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In this tutorial, we will learn to make a text italic using JavaScript. We give the italic property to a text to differentiate it from other text within the sentence.

In HTML, we can make a text italic by just adding the text within the ‘i’ tag. But sometimes, we have to make it dynamic, that not possible with HTML. Then, we have to use a programming language like JavaScript to make the text italic.

Let’s have a look at how to make a text italic using JavaScript. Following are the ways by which we can make a text italic in JavaScript −

Using String italics() Method.

Using Style fontStyle Property.

By Creating DOM Italic Object.

Using String italics() Method

The italics() method is the method in JavaScript used to make the text italic. It does the same thing that the ‘i’ tag in HTML. This method can be invoked only on the strings. It also does not takes any arguments.

Syntax

All the users can follow the below syntax to use the italics() method to make a text italic using JavaScript −

string.italics();

Example

In the example below, we used the italics() method to make a text italic using JavaScript.

var

text

=

“Welcome to the JavaScript”

;

document

.

getElementById

(

‘para’

)

.

innerHTML

=

text

.

italics

(

)

;

In the above example, users can see that we have used the italics() method to make a text italic using JavaScript.

Using Style fontStyle Property

We can use the fontStyle property of the DOM style to make a text italic. We set this property to string value “italic”.

Syntax

Following is the syntax to make a text italic using HTML DOM style fontStyle property −

document.getElementById("paragraph").style.fontStyle = "italic"; Example

function

italic

(

)

{

document

.

getElementById

(

“paragraph”

)

.

style

.

fontStyle

=

“italic”

;

}

By Creating DOM Italic Object

The DOM(document object model) is a standard in JavaScript to access the elements in the document. We can add or access all elements of HTML through the DOM in JavaScript. We make our text italic in HTML by placing it inside the ‘i’ tag. In the same way, we can add the italic element and append the text inside it through the DOM in JavaScript.

Syntax

All the users can follow the below syntax to use the DOM Objects to make a text italic using JavaScript −

var2.appendChild(var3);

Algorithms

Step-1 − Create an ‘i’ element using DOM.

Step-2 − Create a text node and add text to it.

Step-3 − Append the text node in the ‘i’ element.

Step-4 − Now, append the ‘i’ element to any container element.

Example

function

italic

(

)

{

var

para

=

document

.

getElementById

(

“para”

)

;

var

add_element

=

document

.

createElement

(

‘i’

)

;

var

text

=

document

.

createTextNode

(

para

.

innerHTML

)

;

add_element

.

appendChild

(

text

)

;

para

.

innerHTML

=

“”

;

para

.

appendChild

(

add_element

)

;

}

In this tutorial, we have learned about the two ways by which we can make a text italic in JavaScript. Among these, italics() is the method that makes a string italic. We also have used the DOM Objects to make a text italic using a custom logic in JavaScript. You can use both ways to make a text italic while the italics() is the simplest.

You're reading How To Make A Text Italic Using Javascript

How To Add Rows To A Table Using Javascript Dom?

We will learn how to add a row to a table using JavaScript dom. To achieve this, we have multiple methods. Some of them are the following.

Using the insertRow() method

By creating the new Element

Using the insertRow() Method Syntax table.insertRow(index)

Return value − The element which was inserted.

Below is the syntax to insert cell −

table.insertCell(index)

Return value − The element which was inserted.

Steps to add a row to the table

Get the data table element.

Create a row using the insertRow method and inset it into the table.

Create new cell(s) using the insertCell method and insert them into the row you created.

Add data to the newly created cells.

Example

In this example, we have a table that contains the name of the students and their ages. We are adding a new student at the end of the table.

table

,

td

,

th

{

border

:

1

px solid black

;

}

function

addRow

(

)

{

let

table

=

document

.

getElementById

(

“myTable”

)

;

let

row

=

table

.

insertRow

(

1

)

;

let

c1

=

row

.

insertCell

(

0

)

;

let

c2

=

row

.

insertCell

(

1

)

;

let

c3

=

row

.

insertCell

(

2

)

;

c1

.

innerText

=

“Elon”

c2

.

innerText

=

45

c3

.

innerText

=

“Houston”

}

By Creating New Elements

In this method, we will create new rows and columns by using the document.createElement() method.

Approach

Here are the steps to add a row to a table by creating elements.

