Getting Started with React Native

Originally published by Wern Ancheta at

With the ever-increasing popularity of smartphones, developers are looking into solutions for building mobile applications. For developers with a web background, frameworks such as Cordova and Ionic, React Native, NativeScript, and Flutter allow us to create mobile apps with languages we’re already familiar with: HTML, XML, CSS, and JavaScript.

In this guide, we’ll take a closer look at React Native. You’ll learn the absolute basics of getting started with it. Specifically, we’ll cover the following:

  • what is React Native
  • what is Expo
  • how to set up an React Native development environment
  • how to create an app with React Native


This tutorial assumes that you’re coming from a web development background. The minimum requirement for you to be able to confidently follow this tutorial is to know HTML, CSS, and JavaScript. You should also know how to install software on your operating system and work with the command line. We’ll also be using some ES6 syntax, so it would help if you know basic ES6 syntax as well. Knowledge of React is helpful but not required.

What is React Native?

React Native is a framework for building apps that work on both Android and iOS. It allows you to create real native apps using JavaScript and React. This differs from frameworks like Cordova, where you use HTML to build the UI and it will just be displayed within the device’s integrated mobile browser (WebView). React Native has built in components which are compiled to native UI components, while your JavaScript code is executed through a virtual machine. This makes React Native more performant than Cordova.

Another advantage of React Native is its ability to access native device features. There are many plugins which you can use to access native device features, such as the camera and various device sensors. If you’re in need of a platform-specific feature that hasn’t been implemented yet, you can also build your own native modules — although that will require you to have considerable knowledge of the native platform you want to support (Java or Kotlin for Android, and Objective C or Swift for iOS).

If you’re coming here and you’re new to React, you might be wondering what it is. React is a JavaScript library for the Web for building user interfaces. If you’re familiar with MVC, it’s basically the View in MVC. React’s main purpose is to allow developers to build reusable UI components. Examples of these components include buttons, sliders, and cards. React Native took the idea of building reusable UI components and brought it into mobile app development.

What is Expo?

Before coming here, you might have heard of Expo. It’s even recommended in the official React Native docs, so you might be wondering what it is.

In simple terms, Expo allows you to build React Native apps without the initial headache that comes with setting up your development environment. It only requires you to have Node installed on your machine, and the Expo client app on your device or emulator.

But that’s just how Expo is initially sold. In reality, it’s much more than that. Expo is actually a platform that gives you access to tools, libraries and services for building Android and iOS apps faster with React Native. Expo comes with an SDK which includes most of the APIs you can ask for in a mobile app development platform:

Those are just few of the APIs you get access to out of the box if you start building React Native apps with Expo. Of course, these APIs are available to you as well via native modules if you develop your app using the standard React Native setup.

Plain React Native or Expo?

The real question is which one to pick up — React Native or Expo? There’s really no right or wrong answer. It all depends on the context and what your needs are at the moment. But I guess it’s safe to assume that you’re reading this tutorial because you want to quickly get started with React Native. So I’ll go ahead and recommend that you start out with Expo. It’s fast, simple, and easy to set up. You can dive right into tinkering with React Native code and get a feel of what it has to offer in just a couple of hours.

That said, I’ve still included the detailed setup instructions for standard React Native for those who want to do it the standard way. As you begin to grasp the different concepts, and as the need for different native features arises, you’ll actually find that Expo is kind of limiting. Yes, it has a lot of native features available, but not all the native modules that are available to standard React Native projects are supported.

Note: projects like unimodules are beginning to close the gap between standard React Native projects and Expo projects, as it allows developers to create native modules that works for both React Native and ExpoKit.

Setting Up the React Native Development Environment

In this section, we’ll set up the React Native development environment for all three major platforms: Windows, Linux, and macOS. We’ll also cover how to set up the Android and iOS simulators. Lastly, we’ll cover how to set up Expo. If you just want to quickly get started, I recommend that you scroll down to the “Setting up Expo” section.

Here are the general steps for setting up the environment. Be sure to match these general steps to the steps for each platform:

  1. install JDK
  2. install Android Studio or Xcode
  3. install Watchman
  4. update the environment variable
  5. install the emulator
  6. install Node
  7. install React Native CLI

You can skip to the section relevant to your operating system. Some steps — like setting up Android Studio — are basically the same for each operating system, so I’ve put them in their own section:

  • setting up on Windows
  • setting up on Linux
  • setting up on macOS
  • setting up Android Studio
  • install Node
  • setting up Expo
  • setting up emulators
  • install React Native CLI
  • troubleshooting common errors

Setting Up on Windows

This section will show you how to install and configure the software needed to create React Native apps on Windows. Windows 10 was used in testing for this.

