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How to Use Hotwire Rails

Introduction

We are back with another exciting and much-talked-about Rails tutorial on how to use Hotwire with the Rails application. This Hotwire Rails tutorial is an alternate method for building modern web applications that consume a pinch of JavaScript.

Rails 7 Hotwire is the default front-end framework shipped with Rails 7 after it was launched. It is used to represent HTML over the wire in the Rails application. Previously, we used to add a hotwire-rails gem in our gem file and then run rails hotwire: install. However, with the introduction of Rails 7, the gem got deprecated. Now, we use turbo-rails and stimulus rails directly, which work as Hotwire’s SPA-like page accelerator and Hotwire’s modest JavaScript framework.

What is Hotwire?

Hotwire is a package of different frameworks that help to build applications. It simplifies the developer’s work for writing web pages without the need to write JavaScript, and instead sending HTML code over the wire.

Introduction to The Hotwire Framework:

1. Turbo:

It uses simplified techniques to build web applications while decreasing the usage of JavaScript in the application. Turbo offers numerous handling methods for the HTML data sent over the wire and displaying the application’s data without actually loading the entire page. It helps to maintain the simplicity of web applications without destroying the single-page application experience by using the below techniques:

Turbo Frames: Turbo Frames help to load the different sections of our markup without any dependency as it divides the page into different contexts separately called frames and updates these frames individually.
Turbo Drive: Every link doesn’t have to make the entire page reload when clicked. Only the HTML contained within the tag will be displayed.
Turbo Streams: To add real-time features to the application, this technique is used. It helps to bring real-time data to the application using CRUD actions.

2. Stimulus

It represents the JavaScript framework, which is required when JS is a requirement in the application. The interaction with the HTML is possible with the help of a stimulus, as the controllers that help those interactions are written by a stimulus.

3. Strada

Not much information is available about Strada as it has not been officially released yet. However, it works with native applications, and by using HTML bridge attributes, interaction is made possible between web applications and native apps.

Simple diagrammatic representation of Hotwire Stack:

Prerequisites For Hotwire Rails Tutorial

As we are implementing the Ruby on Rails Hotwire tutorial, make sure about the following installations before you can get started.

• Ruby on Rails
• Hotwire gem
• PostgreSQL/SQLite (choose any one database)
• Turbo Rails
• Stimulus.js

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Create a new Rails Project

Find the following commands to create a rails application.

mkdir ~/projects/railshotwire
cd ~/projects/railshotwire
echo "source 'https://rubygems.org'" > Gemfile
echo "gem 'rails', '~> 7.0.0'" >> Gemfile
bundle install  
bundle exec rails new . --force -d=postgresql

Now create some files for the project, up till now no usage of Rails Hotwire can be seen.
Fire the following command in your terminal.

• For creating a default controller for the application

echo "class HomeController < ApplicationController" > app/controllers/home_controller.rb
echo "end" >> app/controllers/home_controller.rb

• For creating another controller for the application

echo "class OtherController < ApplicationController" > app/controllers/other_controller.rb
echo "end" >> app/controllers/home_controller.rb

• For creating routes for the application

echo "Rails.application.routes.draw do" > config/routes.rb
echo '  get "home/index"' >> config/routes.rb
echo '  get "other/index"' >> config/routes.rb
echo '  root to: "home#index"' >> config/routes.rb
echo 'end' >> config/routes.rb

• For creating a default view for the application

mkdir app/views/home
echo '<h1>This is Rails Hotwire homepage</h1>' > app/views/home/index.html.erb
echo '<div><%= link_to "Enter to other page", other_index_path %></div>' >> app/views/home/index.html.erb

• For creating another view for the application

mkdir app/views/other
echo '<h1>This is Another page</h1>' > app/views/other/index.html.erb
echo '<div><%= link_to "Enter to home page", root_path %></div>' >> app/views/other/index.html.erb

• For creating a database and schema.rb file for the application

bin/rails db:create
bin/rails db:migrate

• For checking the application run bin/rails s and open your browser, your running application will have the below view.

Additionally, you can clone the code and browse through the project. Here’s the source code of the repository: Rails 7 Hotwire application

Now, let’s see how Hotwire Rails can work its magic with various Turbo techniques.

Hotwire Rails: Turbo Drive

Go to your localhost:3000 on your web browser and right-click on the Inspect and open a Network tab of the DevTools of the browser.

Now click on go to another page link that appears on the home page to redirect from the home page to another page. In our Network tab, we can see that this action of navigation is achieved via XHR. It appears only the part inside HTML is reloaded, here neither the CSS is reloaded nor the JS is reloaded when the navigation action is performed.

By performing this action we can see that Turbo Drive helps to represent the HTML response without loading the full page and only follows redirect and reindeer HTML responses which helps to make the application faster to access.

Hotwire Rails: Turbo Frame

This technique helps to divide the current page into different sections called frames that can be updated separately independently when new data is added from the server.
Below we discuss the different use cases of Turbo frame like inline edition, sorting, searching, and filtering of data.

Let’s perform some practical actions to see the example of these use cases.

