Trending November 2023 # Everything You Should Know About Data Structures In Python # Suggested December 2023 # Top 14 Popular

You are reading the article Everything You Should Know About Data Structures In Python updated in November 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested December 2023 Everything You Should Know About Data Structures In Python


Data structures in Python are a key concept to learn before we dive into the nuances of data science and model building

Learn about the different data structures Python offers, including lists, tuples and much more


A Data Structure sounds like a very straightforward topic – and yet a lot of data science and analytics newcomers have no idea what it is. When I quiz these folks about the different data structures in Python and how they work, I’m met with a blank stare. Not good!

Python is an easy programming language to learn but we need to get our basics clear first before we dive into the attractive machine learning coding bits. That’s because behind every data exploration task we perform, even analytics step we take, there is a basic element of storage and organization of the data.

And this is a no-brainer – it’s so much easier for us to extract information when we store our data efficiently. We save ourselves a ton of time thanks to our code running faster – who wouldn’t want that?

And that’s why I implore you to learn about data structures in Python.

In this article, we will explore the basic in-built data structures in Python that will come in handy when you are dealing with data in the real world. So whether you’re a data scientist or an analyst, this article is equally relevant for you.

Make sure you go through our comprehensive FREE Python course if you’re new to this awesome programming language.

Table of Contents

What are Data Structures in Python?

Data Structure #1: Lists in Python

Creating Lists

Accessing List elements

Appending values in Lists

Removing elements from Lists

Sorting Lists

Concatenating Lists

List comprehensions

Stacks & Queues using Lists

Data Structure #2: Tuples in Python

Creating Tuples in Python

Immutability of Tuples

Tuple assignment

Changing Tuples values

Data Structure #3: Dictionary in Python

Generating Dictionary

Accessing keys and values

Data Structure #4: Sets in Python

Add and Remove elements from Sets

Sets Operations

What are Data Structures?

Data structures are a way of storing and organizing data efficiently. This will allow you to easily access and perform operations on the data.

There is no one-size-fits-all kind of model when it comes to data structures. You will want to store data in different ways to cater to the need of the hour. Maybe you want to store all types of data together, or you want something for faster searching of data, or maybe something that stores only distinct data items.

Luckily, Python has a host of in-built data structures that help us to easily organize our data. Therefore, it becomes imperative to get acquainted with these first so that when we are dealing with data, we know exactly which data structure will solve our purpose effectively.

Lists in Python are the most versatile data structure. They are used to store heterogeneous data items, from integers to strings or even another list! They are also mutable, which means that their elements can be changed even after the list is created.

Creating Lists

Lists are created by enclosing elements within [square] brackets and each item is separated by a comma:

Python Code:

Since each element in a list has its own distinct position, having duplicate values in a list is not a problem:

Accessing List elements

To access elements of a list, we use Indexing. Each element in a list has an index related to it depending on its position in the list. The first element of the list has the index 0, the next element has index 1, and so on. The last element of the list has an index of one less than the length of the list.

But indexes don’t always have to be positive, they can be negative too. What do you think negative indexes indicate?

While positive indexes return elements from the start of the list, negative indexes return values from the end of the list. This saves us from the trivial calculation which we would have to otherwise perform if we wanted to return the nth element from the end of the list. So instead of trying to return List_name[len(List_name)-1] element, we can simply write List_name[-1].

Using negative indexes, we can return the nth element from the end of the list easily. If we wanted to return the first element from the end, or the last index, the associated index is -1. Similarly, the index for the second last element will be -2, and so on. Remember, the 0th index will still refer to the very first element in the list.

But what if we wanted to return a range of elements between two positions in the lists? This is called Slicing. All we have to do is specify the start and end index within which we want to return all the elements – List_name[start : end].

One important thing to remember here is that the element at the end index is never included. Only elements from start index till index equaling end-1 will be returned.

