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Assume there is a class called animal, but within that class, there are numerous forms such as dog, cat, and cow. That is, a common class animal consists of a variety of forms that come in a variety of shapes and sizes and perform a variety of functions. This entire phenomenon is defined by a single term: polymorphism. Polymorphism is a term used in Python to refer to an object’s ability to take on multiple forms. The term is derived from two distinct terms: poly, which means numerous, and morphs, which means forms.

A class is a template for creating an object. In the following example, we create a class named-A and declare a variable x, which is then passed a value. The object x is created and the value of x is printed.

A simple example demonstrating the concept of class and object in Python:-


A constructor is a sort of subroutine in object-oriented programming. When an object is created within a class, the function constructor is used to assign values to data members. Almost every time we create an object in Python, we use the __init__() function. We use the __init__() function almost everywhere in polymorphism.

What is polymorphism in Python?

Polymorphism is a term used in Python to refer to a generic function name that may be used for several kinds. This notion is commonly used in Python’s object-oriented programming. As is the case with other programming languages like as Java and C++, polymorphism is implemented in Python for a variety of purposes, most notably Duck Typing, Operator and Method overloading, and Method overriding. This polymorphism may be accomplished in two distinct ways: overloading and overriding.

9 >>>”4″+”5″ 45 >>>”ab”+”cd” abcd

We can see from the above example that the addition operator is utilized in a variety of ways. In the first example, since the data given is two integer values, the operator performed a two-number addition.

And in the second example, the identical values are supplied as string data and the same operator concatenates the two strings (concatenation is the act of joining two strings end-to-end). In the third example, the data is two strings, but the operator is identical to the previous one, and it concatenates the two strings.


Although the first value is an integer, the addition operator concatenated the two texts in this case.

Thus, these were some of the most fundamental instances of polymorphism in Python.

How to use polymorphism? Overloading

Overloading may be classified into two categories.

Operator Overloading

Method Overloading

Operator overloading

Operator overloading is a kind of overloading in which an operator may be used in ways other than

those stated in its predefined definition.

14 >>>print(“a”*3) aaa

Thus, in the first example, the multiplication operator multiplied two numbers; but, in the second, since

multiplication of a string and an integer is not feasible, the character is displayed three times twice.

Thus, it demonstrates how a single operator may be used in a variety of ways.

Overloading operators in practice

Example 1:

class Vehicle: def __init__(self, fare): chúng tôi = fare bus= Vehicle(20) car= Vehicle(30) total_fare=bus+ car print(total_fare)


Traceback (most recent call last): File “G:рythоn рyсhаrm рrоjeсtmаin.рy”, line 7, in total_fare=bus+ car TypeError: unsupported operand type(s) for +: ‘Vehicle’ and ‘Vehicle’

In the above example, an error occurred because Python is unable to combine two objects. In

this case, the item is a vehicle.

Now comes the time for operator overloading to be used.

Now we are going to overload the specific method __add__ operator.

class Vehicle: def __init__(self, fare): self.fare = fare def __add__(self, other)://using the special function __add__ operator return self.fare+ other.fare bus= Vehicle(20) car= Vehicle(30) total_fare=bus+ car print(total_fare)



By overloading the special function, we declare that whenever we use the plus operator in the object

total_fare=bus+car, their fares will be added.

Example 2: In this example, let us compare the fares of several modes of transport.

class Vehicle: def __init__(self, fare): chúng tôi = fare def __lt__(self, other):// relational operator __lt__ is used here as the special function return self.fare< other.fare bus= Vehicle(10) car= Vehicle(30) compare=bus< car print(compare)



In the above example, the relational operator __lt__ is utilized as a special function to enable the operator


Method Overloading

Overloading a method refers to a class that has many methods with the same name but perhaps distinct

parameters. While Python does not natively enable method overloading, there are numerous

techniques to do this. While method overloading is possible, only the most recently specified methods

are usable.

Let’s try to understand with the help of an example.

