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This article was published as a part of the Data Science Blogathon.

Introduction

Source – Unsplash

Welcome to our guide on dealing with sparse datasets! In this guide, we will explore a common problem that can arise when working with data: sparsity.

But what is a sparse dataset, you may ask? Imagine you are trying to build a puzzle but only a few pieces to work with. It will be much harder to complete the puzzle with only a few pieces than if you had all of them. Similarly, it can be harder for a machine learning model to learn and make accurate predictions with a sparse dataset than with a dataset that has a lot of data.

So if you’re ready to learn how to work with sparse datasets, let’s get started!

Background

To understand how to work with sparse datasets, it’s essential first to understand what a sparse dataset is and why it can be a problem.

Whatever the reason, a sparse dataset can make it challenging to use the data to train a machine-learning model. Machine learning models need much data to learn from to make accurate predictions. Without enough data, the model may not be able to learn effectively, and its predictions may not be very accurate.

But don’t worry; there are ways to work with sparse datasets! In the rest of this guide, we will cover some strategies and techniques that you can use to make the most of your sparse data. And remember, even if you only have a few puzzle pieces, you can still put together a pretty good picture!

The Potential Drawbacks and Limitations of Working with Sparse Datasets

Methodology

To work with a sparse dataset, there are a few different approaches that you can take. Here are some of the most common methods:

Gather more data: One way to work with a sparse dataset is to try to gather more data. For example, you could ask other people if they have any puzzle pieces that you could use to complete your puzzle. In the same way, you could try to find more data to add to your dataset to make it less sparse.

Use a different machine learning model: Another way to work with a sparse dataset is to use a different machine learning model. Some models are better at working with sparse data than others, so you could try using a different model to see if it performs better on your dataset. Different models have different strengths and weaknesses; some are better at working with sparse data than others. For example, some models, like decision trees and random forests, can handle missing values and learn from data with many missing values. Other models, like neural networks, can be more sensitive to missing values and may require data imputation or feature engineering to work well with sparse data. By trying out different models, you can see which performs best on your specific dataset and achieve the best results.

Use data imputation: Data imputation is a technique that involves filling in missing values in a dataset. There are a few different ways to do this, like using the average value of a particular feature or the value from the previous or next data point. There are several different methods for data imputation, including using the mean or median value of a particular feature, the value from the previous or next data point, or a more sophisticated method like linear regression or k-nearest neighbors. The specific method used will depend on the dataset’s characteristics and the analysis’s goals. Data imputation can help to improve the performance of a machine learning model by providing more complete and consistent data for the model to learn from. Here are some general guidelines for when to use each technique:

Use the mean or median value of a particular feature: If the data is relatively normally distributed and there are only a few missing values, then using the mean or median value of the feature can be a simple and effective way to fill in the gaps. This can be a good choice if the goal is to preserve the overall distribution of the data.

Use the value from the previous or next data point: If the data is ordered in some way, like time series data, then using the value from the previous or next data point can be a good way to fill in missing values. This can help maintain the data’s continuity and preserve the overall trend or pattern.

Use linear regression or k-nearest neighbors: If the data is more complex and there are many missing values, then a more sophisticated method like linear regression or k-nearest neighbors can be a good choice. These methods can be more effective at capturing the underlying relationships in the data and can provide more accurate estimates of the missing values. However, they can be more computationally intensive and may require more expertise to implement.

It is often helpful to try a combination of these techniques and see which works best for your specific dataset and goals. By experimenting and using a combination of techniques, you can find the best approach for dealing with missing values in your data.

Use feature engineering: Feature engineering creates new features or variables from existing data. This can sometimes make it easier for a machine learning model to learn from the data because the new features may capture patterns or trends that were not visible in the original data. This can be done in several ways, like combining or transforming existing features or using domain knowledge to create new features that capture relevant information about the data. For example, if you were working with a dataset about houses, you may create a new feature that indicates the house size in square feet or another feature that indicates the number of bedrooms. By creating these new features, you can provide the machine learning model with additional information that it can use to learn and make more accurate predictions. In the case of a sparse dataset, feature engineering can be beneficial because it can create new features that may help the model to better capture the underlying patterns and trends in the data, even when there are missing or incomplete values. Some standard techniques for feature engineering include:

One-hot encoding: This technique is used to convert categorical data, which cannot be directly used by machine learning algorithms, into numerical data that can be used.

Aggregation: This technique creates new features by aggregating existing features, like taking the mean or median of a set of features.

Binning: This technique is used to group continuous data into bins or intervals, making the data more manageable and easier to work with.

Normalization: This technique rescales data to a common range, like between 0 and 1, so that all features are on the same scale and can be compared directly.