Appending values in Lists

We can add new elements to an existing list using the append() or insert() methods:

append() – Adds an element to the end of the list

insert() – Adds an element to a specific position in the list which needs to be specified along with the value

Removing elements from Lists

Removing elements from a list is as easy as adding them and can be done using the remove() or pop() methods:

remove() – Removes the first occurrence from the list that matches the given value

pop() – This is used when we want to remove an element at a specified index from the list. However, if we don’t provide an index value, the last element will be removed from the list

Sorting Lists

Most of the time, you will be using a list to sort elements. So it is very important to know about the sort() method. It lets you sort list elements in-place in either ascending or descending order:

But where things get a bit tricky is when you want to sort a list containing string elements. How do you compare two strings? Well, string values are sorted using ASCII values of the characters in the string. Each character in the string has an integer value associated with it. We use these values to sort the strings.

On comparing two strings, we just compare the integer values of each character from the beginning. If we encounter the same characters in both the strings, we just compare the next character until we find two differing characters. It is, of course, done internally so you don’t have to worry about it!

Concatenating Lists

We can even concatenate two or more lists by simply using the + symbol. This will return a new list containing elements from both the lists:

List comprehensions

A very interesting application of Lists is List comprehension which provides a neat way of creating new lists. These new lists are created by applying an operation on each element of an existing list. It will be easy to see their impact if we first check out how it can be done using the good old for-loops:

Now, we will see how we can concisely perform this operation using list comprehensions:

See the difference? List comprehensions are a useful asset for any data scientist because you have to write concise and readable code on a daily basis!

A list is an in-built data structure in Python. But we can use it to create user-defined data structures. Two very popular user-defined data structures built using lists are Stacks and Queues.

Stacks are a list of elements in which the addition or deletion of elements is done from the end of the list. Think of it as a stack of books. Whenever you need to add or remove a book from the stack, you do it from the top. It uses the simple concept of Last-In-First-Out.

Queues, on the other hand, are a list of elements in which the addition of elements takes place at the end of the list, but the deletion of elements takes place from the front of the list. You can think of it as a queue in the real-world. The queue becomes shorter when people from the front exit the queue. The queue becomes longer when someone new adds to the queue from the end. It uses the concept of First-In-First-Out.

Now, as a data scientist or an analyst, you might not be employing this concept every day, but knowing it will surely help you when you have to build your own algorithm!

Tuples are another very popular in-built data structure in Python. These are quite similar to Lists except for one difference – they are immutable. This means that once a tuple is generated, no value can be added, deleted, or edited.

We will explore this further, but let’s first see how you can create a Tuple in Python!

Tuples can be generated by writing values within (parentheses) and each element is separated by a comma. But even if you write a bunch of values without any parenthesis and assign them to a variable, you will still end up with a tuple! Have a look for yourself:

Ok, now that we know how to create tuples, let’s talk about immutability.

Immutability of Tuples

Anything that cannot be modified after creation is immutable in Python. Python language can be broken down into mutable and immutable objects.

Lists, dictionaries, sets (we will be exploring these in the further sections) are mutable objects, meaning they can be modified after creation. On the other hand integers, floating values, boolean values, strings, and even tuples are immutable objects. But what makes them immutable?

Everything in Python is an object. So we can use the in-built id() method which gives us the ability to check the memory location of an object. This is known as the identity of the object. Let’s create a list and determine the location of the list and its elements:

As you can see, both the list and its element have different locations in memory. Since we know lists are mutable, we can alter the value of its elements. Let’s do that and see how it affects the location values:

The location of the list did not change but that of the element did. This means that a new object was created for the element and saved in the list. This is what is meant by mutable. A mutable object is able to change its state, or contents, after creation but an immutable object is not able to do that.

But we can call tuples pseudo-immutable because even though they are immutable, they can contain mutable objects whose values can be modified!

As you can see from the example above, we were able to change the values of an immutable object, list, contained within a tuple.

Tuple assignment

Tuple packing and unpacking are some useful operations that you can perform to assign values to a tuple of elements from another tuple in a single line.

We already saw tuple packing when we made our planet tuple. Tuple unpacking is just the opposite-assigning values to variables from a tuple:

It is very useful for swapping values in a single line. Honestly, this was one of the first things that got me excited about Python, being able to do so much with such little coding!

Changing Tuple values

Although I said that tuple values cannot be changed, you can actually make changes to it by converting it to a list using list(). And when you are done making the changes, you can again convert it back to a tuple using tuple().

