’s Data Center Premier Support offeringVarying support levels depending on the package: Priority Support or Premier Support (purchased separately)
Total Cost of OwnershipTCO includes your subscription fee, plus product administration timeTCO includes your subscription fee and product administration time, plus: costs related to infrastructure provisioning or IaaS fees (for example, AWS costs) planned downtime time and resources needed for software upgrades
Data encryption services✅❌
Data residency services✅❌
Audit loggingOrganization-level audit logging available via Atlassian Access (Jira Software, Confluence) 

Product-level audit logs (Jira Software, Confluence)
Advanced audit logging
Device securityMobile device management support (Jira Software, Confluence, Jira Service Management)

Mobile application management (currently on the roadmap)
Mobile device management support (Jira Software, Confluence, Jira Service Management) 
Content security✅❌
Data Storage limits2 GB (Free)

250 GB (Standard)

Unlimited storage (Premium and Enterprise)
No limits
PerformanceContinuous performance updates to improve load times, search responsiveness, and attachments

Cloud infrastructure hosted in six geographic regions to reduce latency
Rate limitingCDN supports Smart mirrors and mirror farms (Bitbucket)
Backup and data disaster recoveryJira leverages multiple geographically diverse data centers, has a comprehensive backup program, and gains assurance by regularly testing their disaster recovery and business continuity plans. 

Backups are generated daily and retained for 30 days to allow for point-in-time data restoration
Containerization and orchestration✅Docker images

Kubernetes support (on the roadmap for now)
Change management and upgradesAtlassian automatically handles software and security upgrades for you Sandbox instance to test changes (Premium and Enterprise) 

Release track options for Premium and Enterprise (Jira Software, Jira Service Management, Confluence)
Direct access to the databaseNo direct access to change the database structure, file system, or other server infrastructure

Extensive REST APIs for programmatic data access
Direct database access
Insights and reportingOrganization and admin insights to track adoption of Atlassian products, and evaluate the security of your organization.Data Pipeline for advanced insightsConfluence analytics

Pros and cons of Jira Cloud

When talking about pros and cons, there’s always a chance that a competitive advantage for some is a dealbreaker for others. That’s why I decided to talk about pros and cons in matching pairs. 

Pro: Scalability is one of the primary reasons businesses are choosing Jira Cloud. DC is technically also scalable, but you’ll need to scale on your own whereas the cloud version allows for the infrastructure to scale with your business. 

Con: Despite the cloud’s ability to grow with your business, there is still a user limit of 35k users. In addition to that, the costs will grow alongside your needs. New users, licenses, storage, and computing power – all come at an additional cost. So, when your organization reaches a certain size, migrating to Jira DC becomes more cost-efficient.

Pro: Jira takes care of maintenance and support for you.

Con: Your business can suffer from unpredicted downtime. And there are certain security risks.  

Pro: Extra bells and whistles: 

  • Sandbox: Sandbox is a safe environment system admins can use to test applications and integrations before rolling them out to the production environment. 
  • Release tracks: Admins can be more flexible with their product releases as they can access batch and control cloud releases. This means they’ll have much more time to test existing configurations and workflows against a new update. 
  • Insight Discovery: More data means more ways you can impact your business or product in a positive, meaningful way. 
  • Team Calendars: This is a handy feature for synchronization and synergy across teams. 

Con: Most of these features are locked behind a paywall and are only available to either Premium and Enterprise or only Enterprise licenses (either fully or through addition of functionality. For example, Release tracks are only available to Enterprise customers.) In addition, the costs will grow as you scale the offering to fit your growing needs. 

Pros and cons of Jira Data Center

I’ll be taking the same approach to talking about the pros and cons as I did when writing about Atlassian Cloud. Pros and cons are paired. 

Pro: Hosting your own system means you can scale horizontally and vertically through additional hardware. Extension of your systems is seamless, and there is no downtime (if you do everything correctly). Lastly, you don’t have to worry about the user limit – there is none. 

Con: While having more control over your systems is great, it implies a dedicated staff of engineers, additional expenses on software licensing, hardware, and physical space. Moreover, seamless extension and 0% downtime are entirely on you.

Pro: Atlassian has updated the DC offering with native bundled applications such as Advanced Roadmaps, team calendars and analytics for confluence, insight asset management, and insight discovery in Jira Service Management DC.

Con: Atlassian has updated their pricing to reflect these changes. And you are still getting fewer “bells and whistles” than Jira Cloud users (as we can see from the feature comparison). 

Pro: You are technically safer as the system is supported on your hardware by your specialists. Any and all Jira server issues, poor updates, and downtime are simply not your concern.

Con: Atlassian offers excellent security options: data encryption in transit and rest, to mobile app management, to audit offerings and API token controls. In their absence, your team company has to dedicate additional resources to security. 

Pro: Additional benefits from Atlassian, such as the Priority Support bundle (all DC subscriptions have this option), and the Data center loyalty discount (more on that in the pricing section.)

The Pricing

Talking about pricing of SaaS products is always a challenge as there are always multiple tiers and various pay-as-you go features. Barebones Jira Cloud, for instance, is completely free of charge, yet there are a series of serious limitations. 

Standard Jira Cloud will cost you an average of $7.50 per user per month while premium cranks that price up to $14.50. The Enterprise plan is billed annually and the cost is determined on a case-by-case basis. You can see the full comparison of Jira Cloud plans here. And you can use this online calculator to learn the cost of ownership in your particular case.

50 UsersStandard (Monthly/Annually)Premium (Monthly/Annually)
Jira Software$387.50 / $3,900$762.50 / $7,650
Jira Work Management$250 / $2,500❌
Jira Service Management$866.25 / $8,650$2,138.25 / $21,500
Confluence$287.50 / $2,900$550 / $5,500
100 UsersStandard (Monthly/Annually)Premium (Monthly/Annually)
Jira Software$775 / $7,750$1,525 / $15,250
Jira Work Management$500 / $5,000❌
Jira Service Management$1,653.75 / $16,550$4,185.75 / $42,000
Confluence$575 / $5,750$1,100 / $11,000
500 UsersStandard (Monthly/Annually)Premium (Monthly/Annually)
Jira Software$3,140 / $31,500$5,107.50 / $51,000 
Jira Work Management$1,850 / $18,500❌
Jira Service Management$4,541.25 / $45,400$11,693.25 / $117,000
Confluence$2,060 / $20,500$3,780 / $37,800

Please note that these prices were calculated without any apps included. 

Jira Data Center starts at $42,000 per year and the plan includes up to 500 users. If you are a new client and are not eligible for any discounts*, here’s a chart that should give you an idea as to the cost of ownership of Jira DC. You can find more information regarding your specific case here.

UsersCommercial Annual PlanAcademic Annual Plan
1-500USD 42,000USD 21,000
501-1000USD 72,000USD 36,000
1001-2000USD 120,000USD 60,000
Confluence for Data Center  
1-500USD 27,000USD 13,500
501-1000USD 48,000USD 24,000
1001-2000USD 84,000USD 42,000
Bitbucket for Data Center  
1-25USD 2,300USD 1,150
26-50USD 4,200USD 2,100
51-100USD 7,600USD 3,800
Jira Service Management for Data Center  
1-50USD 17,200USD 8,600
51-100USD 28,600USD 14,300
101-250USD 51,500USD 25,750


  • Centralized per-user licensing allows users access all enterprise instances with a single Enterprise license.
  • There’s an option for dual licensing for users who purchase an annual cloud subscription with 1,001 or more users. In this case, Atlassian extends your existing server maintenance or Data Center subscription for up to one year at a 100% discount.
  • There are certain discounts for apps depending on your partnership level.
  • Depending on your situation, you may qualify for several Jira Data Center discount programs:

What should be your User Migration strategy?

Originally, there were several migration methods: Jira Cloud Migration Assistant, Jira Cloud Site Import, and there was an option to migrate via CSV export (though Jira actively discourages you from using this method). However, Jira’s team has focused their efforts on improving the Migration Assistant and have chosen to discontinue Cloud Site Import support.

Thanks to the broadened functionality of the assistant, it is now the only go-to method for migration with just one exception. If you are migrating over 1000 users and you absolutely need to migrate advanced roadmaps – you’ll need to rely on Site Import. At least for now, as Jira is actively working on implementing this feature in their assistant.

Here’s a quick comparison of the options and their limitations.

Cloud Migration AssistantApp migration

Existing data on a Cloud Site is not overwritten

You choose the projects, users, and groups you want to migrate

Jira Service Management customer account migration

Better UI to guide you through the migration

Potential migration errors are displayed in advance

Migration can be done in phases reducing the downtime

Pre- and post-migration reports
You must be on a supported self-managed version of Jira
Site ExportCan migrate Advanced RoadmapsApp data is not migrated

Migration overrides existing data on the Cloud site

Separate user importUsers from external directories are not migrated

No choice of data you want or don’t want migrated

There’s a need to split attachments into up to 5GB chunks

Higher risks of downtime due to the “all or nothing” approach

You must be on a supported self-managed version of Jira

Pro tip: If you have a large base of users (above 2000), migrate them before you migrate projects and spaces. This way, you will not disrupt the workflow as users are still working on Server and the latter migration of data will take less time. 

How to migrate to Jira Cloud

Now that we have settled on one particular offering based on available pricing models as well as the pros and the cons that matter the most to your organization, let’s talk about the “how”. 

How does one migrate from Jira Server to Jira Cloud?