Feature selection: This technique identifies the most relevant and useful features in a dataset and removes irrelevant or redundant features.

Feature extraction: This technique extracts features from unstructured data, like text or images, using techniques like natural language processing or computer vision.

Using dimensionality reduction techniques with sparse data: 

Using dimensionality reduction techniques with sparse data can be a useful way to work with sparsity. Dimensionality reduction is a technique that involves reducing the number of features or dimensions in a dataset. This can help deal with sparse data because it can make it easier for a machine-learning model to learn from it and make accurate predictions. There are several different methods for dimensionality reduction, including principal component analysis (PCA), singular value decomposition (SVD), and independent component analysis (ICA). These methods can be applied to sparse datasets to reduce the dimensions and make it easier for a machine-learning model to learn from the data. For example, if you have a dataset with many features and missing values, you could use PCA to reduce the number of features and make the data less sparse. This can help the model learn from the data more effectively and make more accurate predictions.Additionally, using dimensionality reduction techniques can also improve the performance of a machine learning model by reducing overfitting. Overfitting occurs when a model is too complex and tries to fit the data too closely, leading to poor generalization and inaccurate predictions of new data. By reducing the number of dimensions in the data, you can prevent overfitting and improve your model’s performance.Overall, using dimensionality reduction techniques with sparse data can be a useful approach for dealing with sparsity and improving the performance of your machine learning models. By carefully choosing the right method and applying it to your dataset, you can make the most of your sparse data and achieve better results.

These are some of the most common approaches to dealing with a sparse dataset. You can find the best approach for your specific dataset and goals by trying out different methods and experimenting with different techniques. And remember, even if you only have a few puzzle pieces, you can still create a pretty amazing picture!

Tips and Best Practices for Effectively Working with Sparse Datasets

Here are some tips for working with sparse datasets:

Start by understanding what makes a dataset “sparse” – this will help you identify the challenges you may face when working with your data.

Use techniques like feature engineering, data imputation, and regularization to address sparsity in your data. These methods can help you fill in missing values and make the most of the information you have.

If possible, try to generate additional data to improve the density of your dataset. For example, you could collect more data points or create synthetic data to fill in gaps.

Be aware of the potential drawbacks and limitations of working with sparse datasets. For example, they can be more difficult to analyze and interpret and more susceptible to overfitting.

Use a combination of tools and approaches to work with sparse datasets effectively. For example, you could try different algorithms or use a combination of methods to improve your results.

Just like when you’re trying to put together a puzzle with some missing pieces, working with a sparse dataset can be challenging. But you can still progress and achieve good results using the right tools and approaches.

Common Pitfalls to Avoid When Dealing with Sparse Datasets

Source – Pixabay

Here are some common pitfalls to avoid when dealing with sparse datasets, explained in a way that even a toddler could understand:

Don’t ignore the sparsity in your data. Sparse datasets can be tricky to work with, but ignoring the sparsity won’t make it go away.

Don’t assume that all missing values are the same. Just because some values are missing in your dataset, it doesn’t mean they are all missing for the same reasons.

Don’t use the same method for every sparse dataset. Different methods work better for different types of sparsity, so choosing the right method for your specific dataset is essential.

Don’t forget to evaluate the effectiveness of your chosen method. It’s essential to check whether your method is improving your model’s performance rather than just making the data look less sparse.

Conclusion

In summary, a sparse dataset has a lot of missing or empty values and can be challenging to work with. However, there are ways to work with this dataset, like gathering more data, using a different machine learning model, or applying a technique called imputation to fill in the missing values. It’s essential to consider the potential drawbacks and limitations of working with a sparse dataset and to choose the right approach for your specific situation. By understanding these challenges and using the right tools and techniques, you can still make accurate predictions and draw reliable conclusions from your data.

Some key pointers to remember when addressing sparsity in your data are:

Don’t ignore the sparsity in your data. Ignoring sparsity won’t make it go away, and it can negatively impact the performance of your models.

Don’t assume that all missing values are the same. Different types of sparsity require different approaches, so it’s essential to carefully evaluate your data and choose the right method for dealing with sparsity.

There are ways to work with a sparse dataset, like gathering more data, using a different machine learning model, or applying imputation.

Working with a sparse dataset can have drawbacks and limitations, like difficulty interpreting and analyzing the data.

Choosing the right approach for your specific situation is important when dealing with a sparse dataset.

Don’t forget to evaluate the effectiveness of your chosen method. It’s important to check whether your method is improving your model’s performance, rather than just making the data look less sparse.

Keep experimenting and fine-tuning your approach until you find the best method for your specific dataset. There is no one-size-fits-all solution for dealing with sparsity, so it’s important to keep trying different methods and combinations of methods until you find the one that works best for your data.

