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Introduction to MySQL Boolean

There is none of the built-in datatype present in MySQL for boolean values. However, MySQL provides us with the TINYINT data type, which can store values of integers with small values. We can declare the column’s data type whose behavior is like boolean with TINYINT(1) data type. That will function in the same way as a boolean. The 0(zero) is the FALSE value, while all other non-zero values are 1 in MySQL. MySQL provides keywords such as BOOLEAN or BOOL, which are internally treated in the same manner as TINYINT(1). In this article, we will explore the available data types in MySQL that can be utilized to store boolean values. We will also demonstrate the usage of boolean values in tables through examples.

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How to Declare & Store Boolean Values in MySQL?

We can specify the column’s datatype that might store the boolean value as BOOLEAN, BOOL, or TINYINT(1). All of these behave similarly and are synonyms of each other. The column declared with the data type of any of the mentioned databases evaluates the FALSE value as 0 and stores it as 0. On the other hand, the column considers and stores all other values, including TRUE or any non-zero value, as 1 for the respective record. Let us fire a straightforward command in MySQL.

Command:

SELECT TRUE, FALSE;

The output of the above query statement is as follows

Hence, we can conclude that MySQL considers true as one and false as 0. Note that true and false are treated the same irrespective of the case in which they are used.

Example to Implement of MySQL Boolean

1. Let us create one table named marathon_players that will store the participants’ details in the marathon and have columns that will store boolean values in it, such as healthChecked and runCompleted. We will declare the data type of the healthChecked column as BOOLEAN and runCompleted as BOOL and check the results of the created table. For the table creation, we will use the following CREATE TABLE query.

Query:

CREATE TABLE marathon_players( player_id INT NOT NULL AUTO_INCREMENT, Name VARCHAR(100), Age INT, healthChecked VARCHAR(100), runCompleted VARCHAR(100), completionTime TIME, PRIMARY KEY (player_id) );

Now, let us describe the created table by using the following query statement:

DESC marathon_players;

Output:

Executing the above query gives the following output

2. We can conclude that MySQL automatically converts and treats the data type of the “healthChecked” column as TINYINT(1) and the data type of the “runCompleted” column as BOOL, even though we specified them as BOOLEAN.

Let us now insert some values in the marathon_players table using the following insert queries:

Query:

INSERT INTO marathon_players (player_id, Name, Age, healthChecked, runCompleted, completionTime) VALUES('1','Ramesh','25','true','false','02:50:56'); INSERT INTO marathon_players (player_id, Name, Age, healthChecked, runCompleted, completionTime) VALUES('2','Suresh','27','-12','25','01:30:21');

3. Now, let’s execute the following select query to observe the retrieved results

Query:

SELECT * FROM marathon_players;

Output:

4. In the first record, Ramesh’s name was inserted and we specified the healthChecked and runCompleted columns as true and false, respectively. The insertion stored them as 1 and 0, respectively. In the second record, we stored the values -12 and 25 in the healthChecked and runCompleted columns, respectively, using the same format that we declared. Even though we declared those columns as BOOLEAN and BOOL, the database internally treated them as TINYINT datatypes, expanding the column length to store the values.

5. IS TRUE, IS FALSE, IS NOT TRUE, IS NOT FALSE Clauses.

To know whether a particular column contains the value that evaluates to true or false, MySQL provides us with four clauses: IS TRUE, IS FALSE, IS NOT TRUE, and IS NOT FALSE clauses. Out of them, IS TRUE and IS NOT FALSE behave in the same fashion, and IS NOT TRUE and similarly IS FALSE function and result in the same results. Let us try to find out the records in our table marathon_players whose healthChecked columns value is true or is equivalent to true. For this, I will first use the clause IS TRUE, and my query statement will be as follows –

Code:

SELECT * FROM marathon_players WHERE healthChecked IS TRUE;

Output:

6. From the results, it is evident that both the values 1 and 12 are considered true. Any non-zero value, whether positive or negative, will be considered true. Now, let us use IS NOT FALSE clause and see whether we retrieve the same results. Our query statement will be as follows –

Query:

SELECT * FROM marathon_players WHERE healthChecked IS NOT FALSE;

Output:

7. Now, let’s review the results for all individuals whose run was not completed by checking the value of the “runCompleted” column. This will allow us to verify the functionality of the remaining two clauses. Let us prepare the query using the IS FALSE clause in it. The query will be as follows:

Query:

SELECT * FROM marathon_players WHERE runCompleted IS FALSE;

Output:

8. Now, let us use the IS NOT TRUE clause in our query and retrieve the results. Our query statement is as follows –

Query:

SELECT * FROM marathon_players WHERE runCompleted IS NOT TRUE;

Output:

We can conclude that IS NOT TRUE and IS FALSE give the same output.

