The choice of operating system for a virtual private server depends on the needs of a particular company. But if you are looking for a reliable, efficient, and flexible solution to host your web projects, in most cases it will be Windows VPS. In this article, we'll analyze why many people prefer Microsoft solutions, and what benefits and limitations you can expect if you decide to buy a Windows VPS .
Windows VPS is a virtual private server that uses Windows Server as the underlying operating system. Windows VPS functions effectively as a cloud server with elastic scalability. This means that you get a dedicated server resource that you can use as you see fit, and you can easily add resources as needed.
Typically, a private server under Windows is used in many segments, it can be hosting websites and applications, databases, mail servers, and game servers. The main advantage is that the system allows you to run programs and scripts that require a Windows-compatible environment. For example, you can use Windows VPS for hosting ASP.NET, .NET Core, SQL Server, and other Microsoft technologies.
The main reason Windows VPS is preferred is that the system is developed by the largest player on the market. Microsoft is a multi-billion dollar company that invests huge budgets in its products. Other vendors do not have the same amount of resources, it is difficult for them to compete at this level. Consider the other advantages of using a Windows VPS.
Windows VPS requires no additional configuration or software installation. You can start working with it immediately after purchase and get full control over the server. You can choose the desired version of Windows Server (from 2008 to 2022), that best suits your needs and requirements.
But most importantly, the user gets access to a remote desktop with an intuitive graphical interface. There is no need to program anything from the command line, as is required in UNIX-based systems.
The system easily integrates with other Microsoft services such as Office 365, Azure, and SharePoint. You can install additional Windows software such as IIS, Active Directory, and Exchange Server.
You get a single, seamless software interface where you can use cloud services from Microsoft for data storage, analytics, machine learning, and other purposes. It is possible to synchronize the server with other Windows devices and applications, such as Outlook, OneDrive, and Skype.
Microsoft understands what customers want and for 20 years Windows Server developers have eradicated almost all the technical problems associated with the product.
With VPS on Windows Server, you don't have to wait long for vulnerabilities to be fixed. The company promptly and regularly releases security updates and patches, which are installed automatically. With open-source systems, you achieve the same result only if the product has an extensive community.
Windows VPS is ideal for developing and hosting web applications based on ASP and ASP.Net Core – popular technologies from Microsoft. You can use different programming languages like C#, VB.Net, and Python and connect different databases: SQL Server, MySQL, Oracle, and others.
Windows Server is a great software for hosting and other tasks, but not perfect. The system has some limitations which may be critical for a certain group of users.
The first is the high cost. One of the main disadvantages is that Windows VPS is usually more expensive than Linux VPS or shared hosting. This is because VPS requires an additional license for Windows Server, and it is included in the cost of renting a server. Add to this the fact that most tools and software are paid Microsoft products, and using a Windows VPS will put a noticeable strain on your budget.
But note that the high cost is a relative indicator. You can almost always find free alternatives to Microsoft programs. And due to the stable operation of the server and quality support in the long run, you can save thousands of dollars. Windows servers are much less likely to crash and are easier to resume operation than other operating systems.
Another disadvantage of using a Windows VPS is problems with performance and compatibility. You may encounter conflicts between different versions of Windows Server or between the OS from Microsoft and other operating systems. For example, it is possible to have problems when running Windows Server 2022 on old hardware, or when trying to connect to a Windows VPS from a Linux computer.
But if you rent a VPS server, such cases will be rare. Most likely, the hosting company has already taken care of installing modern performance equipment and configuring compatibility settings.
Some programs or scripts may run better on a Linux VPS than on a Windows VPS since they were originally designed for a Linux-compatible environment. For example, you may have problems running PHP or MySQL on a Windows VPS, since these technologies are optimized to run on Linux. Windows VPS is not suitable for developing and running web applications in Ruby on Rails or Node.js, as these technologies work better on Linux VPS.
Among many users, there is an opinion that Linux VPS is preferable in terms of security, and there is a logic in that. The dominance of Windows on the desktop market has made Microsoft systems a prime target for hackers. But Windows has a lot of tools to protect your server from hackers.
You can protect your data from unauthorized access or loss through a variety of security measures, such as
Firewalls. Allow you to control incoming and outgoing network traffic and block unwanted or dangerous connections. You can use both the built-in Windows Firewall and third-party tools, such as Bitdefender or Norton, to protect your Windows VPS from hackers, viruses, or other attacks.
Intrusion detection and prevention systems (IDS/IPS). These are programs or devices that analyze network traffic and detect and prevent hacking attempts or other anomalous activities. You can use IDS/IPS at the operating system level or at the application level.
Data encryption and SSL certificates. You can use BitLocker or VeraCrypt to encrypt disk space or Let's Encrypt and Comodo to obtain SSL certificates.
Of course, Windows VPS allows you to create real-time backups and regularly scheduled backups.
Windows VPS can be a suitable solution for your projects if you want:
run programs or scripts that require a Windows-compatible environment;
have full control over your server and configure it as you wish;
get high performance and reliability of the server.
Windows VPS provides flexibility and scalability, data security, and ease of management for web projects. However, Windows VPS has some limitations. Therefore, you must carefully choose Windows VPS for your projects, first of all, based on usage scenarios. You should also consider other parameters such as server resources, Windows Server version, and purchase or lease costs.
Java developers are the backbone of many successful businesses. They bring a wealth of experience and knowledge to the table, making them an invaluable asset for any company looking to stay competitive in today’s market. With Java being one of the most widely used programming languages, hiring a Java developer is often seen as the best investment for any business looking to develop their own software applications or products. From its wide industry usage to its mature ecosystem and strong community, there's no doubt that Java developers are some of the most sought-after professionals in tech today. In this article, we'll discuss why investing in a skilled Java developer may be your best bet when it comes to taking your business forward. Java competitors are C#(C Sharp), Python, PHP etc.
Investing in a Java developer can be one of the most profitable decisions you make for your business. Not only are they highly sought after, but the projects that Java developers create have the potential to last years into the future. With advanced backend development, custom applications, and web services all being confidently handled by this platform, there is no doubt that companies who invest in talented Java developers will benefit from their skills as they continue to build valuable systems that drive business success. From secure cloud computing to mobile applications and web development, having a qualified and experienced Java developer at your disposal can improve audience engagement, expedite process automation and enable data storage stability for any company.
With the widespread use of Java, many important systems are built on the Java platform, from high-end enterprise applications to scalable web applications. In addition to creating effective and optimized programs for your business, well-trained Java developers will also serve as consultants for any technical modifications you wish to apply. They can offer suggestions and counsel on potential security hazards, alternative platforms on the market, and their particular insights into your company's goods or market.
Also, because the majority of their talents are transferable to many technologies, they can easily keep up with the constantly shifting demands of the market. Java engineers are absolutely worth hiring for your company in the long run.
Hiring Java developers is an excellent investment for any business. Developed back in 1995 by Sun Microsystems and later purchased by Oracle, Java's longevity has bred a strong community and ecosystem of experts, libraries, and tutorials available for businesses to take advantage of.
Furthermore, it is a cross-platform language that runs everywhere including the most popular operating systems; this opens up the hiring pool of potential developers. Considering that several major corporations, including Uber and Airbnb, have already adopted Java as their primary development language, hiring a skilled Java developer would be a tremendous help to any company trying to take use of available resources.
In conclusion, Java developers offer a broad range of abilities and knowledge required to create solutions that succeed. Hiring a skilled Java developer is frequently considered to be the best investment for your organization due to its high demand in the market and its developed ecosystem. Having a skilled Java developer on staff can help you grow your company because so many firms already rely on Java technology for their apps, web services, and data storage solutions.
Java has established itself as one of the most dependable and robust programming languages for creating software and web applications throughout the years. As a result, it has become increasingly popular among developers, leading many successful companies to make the wise decision of hiring experienced Java developers to work on projects that require advanced knowledge of the language. With improved performance, scalability, and security, writing code in Java is an investment worth making as it ensures optimal output quality with minimal effort.
Furthermore, qualified Java developer demand remains consistently high in the job market, which can benefit your organization by making sure you attract only the best talent. All of these reasons encourage businesses to invest in a group of skilled Java programmers who can create cutting-edge software solutions and advance their company's goals.
You may be confident that employing Java developers is the best decision when considering the long-term success of your business. Java development is a flexible sector with several advantages that your company may take advantage of. Java developers are the perfect fit for any organization since they possess a broad variety of technical skills and the ability to quickly resolve difficult issues. From fixing bugs efficiently to ensuring security and stability throughout the production process, Java developers consistently deliver quality results which in turn adds value to your organization. With their extensive expertise in design patterns, object-oriented programming, and core Java language capabilities, Java programmers are capable of building optimized applications and robust software solutions tailored to your exact needs. All of this makes investing in skilled Java developers perhaps the best decision a business can make when it comes to creating modern enterprise systems.
In conclusion, hiring developers from Java is an excellent choice for any business looking to stay ahead of the competition. With their vast range of skills and expertise in object-oriented programming, design patterns, core Java language capabilities, and more; they are capable of developing innovative solutions tailored to your exact needs with improved performance and scalability. Moreover, as a mature and stable language that runs on multiple platforms supported by a strong community and ecosystem of experts; you can be sure there will always be qualified talent available when it comes time to hire new programmers. Investing in well-trained Java engineers offers long-term returns through increased productivity while also helping propel your organization's objectives forward faster than ever before. All this makes adding experienced Java developers to your team one of the best decisions you could make!
Thank you for joining us today for our in-depth Raspberry Pi programming tutorial. The previous guide covered the steps necessary to connect a fingerprint scanner to a Raspberry Pi 4. In addition, we developed a python script to complement the sensor's ability to identify fingerprints. Yet, in this guide, we'll discover how to interface a ws2812 RGB to a Raspberry Pi 4.
Bright, colorful lights are the best, and this tutorial shows you how to set up Fully Configurable WS2812B led strips to run on a Pi 4 computer as quickly and flexibly as possible. In that manner, you can have the ambiance of your home reflect your tastes.
In most cases, when people talk about a "WS2812B Strip," they mean a long piece of extensible PCB with a bunch of different RGB LED Nodes spread out and dotting all along the top. As a bonus, WS2812B strips can be addressed individually. Each RGB node can independently set its own color and brightness. The functionality of non-addressable RGB strips will be identical to that of WS2812B strips. WS2812B Strips are superior in every way, allowing for more imaginative LED light shows. It is possible to create more complex animations, stripes, and chases by individually programming the actions of each LED Node.