Install Chocolatey

Windows doesn’t really come with its own package manager that we can use to install the needed tools. So the first thing we’ll do is install one called Chocolatey. You can install it by executing the following command on the command line or Windows Powershell:

@"%SystemRoot%\System32\WindowsPowerShell\v1.0\powershell.exe" -NoProfile -InputFormat None -ExecutionPolicy Bypass -Command "iex ((New-Object System.Net.WebClient).DownloadString(''))" && SET "PATH=%PATH%;%ALLUSERSPROFILE%\chocolatey\bin"

We can now install the other tools we need by simply using Chocolatey.

Install Python

Python comes with the command line tools required by React Native:

choco install -y python 2 

Install JDK

The JDK allows your computer to understand and run Java code. Be sure to install JDK version 8 as that’s the one required by React Native:

choco install jdk8 

Install NVM

Node has an installer for Windows. It’s better to use NVM for Windows, as that will enable you to install multiple versions of Node so that you can test new versions, or use a different version depending on the project you’re currently working on. For that, you can use NVM for Windows. Download, extract it and execute nvm-setup.exe to install it.

Install Watchman

Watchman optimizes the compilation time of your React Native app. It’s an optional install if you’re not working on a large project. You can find the install instructions on their website.

Update the Environment Variables

This is the final step in setting up React Native on Windows. This is where we update the environment variables so the operating system is aware of all the tools required by React Native. Follow these steps right before you install the React Native CLI.

  • Go to Control PanelSystem and SecuritySystem. Once there, click the Advanced system settings menu on the left.

  • That will open the system properties window. Click on the Environment Variables button:

  • Under the User variables section, highlight the Path variable and click the edit button.
  • On the edit screen, click the New button and enter the path to the Android SDK and platform tools. For me, it’s on C:\users\myUsername\AppData\Local\Android\Sdk and C:\users\myUsername\AppData\Local\Android\Sdk\platform-tools. Note that this is also where you add the path to the JDK if it isn’t already added:

Setting Up on Linux

This section will show you how to install and configure the tools required for developing React Native apps on Linux. I’ve specifically used Ubuntu 18.04 for testing things out, but you should be able to translate the commands to the Linux distribution you’re using.

Install Prerequisite Tools

The first step is to install the following tools. The first line installs the tools required by Node, and the second line is required by Watchman, which we’ll also install later:

sudo apt-get install build-essential libssl-dev curl 
sudo apt-get install git autoconf automake python-dev 

Install NVM

NVM allows us to install and use multiple versions of Node. You can install it with the following commands:

curl -o- | bash
source ~/.profile

Note: be sure to check out the latest version from the releases page to ensure the NVM version you’re installing is updated.

Install JDK

As seen earlier, React Native actually compiles the corresponding code to each of the platforms you wish to deploy to. The JDK enables your computer to understand and run Java code. The specific JDK version required by React Native is JDK version 8.

sudo apt-get install openjdk-8-jre 

Install Watchman

Watchman is a tool for watching changes in the file system. It’s mainly used to speed up the compilation process. If you’ve enabled live preview on the app that you’re developing, the changes you make to the app will be reflected faster in the live preview. The following steps require Git to already be installed on your system:

git clone
cd watchman
git checkout v4.9.0
sudo make install

You may encounter an issue which looks like the following:

CXX      scm/watchman-Mercurial.o

scm/Mercurial.cpp: In constructor ‘watchman::Mercurial::infoCache::infoCache(std::__cxx11::string)’:

scm/Mercurial.cpp:16:40: error: ‘void* memset(void*, int, size_t)’ clearing an object of non-trivial type ‘struct watchman::FileInformation’; use assignment or value-initialization instead [-Werror=class-memaccess]

   memset(&dirstate, 0, sizeof(dirstate));


In file included from scm/Mercurial.h:10,

                 from scm/Mercurial.cpp:3:

./FileInformation.h:18:8: note: ‘struct watchman::FileInformation’ declared here

 struct FileInformation {


cc1plus: all warnings being treated as errors

make: *** [Makefile:4446: scm/watchman-Mercurial.o] Error 1

Try the following command instead:

./configure --without-python --without-pcre --enable-lenient

Update the Environment Variables

Updating the environment variables is necessary in order for the operating system to be aware of the tools you installed, so you can use them directly from the terminal. Note that this is the final step for setting up all the tools required by React Native. Follow this right before the step for installing the React Native CLI.

To update the environment variables, open your .bash_profile file:

sudo nano ~/.bash_profile

Add the following at the beginning then save the file:

export ANDROID_HOME=$HOME/Android/Sdk
export PATH=$PATH:$ANDROID_HOME/emulator
export PATH=$PATH:$ANDROID_HOME/tools/bin
export PATH=$PATH:$ANDROID_HOME/platform-tools

Note that the path above assumes that the Android SDK is installed on your user’s home directory:

echo $HOME

Setting up on macOS

Having a Mac allows you to develop both Android and iOS apps with React Native. In this section, I’ll show how you can set up the development environment for both Android and iOS.