Conclusion

MySQL does not provide any specific datatype that will store the boolean values. However, we can use the keywords “BOOLEAN” and “BOOL” to declare the data type of the column, which will be internally treated and considered as TINYINT(1) data type. Hence, we can say that BOOLEAN and BOOL are synonyms of the TINYINT(1) data type. In MySQL, any truth value, regardless of its case, is considered and stored as 1. Similarly, any non-zero value and the value “1” are treated as TRUE when using clauses such as IS TRUE, IS FALSE, IS NOT TRUE, or IS NOT FALSE. The opposite is applicable for 0 and FALSE in MySQL.

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Swift: Declare An Empty Dictionary

In Swift, there are some different syntaxes to declare an empty dictionary. It is important to remember that all syntaxes produce the same result. In this article, you will see examples of how to declare an empty dictionary and define a dictionary in Swift.

What is a Swift Dictionary?

A dictionary is a collection in Swift that lets you keep key-value pairs. Each key in the dictionary has a corresponding value, and each word in the dictionary needs to be distinct. Because dictionaries are unordered, the sequence in which key-value pairs are introduced does not matter.

Example

In this example, we create a dictionary with three key-value pairs. The keys are “apple”, “banana”, and “orange”, and the corresponding values are 1, 2, and 3. Also, we can access dictionary values using their corresponding keys.

Here’s an example of a simple dictionary in Swift −

import Foundation var dictionary = ["apple": 1, "banana": 2, "orange": 3] print("Original Dictionary: (dictionary)") print(dictionary["apple"] ?? 0) print(dictionary["banana"] ?? 0) dictionary["pear"] = 4 dictionary.removeValue(forKey: "banana") dictionary["apple"] = 5 print("Updated Dictionary: (dictionary)") Output Original Dictionary: ["banana": 2, "apple": 1, "orange": 3] 1 2 Updated Dictionary: ["pear": 4, "orange": 3, "apple": 5]

Overall, dictionaries provide a convenient way to store and retrieve data associated with a specific key. This makes it a powerful tool for organizing and manipulating data in your Swift programs.

Here are a few more examples of how to declare an empty dictionary in Swift Example 1: Dictionary with String keys and Any Values var dictionary = [String: Any]()

In this example, we declare an empty dictionary with String keys and Any values. This means that we can add any type of value to the dictionary, including strings, numbers, arrays, and other dictionaries.

Example 2: Dictionary with Int keys and String Values var dictionary = [Int: String]()

Here, we declare an empty dictionary with Int keys and String values. This is useful when we want to store a list of items that can be accessed by an index, such as an array.

Example 3: Dictionary with Custom object keys and Values import Foundation class Person: Hashable { var name: String var age: Int init(name: String, age: Int) { chúng tôi = name chúng tôi = age } return chúng tôi == chúng tôi && chúng tôi == rhs.age } func hash(into hasher: inout Hasher) { } } var dictionary = [Person: Int]() print(dictionary) Output [:]

In this example, we declare an empty dictionary with custom object keys and Int values. This allows us to store values that are associated with a specific instance of a custom class called Person. Note that the custom class Person needs to conform to the Hashable protocol in order to be used as a dictionary key as shown in the above example.

Effective lookups − Even when you have a lot of data, dictionaries let you look up numbers rapidly using their corresponding keys. This is due to the fact that dictionaries employ a hash table algorithm, which enables constant time (O(1)) lookups.

Key-value combinations in dictionaries make it simple to organize data in a way that is both understandable and manipulative. When dealing with complicated data sets or data with a hierarchical structure, this can be particularly helpful.

Flexibility − Swift dictionaries are changeable and capable of storing any kind of information, including custom objects and data from other dictionaries. They are therefore flexible for managing and organizing data in your programs.