The "WS" in WS2812B indicates that this is the second major revision of this design; "World Semi" refers to the chip's original equipment manufacturer, "2812" to its specific part number, and "B" to its revision number. Each WS2812B LED Node contains an integrated circuit chip, as shown below.
Fully Configurable Strip LEDs now come in wide varieties. It's important to remember whether the strip operates on 5 volts or 12-volt power. To power our 5V-powered Raspberry Pi Computer, we need to use a 5V power supply. Or we'll have to install extra gear to meet the 12v Dc power specifications. LED Node densities also vary across WS2812B LED strips. Since our strip consists of WS2812B nodes soldered onto rigid PCBs, it may also be controlled by the Python script included in this article. ws2812B LED strips are the ideal LEDs to use because of the benefits above. What this manual entails is listed below.
The control mechanism implemented in this tutorial works with WS2812B LED nodes, whether they are rigidly coupled on a PCB or flexibly arranged over a long strip. No prior experience with Raspberry Pi has required, thanks to our comprehensive online raspberry pi 4 for the introduction. You should be able to follow these guidelines if you've read at least chapter 1.
| Where To Buy? | ||||
|---|---|---|---|---|
| No. | Components | Distributor | Link To Buy | |
| 1 | Jumper Wires | Amazon | Buy Now | |
| 2 | Raspberry Pi 4 | Amazon | Buy Now | |
Let's go over the protocol for these addressable LEDs so you can see how they function. Each WS2812B unit's red, green, and blue LEDs can be independently controlled to one of 256 brightness levels. Each LED module needs 24 bits of data or three groups of eight brightness bits. Here's a quick rundown of the steps involved:
The first LED in the string receives this data stream from the microcontroller, which consists of eight green bits, eight red bits, and eight blue bits.
In the case of several LEDs, the data sequence for the second LED begins with green, red, and blue information immediately after the information for the first LED. The process repeats itself until each LED is turned on.
The first LED acts as a receiver for data for all subsequent LEDs in the chain and then sends that data to the second LED without applying the same sequence.
When the original "number one" LED unit runs out of binary LED sequences, it passes the baton to the next available unit.
What follows are detailed instructions for configuring a single Raspberry Pi board to manage many individually addressable LEDs.
A pi 4
5v led strip
A DC barrel jack to 2-pin terminal block adapter.
More than 30 WS2812B strip Nodes will necessitate the usage of an external power supply. Even with highly efficient LEDs, producing brilliant light takes a lot of energy. On average, each pixel will consume 20mA, and while projecting white light at full brightness, each pixel will consume 60mA. This information shows us that 30 Pixels can consume 600mA on average and 1.8A at full brightness. As a side note, staring directly into a room with the lights set to full white causes blind spots in my vision. Because of this, I typically set the brightness of my WS2812B lights to 20%. Ensure your WS2812B strip's power source is adequate for the illumination level you intend to achieve.
In today's world, 3.3V data logic is compatible with most WS2812B LED nodes. The GPIO Pins on a Raspberry Pi have an operational voltage range of 0 to 3.3V; therefore, this works out perfectly. This surface-mounted WS2812B strip Node has seen five minor changes since its inception. Since the earlier WS2812B strip can only work on 5V Data Logic, a logical level shifter may be necessary if you're experiencing strange or intermittent issues.
We'll configure our Raspberry PI to look and act like a desktop PC because that's how most people use computers at home and because it's the most user-friendly setup for novice makers. To convert our Raspberry Pi into a desktop computer, we'll need to install a micro-SD card pre-flashed with the operating system. Then hook it up to a monitor via HDMI and a mouse/keyboard combo. Also, this is a good moment to install a small heatsink on the Raspberry Pi Board's central processing unit IC. Look at the image below to see the setup adjacent to a short WS2812B LED strip with three wires protruding from it. The WS2812B has three wires that have not yet been connected to the Raspberry Pi. WS2812B LED Strip PCBs are often marked with arrows to indicate the direction of data flow. It is a common problem in troubleshooting when this is ignored.
How to Configure Hardware for WS2812B Chains with fewer than 30 Nodes We need to plug in the 5V WS2812B Strip's three wires to the Raspberry Pi before turning the power on. Link the Raspberry Pi's 5V Power Input Pin with the Red Power Cable. Join the white wire labeled "Ground" to the Pi's Ground pin. Join the Raspberry Pi's Green Input Pin to its GPIO 18 port. It is important to remember that the Green Input Pin could be any GPIO pin as long as the appropriate adjustments are made in your Python programs. In this manual, GPIO 18 will be used for the Data Line in all scripts. The picture below shows how these three wires are joined together. This straightforward system can provide power and data transmission for a limited amount of LEDs.
Hardware Configuration for a WS2812B Strip with more than 30 Nodes in Length. However, the Pi 4 Power Pin Out is not powerful enough to power a full LED strip. It's important to know the power requirements of fully addressable WS2812B LEDs, especially when used in large quantities. 150 fully illuminated RGBW LEDs should be manageable by a high-quality 5v 4A power supply.
Following the diagram below, we will connect a 5V 4A power source via a DC barrel jack. Our WS2812B LEDs are pre-soldered with one green data wire, two red power wires, and two white ground wires. The white wire should be attached to the DC barrel jack's negative terminal using a screwdriver. Join the Red Wire to the Terminal With the Positive Screw Down. The White Connectors all link to one other, indicating a Common Ground shared by the Raspberry Pi and the power source. The two devices can't be connected without ground, which will prevent voltage fluctuations and data transmission mistakes.
Furthermore, the WS2812B Strip is no longer connected to the Pi 4 computer via a red power wire. We have found an alternative to using a Raspberry Pi to run our system. See the diagram below for details on connecting a Raspberry Pi to a string of WS2812B LEDs so that they may be controlled remotely.
There are several great best practices to keep in mind if you're planning on powering many LED strips. If you need to run more than a 5-meter roll of LEDs, I recommend consulting the adafruit documentation on the subject. It's important to think about heat and currents.
Now that everything is hooked up how you like it, you can turn on the Pi 4 System by inserting a USB-C cable.
You'll need to install a few packages if you're starting with a clean install of Raspberry Pi OS. Since this is the case, WS2812B LEDs will function properly. When you plug the Raspberry Pi into an electrical outlet and finish the initial boot-up wizard, you will be taken to the operating system's familiar desktop.
Click the black button in the upper left corner to open a new terminal window. An interactive terminal will launch. Below is a picture of this happening, with a huge red arrow indicating the pressed terminal icon.
With this terminal window, we can search for specific programs and download them from the internet. Here are the command lines you use in the terminal to install everything you need. Type | Y | to confirm installs if prompted.
sudo pip3 install rpi_ws281x
sudo pip3 install adafruit-circuitpython-neopixel
sudo python3 -m pip install --force-reinstall adafruit-blinka
You now have the software and programming packages installed on your Device to power and run WS2812B properly.
You may trigger the Raspberry Pi to turn on the associated WS2812B LED strips in various ways. To begin, I will utilize the simplest and quickest technique, which involves hooking up this Raspberry Pi to a monitor, mouse, and keyboard so that I can execute Python scripts directly to control it. Below, you'll find the best and most fun Python script to start with: | strandtest.py |. Even though it's far more involved than the rest of the Python scripts in this tutorial, we felt it was essential to include it first because it provides a comprehensive overview of the various possible patterns and lighting styles.
import time
from rpi_ws281x import *
import argparse
LED_COUNT = 30
LED_PIN = 18
LED_FREQ_HZ = 800000 # LED signal frequency in hertz (usually 800khz)
LED_DMA = 10 # DMA channel to use for generating a signal (try 10)
LED_BRIGHTNESS = 65 # Set to 0 for darkest and 255 for brightest
LED_INVERT = False # True to invert the signal (when using NPN transistor level shift)
LED_CHANNEL = 0 # set to '1' for GPIOs 13, 19, 41, 45 or 53
def colorWipe(strip, color, wait_ms=50):
"""Wipe color across display a pixel at a time."""
for i in range(strip.numPixels()):
strip.setPixelColor(i, color)
strip.show()
time.sleep(wait_ms/1000.0)
def theaterChase(strip, color, wait_ms=50, iterations=10):
"""Movie theater light style chaser animation."""
for j in range(iterations):
for q in range(3):
for i in range(0, strip.numPixels(), 3):
strip.setPixelColor(i+q, color)
strip.show()
time.sleep(wait_ms/1000.0)
for i in range(0, strip.numPixels(), 3):
strip.setPixelColor(i+q, 0)
def wheel(pos):
"""Generate rainbow colors across 0-255 positions."""
if pos < 85:
return Color(pos * 3, 255 - pos * 3, 0)
elif pos < 170:
pos -= 85
return Color(255 - pos * 3, 0, pos * 3)
else:
pos -= 170
return Color(0, pos * 3, 255 - pos * 3)
def rainbow(strip, wait_ms=20, iterations=1):
"""Draw rainbow that fades across all pixels at once."""
for j in range(256*iterations):
for i in range(strip.numPixels()):
strip.setPixelColor(i, wheel((i+j) & 255))
strip.show()
time.sleep(wait_ms/1000.0)
def rainbowCycle(strip, wait_ms=20, iterations=5):
"""Draw rainbow that uniformly distributes itself across all pixels."""
for j in range(256*iterations):
for i in range(strip.numPixels()):
strip.setPixelColor(i, wheel((int(i * 256 / strip.numPixels()) + j) & 255))
strip.show()
time.sleep(wait_ms/1000.0)
def theaterChaseRainbow(strip, wait_ms=50):
"""Rainbow movie theater light style chaser animation."""
for j in range(256):
for q in range(3):
for i in range(0, strip.numPixels(), 3):
strip.setPixelColor(i+q, wheel((i+j) % 255))
strip.show()
time.sleep(wait_ms/1000.0)
for i in range(0, strip.numPixels(), 3):
strip.setPixelColor(i+q, 0)
# Main program logic follows:
if __name__ == '__main__':
# Process arguments
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--clear', action='store_true', help='clear the display on exit')
args = parser.parse_args()
# Create NeoPixel object with appropriate configuration.
strip = Adafruit_NeoPixel(LED_COUNT, LED_PIN, LED_FREQ_HZ, LED_DMA, LED_INVERT, LED_BRIGHTNESS, LED_CHANNEL)
# Intialize the library (must be called once before other functions).
strip.begin()
print ('Press Ctrl-C to quit.')
if not args.clear:
print('Use "-c" argument to clear LEDs on exit')
try:
while True:
print ('Color wipe animations.')
colorWipe(strip, Color(255, 0, 0)) # Red wipe
colorWipe(strip, Color(0, 255, 0)) # Blue wipe
colorWipe(strip, Color(0, 0, 255)) # Green wipe
print ('Theater chase animations.')
theaterChase(strip, Color(127, 127, 127)) # White theater chase
theaterChase(strip, Color(127, 0, 0)) # Red theater chase
theaterChase(strip, Color( 0, 0, 127)) # Blue theater chase
print ('Rainbow animations.')
rainbow(strip)
rainbowCycle(strip)
theaterChaseRainbow(strip)
except KeyboardInterrupt:
if args.clear:
colorWipe(strip, Color(0,0,0), 10)
Get started with Python using an IDE like Thonny IDE. You can use any Python Interpreter you like, as Thonny IDE is one of many. Thonny IDE can be accessed through the Application Menu under the Programming submenu. Then, copy the above code inside Thonny Python Editor and save it as strandtest.py on your desktop. Proceed by clicking the large green Run button. See what happens when you do that in the image below.