Assume a class A, within the class we have taken a function show which has a constructor self and

arguments with the default value None and None. Then I created an object and executed the function

with the object, but I didn’t supply any arguments, despite the fact that it would display None

and None since we set default values in the function area.


class A: def show(self, a=None, b=None): print(a,b) obj=A()


None None

To supply another value, I must now use another method with an argument.


class A: def show(self, a=None, b=None): print(a,b) obj=A()


None None 4 None

The None value supplied to an in the function portion is substituted with 4 in the output. Part 4 is given

as an argument to the function call.

Now, let’s examine what happens if we send two arguments to the function call in the following example.


class A: def show(self, a=None, b=None): print(a,b) obj=A(),5)


None None 4 4 5

Due to the fact that we sent two parameters 4 and 5 during the function call, two distinct values for a

and b are allocated.

Thus, in the preceding example, we saw how we may utilize the same method and call distinct functions

in a variety of ways.

Consider another example in which we utilized conditional statements to invoke several functions in

distinct ways.


class Area: def find_area(self, a=None, b=None): if a != None and b != None: print("Rectangle:", (a * b)) elif a != None: print("square:", (a * a)) else: print("No figure assigned") obj1=Area() obj1.find_area() obj1.find_area(10) obj1.find_area(10,20)


No figure assigned square: 100 Rectangle: 200

If no arguments are supplied during the function call, no value is assigned; if a single argument is passed, the area of a square is shown; and if two values are passed, the area of a rectangle is displayed.


Before we get into method overriding, it’s necessary to understand Python’s initial inheritance. Inheritance is the method by which a class may be derived from any base class, with the derived class inheriting all of the base class’s attributes. Inheritance alleviates the challenge of repeatedly writing the same code and enhances reusability.

Example of inheritance:

class Bird://base class Bird def sound(self): print("Birds Sounds") #сhild сlаss Dоg inherits the bаse сlаss Аnimаl class Sparrow(Bird)://child class Sparrow def tweet(self): print("sparrow tweeting") d = Sparrow() d.tweet() d.sound()


Sparrow tweeting Birds Sound Method Overriding

Method overriding is the process of changing a base class using the methods and parameters of a derived class.

Consider an example to demonstrate how it works. To begin, we’ll design a base class containing a method and then a derived class devoid of methods.


class Vehicle: def run(self): print("Saves Energy") class EV(Vehicle): pass ev = EV()

As a result, when the function is invoked, the output will display the method of the base class, since the derived class lacks a method.


Saves Energy

Now, in the following example, we define another method with the same name as the base class but with a different parameter under the derived class. Due to the fact that the base class’s method has been overridden by the derived class, the output will include just the derived class’s method.


class Vehicle: def run(self): print("Saves Energy") class EV(Vehicle): def run(self): print("Run on Electricity") ev = EV()


Run on Electricity Super() Function

Due to the fact that the base class’s method has been overridden, the base class’s method cannot be invoked normally. Thus, in order to invoke the base class method, we must utilize the super function in the overridden method of the derived class.


class Vehicle: def run(self): print("Saves Energy") class EV(Vehicle): def run(self): super().run()//super function is used to call the method of base class print("Run on Electricity") ev = EV()


Saves Energy Run on Electricity Duck Typing

Duck typing is a polymorphism notion. The term duck typing is derived from a proverb that says

everything that walks like a duck quacks like a duck, and swims like a duck is referred to like a duck

regardless of the item. In basic terms, it indicates that if anything matches its behavior to another, it

will be considered a member of the category to which i

When discussing object-oriented programming in Python, the word polymorphism is unavoidably used.

In object-oriented programming, objects must take on a variety of shapes. This characteristic is critical in

software development. The same action may be executed in a variety of ways because of polymorphism.

This notion is often used while discussing loose coupling, dependency injection, and interfaces, among

other things.