Installing prerequisite tools

Since macOS already comes with Ruby and cURL by default, the only tool you need to install is Homebrew, a package manager for macOS:

/usr/bin/ruby -e “$(curl -fsSL”

If you already have it installed, simply update it with the following:

brew update

For iOS, the following are required:

  • Latest version of Xcode: installs the tools required for compiling iOS apps
  • Watchman: for watching file changes
  • NVM: for installing Node

Install JDK

Install JDK version 8 for macOS, as that’s the one required by React Native:

brew tap AdoptOpenJDK/openjdk
brew cask install adoptopenjdk8

Install Watchman

Watchman speeds up the compilation process of your source code. Installing it is required for both Android and iOS:

brew install watchman

Install NVM

NVM allows you to install multiple versions of Node for testing purposes:

brew install nvm
echo “source $(brew — prefix nvm)/” >> .bash_profile

Update Environment Variables

After you’ve installed all the required tools, and right before you install the React Native CLI, it’s time to update the environment variables. This is an important step, because without doing it, the operating system won’t be aware of the tools required by React Native.

To update it, open your .bash_profile file:

sudo nano ~/.bash_profile

Then add the path to the Android SDK and platform tools:

export ANDROID_HOME=/Users/$USER/Library/Android/sdk
export PATH=${PATH}:$ANDROID_HOME/tools:$ANDROID_HOME/platform-tools:$ANDROID_HOME/emulator

Setting up Android Studio

Android Studio is the easiest way to install the tools required for Android (Android SDK, Android Emulator) so it’s the method I usually recommend for beginners. You can download the installer for your specific platform here.

On Windows and macOS, it has a setup wizard which you can just run, clicking on Next until the install is complete. Be sure to select the .dmg file for macOS or the .exe file for Windows from here if the default download button downloads something different. You can use the following screenshots as a basis for your install.

If you’re on Linux, you need to follow these steps first before you can proceed to installing Android Studio:

  1. Download and extract the .tar.gz file:
tar -xzvf android-studio-ide-183.5522156-linux.tar.gz
  1. Navigate to the extracted directory and go inside the bin directory.
  2. Execute This opens up the installer for Android Studio:

The Setup Wizard will first greet you with the following screen:

Just click on Next until you see the screen below. The Android SDK, and the latest Android API version, are checked by default. You can also check the Android Virtual Device if you want. This installs the default Android emulator for testing your apps. But I generally recommend using Genymotion instead, as it comes with better tools for testing out different device features:

Once it has installed everything, it will show the following. Just click on Finish to close the Setup Wizard:

The next step is to open Android Studio to configure the SDK platform and tools:

Note: if you’ve previously installed Android Studio, you might have an existing project opened already. In that case, you can launch the SDK Manager from the ToolsSDK Manager top menu. Then check the Show Package Details at the bottom right. This way, you could choose only the sub-components instead of installing the whole thing.

Under the SDK Platforms tab, make sure that the following are checked:

  • Android 9.0 (Pie)
  • Android SDK Platform 28
  • Google APIs Intel x86 Atom_64 System Image or Intel x86 Atom_64 System Image

Under the SDK Tools tab, check the Show Package Details again and make sure that the following are checked:

  • Android SDK Build-Tools 29
  • 28.0.3
  • Android SDK Platform-Tools
  • Android SDK Tools
  • Android Support Repository
  • Google Repository

Check the following if you decided to install the Android Emulator:

  • Intel x86 Emulator Accelerator (HAXM installer)

That will optimize the emulator’s performance.

Install Node

Execute the following commands to install a specific version of Node and set it as the default:

nvm install 11.2.0
nvm alias default 11.2.0

Once installed, you can verify that it works by executing the following:

node --version
npm --version

Here’s what the output will look like:

Setting Up Expo

In this section, we’ll set up Expo, an alternative way of developing React Native apps. This section assumes that you already have Node and Watchman installed.

To set up Expo, all you have to do is install their command line tool via npm:

npm install -g expo-cli

That’s really all there is to it! The next step is to download the Expo client App for Android or iOS. Note that this is the only way you can run Expo apps while you’re still on development. Later on, you can build the standalone version.

From here, you can either proceed to the Hello World App section if you plan on running apps on your device, or the Setting up Emulators section if you want to run it on the Android Emulator or iOS Simulator.

Setting Up Emulators

Emulators allow you to test out apps you’re developing right from your development machine.