With that, you should see vibrant hues coming from your LED strip. If individual LEDs on your WS2812B LED Strip aren't lighting up, but the others are, open the Python script, locate the variable labeled LED COUNT, and raise its value till it's in line with the number of LEDs on your strip. The script can be saved and re-run by clicking the Run button. To accommodate the enormous quantity of WS2812B LED Strip, I set this variable to 150. In the next image, you can see a long WS2812B strip that has been wired to an external power source and is controlled by the modified Python script.
Now you can check if your Device is properly constructed and functioning with the help of the above strandtest.py script. Moreover, the resulting illumination is just breathtaking. Maker may be left wanting more, though. This is of special importance to the readability of the code, which facilitates its adaptation to specific tasks. This section will show you how to control the WS2812B strip with a few basic Python programs easily modified to suit your needs.
To get started, look at OneStripNeopixels.py, a very basic WS2812B Control Script.
import time
import board
import neopixel
#Initialise, a strips variable, provide the GPIO Data Pin
# utilized and the amount of LED Nodes on the strip and brightness (0 to 1 value)
pixels1 = neopixel.NeoPixel(board.D18, 55, brightness=1)
#Also, create an arbitrary count variable
x=0
pixels1.fill((0, 220, 0))
#LED Node 10 and the color Blue were selected
pixels1[10] = (0, 20, 255)
#Showing a different color
time.sleep(4)
#Below will loop until variable x has a value of 35
while x<35:
pixels1[x] = (255, 0, 0)
pixels1[x-5] = (255, 0, 100)
pixels1[x-10] = (0, 0, 255)
#Add 1 to the counter
x=x+1
#Add a small time pause which will translate to 'smoothly' changing color
time.sleep(0.05)
#Below section is the same process as the above loop, just in reverse
while x>-15:
pixels1[x] = (255, 0, 0)
pixels1[x+5] = (255, 0, 100)
pixels1[x+10] = (0, 255, 0)
x=x-1
time.sleep(0.05)
#Add a brief time delay to appreciate what has happened
time.sleep(4)
#Complete the script by returning all the LEDs to the off
pixels1.fill((0, 0, 0))
The Python script has been thoroughly commented on, so you should have no trouble following along. The script in the previous paragraph includes readily apparent dials and switches. You'll learn the fundamentals of making a captivating LED Pattern, including how to adjust the number of LED Nodes, the brightness of the LED strips, whether to illuminate the entire board or just a section of it and how to illuminate individual LEDs. Each Node color can be customized by entering a corresponding Red, Blue, or Green numeric value. A number can have any value between zero and 255. The term "RGB Color Code" is commonly used to describe this. While experimenting with different hues can be a lot of fun, the best method to find the precise shade you need is to use a website that lists RGB color codes. Choose any color on this site, and you'll get its corresponding RGB color code in three numbers. After you've located the three-digit color code, you can easily enter it into the Python program.
You can copy and paste the script into Thonny IDE and run it. Once you've saved the file, you can press the run button to activate the script. Below are some screenshots displaying the output.
Next is to hook up a few different LED Strips to the Pi 4 SBC and run a Python script to manage everything. Hardware-wise, I've replicated the procedure described in Hardware Configuration for Small, fewer than 30 Nodes long WS2812B, adding a second, similarly-sized WS2812B LED Strip.
I wired an additional WS2812B strip of lights up to the GPIO in the manner described below. The Raspberry Pi's other 5V Pin was connected to the red power line. The Raspberry Pi's White Ground Wire was attached to the board's second Ground Port. In this case, GPIO 21 received the green data wire. Check out the diagram of the wired components below; using two WS2812B LED strips of varying density is fine.
Using Thonny IDE as previously, run the python script titled TwoStripsNeopixels.py to control these two WS2812B Strips in a no-frills fashion. Look at this thoroughly documented Python script down here.
#include all necessary packages to get LEDs to work with Raspberry Pi
import time
import board
import neopixel
#Initialise two strips variables, provide the GPIO Data Pin
# utilized and the amount of LED Nodes and brightness (0 to 1 value)
pixels1 = neopixel.NeoPixel(board.D18, 30, brightness=1)
pixels2 = neopixel.NeoPixel(board.D21, 6, brightness=1)
#Focusing on a particular strip, use the command Fill to make it all a single color
#based on decimal code R, G, B. Number can be anything from 255 - 0. Use an RGB Colour
#Code Chart Website to quickly identify a desired fill color.
pixels1.fill((0, 255, 0))
pixels2.fill((0, 0, 255))
#Sleep for one second, and then code repeats for different color combinations. Light changes
#Could happen instead in response to certain buttons being pressed or due to threshold values
time.sleep(1.5)
pixels1.fill((200, 200, 0))
pixels2.fill((0, 200, 200))
time.sleep(1.5)
pixels1.fill((50, 70, 215))
pixels2.fill((215, 50, 70))
time.sleep(1.5)
pixels1.fill((0, 0, 0))
pixels2.fill((0, 0, 0))
Note that a Pi 4Computer has four Data Wire locations that can control individual WS2812B LED Strips, provided the corresponding Python script is updated correctly. The pinouts are numbered as follows: GPIO18, GPIO21, GPIO12, and GPIO10. With some tweaking and development of this technique, you can use a single Raspberry Pi as a standalone controller for four individually addressable WS2812B strips.
Ensure the right number of LED Nodes has been assigned to both LED WS2812B Strips before running the script. Once the script is ready to be executed, click the Thonny IDE's large green Run button. Both strands will suddenly come to life, each filled with a unique color. In the picture below, you can see this in action.
In this tutorial, we looked at connecting a WS2812B led strip to a Raspberry Pi. We also programmed the LEDs to blink in various patterns and colors using Python. You may now explore with your raspberry pi and these led strips to create patterns or whatever you like. Next, we'll look at connecting a Pi 4 to a sharp infrared distance measurement sensor.
We all know technology is not only changing trends and habits, but it is also reshaping the way we live our daily lives. We are not talking about centuries ago, but even if we examine the previous two decades, everything, from our communication style to our travel means, from our shopping habits to our entertainment industry, is changing rapidly, and this has both pros and cons. In a broader view, we can say that positive impacts are more dominant than negative effects on our lives. Maybe you have the opposite opinion, but I can prove this with the help of a simple and general example that I am going to discuss in detail with you. Just like almost every other sport and game, poker has some visible technical changes, and people can play poker online. Clients and casinos are getting more and more benefits from technical assistance. We are talking about the facts and ways in which modern technologies are reshaping and redesigning online poker. Different technologies, such as artificial intelligence, are changing the way online poker works, and we will try to find the ways and facts by which this change is being seen by experts.
Collectively, we can say that the advancement in technology has positively revolutionized online poker. There are some negative points about modern technology, but the score of the positive points is higher; therefore, we have talked more about these. The techniques such as AI, machine learning, 3D machines, AR, VR, and some others are the backbone of this change, and it seems like these changes are unending because of the advancement in technology every year. If you are interested in more details then you must visit Ignition starting hands guide for the best tips and tricks on online poker.
The history of online poker is just as important as the history of the internet because, with the advancement of the internet in the 1990s, many games, including online poker, were introduced for game lovers. This growth was so interesting that everyone was attracted to the internet and online gaming, whether they were players or game developers. Many big names in the world of online poker made a great audience out of this niche, and people are moving more and more towards online poker every year.
In the records of online poker, the first visible name is Planet Poker, which was developed in 1998. This was just the start, and the real fame of online poker started in the 2000s. Another name in the poker game is more interesting. After qualifying through an online satellite tournament, Chris Moneymaker won the 2003 World Series of Poker (WSOP) Main Event, helping to popularize the game and attract more players to online poker sites.
In the market for online poker, the next few years were significant, and these played a vital role in attracting a great community to online poker. The main reason for this was, the great bonuses and discounts offered by these online websites and the great community that was involved with amazing skills there. People were getting more and more discounts by simply signing up the new accounts, and this was an attractive point in the growth of online poker.
But in this journey, there were many ups and downs, and here it is important to mention that the industry was rocked in 2006 by the passage of the Unlawful Internet Gambling Enforcement Act (UIGEA) in the United States, which made it illegal for banks to process online gambling transactions. This resulted in a decline in the US market, which had previously been one of the largest for online poker.
Despite this time, the history of online poker has undergone a positive reshaping with the advancement in technology. People seem more convenient to have this game online because of the vast community and more facilities. This will be clear with the help of the points that we are going to share with you.
Now we are talking about some points that will justify that there are many effects of technology on online poker and in every era, we are facing the ups and downs of poker and the conclusion will be made after this discussion.
When players are connected virtually, the first thing that comes to their mind is the transparent results and the security of the rights of the players. The developers have made the system so secure to win the confidence of the players. Modern technology has helped the owners of the system to minimize the chance of cheating and to reduce the chance of using chat bots and cheating codes to win the game. With the advancement of technology, the algorithms are becoming more and more secure to win the trust of thousands of people at the same time.
An interesting point here is to mention of the involvement of the companies like poker operators that are playing a vital role to improve security and to enhance the level of trust towards online poker platforms.