Code readability can be improved by using dictionaries to hold data, particularly if you use descriptive keywords that clarify the meaning of the data.

Effective management of data relationships − Dictionaries offer a means of expressing the connections between various data sets. To keep informed about the parts of a vehicle, for instance, you might use a dictionary where the keys are the titles of the parts and the values are specifics like their height, weight, and material.

Conclusion

In conclusion, dictionaries are an important data structure in Swift programming. They provide a convenient way to store and retrieve data that is associated with a specific key, making it a powerful tool for organizing and manipulating data in your programs. Dictionaries offer benefits such as efficient lookups, easy organization of data, flexibility, improved code readability, and effective handling of data relationships. Therefore, it is essential to learn how to use dictionaries effectively to build robust and efficient Swift programs.

How To Reset Windows: Learn How To Factory Reset Windows

A full reset is normally should only be performed as a last resort, so ensure you’ve tried all other potential solutions and backed up your files before you go ahead.

If your device is just running slowly, you might only need our guide to speeding up Windows, but for more serious performance problems, a full reset might be in order.

The process of resetting a computer has changed slightly in Windows 10, so we offer separate guides on how to reset Windows 10 and how to reset Windows 7 or 8 below.

Factory reset a Windows 10 computer or tablet

In Windows 10 the process is pretty easy, thanks to a built-in tool for resetting a PC. Open the Start menu and select Settings (the cog wheel). Now type ‘reset’ in the search bar and select ‘Reset this PC‘ on the left when the results appear.

Under the Recovery section of Update & Security you can now hit ‘Get started‘ to begin the process. During this you can select if you would like to keep files or do a full reset.

The process is identical for a Windows 10 tablet, because the operating system is almost identical across computers and tablets.

Factory reset a Windows 7 or 8 computer

Factory resetting a computer running an older version of Windows is a little trickier, because there’s no reset tool built into the operating system.

Some computers come with recovery discs, which is a fairly easy way of performing a factory reset – you can just insert the disc and follow the instructions.

Windows 8 computers will sometimes have a recovery application which is launched from within Windows, rather than from a disc, so check your app menu.

If you didn’t get any discs in the box, and don’t have a preinstalled recovery app, then there’s a good chance your PC or laptop has a recovery partition instead.

This is a hidden part of the hard drive which safely stores a complete copy of Windows, drivers and extra programs. You can use it return your computer to the exact state it was in the first day you had it – and it will perform just the same, too.

Please bear in mind that the process will vary between different brands and even different models. The recovery software will be called various names for each brand of computer, usually along the lines of ‘recovery manager’. We’ve done our best to put together a ‘one size fits all’ guide.

Back up any data which you wish to keep before performing a factory reset. You will probably want to copy everything from your user folders, including documents, photos, music and videos. The factory reset will delete all these along with any programs you’ve installed since you got your laptop.

1. Start up or reboot your laptop.

2. During the startup process, hit the appropriate F key or key combination which we have listed below for your manufacturer.

Acer – Alt + F10

Asus – F9

Dell/Alienware – F8

HP – F11

Lenovo – F11

MSI – F3

Samsung – F4

Sony – F10

Toshiba – 0 (not numpad) while turning on, release key when Toshiba logo appears

If these keys don’t work then look out for a message in the screen during startup which might indicate how to start the recovery process. You might need to check your recovery partition is enabled in the BIOS. It’s also possible your laptop might not have a recovery partition (or it may have been deleted) so we’re making no guarantees here. You might need to contact your manufacturer.

3. You should see instructions on the screen explaining how to proceed. Different manufacturers use different software to restore the ‘disk image’ from the recovery partition, so we can’t provide specific guidance. However, the process is almost always automatic once you’ve confirmed you definitely want to proceed.

It might be a case of waiting for 30 minutes for the job to happen in one go, but some systems restore Windows first, and then install drivers and programs automatically when Windows first boots. If that’s the case, don’t try to do anything until you see a message saying the restore has finished.

If you’re just having problems with search, you don’t need to do a full reset. Just reset Windows search – here’s how.

How To Store Data To Dom?

Storing data in the DOM means storing data in plain text format. For example, we store data in the state variable while using React or any other reactive framework. When the user updates the data in the input field, it stores updated data in the state variable.