Note: emulators require your machine to have at least 4GB of RAM. Otherwise, they’ll really slow down your machine to the point where you get nothing done because of waiting for things to compile or load.

iOS Simulator

Xcode already comes pre-installed with iOS simulators, so all you have to do is launch it before you run your apps. To launch an iOS simulator from the command line, you first have to list the available simulators:

xcrun simctl list

Take note of the device UUID of the device you wish to run and substitute it with the value of UUID of the command below:

 open -a Simulator --args -CurrentDeviceUDID “UUID”


As mentioned earlier, I recommend Genymotion as the emulator for Android, as it has more device features that you can test out — for example, when testing out apps that make use of GPS. Genymotion allows you to select a specific place via a map interface:

To install Genymotion, you first have to download and install VirtualBox. Once that’s done, sign up for a Genymotion account, log in, and download the installer. Windows and macOS come with a corresponding installer. But for Linux, you have to download the installer and make it executable:

chmod +x genymotion-<version><arch>.bin

After that, you can now run the installer:


Once it’s done installing, you should be able to search for it on your launcher.

Android Emulator

Even though Genymotion is the first thing I recommend, I believe that Android Emulator has its merits as well. For example, it boots up faster and it feels faster in general. I recommend it if your machine has lower specs or if you have no need for Genymotion’s additional features.

When you launch Android Studio, you can select AVD Manager from the configuration options (or ToolsAVD Manager if you currently have an open project).

Click on the Create Virtual Device button on the window that shows up. It will then ask you to choose the device you wish to install:

You can choose any that you want, but ideally, you want to have the ones which already have a Play Store included. It will be especially useful if your app integrates with Google Sign in or other apps, as it will allow you to install those apps with ease.

Next, it will ask you to download the version of Android you wish to install on the device. Simply select the latest version of Android that’s supported by React Native. At the time of writing this tutorial, it’s Android Pie. Note that this is also the same version that we installed for the Android SDK Platform earlier:

Once installed, click Finish to close the current window then click on Next once you see this screen:

Click on Finish on the next screen to create the emulator. It will then be listed on Android Virtual Device Manager. Click on the play button next to the emulator to launch it.

Install React Native CLI

The final step is to install the React Native CLI. This is the command line tool that allows you to bootstrap a new React Native project, link native dependencies, and run the app on a device or emulator:

npm install -g react-native-cli

Once it’s installed, you can try creating a new project and run it on your device or emulator:

react-native init HelloWorldApp
cd HelloWorldApp
react-native run-android
react-native run-ios

Here’s what the app will look like by default:

At this point, you now have a fully functional React Native development environment set up.

Troubleshooting Common Errors

In this section, we’ll look at the most common errors you may encounter when trying to set up your environment.

Could Not Find tools.jar

You may get the following error:

Could not find tools.jar. Please check that /usr/lib/jvm/java-8-openjdk-amd64 contains a valid JDK installation

This means that the system doesn’t recognize your JDK installation. The solution is to re-install it:

sudo apt-get install openjdk-8-jdk

SDK Location Not Found

You may get the following error when you run your app:

FAILURE: Build failed with an exception.

  • What went wrong:

A problem occurred configuring project ‘:app’.

> SDK location not found. Define location with sdk.dir in the file or with an ANDROID_HOME environment variable.

 This means that you haven’t properly added the path to all of the Android tools required by React Native. You can check it by executing the following:

echo $PATH

It should show the following path:


If not, then you have to edit either your .bashrc or .bash_profile file to add the missing path. The config below adds the path to the platform tools:

sudo nano ~/.bash_profile
export PATH=$PATH:$ANDROID_HOME/tools:$ANDROID_HOME/platform-tools

Unable to Find Utility “instruments”

If you’re developing for iOS, you might encounter the following error when you try to run the app:

Found Xcode project TestProject.xcodeproj
xcrun: error: unable to find utility “instruments”, not a developer tool or in PATH

The problem is that Xcode command line tools aren’t installed yet. You can install them with the following command:

xcode-select --install

“Hello World” App

Now that your development environment is set up, you can start creating the obligatory “hello world” app. The app you’re going to create is a Pokemon search app. It will allow the user to type the name of a Pokemon and view its details.

Here’s what the final output will look like:

You can find the source code on this GitHub repo.

Bootstrapping the App

On your terminal, execute the following command to create a new React Native project:

react-native init RNPokeSearch

For those of you who decided to use Expo instead, here’s the equivalent command for bootstrapping a new React Native project on Expo. Under Managed Workflow, select blank, enter “RNPokeSearch” for the project name, and install dependencies using Yarn:

expo init RNPokeSearch

Just like in the web environment, you can install libraries to easily implement different kinds of functionality in React Native. Once the project is created, we need to install a couple of dependencies: pokemon and axios. The former is used for verifying if the text entered in the search box is a real Pokemon name, while axios is used to make an HTTP request to the API that we’re using: PokeAPI:

yarn add pokemon axios

Note that the above command works on both standard React Native projects and Expo’s managed workflow, since they don’t have any native dependencies. If you’re using Expo’s managed workflow, you won’t be able to use packages that have native dependencies.