Make changes in the app/controllers/home_controller.rb file

#CODE

class HomeController < ApplicationController
   def turbo_frame_form
   end
   
   def turbo_frame submit
      extracted_anynumber = params[:any][:anynumber]
      render :turbo_frame_form, status: :ok, locals: {anynumber: extracted_anynumber,      comment: 'turbo_frame_submit ok' }
   end
end

Add app/views/home/turbo_frame_form.html.erb file to the application and add this content inside the file.

#CODE

<section>

    <%= turbo_frame_tag 'anyframe' do %>
            
      <div>
          <h2>Frame view</h2>
          <%= form_with scope: :any, url: turbo_frame_submit_path, local: true do |form| %>
              <%= form.label :anynumber, 'Type an integer (odd or even)', 'class' => 'my-0  d-inline'  %>
              <%= form.text_field :anynumber, type: 'number', 'required' => 'true', 'value' => "#{local_assigns[:anynumber] || 0}",  'aria-describedby' => 'anynumber' %>
              <%= form.submit 'Submit this number', 'id' => 'submit-number' %>
          <% end %>
      </div>
      <div>
        <h2>Data of the view</h2>
        <pre style="font-size: .7rem;"><%= JSON.pretty_generate(local_assigns) %></pre> 
      </div>
      
    <% end %>

</section>

Make some adjustments in routes.rb

#CODE

Rails.application.routes.draw do
  get 'home/index'
  get 'other/index'

  get '/home/turbo_frame_form' => 'home#turbo_frame_form', as: 'turbo_frame_form'
  post '/home/turbo_frame_submit' => 'home#turbo_frame_submit', as: 'turbo_frame_submit'


  root to: "home#index"
end

• Next step is to change homepage view in app/views/home/index.html.erb

#CODE

<h1>This is Rails Hotwire home page</h1>
<div><%= link_to "Enter to other page", other_index_path %></div>

<%= turbo_frame_tag 'anyframe' do %>        
  <div>
      <h2>Home view</h2>
      <%= form_with scope: :any, url: turbo_frame_submit_path, local: true do |form| %>
          <%= form.label :anynumber, 'Type an integer (odd or even)', 'class' => 'my-0  d-inline'  %>
          <%= form.text_field :anynumber, type: 'number', 'required' => 'true', 'value' => "#{local_assigns[:anynumber] || 0}",  'aria-describedby' => 'anynumber' %>
          <%= form.submit 'Submit this number', 'id' => 'submit-number' %>
      <% end %>
  <div>
<% end %>

After making all the changes, restart the rails server and refresh the browser, the default view will appear on the browser.

Now in the field enter any digit, after entering the digit click on submit button, and as the submit button is clicked we can see the Turbo Frame in action in the below screen, we can observe that the frame part changed, the first title and first link didn’t move.

Hotwire Rails: Turbo Streams

Turbo Streams deliver page updates over WebSocket, SSE or in response to form submissions by only using HTML and a series of CRUD-like operations, you are free to say that either

• Update the piece of HTML while responding to all the other actions like the post, put, patch, and delete except the GET action.
• Transmit a change to all users, without reloading the browser page.

This transmit can be represented by a simple example.

• Make changes in app/controllers/other_controller.rb file of rails application

#CODE

class OtherController < ApplicationController

  def post_something
    respond_to do |format|
      format.turbo_stream {  }
    end
  end

   end

Add the below line in routes.rb file of the application

#CODE

post '/other/post_something' => 'other#post_something', as: 'post_something'

Superb! Rails will now attempt to locate the app/views/other/post_something.turbo_stream.erb template at any moment the ‘/other/post_something’ endpoint is reached.

For this, we need to add app/views/other/post_something.turbo_stream.erb template in the rails application.

#CODE

<turbo-stream action="append" target="messages">
  <template>
    <div id="message_1">This changes the existing message!</div>
  </template>
</turbo-stream>

This states that the response will try to append the template of the turbo frame with ID “messages”.

Now change the index.html.erb file in app/views/other paths with the below content.

#CODE

<h1>This is Another page</h1>
<div><%= link_to "Enter to home page", root_path %></div>

<div style="margin-top: 3rem;">
  <%= form_with scope: :any, url: post_something_path do |form| %>
      <%= form.submit 'Post any message %>
  <% end %>
  <turbo-frame id="messages">
    <div>An empty message</div>
  </turbo-frame>
</div>

• After making all the changes, restart the rails server and refresh the browser, and go to the other page.

• Once the above screen appears, click on the Post any message button

This action shows that after submitting the response, the Turbo Streams help the developer to append the message, without reloading the page.

Another use case we can test is that rather than appending the message, the developer replaces the message. For that, we need to change the content of app/views/other/post_something.turbo_stream.erb template file and change the value of the action attribute from append to replace and check the changes in the browser.

#CODE

<turbo-stream action="replace" target="messages">
  <template>
    <div id="message_1">This changes the existing message!</div>
  </template>
</turbo-stream>

When we click on Post any message button, the message that appear below that button will get replaced with the message that is mentioned in the app/views/other/post_something.turbo_stream.erb template

Stimulus

There are some cases in an application where JS is needed, therefore to cover those scenarios we require Hotwire JS tool. Hotwire has a JS tool because in some scenarios Turbo-* tools are not sufficient. But as we know that Hotwire is used to reduce the usage of JS in an application, Stimulus considers HTML as the single source of truth. Consider the case where we have to give elements on a page some JavaScript attributes, such as data controller, data-action, and data target. For that, a stimulus controller that can access elements and receive events based on those characteristics will be created.

