How to Create and Deploy NFTs with JavaScript

Learn to code an NFT (non-fungible token) using the Ethereum blockchain, the Ropsten Testnet, and a series of JavaScript libraries.

In February 2021, Figma CEO Dylan Fields sold a piece of NFT art for $7.5 million. Similarly, Twitter co-founder Jack Dorsey sold his first tweet on Twitter as an NFT for $2,915,835.47.

Jack Dorsey First Tweet NFT

An NFT (non-fungible token) is a fascinating new technology that represents ownership of an asset digitally. In this tutorial, we’ll cover some important background information, set up third-party services, and finally code and deploy our very own NFT to the Ropsten Testnet.

Let’s get started!

#javascript #blockchain

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How to Create and Deploy NFTs with JavaScript

Easter Deckow

1655630160

PyTumblr: A Python Tumblr API v2 Client

PyTumblr

Installation

Install via pip:

$ pip install pytumblr

Install from source:

$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install

Usage

Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(
    '<consumer_key>',
    '<consumer_secret>',
    '<oauth_token>',
    '<oauth_secret>',
)

client.info() # Grabs the current user information

Two easy ways to get your credentials to are:

The built-in interactive_console.py tool (if you already have a consumer key & secret)
The Tumblr API console at https://api.tumblr.com/console
Get sample login code at https://api.tumblr.com/console/calls/user/info

Supported Methods

User Methods

client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

state - a string, the state of the post. Supported types are published, draft, queue, private
tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
date - a string, the customized GMT that you want
format - a string, the format that your post is in. Support types are html or markdown
slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
                    source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",
                    data="/Users/johnb/path/to/my/image.jpg")

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

title - a string, the title of post that you want. Supports HTML entities.
url - a string, the url that you want to create a link post for.
description - a string, the desciption of the link that you have

#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
                    embed="http://www.youtube.com/watch?v=40pUYLacrj4")

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python interactive-console.py

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python setup.py test

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license

#python #api

Brook Hudson

1659396000

Humidifier: A Ruby tool for Managing AWS CloudFormation Stacks

Humidifier

Humidifier is a ruby tool for managing AWS CloudFormation stacks. You can use it to build and manage stacks programmatically or you can use it as a command line tool to manage stacks through configuration files.

Installation
Getting started
CLI
Development

Installation

Add this line to your application's Gemfile:

gem 'humidifier'

And then execute:

$ bundle

Or install it yourself as:

$ gem install humidifier

Getting started

Stacks are represented by the Humidifier::Stack class. You can set any of the top-level JSON attributes (such as name and description) through the initializer.

Resources are represented by an exact mapping from AWS resource names to Humidifier resources names (e.g. AWS::EC2::Instance becomes Humidifier::EC2::Instance). Resources have accessors for each JSON attribute. Each attribute can also be set through the initialize, update, and update_attribute methods.

Example usage

The below example will create a stack with two resources, a loader balancer and an auto scaling group. It then deploys the new stack and pauses execution until the stack is finished being created.

stack = Humidifier::Stack.new(name: 'Example-Stack')

stack.add(
  'LoaderBalancer',
  Humidifier::ElasticLoadBalancing::LoadBalancer.new(
    scheme: 'internal',
    listeners: [
      {
        load_balancer_port: 80,
        protocol: 'http',
        instance_port: 80,
        instance_protocol: 'http'
      }
    ]
  )
)

stack.add(
  'AutoScalingGroup',
  Humidifier::AutoScaling::AutoScalingGroup.new(
    min_size: '1',
    max_size: '20',
    availability_zones: ['us-east-1a'],
    load_balancer_names: [Humidifier.ref('LoadBalancer')]
  )
)

stack.deploy_and_wait

Interfacing with AWS

Once stacks have the appropriate resources, you can query AWS to handle all stack CRUD operations. The operations themselves are intuitively named (i.e. #create, #update, #delete). There are also convenience methods for validating a stack body (#valid?), checking the existence of a stack (#exists?), and creating or updating based on existence (#deploy).

There are additionally four functions on Humidifier::Stack that support waiting for execution in AWS to finish. They all have non-blocking corollaries, and are named after them. They are: #create_and_wait, #update_and_wait, #delete_and_wait, and #deploy_and_wait.

CloudFormation functions

You can use CFN intrinsic functions and references using Humidifier.fn.[name] and Humidifier.ref. They will build appropriate structures that know how to be dumped to CFN syntax.

Change Sets

Instead of immediately pushing your changes to CloudFormation, Humidifier also supports change sets. Change sets are a powerful feature that allow you to see the changes that will be made before you make them. To read more about change sets see the announcement article. To use them in Humidifier, Humidifier::Stack has the #create_change_set and #deploy_change_set methods. The #create_change_set method will create a change set on the stack. The #deploy_change_set method will create a change set if the stack currently exists, and otherwise will create the stack.

Introspection

To see the template body, you can check the #to_cf method on stacks, resources, fns, and refs. All of them will output a hash of what will be uploaded (except the stack, which will output a string representation).

Humidifier itself contains a registry of all possible resources that it supports. You can access it with Humidifier::registry which is a hash of AWS resource name pointing to the class.

Resources have an ::aws_name method to see how AWS references them. They also contain a ::props method that contains a hash of the name that Humidifier uses to reference the prop pointing to the appropriate prop object.

Large templates

When templates are especially large (larger than 51,200 bytes), they cannot be uploaded directly through the AWS SDK. You can configure Humidifier to seamlessly upload the templates to S3 and reference them using an S3 URL instead by:

Humidifier.configure do |config|
  config.s3_bucket = 'my.s3.bucket'
  config.s3_prefix = 'my-prefix/' # optional
end

Forcing uploading

You can force a stack to upload its template to S3 regardless of the size of the template. This is a useful option if you're going to be deploying multiple copies of a template or if you want a backup. You can set this option on a per-stack basis:

stack.deploy(force_upload: true)

or globally, by setting the configuration option:

Humidifier.configure do |config|
  config.force_upload = true
end

CLI

Humidifier can also be used as a CLI for managing resources through configuration files. For a step-by-step guide, read on, but if you'd like to see a working example, check out the example directory.

To get started, build a ruby script (for example humidifier) that executes the Humidifier::CLI class, like so:

#!/usr/bin/env ruby
require 'humidifier'

Humidifier.configure do |config|
  # optional, defaults to the current working directory, so that all of the
  # directories from the location that you run the CLI are assumed to contain
  # resource specifications
  config.stack_path = 'stacks'

  # optional, a default prefix to use before deploying to AWS
  config.stack_prefix = 'humidifier-'

  # specifies that `users.yml` files contain specifications for `AWS::IAM::User`
  # resources
  config.map :users, to: 'IAM::User'
end

Humidifier::CLI.start(ARGV)

Resource files

Inside of the stacks directory configured above, create a subdirectory for each CloudFormation stack that you want to deploy. With the above configuration, we can create YAML files in the form of users.yml for each stack, which will specify IAM users to create. The file format looks like the below:

EngUser:
  path: /humidifier/
  user_name: EngUser
  groups:
  - Engineering
  - Testing
  - Deployment

AdminUser:
  path: /humidifier/
  user_name: AdminUser
  groups:
  - Management
  - Administration

The top-level keys are the logical resource names that will be displayed in the CloudFormation screen. They point to a map of key/value pairs that will be passed on to humidifier. Any humidifier (and therefore any CloudFormation) attribute may be specified. For more information on CloudFormation templates and which attributes may be specified, see both the humidifier docs and the CloudFormation docs.