So, we store data in the state variable before we submit the form. At the time of submitting the form, we use the values of the state variables.

In vanilla JavaScript, we can do the same, like storing data in plain text format, and whenever we require to submit the form, we can fetch data from DOM rather than getting it from the input fields.

Here, we will learn to store data in the DOM using JavaScript and Jquery both.

Use JavaScript to Store Data in the DOM

In JavaScript, we can create an object to store the data. We can store the data in the object in plain text format and fetch from the object whenever required.

Syntax

Users can follow the syntax below to store the data in the DOM using JavaScript.

let data_obj = { prop1: "", } data_obj.prop1 = value;

In the above syntax, we created the data_obj object to store data, and we can update its value.

Example

let data_obj = { fname: “”, lname: “” } function storeInDOM() { var fname = document.getElementById(“fname”).value; var lname = document.getElementById(“lname”).value; data_obj.fname = fname; data_obj.lname = lname; } function getFromDOM() { }

The jQuery contains the data API, which we can invoke using the data() method. We can store the data for a particular element. When we pass the two parameters to the data API, it stores the data for a particular element; Otherwise, it returns the data stored for a particular element.

Syntax

Users can follow the syntax below to store the data in the DOM using Jquery’s data() method.

$("CSS_identifier ").data("key_name", value);

A CSS identifier is used to select an element in the above syntax. The data() method takes the key as a first parameter and the respected value as a second parameter.

Example

The form contains the email and password input fields in the example below. Whenever the user presses the button to store data, we fetch the input’s value using Jquery and store it in the DOM for a particular element using the data() method. Here, $(“#email”).data(“email”, email) will access the input with id equal to email and store ‘email’ as a key and input value as a value of the ‘email’ key.

So, we can store key-value pairs by taking any element as a reference using the data() method, and also users need to take the same element as a reference while accessing the data.

function storeInDOM() { var email = $(“#email”).val(); var password = $(“#password”).val(); $(“#email”).data(“email”, email); $(“#password”).data(“password”, password); } function getFromDOM() { var email = $(“#email”).data(“email”); var password = $(“#password”).data(“password”); $(“#content”).html(“Email: ” + email + ” Password: ” + password); }

Users learned to store data in the DOM. However, storing data in the DOM is a bad practice as it is temporary. Users can use the local or session storage of the browser to store the data, which also has straightforward syntax.

In JQuery, users can store the data for a particular element using the data API. In JavaScript, users need to store all data in single or multiple objects.

How To Find Outliers

Outliers are extreme values that differ from most other data points in a dataset. They can have a big impact on your statistical analyses and skew the results of any hypothesis tests.

It’s important to carefully identify potential outliers in your dataset and deal with them in an appropriate manner for accurate results.

What are outliers?

Outliers are values at the extreme ends of a dataset.

An outlier isn’t always a form of dirty or incorrect data, so you have to be careful with them in data cleansing. What you should do with an outlier depends on its most likely cause.

True outliers

True outliers should always be retained in your dataset because these just represent natural variations in your sample.

Example: True outlierYou measure 100-meter running times for a representative sample of 560 college students. Your data are normally distributed with a couple of outliers on either end.

Most values are centered around the middle, as expected. But these extreme values also represent natural variations because a variable like running time is influenced by many other factors.

True outliers are also present in variables with skewed distributions where many data points are spread far from the mean in one direction. It’s important to select appropriate statistical tests or measures when you have a skewed distribution or many outliers.

Other outliers

Outliers that don’t represent true values can come from many possible sources:

Measurement errors

Unrepresentative sampling

Example: Other outliersYou repeat your running time measurements for a new sample.

For one of the participants, you accidentally start the timer midway through their sprint. You record this timing as their running time.

This data point is a big outlier in your dataset because it’s much lower than all of the other times.

This type of outlier is problematic because it’s inaccurate and can distort your research results.

Example: Distortion of results due to outliersYou calculate the average running time for all participants using your data.

The average is much lower when you include the outlier compared to when you exclude it.  Your standard deviation also increases when you include the outlier, so your statistical power is lower as well.

In practice, it can be difficult to tell different types of outliers apart. While you can use calculations and statistical methods to detect outliers, classifying them as true or false is usually a subjective process.

Four ways of calculating outliers

You can choose from several methods to detect outliers depending on your time and resources.