Make a change in app/views/other/index.html.erb template file in rails application

#CODE

<h1>This is Another page</h1>
<div><%= link_to "Enter to home page", root_path %></div>

<div style="margin-top: 2rem;">
  <%= form_with scope: :any, url: post_something_path do |form| %>
      <%= form.submit 'Post something' %>
  <% end %>
  <turbo-frame id="messages">
    <div>An empty message</div>
  </turbo-frame>
</div>

<div style="margin-top: 2rem;">
  <h2>Stimulus</h2>  
  <div data-controller="hello">
    <input data-hello-target="name" type="text">
    <button data-action="click->hello#greet">
      Greet
    </button>
    <span data-hello-target="output">
    </span>
  </div>
</div>

Make changes in the hello_controller.js in path app/JavaScript/controllers and add a stimulus controller in the file, which helps to bring the HTML into life.

#CODE

import { Controller } from "@hotwired/stimulus"

export default class extends Controller {
  static targets = [ "name", "output" ]

  greet() {
    this.outputTarget.textContent =
      `Hello, ${this.nameTarget.value}!`
  }
}

Go to your browser after making the changes in the code and click on Enter to other page link which will navigate to the localhost:3000/other/index page there you can see the changes implemented by the stimulus controller that is designed to augment your HTML with just enough behavior to make it more responsive.

With just a little bit of work, Turbo and Stimulus together offer a complete answer for applications that are quick and compelling.

Using Rails 7 Hotwire helps to load the pages at a faster speed and allows you to render templates on the server, where you have access to your whole domain model. It is a productive development experience in ROR, without compromising any of the speed or responsiveness associated with SPA.

Conclusion

We hope you were satisfied with our Rails Hotwire tutorial. Write to us at service@bacancy.com for any query that you want to resolve, or if you want us to share a tutorial on your query.

For more such solutions on RoR, check out our Ruby on Rails Tutorials. We will always strive to amaze you and cater to your needs.

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

#rails #ruby 

A Practical Guide of Unsupervised Learning Algorithms

In this article, learn about Machine Learning Tutorial: A Practical  Guide of Unsupervised Learning Algorithms. Machine learning is a fast-growing technology that allows computers to learn from the past and predict the future. It uses numerous algorithms for building mathematical models and predicting future trends. Machine learning (ML) has widespread applications in the industry, including speech recognition, image recognition, churn prediction, email filtering, chatbot development, recommender systems, and much more.

Machine learning (ML) can be classified into three main categories; supervised, unsupervised, and reinforcement learning. In supervised learning, the model is trained on labeled data. While in unsupervised learning, unlabeled data is provided to the model to predict the outcomes. Reinforcement learning is feedback learning in which the agent collects a reward for each correct action and gets a penalty for a wrong decision. The goal of the learning agent is to get maximum reward points and deduce the error.

What is Unsupervised Learning?

In unsupervised learning, the model learns from unlabeled data without proper supervision.

Unsupervised learning uses machine learning techniques to cluster unlabeled data based on similarities and differences. The unsupervised algorithms discover hidden patterns in data without human supervision. Unsupervised learning aims to arrange the raw data into new features or groups together with similar patterns of data.

For instance, to predict the churn rate, we provide unlabeled data to our model for prediction. There is no information given that customers have churned or not. The model will analyze the data and find hidden patterns to categorize into two clusters: churned and non-churned customers.

Unsupervised Learning Approaches

Unsupervised algorithms can be used for three tasks—clustering, dimensionality reduction, and association. Below, we will highlight some commonly used clustering and association algorithms.

Clustering Techniques

Clustering, or cluster analysis, is a popular data mining technique for unsupervised learning. The clustering approach works to group non-labeled data based on similarities and differences. Unlike supervised learning, clustering algorithms discover natural groupings in data. 

A good clustering method produces high-quality clusters having high intra-class similarity (similar data within a cluster) and less intra-class similarity (cluster data is dissimilar to other clusters). 

It can be defined as the grouping of data points into various clusters containing similar data points. The same objects remain in the group that has fewer similarities with other groups. Here, we will discuss two popular clustering techniques: K-Means clustering and DBScan Clustering.

K-Means Clustering

K-Means is the simplest unsupervised technique used to solve clustering problems. It groups the unlabeled data into various clusters. The K value defines the number of clusters you need to tell the system how many to create.

K-Means is a centroid-based algorithm in which each cluster is associated with the centroid. The goal is to minimize the sum of the distances between the data points and their corresponding clusters.

It is an iterative approach that breaks down the unlabeled data into different clusters so that each data point belongs to a group with similar characteristics.

K-means clustering performs two tasks:

1. Using an iterative process to create the best value of K.
2. Each data point is assigned to its closest k-center. The data point that is closer to the particular k-center makes a cluster.