Pre-migration checklist

Jira’s Cloud Migration Assistant is a handy tool. It will automatically review your data for common errors. But it is incapable of doing all of the work for you. That’s why we – and Atlassian for that matter – recommend creating a pre-migration checklist.   

Smart Checklist will help you craft an actionable, context-rich checklist directly inside a Jira ticket. This way, none of the tasks will be missed, lost, or abandoned. 

Below is an example of how your migration checklist will look like in Jira. 

Feel free to copy the code and paste it into your Smart Checklist editor and you’ll have the checklist at the ready. 

# Create a user migration plan #must
> Please keep in mind that Jira Cloud Migration Assistant migrates all users and groups as well as users and groups related to selected projects
- Sync your user base
- Verify synchronization
- External users sync verification
- Active external directory verification
## Check your Jira Server version #must
- Verify via user interface or Support Zip Product Version Verification
> Jira Migration Assistant will not work unless Jira is running on a supported version
## Fix any duplicate email addresses #must
- Verify using SQL
> Duplicate email addresses are not supported by Jira Cloud and therefore can't be migrated with the Jira Cloud Migration Assistant. To avoid errors, you should find and fix any duplicate email addresses before migration. If user information is managed in an LDAP Server, you will need to update emails there and sync with Jira before the migration. If user information is managed locally, you can fix them through the Jira Server or Data Center user interface.
## Make sure you have the necessary permissions #must
- System Admin global permissions on the Server instance
- Exists in the target Cloud site
- Site Administrator Permission in the cloud
## Check for conflicts with group names #must
- Make sure that the groups in your Cloud Site don't have the same names as groups in Server
> Unless you are actively trying to merge them
- Delete or update add-on users so not to cause migration issues
- Verify via SQL
## Update firewall allowance rules #must
- None of the domains should be blocked by firewall or proxy
## Find a way to migrate apps #must
- Contact app vendors
## Check public access settings #must
- Projects
- Filters
- Filters
- Boards
- Dashboards
## Review server setup #mst
- at least 4gb Heap Allocation
- Open Files limit review
- Verify via support zip
## Check Server timezone #must for merging Cloud sites
- Switch to UTC is using any other timezone
> Add a system flag to the Jira Server instance -Duser.timezone=UTC as outlined in this article about updating documentation to include timezone details.
## Fix any duplicate shared configuration
## Storage limits
## Prepare the server instance
- Check data status
- All fields have value and are not null
-Any archived projects you wish to migrate are activated
## Prepare your cloud site
- Same Jira products enabled
- Same language
- User migration strategy
## Data backup
- Backup Jira Server site
- Backup Cloud site
## Run a test migration
- Done
## Notify Jira support
- Get in touch with Jira migration support

Use backups

On the one hand, having all of your Jira products on a server may seem like a backup in and of itself. On the other hand, there are data migration best practices we should follow even if it’s just a precaution. No one has ever felt sorry for their data being too safe. 

In addition, there are certain types of migration errors that can be resolved much faster with having a backup at hand. 

  1. Jira Server Database backup: this step creates a DB backup in an XML format.
    1. Log in with Jira System Admin permissions
    2. Go to system -> Import and Export -> Backup Manager -> Backup for server.
    3. Click the create Backup for server button. 
    4. Type in the name for your backup. 
    5. Jira will create a zipped XML file and notify you once the backup is ready. 

  1. Jira Cloud Backup: This backup also saves your data in an XML format. The process is quite similar to creating a Jira Server backup with the only difference taking place on the Backups page.
    1. Select the option to save your attachments, logos, and avatars.
    2. Click on the Create backup button. 

  1. As you can see, the Cloud backup includes the option to save attachments, avatars, and logos. This step should be done manually when backing up Server data.
    1. Create a Zip archive for this data
    2. Make sure it follows the structure suggested by Atlassian

Migrating your Jira instance to the cloud via the Jira Migration Assistant

Jira Cloud Migration Assistant is a free add-on Atlassian recommends using when migrating to the cloud. It accesses and evaluates your apps and helps migrate multiple projects. 

Overall, the migration assistant offers a more stable and reliable migration experience. It automatically checks for certain errors. It makes sure all users have unique and valid emails, and makes sure that none of the project names and keys conflict with one another. 

This is a step-by-step guide for importing your Jira Server data backup file into Jira Cloud.

  1. Log into Jira Cloud with admin permissions
  2. Go to System -> Import and Export -> External System Import
  3. Click on the Jira Server import option

  1. Select the backup Zip you have created 
  2. Jira will check the file for errors and present you with two options: enable or disable outgoing mail. Don’t worry, you will be able to change this section after the migration process is complete. 
  3. Then you will be presented with an option to merge Jira Server and Jira Cloud users
    1. Choosing overwrite will replace the users with users from the imported files
    2. The merge option will merge groups with the same name
    3. Lastly, you can select the third option if you are migrating users via Jira’s assistant
  4. Run the import

How do you migrate Jira Server into Jira DC?

Before we can proceed with the migration process, please make sure you meet the following prerequisites:

  1. Make sure you are installing Jira on one of the supported platforms. Atlassian has a list of supported platforms for Jira 9.1.
  2. Make sure the applications you are using are compatible with Jira DC. You will be required to switch to datacenter-compatible versions of your applications (they must be available). 
  3. Make sure you meet the necessary software and hardware requirements:
    1. You have a DC license
    2. You are using a supported database, OS, and Java version
    3. You are using OAuth authentication if your application links to other Atlassian products

Once you are certain you are ready to migrate your Jira Server to Jira Data Center, you can proceed with an installation that’s much simpler than one would expect.

  1. Upgrade your apps to be compatible with Jira DC
  2. Go to Administration -> Applications -> Versions and licenses
  3. Enter your Jira DC License Key
  4. Restart Jira

That’s it. You are all set. Well, unless your organization has specific needs such as continuous uptime, performance under heavy loads, and scalability, in which case you will need to set up a server cluster. You can find out more about setting up server clusters in this guide.  

Everything You Need to Know About Instagram Bot with Python

How to build an Instagram bot using Python

Instagram is the fastest-growing social network, with 1 billion monthly users. It also has the highest engagement rate. To gain followers on Instagram, you’d have to upload engaging content, follow users, like posts, comment on user posts and a whole lot. This can be time-consuming and daunting. But there is hope, you can automate all of these tasks. In this course, we’re going to build an Instagram bot using Python to automate tasks on Instagram.

What you’ll learn:

  • Instagram Automation
  • Build a Bot with Python

Increase your Instagram followers with a simple Python bot

I got around 500 real followers in 4 days!

Growing an audience is an expensive and painful task. And if you’d like to build an audience that’s relevant to you, and shares common interests, that’s even more difficult. I always saw Instagram has a great way to promote my photos, but I never had more than 380 followers… Every once in a while, I decide to start posting my photos on Instagram again, and I manage to keep posting regularly for a while, but it never lasts more than a couple of months, and I don’t have many followers to keep me motivated and engaged.

The objective of this project is to build a bigger audience and as a plus, maybe drive some traffic to my website where I sell my photos!

A year ago, on my last Instagram run, I got one of those apps that lets you track who unfollowed you. I was curious because in a few occasions my number of followers dropped for no apparent reason. After some research, I realized how some users basically crawl for followers. They comment, like and follow people — looking for a follow back. Only to unfollow them again in the next days.

I can’t say this was a surprise to me, that there were bots in Instagram… It just made me want to build one myself!

And that is why we’re here, so let’s get to it! I came up with a simple bot in Python, while I was messing around with Selenium and trying to figure out some project to use it. Simply put, Selenium is like a browser you can interact with very easily in Python.

Ideally, increasing my Instagram audience will keep me motivated to post regularly. As an extra, I included my website in my profile bio, where people can buy some photos. I think it is a bit of a stretch, but who knows?! My sales are basically zero so far, so it should be easy to track that conversion!

Just what the world needed! Another Instagram bot…

After giving this project some thought, my objective was to increase my audience with relevant people. I want to get followers that actually want to follow me and see more of my work. It’s very easy to come across weird content in the most used hashtags, so I’ve planed this bot to lookup specific hashtags and interact with the photos there. This way, I can be very specific about what kind of interests I want my audience to have. For instance, I really like long exposures, so I can target people who use that hashtag and build an audience around this kind of content. Simple and efficient!

My gallery is a mix of different subjects and styles, from street photography to aerial photography, and some travel photos too. Since it’s my hometown, I also have lots of Lisbon images there. These will be the main topics I’ll use in the hashtags I want to target.

This is not a “get 1000 followers in 24 hours” kind of bot!

So what kind of numbers are we talking about?

I ran the bot a few times in a few different hashtags like “travelblogger”, “travelgram”, “lisbon”, “dronephotography”. In the course of three days I went from 380 to 800 followers. Lots of likes, comments and even some organic growth (people that followed me but were not followed by the bot).

To be clear, I’m not using this bot intensively, as Instagram will stop responding if you run it too fast. It needs to have some sleep commands in between the actions, because after some comments and follows in a short period of time, Instagram stops responding and the bot crashes.

You will be logged into your account, so I’m almost sure that Instagram can know you’re doing something weird if you speed up the process. And most importantly, after doing this for a dozen hashtags, it just gets harder to find new users in the same hashtags. You will need to give it a few days to refresh the user base there.

But I don’t want to follow so many people in the process…

The most efficient way to get followers in Instagram (apart from posting great photos!) is to follow people. And this bot worked really well for me because I don’t care if I follow 2000 people to get 400 followers.