With the advancement in the technologies like artificial intelligence, poker is becoming more and more interesting because of the fantastic features and the availability of the options of personalization that not only gives a quality experience to the players but the rules and options are so clear that even a new player can understand all of them easily without any major issue. This is making online poker more common among the new generation and is still the favorite of the previous one.
For the best gaming experiences, the personalization of the profile and the game is playing an important role. It seems like many controls of the profile are in the hand of the player and he can easily use his profile image in different ways for the best experience.
Data fusion is one of the most significant points in modern technology, and if we purely talk about poker, this game is full of thrill because of its complex decisions and tactics of the game that every player must be pro in to get the best position. The advanced technology helps the player to get a better experience with the help of data fusion. This makes the game more realistic as all the players are in the same database and there is no need of putting the information manually, this makes the user experience better.
With the advancement in artificial technology, machine learning has helped the system to perform better every day by learning from the behaviour of the users every time they play.
The start of the technology was amazing and easy, but with the passage of time and the introduction of new games and platforms, all of them are using modern technology. With the advancement in it, people have the easiest way to reach these platforms, and by the same token, the availability of multiple choices for online poker has made it more interesting to have the game as every online platform has its features and expertise. So, every user has a variety of online poker platforms with the advancement of technology, and this is the winning point of online poker.
As we have said, with the advancement in technology, the major work is done on the database of the players, which makes it possible to play with your groups at the same time without any issues. The traditional way of playing poker is to play at a table, and it seems perfect to play with friends in such an environment, but technology has made it possible to play with your own friends on the same table with pre-defined and decided rules according to your mode and choice. Any game with a friend is interesting, and poker is the best option to spend quality time with your friends and loved ones.
This is the nature of the human that more variety means more fun, especially if it is about the games. It is observed that real games do not offer more variety than online games because, because of the advancement in technology, the variations in the games are provided to the players so that every time they play, they enjoy a different type of game according to the mode.
Poker is a mind game, and every type of player needs a different game according to their skills. For example, a player at the beginner level will need some relaxation and want to play with the new players for practice. These types of facilities are provided with an online platform with the help of modern technology that gives challenges to players of the same rank and the same skills. The same is true for vice versa.
Till now, our focus was to justify the points to make clear why the reshaping of online poker is related to modern technology. This time, we are moving to the expert level and will talk deeply about the technology to prove our previous discussion. Online poker and casinos are popular niches globally, and people find it more convenient to play on their mobile devices, desktops, and computers rather than going to the real markets where less security and transparency are provided. To make it more popular, many techniques are being used, and some of these are given next:
One of the most popular technologies that we have seen in the market for online poker is virtual reality, which is not only related to this particular topic but to several fields of gaming. When we think about online poker, the only thing that we might miss is the interaction of the people just like in the real world. To overcome this deficiency, now techniques like virtual reality are used with the help of which, the players can interact and deal with the other players conveniently and can read their profiles to learn about their games and their positions. This helps a lot to the players to make the strategy and to have the perfect move at the perfect time. To make it more real, headsets and motion sensors are used for augmented reality, or simply AR, which helps the user gain knowledge about other players and the rules of the game they are playing so that they may have the strategy to win the game. The information about the objects and the working of the object is also related to it and this gives the player a more engaging and realistic experience.
If we say that blockchain technology has revolutionized the world of online poker, then it will not be wrong because it is proven in many ways, and in such games, the most important feature is the security and privacy that are provided by the blockchain.
To make sure that every transaction is safe and clear, the blockchain provides many features, that help the online platform win the trust of its users. This means that the player will have a tension-free game with the confidence of having the perfect result without any cheating.
If we are talking about modern techniques, then how can we forget the 3D machines? To have an immense and real-time experience in online poker, people are using 3D machines, which give a highly interesting experience in such games. The 3D animation is closer to the real world and the fantastic design gives the best experience with creativity.
The graphics and character modelling of these machines make the game more interesting. The animations make the game more engaging, and the players feel it is more realistic and have a better experience.
We should mention here that the technologies we have mentioned here are not only convenient for the players but also for the developers and the owners of online poker. This gives the player the independence of playing and getting the reward most transparently and conveniently. So, this reshaping of online poker with modern technology is helpful.
Hello learners! Welcome to the next episode of the arrays, in which we are moving towards the details of the arrays at an advanced level. In the previous lecture, we covered the introductions and fundamentals of arrays, dimensional arrays, and array operations. One must know that the working of the arrays does not end with simple operations, and there is a lot to learn about them. Arrays and their types are important topics in programming, and if we talk about Python, the working and concepts of the array in Python are relatively simple and more effective. The details of the advanced types of arrays will prove this statement. We have a lot of data to share with you, and for this reason, we have arranged this lecture. It is important to understand the reasons behind the reading of this lecture.
What are two-dimensional arrays, and how can we perform them in a Jupyter notebook?
How can we access all the elements of the two-dimensional arrays?
Can we insert the elements in the two-dimensional arrays?
How do we update our 2D array?
Is the appending process easy in the 2D arrays in Python?
Explain three-dimensional arrays and provide an example of how the array is updated.
All of these are important interview questions, and you will learn the concept through its practical implementation, so stay with us till the end of this lecture to get all the answers.
We all know a lot about one-dimensional arrays and have seen these concepts at different levels. In this type, we are dealing with the arrays at an advanced level, and to understand them, we will work on the Jupyter notebook. The second type of array is a little bit different, and we can say it is twice as difficult as the one discussed before. We all know the concept of dimensions from physics classes, and these concepts are also true for arrays. These are also referred to as "multi-dimensional arrays," and we define them as:
“The 2-D arrays are the type of arrays that are shown by two indices, and these contain rows and columns to save the data in the form of matrices.”
As you can guess, these are comparatively more complex than the one-dimensional arrays, and storing the data in them is more useful and has many applications in real life. While performing the example of a 2D array, we are going to use the simpler way where the user just has to mention the numbers of rows and columns and an array is ready.
array-name = [ [d1, d2, .... ,dn], [e1, e2, .... ,en] ]
Here,
d1,d2,...,dn=number of elements in the first dimension
e1,e2,...,en=number of elements in the second dimension
The numbers of the elements vary from zero to infinity. For more detail, have a look at the example given next:
#Initializing the two-dimensional array
array_input = [ [10.9,78.9,111.90] ,[8.0,771.2,2.0] ]
#showing the elements of each dimension separately
print("The elements in the first dimension = " ,array_input[0])
print("The elements in the first dimension = " ,array_input[1])
So you can see that the initializing of the array with the floating elements is done in the first step. Here, the point to notice is the usage of square brackets, and you have to remember that no other type of array is used in this case.
In the previous case, you have seen that if we wanted two or more elements as output, there was a need for more than one print function. Yet, there are also cases where the whole elements of an array are required on the screen. For this, the programmers use different types of iterations according to the requirements and get the results. We have seen the working of the iterations many times in this course and therefore, there is no need to explain why we use it. Arrays are one of the best examples to check the working of the iterations. In all the examples of the arrays, you will see the use of for loop in the nested form because we are dealing with two-dimensional arrays.
One must be wondering how we can add more elements to the two-dimensional arrays because of the matrix-type arrangement of the lament. For this, the method is simple, and by discussing it, we will learn the “input” method of Python. Have a look at the code given next, and we will discuss the details in just a bit.
#importing the array from the Python
import array as twoDArray
#declaring our array with two dimensions of the order 2 by 2
twoDArray = [[2,2,2,2,2,2,2], [6,6]]
print("Array before insertion of elements: ", twoDArray)
#Inserting the elements in the matrix at position three
twoDArray.insert(3, [4,7,4,2,9])
print("Array after insertion of elements: ")
#Using nested for loop to inset the elements one after the other at the required position
for x in twoDArray:
for y in x:
print(y,end = " ")
print()
The following points are to be discussed according to this concept:
Importing the array makes the code easy and effective.
The two-dimensional array is nothing but the arrangement of elements in the form of rows and columns.
The number of rows and columns is not restricted to each other, that is, there is no restriction on using the square array all the time, and the number of elements in rows and columns may be different from each other.
The square brackets are used in the two-dimensional arrays in every case, whether it is the outermost bracket or dimensional bracket.
Special functions such as insert, delete, etc. can be used by using the dot operator with the name of the array.
To insert the elements in the two-dimensional array, a nested for loop is efficient where two loops are being used. The syntax is important in this case.
Other rules of the code are discussed in different lectures in this course.
The next case that we are going to discuss is the updating of the elements in the arrays. Think about the situation when you first declare the array and work with it, but after some time, you have to change some of the elements of the arrays. In such cases, the insertion and deletion operations that must be done are time-consuming. The best way to do this is to update the elements directly. The reason why I mention this detail is that arrays are unchangeable data types in other programming languages such as C++, and once declared, the arrays can not be altered in such languages. Yet, Python gives us the ease to update, delete, insert, and change the arrays in the programs. We know programming is a vast procedure and there are several ways to do the same task, but for you, we have picked the simplest and most interesting way to do so that is given in the code next:
#Importing the array
import array as Updation
#Declaring a two-dimensional array
Updation = [[22,56,4,1], [2,4,6,8,10]]
print("Array before Updation of elements: ", Updation)
#updating the elements in the second array and the third element of the first array
Updation[1] = [44,89,2,0]
Updation[0][3] = 5
print("Array after update of elements: ")
#using nested for loop to print the array
for x in Updation:
for y in x:
print(y,end = " ")
We are now able to understand such codes; therefore, there is no need for long descriptions. But, the point here to notice is, in the same code, merely by declaring the elements at certain positions, the array can be updated and the previous values are then ignored.
In the previous lecture, the element at the end of the one-dimensional array was appended easily. Yet, you must think that more detail is required for the appending of the data because it has to be mentioned where the exact place is where you want to append the data. So, if you remember the previous lecture then you will find it different to append this type of array.
#importing the array
import array as myArray
#initializing our array
myArray = [[1.4,66.90,234.0], [009.6,127.8,34.0,11.34], [0,2]]
print("Elements of my array = ", myArray)
#Using the length operator to check the length before appending the elements
length=len(myArray)
print("Length of the array before appending: ", length)
#The process of appending
myArray.append([7.9,334.90,2303])
#calculating and printing the length after appending elements
length=len(myArray)
print("Length of the array after appending: ", length)
#printing the results after appending
for x in myArray:
for y in x:
print(y,end = " ")
print()
The difference is clear; the number of elements can be easily compared with the help of the length function. We have used the length function many times in this course, and here you can see a better example of how to use it.