Get the table body element in which you want to add a row

Create row element

Create cells Insert data into cells

Append cells to the row

Append row to table body

Example

table

,

td

,

th

{

border

:

1

px solid black

;

}

function

addRow

(

)

{

let

table

=

document

.

getElementById

(

“tableBody”

)

;

let

row

=

document

.

createElement

(

“tr”

)

let

c1

=

document

.

createElement

(

“td”

)

let

c2

=

document

.

createElement

(

“td”

)

let

c3

=

document

.

createElement

(

“td”

)

let

c4

=

document

.

createElement

(

“td”

)

c1

.

innerText

=

“Elon”

c2

.

innerText

=

“42”

c3

.

innerText

=

“Houston”

c4

.

innerText

=

“C++”

row

.

appendChild

(

c1

)

;

row

.

appendChild

(

c2

)

;

row

.

appendChild

(

c3

)

;

row

.

appendChild

(

c4

)

;

table

.

appendChild

(

row

)

}

Extracting Text Strings Using Excel’s Text To Columns

The Easy Way

In this example I’m going to show you how you can extract the domain name from a URL in Excel.

There are two approaches:

The really complicated, banging head on desk, formula method or

I don’t know why but most people choose the formula option. Probably because they don’t know Text to Columns exists.

Don’t get me wrong. Formulas have their place (and I’ll cover them next week), but unless you’re setting up a template that you’re going to use over and over again, Text to Columns is your best friend.

Text to Columns

You’ll find the Text to Columns tool on the Data tab of the ribbon in the Data Tools section:

Here you have to choose the file type that describes your data.

The rules are simple:

If you can draw a straight vertical line (that represents a column), between the data you want to separate, then you choose Fixed Width. If you can’t, then you need to choose Delimited.

You can see in the image below I’ve put a red line where I want the text separated, but the last row is going to leave a forward slash at the front of my domain.

So, in this case I need to choose the Delimited data type. Pressing ‘Next’ brings me to step 2 of the Wizard:

Here I can choose the delimiter I want to use to separate my data. Common delimiters are Tab, Semicolon, Comma (think .csv files) or Space, but I can also stipulate another delimiter that occurs in my data.

As soon as I enter the slash in the ‘Other’ field the Wizard gives me a preview of how my data will be separated.

Each set of data between a / is separated into its own column as indicated by the vertical lines in the Data preview section below:

In the next step I can choose which columns I want to keep (1) and in what format (2), and where to place the extracted data (3):

You can see in the image below all of the column headers now read ‘Skip Column’ except the column containing the domain which I’m going to keep.

Lastly I can choose the cell Destination for the domain names (3).

Tip: if you want to retain the original URL make sure you change the destination cell (3) to a cell in an empty column, otherwise it will replace your original data.

Finished Product

Here we have our domain names in under a minute, and without bruising to your head. 🙂

Tip: Sometimes you need to run your data through Text to Columns in stages, extracting one part of the data in the first pass, then the next part, and so on. It all depends on the delimiters in your data.

Let’s take the data below as an example:

It’s almost in the right format to just use the Fixed Width format in step 1 of the Wizard, but we can see (image below) when I choose this option that it’s not quite right because the first row has 1 character too many in the second column:

That’s ok, I can use the Delimiter method like this:

See in the image above how I’ve selected several delimiters based on what is present in my text.

I’ll need to run it through the Text to Columns twice though because I also want to separate the 01 from the preceding text and I can only choose one ‘Other’ delimiter at a time.

Other Uses for Text to Columns

Another great use for Text to Columns is fixing dates formatted as text.

Next week I’ll step you through the formula method. Don’t worry there’s no need to tape bubble wrap to your head in anticipation of banging your head on the desk.

I’m going to show you an easy way to build complex MID formulas without the usual frustration.

A Quick Guide To Text Cleaning Using The Nltk Library

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

Introduction

To get an understanding of the basic text cleaning processes I’m using the NLTK library which is great for learning.

Removing extra spaces

Most of the time the text data that you have may contain extra spaces in between the words, after or before a sentence. So to start with we will remove these extra spaces from each sentence by using regular expressions.

CODE:



2. Removing punctuations

The punctuations present in the text do not add value to the data. The punctuation, when attached to any word, will create a problem in differentiating with other words.

CODE:

"I like NLP." == 'I like NLP'

Punctuations can be removed by using regular expressions.

CODE:

text = "Hello! How are you!! I'm very excited that you're going for a trip to Europe!! Yayy!" re.sub("[^-9A-Za-z ]", "" , text)

Punctuations can also be removed by using a package from the string library.

CODE:

import string text = "Hello! How are you!! I'm very excited that you're going for a trip to Europe!! Yayy!" text_clean = "".join([i for i in text if i not in string.punctuation]) text_clean

str.lower() or str.upper().

For example, you can convert the character to either lower case or upper case at the time of checking for the punctuations.

CODE:

import string

text = "Hello! How are you!! I'm very excited that you're going for a trip to Europe!! Yayy!"

text_clean = "".join([i.lower() for i in text if i not in string.punctuation])

text_clean

4. Tokenization: Splitting a sentence into words and creating a list, ie each sentence is a list of words. There are mainly 3 types of tokenizers.

a. word_tokenize: It is a generic tokenizer that separates words and punctuations. An apostrophe is not considered as punctuation here.