This change, however, is expensive as it involves making a copy of the tuple. But tuples come in handy when you don’t want others to change the content of the data structure.

Data Structure #3: Dictionary in Python

Dictionary is another Python data structure to store heterogeneous objects that are immutable but unordered. This means that when you try to access the elements, they might not be in exactly the order as the one you inserted them in.

But what sets dictionaries apart from lists is the way elements are stored in it. Elements in a dictionary are accessed via their key values instead of their index, as we did in a list. So dictionaries contain key-value pairs instead of just single elements.

Generating Dictionary

Dictionaries are generated by writing keys and values within a { curly } bracket separated by a semi-colon. And each key-value pair is separated by a comma:

Using the key of the item, we can easily extract the associated value of the item:

These keys are unique. But even if you have a dictionary with multiple items with the same key, the item value will be the one associated with the last key:

Dictionaries are very useful to access items quickly because, unlike lists and tuples, a dictionary does not have to iterate over all the items finding a value. Dictionary uses the item key to quickly find the item value. This concept is called hashing.

Accessing keys and values

You can access the keys from a dictionary using the keys() method and the values using the values() method. These we can view using a for-loop or turn them into a list using list():

We can even access these values simultaneously using the items() method which returns the respective key and value pair for each element of the dictionary.

Sometimes you don’t want multiple occurrences of the same element in your list or tuple. It is here that you can use a set data structure. Set is an unordered, but mutable, collection of elements that contains only unique values.

You will see that the values are not in the same order as they were entered in the set. This is because sets are unordered.

Add and Remove elements from a Set

To add values to a set, use the add() method. It lets you add any value except mutable objects:

To remove values from a set, you have two options to choose from:

The first is the remove() method which gives an error if the element is not present in the Set

The second is the discard() method which removes elements but gives no error when the element is not present in the Set

If the value does not exist, remove() will give an error but discard() won’t.

Set operations

Using Python Sets, you can perform operations like union, intersection and difference between two sets, just like you would in mathematics.

Union of two sets gives values from both the sets. But the values are unique. So if both the sets contain the same value, only one copy will be returned:

Intersection of two sets returns only those values that are common to both the sets:

Difference of a set from another gives only those values that are not present in the first set:

End Notes

Isn’t Python a beautiful language? It provides you with so many options to handle your data more efficiently. And learning about data structures in Python is a key aspect of your own learning journey.

This article should serve as a good introduction to the in-built data structures in Python. And if it got you interested in Python, and you are itching to know more about it in detail and how to use it in your everyday data science or analytics work, I recommend going through the following articles and courses:


You're reading Everything You Should Know About Data Structures In Python

Everything You Need To Know About Uranium

Since the German chemist Martin Heinrich Klaproth identified uranium in 1789, atomic number 92 has become one of the most troubling substances on the planet. It’s naturally radioactive, but its isotope uranium-235 also happens to be fissile, as Nazi nuclear chemists learned in 1938, when they did the impossible and split a uranium nucleus in two. American physicists at U.C. Berkeley were soon to discover they could force uranium-238 to decay into plutonium-239; the substance has since been used in weapons and power plants around the world. Today, the element continues to stoke international tensions as Iran stockpiles uranium in defiance of an earlier treaty, and North Korea’s “Rocket Man” leader Kim Jong-un continues to resist denuclearization.

But what is uranium, exactly? And what do you need to know about it beyond the red-hot headlines? Here we answer your most pressing nuclear questions:

Where does uranium come from?

Uranium is a common metal. “It can be found in minute quantities in most rocks, soils, and waters,” geologist Dana Ulmer-Scholle writes in an explainer from the New Mexico Bureau of Geology and Mineral Resources. But finding richer deposits—the ones with concentrated uranium actually worth mining—is more difficult.

When engineers find a promising seam, they mine the uranium ore. “It’s not people with pickaxes anymore,” says Jerry Peterson, a physicist at the University of Colorado, Boulder. These days, it comes from leaching, which Peterson describes as pouring “basically PepsiCola—slightly acidic” down into the ground and pumping the liquid up from adjacent holes. As the fluid percolates through the deposit, it separates out the uranium for harvesting.

Uranium ore. Deposit Photos

What are the different types of uranium?