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How To Download Kaggle Datasets Using Jupyter Notebook

This article was published as a part of the Data Science Blogathon.

What is Kaggle?

 where they meet and share their knowledge. Whether you are a beginner or an expert, Kaggle may have come or might come in handy during your journey in the domain. Kaggle was established in 2010, where it used to host Machine Learning competitions, later acquired by Google.

Using Kaggle gives a variety of features for the user’s help. One can come and show off their skills by participating in competitions which would help them fill their pocket. Kaggle also hosts several fora based on different topics of highly qualified and kind people from the globe. Apart from this, you can learn to code and solve numerous problems available on the platform. One of the leading reasons you may or you might have heard about Kaggle is the number and variety of open-source datasets it hosts. The best part of Kaggle is, even you can host your dataset if it holds a little value to be analyzed or to train one’s model.

Kaggle Homepage (Source – Personal Computer)

Downloading Kaggle Datasets (Conventional Way):

The conventional way of downloading datasets from Kaggle is:

1. First, go to Kaggle and you will land on the Kaggle homepage.

2. Sign up or Sign in with required credentials.

3. Then select the Data option from the left pane and you will land on the Datasets page.

4. Now from the variety of domains, select the datasets that match best of your needs and press the Download button.

Kaggle Datasets Page (Source – Personal Computer)

If you are unaware or confused about which dataset you should select, Kaggle has got you covered. Kaggle has several updated lists of Datasets based on the interest of the viewer. For example, when you land upon the Kaggle Datasets page, you will find multiple lists of Datasets, such as Trending Datasets, Popular Datasets, Datasets related to Businesses, Datasets related to COVID, and so on.

Apart from this, if you are specific with the dataset you want, you can always use the Filters and select the file type and the desired dataset’s file size.

Downloading Kaggle Dataset in Jupyter Notebook

Now, let’s look at the new method to download Kaggle Dataset.

Before starting, you need to have the opendatasets library installed in your system. If it’s not present in your system, use Python’s package manager pip and run:

!pip install opendatasets

in a Jupyter Notebook cell. Python’s opendatasets library is used for downloading open datasets from platforms such as Kaggle.

The process to Download is as follows:

1. Import the opendatasets library

import opendatasets as od

2. Now use the download function of the opendatasets library, which as the name suggests, is used to download the dataset. It takes the link to the dataset as an argument.

For example, If I have selected the Heart Attack Analysis & Prediction Dataset to download. I will select its hyperlink. Now, this hyperlink is used as an argument in the .download() function.

3. On executing the above line, it will prompt for Kaggle username. Kaggle username can be fetched from the Account tab of the My Profile section.

Account Tab in Kaggle Profile Section (Source – Personal Computer)

5. On opening this file, you will find the username and key in it. Copy the key and paste it into the prompted Jupyter Notebook cell. The content of the downloaded file would look like this:

6. A progress bar will show if the dataset is downloaded completely or not.

7. After successful completion of the download, a folder will be created in the current working directory of your Jupyter Notebook. This folder contains our dataset.

Your Jupyter Notebook should look like this:

Jupyter Notebook after execution of Code (Source – Personal Computer)

NOTE: Remember, that you don’t have to create a new API Token from Kaggle every time you want to download a dataset. You can use the same key for every single download.

The Kaggle Dataset Page

Datasets play a vital role in one’s journey in achieving higher highs in the domain of Machine Learning. Thus, one must know every possible way to fetch the datasets. Kaggle is the most widely used platform for downloading dataset. Thus, you can get large varieties of datasets uploaded by the field experts.

Apart from the title, each dataset in Kaggle has more attributes such as Usability Score, the publisher, the size, and the dataset format. When you open a dataset, you will find these details. The Usability Score is given by certain parameters. For this score, it is not mentioned what range of score is a good Usability Score, but it is always good to start with high Usability Score dataset. Each dataset also shows the size of the dataset to be downloaded. A larger file size would take more time to load in the data frame. For example, the popular dataset US Accidents has about 4.2 Million Rows and has a file size of about 300 MBs. Thus, it would take varied times to load into a dataframe. It also shows the file format in which data is present. Knowing these details of your dataset can also be beneficial.

Heart Attack Analysis & Prediction Dataset. Notice the details we talked about. (Source – Personal Computer)

One can practice and share their findings in the Code section of Dataset’s page for each dataset. You will find several submissions by Kaggle members on every dataset page. Also, the Publisher of the dataset can post any Task which one can aim and work towards it. Since there is no single solution to any problem in Machine Learning, it is always good to see and learn from others. This may help you in your next projects. For example, the COVID-19 Open Research Dataset Challenge dataset has a file size of 9 GBs and over 1500 Code Submissions.