Mappers

Oftentimes, specifying these attributes can become repetitive, e.g., each user should automatically receive the same "path" attribute. Other times, you may want custom logic to execute depending on which AWS environment you're running in. Finally, you may want to reference resources in the same or other stacks.

Humidifier's solution for this is to allow customized "mapper" classes to take the user-provided attributes and transform them into the attributes that CloudFormation expects. Consider the following example for mapping a user:

class UserMapper < Humidifier::Config::Mapper
  GROUPS = {
    'eng' => %w[Engineering Testing Deployment],
    'admin' => %w[Management Administration]
  }

  defaults do |logical_name|
    { path: '/humidifier/', user_name: logical_name }
  end

  attribute :group do |group|
    groups = GROUPS[group]
    groups.any? ? { groups: GROUPS[group] } : {}
  end
end

Humidifier.configure do |config|
  config.map :users, to: 'IAM::User', using: UserMapper
end

This means that by default, all entries in the users.yml files will get a /humidifier/ path, the user_name attribute will be set based on the logical name that was provided for the resource, and you can additionally specify a group attribute, even though it is not native to CloudFormation. With this group attribute, it will actually map to the groups attribute that CloudFormation expects.

With this new mapper in place, we can simplify our YAML file to:

EngUser:
  group: eng

AdminUser:
  group: admin

Using the CLI

Now that you've configured your CLI, your resources, and your mappers, you can use the CLI to display, validate, and deploy your infrastructure to CloudFormation. Run your script without any arguments to get the help message and explanations for each command.

Each command has an --aws-profile (or -p) option for specifying which profile to authenticate against when querying AWS. You should ensure that this profile has the correct permissions for creating whatever resources are going to part of your stack. You can also rely on the AWS_* environment variables, or the EC2 instance profile if you're deploying from an instance. For more information, see the AWS docs under the "Configuration" section.

Below are the list of commands and some of their options.

`change [?stack]`

Creates a change set for either the specified stack or all stacks in the repo. The change set represents the changes between what is currently deployed versus the resources represented by the configuration.

`deploy [?stack] [*parameters]`

Creates or updates (depending on if the stack already exists) one or all stacks in the repo.

The deploy command also allows a --prefix command line argument that will override the default prefix (if one is configured) for the stack that is being deployed. This is especially useful when you're deploying multiple copies of the same stack (for instance, multiple autoscaling groups) that have different purposes or semantically mean newer versions of resources.

`display [stack] [?pattern]`

Displays the specified stack in JSON format on the command line. If you optionally pass a pattern argument, it will filter the resources down to just ones whose names match the given pattern.

`stacks`

Displays the names of all of the stacks that humidifier is managing.

`upgrade`

Downloads the latest CloudFormation resource specification. Periodically AWS will update the file that humidifier is based on, in which case the attributes of the resources that were changed could change. This gem usually stays relatively in sync, but if you need to use the latest specs and this gem has not yet released a new version containing them, then you can run this command to download the latest specs onto your system.

`upload [?stack]`

Upload one or all stacks in the repo to S3 for reference later. Note that this must be combined with the humidifier s3_bucket configuration option.

`validate [?stack]`

Validate that one or all stacks in the repo are properly configured and using values that CloudFormation understands.

`version`

Output the version of Humidifier as well as the version of the CloudFormation resource specification that you are using.

Parameters

CloudFormation template parameters can be specified by having a special parameters.yml file in your stack directory. This file should contain a YAML-encoded object whose keys are the names of the parameters and whose values are the parameter configuration (using the same underscore paradigm as humidifier resources for specifying configuration).

You can pass values to the CLI deploy command after the stack name on the command line as in:

humidifier deploy foobar Param1=Foo Param2=Bar

Those parameters will get passed in as values when the stack is deployed.

Shortcuts

A couple of convenient shortcuts are built into humidifier so that writing templates and mappers both can be more concise.

Automatic id properties

There are a lot of properties in the AWS CloudFormation resource specification that are simply pointers to other entities within the AWS ecosystem. For example, an AWS::EC2::VPCGatewayAttachment entity has a VpcId property that represents the ID of the associated AWS::EC2::VPC.

Because this pattern is so common, humidifier detects all properties ending in Id and allows you to specify them without the suffix. If you choose to use this format, humidifier will automatically turn that value into a CloudFormation resource reference.

Anonymous mappers

A lot of the time, mappers that you create will not be overly complicated, especially if you're using automatic id properties. So, the config.map method optionally takes a block, and allows you to specify the mapper inline. This is recommended for mappers that aren't too complicated as to warrant their own class (for instance, for testing purposes). An example of this using the UserMapper from above is below:

Humidifier.configure do |config|
  config.map :users, to: 'IAM::User' do
    GROUPS = {
      'eng' => %w[Engineering Testing Deployment],
      'admin' => %w[Management Administration]
    }

    defaults do |logical_name|
      { path: '/humidifier/', user_name: logical_name }
    end

    attribute :group do |group|
      groups = GROUPS[group]
      groups.any? ? { groups: GROUPS[group] } : {}
    end
  end
end

Cross-stack references

AWS allows cross-stack references through the intrinsic Fn::ImportValue function. You can take advantage of this with humidifier by using the export: true option on resources in your stacks. For instance, if in one stack you have a subnet that you need to reference in another, you could (stacks/vpc/subnets.yml):

ProductionPrivateSubnet2a:
  vpc: ProductionVPC
  cidr_block: 10.0.0.0/19
  availability_zone: us-west-2a
  export: true

ProductionPrivateSubnet2b:
  vpc: ProductionVPC
  cidr_block: 10.0.64.0/19
  availability_zone: us-west-2b
  export: true

ProductionPrivateSubnet2c:
  vpc: ProductionVPC
  cidr_block: 10.0.128.0/19
  availability_zone: us-west-2c
  export: true

And then in another stack, you could reference those values (stacks/rds/db_subnets_groups.yml):

ProductionDBSubnetGroup:
  db_subnet_group_description: Production DB private subnet group
  subnets:
  - ProductionPrivateSubnet2a
  - ProductionPrivateSubnet2b
  - ProductionPrivateSubnet2c

Within the configuration, you would specify to use the Fn::ImportValue function like so:

Humidifier.configure do |config|
  config.stack_path = 'stacks'

  config.map :subnets, to: 'EC2::Subnet'

  config.map :db_subnet_groups, to: 'RDS::DBSubnetGroup' do
    attribute :subnets do |subnet_names|
      subnet_ids =
        subnet_names.map do |subnet_name|
          Humidifier.fn.import_value(subnet_name)
        end

      { subnet_ids: subnet_ids }
    end
  end
end

If you specify export: true it will by default export a reference to the resource listed in the stack. You can also choose to export a different attribute by specifying the attribute as the value to export. For example, if we were creating instance profiles and wanted to export the Arn so that it could be referenced by an instance later, we could:

APIRoleInstanceProfile:
  depends_on: APIRole
  roles:
  - APIRole
  export: Arn

Development

To get started, ensure you have ruby installed, version 2.4 or later. From there, install the bundler gem: gem install bundler and then bundle install in the root of the repository.