The bot saves a list with all the users that were followed while it was running, so someday I may actually do something with this list. For instance, I can visit each user profile, evaluate how many followers or posts they have, and decide if I want to keep following them. Or I can get the first picture in their gallery and check its date to see if they are active users.

If we remove the follow action from the bot, I can assure you the growth rate will suffer, as people are less inclined to follow based on a single like or comment.

Why will you share your code?!

That’s the debate I had with myself. Even though I truly believe in giving back to the community (I still learn a lot from it too!), there are several paid platforms that do more or less the same as this project. Some are shady, some are used by celebrities. The possibility of starting a similar platform myself, is not off the table yet, so why make the code available?

With that in mind, I decided to add an extra level of difficulty to the process, so I was going to post the code below as an image. I wrote “was”, because meanwhile, I’ve realized the image I’m getting is low quality. Which in turn made me reconsider and post the gist. I’m that nice! The idea behind the image was that if you really wanted to use it, you would have to type the code yourself. And that was my way of limiting the use of this tool to people that actually go through the whole process to create it and maybe even improve it.

I learn a lot more when I type the code myself, instead of copy/pasting scripts. I hope you feel the same way!

The script isn’t as sophisticated as it could be, and I know there’s lots of room to improve it. But hey… it works! I have other projects I want to add to my portfolio, so my time to develop it further is rather limited. Nevertheless, I will try to update this article if I dig deeper.

This is the last subtitle!

You’ll need Python (I’m using Python 3.7), Selenium, a browser (in my case I’ll be using Chrome) and… obviously, an Instagram account! Quick overview regarding what the bot will do:

  • Open a browser and login with your credentials
  • For every hashtag in the hashtag list, it will open the page and click the first picture to open it
  • It will then like, follow, comment and move to the next picture, in a 200 iterations loop (number can be adjusted)
  • Saves a list with all the users you followed using the bot

If you reached this paragraph, thank you! You totally deserve to collect your reward! If you find this useful for your profile/brand in any way, do share your experience below :)

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from time import sleep, strftime
from random import randint
import pandas as pd

chromedriver_path = 'C:/Users/User/Downloads/chromedriver_win32/chromedriver.exe' # Change this to your own chromedriver path!
webdriver = webdriver.Chrome(executable_path=chromedriver_path)

username = webdriver.find_element_by_name('username')
password = webdriver.find_element_by_name('password')

button_login = webdriver.find_element_by_css_selector('#react-root > section > main > div > article > div > div:nth-child(1) > div > form > div:nth-child(3) > button')

notnow = webdriver.find_element_by_css_selector('body > div:nth-child(13) > div > div > div > div.mt3GC > button.aOOlW.HoLwm') #comment these last 2 lines out, if you don't get a pop up asking about notifications

In order to use chrome with Selenium, you need to install chromedriver. It’s a fairly simple process and I had no issues with it. Simply install and replace the path above. Once you do that, our variable webdriver will be our Chrome tab.

In cell number 3 you should replace the strings with your own username and the respective password. This is for the bot to type it in the fields displayed. You might have already noticed that when running cell number 2, Chrome opened a new tab. After the password, I’ll define the login button as an object, and in the following line, I click it.

Once you get in inspect mode find the bit of html code that corresponds to what you want to map. Right click it and hover over Copy. You will see that you have some options regarding how you want it to be copied. I used a mix of XPath and css selectors throughout the code (it’s visible in the find_element_ method). It took me a while to get all the references to run smoothly. At points, the css or the xpath directions would fail, but as I adjusted the sleep times, everything started running smoothly.

In this case, I selected “copy selector” and pasted it inside a find_element_ method (cell number 3). It will get you the first result it finds. If it was find_elements_, all elements would be retrieved and you could specify which to get.

Once you get that done, time for the loop. You can add more hashtags in the hashtag_list. If you run it for the first time, you still don’t have a file with the users you followed, so you can simply create prev_user_list as an empty list.

Once you run it once, it will save a csv file with a timestamp with the users it followed. That file will serve as the prev_user_list on your second run. Simple and easy to keep track of what the bot does.

Update with the latest timestamp on the following runs and you get yourself a series of csv backlogs for every run of the bot.

Instagram bot with Python

The code is really simple. If you have some basic notions of Python you can probably pick it up quickly. I’m no Python ninja and I was able to build it, so I guess that if you read this far, you are good to go!

hashtag_list = ['travelblog', 'travelblogger', 'traveler']

# prev_user_list = [] - if it's the first time you run it, use this line and comment the two below
prev_user_list = pd.read_csv('20181203-224633_users_followed_list.csv', delimiter=',').iloc[:,1:2] # useful to build a user log
prev_user_list = list(prev_user_list['0'])

new_followed = []
tag = -1
followed = 0
likes = 0
comments = 0

for hashtag in hashtag_list:
    tag += 1
    webdriver.get(''+ hashtag_list[tag] + '/')
    first_thumbnail = webdriver.find_element_by_xpath('//*[@id="react-root"]/section/main/article/div[1]/div/div/div[1]/div[1]/a/div')
        for x in range(1,200):
            username = webdriver.find_element_by_xpath('/html/body/div[3]/div/div[2]/div/article/header/div[2]/div[1]/div[1]/h2/a').text
            if username not in prev_user_list:
                # If we already follow, do not unfollow
                if webdriver.find_element_by_xpath('/html/body/div[3]/div/div[2]/div/article/header/div[2]/div[1]/div[2]/button').text == 'Follow':
                    followed += 1

                    # Liking the picture
                    button_like = webdriver.find_element_by_xpath('/html/body/div[3]/div/div[2]/div/article/div[2]/section[1]/span[1]/button/span')
                    likes += 1

                    # Comments and tracker
                    comm_prob = randint(1,10)
                    print('{}_{}: {}'.format(hashtag, x,comm_prob))
                    if comm_prob > 7:
                        comments += 1
                        comment_box = webdriver.find_element_by_xpath('/html/body/div[3]/div/div[2]/div/article/div[2]/section[3]/div/form/textarea')

                        if (comm_prob < 7):
                            comment_box.send_keys('Really cool!')
                        elif (comm_prob > 6) and (comm_prob < 9):
                            comment_box.send_keys('Nice work :)')
                        elif comm_prob == 9:
                            comment_box.send_keys('Nice gallery!!')
                        elif comm_prob == 10:
                            comment_box.send_keys('So cool! :)')
                        # Enter to post comment

                # Next picture
    # some hashtag stops refreshing photos (it may happen sometimes), it continues to the next

for n in range(0,len(new_followed)):
updated_user_df = pd.DataFrame(prev_user_list)
print('Liked {} photos.'.format(likes))
print('Commented {} photos.'.format(comments))
print('Followed {} new people.'.format(followed))

Instagram bot with Python

The print statement inside the loop is the way I found to be able to have a tracker that lets me know at what iteration the bot is all the time. It will print the hashtag it’s in, the number of the iteration, and the random number generated for the comment action. I decided not to post comments in every page, so I added three different comments and a random number between 1 and 10 that would define if there was any comment at all, or one of the three. The loop ends, we append the new_followed users to the previous users “database” and saves the new file with the timestamp. You should also get a small report.

Instagram bot with Python

And that’s it!

After a few hours without checking the phone, these were the numbers I was getting. I definitely did not expect it to do so well! In about 4 days since I’ve started testing it, I had around 500 new followers, which means I have doubled my audience in a matter of days. I’m curious to see how many of these new followers I will lose in the next days, to see if the growth can be sustainable. I also had a lot more “likes” in my latest photos, but I guess that’s even more expected than the follow backs.

Instagram bot with Python

It would be nice to get this bot running in a server, but I have other projects I want to explore, and configuring a server is not one of them! Feel free to leave a comment below, and I’ll do my best to answer your questions.

I’m actually curious to see how long will I keep posting regularly! If you feel like this article was helpful for you, consider thanking me by buying one of my photos.

Instagram bot with Python

How to Make an Instagram Bot With Python and InstaPy

Instagram bot with Python

What do SocialCaptain, Kicksta, Instavast, and many other companies have in common? They all help you reach a greater audience, gain more followers, and get more likes on Instagram while you hardly lift a finger. They do it all through automation, and people pay them a good deal of money for it. But you can do the same thing—for free—using InstaPy!

In this tutorial, you’ll learn how to build a bot with Python and InstaPy, which automates your Instagram activities so that you gain more followers and likes with minimal manual input. Along the way, you’ll learn about browser automation with Selenium and the Page Object Pattern, which together serve as the basis for InstaPy.

In this tutorial, you’ll learn:

  • How Instagram bots work
  • How to automate a browser with Selenium
  • How to use the Page Object Pattern for better readability and testability
  • How to build an Instagram bot with InstaPy

You’ll begin by learning how Instagram bots work before you build one.

Table of Contents

  • How Instagram Bots Work
  • How to Automate a Browser
  • How to Use the Page Object Pattern
  • How to Build an Instagram Bot With InstaPy
    • Essential Features
    • Additional Features in InstaPy
  • Conclusion

Important: Make sure you check Instagram’s Terms of Use before implementing any kind of automation or scraping techniques.

How Instagram Bots Work

How can an automation script gain you more followers and likes? Before answering this question, think about how an actual person gains more followers and likes.

They do it by being consistently active on the platform. They post often, follow other people, and like and leave comments on other people’s posts. Bots work exactly the same way: They follow, like, and comment on a consistent basis according to the criteria you set.