The three-dimensional array is the most complex form of the array discussed so far, and usually, at this level, the learner does not go into the details of the three-dimensional arrays to avoid complexity. The 2D array has many interesting applications and has the ability to store a lot of data in a cleaner way; therefore, 3D arrays are not used much for learning purposes, but if we talk about complex applications such as gaming and other fields, the 3D arrays have a great scope.
The form of the 3D array can be understood by keeping the matrix of the order 3x3 in mind. Here is a little information about the initialization of the three-dimensional arrays in the Jupyter notebook.
#initializing my three-dimensional array
ThreeD=[[[22.5,[67.9],[44.4]],[5],[23.8]],[[12,[123],[893]],[0],[78],[126]],[[70],[11]]]
print("The three dimensional array = ", ThreeD)
#Simply updates the elements by mentioning the locations and values.
ThreeD[0][0][0] =11
ThreeD[1][0][0] =21
ThreeD[0][0][1] =111
#Printing the results
print("After updating the elements the array = " ,ThreeD)
Hence, this picture gives us information about the shape and form of the three-dimensional arrays. Do not think that 3D arrays have the form of a 3-by-3 matrix. I suggest you take a deep look at the brackets of the array that we have mentioned here. The complexity of the three-dimensional array is much greater than in previous cases. To make it simple, we have used the single element in the single brackets, but there are more options for the same work that will be unnecessary to learn right now.
Another thing to be mentioned here is that, for simple operations such as displaying and updating the elements, there is no need to import the array every time. Yet, we have used it to display the proper professional coding of the array. We are not talking about the three-dimensional arrays in more detail because they are very difficult to understand and because there are some other options that work better than the three-dimensional arrays. You will learn it in detail in the next lectures.
Therefore, we can say that we have learned a lot about the advanced level of arrays in Python which are two-dimensional arrays and three-dimensional arrays. It is interesting to note that dealing with arrays is much easier in Python than in any other programming language. Therefore, we always say that Python is interesting and easy to learn. The topic ends here, but the detail of data type is not limited to just the types of array. You have to learn a lot about iit,and we are here to share every single and necessary detail in a simple way. So, stay with us in this learning phase.
Hello Python programmers! Welcome to the engineering projects where you will find the best learning data in a precise way. We are dealing with Python nowadays, and today, the topic of discussion is the arrays in the language. We have seen different data types in Python and discussed a lot about them in detail till now. In the previous lecture, we saw the details of the procedures in dictionaries. There are certain ways to store the data in the different types of sequences, and we have highlighted a lot about almost all of them. It is time to discuss the arrays, but before this, it is better to understand the objectives of this lecture:
Introduction to arrays
Difference between contiguous and non-contiguous memory locations
One-dimensional arrays
Functions in arrays
If you are from a programming background, then you have surely heard the name "arrays" and used them in your practice. Yet, if you are a beginner, you must know that arrays are present in almost all types of high-level programming languages. The array is also the way to organize and store the data in a sequential way, but it has some very basic characteristics, most of which are similar to those of other data types, but some are a little bit different, and we will work on both of these types. But before that, let's take a look at the basic definitions of an array:
“An array is a collection of items that are stored in contiguous memory locations. The idea is to group together items of the same type.”
This makes calculating the position of each element easier by simply adding an offset to a base value, i.e., the memory location of the array's first element. These are used to store a gigantic amount of data in different departments and offices, and because it is an old technique, people prefer it to other types such as sets, lists, and dictionaries.
When working with arrays, you will always notice the term "contiguous," and it makes sense because it is the best word to describe the structure of the array. It is the finite collection of data in which the elements are arranged strictly one after the other in the successive locations of memory, and in this way, a contiguous structure is obtained just like a ladder in a horizontal way. If you think it is all about knowing about the contiguous memory locations, then you are wrong. But, it is better to understand the concept by comparing it with the help of the opposite of it, that is the non-contiguous memory places.
Sr # |
Feature |
Contiguous Memory Allocation |
Non-Contiguous Memory Allocation |
1 |
Definition |
Contiguous memory locations are the type of allocation that allocates a continuous block of memory without skipping any places. |
Non-contiguous memory allocation, on the other hand, is the type in which memory is not continuous but separate blocks of memory are allocated regardless of how little space exists between them. |
2 |
Complexity |
The control of the operating system is easy in it because the memory can be smoothly be accessed. |
In this case, controlling the OS is comparatively difficult. |
3 |
Execution time |
The execution time is faster as compared to the other type, and it makes sense because the compiler does not have to sense the gap and then jump according to the length of the gap. |
In this case, the execution time is longer than in the first case because the compiler jumps from one memory location to the other. The time is determined by the gaps between the allocated memory. |
4 |
Overhead |
Because there are fewer address translations when dealing with contiguous memory allocation, there is less overhead. |
Because the address translation is greater in this case, we have more overhead. |
5 |
Special Features |
The contiguous memory allocation contains single-partition allocation as well as multi-partition allocation. |
When dealing with non-contiguous memory allocation, paging and segmentation are observed. |
6 |
Type of Fragmentation |
These include both internal and external fragmentation in the case of contiguous memory allocation. |
The internal fragmentation does not occur in this case, and we get only external fragmentation. |
7 |
Swap-in process |
It is noted that the swap-in process can only be arranged in the space that was originally allocated. |
If we talk about the non-contiguous memory allocation, the swap-in process is arranged in any memory allocation, so we get versatility in this case. |
8 |
Wastage of memory |
In this case, the wasted memory varies according to the array we are using. |
It is an advantage of this type of allocation that no memory is wasted in this case. |
9 |
Types |
There are only two types of contiguous memory allocation:
The hint for this point is also given in another feature. |
We observe five types in this case that are mentioned below:
All of this is unnecessary in our case; thus, do not go into detail about this type of situation. |
This is the distinguishing feature of arrays in Python when compared to other programming languages. Arrays, like many other functions and data types, are pre-defined in Python for the convenience of programmers. Because arrays are very general data types that programmers frequently use, it is simple to declare and import arrays in the same line and then begin working with the arrays. There are several ways to use arrays in Python, one of which is to import the arrays. Have a look at the details of this process:
There are different steps that are to be followed when dealing with the arrays in Python, and these are mentioned here:
Import the arrays.
Declare the name.
Use the array in different operations.
Hence using these steps, the arrays are used in different ways and it is interesting to notice that the declaration and the usage of an array are very different in Python as compared to other higher-level programming languages such as C++, C#, etc.
To understand well, let us see all these steps in action, but I want to discuss the type of the arrays with you while performing all these steps so that we may understand both these concepts one after the other.
Here is the point of the array that makes it different from the dictionaries and sets we have mentioned before. The arrays can be made in three ways:
One-dimensional array
Two-dimensional array
Three-dimensional array
Now it's the choice of the programmer according to the requirement, which type of array is he or she using. The details of each of them will be shared with you in just a bit, but before that, open your Jupyter notebook so that we can apply these arrays practically side by side.
Go to the search area of the window and get the Jupyter notebook there.
A screen will appear in front of you, and you have to go to the drop-down menu that is present on the right side of the screen.
Choose python there and wait for the new tab to open on your browser.
Click on the new cell location and start coding.
The most basic and commonly used array is one, and it does not require much effort for you to understand its concept. The very basic structure of the array that we see in the examples and discussion The elements are arranged one after the other, and the index starts at the first place where the zero number is assigned to that location. All the elements are linearly arranged, and it is the simplest form of array that is easy to create and access. In this way, the programmer gets the collection of the same data type in a linear format. The array is then stored in a variable to be used in different ways.
#importing the arrays from the Python library
import array as myArray
result = myArray.array('i', [22,68,123,90,49,167,66,129])
# accessing elements of array and printing it
print("Accessing the first element ", result[0])
print("Accessing the fourth element ", result[3])
The following output is observed as a result:
Hence, the single elements can be accessed in this way. Have you noticed that in the array, we have mentioned the “i” before, and after that, a list is being used? It has a very special reason, and you can check this by deleting the “i” in the code. You will observe that the compiler is throwing an error about the declaration of the Unicode character. Always remember that if the programmer is using the import array method in Python, there is a rule in Python for using arrays that the first argument of the array is always a Unicode character.
Here comes the reason why we are importing the arrays into Python. The primary reason is, it has multiple functions that make the work super easy, and the programmers do not have to write the codes again and again. I am just sharing the very basic and common functions. So let’s jump to the codes:
Take the example in your mind that when you are dealing with a gigantic amount of data at the same array and it becomes tedious to search for the frequency of the particular item. In such cases, when the detail of the element appearing in the array is required, the count function is used. The working of this function can be understood with this example:
#Declaring our array of strings
import array as myArray
result = myArray.array('i', [22,68,123,90,49,34,67,22,90,33,7,11,8,44,11,77,23,90,28,541,490,299,611,20,561,112,3,88,167,66,129])
#Using the count function to get frequency
print(result.count(90))
So, when we check the frequency of the number 90, we get the following output:
Hence, we can easily conclude that in our array, the integer 90 appears three times.
The next function that is to be tested is the append function. For this, we add a new element at the end of the array, and doing this is very simple, as you can see in the code given next:
#Declaring our array of integers
import array as myArray
result = myArray.array('i', [22,68,123,90,49,34,67,22,90,33,7,11,8,44,
11,77,23,90,28,541,490,299,611,20,561,112,3,88,167,66,129])
#appending a new 90 at the end of array
result.append(90)
print(result)
The next function is reverse(), and as you can guess, the reverse of the array can be obtained with the help of a short command that we are going to test in the code next:
#Declaring our array of strings
import array as myArray
result = myArray.array('i', [22,68,123,90,49,34,67,22,90,33,7,11,8,44,
11,77,23,90,28,541,490,299,611,20,561,112,3,88,167,66,129])
result.reverse()
print(result)
As you can see, the order of all the elements is reversed in such a way that the first element is the last and vice versa.
Consequently, in this lecture, we have seen the details of arrays in a different way than when the introduction was done at the start. After that, we have seen what the workings of the contiguous memory location are and how it is different from the other types of contiguous memory locations. Different functions were also discussed in this regard, and more will be discussed in the next lecture.