Sorting method

You can sort quantitative variables from low to high and scan for extremely low or extremely high values. Flag any extreme values that you find.

This is a simple way to check whether you need to investigate certain data points before using more sophisticated methods.

Example: Sorting methodYour dataset for a pilot experiment consists of 8 values.

180 156 9 176 163 1827 166 171

You sort the values from low to high and scan for extreme values.

9 156 163 166 171 176 180 1872

Using visualizations

You can use software to visualize your data with a box plot, or a box-and-whisker plot, so you can see the data distribution at a glance. This type of chart highlights minimum and maximum values (the range), the median, and the interquartile range for your data.

Many computer programs highlight an outlier on a chart with an asterisk, and these will lie outside the bounds of the graph.

Statistical outlier detection

Statistical outlier detection involves applying statistical tests or procedures to identify extreme values.

You can convert extreme data points into z scores that tell you how many standard deviations away they are from the mean.

If a value has a high enough or low enough z score, it can be considered an outlier. As a rule of thumb, values with a z score greater than 3 or less than –3 are often determined to be outliers.

Using the interquartile range

The interquartile range (IQR) tells you the range of the middle half of your dataset. You can use the IQR to create “fences” around your data and then define outliers as any values that fall outside those fences.

This method is helpful if you have a few values on the extreme ends of your dataset, but you aren’t sure whether any of them might count as outliers.

Interquartile range method

Sort your data from low to high

Identify the first quartile (Q1), the median, and the third quartile (Q3).

Calculate your IQR = Q3 – Q1

Calculate your upper fence = Q3 + (1.5 * IQR)

Calculate your lower fence = Q1 – (1.5 * IQR)

Use your fences to highlight any outliers, all values that fall outside your fences.

Your outliers are any values greater than your upper fence or less than your lower fence.

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Example: Using the interquartile range to find outliers

We’ll walk you through the popular IQR method for identifying outliers using a step-by-step example.

Your dataset has 11 values. You have a couple of extreme values in your dataset, so you’ll use the IQR method to check whether they are outliers.

25 37 24 28 35 22 31 53 41 64 29

Step 1: Sort your data from low to high

First, you’ll simply sort your data in ascending order.

22 24 25 28 29 31 35 37 41 53 64

Step 2: Identify the median, the first quartile (Q1), and the third quartile (Q3)

The median is the value exactly in the middle of your dataset when all values are ordered from low to high.

Since you have 11 values, the median is the 6th value. The median value is 31.

22 24 25 28 29 31 35 37 41 53 64

Next, we’ll use the exclusive method for identifying Q1 and Q3. This means we remove the median from our calculations.

The Q1 is the value in the middle of the first half of your dataset, excluding the median. The first quartile value is 25.

22 24 25 28 29

Your Q3 value is in the middle of the second half of your dataset, excluding the median. The third quartile value is 41.

35 37 41 53 64

Step 3: Calculate your IQR

The IQR is the range of the middle half of your dataset. Subtract Q1 from Q3 to calculate the IQR.

Formula Calculation

IQR = Q3 – Q1

Q1 = 26

Q3 = 41

IQR = 41 – 26

= 15

Step 4: Calculate your upper fence

The upper fence is the boundary around the third quartile. It tells you that any values exceeding the upper fence are outliers.

Formula Calculation

Upper fence = Q3 + (1.5 * IQR)

Upper fence = 41 + (1.5 * 15)

= 41 + 22.5

= 63.5

Step 5: Calculate your lower fence

The lower fence is the boundary around the first quartile. Any values less than the lower fence are outliers.

Formula Calculation

Lower fence = Q1 – (1.5 * IQR)

Lower fence = 26 – (1.5 * IQR)

= 26 – 22.5

= 3.5

Step 6: Use your fences to highlight any outliers

Go back to your sorted dataset from Step 1 and highlight any values that are greater than the upper fence or less than your lower fence. These are your outliers.

Upper fence = 63.5

Lower fence = 3.5

22 24 25 28 29 31 35 37 41 53 64

You find one outlier, 64, in your dataset.

Dealing with outliers

Once you’ve identified outliers, you’ll decide what to do with them. Your main options are retaining or removing them from your dataset. This is similar to the choice you’re faced with when dealing with missing data.