The better the criteria you set, the better your results will be. You want to make sure you’re targeting the right groups because the people your bot interacts with on Instagram will be more likely to interact with your content.

For example, if you’re selling women’s clothing on Instagram, then you can instruct your bot to like, comment on, and follow mostly women or profiles whose posts include hashtags such as #beauty, #fashion, or #clothes. This makes it more likely that your target audience will notice your profile, follow you back, and start interacting with your posts.

How does it work on the technical side, though? You can’t use the Instagram Developer API since it is fairly limited for this purpose. Enter browser automation. It works in the following way:

  1. You serve it your credentials.
  2. You set the criteria for who to follow, what comments to leave, and which type of posts to like.
  3. Your bot opens a browser, types in on the address bar, logs in with your credentials, and starts doing the things you instructed it to do.

Next, you’ll build the initial version of your Instagram bot, which will automatically log in to your profile. Note that you won’t use InstaPy just yet.

How to Automate a Browser

For this version of your Instagram bot, you’ll be using Selenium, which is the tool that InstaPy uses under the hood.

First, install Selenium. During installation, make sure you also install the Firefox WebDriver since the latest version of InstaPy dropped support for Chrome. This also means that you need the Firefox browser installed on your computer.

Now, create a Python file and write the following code in it:

from time import sleep

from selenium import webdriver

browser = webdriver.Firefox()




Run the code and you’ll see that a Firefox browser opens and directs you to the Instagram login page. Here’s a line-by-line breakdown of the code:

  • Lines 1 and 2 import sleep and webdriver.
  • Line 4 initializes the Firefox driver and sets it to browser.
  • Line 6 types on the address bar and hits Enter.
  • Line 8 waits for five seconds so you can see the result. Otherwise, it would close the browser instantly.
  • Line 10 closes the browser.

This is the Selenium version of Hello, World. Now you’re ready to add the code that logs in to your Instagram profile. But first, think about how you would log in to your profile manually. You would do the following:

  1. Go to
  2. Click the login link.
  3. Enter your credentials.
  4. Hit the login button.

The first step is already done by the code above. Now change it so that it clicks on the login link on the Instagram home page:

from time import sleep

from selenium import webdriver

browser = webdriver.Firefox()



login_link = browser.find_element_by_xpath("//a[text()='Log in']")



Note the highlighted lines:

  • Line 5 sets five seconds of waiting time. If Selenium can’t find an element, then it waits for five seconds to allow everything to load and tries again.
  • Line 9 finds the element <a> whose text is equal to Log in. It does this using XPath, but there are a few other methods you could use.
  • Line 10 clicks on the found element <a> for the login link.

Run the script and you’ll see your script in action. It will open the browser, go to Instagram, and click on the login link to go to the login page.

On the login page, there are three important elements:

  1. The username input
  2. The password input
  3. The login button

Next, change the script so that it finds those elements, enters your credentials, and clicks on the login button:

from time import sleep

from selenium import webdriver

browser = webdriver.Firefox()



login_link = browser.find_element_by_xpath("//a[text()='Log in']")


username_input = browser.find_element_by_css_selector("input[name='username']")

password_input = browser.find_element_by_css_selector("input[name='password']")

username_input.send_keys("<your username>")

password_input.send_keys("<your password>")

login_button = browser.find_element_by_xpath("//button[@type='submit']")



Here’s a breakdown of the changes:

  1. Line 12 sleeps for two seconds to allow the page to load.
  2. Lines 14 and 15 find username and password inputs by CSS. You could use any other method that you prefer.
  3. Lines 17 and 18 type your username and password in their respective inputs. Don’t forget to fill in <your username> and <your password>!
  4. Line 20 finds the login button by XPath.
  5. Line 21 clicks on the login button.

Run the script and you’ll be automatically logged in to to your Instagram profile.

You’re off to a good start with your Instagram bot. If you were to continue writing this script, then the rest would look very similar. You would find the posts that you like by scrolling down your feed, find the like button by CSS, click on it, find the comments section, leave a comment, and continue.

The good news is that all of those steps can be handled by InstaPy. But before you jump into using Instapy, there is one other thing that you should know about to better understand how InstaPy works: the Page Object Pattern.

How to Use the Page Object Pattern

Now that you’ve written the login code, how would you write a test for it? It would look something like the following:

def test_login_page(browser):
    username_input = browser.find_element_by_css_selector("input[name='username']")
    password_input = browser.find_element_by_css_selector("input[name='password']")
    username_input.send_keys("<your username>")
    password_input.send_keys("<your password>")
    login_button = browser.find_element_by_xpath("//button[@type='submit']")

    errors = browser.find_elements_by_css_selector('#error_message')
    assert len(errors) == 0

Can you see what’s wrong with this code? It doesn’t follow the DRY principle. That is, the code is duplicated in both the application and the test code.

Duplicating code is especially bad in this context because Selenium code is dependent on UI elements, and UI elements tend to change. When they do change, you want to update your code in one place. That’s where the Page Object Pattern comes in.

With this pattern, you create page object classes for the most important pages or fragments that provide interfaces that are straightforward to program to and that hide the underlying widgetry in the window. With this in mind, you can rewrite the code above and create a HomePage class and a LoginPage class:

from time import sleep

class LoginPage:
    def __init__(self, browser):
        self.browser = browser

    def login(self, username, password):
        username_input = self.browser.find_element_by_css_selector("input[name='username']")
        password_input = self.browser.find_element_by_css_selector("input[name='password']")
        login_button = browser.find_element_by_xpath("//button[@type='submit']")

class HomePage:
    def __init__(self, browser):
        self.browser = browser

    def go_to_login_page(self):
        self.browser.find_element_by_xpath("//a[text()='Log in']").click()
        return LoginPage(self.browser)

The code is the same except that the home page and the login page are represented as classes. The classes encapsulate the mechanics required to find and manipulate the data in the UI. That is, there are methods and accessors that allow the software to do anything a human can.

One other thing to note is that when you navigate to another page using a page object, it returns a page object for the new page. Note the returned value of go_to_log_in_page(). If you had another class called FeedPage, then login() of the LoginPage class would return an instance of that: return FeedPage().

Here’s how you can put the Page Object Pattern to use:

from selenium import webdriver

browser = webdriver.Firefox()

home_page = HomePage(browser)
login_page = home_page.go_to_login_page()
login_page.login("<your username>", "<your password>")


It looks much better, and the test above can now be rewritten to look like this:

def test_login_page(browser):
    home_page = HomePage(browser)
    login_page = home_page.go_to_login_page()
    login_page.login("<your username>", "<your password>")

    errors = browser.find_elements_by_css_selector('#error_message')
    assert len(errors) == 0

With these changes, you won’t have to touch your tests if something changes in the UI.

For more information on the Page Object Pattern, refer to the official documentation and to Martin Fowler’s article.

Now that you’re familiar with both Selenium and the Page Object Pattern, you’ll feel right at home with InstaPy. You’ll build a basic bot with it next.

Note: Both Selenium and the Page Object Pattern are widely used for other websites, not just for Instagram.

How to Build an Instagram Bot With InstaPy

In this section, you’ll use InstaPy to build an Instagram bot that will automatically like, follow, and comment on different posts. First, you’ll need to install InstaPy:

$ python3 -m pip install instapy

This will install instapy in your system.

Essential Features

Now you can rewrite the code above with InstaPy so that you can compare the two options. First, create another Python file and put the following code in it:

from instapy import InstaPy

InstaPy(username="<your_username>", password="<your_password>").login()

Replace the username and password with yours, run the script, and voilà! With just one line of code, you achieved the same result.

Even though your results are the same, you can see that the behavior isn’t exactly the same. In addition to simply logging in to your profile, InstaPy does some other things, such as checking your internet connection and the status of the Instagram servers. This can be observed directly on the browser or in the logs:

INFO [2019-12-17 22:03:19] [username]  -- Connection Checklist [1/3] (Internet Connection Status)
INFO [2019-12-17 22:03:20] [username]  - Internet Connection Status: ok
INFO [2019-12-17 22:03:20] [username]  - Current IP is "" and it's from "Germany/DE"
INFO [2019-12-17 22:03:20] [username]  -- Connection Checklist [2/3] (Instagram Server Status)
INFO [2019-12-17 22:03:26] [username]  - Instagram WebSite Status: Currently Up

Pretty good for one line of code, isn’t it? Now it’s time to make the script do more interesting things than just logging in.

For the purpose of this example, assume that your profile is all about cars, and that your bot is intended to interact with the profiles of people who are also interested in cars.