Hello peeps! Welcome to the new episode of the Python tutorial. We have been working with different types of data collection in Python, and it is amazing to see that there are several ways to store and retrieve data. In the previous lecture, our focus was on the basics of dictionaries. We observed that there are many important topics in the dictionaries, and we must know all of them to make our base of concepts solid. For this, we are now dealing with the dictionaries in different ways, and this tutorial is going to be very interesting because we will pay more heed to the practical work and, by choosing a few cases in our codes, we will apply multiple operations to them. So have the highlights of today’s learning, and then we will move forward with the concepts.
What are nested dictionaries, and how do we use them?
How do you access the elements in the nested dictionary?
Tell us the procedure to add and delete elements from the dictionary.
Can we modify the elements of the dictionary? If yes, then how can we do so?
What is the membership test, and what is the procedure to use it in the dictionary?
Write the simple example in which the iteration is used with the dictionary.
In the previous lecture, we saw simple dictionaries and applied their functions to them. In the present lecture, the details of the dictionaries will be discussed in detail. These functions are not new to us but have been learned while dealing with sets and lists. Yet, the working and the results of every function are different with different types of data, and therefore, we are using the predefined functions again but with a twist. In the previous lecture, we saw the nested dictionary in an example but have not mentioned the details because we were discussing the list in dictionaries and it was not suitable to discuss it at that moment. Yet, have a look at the definition of a nested dictionary:
“A nested dictionary is a type of dictionary in Python that contains more than one dictionary separated by commas in it with different indexes, and each dictionary has a specific number according to its order.”
Accessing the data is a little bit more difficult in the nested dictionaries than in the simpler ones because the index is so important. The dictionaries are then divided with the help of commas between them. Open your Jupyter notebook by following these steps:
Go to the search bar on your PC.
Search for the Jupyter notebook.
Now go to the drop-down menu and choose Python there.
Wait for the new local host to be opened in your browser.
Go to the new cell and start coding.
Copy this code and paste it into the cell. By pushing the run button, you will get the perfect nested dictionary.
Do you know the meaning of our statement that accessing the data from a nested dictionary is a little bit different? It is because we need to type more of the data, and therefore, it is important to mention all the information accurately, as with all types of data, and this becomes tricky for non-programmers.
#Declaring the nested dictionary containing the information of the books in a library
bookData = {1: {'subjectName': 'Physic', 'coverColor': 'red', 'Pages': '125'},
2: {'subjectName': 'Chemistry', 'coverColor': 'blue', 'Pages': '234'},
3: { 'subjectName': 'Biology', 'coverColor': 'green and blue', 'Pages': '564'}}
#Printing the required value
print("Your required data = ", bookData[2]['coverColor'] )
So, as you can see, the real use of a nested dictionary is to get more readable and clean data every time. These are the advantages of the nested dictionary it is more practical, and a large amount of data can be stored and accessed easily.
It is the plus point of the dictionary that programmers can easily add the element in the nested dictionary by using some simple steps. The point here is to e discussed is to show you that the concept of an empty dictionary is present in Python. Many other data types do not have this characteristic. Hence, the addition of a new element becomes easy while using the dictionary. Have a look at the output of the code and after that, we will discuss the points about it.
#Declaring the nested dictionary containing the information of the books in a library
bookData = {1: {'subjectName': 'Physic', 'coverColor': 'red', 'Pages': 12},
2: {'subjectName': 'Chemistry', 'coverColor': 'blue', 'Pages': 234},
3: { 'subjectName': 'Biology', 'coverColor': 'green and blue', 'Pages': 564}}
print("Dictionary before modification = ",bookData )
print()
#Making the room for the new element by creating the empty dictionary
bookData[4]={}
#inserting elements in the next space
bookData[4]['subjectName']= 'English'
bookData[4]['coverColor']= 'yellow'
bookData[4]['Pages']= 611
print("The modified dictionary= ", bookData)
We can conclude the following points from the data given above:
The addition of the new dictionary to the existing nested dictionary is easy.
There is a need for a blank element so that we may fill in the required items.
The programmer in the code has declared the number of the dictionary and not the index, therefore, the data is not started with a zero and instead, the numbers of our choice are used. The programmer may name its dictionary anything no matter if it is the number of the alphabet.
Let us discuss the case in which the programmer wants to delete the whole dictionary from the nested collection for some reason. For this, the procedure is not too long and the simple delete command is used for it. For better elaboration, we are using the same example and deleting the second dictionary from our list.
#Declaring the nested dictionary containing the information of the books in a library
bookData = {1: {'subjectName': 'Physic', 'coverColor': 'red', 'Pages': 12},
2: {'subjectName': 'Chemistry', 'coverColor': 'blue', 'Pages': 234},
3: { 'subjectName': 'Biology', 'coverColor': 'green and blue', 'Pages': 564}}
print("Dictionary before modification = ",bookData )
print()
#Deleting the 2nd dictionary
del bookData[2]
print("The modified dictionary= ", bookData
Here, the question arises if the single element is to be deleted only, what will be the code? There are only minor changes in the code given above, and only the particular element of the mentioned dictionary will be deleted.
#Declaring the nested dictionary containing the information of the books in a library
bookData = {1: {'subjectName': 'Physic', 'coverColor': 'red', 'Pages': 12},
2: {'subjectName': 'Chemistry', 'coverColor': 'blue', 'Pages': 234},
3: { 'subjectName': 'Biology', 'coverColor': 'green and blue', 'Pages': 564}}
print("Dictionary before modification = ",bookData )
print()
#Deleting the specific elements from the dictionary
del bookData[2]['coverColor']
del bookData[3]['subjectName']
print("The modified dictionary= ", bookData)
The next step is to learn about the modification process of the dictionary, and for that, we are choosing another example because it is boring to check the conditions from the same example all the time. For the sake of simplicity, we are using the simple dictionary instead of the nested type of the dictionary.
#Declaring the initial data of the staff
staffInfo = {"2C32": "HR", "2C34": "Chemist", "2C20": "Doctor"}
print("Initial Staff Information: ", staffInfo)
#Changing the staff information
staffInfo["2C32"] = "Compounder"
#Printing the results
print("Updated Staff Information: ", staffInfo)
Hence, in this way, we do not have to first delete and then add the new element in its place; simply changing the element is enough. The new value is masked over the previous one, and in this way, the task becomes easy.
Until now, we have seen some simple and short examples so that the concepts may be easily understood. Yet you must know that in real-life applications, dictionaries are so large that it becomes difficult to notice the existence of specific elements in them. For such cases, it is important to learn the command to check the availability.
# Membership Test for our new dictionary
AvailableFood = {1: "chicken", 2: "beef", 3: "barbeque", 4: "burger", 5: "soup",
6: "salad", 7: "cake", 8: "lasagna", 9: "pizza", 10: "pie",11: "sandwiches",
12: "pasta", 13: "mushrooms", 14: "sausage", 15: "ice cream", 16: "cola", 17: "cupcakes",
18: "chocolate", 19: "biryani", 20: "golgappy", 21: "bread", 22: "jam", 23: "eggs" }
#Checking the availability by using just the key
print(6 in AvailableFood)
#checking if the certain key is "not" available
print(18 not in AvailableFood)
#Using a false value to check the behaviour of the keyword
print(49 in AvailableFood)
If we go into the details of this code, we may get the idea that:
Dictionaries can be used to store a massive amount of data.
The addition of the data is so simple and uncomplicated, and we can access any element easily.
The keys are useful to get the data easily without any complexity in typing in such cases if we use the integers as keys.
The “in” function searches for the required elements of the user and gives the result about the availability of the element.
The “not in” function is totally opposite from the former case, and the programmer can use it to check whether the specific element is absent or not.
The third case is interesting, we simply put the value to check how the “in” function responds when the element is not present in the dictionary, and the result was as expected.
The iteration process is fascinating, and we always try to provide you with examples that you will use in the practical implementation over and over again. The iterations are an important concept, and you will learn them in detail in the coming sessions, but we believe that, for now, you have an idea of what they are and how the programmers use them in their codes. It is time to check what the dictionary will do if we use it in the for loop.
# Membership Test for our new dictionary
AvailableFood = {1: "chicken", 2: "beef", 3: "barbeque", 4: "burger", 5: "soup",
6: "salad", 7: "cake", 8: "lasagna", 9: "pizza", 10: "pie",11: "sandwiches",
12: "pasta", 13: "mushrooms", 14: "sausage", 15: "ice cream", 16: "cola", 17: "cupcakes",
18: "chocolate", 19: "biryani", 20: "golgappy", 21: "bread", 22: "jam", 23: "eggs" }
#using the for loop with the "in" keyword to get the list of each and every food item
for element in AvailableFood:
print(AvailableFood[element])
Hence, all the elements are printed one after the other in a new line, and the programmers can simply get the available food items by using a simple command.
For more practice, I want to check the behaviour of this loop when I use it with the nested dictionary:
#Declaring the nested dictionary containing the information of the books in a library
bookData = {1: {'subjectName': 'Physic', 'coverColor': 'red', 'Pages': 12},
2: {'subjectName': 'Chemistry', 'coverColor': 'blue', 'Pages': 234},
3: { 'subjectName': 'Biology', 'coverColor': 'green and blue', 'Pages': 564}}
print("Elements of the dictionaries = ",bookData )
print()
#printing All the dictionaries using iteration.
for myBooks in bookData:
print("Elements of single dictionary using the for loop = ", bookData[myBooks])
The results of the code given above are clean and more understandable:
Hence, the use of iterations makes our output uncomplicated and ordered. So it was all about the dictionary in this episode of the Python programming language. It was amazing to see the different examples, and all of them were related to the dictionaries. The usage of the dictionary in Python is important to understand; therefore, we used the Jupyter notebook to access, add, delete, and modify the elements of nested dictionaries and simple dictionaries with the help of examples. Moreover, the membership test was also interesting to learn and easy. In the end, the iterations made our work easy, and we got a cleaner output with the help of the iterations. We hope it was an attractive way to learn the dictionaries and stay with us for more concepts of Python. Happy learning to all of you.
Hello students! Welcome to the new Python tutorial, where we are learning a lot about this programming language by applying the codes in a Jupyter notebook. It is interesting to see how simple codes with easy syntax can be useful to store, retrieve, and use data in different ways. Many things in Python are so unadorned and pre-defined that programmers may use them in a productive way without any issue.
In the previous lecture, we have seen the details of the sets and how different types of mathematical operations are used to get the required results. We have seen many codes, the examples of which were taken from day-to-day life. In the present lecture, the mission is to learn a new topic that is related to the concepts of previous lectures. Yet, it is important to have a glance at the points that will be discussed in detail here:
Introduction to the dictionaries in the python programming language.