Testing

The default rake task runs the tests. Styling is governed by rubocop. The docs are generated with yard. To run all three of these, run:

$ bundle exec rake
$ bundle exec rubocop
$ bundle exec rake yard

Specs

The specs pulled from the CFN docs is saved to CloudFormationResourceSpecification.json. You can update it by running bundle exec rake specs. This script will pull down the latest resource specification to be used with Humidifier.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/kddnewton/humidifier.

License

The gem is available as open source under the terms of the MIT License.

Author: kddnewton
Source code: https://github.com/kddnewton/humidifier
License: MIT license

#ruby #ruby-on-rails

Tamale Moses

1669003576

Exploring Mutable and Immutable in Python

In this Python article, let's learn about Mutable and Immutable in Python.

Mutable and Immutable in Python

Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.

Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.

Mutable Definition

Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.

Immutable Definition

Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.

List of Mutable and Immutable objects

Objects of built-in type that are mutable are:

Lists
Sets
Dictionaries
User-Defined Classes (It purely depends upon the user to define the characteristics)

Objects of built-in type that are immutable are:

Numbers (Integer, Rational, Float, Decimal, Complex & Booleans)
Strings
Tuples
Frozen Sets
User-Defined Classes (It purely depends upon the user to define the characteristics)

Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.

Objects in Python

In Python, everything is treated as an object. Every object has these three attributes:

Identity – This refers to the address that the object refers to in the computer’s memory.
Type – This refers to the kind of object that is created. For example- integer, list, string etc.
Value – This refers to the value stored by the object. For example – List=[1,2,3] would hold the numbers 1,2 and 3

While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.

Check out this free python certificate course to get started with Python.

Mutable Objects in Python

I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:

#Creating a list which contains name of Indian cities

cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]

# Printing the elements from the list cities, separated by a comma & space

for city in cities:
		print(city, end=’, ’)

Output [1]: Delhi, Mumbai, Kolkata

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [2]: 0x1691d7de8c8

#Adding a new city to the list cities

cities.append(‘Chennai’)

#Printing the elements from the list cities, separated by a comma & space

for city in cities:
	print(city, end=’, ’)

Output [3]: Delhi, Mumbai, Kolkata, Chennai

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [4]: 0x1691d7de8c8

The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.

Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.

Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0.

#Creating a Tuple with variable name ‘foo’

foo = (1, 2)

#Changing the index[0] value from 1 to 3

foo[0] = 3
	
TypeError: 'tuple' object does not support item assignment

Immutable Objects in Python

Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:

#Creating a Tuple which contains English name of weekdays

weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’

# Printing the elements of tuple weekdays

print(weekdays)

Output [1]:  (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [2]: 0x1691cc35090

#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’

weekdays  +=  ‘Pythonday’,

#Printing the elements of tuple weekdays

print(weekdays)

Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [4]: 0x1691cc8ad68

This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it. Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.

Also Read: Understanding the Exploratory Data Analysis (EDA) in Python

Where can you use mutable and immutable objects:

Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.

Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.

Watch outs: Non transitive nature of Immutability:

OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–

#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements

#The elements (lists) contains the name, age & gender

person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the tuple

print(person)

Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [2]: 0x1691ef47f88

#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4

person[0][1] = 4

#printing the updated tuple

print(person)

Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [4]: 0x1691ef47f88

In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.

Also Read: Real-Time Object Detection Using TensorFlow

Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–

#creating a list (mutable object) which contains tuples(immutable) as it’s elements

list1 = [(1, 2, 3), (4, 5, 6)]

#printing the list

print(list1)

Output [1]: [(1, 2, 3), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [2]: 0x1691d5b13c8

#changing object reference at index 0

list1[0] = (7, 8, 9)

#printing the list

Output [3]: [(7, 8, 9), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [4]: 0x1691d5b13c8

As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.

Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’

x = 10

#printing the value of ‘x’

print(x)

Output [1]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(x)))

Output [2]: 0x538fb560

#creating an object of integer type with value 10 and reference variable name ‘y’

y = 10

#printing the value of ‘y’

print(y)

Output [3]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(y)))

Output [4]: 0x538fb560

As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.

Quick check – Python Data Structures

Immutability of Tuple

Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.

Exceptions in immutability

Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.

Consider a tuple ‘tup’.

Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;

We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.

FAQs

1. Difference between mutable vs immutable in Python?

Mutable Object	Immutable Object
State of the object can be modified after it is created.	State of the object can’t be modified once it is created.
They are not thread safe.	They are thread safe
Mutable classes are not final.	It is important to make the class final before creating an immutable object.

2. What are the mutable and immutable data types in Python?

Some mutable data types in Python are:

list, dictionary, set, user-defined classes.

Some immutable data types are:

int, float, decimal, bool, string, tuple, range.

3. Are lists mutable in Python?

Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)

4. Why are tuples called immutable types?

Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.

A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.

5. Are sets mutable in Python?

A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.

6. Are strings mutable in Python?

Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.

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Original article source at: https://www.mygreatlearning.com

#python

Arvilla Medhurst

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What Is R Programming Language? introduction & Basics

In this R article, we will learn about What Is R Programming Language? introduction & Basics. R is a programming language developed by Ross Ihaka and Robert Gentleman in 1993. R possesses an extensive catalog of statistical and graphical methods. It includes machine learning algorithms, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R, but for heavy computational tasks, C, C++, and Fortran codes are preferred.

Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicating the results

Program: R is a clear and accessible programming tool
Transform: R is made up of a collection of libraries designed specifically for data science
Discover: Investigate the data, refine your hypothesis and analyze them
Model: R provides a wide array of tools to capture the right model for your data
Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world.

What is R used for?

Statistical inference
Data analysis
Machine learning algorithm

As conclusion, R is the world’s most widely used statistics programming language. It’s the 1st choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in mission-critical business applications.

R-environment setup

Windows Installation – We can download the Windows installer version of R from R-3.2.2 for windows (32/64)

As it is a Windows installer (.exe) with the name “R-version-win.exe”. You can just double click and run the installer accepting the default settings. If your Windows is a 32-bit version, it installs the 32-bit version. But if your windows are 64-bit, then it installs both the 32-bit and 64-bit versions.

After installation, you can locate the icon to run the program in a directory structure “R\R3.2.2\bin\i386\Rgui.exe” under the Windows Program Files. Clicking this icon brings up the R-GUI which is the R console to do R Programming.