First, you can like some posts that are tagged #bmw or #mercedes using like_by_tags():

from instapy import InstaPy

session = InstaPy(username="<your_username>", password="<your_password>")


session.like_by_tags(["bmw", "mercedes"], amount=5)

Here, you gave the method a list of tags to like and the number of posts to like for each given tag. In this case, you instructed it to like ten posts, five for each of the two tags. But take a look at what happens after you run the script:

INFO [2019-12-17 22:15:58] [username]  Tag [1/2]
INFO [2019-12-17 22:15:58] [username]  --> b'bmw'
INFO [2019-12-17 22:16:07] [username]  desired amount: 14  |  top posts [disabled]: 9  |  possible posts: 43726739
INFO [2019-12-17 22:16:13] [username]  Like# [1/14]
INFO [2019-12-17 22:16:13] [username]
INFO [2019-12-17 22:16:15] [username]  Image from: b'mattyproduction'
INFO [2019-12-17 22:16:15] [username]  Link: b''
INFO [2019-12-17 22:16:15] [username]  Description: b'Mal etwas anderes \xf0\x9f\x91\x80\xe2\x98\xba\xef\xb8\x8f Bald ist das komplette Video auf YouTube zu finden (n\xc3\xa4here Infos werden folgen). Vielen Dank an @patrick_jwki @thehuthlife  und @christic_  f\xc3\xbcr das bereitstellen der Autos \xf0\x9f\x94\xa5\xf0\x9f\x98\x8d#carporn#cars#tuning#bagged#bmw#m2#m2competition#focusrs#ford#mk3#e92#m3#panasonic#cinematic#gh5s#dji#roninm#adobe#videography#music#bimmer#fordperformance#night#shooting#'
INFO [2019-12-17 22:16:15] [username]  Location: b'K\xc3\xb6ln, Germany'
INFO [2019-12-17 22:16:51] [username]  --> Image Liked!
INFO [2019-12-17 22:16:56] [username]  --> Not commented
INFO [2019-12-17 22:16:57] [username]  --> Not following
INFO [2019-12-17 22:16:58] [username]  Like# [2/14]
INFO [2019-12-17 22:16:58] [username]
INFO [2019-12-17 22:17:01] [username]  Image from: b'davs0'
INFO [2019-12-17 22:17:01] [username]  Link: b''
INFO [2019-12-17 22:17:01] [username]  Description: b'Someone said cloud? \xf0\x9f\xa4\x94\xf0\x9f\xa4\xad\xf0\x9f\x98\x88 \xe2\x80\xa2\n\xe2\x80\xa2\n\xe2\x80\xa2\n\xe2\x80\xa2\n#bmw #bmwrepost #bmwm4 #bmwm4gts #f82 #bmwmrepost #bmwmsport #bmwmperformance #bmwmpower #bmwm4cs #austinyellow #davs0 #mpower_official #bmw_world_ua #bimmerworld #bmwfans #bmwfamily #bimmers #bmwpost #ultimatedrivingmachine #bmwgang #m3f80 #m5f90 #m4f82 #bmwmafia #bmwcrew #bmwlifestyle'
INFO [2019-12-17 22:17:34] [username]  --> Image Liked!
INFO [2019-12-17 22:17:37] [username]  --> Not commented
INFO [2019-12-17 22:17:38] [username]  --> Not following

By default, InstaPy will like the first nine top posts in addition to your amount value. In this case, that brings the total number of likes per tag to fourteen (nine top posts plus the five you specified in amount).

Also note that InstaPy logs every action it takes. As you can see above, it mentions which post it liked as well as its link, description, location, and whether the bot commented on the post or followed the author.

You may have noticed that there are delays after almost every action. That’s by design. It prevents your profile from getting banned on Instagram.

Now, you probably don’t want your bot liking inappropriate posts. To prevent that from happening, you can use set_dont_like():

from instapy import InstaPy

session = InstaPy(username="<your_username>", password="<your_password>")
session.like_by_tags(["bmw", "mercedes"], amount=5)
session.set_dont_like(["naked", "nsfw"])

With this change, posts that have the words naked or nsfw in their descriptions won’t be liked. You can flag any other words that you want your bot to avoid.

Next, you can tell the bot to not only like the posts but also to follow some of the authors of those posts. You can do that with set_do_follow():

from instapy import InstaPy

session = InstaPy(username="<your_username>", password="<your_password>")
session.like_by_tags(["bmw", "mercedes"], amount=5)
session.set_dont_like(["naked", "nsfw"])
session.set_do_follow(True, percentage=50)

If you run the script now, then the bot will follow fifty percent of the users whose posts it liked. As usual, every action will be logged.

You can also leave some comments on the posts. There are two things that you need to do. First, enable commenting with set_do_comment():

from instapy import InstaPy

session = InstaPy(username="<your_username>", password="<your_password>")
session.like_by_tags(["bmw", "mercedes"], amount=5)
session.set_dont_like(["naked", "nsfw"])
session.set_do_follow(True, percentage=50)
session.set_do_comment(True, percentage=50)

Next, tell the bot what comments to leave with set_comments():

from instapy import InstaPy

session = InstaPy(username="<your_username>", password="<your_password>")
session.like_by_tags(["bmw", "mercedes"], amount=5)
session.set_dont_like(["naked", "nsfw"])
session.set_do_follow(True, percentage=50)
session.set_do_comment(True, percentage=50)
session.set_comments(["Nice!", "Sweet!", "Beautiful :heart_eyes:"])

Run the script and the bot will leave one of those three comments on half the posts that it interacts with.

Now that you’re done with the basic settings, it’s a good idea to end the session with end():

from instapy import InstaPy

session = InstaPy(username="<your_username>", password="<your_password>")
session.like_by_tags(["bmw", "mercedes"], amount=5)
session.set_dont_like(["naked", "nsfw"])
session.set_do_follow(True, percentage=50)
session.set_do_comment(True, percentage=50)
session.set_comments(["Nice!", "Sweet!", "Beautiful :heart_eyes:"])

This will close the browser, save the logs, and prepare a report that you can see in the console output.

Additional Features in InstaPy

InstaPy is a sizable project that has a lot of thoroughly documented features. The good news is that if you’re feeling comfortable with the features you used above, then the rest should feel pretty similar. This section will outline some of the more useful features of InstaPy.

Quota Supervisor

You can’t scrape Instagram all day, every day. The service will quickly notice that you’re running a bot and will ban some of its actions. That’s why it’s a good idea to set quotas on some of your bot’s actions. Take the following for example:

session.set_quota_supervisor(enabled=True, peak_comments_daily=240, peak_comments_hourly=21)

The bot will keep commenting until it reaches its hourly and daily limits. It will resume commenting after the quota period has passed.

Headless Browser

This feature allows you to run your bot without the GUI of the browser. This is super useful if you want to deploy your bot to a server where you may not have or need the graphical interface. It’s also less CPU intensive, so it improves performance. You can use it like so:

session = InstaPy(username='test', password='test', headless_browser=True)

Note that you set this flag when you initialize the InstaPy object.

Using AI to Analyze Posts

Earlier you saw how to ignore posts that contain inappropriate words in their descriptions. What if the description is good but the image itself is inappropriate? You can integrate your InstaPy bot with ClarifAI, which offers image and video recognition services:

session.set_use_clarifai(enabled=True, api_key='<your_api_key>')

Now your bot won’t like or comment on any image that ClarifAI considers NSFW. You get 5,000 free API-calls per month.

Relationship Bounds

It’s often a waste of time to interact with posts by people who have a lot of followers. In such cases, it’s a good idea to set some relationship bounds so that your bot doesn’t waste your precious computing resources:

session.set_relationship_bounds(enabled=True, max_followers=8500)

With this, your bot won’t interact with posts by users who have more than 8,500 followers.

For many more features and configurations in InstaPy, check out the documentation.


InstaPy allows you to automate your Instagram activities with minimal fuss and effort. It’s a very flexible tool with a lot of useful features.

In this tutorial, you learned:

  • How Instagram bots work
  • How to automate a browser with Selenium
  • How to use the Page Object Pattern to make your code more maintainable and testable
  • How to use InstaPy to build a basic Instagram bot

Read the InstaPy documentation and experiment with your bot a little bit. Soon you’ll start getting new followers and likes with a minimal amount of effort. I gained a few new followers myself while writing this tutorial.

Automating Instagram API with Python

Instagram bot with Python

Gain active followers - Algorithm

Maybe some of you do not agree it is a good way to grow your IG page by using follow for follow method but after a lot of researching I found the proper way to use this method.

I have done and used this strategy for a while and my page visits also followers started growing.

The majority of people failing because they randomly targeting the followers and as a result, they are not coming back to your page. So, the key is to find people those have same interests with you.

If you have a programming page go and search for IG pages which have big programming community and once you find one, don’t send follow requests to followers of this page. Because some of them are not active even maybe fake accounts. So, in order to gain active followers, go the last post of this page and find people who liked the post.

Unofficial Instagram API

In order to query data from Instagram I am going to use the very cool, yet unofficial, Instagram API written by Pasha Lev.

**Note:**Before you test it make sure you verified your phone number in your IG account.

The program works pretty well so far but in case of any problems I have to put disclaimer statement here:

Disclaimer: This post published educational purposes only as well as to give general information about Instagram API. I am not responsible for any actions and you are taking your own risk.

Let’s start by installing and then logging in with API.

pip install InstagramApi

from InstagramAPI import InstagramAPI

api = InstagramAPI("username", "password")

Once you run the program you will see “Login success!” in your console.

Get users from liked list

We are going to search for some username (your target page) then get most recent post from this user. Then, get users who liked this post. Unfortunately, I can’t find solution how to paginate users so right now it gets about last 500 user.

users_list = []

def get_likes_list(username):
    result = api.LastJson
    username_id = result['user']['pk'] # Get user ID
    user_posts = api.getUserFeed(username_id) # Get user feed
    result = api.LastJson
    media_id = result['items'][0]['id'] # Get most recent post
    api.getMediaLikers(media_id) # Get users who liked
    users = api.LastJson['users']
    for user in users: # Push users to list
        users_list.append({'pk':user['pk'], 'username':user['username']})

Follow Users

Once we get the users list, it is time to follow these users.