React Native Project Directory Structure

Before we proceed to coding, let’s first take a look at the directory structure of a standard React Native project:

Here’s a break down of the most important files and folders that you need to remember:

  • App.js: the main project file. This is where you’ll start developing your app. Any changes you make to this file will be reflected on the screen.
  • index.js: the entry point file of any React Native project. This is responsible for registering the App.js file as the main component.
  • package.json: where the name and versions of the libraries you installed for this project are added.
  • node_modules: where the libraries you installed are stored. Note that this already contains a lot of folders before you installed the two libraries earlier. This is because React Native also has its own dependencies. The same is true for all the other libraries you install.
  • src: acts as the main folder which stores all the source code related to the app itself. Note that this is only a convention. The name of this folder can be anything. Some people use app as well.
  • android: where the Android-related code is. React Native isn’t a native language. That’s why we need this folder to bootstrap the Android app.
  • ios: where the iOS-related code is. This accomplishes the same thing as the android folder, but for iOS.

Don’t mind the rest of the folders and files for now, as we won’t be needing them when just getting started.

For Expo users, your project directory will look like this:

As you can see, it’s pretty much the same. The only difference is that there’s no android and ios folders. This is because Expo takes care of running the app for you on both platforms. There’s also the addition of the assets folder. This is where app assets such as icons and splash screens are stored.

Running the App

At this point, you can now run the app. Be sure to connect your device to your computer, or open your Android emulator or iOS simulator before doing so:

react-native run-android
react-native run-ios

You already saw what the default screen looks like earlier.

If you’re on Expo, you can run the project with the following command. Be sure you’ve already installed the corresponding Expo client for your phone’s operating system before doing so:

yarn start

Once it’s running, it will display the QR code:

Open your Expo client app, and in the projects tab click on Scan QR Code. This will open the app on your Android or iOS device. If you have an emulator running, you can either press i to run it on the iOS simulator or a to run it on the Android emulator:

If you’re testing on a real device, shake it so the developer menu will show up:

Click on the following:

  • Enable Live Reload: automatically reloads your app when you hit save on any of its source code.
  • Start Remote JS Debugging: for debugging JavaScript errors on the browser. You can also use react-native log-android or react-native log-ios for this, but remote debugging has a nicer output, so it’s easier to inspect.

If you want, you can also set the debug server. This allows you to disconnect your mobile device from your computer while you develop the app. You can do that by selecting Dev Settings in the developer menu. Then under the Debugging section, select Debug server host & port for device. This opens up a prompt where you can enter your computer’s internal IP address and the port where Metro Bundler runs on (usually port 8081).

Once you’ve set that, select Reload from the developer menu to commit the changes. At this point, you can now disconnect your device from the computer. Note that any time you install a package, you have to connect your device, quit Metro Bundler, and run the app again (using react-native run-android or react-native run-ios). That’s the only way for the changes to take effect.

Coding the App

Both standard React Native projects and Expo have built-in components which you can use to accomplish what you want. Simply dig through the documentation and you’ll find information on how to implement what you need. In most cases, you either need a specific UI component or an SDK which works with a service you plan on using. You can use Native Directory to look for those or just plain old Google. More often than not, here’s what your workflow is going to look like:

  1. Look for an existing package which implements what you want.
  2. Install it.
  3. Link it — only for native modules. If you’re on Expo, you don’t really need to do this because you can only install pure JavaScript libraries — although this might change soon because of the introduction of unimodules and bare workflow.
  4. Use it on your project.

Now that you’ve set up your environment and learned a bit about the workflow, we’re ready to start coding the app.

Start by replacing the contents of the App.js file with the following code:

import React from ‘react’;

import Main from ‘./src/Main’;


function App() {


  return <Main />




export default App;

The first line in the code above code imports React. You need to import this class any time you want create a component.

The second line is where we import a custom component called Main. We’ll create it later. For now, know that this is where we’ll put the majority of our code.

After that, we create the component by creating a new function. All this function does is return the Main component.

Lastly, we export the class so that it can be imported somewhere else. In this case, it’s actually imported from the index.js file.

Next, create the src/Main.js file and add the following:

// src/Main.js
import React, { Component } from ‘react’;
import { SafeAreaView, View, Text, TextInput, Button, Alert, StyleSheet, ActivityIndicator } from ‘react-native’;

The second line imports the components that are built into React Native. Here’s what each one does:

  • SafeAreaView: for rendering content within the safe area boundaries of a device. This automatically adds a padding that wraps its content so that it won’t be rendered on camera notches and sensor housing area of a device.
  • View: a fundamental UI building block. This is mainly used as a wrapper for all the other components so they’re structured in such a way that you can style them with ease. Think of it as the equivalent of <div>: if you want to use Flexbox, you have to use this component.
  • Text: for displaying text.
  • TextInput: the UI component for inputting text. This text can be plain text, email, password, or a number pad.
  • Button: for showing a platform-specific button. This component looks different based on the platform it runs on. If it’s Android, it uses Material Design. If it’s iOS, it uses Cupertino.
  • Alert: for showing alerts and prompts.
  • ActivityIndicator: for showing a loading animation indicator.
  • StyleSheet: for defining the component styles.