Selecting perfect data for your need needs time. It may happen several times that you might download a dataset that is not prepared as per your need. Thus it is always propitious to read the Dataset descriptions of what it is offering. For example, if you want to analyse the dataset based on COVID 19 Vaccination Programs worldwide, you will find an enormous number of such datasets fulfilling your interest. In such a situation, it is always helpful to read and select the perfect dataset for you.

Apart from this, Kaggle also provides Free Courses to improve Data Science skills such as Python, Data Cleaning, Data Visualization et cetera. You will get a completion certificate as well on the successful completion of a course.

 

Conclusion

Thus, opendatasets is a boon for the practitioners who are aiming to excel in the domain. Dataset is an essential part of every Data Science project. Your every bit of analysis starts with the data. Execution of a task in Python can be done most efficiently. And when it comes to downloading datasets, your ultimate task is to get the datasets with the least efforts possible.

1. You don’t have to necessarily sign in to Kaggle if you are downloading directly from Jupyter Notebook. You can use your username and key when prompted.

2. You save yourself from the hassle of transferring the downloaded file from the browser to copying it to your Notebook’s directory. The file will get downloaded at the current working directory only.

3. It’s always good to know alternate solutions to an existing solution.

Don’t forget to check out my previous article here.

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Guide To Namedtuple Python With Examples

Introduction to Namedtuple Python

Web development, programming languages, Software testing & others

Working of Namedtuple

As tuple has an integer index to access as they have no names, there might be ambiguity to store and access the data to that particular integer index. Tuples are an ad-hoc structure that means if two tuples have the same number of fields and the same data stored, there is an ambiguity for accessing. So to overcome all such problems, Python introduces Namedtuple in the collections module. Python has containers like dictionaries called namedtuple(), supporting access to the value through key similar to dictionaries. Namedtuple is the class of collections module which also contains many other classes for storing data like sets, dictionaries, tuple, etc.

Nametuple is an extension to the built-in tuple data type, where tuples are immutable, which means once created, they cannot be modified. Here it shows how we can access the elements using keys and indexes. To use this namedtuple(), you should always import collections in the program. Namedtuples offers a few users access and conversion methods that all start with a _ underscores. Namedtuple is mostly used on unstructured tuples and dictionaries, which makes it easier for data accessing. Namedtuple makes an easy way to clean up the code and make it more readable, which makes it a better structure for the data.

There are different access and conversion operations on namedtuple.

They are as follows:

Access operations on Namedtuple() which we can access values using indexes, keys, and getattr() methods.

Access by index: In this, the values are accessed using index number because the attribute values of namedtuple() are in order so indexes can easily access it.

Access by keys: In this, the working is similar to a dictionary where the values can be accessed using the keys given as allowed in dictionaries.

Access using getattr(): This is one of another method in which it takes namedtuple and key-value as its argument.

Examples of Namedtuple Python

Given below are the examples mentioned:

Example #1

Code:

import collections Employee = collections.namedtuple('Employee',['name','age','designation']) E = Employee('Alison','30','Software engineer') print ("Demonstration using index, The Employee name is: ",E.name) print ("Demonstration using keynames, The Employee age is : ",E[1]) print ("Demonstration using getattr(), The Employee designation is : ",getattr(E,'designation'))

In the above example, firstly, we create namedtuple() with tuple name as “Employee”, and it has different attributes in the tuple named “name”, “age”, “designation”. The Employee tuple’s key-value can be accessed using 3 different ways, as demonstrated in the program.

There are some conversion operations that can be applied on namedtuple().

They are as follows:

_make(): This function converts any iterable passed as argument to this function to namedtuple() and this function returns namedtuple().

_asdict(): This function converts the values of namedtuple that are constructed by mapping the values of namedtuple and returns the OrderDict().

** (double star) operator: This operator is used to convert to namedtuple() from the dictionary.

_fields: This function returns all the keynames of the namedtuple that is declared. We can also check how many fields and which fields are there in the namedtuple().

_replace(): This function replaces the values that are mapped with keynames that are passed as an argument to this function.

Example #2

Code:

import collections Employee = collections.namedtuple('Employee',['name','age','designation']) E = Employee('Alison','30','Software engineer') El = ['Tom', '39', 'Sales manager' ] Ed = { 'name' : "Bob", 'age' : 30 , 'designation' : 'Manager' } print ("The demonstration for using namedtuple as iterable is : ") print (Employee._make(El)) print("n") print ("The demonstration of OrderedDict instance using namedtuple is : ") print (E._asdict()) print("n") print ("The demonstration of converstion of namedtuple instance to dict is :") print (Employee(**Ed)) print("n") print ("All the fields of Employee are :") print (E._fields) print("n") print ("The demonstration of replace() that modifies namedtuple is : ") print(E._replace(name = 'Bob'))

Output:

The above program creates the namedtuple() “Employee” and then it creates iterable list and dictionary “El” and “Ed” which uses the conversion operations _make() which will convert the “El” to namedtuple(), _asdict() is also used to display the namedtuple() in the order that is using OrderDict(), the double start (**) which converts dictionary “Ed” to namedtuple(), E.fields which will print the fields in the declared namedtuple(), E.replace(name = “Bob”) this function will replace the name field value of the namedtuple() in this it replaces “Alison” to “Bob”.