IMPORTANT NOTE: set time limit as much as you can to avoid automation detection.

from time import sleep

following_users = []

def follow_users(users_list):
    api.getSelfUsersFollowing() # Get users which you are following
    result = api.LastJson
    for user in result['users']:
    for user in users_list:
        if not user['pk'] in following_users: # if new user is not in your following users                   
            print('Following @' + user['username'])
            # after first test set this really long to avoid from suspension
            print('Already following @' + user['username'])

Unfollow Users

This function will look users which you are following then it will check if this user follows you as well. If user not following you then you are unfollowing as well.

follower_users = []

def unfollow_users():
    api.getSelfUserFollowers() # Get your followers
    result = api.LastJson
    for user in result['users']:
        follower_users.append({'pk':user['pk'], 'username':user['username']})

    api.getSelfUsersFollowing() # Get users which you are following
    result = api.LastJson
    for user in result['users']:
    for user in following_users:
        if not user['pk'] in follower_users: # if the user not follows you
            print('Unfollowing @' + user['username'])
            # set this really long to avoid from suspension

Full Code with extra functions

Here is the full code of this automation

import pprint
from time import sleep
from InstagramAPI import InstagramAPI
import pandas as pd

users_list = []
following_users = []
follower_users = []

class InstaBot:

    def __init__(self):
        self.api = InstagramAPI("your_username", "your_password")

    def get_likes_list(self,username):
        api = self.api
        api.searchUsername(username) #Gets most recent post from user
        result = api.LastJson
        username_id = result['user']['pk']
        user_posts = api.getUserFeed(username_id)
        result = api.LastJson
        media_id = result['items'][0]['id']

        users = api.LastJson['users']
        for user in users:
            users_list.append({'pk':user['pk'], 'username':user['username']})

    def follow_users(self,users_list):
        api = self.api
        result = api.LastJson
        for user in result['users']:
        for user in users_list:
            if not user['pk'] in following_users:
                print('Following @' + user['username'])
                # set this really long to avoid from suspension
                print('Already following @' + user['username'])

     def unfollow_users(self):
        api = self.api
        result = api.LastJson
        for user in result['users']:
            follower_users.append({'pk':user['pk'], 'username':user['username']})

        result = api.LastJson
        for user in result['users']:

        for user in following_users:
            if not user['pk'] in [user['pk'] for user in follower_users]:
                print('Unfollowing @' + user['username'])
                # set this really long to avoid from suspension

bot =  InstaBot()
# To follow users run the function below
# change the username ('instagram') to your target username

# To unfollow users uncomment and run the function below
# bot.unfollow_users()

it will look like this:

Reverse Python

some extra functions to play with API:

def get_my_profile_details():
    result = api.LastJson
    username = result['user']['username']
    full_name = result['user']['full_name']
    profile_pic_url = result['user']['profile_pic_url']
    followers = result['user']['follower_count']
    following = result['user']['following_count']
    media_count = result['user']['media_count']
    df_profile = pd.DataFrame(
        'full name': full_name,
        'profile picture URL':profile_pic_url,
        'media count': media_count,
        }, index=[0])
    df_profile.to_csv('profile.csv', sep='\t', encoding='utf-8')

def get_my_feed():
    image_urls = []
    result = api.LastJson
    # formatted_json_str = pprint.pformat(result)
    # print(formatted_json_str)
    if 'items' in result.keys():
        for item in result['items'][0:5]:
            if 'image_versions2' in item.keys():
                image_url = item['image_versions2']['candidates'][1]['url']

    df_feed = pd.DataFrame({
                'image URL':image_urls
    df_feed.to_csv('feed.csv', sep='\t', encoding='utf-8')

Building an Instagram Bot with Python and Selenium to Gain More Followers

This is image title

Let’s build an Instagram bot to gain more followers! — I know, I know. That doesn’t sound very ethical, does it? But it’s all justified for educational purposes.

Coding is a super power — we can all agree. That’s why I’ll leave it up to you to not abuse this power. And I trust you’re here to learn how it works. Otherwise, you’d be on GitHub cloning one of the countless Instagram bots there, right?

You’re convinced? — Alright, now let’s go back to unethical practices.

The Plan

So here’s the deal, we want to build a bot in Python and Selenium that goes on the hashtags we specify, likes random posts, then follows the posters. It does that enough — we get follow backs. Simple as that.

Here’s a pretty twisted detail though: we want to keep track of the users we follow so the bot can unfollow them after the number of days we specify.


So first things first, I want to use a database to keep track of the username and the date added. You might as well save/load from/to a file, but we want this to be ready for more features in case we felt inspired in the future.

So make sure you create a database (I named mine instabot — but you can name it anything you like) and create a table called followed_users within the database with two fields (username, date_added)

Remember the installation path. You’ll need it.

You’ll also need the following python packages:

  • selenium
  • mysql-connector

Getting down to it

Alright, so first thing we’ll be doing is creating settings.json. Simply a .json file that will hold all of our settings so we don’t have to dive into the code every time we want to change something.



  "db": {
    "host": "localhost",
    "user": "root",
    "pass": "",
    "database": "instabot"
  "instagram": {
    "user": "",
    "pass": ""
  "config": {
    "days_to_unfollow": 1,
    "likes_over": 150,
    "check_followers_every": 3600,
    "hashtags": []

As you can see, under “db”, we specify the database information. As I mentioned, I used “instabot”, but feel free to use whatever name you want.

You’ll also need to fill Instagram info under “instagram” so the bot can login into your account.

“config” is for our bot’s settings. Here’s what the fields mean:

days_to_unfollow: number of days before unfollowing users

likes_over: ignore posts if the number of likes is above this number

check_followers_every: number of seconds before checking if it’s time to unfollow any of the users

hashtags: a list of strings with the hashtag names the bot should be active on


Now, we want to take these settings and have them inside our code as constants.


import json

def init():
    # read file
    data = None
    with open('settings.json', 'r') as myfile:
        data =
    obj = json.loads(data)
    INST_USER = obj['instagram']['user']
    INST_PASS = obj['instagram']['pass']
    USER = obj['db']['user']
    HOST = obj['db']['host']
    PASS = obj['db']['pass']
    DATABASE = obj['db']['database']
    LIKES_LIMIT = obj['config']['likes_over']
    CHECK_FOLLOWERS_EVERY = obj['config']['check_followers_every']
    HASHTAGS = obj['config']['hashtags']
    DAYS_TO_UNFOLLOW = obj['config']['days_to_unfollow']

the init() function we created reads the data from settings.json and feeds them into the constants we declared.


Alright, time for some architecture. Our bot will mainly operate from a python script with an init and update methods. Create

import Constants

def init(webdriver):

def update(webdriver):

We’ll be back later to put the logic here, but for now, we need an entry point.

Entry Point

Create our entry point,

from selenium import webdriver
import BotEngine

chromedriver_path = 'YOUR CHROMEDRIVER PATH' 
webdriver = webdriver.Chrome(executable_path=chromedriver_path)



chromedriver_path = ‘YOUR CHROMEDRIVER PATH’ webdriver = webdriver.Chrome(executable_path=chromedriver_path)



Of course, you’ll need to swap “YOUR CHROMEDRIVER PATH” with your actual ChromeDriver path.

Time Helper

We need to create a helper script that will help us calculate elapsed days since a certain date (so we know if we should unfollow user)


import datetime

def days_since_date(n):
    diff = - n
    return diff.days


Create It’ll contain a class that handles connecting to the Database for us.

import mysql.connector
import Constants
class DBHandler:
    def __init__(self):
        DBHandler.HOST = Constants.HOST
        DBHandler.USER = Constants.USER
        DBHandler.DBNAME = Constants.DATABASE
        DBHandler.PASSWORD = Constants.PASS
    HOST = Constants.HOST
    USER = Constants.USER
    DBNAME = Constants.DATABASE
    PASSWORD = Constants.PASS
    def get_mydb():
        if DBHandler.DBNAME == '':
        db = DBHandler()
        mydb = db.connect()
        return mydb

    def connect(self):
        mydb = mysql.connector.connect(
            database = DBHandler.DBNAME
        return mydb

As you can see, we’re using the constants we defined.

The class contains a static method get_mydb() that returns a database connection we can use.

Now, let’s define a DB user script that contains the DB operations we need to perform on the user.


import datetime, TimeHelper
from DBHandler import *
import Constants

#delete user by username
def delete_user(username):
    mydb = DBHandler.get_mydb()
    cursor = mydb.cursor()
    sql = "DELETE FROM followed_users WHERE username = '{0}'".format(username)

#add new username
def add_user(username):
    mydb = DBHandler.get_mydb()
    cursor = mydb.cursor()
    now =
    cursor.execute("INSERT INTO followed_users(username, date_added) VALUES(%s,%s)",(username, now))

#check if any user qualifies to be unfollowed
def check_unfollow_list():
    mydb = DBHandler.get_mydb()
    cursor = mydb.cursor()
    cursor.execute("SELECT * FROM followed_users")
    results = cursor.fetchall()
    users_to_unfollow = []
    for r in results:
        d = TimeHelper.days_since_date(r[1])
        if d > Constants.DAYS_TO_UNFOLLOW:
    return users_to_unfollow

#get all followed users
def get_followed_users():
    users = []
    mydb = DBHandler.get_mydb()
    cursor = mydb.cursor()
    cursor.execute("SELECT * FROM followed_users")
    results = cursor.fetchall()
    for r in results:

    return users

Account Agent

Alright, we’re about to start our bot. We’re creating a script called that will contain the agent behavior.