CODE:

text = "Hello! How are you!! I'm very excited that you're going for a trip to Europe!! Yayy!" nltk.tokenize.word_tokenize(text)

word_tokenize

Notice that the highlighted words are split based on the punctuations.

b. TweetTokenizer: This is specifically used while dealing with text data from social media consisting of #,@, emoticons.

CODE:

text = "Hello! How are you!! I'm very excited that you're going for a trip to Europe!! Yayy!" from nltk.tokenize import TweetTokenizer tweet = TweetTokenizer() tweet.tokenize(text)

Observe the highlighted part here and in word tokenize

c. regexp_tokenize: It can be used when we want to separate words of our interests which follows a common pattern like extracting all hashtags from tweets, addresses from tweets, or hyperlinks from the text. In this, you can use the normal regular expression functions to separate the words.

CODE:

import re a = 'What are your views related to US elections @nitin' re.split('[email protected]', a) 5. Removing Stopwords

Stopwords include: I, he, she, and, but, was were, being, have, etc, which do not add meaning to the data. So these words must be removed which helps to reduce the features from our data. These are removed after tokenizing the text.

CODE:

stopwords = nltk.corpus.stopwords.words('english') text = "Hello! How are you!! I'm very excited that you're going for a trip to Europe!! Yayy!" text_new = "".join([i for i in text if i not in string.punctuation]) print(text_new) words = nltk.tokenize.word_tokenize(text_new) print(words) words_new = [i for i in words if i not in stopwords] print(words_new)

Can give condition to get words with length greater than 2 after removing stopwords

6. Lemmatization & Stemming

CODE:

ps = nltk.PorterStemmer() w = [ps.stem(word) for word in words_new] print(w)

OR

ss = nltk.SnowballStemmer(language = 'english') w = [ss.stem(word) for word in words_new] print(w)

b. Lemmatization: Takes the word to its root form called Lemma. It helps to bring words to their dictionary form. It is applied to nouns by default. It is more accurate as it uses more informed analysis to create groups of words with similar meanings based on the context, so it is complex and takes more time. This is used where we need to retain the contextual information.

CODE:

wn = nltk.WordNetLemmatizer() w = [wn.lemmatize(word) for word in words_new] print(w)

Based on the problem we have to use either Stemming or Lemmatizing.

End Notes

These are the cleaning techniques that must be applied to make our text data ready for analysis and model building. It is not necessary that you have to perform all these steps for cleaning.

Sometimes, you want to create new features for analysis such as the percentage of punctuation in each text, length of each review of any product/movie in a large dataset or you can check that if there are more percentage of punctuations in a spam mail or ham mail or positive sentiment reviews are having more punctuations than negative sentiment reviews or vice-versa.

Once the text cleaning is done we will proceed with text analytics. Before model building, it is necessary to bring the text data to numeric form(called vectorization) so that it is understood by the machine.

Related

How To Use Checkbox Inside Select Option Using Javascript?

Create a custom select menu Syntax

Users can follow the syntax below to manage the checkboxes of a custom dropdown menu using JavaScript.

function showOptions() { if (showCheckBoxes) { showCheckBoxes = false; } else { showCheckBoxes = true; } } function getOptions() { var selectedOptions = document.querySelectorAll('input[type=checkbox]:checked') }

In the above syntax, we show the options of custom dropdown based on the value of the showCheckBoxes variable. Also, we can iterate through the array of selectedOptions array to get all checked checkboxes one by one.

Steps

Step 1 − Create a div containing the menu text.

Step 2 − Now, use the custom HTML, and make options using the checkbox input type.

Step 4 − In JavaScript, declare the showCheckBoxes variable, and initialize it with the true Boolean value. We will show the options of custom dropdown based on the showCheckBoxes variable.

Step 6 − Now, define a getOptions() function. In the getOptions() function, access all checked checkboxes and print the value of all selected checkboxes by iterating through the selectedOptions array using the for-loop.

Example 1

In the example below, we have created the custom select menu as explained in the above algorithm. Users can select multiple options by checking the multiple checkboxes.