Conclusion

In Python, we use namedtuple instead of the tuple as in other programming languages as this is a class of collections module that will provide us with some helper methods like getattr(), _make(), _asdict(), _fileds, _replace(), accessing with keynames, ** double star, etc. This function helps us access the values by having keys as the arguments with the above different access and conversion functions on the namedtuple() class of collections module. It is easier than tuples to use and is very efficient and readable than tuples.

Recommended Articles

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Guide To How Mixin Works In Ruby With Examples

Introduction to Ruby Mixin

Web development, programming languages, Software testing & others

Syntax:

Below is the simple syntax which shows the flow of the mixin in Ruby, here in the below syntax we have created a module called MIXIN and in this module we are including inside the MAINMODULE class. .

#Module1 defining with some methods module MIXIN def method1 end def method2 end end class MAINMODULE include MIXIN defmain_method end end How Mixin works in Ruby?

To see the working of the mixin in Ruby we will follow a diagram and some steps which will show the actual meaning of the mixin concept inside Ruby.

First we have defined a module MIXIN1 and MIXIN2. We can put some class methods and constants inside the modules.

Both of the modules MIXIN1 and MIXIN2 contain some methods like method1, method2, method3 and method4. These methods are going to be accessed by a single class without the concept of the multiple inheritance, simply this technique is called the mixin .

Next we have defined a class MAINMODULE and this class includes the two above modules inside it. Once we include the modules inside the class, the class is eligible to have all the properties of the both modules (MIXIN1 and MIXIN2).

Next we are creating the object of the MAINMODULE and with that object we are able to access the methods of the both modules which we have included into our MAINMODULE class.

This mechanism of including and using two or more modules inside one class is nothing but a mixin.

So we can understand from syntax it is replacing the need of multi inheritance in Ruby.

Note 2: We can use many modules inside one class and this is the main benefit of using mixin. Suppose we wanted to use some functionality, all the functionality belongs to different modules, so with the help of mixin concept we are not required to write the same code again and again.

Given below is the diagram:

Examples of Ruby Mixin Example #1

In the below example we are performing arithmetic operations inside the module called CALCULATE and this module will be included inside the USECALCULATION class.

Code:

# Defining CALCULATE modules which consists of some methods for arithmetic operations. module CALCULATE def add(a,b) puts "The sum of two number is #{a+b}" end def multi(a,b) puts "The multiplication of two number is #{a*b}" end def div(a,b) puts "The division of two number is #{a/b}" end defsubstract(a,b) puts "The subtraction of two number is #{a-b}" end end # Defining a main class method and inside the method we are simply including all the three modules which we have defined above. class USECALCULATION include CALCULATE defdisplay_output puts 'This is inside the USECALCULATION and method CALCULATE' end end # Creating object mainObject = USECALCULATION.new # Calling methods mainObject.display_output mainObject.add(1,3) mainObject.multi(2,4) mainObject.div(2,4) mainObject.substract(2,4)

Output:

Example #2

Code:

# Defining three modules which consists of some methods . module MIXIN1 def method1 puts 'This is inside the MIXIN1 and method1.' end end module MIXIN2 def method2 puts 'This is inside the MIXIN1 and method1.' end end module MIXIN3 def method3 puts 'This is inside the MIXIN1 and method1.' end end # Defining a main class method and inside the method we are simply including all the three modules which we have defined abbove. class MAINMODULECLASS include MIXIN1 include MIXIN2 include MIXIN3 defdisplay_main puts 'This is indide the MAINMODULECLASS and method display_main' end end # Creating object mainObject = MAINMODULECLASS.new # Calling methods mainObject.display_main mainObject.method1 mainObject.method2 mainObject.method3

Output:

Conclusion

From these tutorials we saw about the basics of the mixin in Ruby, we also saw about the working of the mixin with help of diagram, we came to know that we can use mixin where we need to include one or many modules into another class and from that class object we can access attributes of modules like its method.

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How To Get Started With Linux: A Beginner’s Guide

The world of Linux is ready to welcome you, with a shower of free open-source software you can use on any PC: hundreds of active Linux distributions, and dozens of different desktop environments you could run on them. It’s a far cry from the one-size-fits-all, this-is-just-what-comes-with-your-PC vision of Windows.