Import some modules, some of which we need for later and write a login function that will make use of our webdriver.

Notice that we have to keep calling the sleep function between actions. If we send too many requests quickly, the Instagram servers will be alarmed and will deny any requests you send.

from time import sleep
import datetime
import DBUsers, Constants
import traceback
import random

def login(webdriver):
    #Open the instagram login page
    #sleep for 3 seconds to prevent issues with the server
    #Find username and password fields and set their input using our constants
    username = webdriver.find_element_by_name('username')
    password = webdriver.find_element_by_name('password')
    #Get the login button
        button_login = webdriver.find_element_by_xpath(
        button_login = webdriver.find_element_by_xpath(
    #sleep again
    #click login
    #In case you get a popup after logging in, press not now.
    #If not, then just return
        notnow = webdriver.find_element_by_css_selector(
            'body > div.RnEpo.Yx5HN > div > div > div.mt3GC > button.aOOlW.HoLwm')

Also note how we’re getting elements with their xpath. To do so, right click on the element, click “Inspect”, then right click on the element again inside the inspector, and choose Copy->Copy XPath.

Another important thing to be aware of is that element hierarchy change with the page’s layout when you resize or stretch the window. That’s why we’re checking for two different xpaths for the login button.

Now go back to, we’re ready to login.

Add more imports that we’ll need later and fill in the init function

import AccountAgent, DBUsers
import Constants
import datetime

def init(webdriver):

def update(webdriver):

If you run our entry script now ( you’ll see the bot logging in.

Perfect, now let’s add a method that will allow us to follow people to

def follow_people(webdriver):
    #all the followed user
    prev_user_list = DBUsers.get_followed_users()
    #a list to store newly followed users
    new_followed = []
    followed = 0
    likes = 0
    #Iterate theough all the hashtags from the constants
    for hashtag in Constants.HASHTAGS:
        #Visit the hashtag
        webdriver.get('' + hashtag+ '/')

        #Get the first post thumbnail and click on it
        first_thumbnail = webdriver.find_element_by_xpath(

            #iterate over the first 200 posts in the hashtag
            for x in range(1,200):
                t_start =
                #Get the poster's username
                username = webdriver.find_element_by_xpath('/html/body/div[3]/div[2]/div/article/header/div[2]/div[1]/div[1]/h2/a').text
                likes_over_limit = False
                    #get number of likes and compare it to the maximum number of likes to ignore post
                    likes = int(webdriver.find_element_by_xpath(
                    if likes > Constants.LIKES_LIMIT:
                        print("likes over {0}".format(Constants.LIKES_LIMIT))
                        likes_over_limit = True

                    print("Detected: {0}".format(username))
                    #If username isn't stored in the database and the likes are in the acceptable range
                    if username not in prev_user_list and not likes_over_limit:
                        #Don't press the button if the text doesn't say follow
                        if webdriver.find_element_by_xpath('/html/body/div[3]/div[2]/div/article/header/div[2]/div[1]/div[2]/button').text == 'Follow':
                            #Use DBUsers to add the new user to the database
                            #Click follow
                            followed += 1
                            print("Followed: {0}, #{1}".format(username, followed))

                        # Liking the picture
                        button_like = webdriver.find_element_by_xpath(

                        likes += 1
                        print("Liked {0}'s post, #{1}".format(username, likes))
                        sleep(random.randint(5, 18))

                    # Next picture
                    sleep(random.randint(20, 30))
                t_end =

                #calculate elapsed time
                t_elapsed = t_end - t_start
                print("This post took {0} seconds".format(t_elapsed.total_seconds()))


        #add new list to old list
        for n in range(0, len(new_followed)):
        print('Liked {} photos.'.format(likes))
        print('Followed {} new people.'.format(followed))

It’s pretty long, but generally here’s the steps of the algorithm:

For every hashtag in the hashtag constant list:

  • Visit the hashtag link
  • Open the first thumbnail
  • Now, execute the following code 200 times (first 200 posts in the hashtag)
  • Get poster’s username, check if not already following, follow, like the post, then click next
  • If already following just click next quickly

Now we might as well implement the unfollow method, hopefully the engine will be feeding us the usernames to unfollow in a list:

def unfollow_people(webdriver, people):
    #if only one user, append in a list
    if not isinstance(people, (list,)):
        p = people
        people = []

    for user in people:
            webdriver.get('' + user + '/')
            unfollow_xpath = '//*[@id="react-root"]/section/main/div/header/section/div[1]/div[1]/span/span[1]/button'

            unfollow_confirm_xpath = '/html/body/div[3]/div/div/div[3]/button[1]'

            if webdriver.find_element_by_xpath(unfollow_xpath).text == "Following":
                sleep(random.randint(4, 15))

        except Exception:

Now we can finally go back and finish the bot by implementing the rest of

import AccountAgent, DBUsers
import Constants
import datetime

def init(webdriver):

def update(webdriver):
    #Get start of time to calculate elapsed time later
    start =
    #Before the loop, check if should unfollow anyone
    while True:
        #Start following operation
        #Get the time at the end
        end =
        #How much time has passed?
        elapsed = end - start
        #If greater than our constant to check on
        #followers, check on followers
        if elapsed.total_seconds() >= Constants.CHECK_FOLLOWERS_EVERY:
            #reset the start variable to now
            start =
            #check on followers

def _check_follow_list(webdriver):
    print("Checking for users to unfollow")
    #get the unfollow list
    users = DBUsers.check_unfollow_list()
    #if there's anyone in the list, start unfollowing operation
    if len(users) > 0:
        AccountAgent.unfollow_people(webdriver, users)


And that’s it — now you have yourself a fully functional Instagram bot built with Python and Selenium. There are many possibilities for you to explore now, so make sure you’re using this newly gained skill to solve real life problems!

You can get the source code for the whole project from this GitHub repository.

Building a simple Instagram bot with Python tutorial

Here we build a simple bot using some simple Python which beginner to intermediate coders can follow.

Here’s the code on GitHub

Build A (Full-Featured) Instagram Bot With Python

Source Code: 

How to Get Instagram Followers/Likes Using Python

In this video I show you how to program your own Instagram Bot using Python and Selenium. 

Code Link:

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time
import random
import sys

def print_same_line(text):

class InstagramBot:

    def __init__(self, username, password):
        self.username = username
        self.password = password
        self.driver = webdriver.Chrome()

    def closeBrowser(self):

    def login(self):
        driver = self.driver
        login_button = driver.find_element_by_xpath("//a[@href='/accounts/login/?source=auth_switcher']")
        user_name_elem = driver.find_element_by_xpath("//input[@name='username']")
        passworword_elem = driver.find_element_by_xpath("//input[@name='password']")

    def like_photo(self, hashtag):
        driver = self.driver
        driver.get("" + hashtag + "/")

        # gathering photos
        pic_hrefs = []
        for i in range(1, 7):
                driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
                # get tags
                hrefs_in_view = driver.find_elements_by_tag_name('a')
                # finding relevant hrefs
                hrefs_in_view = [elem.get_attribute('href') for elem in hrefs_in_view
                                 if '.com/p/' in elem.get_attribute('href')]
                # building list of unique photos
                [pic_hrefs.append(href) for href in hrefs_in_view if href not in pic_hrefs]
                # print("Check: pic href length " + str(len(pic_hrefs)))
            except Exception:

        # Liking photos
        unique_photos = len(pic_hrefs)
        for pic_href in pic_hrefs:
            driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
                time.sleep(random.randint(2, 4))
                like_button = lambda: driver.find_element_by_xpath('//span[@aria-label="Like"]').click()
                for second in reversed(range(0, random.randint(18, 28))):
                    print_same_line("#" + hashtag + ': unique photos left: ' + str(unique_photos)
                                    + " | Sleeping " + str(second))
            except Exception as e:
            unique_photos -= 1

if __name__ == "__main__":

    username = "USERNAME"
    password = "PASSWORD"

    ig = InstagramBot(username, password)

    hashtags = ['amazing', 'beautiful', 'adventure', 'photography', 'nofilter',
                'newyork', 'artsy', 'alumni', 'lion', 'best', 'fun', 'happy',
                'art', 'funny', 'me', 'followme', 'follow', 'cinematography', 'cinema',
                'love', 'instagood', 'instagood', 'followme', 'fashion', 'sun', 'scruffy',
                'street', 'canon', 'beauty', 'studio', 'pretty', 'vintage', 'fierce']

    while True:
            # Choose a random tag from the list of tags
            tag = random.choice(hashtags)
        except Exception:
            ig = InstagramBot(username, password)

Build An INSTAGRAM Bot With Python That Gets You Followers

Instagram Automation Using Python

How to Create an Instagram Bot | Get More Followers

Building a simple Instagram Influencer Bot with Python tutorial

#python #chatbot #web-development

Shubham Ankit

Shubham Ankit


How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)

How to Automate Excel with Python

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation


Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

Sheet: A sheet is a single page composed of cells for organizing data.

Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

Row: A row is a horizontal line represented by a number (1,2, etc.).

Column: A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

pip install openpyxl


We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.

from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws =
#creating new worksheets by using the create_sheet method

ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position

#Renaming the sheet
ws.title = "Example"

#save the workbook = "example.xlsx")


We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.