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All You Should Know About Datetime Variables In Python And Pandas

The Complex yet Powerful World of DateTime in Data Science

I still remember coming across my first DateTime variable when I was learning Python. It was an e-commerce project where I had to figure out the supply chain pipeline – the time it takes for an order to be shipped, the number of days it takes for an order to be delivered, etc. It was quite a fascinating problem from a data science perspective.

The issue – I wasn’t familiar with how to extract and play around with the date and time components in Python.

There is an added complexity to the DateTime features, an extra layer that isn’t present in numerical variables. Being able to master these DateTime features will help you go a long way towards becoming a better (and more efficient) data scientist. It’s definitely helped me a lot!

And the date and time features are ubiquitous in data science projects. Think about it – they are a rich source of valuable information, and hence, can give some deep insights about any dataset at hand. Plus the amount of flexibility they offer when we’re performing feature engineering – priceless!

In this article, we will first have a look at how to handle date and time features with Python’s DateTime module and then we will explore Pandas functions for the same!

Note: I assume you’re familiar with Python and the Pandas library. If not, I highly recommend taking the awesome free courses below:

Table of Contents

The Importance of the Date-Time Component

Working with Dates in Python

Working with Time in Python

DateTime in Python

Updating old dates

Extracting Weekday from DateTime

What week is it?

Leap year or not? Use the calendar!

The Different Datetime formats

Advanced DateTime formatting with Strptime & Strftime


DateTime with Pandas

DateTime and Timedelta objects in Pandas

Date range in Pandas

Making DateTime features in Pandas

The Importance of the Date-Time Component

It’s worth reiterating, dates and times are a treasure trove of information and that is why data scientists love them so much.

Before we dive into the crux of the article, I want you to experience this yourself. Take a look at the date and time right now. Try and imagine all kinds of information that you can extract from it to understand your reading habit. The year, month, day, hour, and minute are the usual suspects.

But if you dig a little further, you can determine whether you prefer reading on weekdays or weekends, whether you are a morning person or a night owl (we are in the same boat here!), or whether you accumulate all the interesting articles to read at the end of the month!

Clearly, the list will go on and you will gradually learn a lot about your reading habits if you repeat this exercise after collecting the data over a period of time, say a month. Now imagine how useful this feature would be in a real-world scenario where information is collected over a long period of time.

Date and time features find importance in data science problems spanning industries from sales, marketing, and finance to HR, e-commerce, retail, and many more. Predicting how the stock markets will behave tomorrow, how many products will be sold in the upcoming week, when is the best time to launch a new product, how long before a position at the company gets filled, etc. are some of the problems that we can find answers to using date and time data.

This incredible amount of insight that you can unravel from the data is what makes date and time components so fun to work with! So let’s get down to the business of mastering date-time manipulation in Python.

Working with Dates in Python

The date class in the DateTime module of Python deals with dates in the Gregorian calendar. It accepts three integer arguments: year, month, and day. Let’s have a look at how it’s done:

You can see how easy it was to create a date object of datetime class. And it’s even easier to extract features like day, month, and year from the date. This can be done using the day, month, and year attributes. We will see how to do that on the current local day date object that we will create using the today() function:

Python Code:

Working with Time in Python

time is another class of the DateTime module that accepts integer arguments for time up to microseconds and returns a DateTime object:

You can extract features like hour, minute, second, and microsecond from the time object using the respective attributes. Here is an example:

This is just the tip of the iceberg. There is so much more we can do with DateTime features in Python and that’s what we’ll look at in the next section.

DateTime in Python

So far, we have seen how to create a date and a time object using the DateTime module. But the beauty of the DateTime module is that it lets you dovetail both the properties into a single object, DateTime!

datetime is a class and an object in Python’s DateTime module, just like date and time. The arguments are a combination of date and time attributes, starting from the year and ending in microseconds.

So, let’s see how you can create a DateTime object:

Or you could even create an object on the local date and time using the now() method:

You can go on and extract whichever value you want to from the DateTime object using the same attributes we used with the date and time objects individually.

Next, let’s look at some of the methods in the DateTime class.