Choose and download a Linux distro

The first step is choosing the Linux distribution you’ll want to use.

Fedora Linux with the Gnome Shell desktop.

Unlike Windows 10, there’s no single version of Linux. Linux distributions take the Linux kernel and combine it with other software like the GNU core utilities, chúng tôi graphical server, a desktop environment, web browser, and more. Each distribution unites some combination of these elements into a single operating system you can install.

DistroWatch offers a good, in-depth summary of all the major Linux distributions you might want to try. Ubuntu is a fine place to start for former (or curious) Windows users. Ubuntu strives to eliminate many of Linux’s rougher edges. Many Linux users now prefer Linux Mint, which ships with either the Cinnamon or MATE desktops—both are a bit more traditional than Ubuntu’s Unity desktop.

The Cinnamon desktop environment running on Linux Mint 18.2.

Choosing the single best isn’t your first priority, though. Just choose a fairly popular one like Linux Mint, Ubuntu, Fedora, or openSUSE. Head to the Linux distribution’s website and download the ISO disc image you’ll need. Yes, it’s free.

You can use the Universal USB Installer to easily create a bootable thumb drive using an .ISO image of a Linux distribution.

You can now either burn that ISO image to a DVD or USB. Note that booting from USB 3.0 is faster than booting from DVD these days, and more versatile given that most laptops and many desktops no longer include a DVD drive. 

Fedora’s Media Writer utility is a thing of beauty and can run on Windows or Mac OS. It’s the easiest way to make a bootable Linux USB stick.

For most desktops and laptops, the above instructions will suffice. However, if you want to use Linux on a Chromebook, Raspberry Pi, or another type of device, there are special instructions you’ll need to follow.

Running Linux live off an external drive

If you’re not sure whether you’re running UEFI or BIOS, you’re probably running UEFI, unless your PC is five years old or more. To enter your BIOS or UEFI on a desktop, you’ll generally have to hit the Del or F12 key during the POST process (before Windows starts booting). 

On younger Windows PCs running Windows 10, you may have to disable Secure Boot before booting Linux. (Secure Boot has been a headache for many Linux users.) Most of the larger Linux distributions will boot normally with Secure Boot enabled, but others won’t.  

Your Linux distribution of choice probably allows you to use it in a “live” environment, meaning it runs entirely off the disc or USB drive and doesn’t actually need to be installed to your computer’s hard drive. Just use the Linux desktop normally and get a feel for it. You can even install software, and it’ll remain installed in the live system until you reboot.

Fedora’s Live CD interface, like most Linux distributions, lets you choose to run the operating system from your bootable media or install it to your hard drive.

Even if you don’t want to use Linux as your everyday operating system, having this Linux live DVD or USB drive around can be useful. You can insert it into any computer and boot Linux whenever you want. Use it to troubleshoot Windows problems, recover files from a corrupted system, scan an infected system for malware, or provide a secure environment for online banking and other important tasks.

To leave the live Linux system, just reboot your computer and remove the disc or USB drive.

Use Linux in a virtual machine

With free virtualization tools like VirtualBox, you can have multiple virtual machines (VMs), complete with their own boot sequences and isolated storage. One of the most popular things to do with virtual machines is to run different operating systems on one computer without needing to reboot.

It’s pretty easy to create a VM on Windows to create a virtual environment to run Linux in. VMs are easy to manage, and when you’re done using them, you can delete them. You can even back up copies of the entire virtualized (guest) operating system if you need to.

You can use VirtualBox to test different Linux distros while running Windows or Linux. Here, Debian 9 (the guest) is running in VirtualBox on Arch Linux (the host).

In addition to a performance hit, virtual machines generally won’t have direct hardware access to things like video cards.

Your Linux desktop environment

The Fedora 25 desktop running GNOME’s Software and Nautilus applications.

Ubuntu 16.04’s Unity desktop can be quirky, but it’s packed with useful features you’d never find on your own, like the HUD. If you’re going with Ubuntu 16.04 or earlier, be aware that Ubuntu will be abandoning its Unity desktop in future versions. Ubuntu dropped Unity in favor of the GNOME shell that comes default on Fedora and other distributions. If you want to try Ubuntu, we recommend trying Ubuntu GNOME, which uses the GNOME desktop instead of Unity.

Additionally, be sure to enable virtual desktops (most modern Linux desktops have disabled them by default) and give them a shot, too.

Every desktop environment has a set of tools to help you customize the look and feel how you want it to. Here, Cinnamon’s System Settings running on Linux Mint 18.2 shows the options available.