Uranium has several important isotopes—different flavors of the same substance that vary only in their neutron count (also called atomic mass). The most common is uranium-238, which accounts for 99 percent of the element’s presence on Earth. The least common isotope is uranium-234, which forms as uranium-238 decays. Neither of these products are fissile, meaning their atoms don’t easily split, so they can’t sustain a nuclear chain reaction.

That’s what makes the isotope uranium-235 so special—it’s fissile, so with a bit of finessing, it can support a nuclear chain reaction, making it ideal for nuclear power plants and weapons manufacturing. But more on that later.

There’s also uranium-233. It’s another fissile product, but its origins are totally different. It’s a product of thorium, a metallic chemical much more abundant than uranium. If nuclear physicists expose thorium-232 to neutrons, the thorium is liable to absorb a neutron, causing the material to decay into uranium-233.

Just as you can turn thorium into uranium, you can turn uranium into plutonium. Even the process is similar: Expose abundant uranium-238 to neutrons, and it will absorb one, eventually causing it to decay to plutonium-239, another fissile substance that’s been used to create nuclear energy and weapons. Whereas uranium is abundant in nature, plutonium is really only seen in the lab, though it can occur naturally alongside uranium.

How do you go from a rock to a nuclear fuel source?

People don’t exactly lay out step-by-step guides to refining nuclear materials. But Peterson got pretty close. After you’ve extracted uranium from the earth, he says chemical engineers separate the uranium-rich liquid from other minerals in the sample. When the resulting uranium oxide dries, it’s the color of semolina flour, hence the nickname “yellowcake” for this intermediate product.

From there, a plant can purchase a pound of yellowcake for $20 or $30. They mix the powder with hydrofluoric acid. The resulting gas is spun in a centrifuge to separate from uranium-238 and uranium-235. This process is called “enrichment.” Instead of the natural concentration of 0.7 percent, nuclear power plants want a product that’s enriched to between 3 and 5 percent uranium-235. For a weapon, you need much more: These days, upwards of 90 percent is the goal.

Once that uranium is enriched, power plant operators pair it with a moderator, like water, that slows down the neutrons in the uranium. This increases the probability of a consistent chain reaction. When your reaction is finally underway, each individual neutron will transform into 2.4 neutrons, and so on, creating energy all the while.

Uranium glass dinnerware. Deposit Photos

Any fun facts I should take with me to my next dinner party?

Try this: In PopSci‘s “Danger” issue earlier this year, David Meier, a research scientist at Pacific Northwest National Lab, talked about his work to create a database of plutonium sources. Turns out, every plutonium product has a visible origin story, because “there’s not one way of processing it,” Meier says. The United States had two plutonium production sites. While the intermediate product from Hanford, Washington (the Manhattan Project site from which PNNL grew) was brown and yellow, the Savannah River site in Akon, South Carolina, produced “a nice blue material,” Meier says. Law enforcement officials hope these subtle differences—which may also correspond to changes in the chemical signature, particle size, or shape of the material—will one day help them track down illicit nuclear development.

Or, dazzle your guests with a short history of radioactive dinnerware. The manufacture of uranium glass, also called canary glass or Vaseline glass began in the 1830s. Before William Henry Perkin created the first synthetic color in 1856, dyes were terribly expensive and even then they didn’t last. Uranium became a popular way to give plates, vases, and glasses a deep yellow or minty green tinge. But put these household objects under a UV light and they all fluoresce a shocking neon chartreuse. Fortunately for the avid collectors who actively trade in uranium glass, most of these objects aren’t so radioactive as to pose a risk to human health.

Last one: In 2002, the medical journal The Lancet published an article on the concerning potential for depleted uranium—the waste leftover after uranium-235 extraction—to end up on the battlefield. The concern is that its high density would make it an incredible projectile, capable of piercing even the most well-enforced battle tank. Worse yet, it could then contaminate the surrounding landscape and anyone it.

Everything You Need To Know About Using “Shared With You” In Ios 15

iOS 15 has brought a new way to share images, videos, links, etc. universally: Shared with You. It works across apps like Safari, Photos, and Notes and allows users to share the files and information from those apps with others. This article takes a look at how to share photos and links using “Shared With You” share menu in iOS 15.