Why do we need dictionaries if we have different ways to store the data?
How can we use dictionaries in Jupyter notebook?
How do you define the concept of keys in dictionaries?
What are some characteristics that make the dictionaries different from others?
How do you justify that using the list with dictionaries makes the working better?
These points are the important interview questions and all of these will be crystal clear in your mind when we will go deep into these concepts one after the other. So stay in this lecture till the end and we will discuss the basic concept today.
To understand the topic, the first question that arises in the mind is what actually constitutes a dictionary and why it is important to understand this concept. An interesting piece of information to share here is that dictionaries are not new concepts; these were defined in the older versions of Python as well, but the difference is that in the older versions, from Python 3.6 onward, dictionaries were considered unordered, and if we talk about the current introduction, we will get the following lines:
"In Python 3.6 and later, a Python dictionary is an ordered collection of data that stores data in the form of pairs in keys or values."
The word “ordered” is not new to us and from the above information, we can understand that in the older versions of Python dictionaries when displayed in the output, does not have the proper indexing and the order of the element was unpredictable but when talking about the updated version (that we are using during this tutorial) the order of the elements in the dictionaries is well defined and the elements will be arranged in the same manner always that is defined by the programmer. Here it is important to notice that usually, the students are using the updated version of Python and there is no need to mention the older versions but the reason to mention this is, the older versions are preferred by the users that have older personal computers or because of any other reason, if they are using the previous versions, then they will find the dictionaries a little bit different from this tutorial.
We have been working with the data types in Python that all are used to have the collection of data and the question here is why we need another entity that can store the data if we have lists, sets, and others. The answer here is simply because the way to store the data is different for all of these and as the topic of discussion today is dictionaries, you must know that it uses the key values to store the data and therefore, it becomes uncomplicated to store the data using the key value mapping format. The most common advantage is, the retrieval of data from the dictionary becomes super easy with the help of these key values. If we talk about the complexity of the dictionaries, we will get the following data:
The best case of a dictionary has O(1) complexity.
The worst case of a dictionary has the O(n) complexity.
Now, that you understand the basics of dictionaries, to make the discussion clear, examples are being used in the Jupyter notebook. We have been using it in our tutorial, and therefore, we know that we simply have to start the software and get the new tab in the browser by clicking on the “new” dialogue box and clicking on “Python 3."
Once you get the worksheet now it's time to get used to all the codes that will clarify the concepts in a better way. So let’s start learning the concept and then simply use all the learning in our codes. This will be easy to understand because we have been working with such types of codes in this whole course but the difference here is the key of the dictionary.
You must know that dictionaries are declared by using the curly brackets in Python and if you have seen our previous tutorial, you must have the idea that the same representation was used in the sets then how can the compiler differentiate between these two and what is the reason to introduce the concept of dictionary in Python? Well, as we move towards the complex problems, we get the idea that simply storing the data is not enough, there is a need of providing the reference of the data and this happens when we use the keys in dictionaries.
“A key in Python dictionary is the specific and unique value assigned to each element and that has the immutable data type.”
The role of keys in the dictionaries will be clear by using the output of the code given next:
#declaring the dictionaries containing the grocery items
GroceryItems={"lettuce":4, "onion":34,"pepper":8,"plum":12, "frozen fruits":22}
print(GroceryItems)
Hence, the keys add more details to the dictionaries, and the data is stored in a better manner while being used in dictionaries. The usage of the keys is still ambiguous in the mind, hence, we are using different examples to express it well. There are different ways to use the dictionaries, and these procedures are given next:
#declaring the dictionaries containing the grocery items
GroceryItems={"lettuce":4, "onion":34,"pepper":8,"plum":12, "frozen fruits":22}
print(GroceryItems)
#retriving the data using the concept of keys in a dictionary
print("The required data = ", GroceryItems["pepper"])
Hence, the data stored in the key can be used easily to retrieve the data. In the example given above, when the person wants to get the number of groceries they want with the help of items.
We have focused on the examples, but before going into the details of them, you must know some important points about the dictionaries.
The dictionaries are declared with the help of curly brackets.
The keys are used with the elements and to declare them, the colon is used along with each element.
The value of the keys can be obtained by mentioning the element.
The dictionaries are ordered and every time when the same dictionary is declared, we get the element as same as that was mentioned in the code.
The usage of any data type is allowed in the dictionaries, and therefore, the working of a large amount of data is possible in the dictionaries.
Same as the sets, duplicate elements in the dictionaries are not possible. It means when the element is repeating in the code, the compiler automatically ignores the second third, or any type of repeating of the same element and therefore, the repetition is not allowed in dictionaries.
Let us now look at some examples of dictionaries where we will use our Python concepts and some functions to interact with the dictionaries.
We know about the details of lists in the Python programming language, and now we want to connect things together so that we may understand why the students had to learn a lot about lists. Python provides uncomplicated ways to use different functions and concepts and get useful results. In the Jupyter Notebook, we are now inserting the list into our dictionaries in two ways, and in this way, the concepts of lists will also refresh our minds.
During discussion about some important characteristics of dictionaries, it was mentioned that any data type can be inserted into the dictionaries, and this point will be proved with the help of the following code:
#declaring the dictionary of tooth brush
toothBrush = {
"Name of Item": "toothbrush",
"electric": False,
"price": 60,
"available colours": ["red", "white", "blue"]
}
print("Features of tooth brush in detail: ",toothBrush)
print("Price of the tooth brush= ", toothBrush["price"])
When we input this code in the new cell of our local host, after pushing the run button, the following results can be obtained:
Hence, it is proved that any data type in Python can be inserted into the dictionary, and this characteristic gives the programmers the independence to use the collection of data and relate it to a single item in the dictionary. In the example we have just seen, there were multiple options for the same brush about its colours, and hence, in such cases, the programmers use dictionaries to get the required results.
Here comes a different kind of concept that we have not learned in this tutorial until now. The list can also have the dictionaries as the element and if you are wondering why the programmers use them then you must know, the characteristics of the data types matter, and each data type has its own speciality therefore, the combination of both these data types in different ways gives us the variety of uses.
#Create a list of dictionaries that represent the prices of different items
#along with their serial number in the grocery store.
myStoreItems = [{345: 'chicken', 567: 'eggs',
561: 'beef', 879: 'mutton'},
{348: 'milk', 670: 'butter',
127: 'bread', 445: 'tea bags'},
{237: 'chocolate spread', 381: 'jam',
890: 'sauce', 340: 'sandwich'}]
print(myStoreItems)
#accessing the data from the first dictionary
print("The required item is ",myStoreItems[0][561])
#accessing the data from the second dictionary
print("The required item is ",myStoreItems[1][127])
#accessing the data from the second dictionary
print("The required item is ",myStoreItems[2][340])
In this way, we can extract the following concept from this code:
The dictionaries can be used in the lists.
There is the possibility of using different dictionaries in the same code.
The data can be accessed from any dictionary by using the index of the data.
To get the exact element, the programmers have to write the index of the element.
For using the right indexing method, first, we have to mention the dictionary number and then the key of the element.
It's a reminder that indexing starts at 0.
It becomes easy to manage the data with the keys and more data can be stored in a single dictionary in a more manageable way.
The declaration of the dictionaries resembles the sets and we see many features of these two concepts that resemble each other.
In this lecture, we started our discussion with the introduction of dictionaries. The list has many informative features, and the combination of dictionaries with the list makes many useful tasks easy to complete without facing any issues because of the smooth process. The user simply has to feed the required data, and at the appropriate time, the data can be retrieved easily. So, that was all for today. Dictionaries have applications in different ways, and therefore, it is not possible to provide students with all the necessary information in a single lecture. Therefore, in the second part, we will discuss more details about dictionaries and learn a lot through examples.
Online technology has had a profound impact on the real world, revolutionizing various industries and areas of our lives. From e-commerce and communication to education, healthcare, and entertainment, online technology has made many aspects of daily life more convenient, accessible, and efficient.
This technology has fundamentally changed the way people interact with the world around them — and there is no denying — another industry that has been significantly transformed by online technology is — the gaming industry.
With the rise of Live casino online technology and advancements in internet connectivity, players can now connect and compete with others from anywhere in the world. Online technology has also enabled the development of massively multiplayer online games (MMOs) that allow thousands of players to interact in shared virtual worlds.
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And online casinos are no different.
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However, it is important to note that gambling can be addictive, and players should always gamble responsibly and within their means to ensure that their online casino experience remains enjoyable and is not detrimental to their overall well-being.
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Players can access online casinos using their computer or mobile device, and the games are played using software that simulates the experience of a traditional casino. The outcome of the games is determined by a random number generator , which ensures that the results are fair and unbiased.
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Online casinos use a variety of technologies to provide players with a seamless gaming experience. Some of the most common technologies include:
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Artificial Intelligence:
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Online casinos strive to replicate the real-world casino experience as closely as possible. The following are some ways that online casinos replicate the real-world casino experience:
Online casinos offer a wide range of games that are similar to those found in real-world casinos. Some of the most popular games found in online casinos include:
Online casinos offer online slots as one of their most popular games. They come in different themes, graphics, and features and offer various ways to win.
Online casinos offer a variety of table games, including blackjack, roulette, baccarat, craps, and sic bo. These games are usually played against the computer, but some online casinos offer live dealer versions.
Video poker is a game that combines the elements of slots and poker. Players are dealt a hand of cards and must decide which cards to keep and which to discard in the hopes of making a winning hand.
Online casinos offer live dealer games streamed in real-time from a casino studio. Players can interact with a real dealer and other players, creating a more authentic gaming experience.
These virtual casino games are designed with high-quality graphics and sound effects that help to replicate the look and feel of a real-world casino.
The following are some of the ways that graphics and sound effects enhance the online casino experience:
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Graphics and sound effects are used to create a theme and story for each game. This can include anything from a classic slot machine theme that tells a story or transports players to a different time or place.
These graphics and sound effects can offer more realistic exposure. For example, in live dealer games, the sound of the dealer shuffling cards and the cards' visuals can create a more authentic event like you’re part of the entire process.
These visuals and effects are often used to provide feedback and rewards to players. For example, when a player wins a game, the graphics and sound effects may create a celebratory experience to enhance the player's excitement.