#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")




#getting sheet names
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']

#getting a particular sheet
sheet1 = wb["sheet2"]

#getting sheet title
result = 'sheet2'

#Getting the active sheet
sheetactive =
result = 'sheet1'




#get a cell from the sheet
sheet1["A1"] <
  Cell 'Sheet1'.A1 >

  #get the cell value
ws["A1"].value 'Segment'

#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)




#looping through each row and column
for x in range(1, 5):
  for y in range(1, 5):
  print(x, y, ws.cell(row = x, column = y)

#getting the highest row number

#getting the highest column number

There are two functions for iterating through rows and columns.

Iter_rows() => returns the rows
Iter_cols() => returns the columns {
  min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.


#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
  for cell in row:
  print(cell) <
  Cell 'Sheet1'.A2 >
  Cell 'Sheet1'.B2 >
  Cell 'Sheet1'.C2 >
  Cell 'Sheet1'.A3 >
  Cell 'Sheet1'.B3 >
  Cell 'Sheet1'.C3 >

  #iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
  for cell in col:
  print(cell) <
  Cell 'Sheet1'.A2 >
  Cell 'Sheet1'.A3 >
  Cell 'Sheet1'.B2 >
  Cell 'Sheet1'.B3 >
  Cell 'Sheet1'.C2 >
  Cell 'Sheet1'.C3 >

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.


for row in ws.values:
  for value in row:



Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.




#creates a new workbook
wb = openpyxl.Workbook()

#saving the workbook"new.xlsx")




#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")

#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")

#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']

#deleting a sheet
del wb['sheet 0']

#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']




#checking the sheet value

#adding value to cell
ws['B2'] = 367

#checking value




We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.

For example:

import openpyxl
from openpyxl
import Workbook

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']

ws['A9'] = '=SUM(A2:A8)'"new2.xlsx")

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.




Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

For example:
Merge cells

#merge cells B2 to C9
ws['B2'] = "Merged cells"

Adding the above code to the previous example will merge cells as below.




#unmerge cells B2 to C9

The above code will unmerge cells from B2 to C9.


To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.


import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3

ws.add_image(img, 'A3')"new2.xlsx")




Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:


import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series

wb = openpyxl.load_workbook("example.xlsx")
ws =

values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
ws.add_chart(chart, "E3")"MyChart.xlsx")


How to Automate Excel with Python with Video Tutorial

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling

📄 Resources 📄
OpenPyXL Docs: 
Code Written in This Tutorial: 


Bongani  Ngema

Bongani Ngema


How to Create & Add Content - Images, Text To Modern SharePoint Pages


Requirement is to create Modern pages with content, which includes images and text. 

The Content is in SharePoint List. The pages are created from a Page Template.

To get Text part from Page template, use below PowerShell,

#get page textpart instance id
$parts=Get-PnPPageComponent -Page <pagename.aspx>

Execute the below PowerShell to create pages with HTML content from SharePoint List.

$logFile = "Logs\LogFile.log"
Start - Transcript - Path $logFile - Append
$libName = "Site Pages"
$siteURL = ""
$contentType = "Group and Division Page"
$listname = "Content"
$sectionCategoy = "Our organisation"
Try {
    #Connect to PnP Online
    $connection = Connect - PnPOnline - Url $siteURL - UseWebLogin - ReturnConnection - WarningAction Ignore
    #Get items from Content list
    $items = Get - PnPListItem - List $listName - PageSize 100
    foreach($item in $items) {
        if ($null - ne $item["Title"] - and $null - ne $item["Content"]) {
            #Get Page webparts instance Id
            #$parts = Get - PnPPageComponent - Page PageTemplate.aspx
            # load the page template
            $template = Get - PnPClientSidePage - Identity "Templates/Division-page-template"
            #Get page name
            $fullFileName = $item["Title"].Replace(" ", "_") + ".aspx"
            #Create fileURL
            $fileURL = $siteURL + $libName + "/" + $fullFileName
            # save a new SharePoint Page based on the Page Template
            $page = Get - PnPPage - Identity $fullFileName
            $htmlToInject = $item["Content"]
            $htmlToInject = $htmlToInject.TrimStart('{"Html":"').TrimEnd('"}') - replace([regex]::Escape('\n')), '' - replace([regex]::Escape('<a href=\')),' < a href = ' -replace ([regex]:: Escape('\
                        ">')),'" > ' -replace ([regex]::Escape(' & bull; % 09 ')),'
                        ' -replace '
            #Set PnP Page Text

            Set-PnPPageTextPart -Page $page -InstanceId "9fab3ce6-0638-4008-a9b9-cf2b784245b5" -Text $htmlToInject

            #publish page
            Set-PnPPage -Identity $fullFileName -Title $item["Title"] -ContentType $contentType -Publish

            #get site pages library
            $sitepagelist= Get-PnPList -Identity 'Site Pages'
            #get page Id and page Item to update section category
            $pageItem=Get-PnPListItem -List $sitepagelist -Id $page.PageId
            Set-PnPListItem -Values @{"SectionCategory" = $sectionCategoy} -List $sitepagelist -Identity $pageItem

            Write-Host "Title or Content has no value"
Catch {
    Write-Host "Error: $($_.Exception.Message)" -Foregroundcolor Red

Original article source at:

#sharepoint #image #text 

Wilford  Pagac

Wilford Pagac


Best Custom Web & Mobile App Development Company

Everything around us has become smart, like smart infrastructures, smart cities, autonomous vehicles, to name a few. The innovation of smart devices makes it possible to achieve these heights in science and technology. But, data is vulnerable, there is a risk of attack by cybercriminals. To get started, let’s know about IoT devices.

What are IoT devices?

The Internet Of Things(IoT) is a system that interrelates computer devices like sensors, software, and actuators, digital machines, etc. They are linked together with particular objects that work through the internet and transfer data over devices without humans interference.

Famous examples are Amazon Alexa, Apple SIRI, Interconnected baby monitors, video doorbells, and smart thermostats.

How could your IoT devices be vulnerable?

When technologies grow and evolve, risks are also on the high stakes. Ransomware attacks are on the continuous increase; securing data has become the top priority.

When you think your smart home won’t fudge a thing against cybercriminals, you should also know that they are vulnerable. When cybercriminals access our smart voice speakers like Amazon Alexa or Apple Siri, it becomes easy for them to steal your data.

Cybersecurity report 2020 says popular hacking forums expose 770 million email addresses and 21 million unique passwords, 620 million accounts have been compromised from 16 hacked websites.

The attacks are likely to increase every year. To help you secure your data of IoT devices, here are some best tips you can implement.

Tips to secure your IoT devices

1. Change Default Router Name

Your router has the default name of make and model. When we stick with the manufacturer name, attackers can quickly identify our make and model. So give the router name different from your addresses, without giving away personal information.

2. Know your connected network and connected devices

If your devices are connected to the internet, these connections are vulnerable to cyber attacks when your devices don’t have the proper security. Almost every web interface is equipped with multiple devices, so it’s hard to track the device. But, it’s crucial to stay aware of them.

3. Change default usernames and passwords

When we use the default usernames and passwords, it is attackable. Because the cybercriminals possibly know the default passwords come with IoT devices. So use strong passwords to access our IoT devices.

4. Manage strong, Unique passwords for your IoT devices and accounts

Use strong or unique passwords that are easily assumed, such as ‘123456’ or ‘password1234’ to protect your accounts. Give strong and complex passwords formed by combinations of alphabets, numeric, and not easily bypassed symbols.

Also, change passwords for multiple accounts and change them regularly to avoid attacks. We can also set several attempts to wrong passwords to set locking the account to safeguard from the hackers.

5. Do not use Public WI-FI Networks

Are you try to keep an eye on your IoT devices through your mobile devices in different locations. I recommend you not to use the public WI-FI network to access them. Because they are easily accessible through for everyone, you are still in a hurry to access, use VPN that gives them protection against cyber-attacks, giving them privacy and security features, for example, using Express VPN.

6. Establish firewalls to discover the vulnerabilities

There are software and firewalls like intrusion detection system/intrusion prevention system in the market. This will be useful to screen and analyze the wire traffic of a network. You can identify the security weakness by the firewall scanners within the network structure. Use these firewalls to get rid of unwanted security issues and vulnerabilities.

7. Reconfigure your device settings

Every smart device comes with the insecure default settings, and sometimes we are not able to change these default settings configurations. These conditions need to be assessed and need to reconfigure the default settings.

8. Authenticate the IoT applications

Nowadays, every smart app offers authentication to secure the accounts. There are many types of authentication methods like single-factor authentication, two-step authentication, and multi-factor authentication. Use any one of these to send a one time password (OTP) to verify the user who logs in the smart device to keep our accounts from falling into the wrong hands.

9. Update the device software up to date

Every smart device manufacturer releases updates to fix bugs in their software. These security patches help us to improve our protection of the device. Also, update the software on the smartphone, which we are used to monitoring the IoT devices to avoid vulnerabilities.

10. Track the smartphones and keep them safe

When we connect the smart home to the smartphone and control them via smartphone, you need to keep them safe. If you miss the phone almost, every personal information is at risk to the cybercriminals. But sometimes it happens by accident, makes sure that you can clear all the data remotely.

However, securing smart devices is essential in the world of data. There are still cybercriminals bypassing the securities. So make sure to do the safety measures to avoid our accounts falling out into the wrong hands. I hope these steps will help you all to secure your IoT devices.

If you have any, feel free to share them in the comments! I’d love to know them.

Are you looking for more? Subscribe to weekly newsletters that can help your stay updated IoT application developments.

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