How “Share With You” Works in Safari

We share links to interesting web pages and articles with friends all the time. You read something like a new healthy recipe or some local news, and you want to share a link to it with someone you know.

In previous iOS versions, the recipient would have to open the Messages app, dig deep into the conversation to find the link shared, and tap on it to open it in Safari. Now, with “Shared With You”, things are much simpler.

Let’s say someone sends me a link to an interesting article. Instead of messing around in the Messages app, It will be automatically added to Safari. It doesn’t asks for any permissions; however, you can control them from settings.

The link will appear on the home page under the “Shared with You” heading. Simply tap on the link to open it in a Safari tab. It’s that easy.

The feature works in all messaging apps and not just iMessage. iOS 15 scans and collects all links and displays them in Safari.

You will also notice the name of the person who shared the link below in Safari. Tap on it to open the conversation in the Messages app. Tap and hold to send a reply in a pop-up or remove the link and declutter the space.

How do you find the link again in a sea of links in the Messages app? Simple. Long-press the link and find the option to pin it. You can now find these pinned links in the Messages search menu, Shared With You, and the conversation’s Details view.

How Does “Share With You” Work in the Photos App

Whenever someone shares a photo with you, it will appear in the Photos app under a new “Shared With You” section located under the “For You” tab in the Photos app. It could be an image you received in the Messages app or some other chat app.

You will see a download icon next to the image in the Messages app. In previous iOS versions, you would need to tap on that to download and save the image to the Photos app.

Now, you don’t need to download it initially. Open the Photos app to view all shared images under the “Shared With You” section in the “For You” tab. As seen in the below screenshot, you will see the profile picture of the sender at the top. Tapping on the same will lead you back to the conversation in the Messages app where the image was first seen.

Not shown in the image is a link at the bottom that says “Save Shared Photo,” which allows you to save the photo so that is no longer only connected to the original message. Once you save the image, the option will no longer appear.

You may want to save the photo if you are the type to often delete old messages. If you delete the message before saving the photo, the photo will be gone.

If you are receiving files in the Messages app, you can easily save it for later use.

How to Share Photos From iCloud Directly

With iOS 15, you can now share photos via an iCloud link instead of sharing the actual photo. Open the Photos app and long-press on the photo that you want to share with someone. Tap on the “Share” button, then select “Copy iCloud link.”

The generated link can be shared via any messaging app you use. If you think the photo shouldn’t be shared anymore or was shared mistakenly, you can quickly stop sharing the photo. Open the Photos app and locate the photo you shared under the “For You” tab. Tap once to open it and select the three-dot menu icon to find the “Stop Sharing” button.

Disable “Shared with You” Completely Frequently Asked Questions 1. Why can’t I see “Shared With You” in Photos?

There are many people reporting on social media sites like Reddit and Twitter about this. It is not working for some users, but that is expected to be fixed in subsequent iOS updates, hopefully.

2. When do the shared links for iCloud Photos expire?

The link is available for 30 days, after which it will expire automatically. The recipient will be greeted with a “Failed to Retrieve” message after that. A new link would have to be generated then.

Wrapping Up

This is a neat feature that would make lives easier for many people. The rollout could have been smoother though. Shared With You works in a number of apps and collects valuable data and displays it in the apps. All links are available in the Safari browser, while all photos are visible in Photos app.

It will be interesting to see where Apple takes Shared With You in future updates.

Gaurav Bidasaria

A C.A. by profession and a tech enthusiast by passion, Gaurav loves tinkering with new tech and gadgets. He dropped out of CA in the final year to follow his passion. He has over seven years of experience as a writer covering consumer tech and writes how-to guides, comparisons, listicles, and explainers for B2B and B2C apps and services. He recently started working out but mostly, you will find him either gaming or streaming.

Subscribe to our newsletter!

Our latest tutorials delivered straight to your inbox

Sign up for all newsletters.

By signing up, you agree to our Privacy Policy and European users agree to the data transfer policy. We will not share your data and you can unsubscribe at any time.