Live casino online technology offers live dealer games streamed in real time from a casino studio. This allows players to interact with a real dealer and other players, creating a more authentic gaming atmosphere that both parties can enjoy.
Some of the key aspects of live dealer games include:
Live dealer games are hosted by real dealers who operate the game in real time from a casino studio. Players can interact with the dealer and other players like in a real-world casino.
Many of the most popular table games are available as live dealer games, including blackjack, roulette, baccarat, and casino hold'em.
Live dealer games offer a range of betting options, from low-stakes games to high-stakes games for high rollers.
These games provide the convenience of online gaming, allowing players to enjoy the casino experience from the comfort of their own homes.
Bonuses and promotions are an essential part of the online casino experience, offering players the opportunity to boost their bankroll and increase their chances of winning. Some of the most common types of bonuses and promotions offered by online casinos include:
Welcome bonuses are offered to new players when they sign up for an online casino. These bonuses usually come in the form of a match bonus, where the casino matches a percentage of the player's first deposit.
Free spins are a type of bonus that allows players to spin the reels of a slot machine without having to place a bet. Free spins may be awarded as part of a welcome bonus or as a promotion to existing players.
Reload bonuses are bonuses offered to existing players when they make a deposit. These bonuses are usually smaller than welcome bonuses, but they still offer players the opportunity to boost their bankroll.
Many online casinos offer loyalty rewards programs that reward players for their continued play. These programs may offer players points that can be redeemed for cash or prizes, or access to exclusive bonuses and promotions.
Cashback bonuses offer players a percentage of their losses back as a bonus. For example, a casino may offer a 10% cashback bonus on losses incurred over a certain period.
Online casinos offer a range of payment methods that are similar to those offered in real-world casinos. The following are some of the most common payment methods used by online casinos:
Visa and Mastercard are the most commonly accepted credit and debit cards at online casinos. These payment methods offer fast and convenient transactions, but players should be aware that some banks may decline transactions with online casinos due to local gambling regulations.
E-wallets like PayPal, Neteller, and Skrill are popular payment methods for online casino players. These payment methods offer fast and secure transactions, with many online casinos offering bonuses and promotions for players who use e-wallets.
Bank transfers are a standard payment method for players who want to transfer large amounts of money. However, bank transfers can take several days to process, which may not be ideal for players who want to start playing immediately.
Some online casinos accept cryptocurrencies like Bitcoin and Ethereum as payment methods. Cryptocurrencies offer fast and secure transactions, but players should be aware of the volatility of these currencies and the potential for significant value fluctuations.
In conclusion, online casinos have revolutionized the gambling industry by offering players a convenient and accessible way to play their favorite casino games from the comfort of their own homes.
With advancements in technology, online casinos have been able to replicate the real-world casino experience through features such as live dealer games, high-quality graphics and sound effects, and a wide selection of games.
Additionally, the availability of various payment methods and bonuses, and promotions have made online gambling more attractive to players. However, it is important to note that online gambling can be addictive, and players should always gamble responsibly and within their means.
Needless to say, online casinos provide a convenient and entertaining way to enjoy the thrill of gambling, but it is important to approach online gambling with caution and moderation.
That’s all for today. Hope you’ve enjoyed reading the article. If you have experienced playing in online casinos, we’d appreciate your input in the section below. Thanks for your precious time. Until next time!
Hey people! Welcome to another tutorial on Python programming. We hope you are doing great in Python. Python is one of the most popular languages around the globe, and it is not surprising that you are interested in learning about the comparatively complex concepts of Python. In the previous class, our focus was to learn the basics of sets, and we saw the properties of sets in detail with the help of coding examples. We found it useful in many general cases, and in the present lecture, our interest is in the built-in functions that can be used with the sets. Still, before going deep into the topic, it is better to have a glance at the following headings:
What are the built-in functions?
How do we perform the all() function with examples?
What is the difference between any() and all()?
What are the parameters of sorted()?
From the beginning of this course, we have been using the built-in functions for different purposes, but here, the main idea must be cleared, so for the purpose of revision, have a look at the basic definition of these functions:
"Built-in functions are the pieces of code that have a particular result all the time and are pre-defined in the programming languages so that the programmer does not have to type the same code but simply put the values in the functions and get the required output as expected with minimum effort."
For a better understanding of the sets and the practice of these in codes, the best way is to use the built-in functions so that whenever you are coding these built-in functions arise instantly into your mind and the effort to code the same line again and again in different programs. There are certain types of built-in functions, the details of which were covered in the previous lecture, so for now, we are trying to introduce the new functions and repeat just the most common ones with the sets.
The first function of discussed by us is very interesting and has application in many general cases. This function checks the element of the set one after the other and on the basis of a combination of results, it provides the output. The working of all() is a little bit tricky. Consider the case when we define a set with a particular data type and all function and then keeps that data type in the memory. Afterwards, it checks the result by following the sequence of combinations and then provides a single output in the form of true or false. The combination of all() is given in the table:
True |
False |
Result |
All values |
No value |
True |
No value |
All values |
False |
One value |
Remaining |
False |
Remaining |
One value |
False |
Empty set |
True |
|
Hence, just like the logical gates, the all() function works on the logics, and this will be proved when you see these examples:
Before going into the details of the all() function, have a look at the code where only integers anf floats are used with different values.
# integer set with non-zero values
myIntegerSet = {33,6,5,12,44,9}
print(all(myIntegerSet))
# Float and false value
myFloatSet = {33.6,55.9,12,4, False}
print(all(myFloatSet))
# Float with only one false value
FloatWithOneFalse = {1.8, 3.55, 4.34, False}
print(all(FloatWithOneFalse))
# Integer set with only one true value
integerWihOneTrueValue= {44,False,0}
print(all(integerWihOneTrueValue))
# empty set
emptySet={}
print(all(emptySet))
The following points are extracted from the results given above:
The digit zero is always considered false if you are using the float or integer sets.
The all() function checks each and every element and stores the results in memory, then checks for the combination and provides the results.
It is obvious that if the set contains the value "false,” then it will be considered the false function, and the compiler will not consider it a string.
The empty set is always true.
Now, this can also be performed with the string sets, and all the strings will be considered true. Hence, any element with double quotation marks will be considered a string, and the compiler will provide the output in the form of a single result.
Your task is to design the code using the information given above and check the results by matching the table given above.
If you have understood the previous function, then this will be more fun for you because, in some cases, we can say that the any() function is the opposite of the all() function. Therefore, they are named so. So, look at the code of the any() function, where only the name of the function is being replaced, and you will see the clear difference in the results.
# integer set with non-zero vlaues
myIntegerSet = {33,6,5,12,44,9}
print(any(myIntegerSet))
# Float and false value
myFloatSet = {33.6,55.9,12,4, False}
print(any(myFloatSet))
# Float with only one false value
FloatWithOneFalse = {1.8, 3.55, 4.34, False}
print(any(FloatWithOneFalse))
# Integer set with only one true value
integerWihOneTrueValue= {44,False,0}
print(any(integerWihOneTrueValue))
# empty set
emptySet={}
print(any(emptySet))
The results can easily be understood, and this time, we have extracted the table of combinations from the output of our code:
True |
False |
Result |
All values |
No value |
True |
No value |
All values |
False |
One value |
Remaining |
True |
Remaining |
One value |
True |
Empty set |
False |
|
So, the same task is for you as we have given the exercide with the all() function.
It is a different kind of function and is meant to represent the set in a better manner. While using it with the set, the output is obtained has the number of index with it automatically. It is useful in the long sets where the number of elements matters, and by using this, the programmer does not have to do the counting manually, but the index number is shown with each element. For this, the results are saved into the variable, and then the results are fed into the list. If it is confusing at the moment, have a look at the code given next:
#Making the set of books
Books={'Biology', 'Chemistry', 'Physics', 'English', 'General Knowledge',
'geography', 'poems', ' grammer','programming', 'software', 'technology'}
#declaring the varialbe
i=enumerate(Books)
#using the varialbe in list and printing the results
print(list(i))
So, all the books are arranged and have an index according to their position. Take the case into your mind, where the set contains hundreds or thousands of elements, and the indexing helps us to understand the collection.
The next function that is going to be discussed is the sorted() function, that plays an important role in the numeric calculations. Usually, programmers feed the data in an unordered manners.
If all the discussion above is not enough for you to understand the whole concept, then do not worry because these examples and their explanation will help you understand the concepts deeply.
It is a simple task, and the sorting is easy when you have few elements, but with the complex cases, the sorting needs logic. You will see some examples with the parameters in the same lecture, but for a start, have a look at the code below:
So, the sorting process of integers and
#Declaring the set
print("The unsorted set = ", myIntegers)
print()
#Using sorted() function on the set
print("The sorted set= ", sorted(myIntegers))
This is the big array, and it is difficult to arrange it manually; therefore, we are using sets here to eliminate the duplicate elements and to sort them with the help of a simple command.
Once the set is complete, it becomes difficult to read and understand the data of the set if it is large. In such cases, the sorted function is used, which sorts, or, in simpler words, arranges the data into a specific order. To learn about the syntax of this function, you must know three parameters:
This is the general term used to present a collection of things, groups, loops, or sequences. These are the necessary parts of the sorted function, and as “set” is the collection of data types, we can use it in this function to sort the elements according to our wishes. Without this parameter, the compiler will show the error.
It is a relatively difficult parameter to grasp, but it is responsible for the function's versatility. It may be tedious to realize that the set you are entering is only sorted in ascending and descending order. In programming, the data is stored in the form of different collections of numbers, and the key is the way to tell the compiler which pattern must be followed for sorting the data. This will be clear with the help of an example, and after that, we will examine the results:
The second parameter in the sorted() function is optional, and it is used to ask the programmer about the way to sort the data, that is if the programmer needs the iteration in an ascending or descending manner. It just has two possible inputs: true or false. As the name of the parameter indicates, if the reverse is set to true, the results will be in reverse order, that is, from the highest value to the lowest. But by default, it is always true, and if the programmer inputs true or even does not use this parameter, the output of the set will be in ascending rather than descending order.
# My string list declaration
mySet ={ ('apple'), ('red'), ('black'), ('strawberry')}
# sort set taking length as key and reversing the order
Result = sorted(mySet, key=len, reverse=True)
# print set
print('Sorted results:', Result)
So, it was an interesting tutorial on the sets, and with the help of different examples, we have learned built-in functions such as all(), any(), and the sorted function.