For each outlier, think about whether it’s a true value or an error before deciding.

Does the outlier line up with other measurements taken from the same participant?

Is this data point completely impossible or can it reasonably come from your population?

What’s the most likely source of the outlier? Is it a natural variation or an error?

In general, you should try to accept outliers as much as possible unless it’s clear that they represent errors or bad data.

Retain outliers

Just like with missing values, the most conservative option is to keep outliers in your dataset. Keeping outliers is usually the better option when you’re not sure if they are errors.

With a large sample, outliers are expected and more likely to occur. But each outlier has less of an effect on your results when your sample is large enough. The central tendency and variability of your data won’t be as affected by a couple of extreme values when you have a large number of values.

If you have a small dataset, you may also want to retain as much data as possible to make sure you have enough statistical power. If your dataset ends up containing many outliers, you may need to use a statistical test that’s more robust to them. Non-parametric statistical tests perform better for these data.

Remove outliers

Outlier removal means deleting extreme values from your dataset before you perform statistical analyses. You aim to delete any dirty data while retaining true extreme values.

It’s a tricky procedure because it’s often impossible to tell the two types apart for sure. Deleting true outliers may lead to a biased dataset and an inaccurate conclusion.

For this reason, you should only remove outliers if you have legitimate reasons for doing so. It’s important to document each outlier you remove and your reasons so that other researchers can follow your procedures.

Other interesting articles

If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions about outliers Cite this Scribbr article

Bhandari, P. Retrieved July 20, 2023,

Cite this article

How To Add Music To Powerpoint Presentations

Microsoft PowerPoint allows you to add different types of multimedia content. Music is just one more content type you can add to catch attention. Before you start adding music to your PowerPoint presentations though, do remember that not all types of music and even not every sound effect may be suitable for your presentation. 

As a general rule for creating engaging PowerPoint presentations, always tailor your presentation around the content and not the other way around.

Table of Contents

Let’s learn how to add music to your PowerPoint slides and then customize the music file with the playback controls in the PowerPoint Ribbon. 

Here are a few things you should know before you upload the first music file: 

You can add one or several audio files to your slides. 

You can download a song or a music file from the internet and then insert it in your slides.

You can record your own voice and add it as a narration to your presentation. 

You must use an external audio editor to string together multiple music files when you want to cover a long presentation. 

PowerPoint supports WAV, WMA, MP3, and a few other file formats. 

In this article, we won’t talk about adding audio narration to PowerPoint. Instead, let’s look at how to add music to your PowerPoint presentation with a sound file of your choice. 

Add Music To Your PowerPoint Presentation

Adding music to PowerPoint slides is just like uploading any other file type.

The Audio Tools Playback tab appears on the Ribbon as soon as the file is uploaded on the slide. You can also select the icon of the audio file in the Normal slide view and display the Audio Tools Playback Tab.

Customize The Music With The Audio Tools Playback Tab

By default, the Audio Style is automatically set to No Style.

You can select Play in Background. Play in Background makes the audio file start automatically during a slide show and also play across multiple slides.

There are three ways to start the playback. 

The other choices like Volume, Play Across Slides, Loop until Stopped, and Rewind After Playing are all self-explanatory. 

How To Trim Your Music Clip

The Editing group on the Playback tab gives you a few ways to change the way your music file will sound. You can add Fade effects with the Fade In and Fade Out timers to gradually introduce the sound clip in your presentation. 

Drag the green marker (at the start of the clip) to the marker position. To trim the end of the clip, drag the red marker on the right to the new point where you want to end it. 

Instead of dragging the markers, you can also note the time of the end points and then enter it in the Start and End time boxes. 

When you save the presentation, the trimmed information is saved in the file. You can also save a separate copy of the trimmed music file outside PowerPoint too.

How To Add Bookmarks To An Audio File When You Want To Change The Audio Icon

By default, an audio file will appear as a speaker icon in the slide. If you want, you can change the icon to a different picture.

Play The Perfect Audio For Your Presentation

You can consider using songs or an instrumental score to your slides when you want to convert PowerPoint into a video. 

But do remember that these media shouldn’t overpower the content of your presentation. Every rule of effective presentations says that it’s important to know what to leave out as well as what music to add into your PowerPoint slides. 

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