 

An illustration of K-means clustering. Image source

DBScan Clustering

“DBScan” stands for “Density-based spatial clustering of applications with noise.” There are three main words in DBscan: density, clustering, and noise. Therefore, this algorithm uses the notion of density-based clustering to form clusters and detect the noise.

Clusters are usually dense regions that are separated by lower density regions. Unlike the k-means algorithm, which works only on well-separated clusters, DBscan has a wider scope and can create clusters within the cluster. It discovers clusters of various shapes and sizes from a large set of data, which consists of noise and outliers.

There are two parameters in the DBScan algorithm:

minPts: The threshold, or the minimum number of points grouped together for a region considered as a dense region.

eps(ε): The distance measure used to locate the points in the neighborhood. 

 

An illustration of density-based clustering. Image Source 

Association Rule Mining

An association rule mining is a popular data mining technique. It finds interesting correlations in large numbers of data items. This rule shows how frequently items occur in a transaction.

Market Basket Analysis is a typical example of an association rule mining that finds relationships between items in the grocery store. It enables retailers to identify and analyze the associations between items that people frequently buy.

Important terminology used in association rules:

Support: It tells us about the combination of items bought frequently or frequently bought items.

Confidence: It tells us how often the items A and B occur together, given the number of times A occurs.

Lift: The lift indicates the strength of a rule over the random occurrence of A and B. For instance, A->B, the life value is 5. It means that if you buy A, the occurrence of buying B is five times.

The Apriori algorithm is a well-known association rule based technique.

Apriori algorithm 

The Apriori algorithm was proposed by R. Agarwal and R. Srikant in 1994 to find the frequent items in the dataset. The algorithm’s name is based on the fact that it uses prior knowledge of frequently occurring things. 

The Apriori algorithm finds frequently occurring items with minimum support. 

It consists of two steps:

• Generation of candidate itemsets.
• Removing items that are infrequent and don’t fulfill the criteria of minimum support.

Practical Implementation of Unsupervised Algorithms 

In this tutorial, you will learn about the implementation of various unsupervised algorithms in Python. Scikit-learn is a powerful Python library widely used for various unsupervised learning tasks. It is an open-source library that provides numerous robust algorithms, which include classification, dimensionality reduction, clustering techniques, and association rules.

Let’s begin!

1. K-Means algorithm 

Now let’s dive deep into the implementation of the K-Means algorithm in Python. We’ll break down each code snippet so that you can understand it easily.

Import libraries

First of all, we will import the required libraries and get access to the functions.

#Let's import the required libraries
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns

Loading the dataset 

The dataset is taken from the kaggle website. You can easily download it from the given link. To load the dataset, we use the pd.read_csv() function. head() returns the first five rows of the dataset.

my_data = pd.read_csv('Customers_Mall.csv.') my_data.head()

The dataset contains five columns: customer ID, gender, age, annual income in (K$), and spending score from 1-100. 

Data Preprocessing 

The info() function is used to get quick information about the dataset. It shows the number of entries, columns, total non-null values, memory usage, and datatypes. 

my_data.info()

 

To check the missing values in the dataset, we use isnull().sum(), which returns the total number of null values.

 

#Check missing values 
my_data.isnull().sum()

 

The box plot or whisker plot is used to detect outliers in the dataset. It also shows a statistical five number summary, which includes the minimum, first quartile, median (2nd quartile), third quartile, and maximum.

my_data.boxplot(figsize=(8,4))

Using Box Plot, we’ve detected an outlier in the annual income column. Now we will try to remove it before training our model. 

#let's remove outlier from data
med =61
my_data["Annual Income (k$)"] = np.where(my_data["Annual Income (k$)"] >
 120,med,my_data['Annual Income (k$)'])

The outlier in the annual income column has been removed now to confirm we used the box plot again.

my_data.boxplot(figsize=(8,5))

Data Visualization

A histogram is used to illustrate the important features of the distribution of data. The hist() function is used to show the distribution of data in each numerical column.

my_data.hist(figsize=(6,6)) 

The correlation heatmap is used to find the potential relationships between variables in the data and to display the strength of those relationships. To display the heatmap, we have used the seaborn plotting library.

plt.figure(figsize=(10,6)) sns.heatmap(my_data.corr(), annot=True, cmap='icefire').set_title('seaborn') plt.show()

Choosing the Best K Value

The iloc() function is used to select a particular cell of the data. It enables us to select a value that belongs to a specific row or column. Here, we’ve chosen the annual income and spending score columns.