R basic Syntax

R Programming is a very popular programming language that is broadly used in data analysis. The way in which we define its code is quite simple. The “Hello World!” is the basic program for all the languages, and now we will understand the syntax of R programming with the “Hello world” program. We can write our code either in the command prompt, or we can use an R script file.

R command prompt

Once you have R environment setup, then it’s easy to start your R command prompt by just typing the following command at your command prompt −
$R
This will launch R interpreter and you will get a prompt > where you can start typing your program as follows −

>myString <- "Hello, World"
>print (myString)
[1] "Hello, World!"

Here the first statement defines a string variable myString, where we assign a string “Hello, World!” and then the next statement print() is being used to print the value stored in myString variable.

R data-types

While doing programming in any programming language, you need to use various variables to store various information. Variables are nothing but reserved memory locations to store values. This means that when you create a variable you reserve some space in memory.

In contrast to other programming languages like C and java in R, the variables are not declared as some data type. The variables are assigned with R-Objects and the data type of the R-object becomes the data type of the variable. There are many types of R-objects. The frequently used ones are −

Vectors
Lists
Matrices
Arrays
Factors
Data Frames

Vectors

#create a vector and find the elements which are >5
v<-c(1,2,3,4,5,6,5,8)
v[v>5]

#subset
subset(v,v>5)

#position in the vector created in which square of the numbers of v is >10 holds good
which(v*v>10)

#to know the values 
v[v*v>10]

Output: [1] 6 8 Output: [1] 6 8 Output: [1] 4 5 6 7 8 Output: [1] 4 5 6 5 8

Matrices

A matrix is a two-dimensional rectangular data set. It can be created using a vector input to the matrix function.

#matrices: a vector with two dimensional attributes
mat<-matrix(c(1,2,3,4))
 
mat1<-matrix(c(1,2,3,4),nrow=2)
mat1

Output: [,1] [,2] [1,] 1 3 [2,] 2 4

mat2<-matrix(c(1,2,3,4),ncol=2,byrow=T)
mat2

Output: [,1] [,2] [1,] 1 2 [2,] 3 4

mat3<-matrix(c(1,2,3,4),byrow=T)
mat3

#transpose of matrix
mattrans<-t(mat)
mattrans

#create a character matrix called fruits with elements apple, orange, pear, grapes
fruits<-matrix(c("apple","orange","pear","grapes"),2)
#create 3×4 matrix of marks obtained in each quarterly exams for 4 different subjects 
X<-matrix(c(50,70,40,90,60, 80,50, 90,100, 50,30, 70),nrow=3)
X

#give row names and column names
rownames(X)<-paste(prefix="Test.",1:3)
subs<-c("Maths", "English", "Science", "History")
colnames(X)<-subs
X

Output: [,1] [1,] 1 [2,] 2 [3,] 3 [4,] 4 Output: [,1] [,2] [,3] [,4] [1,] 1 2 3 4 Output: [,1] [,2] [,3] [,4] [1,] 50 90 50 50 [2,] 70 60 90 30 [3,] 40 80 100 70 Output: Maths English Science History Test. 1 50 90 50 50 Test. 2 70 60 90 30 Test. 3 40 80 100 70

Arrays

While matrices are confined to two dimensions, arrays can be of any number of dimensions. The array function takes a dim attribute which creates the required number of dimensions. In the below example we create an array with two elements which are 3×3 matrices each.

#Arrays
arr<-array(1:24,dim=c(3,4,2))
arr

#create an array using alphabets with dimensions 3 rows, 2 columns and 3 arrays
arr1<-array(letters[1:18],dim=c(3,2,3))

#select only 1st two matrix of an array
arr1[,,c(1:2)]

#LIST
X<-list(u=2, n='abc')
X
X$u

 [,1] [,2] [,3] [,4]

 [,1] [,2] [,3] [,4]

 [,1] [,2]

 [,1] [,2]

Dataframes

Data frames are tabular data objects. Unlike a matrix in a data frame, each column can contain different modes of data. The first column can be numeric while the second column can be character and the third column can be logical. It is a list of vectors of equal length.

#Dataframes
students<-c("J","L","M","K","I","F","R","S")
Subjects<-rep(c("science","maths"),each=2)
marks<-c(55,70,66,85,88,90,56,78)
data<-data.frame(students,Subjects,marks)
#Accessing dataframes
data[[1]]

data$Subjects
data[,1]

Output: [1] J L M K I F R S Levels: F I J K L M R S Output: data$Subjects [1] science science maths maths science science maths maths Levels: maths science

Factors

Factors are the r-objects which are created using a vector. It stores the vector along with the distinct values of the elements in the vector as labels. The labels are always character irrespective of whether it is numeric or character or Boolean etc. in the input vector. They are useful in statistical modeling.

Factors are created using the factor() function. The nlevels function gives the count of levels.

#Factors
x<-c(1,2,3)
factor(x)

#apply function
data1<-data.frame(age=c(55,34,42,66,77),bmi=c(26,25,21,30,22))
d<-apply(data1,2,mean)
d

#create two vectors age and gender and find mean age with respect to gender
age<-c(33,34,55,54)
gender<-factor(c("m","f","m","f"))
tapply(age,gender,mean)

Output: [1] 1 2 3 Levels: 1 2 3 Output: age bmi 54.8 24.8 Output: f m 44 44

R Variables

A variable provides us with named storage that our programs can manipulate. A variable in R can store an atomic vector, a group of atomic vectors, or a combination of many R objects. A valid variable name consists of letters, numbers, and the dot or underlines characters.

Rules for writing Identifiers in R

Identifiers can be a combination of letters, digits, period (.), and underscore (_).
It must start with a letter or a period. If it starts with a period, it cannot be followed by a digit.
Reserved words in R cannot be used as identifiers.

Valid identifiers in R

total, sum, .fine.with.dot, this_is_acceptable, Number5

Invalid identifiers in R

tot@l, 5um, _fine, TRUE, .0ne

Best Practices

Earlier versions of R used underscore (_) as an assignment operator. So, the period (.) was used extensively in variable names having multiple words. Current versions of R support underscore as a valid identifier but it is good practice to use a period as word separators.
For example, a.variable.name is preferred over a_variable_name or alternatively we could use camel case as aVariableName.

Constants in R

Constants, as the name suggests, are entities whose value cannot be altered. Basic types of constant are numeric constants and character constants.

Numeric Constants

All numbers fall under this category. They can be of type integer, double or complex. It can be checked with the typeof() function.
Numeric Constants followed by L are regarded as integers and those followed by i are regarded as complex.

> typeof(5)
> typeof(5L)
> typeof(5L)

[1] “double” [1] “double” [[1] “double”

Character Constants

Character constants can be represented using either single quotes (‘) or double quotes (“) as delimiters.

> 'example'
> typeof("5")

[1] "example" [1] "character"

R Operators

Operators – Arithmetic, Relational, Logical, Assignment, and some of the Miscellaneous Operators that R programming language provides.

There are four main categories of Operators in the R programming language.