If you ever get lost, there is plenty of help online. Generally Googling your distribution’s name followed by the question will lead you in the right direction. If you prefer a more structured help environment, the Ubuntu and Fedora documentation websites are great resources. While the Arch Wiki is written with users of Arch Linux in mind, it is a great in-depth resource for Linux programs in general.

Install Linux, or not

You have choices about when and how to install Linux. You can leave it on a disc or USB drive and boot it up whenever you want to play with it. Play with it several times until you’re sure you want to install it. You can try several Linux distributions in this way—you can even re-use the same USB drive.

The big reasons to install Linux instead of just running it from a USB drive or disc are productivity and convenience. Unlike running Linux live, installed Linux will remember your settings, keep your installed software, and maintain your files between reboots.

Want to stay up to date on Linux, BSD, Chrome OS, and the rest of the World Beyond Windows? Bookmark the World Beyond Windows column page or follow our RSS feed.

Of course, you can always choose to install Windows in a virtual machine as well.

How to install more software

OpenSUSE’s YaST software management tool.

Software installation on Linux works very differently from software installation on Windows. You don’t need to open your web browser and search for applications. Instead, look for the software installer on your system. On Ubuntu and Fedora, you can install software using GNOME’s software store application (aptly called “Software”).

Software managers aren’t just fancy interfaces for downloading software from the web. Your Linux distribution hosts its own “software repositories,” containing software compiled to work with it. This software is tested and provided by the Linux distribution. (If you choose a rolling-release distribution like Arch or openSUSE Tumbleweed, the newer software can cause problems. If you prefer stability over the latest-and-greatest versions of software, stick to a “versioned” Linux distribution to start out.) If security patches are necessary, your Linux distribution will provide them to you in a standard way.

GNOME Software is an application that uses store-like interface to browse for and install software. GNOME Software is available on Ubuntu and any distribution that uses the GNOME desktop.

While most major distributions offer GUI programs to help you install software, all distributions have command-line tools that can do the same thing. Though it can be intimidating for newbies, we recommend users familiarize themselves with how to install applications from the command line, even if they prefer using the GUI. If an installation fails for some reason, using the command line will offer hints as to why the installation failed.

Some applications—particularly closed-source applications like Google Chrome, Steam, Skype, Minecraft, and others—may have to be installed from outside your Linux distribution’s package manager. But check your package manager first—you’ll be surprised what apps may be available through your distro’s repositories.

If you can’t find the app you need, you can download these applications from their official websites, just as you would on Windows. Be sure to download the installer package designed for the Linux distribution you’re using.

Contrary to widespread belief, you probably don’t need to install hardware drivers manually when you install the operating system. Most of the hardware drivers you’ll need are built-in on Linux. There are a few closed-source drivers you might want—the Nvidia and AMD drivers for optimal 3D graphics performance, or Wi-Fi drivers to make your Wi-Fi hardware work right. However, most of the hardware you have (even touchscreens) should work out of the box. 

It’s worth mentioning that while Nvidia’s proprietary Linux drivers are great performance-wise, Nvidia’s proprietary drivers don’t always play nice with the open-source community. (Linus Torvalds, the guy who wrote the Linux kernel, famously gave the finger to Nvidia on camera because of this.) If you’re not planning on doing a lot of gaming on Linux, Intel’s integrated graphics (which is present on pretty much all non-enthusiast Intel Core CPUs) will do the job just fine.

The Software Manager in Linux Mint 18.2’s Cinnamon desktop has a user-friendly interface. 

Ubuntu and Linux Mint will recommend drivers to you via their hardware driver tools, if necessary. Some Linux distributions may not help you install these at all. For example, Fedora doesn’t want to endorse closed-source Linux drivers. If you need specialized drivers, check your distribution’s documentation. Most distros have help pages for people who have AMD or Nvidia video cards, for instance.

Now you have the basic knowledge you need to get started using Linux. Happy exploring!

Guide To 2 Types Of Checkboxes In Css With Examples

Introduction to Checkbox CSS

Real-Time Example: Suppose the particular question has four options in an examination. In that, more questions if more than two options are correct. If we take the general radio button, we can select at a time only one option, but that is not the case. We must select more than two options at a time and use the checkbox to achieve this requirement.

Advantages

More than one option can be selected by using the checkbox.

Types of Checkboxes

There are two types of Checkboxes in CSS

Default checkboxes

Custom checkboxes

1. Default checkboxes

The default checkbox is not required to add any additional styles. CSS libraries, by default, provided some styles for this checkbox.

Syntax:

2. Custom checkboxes

This Custom checkbox must require adding additional styles because this is a user-required checkbox, so they have to provide CSS styles based on their requirement.