6 Unusual Signs Of Dehydration You Should Know About

It is essential to keep an eye on signs of dehydration as soon as possible to provide efficient treatment and avoid a scenario that might be life-threatening. Dehydration is characterized by several common symptoms, some of which are more obvious than others. Because they require immediate treatment, the signs of mental and physical impairment caused by dehydration are extremely important to be aware of. In severe situations of dehydration, seeking medical assistance can be necessary.

1. Not Urinating or Very Dark Urine

The color of a person’s urine is a simple way to decide whether he is dehydrated. Urine from a healthy person should have the color of healthy pale-yellow that resembles apple juice but is much darker than normal.

If you are not passing any urine at all, you are certainly dangerously dehydrated. You really ought to go to the doctor as soon as possible.

If you discover that your pee is a dark yellow color, you should increase your water intake. Drink no more water than your body can process in a single gulp. This will help you avoid dehydration.

If you are concerned that you are not getting enough fluids daily, you should attempt to keep a daily track; you should always try, regardless of where you are.

2. Dry Skin

Examining the color of your urine is only one of several methods to determine whether or not you are dehydrated. There are other additional methods also. Another trait that can be used to tell a lot about a person is how soft his skin is.

Try your hand at this little test: to see what happens, squeeze the skin on the top of your hand. The reluctance to return indicates that it is somewhat moderately dehydrated. Extreme dehydration can be diagnosed by looking for signs of the skin “tenting” or sticking together.

You should drink extra water if you are mild to moderately dehydrated. It is the same thing you should do if your urine is darker. If you’ve just finished a particularly strenuous workout, a fluidand electrolyte-rich beverage like a sports drink or coconut water could be the ideal choice to rehydrate. Water is, of course, always a good option to go with.

If your skin begins to show indications of tenting due to dehydration, it is recommended that you consult a medical professional as soon as possible.

3. Rapid Heartbeat and Breathing

Your heart rate and respiratory rate will increase directly from your physical activity. However, if you continue to experience these symptoms even after you have lowered your body temperature or halted your physical activity, it may be a sign that you are severely dehydrated. When electrolyte levels fall below normal, the heart’s ability to pump blood is negatively impacted.

How to stop it − Without the proper amount of fluid in the body, your organs, most notably your heart, liver, kidneys, and lungs, cannot perform their functions properly. Because of this, if you notice any of these symptoms, you must make an appointment with a medical professional to rule out the possibility of dehydration.

Suppose a medical professional or nurse determines, after doing a comprehensive assessment on you, that you are dehydrated, you will most likely be given an intravenous line and a potent rehydration solution to administer through the line. This water contains a variety of salts and sugars, some of which include sodium chloride and potassium. These components work together to facilitate the rapid delivery of vital fluids to the parts of the body that require them the most.

4. Dizziness

Did you know that water accounts for around 75% of the mass of your brain? Consequently, enhancing cognitive function can be as easy as increasing one’s water consumption and eating naturally high-water-content foods.

On the other hand, not getting enough water might affect one’s mental function. Feeling faint, lightheaded, or dizzy are all symptoms of acute dehydration, but they are not the only symptoms that can occur.

It would be a mistake to disregard these indicators as being trivial. Rest and replenish your body’s fluids by drinking water in small sips and eating meals high in water, such as fruits and vegetables.

Cucumbers, watermelons, tomatoes, strawberries, apples, and grapes are some featured fruits and vegetables. They help restore your body’s electrolyte and mineral balance, which can deplete the brain and other tissues if they are not routinely restored. One can avoid it by drinking enough water and eating healthy foods.

On the other hand, if you feel lightheaded or dizzy to the point that you need medical attention, you should go to the emergency room of the hospital closest to your location as soon as possible.

5. Fever and Chills

Fever and chills are two symptoms frequently experienced by people afflicted with viral infections such as the flu. Nevertheless, it would be best if you did not disregard this warning sign.Additionally, it should serve as a warning sign for severe dehydration. When the body is deprived of fluids and tries to maintain a normal temperature, hyperthermia, and symptoms similar to fever can occur. Some of these symptoms include chills.

If something like this happens while you’re working out or participating in a sport, you should immediately stop what you’re doing and sit down. The stress you’re placing on the many systems in your body makes your symptoms even more severe.