 

X_val = my_data.iloc[:, 3:].values X_val

 

# Loading Kmeans Library

from sklearn.cluster import KMeans

Now we will select the best value for K using the Elbow’s method. It is used to determine the optimal number of clusters in K-means clustering.

my_val = []

for i in range(1,11):

    kmeans = KMeans(n_clusters = i, init='k-means++', random_state = 123)

    kmeans.fit(X_val)

    my_val.append(kmeans.inertia_)

The sklearn.cluster.KMeans() is used to choose the number of clusters along with the initialization of other parameters. To display the result, just call the variable.

my_val #Visualization of clusters using elbow’s method plt.plot(range(1,11),my_val) plt.xlabel('The No of clusters') plt.ylabel('Outcome') plt.title('The Elbow Method') plt.show()

Through Elbow’s Method, when the graph looks like an arm, then the elbow on the arm is the best value of K. In this case, we’ve taken K=3, which is the optimal value for K.

kmeans = KMeans(n_clusters = 3, init='k-means++') kmeans.fit(X_val) #To show centroids of clusters  kmeans.cluster_centers_ #Prediction of K-Means clustering  y_kmeans = kmeans.fit_predict(X_val) y_kmeans

Splitting the dataset into three clusters

The scatter graph is used to plot the classification results of our dataset into three clusters.

plt.scatter(X_val[y_kmeans == 0,0], X_val[y_kmeans == 0,1], c='red',s=100)

plt.scatter(X_val[y_kmeans == 1,0], X_val[y_kmeans == 1,1], c='green',s=100)

plt.scatter(X_val[y_kmeans == 2,0], X_val[y_kmeans == 2,1], c='orange',s=100)

plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=300, c='brown')

plt.title('K-Means Unsupervised Learning')

plt.show()

2. Apriori Algorithm

To implement the apriori algorithm, we will utilize “The Bread Basket” dataset. The dataset is available on Kaggle and you can download it from the link. This algorithm suggests products based on the user’s purchase history. Walmart has greatly utilized the algorithm to recommend relevant items to its users.

Let’s implement the Apriori algorithm in Python. 

Import libraries 

To implement the algorithm, we need to import some important libraries.

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

import seaborn as sns

Loading the dataset 

The dataset contains five columns and 20507 entries. The data_time is a prominent column and we can extract many vital insights from it.

my_data= pd.read_csv("bread basket.csv") my_data.head()

Data Preprocessing 

Convert the data_time into an appropriate format.

my_data['date_time'] = pd.to_datetime(my_data['date_time'])

#Total No of unique customers

my_data['Transaction'].nunique()

Now we want to extract new columns from the data_time to extract meaningful information from the data.

#Let's extract date

my_data['date'] = my_data['date_time'].dt.date

#Let's extract time

my_data['time'] = my_data['date_time'].dt.time

#Extract month and replacing it with String

my_data['month'] = my_data['date_time'].dt.month

my_data['month'] = my_data['month'].replace((1,2,3,4,5,6,7,8,9,10,11,12), 

                                          ('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug',

                                          'Sep','Oct','Nov','Dec'))

#Extract hour

my_data[‘hour’] = my_data[‘date_time’].dt.hour

# Replacing hours with text

# Replacing hours with text

hr_num = (1,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23)

hr_obj = (‘1-2′,’7-8′,’8-9′,’9-10′,’10-11′,’11-12′,’12-13′,’13-14′,’14-15’,

               ’15-16′,’16-17′,’17-18′,’18-19′,’19-20′,’20-21′,’21-22′,’22-23′,’23-24′)

my_data[‘hour’] = my_data[‘hour’].replace(hr_num, hr_obj)

# Extracting weekday and replacing it with String 

my_data[‘weekday’] = my_data[‘date_time’].dt.weekday

my_data[‘weekday’] = my_data[‘weekday’].replace((0,1,2,3,4,5,6), 

                                          (‘Mon’,’Tues’,’Wed’,’Thur’,’Fri’,’Sat’,’Sun’))

#Now drop date_time column

my_data.drop(‘date_time’, axis = 1, inplace = True)

After extracting the date, time, month, and hour columns, we dropped the data_time column.

Now to display, we simply use the head() function to see the changes in the dataset.

my_data.head()

# cleaning the item column

my_data[‘Item’] = my_data[‘Item’].str.strip()

my_data[‘Item’] = my_data[‘Item’].str.lower()

my_data.head()

Data Visualization 

To display the top 10 items purchased by customers, we used a barplot() of the seaborn library. 

plt.figure(figsize=(10,5))

sns.barplot(x=my_data.Item.value_counts().head(10).index, y=my_data.Item.value_counts().head(10).values,palette='RdYlGn')

plt.xlabel('No of Items', size = 17)

plt.xticks(rotation=45)

plt.ylabel('Total Items', size = 18)

plt.title('Top 10 Items purchased', color = 'blue', size = 23)

plt.show()

From the graph, coffee is the top item purchased by the customers, followed by bread.