Next, import the libraries we installed earlier:

import axios from ‘axios’;
import pokemon from ‘pokemon’;

We’ll also be creating a custom Pokemon component later. This one is used for displaying Pokemon data:

import Pokemon from ‘./components/Pokemon’;

Because getting the required Pokemon data involves making two API requests, we have to set the API’s base URL as a constant:

const POKE_API_BASE_URL = “”;

Next, define the component class and initialize its state:

export default class Main extends Component {


  state = {

    isLoading: false, // decides whether to show the activity indicator or not

    searchInput: ‘’, // the currently input text

    name: ‘’, // Pokemon name

    pic: ‘’, // Pokemon image URL

    types: [], // Pokemon types array

    desc: ‘’ // Pokemon description



  // next: add render() method


 In the code above, we’re defining the main component of the app. You can do this by defining an ES6 class and having it extend React’s Component class. This is another way of defining a component in React. In the App.js file, we created a functional component. This time we’re creating a class-based component.

The main difference between the two is that functional components are used for presentation purposes only. Functional components have no need to keep their own state because all the data they require is just passed to them via props. On the other hand, class-based components maintain their own state and they’re usually the ones passing data to functional components.

If you want to learn more about the difference between functional and class-based components, read this tutorial: Functional vs Class-Components in React.

Going back to the code, we’re initializing the state inside our component. You define it as a plain JavaScript object. Any data that goes into the state should be responsible for changing what’s rendered by the component. In this case, we put in isLoading to control the visibility of the activity indicator and searchInput to keep track of the input value in the search box.

This is an important concept to remember. React Native’s built-in components, and even the custom components you create, accept properties that control the following:

  • what’s displayed on the screen (data source)
  • how they present it (structure)
  • what it looks like (styles)
  • what actions to perform when user interacts with it (functions)

We’ll go through those properties in more detail in the next section. For now, know that the value of those properties are usually updated through the state.

The rest of the state values are for the Pokemon data. It’s a good practice to set the initial value with the same type of data you’re expecting to store later on — as this serves as documentation as well.

Anything that’s not used for rendering or data flow can be defined as an instance variable, like so:

class Main extends Component {


    this.appTitle = “RNPokeSearch”;



Alternatively, it can be defined as an outside-the-class variable, just like what we did with the POKEAPIBASE_URL earlier:

const POKE_API_BASE_URL = ‘’;


class Main extends Component {



Structuring and Styling Components

Let’s return to the component class definition. When you extend React’s Component class, you have to define a render() method. This contains the code for returning the component’s UI and it’s made up of the React Native components we imported earlier.

Each component has its own set of props. These are basically attributes that you pass to the component to control a specific aspect of it. In the code below, most of them have the style prop, which is used to modify the styles of a component. You can pass any data type as a prop. For example, the onChangeText prop of the TextInput is a function, while the types prop in the Pokemon is an array of objects. Later on in the Pokemon component, you’ll see how the props will be used:

render() {

  const { name, pic, types, desc, searchInput, isLoading } = this.state; // extract the Pokemon data from the state

  return (

    <SafeAreaView style={styles.wrapper}>

      <View style={styles.container}>

        <View style={styles.headContainer}>

          <View style={styles.textInputContainer}>



              onChangeText={(searchInput) => this.setState({searchInput})}


              placeholder={“Search Pokemon”}



          <View style={styles.buttonContainer}>









        <View style={styles.mainContainer}>


            isLoading &&

            <ActivityIndicator size=“large” color=“#0064e1” />




            !isLoading &&





              desc={desc} />







 Breaking down the code above, we first extract the state data:

const { name, pic, types, desc, searchInput, isLoading } = this.state;

Next, we return the component’s UI, which follows this structure:











The above structure is optimized for using Flexbox. Go ahead and define the component styles at the bottom of the file:

const styles = StyleSheet.create({

  wrapper: {

    flex: 1


  container: {

    flex: 1,

    padding: 20,

    backgroundColor: ‘#F5FCFF’,


  headContainer: {

    flex: 1,

    flexDirection: ‘row’,

    marginTop: 100


  textInputContainer: {

    flex: 2


  buttonContainer: {

    flex: 1


  mainContainer: {

    flex: 9


  textInput: {

    height: 35,

    marginBottom: 10,

    borderColor: “#ccc”,

    borderWidth: 1,

    backgroundColor: “#eaeaea”,

    padding: 5



In React Native, you define styles by using StyleSheet.create() and passing in the object that contains your styles. These style definitions are basically JavaScript objects, and they follow the same structure as your usual CSS styles:

element: {

  property: value


The wrapper and container is set to flex: 1, which means it will occupy the entirety of the available space because they have no siblings. React Native defaults to flexDirection: ‘column’, which means it will lay out the flex items vertically, like so:

In contrast, (flexDirection: ‘row’) lays out items horizontally:

It’s different for headContainer, because even though it’s set to flex: 1, it has mainContainer as its sibling. This means that headContainer and mainContainer will both share the same space. mainContainer is set to flex: 9 so it will occupy the majority of the available space (around 90%), while headContainer will only occupy about 10%.