Arithmetic Operators
Relational Operators
Logical Operators
Assignment Operators
Mixed Operators

x <- 35
y<-10

x+y > x-y > x*y > x/y > x%/%y > x%%y > x^y [1] 45 [1] 25 [1] 350 [1] 3.5 [1] 3 [1] 5 [1]2.75e+15

Logical Operators

The below table shows the logical operators in R. Operators & and | perform element-wise operation producing result having a length of the longer operand. But && and || examines only the first element of the operands resulting in a single length logical vector.

a <- c(TRUE,TRUE,FALSE,0,6,7)
b <- c(FALSE,TRUE,FALSE,TRUE,TRUE,TRUE)
a&b 
[1] FALSE TRUE FALSE FALSE TRUE TRUE
a&&b
[1] FALSE
> a|b
[1] TRUE TRUE FALSE TRUE TRUE TRUE
> a||b
[1] TRUE
> !a
[1] FALSE FALSE TRUE TRUE FALSE FALSE
> !b
[1] TRUE FALSE TRUE FALSE FALSE FALSE

R functions

Functions are defined using the function() directive and are stored as R objects just like anything else. In particular, they are R objects of class “function”. Here’s a simple function that takes no arguments simply prints ‘Hi statistics’.

#define the function
f <- function() {
print("Hi statistics!!!")
}
#Call the function
f()

Output: [1] "Hi statistics!!!"

Now let’s define a function called standardize, and the function has a single argument x which is used in the body of a function.

#Define the function that will calculate standardized score.
standardize = function(x) {
m = mean(x)
sd = sd(x)
result = (x – m) / sd
result
}
input<- c(40:50) #Take input for what we want to calculate a standardized score.
standardize(input) #Call the function

Output: standardize(input) #Call the function [1] -1.5075567 -1.2060454 -0.9045340 -0.6030227 -0.3015113 0.0000000 0.3015113 0.6030227 0.9045340 1.2060454 1.5075567

Loop Functions

R has some very useful functions which implement looping in a compact form to make life easier. The very rich and powerful family of applied functions is made of intrinsically vectorized functions. These functions in R allow you to apply some function to a series of objects (eg. vectors, matrices, data frames, or files). They include:

lapply(): Loop over a list and evaluate a function on each element
sapply(): Same as lapply but try to simplify the result
apply(): Apply a function over the margins of an array
tapply(): Apply a function over subsets of a vector
mapply(): Multivariate version of lapply

There is another function called split() which is also useful, particularly in conjunction with lapply.

R Vectors

A vector is a sequence of data elements of the same basic type. Members in a vector are officially called components. Vectors are the most basic R data objects and there are six types of atomic vectors. They are logical, integer, double, complex, character, and raw.

The c() function can be used to create vectors of objects by concatenating things together. 
x <- c(1,2,3,4,5) #double
x #If you use only x auto-printing occurs
l <- c(TRUE, FALSE) #logical
l <- c(T, F) ## logical
c <- c("a", "b", "c", "d") ## character
i <- 1:20 ## integer
cm <- c(2+2i, 3+3i) ## complex
print(l)
print(c)
print(i)
print(cm)

You can see the type of each vector using typeof() function in R.
typeof(x)
typeof(l)
typeof(c)
typeof(i)
typeof(cm)

Output: print(l) [1] TRUE FALSE print(c) [1] "a" "b" "c" "d" print(i) [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 print(cm) [1] 2+2i 3+3i Output: typeof(x) [1] "double" typeof(l) [1] "logical" typeof(c) [1] "character" typeof(i) [1] "integer" typeof(cm) [1] "complex"

Creating a vector using seq() function:

We can use the seq() function to create a vector within an interval by specifying step size or specifying the length of the vector.

seq(1:10) #By default it will be incremented by 1
seq(1, 20, length.out=5) # specify length of the vector
seq(1, 20, by=2) # specify step size

Output: > seq(1:10) #By default it will be incremented by 1 [1] 1 2 3 4 5 6 7 8 9 10 > seq(1, 20, length.out=5) # specify length of the vector [1] 1.00 5.75 10.50 15.25 20.00 > seq(1, 20, by=2) # specify step size [1] 1 3 5 7 9 11 13 15 17 19

Extract Elements from a Vector:

Elements of a vector can be accessed using indexing. The vector indexing can be logical, integer, or character. The [ ] brackets are used for indexing. Indexing starts with position 1, unlike most programming languages where indexing starts from 0.

Extract Using Integer as Index:

We can use integers as an index to access specific elements. We can also use negative integers to return all elements except that specific element.

x<- 101:110
x[1]   #access the first element
x[c(2,3,4,5)] #Extract 2nd, 3rd, 4th, and 5th elements
x[5:10]        #Extract all elements from 5th to 10th
x[c(-5,-10)] #Extract all elements except 5th and 10th
x[-c(5:10)] #Extract all elements except from 5th to 10th

Output: x[1] #Extract the first element [1] 101 x[c(2,3,4,5)] #Extract 2nd, 3rd, 4th, and 5th elements [1] 102 103 104 105 x[5:10] #Extract all elements from 5th to 10th [1] 105 106 107 108 109 110 x[c(-5,-10)] #Extract all elements except 5th and 10th [1] 101 102 103 104 106 107 108 109 x[-c(5:10)] #Extract all elements except from 5th to 10th [1] 101 102 103 104

Extract Using Logical Vector as Index:

If you use a logical vector for indexing, the position where the logical vector is TRUE will be returned.

x[x < 105]
x[x>=104]

Output: x[x < 105] [1] 101 102 103 104 x[x>=104] [1] 104 105 106 107 108 109 110

Modify a Vector in R:

We can modify a vector and assign a new value to it. You can truncate a vector by using reassignments. Check the below example.

x<- 10:12
x[1]<- 101 #Modify the first element
x
x[2]<-102 #Modify the 2nd element
x
x<- x[1:2] #Truncate the last element
x

Output: x [1] 101 11 12 x[2]<-102 #Modify the 2nd element x [1] 101 102 12 x<- x[1:2] #Truncate the last element x [1] 101 102

Arithmetic Operations on Vectors:

We can use arithmetic operations on two vectors of the same length. They can be added, subtracted, multiplied, or divided. Check the output of the below code.