Updating old Dates

First, we’ll see how to separate date and time from the DateTime object using the date() and time() methods. But you could also replace a value in the DateTime objects without having to change the entire date using the replace() method:

Weekday from DateTime

One really cool thing that you can do with the DateTime function is to extract the day of the week! This is especially helpful in feature engineering because the value of the target variable can be dependent on the day of the week, like sales of a product are generally higher on a weekend or traffic on StackOverflow could be higher on a weekday when people are working, etc.

The weekday() method returns an integer value for the day of the week, where Monday is 0 and Sunday is 6. But if you wanted it to return the weekday value between 1 and 7, like in a real-world scenario, you should use isoweekday():

What Week is it?

Alright, you know the day of the week, but do you know what week of the year is it? This is another very important feature that you can generate from the given date in a dataset.

Sometimes the value of the target variable might be higher during certain times of the year. For example, the sales of products on e-commerce websites are generally higher during vacations.

You can get the week of the year by slicing the value returned by the isocalendar() method:

Leap Year or Not? Use Calendar!

Want to check whether it is a leap year or not? You will need to use the isleap() method from the calendar module and pass the year as an attribute:

View the code on Gist.

Congratulations – you are living in a leap year! What did you do with the extra day? Oh, you missed it? Don’t worry! Just take a day this month and do the stuff that you love! But where are you going? You got your calendar right here!

Not free this month? You can have a look at the entire calendar for the year:

Pretty cool, right? Plan your year wisely and take out some time to do the things you love!

DateTime Formats

The Datetime module lets you interchange the format of DateTime between a few options.

First up is the ISO format. If you wanted to create a DateTime object from the string form of the date in ISO format, use the fromisoformat() method. And if you intended to do the reverse, use the isoformat() method:

If you wanted to convert DateTime into a string format, you could use the ctime() method. This returns the date in a string format. And if you wanted to extract just the date from that, well, you would have to use slicing:

And if none of these functions strike your fancy, you could use the format() method which lets you define your own format:

Wait – what are these arguments I passed to the function? These are called formatted string codes and we will look at them in detail in the next section.

Advanced DateTime Formatting with Strptime & Strftime

These functions are very important as they let you define the format of the DateTime object explicitly. This can give you a lot of flexibility with handling DateTime features.

strptime() creates a DateTime object from a string representing date and time. It takes two arguments: the date and the format in which your date is present. Have a look below:

You define the format using the formatting codes as I did above. There are a number of formatting codes and you can have a look at them in the documentation.

The stftime() method, on the other hand, can be used to convert the DateTime object into a string representing date and time:

But you can also extract some important information from the DateTime object like weekday name, month name, week number, etc. which can turn out to be very useful in terms of features as we saw in previous sections.


So far, we have seen how to create a DateTime object and how to format it. But sometimes, you might have to find the duration between two dates, which can be another very useful feature that you can derive from a dataset. This duration is, however, returned as a timedelta object.

As you can see, the duration is returned as the number of days for the date and seconds for the time between the dates. So you can actually retrieve these values for your features:

View the code on Gist.

But what if you actually wanted the duration in hours or minutes? Well, there is a simple solution for that.

timedelta is also a class in the DateTime module. So, you could use it to convert your duration into hours and minutes as I’ve done below:

Now, what if you wanted to get the date 5 days from today? Do you simply add 5 to the present date?

Not quite. So how do you go about it then? You use timedelta of course!

timedelta makes it possible to add and subtract integers from a DateTime object.

DateTime in Pandas

We already know that Pandas is a great library for doing data analysis tasks. And so it goes without saying that Pandas also supports Python DateTime objects. It has some great methods for handling dates and times, such as to_datetime() and to_timedelta().

DateTime and Timedelta objects in Pandas

The to_datetime() method converts the date and time in string format to a DateTime object:

You might have noticed something strange here. The type of the object returned by to_datetime() is not DateTime but Timestamp. Well, don’t worry, it is just the Pandas equivalent of Python’s DateTime.