It is possible to cure the symptoms of dehydration at home by increasing the amount of fluid consumed and applying a cold compress to the face or immersing in an ice bath. You should seek emergency medical assistance if your fever lasts more than 103 degrees Fahrenheit or rises over that temperature.

If something like this happens to an adult, it might indicate severe dehydration, which requires prompt medical attention.

6. Unconsciousness

Dehydration can bring on unconsciousness by lowering blood pressure or making a person feel faint. Your condition will remain under observation by the medical staff until it is certain that it has stabilized and your fluid levels have returned to normal.


Several factors influence how much water you need to drink daily, including your age, gender, whether you are pregnant or nursing, and whether or not you have any preexisting medical concerns.

This Is What You Should Know About Investing In Doge, Cardano, Xrp

Crypto-adoption across industries has not really been gradual and steady. The market has seen erratic waves of speculation, exuberance, and acceptance. The growth of altcoins such as Dogecoin, Cardano, and XRP has underlined the evolution of the crypto-ecosystem. These alts, technically, are the market’s “old” coins.

Now, amidst the presence of hyped-up newer coins like MATIC, DOT, and ICP, how do the old coins fare? Is it worth investing in them at this stage? Well, let’s find out.

Dogecoin [DOGE]

Created in 2013 as a joke by its founders, the market’s most popular meme-coin is actually one of the oldest cryptos in the market. Leaving aside its meme and joke aspects, DOGE pumped by 8% over the past week. In fact, the alt’s yearly ROI shared an impressive figure of 6388.56%, at press time.

For the most part of 2023, DOGE’s Sharpe ratio (the risk-adjusted returns) has remained well above zero. However, at the time of writing, the same stood in the negative (-3.31, to be precise). A lower ratio indicates that the asset’s HODLers are exposed to comparatively higher risks. The odds of negative portfolio returns also intensify as and when the Sharpe ratio declines.

Though DOGE’s development activity did pick up the pace in the initial few weeks of June, it has been on a downfall since. With no explicit use-case, this alt’s long-term future, arguably, is dull too. Furthermore, when competition increases in the space, DOGE will have to improve on its developmental side too to sustain itself in the market.

Cardano [ADA]

Cardano made headlines yesterday after Morningstar’s Amy Arnott claimed that ADA would end up seizing a spot for itself in the top 3. ADA’s dominance, in the macro frame, has pretty much been on the rise. Even though there have been a few hiccups of late, the larger trend seems to be appealing, unlike DOGE.

When compared to last year, ADA’s development activity has been dwindling since May. The halt in ADA’s growth stage was also evidently visible on the asset’s adjusted NVT ratio reading. This indicator typically describes the relationship between the market cap and transfer volume.

Notably, Cardano’s adjusted NVT ratio was as high as 56.7 on 11 July. However, the same had fallen to 17.19 at the time of writing.

The press time ratio for Cardano implied that network value has not been able to keep up with the hike in usage of the network. However, the same can be expected to rise by the time Alonzo is in full flow.

ADA’s volume, which was oscillating around its December 2023 levels (around $200 million), has gradually started picking up pace now. For instance, the volume on 26 July reflected a value of $1.3 billion. If the same trend continues, the NVT would get the required push and so would ADA’s long-term price.


Over the past month, the price of this alt has remained rangebound in the $0.5 to $0.7 bracket. Nonetheless, the sixth-largest crypto’s price has appreciated by more than 21% over the past week. Now, that’s definitely something to cheer about. In fact, XRP’s volume also registered consecutive upticks of late. The same stood at $6.5 billion, at the time of writing.

XRP’s development activity too made noteworthy strides of late.

What’s more, Ripple recently announced the launch of Ripplenet’s first ODL service implementation in Japan. In fact, the blockchain firm has been astute enough to tap into developing and unchartered regions to render its services.

The token’s adjusted NVT registered notable spikes as well, denoting a hike in the adoption and utility of the network. Again, this is a good sign.

Looking at the state of all the three altcoins, it would be fair enough to claim that investing in Cardano and XRP, at this stage, seems to be a better prospect than investing in DOGE. It should also be kept in mind that the crypto-community sternly believes that demand for smart contracts and cross-border payments may massively blow up in the coming years. Hence, ADA and XRP seem to be a pretty compulsive duo to cling on to.