Syntax:

CSS Styles /* topClass class styles*/ .topClass { display: block; cursor: pointer; font-size: 22px; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; position: relative; padding-left: 35px; margin-bottom: 12px; } /* Hidden the default check box*/ .topClass input { position: absolute; cursor: pointer; height: 0; width: 0; opacity: 0; } /* creating user custom contentMark */ .contentMark { position: absolute; height: 25px; width: 25px; background-color: #eee; top: 0; left: 0; } /* when hover the mouse green color will be added */ .topClass:hover input ~ . contentMark { background-color: green; } /* When the checkbox is checked, add a blue background */ .toClass input:checked ~ . contentMark { background-color: blue; } /* create checkmark, initially hidden if we not check*/ .contentMark:after { position: absolute; display: none; content: “”; } /* checked and showed if we check the box */ .topClass input:checked ~ . contentMark:after { display: block; } /*It styles the contentMark class indicator */ .topClass. contentMark:after { left: 9px; top: 5px; width: 5px; -ms-transform: rotate(45deg); border-width: 0 3px 3px 0; -webkit-transform: rotate(45deg); transform: rotate(45deg); height: 10px; border: solid white; }

HTML Code:

Examples of Checkbox CSS

Here are the following examples:

Example #1

Code:

h1 { color: green; text-align: center; } h2 { color:blue; } label { color: brown; font-size: 18px; }

Output:

Example #2

Custom checkbox with Question and Answer:

Code:

h1 { color: green; text-align: center; } h2 { color: blue; } label { color: brown; font-size: 18px; } /* The labelClass */ .labelClass { display: block; position: relative; padding-left: 36px; margin-bottom: 13px; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; cursor: pointer; -moz-user-select: none; -ms-user-select: none; user-select: none; cursor: pointer; font-size: 23px; } /* Hide the browser’s default checkbox */ .labelClass input { position: absolute; height: 0; width: 0; opacity: 0; cursor: pointer; width: 0; } /* Create a custom checkbox */ .checkmark { position: absolute; top: 0; height: 24px; width : 24px; background-color : pink; left: 0; width: 24px; background-color: pink; } /* On mouse-over, add a grey background color */ .labelClass:hover input ~ .checkmark { background-color: gray; } /* When the checkbox is checked, add a blue background */ .labelClass input:checked ~ .checkmark { background-color: brown; } /* Create the checkmark/indicator (hidden when not checked) */ .checkmark:after { content: “”; position: absolute; display: none; } /* Show the checkmark when checked */ .labelClass input:checked ~ .checkmark:after { display: block; } /* Style the checkmark/indicator */ .labelClass .checkmark:after { left: 10px; top: 6px; -webkit-transform: rotate(46deg); -ms-transform : rotate( 46deg); transform : rotate( 46deg); width: 6px; height: 11px; border: solid white; border-width: 0 2px 2px 0; -ms-transform: rotate(46deg); transform: rotate(46deg); }

Output:

Explanation: Example 1 has no styles, whereas in Example 2, we have a Custom checkbox with styles that make the font and checkbox beautiful.

Example #3

Auto Select Items

Code:

h1 { color: fuchsia; text-align: center; } h2 { color: brown; } label { color: green; font-size: 18px; } /* The labelClass */ .labelClass { display: block; position: relative; padding-left: 36px; margin-bottom: 13px; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; cursor: pointer; -moz-user-select: none; -ms-user-select: none; user-select: none; cursor: pointer; font-size: 23px; } /* Hide the browser’s default checkbox */ .labelClass input { position: absolute; height: 0; width: 0; opacity: 0; cursor: pointer; width: 0; } /* Create a custom checkbox */ .checkmark { position: absolute; top: 0; height: 24px; width : 24px; background-color : orange; left: 0; width: 24px; background-color: navy; } /* On mouse-over, add a grey background color */ .labelClass:hover input ~ .checkmark { background-color: gray; } /* When the checkbox is checked, add a blue background */ .labelClass input:checked ~ .checkmark { background-color: brown; } /* Create the checkmark/indicator (hidden when not checked) */ .checkmark:after { content: “”; position: absolute; display: none; } /* Show the checkmark when checked */ .labelClass input:checked ~ .checkmark:after { display: block; } /* Style the checkmark/indicator */ .labelClass .checkmark:after { left: 10px; top: 6px; -webkit-transform: rotate(46deg); -ms-transform : rotate( 46deg); transform : rotate( 46deg); width: 6px; height: 11px; border: solid white; border-width: 0 2px 2px 0; -ms-transform: rotate(46deg); transform: rotate(46deg); }

Output:

Conclusion

CSS checkbox can be created by using default styles and custom styles. The default checkbox does not have a rich GUI, whereas the custom checkbox has a rich GUI. You can select multiple items at a time in the checkbox. Initially, we can also auto-check any choice number of checkboxes.

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