We already know that timedelta gives differences in times. The Pandas to_timedelta() method does just this:

Here, the unit determines the unit of the argument, whether that’s day, month, year, hours, etc.

Date Range in Pandas

To make the creation of date sequences a convenient task, Pandas provides the date_range() method. It accepts a start date, an end date, and an optional frequency code:

Instead of defining the end date, you could define the period or number of time periods you want to generate:

Making DateTime Features in Pandas

Let’s also create a series of end dates and make a dummy dataset from which we can derive some new features and bring our learning about DateTime to fruition.

View the code on Gist.

Perfect! So we have a dataset containing start date, end date, and a target variable:

We can create multiple new features from the date column, like the day, month, year, hour, minute, etc. using the dt attribute as shown below:

Our duration feature is great, but what if we would like to have the duration in minutes or seconds? Remember how in the timedelta section we converted the date to seconds? We could do the same here!

Great! Can you see how many new features we created from just the dates?

Now, let’s make the start date the index of the DataFrame. This will help us easily analyze our dataset because we can use slicing to find data representing our desired dates:

Awesome! This is super useful when you want to do visualizations or any data analysis.

End Notes

I hope you found this article on how to manipulate date and time features with Python and Pandas useful. But nothing is complete without practice. Working with time series datasets is a wonderful way to practice what we have learned in this article.

I recommend taking part in a time series hackathon on the DataHack platform. You might want to go through this and this article first in order to gear up for that hackathon.


Everything We Know About How To Buy A Oneplus One

When OnePlus announced that fans of the anticipated OnePlus One would only be able to buy the phone via invitation there was a lot of concern about why this method was chosen and how the system would work.

Why an invite system?

One of the major reasons why rivals such as Xiaomi, Huawei etc, are able to sell their products at such an attractive price is because they choose to manufacturer their product either in small batches, gradually building up to the point they can offer their phones on sale to anyone at anytime. This saves money as it means that stock doesn’t sit around not being sold, but more importantly component costs can come down allowing the company to make money.

Oneplus say this isn’t the reason why they can sell at a low price, and say their pricing is low as their marketing is al handled online and they have no physical retails stores to deal with thus saving money. OnePlus say that the invite system was chosen as the best method to supply phones to their customers as the company is still learning and don’t want to have issues with too much or too little stock.

Grey market invites?

OnePlus hope the invite system will avoid these issues as only fans of the company/phone will be initially invited to buy the phone. In this way those with invites are sure to get the phone, and scalpers will have a real problem getting a hold of an invite.

Gizchina News of the week

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But surely their will be a grey market for invites? This would have been the case if the invites were open-ended however they will only be valid for 1-2 days, if the invite is not used then it will be returned. In special cases (give-aways and raffles) invites might have a longer expiry date.

How to get an invite to buy a OnePlus One

According to the latest update on the OnePlus forums, the first real batch of invitations will sent out to fans in May, these will be for the 64GB Sandstone version of the OPO. OnePlus say that both forum and non-forum fans will be chosen for invitations so you don’t need to worry about hanging around on the forums just in case.

If you aren’t fortunate enough to get one of the first invites, then don’t worry competitions and raffles will be held allowing you the chance to get one, and you can even be invited by a friend who has bought the OnePlus One phone already.

Once you have bought your OnePlus One you will be then be able to invite people to buy the phone too. The number of invitations you can send out will vary depending on the time you get and production schedule, but is sounds as though once you have become a OPO owner you will have the option to invite people for as long as the system is in place. Keep in mind though you won’t automatically receive the invites with the phone but they will be available to you as and when stock is ready.

OnePlus One Invite System

Well I hope that helps clear a few details of the OnePlus One invite system up for you. OnePlus also have a thread on their forums about the invite system which is being updated on a regular basis too, so I suggest you also keep an yet on that too incase things change.