# Create two vectors.
v1 <- c(1:10)
v2 <- c(101:110)

# Vector addition.
add.result <- v1+v2
print(add.result)
# Vector subtraction.
sub.result <- v2-v1
print(sub.result)
# Vector multiplication.
multi.result <- v1*v2
print(multi.result)
# Vector division.
divi.result <- v2/v1
print(divi.result)

Output: print(add.result) [1] 102 104 106 108 110 112 114 116 118 120 print(sub.result) [1] 100 100 100 100 100 100 100 100 100 100 print(multi.result) [1] 101 204 309 416 525 636 749 864 981 1100 print(divi.result) [1] 101.00000 51.00000 34.33333 26.00000 21.00000 17.66667 15.28571 13.50000 12.11111 11.00000

Find Minimum and Maximum in a Vector:

The minimum and the maximum of a vector can be found using the min() or the max() function. range() is also available which returns the minimum and maximum in a vector.

x<- 1001:1010
max(x) # Find the maximum
min(x) # Find the minimum
range(x) #Find the range

Output: max(x) # Find the maximum [1] 1010 min(x) # Find the minimum [1] 1001 range(x) #Find the range [1] 1001 1010

R Lists

The list is a data structure having elements of mixed data types. A vector having all elements of the same type is called an atomic vector but a vector having elements of a different type is called list.
We can check the type with typeof() or class() function and find the length using length()function.

x <- list("stat",5.1, TRUE, 1 + 4i)
x
class(x)
typeof(x)
length(x)

Output: x [[1]] [1] "stat" [[2]] [1] 5.1 [[3]] [1] TRUE [[4]] [1] 1+4i class(x) [1] “list” typeof(x) [1] “list” length(x) [1] 4

You can create an empty list of a prespecified length with the vector() function.

x <- vector("list", length = 10)
x

Output: x [[1]] NULL [[2]] NULL [[3]] NULL [[4]] NULL [[5]] NULL [[6]] NULL [[7]] NULL [[8]] NULL [[9]] NULL [[10]] NULL

How to extract elements from a list?

Lists can be subset using two syntaxes, the $ operator, and square brackets []. The $ operator returns a named element of a list. The [] syntax returns a list, while the [[]] returns an element of a list.

# subsetting
l$e
l["e"]
l[1:2]
l[c(1:2)] #index using integer vector
l[-c(3:length(l))] #negative index to exclude elements from 3rd up to last.
l[c(T,F,F,F,F)] # logical index to access elements

Output: > l$e [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 1 0 0 0 0 0 0 0 0 0 [2,] 0 1 0 0 0 0 0 0 0 0 [3,] 0 0 1 0 0 0 0 0 0 0 [4,] 0 0 0 1 0 0 0 0 0 0 [5,] 0 0 0 0 1 0 0 0 0 0 [6,] 0 0 0 0 0 1 0 0 0 0 [7,] 0 0 0 0 0 0 1 0 0 0 [8,] 0 0 0 0 0 0 0 1 0 0 [9,] 0 0 0 0 0 0 0 0 1 0 [10,] 0 0 0 0 0 0 0 0 0 1 > l["e"] $e [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 1 0 0 0 0 0 0 0 0 0 [2,] 0 1 0 0 0 0 0 0 0 0 [3,] 0 0 1 0 0 0 0 0 0 0 [4,] 0 0 0 1 0 0 0 0 0 0 [5,] 0 0 0 0 1 0 0 0 0 0 [6,] 0 0 0 0 0 1 0 0 0 0 [7,] 0 0 0 0 0 0 1 0 0 0 [8,] 0 0 0 0 0 0 0 1 0 0 [9,] 0 0 0 0 0 0 0 0 1 0 [10,] 0 0 0 0 0 0 0 0 0 1 > l[1:2] [[1]] [1] 1 2 3 4 [[2]] [1] FALSE > l[c(1:2)] #index using integer vector [[1]] [1] 1 2 3 4 [[2]] [1] FALSE > l[-c(3:length(l))] #negative index to exclude elements from 3rd up to last. [[1]] [1] 1 2 3 4 [[2]] [1] FALSE l[c(T,F,F,F,F)] [[1]] [1] 1 2 3 4

Modifying a List in R:

We can change components of a list through reassignment.

l[["name"]] <- "Kalyan Nandi"
l

Output: [[1]] [1] 1 2 3 4 [[2]] [1] FALSE [[3]] [1] “Hello Statistics!” $d function (arg = 42) { print(“Hello World!”) } $name [1] “Kalyan Nandi”

R Matrices

In R Programming Matrix is a two-dimensional data structure. They contain elements of the same atomic types. A Matrix can be created using the matrix() function. R can also be used for matrix calculations. Matrices have rows and columns containing a single data type. In a matrix, the order of rows and columns is important. Dimension can be checked directly with the dim() function and all attributes of an object can be checked with the attributes() function. Check the below example.

Creating a matrix in R

m <- matrix(nrow = 2, ncol = 3)
dim(m)
attributes(m)
m <- matrix(1:20, nrow = 4, ncol = 5)
m

Output: dim(m) [1] 2 3 attributes(m) $dim [1] 2 3 m <- matrix(1:20, nrow = 4, ncol = 5) m [,1] [,2] [,3] [,4] [,5] [1,] 1 5 9 13 17 [2,] 2 6 10 14 18 [3,] 3 7 11 15 19 [4,] 4 8 12 16 20

Matrices can be created by column-binding or row-binding with the cbind() and rbind() functions.

x<-1:3
y<-10:12
z<-30:32
cbind(x,y,z)
rbind(x,y,z)

Output: cbind(x,y,z) x y z [1,] 1 10 30 [2,] 2 11 31 [3,] 3 12 32 rbind(x,y,z) [,1] [,2] [,3] x 1 2 3 y 10 11 12 z 30 31 32

By default, the matrix function reorders a vector into columns, but we can also tell R to use rows instead.

x <-1:9
matrix(x, nrow = 3, ncol = 3)
matrix(x, nrow = 3, ncol = 3, byrow = TRUE)

Output cbind(x,y,z) x y z [1,] 1 10 30 [2,] 2 11 31 [3,] 3 12 32 rbind(x,y,z) [,1] [,2] [,3] x 1 2 3 y 10 11 12 z 30 31 32

R Arrays

In R, Arrays are the data types that can store data in more than two dimensions. An array can be created using the array() function. It takes vectors as input and uses the values in the dim parameter to create an array. If you create an array of dimensions (2, 3, 4) then it creates 4 rectangular matrices each with 2 rows and 3 columns. Arrays can store only data type.

Give a Name to Columns and Rows:

We can give names to the rows, columns, and matrices in the array by setting the dimnames parameter.

v1 <- c(1,2,3)
v2 <- 100:110
col.names <- c("Col1","Col2","Col3","Col4","Col5","Col6","Col7")
row.names <- c("Row1","Row2")
matrix.names <- c("Matrix1","Matrix2")
arr4 <- array(c(v1,v2), dim=c(2,7,2), dimnames = list(row.names,col.names, matrix.names))
arr4

Output: , , Matrix1 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110 , , Matrix2 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110

Accessing/Extracting Array Elements:

# Print the 2nd row of the 1st matrix of the array.
print(arr4[2,,1])
# Print the element in the 2nd row and 4th column of the 2nd matrix.
print(arr4[2,4,2])
# Print the 2nd Matrix.
print(arr4[,,2])

Output: > print(arr4[2,,1]) Col1 Col2 Col3 Col4 Col5 Col6 Col7 2 100 102 104 106 108 110 > > # Print the element in the 2nd row and 4th column of the 2nd matrix. > print(arr4[2,4,2]) [1] 104 > > # Print the 2nd Matrix. > print(arr4[,,2]) Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110

R Factors

Factors are used to represent categorical data and can be unordered or ordered. An example might be “Male” and “Female” if we consider gender. Factor objects can be created with the factor() function.

x <- factor(c("male", "female", "male", "male", "female"))
x
table(x)

Output: x [1] male female male male female Levels: female male table(x) x female male 2 3

By default, Levels are put in alphabetical order. If you print the above code you will get levels as female and male. But if you want to get your levels in a particular order then set levels parameter like this.

x <- factor(c("male", "female", "male", "male", "female"), levels=c("male", "female"))
x
table(x)

Output: x [1] male female male male female Levels: male female table(x) x male female 3 2

R Dataframes

Data frames are used to store tabular data in R. They are an important type of object in R and are used in a variety of statistical modeling applications. Data frames are represented as a special type of list where every element of the list has to have the same length. Each element of the list can be thought of as a column and the length of each element of the list is the number of rows. Unlike matrices, data frames can store different classes of objects in each column. Matrices must have every element be the same class (e.g. all integers or all numeric).