Now, to display the number of orders received each month, the groupby() function is used along with barplot() to visually show the results.

mon_Tran =my_data.groupby('month')['Transaction'].count().reset_index() mon_Tran.loc[:,"mon_order"] =[4,8,12,2,1,7,6,3,5,11,10,9] mon_Tran.sort_values("mon_order",inplace=True) plt.figure(figsize=(12,5)) sns.barplot(data = mon_Tran, x = "month", y = "Transaction") plt.xlabel('Months', size = 14) plt.ylabel('Monthly Orders', size = 14) plt.title('No of orders received each month', color = 'blue', size = 18) plt.show()

To show the number of orders received each day, we applied groupby() to the weekday column.

wk_Tran = my_data.groupby('weekday')['Transaction'].count().reset_index()

wk_Tran.loc[:,"wk_ord"] = [4,0,5,6,3,1,2]

wk_Tran.sort_values("wk_ord",inplace=True)

plt.figure(figsize=(11,4))

sns.barplot(data = wk_Tran, x = "weekday", y = "Transaction",palette='RdYlGn')

plt.xlabel('Week Day', size = 14)

plt.ylabel('Per day orders', size = 14)

plt.title('Orders received per day', color = 'blue', size = 18)

plt.show()

 Implementation of the Apriori Algorithm 

We import the mlxtend library to implement the association rules and count the number of items.

from mlxtend.frequent_patterns import association_rules, apriori

tran_str= my_data.groupby(['Transaction', 'Item'])['Item'].count().reset_index(name ='Count')

tran_str.head(8)

Now we’ll make a mxn matrix where m=transaction and n=items, and each row represents whether the item was in the transaction or not.

Mar_baskt = tran_str.pivot_table(index='Transaction', columns='Item', values='Count', aggfunc='sum').fillna(0)

Mar_baskt.head()

We want to make a function that returns 0 and 1. 0 means that the item wasn’t present in the transaction, while 1 means the item exists.

def encode(val):

    if val<=0:

        return 0

    if val>=1:

        return 1

#Let's apply the function to the dataset

Basket=Mar_baskt.applymap(encode)

Basket.head()

#using apriori algorithm to set min_support 0.01 means 1% freq_items = apriori(Basket, min_support = 0.01,use_colnames = True) freq_items.head()

Using the association_rules() function to generate the most frequent items from the dataset.

App_rule= association_rules(freq_items, metric = "lift", min_threshold = 1) App_rule.sort_values('confidence', ascending = False, inplace = True) App_rule.head()

From the above implementation, the most frequent items are coffee and toast, both having a lift value of 1.47 and a confidence value of 0.70. 

3. Principal Component Analysis 

Principal component analysis (PCA) is one of the most widely used unsupervised learning techniques. It can be used for various tasks, including dimensionality reduction, information compression, exploratory data analysis and Data de-noising.

Let’s use the PCA algorithm!

First we import the required libraries to implement this algorithm.

import numpy as np 

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

from sklearn.decomposition import PCA

from sklearn.datasets import load_digits

Loading the Dataset 

To implement the PCA algorithm the load_digits dataset of Scikit-learn is used which can easily be loaded using the below command. The dataset contains images data which include 1797 entries and 64 columns.

 

#Load the dataset

my_data= load_digits()

#Creating features

X_value = my_data.data

#Creating target

#Let's check the shape of X_value

X_value.shape

 

 

 

#Each image is 8x8 pixels therefore 64px  my_data.images[10] #Let's display the image plt.gray()  plt.matshow(my_data.images[34])  plt.show()

Now let’s project data from 64 columns to 16 to show how 16 dimensions classify the data.

X_val = my_data.data 

y_val = my_data.target

my_pca = PCA(16)  

X_projection = my_pca.fit_transform(X_val)

print(X_val.shape)

print(X_projection.shape)

Using colormap we visualize that with only ten dimensions  we can classify the data points. Now we’ll select the optimal number of dimensions (principal components) by which data can be reduced into lower dimensions.

plt.scatter(X_projection[:, 0], X_projection[:, 1], c=y_val, edgecolor='white',

            cmap=plt.cm.get_cmap("gist_heat",12))

plt.colorbar();

pca=PCA().fit(X_val)

plt.plot(np.cumsum(my_pca.explained_variance_ratio_))

plt.xlabel('Principal components')

plt.ylabel('Explained variance')

Based on the below graph, only 12 components are required to explain more than 80% of the variance which is still better than computing all the 64 features. Thus, we’ve reduced the large number of dimensions into 12 dimensions to avoid the dimensionality curse. pca=PCA().fit(X_val)

plt.plot(np.cumsum(pca.explained_variance_ratio_))

plt.xlabel('Principal components')

plt.ylabel('Explained variance')



#Let's visualize how it looks like

Unsupervised_pca = PCA(12)  

X_pro = Unsupervised_pca.fit_transform(X_val)

print("New Data Shape is =>",X_pro.shape)

#Let's Create a scatter plot

plt.scatter(X_pro[:, 0], X_pro[:, 1], c=y_val, edgecolor='white',

            cmap=plt.cm.get_cmap("nipy_spectral",10))

plt.colorbar();

Wrapping Up 

In this machine learning tutorial, we’ve implemented the Kmeans, Apriori, and PCA algorithms. These are some of the most widely used algorithms, having numerous industrial applications and solve many real world problems. For instance, K-means clustering is used in astronomy to study stellar and galaxy spectra, solar polarization spectra, and X-ray spectra. And, Apriori is used by retail stores to optimize their product inventory. 