[ OnePlus Invite System ]

Everything You Know About Grilling Is Wrong

Everything You Know about Grilling Is Wrong

Greg Blonder is an engineering professor by vocation and a barbecue whiz by avocation. Photo by Jackie Ricciardi


Everything You Know about Grilling Is Wrong ENG prof on the science of a perfect steak

It may not be the most academic topic that Greg Blonder has studied, but it’s the reason he can tell you why sticking a can of beer up the rear of that chicken before grilling will make it succulently juicy. Contrary to conventional wisdom, it’s not the beer.

Science confirms that the long-standing theory—that evaporating beer streams moisten the meat—is wrong. Rather, it’s standing the chicken vertically on the grill that yields that great taste, says Blonder, a College of Engineering professor of the practice of mechanical engineering. “The beer can actually slow down the cooking,” he says, because, cocooned by chicken meat, the can is “the last thing to heat up.…It’s actually causing the meat near the center to be kind of raw.”

He says vertical cooking of the chicken is absolutely a great thing to do, and “sticking a beer can up the butt is a really stupid idea.”

Blonder does what few scientists or backyard barbecuers do, or can do: he applies formidable science training to cooking a great piece of beef, fish, or fowl. And he coauthored the recent book Meathead: The Science of Great Barbecue and Grilling (Houghton Mifflin Harcourt, 2023). The Meathead of the title is Meathead Goldwyn, founder and editor of chúng tôi which Blonder contributes to. (His own website has info ranging from technical articles to recipes.)

“Many chefs mess up because they don’t understand the science,” he says. Good ones intuit their way to being good barbecuers, but even talented chefs can believe in “myths that they’ve picked up over time that they don’t know to abandon.” 

Putting science behind the sizzle is partly the result of a decades-old love of cooking, says Blonder, whose scientific knowledge has also helped his wife, a talented pastry maker. “I’d look at the recipe and say, ‘The claim of the recipe author violates what I know about thermodynamics.’ I would end up measuring temperatures on our ovens to see whether the dial matched the temperature it claimed. And our home oven was 50 degrees off—high.”

BU Today spoke with the science-based guru of the grill about tips for the summer barbecue season.

BU Today: How did you get involved in writing this book?

Blonder: I was smoking a big brisket, and it takes 14 hours to cook all the way through. When you get to about five hours, it stops rising. It’s in a hot oven and it stops rising—that’s weird. It’s called the “stall.” You wait three, four hours and it breaks the stall, but it affects your ability to know when it’s done. Meathead had the country’s largest barbecue website. He had an article on how the stall will occur and ways to stop it, like wrapping it in aluminum foil. But he had no explanation for it, so I wrote him a note. This was eight years ago or so. He wrote back saying, “Do you know why it stalls?” I said sure. We wrote up an article together for his website. I started adding some recipes and collaborating on some of the site’s writing. Meathead obtained a book contract, and I helped him with the book.

You’ve worked with restaurants to debunk some myths. Can you tell us about some of those myths?

Everyone in Texas understands that if you smoke chicken, the chicken will appear pink, for the same reason there’s a pink ring in brisket or in ribs: it has to do with nitric oxide in the combustion gases of a wood fire that fix the color of myoglobin, a chemical with iron in it. It’s what makes meat red. When you live north of the Mason-Dixon line, you say, “Raw meat!” and you return it to the kitchen, which is a problem, because that’s waste. I’ve worked with a bunch of Northern barbecue restaurants to show them that if they do an acid marinade—like buttermilk or a lemon—chemistry suppresses the fixing process. And so the meat looks white. The acid solved the visual problem without hurting the taste.

I also did work with some restaurants to understand that when you salt meat and you give the salt enough time to make it from the surface to the interior—in a thick brisket, that means three days to a week—that salt changes the environment of the water that’s in the meat, and it holds the water in place. So the meat will be juicier. I spoke at the National Barbecue Association a year ago in Nashville. Great guys—guns, God, glory kind of people. I told them, “You need to get either the rub on or the salt on at least three days to a week ahead of time.”

What about the myth that we should “sear first, then cook”?