.dropdown { width: 12rem;       height: 1.5rem;       font-size: 1.3rem;       padding: 0.6 0.5rem;       background-color: aqua;       cursor: pointer;       border-radius: 10px;       border: 2px solid yellow; }     #options {       margin: 0.5rem 0;       width: 12rem;       background-color: lightgrey;       display: none;       flex-direction: column;       border-radius: 12px;     }     label {       padding: 0.2rem;     }     label:hover {       background-color: aqua;     }     button {       font-size: 1rem;       border-radius: 10px;       padding: 0.5rem;       background-color: yellow;       border: 2px solid green;       margin: 1rem 0; } show all options       First Option       Second Option       Third Option       Fourth Option       Fifth Option let output = document.getElementById(‘output’);     var showCheckBoxes = true;

    function showOptions() {       var options =         document.getElementById(“options”);

      if (showCheckBoxes) {         options.style.display = “flex”;         showCheckBoxes = !showCheckBoxes;       } else {         options.style.display = “none”;         showCheckBoxes = !showCheckBoxes;       }     }     function getOptions() {       var selectedOptions = document.querySelectorAll(‘input[type=checkbox]:checked’)       for (var i = 0; i < selectedOptions.length; i++) {         output.innerHTML += selectedOptions[i].value + ” , “;         console.log(selectedOptions[i])       }     }

In this tutorial, users learned to create a custom select menu using the html, CSS, and JavaScript. Also, users can use some CSS libraries like Bootstrap to create a select menu with checkboxes.

How To Clone An Object Using Spread Operator In Javascript?

The spread operator, which was first introduced in ES6, is used to unpack the elements of an iterable like an array. Cloning and merging the array is simple thanks to the spread operator. The spread operator could not be used with objects when it was first introduced in ES6. The spread operator was eventually extended to objects in ES2024.

You’ll learn how to use the JavaScript object spread (…) to clone an object or merge two objects into one in this article. In areas where 0+ arguments are expected, the spread operator allows an iterable to extend.

This is most frequently used in variable arrays where more than one value is required. It provides us with the ability to get a list of parameters from an array. The Spread operator has the same syntax as the Rest argument, but it has the exact opposite effect.

When all elements from an object or array must be included in a list of some sort, spread syntax could be used.

Syntax

Following is the syntax of spread operator

var myVariable = [...value]; Example 1

To unpack elements of an array, you use the spread operator (…) in this example. When cloning an array, the spread operator comes very helpful.

let

team

=

[

‘India’

,

‘Australia’

,

‘England’

,

‘New Zealand’

]

;

let

cricket

=

[

team

]

;

document

.

getElementById

(

“result”

)

.

innerHTML

=

cricket

;

Example 2

In his example the spread operator (…) unpacks elements from the team array and sets them in a new array cricket in this example. To combine two or more arrays into one, use the spread operator (…).

let

cricket

=

[

‘India’

,

‘Australia’

,

‘England’

,

‘New Zealand’

]

;

let

bcci

=

[

‘West Indies’

,

‘Ireland’

,

‘Kenya’

,

‘Bangladesh’

]

;

let

merge

=

[

cricket

,

bcci

]

;

document

.

getElementById

(

“result”

)

.

innerHTML

=

merge

;

Example 3

In this example, you’ll learn how to utilise the JavaScript Object spread operator to clone an object’s own enumerable properties −

const

cricket

=

{

team

:

14

}

;

const

clonedCricket

=

{

cricket

}

;

document

.

write

(

clonedCricket

.

team

)

;

Example 4

Merging objects: In this example, you’ll learn how the spread operator (…) can be used to merge two objects similarly to arrays.

const

cricket

=

{

team

:

12

}

;

const

style

=

{

backgroundColor

:

“blue”

}

;

const

solidCircle

=

{

cricket

,

style

}

;

document

.

getElementById

(

“result1”

)

.

innerHTML

=

solidCircle

.

team

;

document

.

getElementById

(

“result2”

)

.

innerHTML

=

solidCircle

.

backgroundColor

;

console

.

log

(

solidCircle

)

;

Example 5

The tutpoint1 object is being shared. The tutpoint1 object’s key-value pairs are copied to the clonedUser object. Let’s take a look at another example of merging two objects with the spread operator − mergedUsers is a clone of both tutpoint1 and tutpoint2. Each countless property on the objects will be copied to the mergedUsers object in fact. The spread operator is really a shortcut for the Object.assign() function, however there are several variations.

const

tutpoint1

=

{

country

:

‘India’

,

tutorial

:

‘Tutorialspoint’

,

}

;

const

tutpoint2

=

{

name

:

“JavaScript”

,

framework

:

“React JS”

}

;

const

mergedUsers

=

{

tutpoint1

,

tutpoint2

}

;

console

.

log

(

mergedUsers

)

In Brief

The spread operator, the rest operator, and the Object.assign() function are all acceptable ways to clone objects in JavaScript. In addition to cloning objects, object spread and Object.assign() allow you to add or edit properties as the clone is being created.

You can clone an object while adding, updating, or skipping properties from being cloned by combining the object spread and rest in a same line. The spread operator creates new properties whereas Object.assign() assigns them when merging objects.

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