Creating a Data Frame:

Data frames can be created explicitly with the data.frame() function.

employee <- c('Ram','Sham','Jadu')
salary <- c(21000, 23400, 26800)
startdate <- as.Date(c('2016-11-1','2015-3-25','2017-3-14'))
employ_data <- data.frame(employee, salary, startdate)
employ_data
View(employ_data)

Output: employ_data employee salary startdate 1 Ram 21000 2016-11-01 2 Sham 23400 2015-03-25 3 Jadu 26800 2017-03-14 View(employ_data)

Get the Structure of the Data Frame:

If you look at the structure of the data frame now, you see that the variable employee is a character vector, as shown in the following output:

str(employ_data)

Output: > str(employ_data) 'data.frame': 3 obs. of 3 variables: $ employee : Factor w/ 3 levels "Jadu","Ram","Sham": 2 3 1 $ salary : num 21000 23400 26800 $ startdate: Date, format: "2016-11-01" "2015-03-25" "2017-03-14"

Note that the first column, employee, is of type factor, instead of a character vector. By default, data.frame() function converts character vector into factor. To suppress this behavior, we can pass the argument stringsAsFactors=FALSE.

employ_data <- data.frame(employee, salary, startdate, stringsAsFactors = FALSE)
str(employ_data)

Output: 'data.frame': 3 obs. of 3 variables: $ employee : chr "Ram" "Sham" "Jadu" $ salary : num 21000 23400 26800 $ startdate: Date, format: "2016-11-01" "2015-03-25" "2017-03-14"

R Packages

The primary location for obtaining R packages is CRAN.

You can obtain information about the available packages on CRAN with the available.packages() function.
a <- available.packages()

head(rownames(a), 30) # Show the names of the first 30 packages
Packages can be installed with the install.packages() function in R. To install a single package, pass the name of the lecture to the install.packages() function as the first argument.
The following code installs the ggplot2 package from CRAN.
install.packages(“ggplot2”)
You can install multiple R packages at once with a single call to install.packages(). Place the names of the R packages in a character vector.
install.packages(c(“caret”, “ggplot2”, “dplyr”))

Loading packages
Installing a package does not make it immediately available to you in R; you must load the package. The library() function is used to load packages into R. The following code is used to load the ggplot2 package into R. Do not put the package name in quotes.
library(ggplot2)
If you have Installed your packages without root access using the command install.packages(“ggplot2″, lib=”/data/Rpackages/”). Then to load use the below command.
library(ggplot2, lib.loc=”/data/Rpackages/”)
After loading a package, the functions exported by that package will be attached to the top of the search() list (after the workspace).
library(ggplot2)

search()

R – CSV() files

In R, we can read data from files stored outside the R environment. We can also write data into files that will be stored and accessed by the operating system. R can read and write into various file formats like CSV, Excel, XML, etc.

Getting and Setting the Working Directory

We can check which directory the R workspace is pointing to using the getwd() function. You can also set a new working directory using setwd()function.

# Get and print current working directory.
print(getwd())

# Set current working directory.
setwd("/web/com")

# Get and print current working directory.
print(getwd())

Output: [1] "/web/com/1441086124_2016" [1] "/web/com"

Input as CSV File

The CSV file is a text file in which the values in the columns are separated by a comma. Let’s consider the following data present in the file named input.csv.

You can create this file using windows notepad by copying and pasting this data. Save the file as input.csv using the save As All files(*.*) option in notepad.

Reading a CSV File

Following is a simple example of read.csv() function to read a CSV file available in your current working directory −

data <- read.csv("input.csv")
print(data)

  id,   name,    salary,   start_date,     dept

R- Charts and Graphs

R- Pie Charts

Pie charts are created with the function pie(x, labels=) where x is a non-negative numeric vector indicating the area of each slice and labels= notes a character vector of names for the slices.

Syntax

The basic syntax for creating a pie-chart using the R is −

pie(x, labels, radius, main, col, clockwise)

Following is the description of the parameters used −

x is a vector containing the numeric values used in the pie chart.
labels are used to give a description of the slices.
radius indicates the radius of the circle of the pie chart. (value between −1 and +1).
main indicates the title of the chart.
col indicates the color palette.
clockwise is a logical value indicating if the slices are drawn clockwise or anti-clockwise.

Simple Pie chart

# Simple Pie Chart
slices <- c(10, 12,4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie(slices, labels = lbls, main="Pie Chart of Countries")

3-D pie chart

The pie3D( ) function in the plotrix package provides 3D exploded pie charts.

# 3D Exploded Pie Chart
library(plotrix)
slices <- c(10, 12, 4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie3D(slices,labels=lbls,explode=0.1,
   main="Pie Chart of Countries ")

R -Bar Charts

A bar chart represents data in rectangular bars with a length of the bar proportional to the value of the variable. R uses the function barplot() to create bar charts. R can draw both vertical and Horizontal bars in the bar chart. In the bar chart, each of the bars can be given different colors.

Let us suppose, we have a vector of maximum temperatures (in degree Celsius) for seven days as follows.

max.temp <- c(22, 27, 26, 24, 23, 26, 28)
barplot(max.temp)

Some of the frequently used ones are, “main” to give the title, “xlab” and “ylab” to provide labels for the axes, names.arg for naming each bar, “col” to define color, etc.

We can also plot bars horizontally by providing the argument horiz=TRUE.

# barchart with added parameters
barplot(max.temp,
main = "Maximum Temperatures in a Week",
xlab = "Degree Celsius",
ylab = "Day",
names.arg = c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"),
col = "darkred",
horiz = TRUE)

Simply doing barplot(age) will not give us the required plot. It will plot 10 bars with height equal to the student’s age. But we want to know the number of students in each age category.

This count can be quickly found using the table() function, as shown below.

> table(age)
age
16 17 18 19 
1  2  6  1

Now plotting this data will give our required bar plot. Note below, that we define the argument “density” to shade the bars.

barplot(table(age),
main="Age Count of 10 Students",
xlab="Age",
ylab="Count",
border="red",
col="blue",
density=10
)

A histogram represents the frequencies of values of a variable bucketed into ranges. Histogram is similar to bar chat but the difference is it groups the values into continuous ranges. Each bar in histogram represents the height of the number of values present in that range.