This idea of searing went back to the 1800s. In fact, when you sear meat, it makes that sizzling sound because it’s constantly emitting water. The idea that searing seals things in is bogus. Sometimes restaurant chains will say, “We sear in the flavor.” Not true.

There are three ways to cook a steak. Two are right, one is wrong, and the wrong way is the way everyone does it, which is take the steak out of the refrigerator, throw it on a hot grill, and flip it maybe two, three times until the inside gets to medium. When you do that, the outer quarter inch or more might be gray, and gray meat doesn’t have a lot of flavor. When you take it off the grill, that heat continues to work its way to the center. The surface of the meat’s at boiling temperature, 212 degrees. The inside’s at 135, which is what you want. You take it off the grill, the 212 drops as it heats up the 135, and eventually the whole steak’s at 145, which is hotter and grayer than what you want.

The traditional way of cooking on the backyard grill—take cold meat from the fridge, cook it, flipping it occasionally, taking it off when it’s ready—is actually the worst thing. Color is not an indicator of temperature with meat. You can have meat that is 150 degrees that is well done and pink. And you can have meat that’s 125 degrees that is medium rare and gray. And that has to do with the chemistry of the myoglobin that’s inside and the way the animal was slaughtered and what marinades you use.

What are the right ways?

The key is to separate the cooking into two steps. One step is bringing the temperature of the meat up to, say, medium, whatever the target temperature is. And the other step is to brown the surface to build the flavor and the color. There are two ways to separate it:

You can take the meat right out of the fridge, put it on an extremely hot grill for about four minutes total, brown the surfaces, then either put it off on the side of the grill or in your kitchen oven at 200 degrees and let it slowly coast up to 135. Because the heat is coming in slowly instead of forcing its way rapidly in through the outside, the whole piece of meat, surface to center, will be one color. In an hour—depending on how thick the steak is, 45 minutes—the whole steak will go from raw on the inside to medium, and it’ll be beautiful.

The reverse way to do it, called the reverse sear, is start with the meat in a 200-degree oven and raise it over an hour until the meat is, say, 120 degrees top to bottom. Take it out of the oven; go on the grill; cook the two surfaces and get a nice brown color and the Maillard reaction [between amino acids and simple sugars], which gives you this beautiful, aromatic, nutty smell of meat, and then take it right off the grill and eat it. The reverse sear is my favorite way of doing it.

Another myth the book busts is bringing meat to room temperature before cooking. Does science have anything to say about cooking with wood, or charcoal, or gas?

Perfectly combusted propane or natural gas is odorless—it just produces water and CO2, the byproducts of combustion. So there’s no flavor produced by properly combusted gas. To get flavor on a gas grill—in most cases, it comes from burnt fat that falls on the fire.

Wood smoke has a sweeter, more distinct odor from fat. You need wood if you want to have a traditional barbecue aroma and appearance.

I don’t want to dissuade anyone from the convenience of a gas grill. The gas grill is my go-to for vegetables and fish, which cook so quickly that they don’t have time for smoke to make much of a difference.

No meat on a gas grill for you?

Almost never. Charcoal is a much better way to grill a steak. A wood fire grill or smoker does it all, but it’s also the least convenient for people to use. Charcoal is kind of a nice compromise for people. People like it because of its consistency, and they add chemicals to charcoal briquettes so that they burn slowly.

Is it a carcinogen concern if you use lighter fluid or pretreated briquettes?

That’s an abomination against the god of barbecue. That flavor will never disappear, that’s an objectionable flavor and does have some carcinogens. Smoke is already bad; cigarette smoke and barbecue smoke are identical, they’re just as bad for you. 

Does that mean you eat barbecue sparingly?

No, because the danger’s from the smoke, not the finished product. The chemicals in the smoke, which will give you lung cancer when you’re standing there cooking, are not the chemicals on the meat. And the epidemiological studies that have looked at eating smoked meats versus other meats versus being a vegetarian have been relatively inconclusive. I worry about many things environmental. This is not one of the ones I worry about. 

Do your colleagues know you do this hobby?