Let’s move on to the contents of headContainer. It has textInputContainer and buttonContainer as its children. It’s set to flexDirection: ‘row’, so that its children will be laid out horizontally. The same principle applies when it comes to space sharing: textInputContainer occupies thw thirds of the available horizontal space, while buttonContainer only occupies one third.

The rest of the styles are pretty self explanatory when you have a CSS background. Just remember to omit - and set the following character to uppercase. For example, if you want to set background-color, the React Native equivalent is backgroundColor.

Note: not all CSS properties that are available on the Web are supported on React Native. For example, things like floats or table properties aren’t supported. You can find the list of supported CSS properties in the docs for View and Text components. Someone has also compiled a React Native Styling Cheat Sheet. There’s also a style section in the documentation for a specific React Native component that you want to use. For example, here are the style properties that you can use for the Image component.

Event Handling and Updating the State

Let’s now break down the code for the TextInput and Button components. In this section, we’ll talk about event handling, making HTTP requests, and updating the state in React Native.

Let’s start by examining the code for TextInput:



  onChangeText={(searchInput) => this.setState({searchInput})}


  placeholder={“Search Pokemon”}


In the above code, we’re setting the function to execute when the user inputs something in the component. Handling events like these are similar to how it’s handled in the DOM: you simply pass the event name as a prop and set its value to the function you wish to execute. In this case, we’re simply inlining it because we’re just updating the state. The value input by the user is automatically passed as an argument to the function you supply so all you have to do is update the state with that value. Don’t forget to set the value of the TextInput to that of the state variable. Otherwise, the value input by the user won’t show as they type on it.

Next, we move on to the Button component. Here, we’re listening for the onPress event:






Once pressed, the searchPokemon() function is executed. Add this function right below the render() method. This function uses the async/await pattern because performing an HTTP request is an asynchronous operation. You can also use Promises, but to keep our code concise, we’ll stick with async/await instead. If you’re not familiar with it, be sure to read this tutorial:

render() {

  // …



searchPokemon = async () => {

  try {

    const pokemonID = pokemon.getId(this.state.searchInput); // check if the entered Pokemon name is valid



      isLoading: true // show the loader while request is being performed



    const { data: pokemonData } = await axios.get(${POKE_API_BASE_URL}/pokemon/${pokemonID});

    const { data: pokemonSpecieData } = await axios.get(${POKE_API_BASE_URL}/pokemon-species/${pokemonID});


    const { name, sprites, types } = pokemonData;

    const { flavor_text_entries } = pokemonSpecieData;




      pic: sprites.front_default,

      types: this.getTypes(types),

      desc: this.getDescription(flavor_text_entries),

      isLoading: false // hide loader



  } catch (err) {

    Alert.alert(“Error”, “Pokemon not found”);



 Breaking down the code above, we first check if the entered Pokemon name is valid. If it’s valid, the National Pokedex ID (if you open the link, that’s the number on top of the Pokemon name) is returned and we supply it as a parameter for the HTTP request. The request is made using axios’ get() method, which corresponds to an HTTP GET request. Once the data is available, we extract what we need and update the state.

Here’s the getTypes() function. All it does is reassign the slot and type properties of the Pokemon types to id and name:

getTypes = (types) => {

  return{ slot, type }) => {

    return {

      “id”: slot,





Here’s the getDescription() function. This finds the first English version of the flavor_text:

getDescription = (entries) => {

  return entries.find(item => === ‘en’).flavor_text;


Pokemon Component

Earlier, we imported and used a component called Pokemon, but we haven’t really created it yet. Let’s go ahead and do so. Create a src/components/Pokemon.js file and add the following:

// src/components/Pokemon.js

import React from ‘react’;

import { View, Text, Image, FlatList, StyleSheet } from ‘react-native’;


const Pokemon = ({ name, pic, types, desc }) => {

  if (!name) {

    return null



  return (

    <View style={styles.mainDetails}>


        source={{uri: pic}}

        style={styles.image} resizeMode={“contain”} />

        <Text style={styles.mainText}>{name}</Text>






          keyExtractor={(item) =>}

          renderItem={({item}) => {

            return (

              <View style={[styles[], styles.type]}>

                <Text style={styles.typeText}>{}</Text>






        <View style={styles.description}>








const styles = StyleSheet.create({

  mainDetails: {

    padding: 30,

    alignItems: ‘center’


  image: {

    width: 100,

    height: 100


  mainText: {

    fontSize: 25,

    fontWeight: ‘bold’,

    textAlign: ‘center’


  description: {

    marginTop: 20


  types: {

    flexDirection: ‘row’,

    marginTop: 20


  type: {

    padding: 5,

    width: 100,

    alignItems: ‘center’


  typeText: {

    color: ‘#fff’,


  normal: {

    backgroundColor: ‘#8a8a59’


  fire: {

    backgroundColor: ‘#f08030’


  water: {

    backgroundColor: ‘#6890f0’


  electric: {

    backgroundColor: ‘#f8d030’


  grass: {

    backgroundColor: ‘#78c850’


  ice: {

    backgroundColor: ‘#98d8d8’


  fighting: {

    backgroundColor: ‘#c03028’


  poison: {

    backgroundColor: ‘#a040a0’


  ground: {

    backgroundColor: ‘#e0c068’


  flying: {

    backgroundColor: ‘#a890f0’


  psychic: {

    backgroundColor: ‘#f85888’


  bug: {

    backgroundColor: ‘#a8b820’


  rock: {

    backgroundColor: ‘#b8a038’


  ghost: {

    backgroundColor: ‘#705898’


  dragon: {

    backgroundColor: ‘#7038f8’


  dark: {

    backgroundColor: ‘#705848’


  steel: {

    backgroundColor: ‘#b8b8d0’


  fairy: {

    backgroundColor: ‘#e898e8’




export default Pokemon;

 In the code above, we first checked if the name has a falsy value. If it has, we simply return null as there’s nothing to render.

We’re also using two new, built-in React Native components:

  • Image: used for displaying images from the Internet or from the file system
  • FlatList: used for displaying lists

As we saw earlier, we’re passing in the Pokemon data as prop for this component. We can extract those props the same way we extract individual properties from an object:

const Pokemon = ({ name, pic, types, desc }) => {

  // … 


The Image component requires the source to be passed to it. The source can either be an image from the file system, or, in this case, an image from the Internet. The former requires the image to be included using require(), while the latter requires the image URL to be used as the value of the uri property of the object you pass to it.

resizeMode allows you to control how the image will be resized based on its container. We used contain, which means it will resize the image so that it fits within its container while still maintaining its aspect ratio. Note that the container is the Image component itself. We’ve set its width and height to 100, so the image will be resized to those dimensions. If the original image has a wider width than its height, a width of 100 will be used, while the height will adjust accordingly to maintain the aspect ratio. If the original image dimension is smaller, it will simply maintain its original size:


  source={{uri: pic}}


  resizeMode={“contain”} />

 Next is the FlatList component. It’s used for rendering a list of items. In this case, we’re using it to render the types of the Pokemon. This requires the data, which is an array containing the items you want to render, and the renderItem, which is the function responsible for rendering each item on the list. The item in the current iteration can be accessed the same way props are accessed in a functional component:





  keyExtractor={(item) =>}

  renderItem={({item}) => {

    return (

      <View style={[styles[], styles.type]}>

        <Text style={styles.typeText}>{}</Text>





 In the code above, we also supplied the following props:

  • columnWrapperStyle: used for specifying the styles for each column. In this case, we want to render each list item inline, so we’ve specified flexDirection: ‘row’.
  • numColumns: the maximum number of columns you want to render for each row on the list. In this case, we’ve specified 2, because a Pokemon can only have two types at most.
  • keyExtractor: the function to use for extracting the keys for each item. You can actually omit this one if you pass a key prop to the outer-most component of each of the list items.

At this point, you can now test the app on your device or emulator:

react-native run-android
react-native run-ios
yarn start


That’s it! In this tutorial, you’ve learned how to set up the React Native development environment using both the standard procedure and Expo. You also learned how to create your very first React Native app.

Thanks for reading

If you liked this post, share it with all of your programming buddies!

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Further reading about React Native

The Complete React Native and Redux Course

React Native - The Practical Guide

The complete React Native course ( 2nd edition )

Build CRUD Mobile App using React Native, Elements, Navigation, Apollo Client and GraphQL

Which one is best for you? Flutter, React Native, Ionic or NativeScript?

Accepting payments in React Native

Microsoft launches React Native for Windows

Adding Authentication to React Native Chat App Using Auth0

#react-native #mobile-apps #ios #pwa

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Getting Started with React Native
Autumn  Blick

Autumn Blick


How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

  • Performance: It delivers optimal performance.
  • Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
  • UI design: React Native enables you to design simple and responsive UI for your mobile app.
  • 3rd party plugins: This framework supports 3rd party plugins.
  • Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

  • Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
  • Near-native user experience: React Native provides several native components, and these are platform agnostic.
  • The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

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: 


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