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Original article sourced at: https://thedatascientist.com

#machine-learning 

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

1. Design the PCB using your favorite CAD or drawing software.
2. Print the top and bottom copper and top silk screen layers to a PDF file.
3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
4. Use a Gerber viewer to double-check the output against the original PCB design.
5. Make adjustments as needed.
6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

---

pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

---

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl 

Know Everything About HTML With HTML Experts

HTML stands for a hypertext markup language. For the designs to be displayed in web browser HTML is the markup language. Technologies like Cascading style sheets (CSS) and scripting languages such as JavaScript assist HTML. With the help of HTML websites and the web, designs are created. Html has a wide range of academic applications. HTML has a series of elements. HTML helps to display web content. Its elements tell the web how to display the contents.

The document component of HTML is known as an HTML element. HTML element helps in displaying the web pages. An HTML document is a mixture of text nodes and HTML elements.

Basics of HTML are-

The simple fundamental components oh HTML is

1. Head- the setup information for the program and web pages is carried in the head
2. Body- the actual substance that is to be shown on the web page is carried in the body
3. HTML- information starts and ends with and labels.
4. Comments- come up in between

Html versions timeline

1. HTML was created in 1990. Html is a program that is updated regularly. the timeline for the HTML versions is
2. HTML 2- November, 1995
3. HTML 3- January, 1997
4. HTML 4- December, 1997; April, 1998; December, 1999; May, 2000
5. HTML 5- October, 2014; November, 2016; December, 2017

HTML draft version timelines are

1. October 1991
2. June 1992
3. November 1992
4. June 1993
5. November 1993
6. November 1994
7. April 1995
8. January 2008
9. HTML 5-
   2011, last call
   2012 candidate recommendation
   2014 proposed recommendation and recommendation

HTML helps in creating web pages. In web pages, there are texts, pictures, colouring schemes, tables, and a variety of other things. HTML allows all these on a web page.
There are a lot of attributes in HTML. It may get difficult to memorize these attributes. HTML is a tricky concept. Sometimes it gets difficult to find a single mistake that doesn’t let the web page function properly.

Many minor things are to be kept in mind in HTML. To complete an HTML assignment, it is always advisable to seek help from online experts. These experts are well trained and acknowledged with the subject. They provide quality content within the prescribed deadline. With several positive reviews, the online expert help for HTML assignment is highly recommended.

#html assignment help #html assignment writing help #online html assignment writing help #html assignment help service online #what is html #about html

How To Create Tabs in Less Than 12 Minutes Using HTML CSS

In this video, We have created a Tab design in HTML and CSS without using JavaScript. I have also provided HTML and CSS code on my website, you can visit my website by clicking given link. 

Subscribe: https://www.youtube.com/@CodingLabYT/featured 

Source Code :

HTML :

<!DOCTYPE html>
<html lang="en" dir="ltr">
  <head>
    <meta charset="UTF-8">
    <!--<title> CSS Vertical Tabs </title>-->
    <link rel="stylesheet" href="style.css">
    <!-- Fontawesome CDN Link -->
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.2/css/all.min.css"/>
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
   </head>
<body>
  <div class="container">
    <div class="topic">CSS Vertical Tabs.</div>
    <div class="content">
      <input type="radio" name="slider" checked id="home">
      <input type="radio" name="slider" id="blog">
      <input type="radio" name="slider" id="help">
      <input type="radio" name="slider" id="code">
      <input type="radio" name="slider" id="about">
      <div class="list">
        <label for="home" class="home">
        <i class="fas fa-home"></i>
        <span class="title">Home</span>
      </label>
      <label for="blog" class="blog">
        <span class="icon"><i class="fas fa-blog"></i></span>
        <span class="title">Blog</span>
      </label>
      <label for="help" class="help">
        <span class="icon"><i class="far fa-envelope"></i></span>
        <span class="title">Help</span>
      </label>
      <label for="code" class="code">
        <span class="icon"><i class="fas fa-code"></i></span>
        <span class="title">Code</span>
      </label>
      <label for="about" class="about">
        <span class="icon"><i class="far fa-user"></i></span>
        <span class="title">About</span>
      </label>
      <div class="slider"></div>
    </div>
      <div class="text-content">
        <div class="home text">
          <div class="title">Home Content</div>
          <p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Quasi excepturi ducimus sequi dignissimos expedita tempore omnis quos cum, possimus, aspernatur esse nihil commodi est maiores dolorum rem iusto atque, beatae voluptas sit eligendi architecto dolorem temporibus. Non magnam ipsam, voluptas quasi nam dicta ut. Ad corrupti aliquid obcaecati alias, nemo veritatis porro nisi eius sequi dignissimos ea repellendus quibusdam minima ipsum animi quae, libero quisquam a! Laudantium iste est sapiente, ullam itaque odio iure laborum voluptatem quaerat tempore doloremque quam modi, atque minima enim saepe! Dolorem rerum minima incidunt, officia!</p>
        </div>
        <div class="blog text">
          <div class="title">Blog Content</div>
          <p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Alias tempora, unde reprehenderit incidunt excepturi blanditiis ullam dignissimos provident quam? Fugit, enim! Architecto ad officiis dignissimos ex quae iusto amet pariatur, ea eius aut velit, tempora magnam hic autem maiores unde corrupti tenetur delectus! Voluptatum praesentium labore consectetur ea qui illum illo distinctio, sunt, ipsam rerum optio quibusdam cum a? Aut facilis non fuga molestiae voluptatem omnis reprehenderit, dignissimos commodi repellat sapiente natus ipsam, ipsa distinctio. Ducimus repudiandae fuga aliquid, numquam.</p>
        </div>
        <div class="help text">
          <div class="title">Help Content</div>
          <p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Maiores error neque, officia excepturi dolores quis dolor, architecto iusto deleniti a soluta nostrum. Fuga reiciendis beatae, dicta voluptatem, vitae eligendi maxime accusamus. Amet totam aut odio velit cumque autem neque sequi provident mollitia, nisi sunt maiores facilis debitis in officiis asperiores saepe quo soluta laudantium ad non quisquam! Repellendus culpa necessitatibus aliquam quod mollitia perspiciatis ducimus doloribus perferendis autem, omnis, impedit, veniam qui dolorem? Ipsam nihil assumenda, sit ratione blanditiis eius aliquam libero iusto, dolorum aut perferendis modi laboriosam sint dolor.</p>
        </div>
        <div class="code text">
          <div class="title">Code Content</div>
          <p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Tempore magnam vitae inventore blanditiis nam tenetur voluptates doloribus error atque reprehenderit, necessitatibus minima incidunt a eius corrupti placeat, quasi similique deserunt, harum? Quia ut impedit ab earum expedita soluta repellat perferendis hic tempora inventore, accusantium porro consequuntur quisquam et assumenda distinctio dignissimos doloremque enim nemo delectus deserunt! Ullam perspiciatis quae aliquid animi quam amet deleniti, at dolorum tenetur, tempore laborum.</p>
        </div>
        <div class="about text">
          <div class="title">About Content</div>
          <p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Necessitatibus incidunt possimus quas ad, sit nam veniam illo ullam sapiente, aspernatur fugiat atque. Laboriosam libero voluptatum molestiae veniam earum quisquam, laudantium aperiam, eligendi dicta animi maxime sunt non nisi, ex, ipsa! Soluta ex, quibusdam voluptatem distinctio asperiores recusandae veritatis optio dolorem illo nesciunt quos ullam, dicta numquam ipsam cumque sed. Blanditiis omnis placeat, enim sit dicta eligendi voluptatibus laborum consectetur repudiandae tempora numquam molestiae rerum mollitia nemo. Velit perspiciatis, nesciunt, quo illo quas error debitis molestiae et sapiente neque tempore natus?</p>
        </div>
      </div>
    </div>
  </div>