In our next article, we will assess whether or not it is worth betting on DeFi tokens in the long run. Stay tuned! 

Exynos 2100: What You Should Know About Samsung’S Flagship Chipset

Exynos 2100 specs

The Exynos 2100 is a landmark release for the firm, not just because of the fact that it’s a 5nm design. It’s the company’s first flagship SoC without custom CPU cores since 2023’s Exynos 7420. Instead of Samsung’s in-house Mongoose cores, the new SoC exclusively uses Arm’s Cortex cores.

The chipset sports one Arm Cortex-X1 core clocked at 2.9GHz, three Cortex-A78 CPU cores at 2.8GHz, and four Cortex-A55 cores running at 2.2GHz. This is the same CPU core arrangement seen on the rival Snapdragon 888 SoC. Samsung said the new CPU setup enables a 30% boost to multi-core performance over the Exynos 990. The Korean brand also points to the 5nm process as being crucial, saying it enables 20% lower power consumption or 10% better overall performance.

Exynos 2100: 5G and AI

Supplied by Samsung

A modern smartphone processor doesn’t just consist of the CPU and GPU, as cellular connectivity is one of the most important elements. The Exynos 2100 sports an integrated 5G modem with sub-6GHz and mmWave capabilities. Sub-6GHz downlink speeds theoretically top out at 5.1Gbps, while mmWave speeds top out at 7.35Gbps.

The actual speeds aren’t an upgrade over the Exynos 990, but the move to an integrated modem means you can expect better power efficiency compared to 2023’s flagship processor.

Moving to machine learning, the Exynos 2100 delivers a tri-core NPU that boasts 26 TOPS of power. By contrast, the Exynos 990 features 15 TOPS of power. Of course, there’s more to machine learning than a simple measurement, and the new NPU touts twice the power efficiency of the older silicon. So expect tasks like computer vision, scene recognition, and more to sip less juice.

Camera support

Robert Triggs / Android Authority

The Exynos 2100 is a camera powerhouse, offering 200MP camera support as well as support for up to six cameras and concurrent data processing from four sensors. In fact, the chipset features a so-called multi-camera and frame processor (MCFP) within the ISP, which is able to combine data from multiple sensors to deliver improved zoom, better wide-angle shots, and more.

On the video front, you’ve got 8K/30fps recording, 8K/60fps playback, 4K/120fps recording, and AV1 codec support for decoding. In other words, Samsung’s processor is among the top dogs when it comes to photo and video capabilities. Qualcomm’s equivalent chipset (Snapdragon 888) doesn’t support AV1 decoding or encoding, making this a notable feather in Samsung’s cap as more streaming services support the bandwidth-friendly standard.

Supported phones

Exynos SoCs usually find their way into Samsung’s flagship offerings, and the Exynos 2100 continues this trend. The Galaxy S21 series offers this processor in most markets aside from the US. In other words, you’ll be getting an Exynos-powered S21 if you’re in Europe, India, the Middle East, Africa, and several Asian locales.

Flagship Exynos chipsets have traditionally appeared inside the Galaxy Note range as well, but we aren’t getting a Note this year. Meanwhile, both the Galaxy Z Flip and Galaxy Z Fold lineups have generally used Qualcomm silicon for all variants.

How does it fare against the Snapdragon 888? What to know about a successor?

Samsung confirmed to Android Authority earlier this year that the next Exynos flagship processor (i.e. Exynos 2100 successor) would indeed offer AMD graphics. Furthermore, AMD revealed back in June that this mobile GPU would have features like ray tracing and variable refresh rate support. The graphics company added that Samsung would reveal more details “later this year.”

This suggests that the Galaxy S22 will offer AMD graphics, which could theoretically make for a big upgrade over Arm’s Mali GPU tech. But we’ll need to wait for more details and benchmarks first. Otherwise, we’re expecting the next Exynos chipset to potentially make use of the new Armv9 architecture, as well as Arm’s latest CPU cores. These cores include the Cortex-X2, Cortex-A710, and the Cortex-A510.

Update the detailed information about Everything You Should Know About Data Structures In Python on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!