R creates histogram using hist() function. This function takes a vector as an input and uses some more parameters to plot histograms.

Syntax

The basic syntax for creating a histogram using R is −

hist(v,main,xlab,xlim,ylim,breaks,col,border)

Following is the description of the parameters used −

v is a vector containing numeric values used in the histogram.
main indicates the title of the chart.
col is used to set the color of the bars.
border is used to set the border color of each bar.
xlab is used to give a description of the x-axis.
xlim is used to specify the range of values on the x-axis.
ylim is used to specify the range of values on the y-axis.
breaks are used to mention the width of each bar.

Example

A simple histogram is created using input vector, label, col, and border parameters.

The script given below will create and save the histogram in the current R working directory.

# Create data for the graph.
v <-  c(9,13,21,8,36,22,12,41,31,33,19)

# Give the chart file a name.
png(file = "histogram.png")

# Create the histogram.
hist(v,xlab = "Weight",col = "yellow",border = "blue")

# Save the file.
dev.off()

Range of X and Y values

To specify the range of values allowed in X axis and Y axis, we can use the xlim and ylim parameters.

The width of each bar can be decided by using breaks.

# Create data for the graph.
v <- c(9,13,21,8,36,22,12,41,31,33,19)

# Give the chart file a name.
png(file = "histogram_lim_breaks.png")

# Create the histogram.
hist(v,xlab = "Weight",col = "green",border = "red", xlim = c(0,40), ylim = c(0,5),
   breaks = 5)

# Save the file.
dev.off()

R vs SAS – Which Tool is Better?

The debate around data analytics tools has been going on forever. Each time a new one comes out, comparisons transpire. Although many aspects of the tool remain subjective, beginners want to know which tool is better to start with.
The most popular and widely used tools for data analytics are R and SAS. Both of them have been around for a long time and are often pitted against each other. So, let’s compare them based on the most relevant factors.

Availability and Cost: SAS is widely used in most private organizations as it is a commercial software. It is more expensive than any other data analytics tool available. It might thus be a bit difficult buying the software if you are an individual professional or a student starting out. On the other hand, R is an open source software and is completely free to use. Anyone can begin using it right away without having to spend a penny. So, regarding availability and cost, R is hands down the better tool.
Ease of learning: Since SAS is a commercial software, it has a whole lot of online resources available. Also, those who already know SQL might find it easier to adapt to SAS as it comes with PROC SQL option. The tool has a user-friendly GUI. It comes with an extensive documentation and tutorial base which can help early learners get started seamlessly. Whereas, the learning curve for R is quite steep. You need to learn to code at the root level and carrying out simple tasks demand a lot of time and effort with R. However, several forums and online communities post religiously about its usage.
Data Handling Capabilities: When it comes to data handling, both SAS and R perform well, but there are some caveats for the latter. While SAS can even churn through terabytes of data with ease, R might be constrained as it makes use of the available RAM in the machine. This can be a hassle for 32-bit systems with low RAM capacity. Due to this, R can at times become unresponsive or give an ‘out of memory’ error. Both of them can run parallel computations, support integrations for Hadoop, Spark, Cloudera and Apache Pig among others. Also, the availability of devices with better RAM capacity might negate the disadvantages of R.
Graphical Capabilities: Graphical capabilities or data visualization is the strongest forte of R. This is where SAS lacks behind in a major way. R has access to packages like GGPlot, RGIS, Lattice, and GGVIS among others which provide superior graphical competency. In comparison, Base SAS is struggling hard to catch up with the advancements in graphics and visualization in data analytics. Even the graphics packages available in SAS are poorly documented which makes them difficult to use.
Advancements in Tool: Advancements in the industry give way to advancements in tools, and both SAS and R hold up pretty well in this regard. SAS, being a corporate software, rolls out new features and technologies frequently with new versions of its software. However, the updates are not as fast as R since it is open source software and has many contributors throughout the world. Alternatively, the latest updates in SAS are pushed out after thorough testing, making them much more stable, and reliable than R. Both the tools come with a fair share of pros & cons.
Job Scenario: Currently, large corporations insist on using SAS, but SMEs and start-ups are increasingly opting for R, given that it’s free. The current job trend seems to show that while SAS is losing its momentum, R is gaining potential. The job scenario is on the cusp of change, and both the tools seem strong, but since R is on an uphill path, it can probably witness more jobs in the future, albeit not in huge corporates.
Deep Learning Support: While SAS has just begun work on adding deep learning support, R has added support for a few packages which enable deep learning capabilities in the tool. You can use KerasR and keras package in R which are mere interfaces for the original Keras package built on Python. Although none of the tools are excellent facilitators of deep learning, R has seen some recent active developments on this front.
Customer Service Support and Community: As one would expect from full-fledged commercial software, SAS offers excellent customer service support as well as the backing of a helpful community. Since R is free open-source software, expecting customer support will be hard to justify. However, it has a vast online community that can help you with almost everything. On the other hand, no matter what problem you face with SAS, you can immediately reach out to their customer support and get it solved without any hassles.

Final Verdict
As per estimations by the Economic Times, the analytics industry will grow to $16 billion till 2025 in India. If you wish to venture into this domain, there can’t be a better time. Just start learning the tool you think is better based on the comparison points above.

Original article source at: https://www.mygreatlearning.com

#r #programming

Shubham Ankit

1657081614

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

What is OPENPYXL

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

CREATE A NEW WORKBOOK

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 = wb.active
#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
wb.save(filename = "example.xlsx")

READING DATA FROM WORKBOOK

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 SHEETS FROM THE LOADED WORKBOOK

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

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

#getting sheet title
sheet1.title
result = 'sheet2'

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

ACCESSING CELLS AND CELL VALUES

#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)
d.value
10

ITERATING THROUGH ROWS AND COLUMNS

#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)
    .value)

#getting the highest row number
ws.max_row
701

#getting the highest column number
ws.max_column
19

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.

Example:

#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.

Example:

for row in ws.values:
  for value in row:
  print(value)

WRITING DATA TO AN EXCEL FILE

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.

CREATING AND SAVING A NEW WORKBOOK

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

#saving the workbook
wb.save("new.xlsx")

ADDING AND REMOVING SHEETS

#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']

ADDING CELL VALUES

#checking the sheet value
ws['B2'].value
null

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

#checking value
ws['B2'].value
367

ADDING FORMULAS

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)'

wb.save("new2.xlsx")

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

MERGE/UNMERGE CELLS

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.merge_cells('B2:C9')
ws['B2'] = "Merged cells"

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

UNMERGE CELLS

#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')

The above code will unmerge cells from B2 to C9.

INSERTING AN IMAGE

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.

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')

wb.save("new2.xlsx")

Result:

CREATING CHARTS

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:

Example

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

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

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

Result

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.

📄 Resources 📄
OpenPyXL Docs: https://openpyxl.readthedocs.io/en/stable/
Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial
Subscribe: https://www.youtube.com/c/TechWithTim/featured

#python