</body>
</html>

CSS :

@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@200;300;400;500;600;700&display=swap');
*{
  margin: 0;
  padding: 0;
  box-sizing: border-box;
  font-family: 'Poppins', sans-serif;
}
body{
  height: 100vh;
  display: flex;
  align-items: center;
  justify-content: center;
  background: #dad3f8;
}
::selection{
  background: #6d50e2;
  color: #fff;
}
.container{
  max-width: 950px;
  width: 100%;
  padding: 40px 50px  40px  40px;
  background: #fff;
  margin: 0 20px;
  border-radius: 12px;
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.container .topic{
  font-size: 30px;
  font-weight: 500;
  margin-bottom: 20px;
}
.content{
  display: flex;
  align-items: center;
  justify-content: space-between;
}
.content .list{
  display: flex;
  flex-direction: column;
  width: 20%;
  margin-right: 50px;
  position: relative;
}
.content .list label{
  height: 60px;
  font-size: 22px;
  font-weight: 500;
  line-height: 60px;
  cursor: pointer;
  padding-left: 25px;
  transition: all 0.5s ease;
  color: #333;
  z-index: 12;
}
#home:checked ~ .list label.home,
#blog:checked ~ .list label.blog,
#help:checked ~ .list label.help,
#code:checked ~ .list label.code,
#about:checked ~ .list label.about{
  color: #fff;
}
.content .list label:hover{
  color: #6d50e2;
}
.content .slider{
  position: absolute;
  left: 0;
  top: 0;
  height: 60px;
  width: 100%;
  border-radius: 12px;
  background: #6d50e2;
  transition: all 0.4s ease;
}
#home:checked ~ .list .slider{
  top: 0;
}
#blog:checked ~ .list .slider{
  top: 60px;
}
#help:checked ~ .list .slider{
  top: 120px;
}
#code:checked ~ .list .slider{
  top: 180px;
}
#about:checked ~ .list .slider{
  top: 240px;
}
.content .text-content{
  width: 80%;
  height: 100%;
}
.content .text{
  display: none;
}
.content .text .title{
  font-size: 25px;
  margin-bottom: 10px;
  font-weight: 500;
}
.content .text p{
  text-align: justify;
}
.content .text-content .home{
  display: block;
}
#home:checked ~ .text-content .home,
#blog:checked ~ .text-content .blog,
#help:checked ~ .text-content .help,
#code:checked ~ .text-content .code,
#about:checked ~ .text-content .about{
  display: block;
}
#blog:checked ~ .text-content .home,
#help:checked ~ .text-content .home,
#code:checked ~ .text-content .home,
#about:checked ~ .text-content .home{
  display: none;
}
.content input{
  display: none;
}

Download Code Files

#javascript #html #css