They just chalk it up to: every professor is eccentric—this is my eccentricity.

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Python Class: A Complete Guide (Beginner Friendly)

Python is an object-oriented programming language. It means almost everything is an object. When becoming a Python developer, it’s crucial to learn what is a Python class and how to use it to create those objects.

A Python class is a blueprint for creating objects.

For example, here is a simple class Person, that has an attribute name:

class Person: name = "Sofie"

Now you can create a Person object from the class by:

girl = Person()

An object is also known as an instance of a class. The process of creating objects from a class is called instantiation.

You can use a class to instantiate different objects that all represent the class. For example, you can create multiple persons with different names.

How to Crete a Class in Python

To create a new Python class, use the class keyword.

Here is the syntax for defining a class in Python:

class ExampleClass: pass

Now, every piece of code you write into this block belongs to that class.

For now, we are going to pass the implementation of the class, as we will return to it later on.

How to Use a Python Class

In the previous section, you learned the syntax for creating a class in Python. But how can you use this class?

The answer is you can create, or more formally, instantiate objects of the class.

Creating an instance of the above ExampleClass looks like this:

obj = ExampleClass()

Now obj is a Python object that represents the ExampleClass. It thus has all the behavior described in the class. However, now the ExampleClass is empty, thus you can not do much with its representative objects.

In the next section, we are going to learn how to associate properties and behavior with the class. This way you can put your class into use.

Attributes and Methods in a Python Class

A bare class is not much of use. To benefit from using classes, you need to associate some behavior with them.

For example, a Person class could store info about the person and a method that introduces it.

To associate properties and behavior to a class, you need to create attributes and methods in the class.

Let’s first have a look at how to create class attributes in Python.

Attributes in a Class

Attributes in classes are properties that are present in the class and its objects. For example, a Fruit class could have a color attribute.

To create attributes in a class, declare them as variables in the class.

For example, let’s create a Fruit class with a color attribute:

class Fruit: color = "Yellow"

(Keep in mind you can add as many attributes to your class as you want.)

If you now instantiate a Fruit object based on the above Fruit class, you can access its color property using the dot notation.

For example, let’s create a Fruit object called some_fruit and display its color by printing it into the console:

some_fruit = Fruit() print(some_fruit.color)



Now, the color of some_fruit is "Yellow" because that’s what you defined in the class. But you can change it for this particular object if you wish to.

For instance, let’s turn some_fruit to red:

some_fruit.color = "Red" print(some_fruit.color)



This change in color does not affect the Fruit class. Instead, it only changes the object, as you can see.

Now that you understand what class attributes are in Python, let’s take a look at methods in classes.

Methods in a Python Class

A function inside a class is known as a method. A method assigns behavior to the class.

Usually, a method uses the attributes (or the other methods) of the class to perform some useful task. For example, a Weight class could have a kilograms attribute. In addition, it can have a to_pounds() method, that converts the kilograms to pounds.

To create a method for your class in Python, you need to define a function in it.

As mentioned, the method needs to access the attributes of the class. To do it, the method has to accept an argument that represents the class itself.

Let’s put it all together in a form of a simple example:

Let’s create a Person class and define an introduce() method to it. This makes it possible for each Person object to introduce themselves by calling person.introduce():

class Person: name = "Sophie" def introduce(self): print("Hi, I'm",

If you now look at the introduce() method, you can see it takes one argument called self. This is there because as mentioned earlier, the class needs to be able to access its own attributes to use them. In this case, the person needs to know the name of itself.

Now you can create a person objects and make them introduce themselves using the introduce() method.

For instance:

worker = Person() = "Jack" worker.introduce()


Hi, I'm Jack

Wonderful! You know the basics of defining a class and creating objects that contain attributes and some useful behavior.

But in the above example, the name of a Person is always Sophie to begin with. When you create a person object, you need to separately change its name if you want to. Even though it works, it is not practical.

A better for instantiating objects would be to directly give them a name upon creation:

dude = Person("Jack")

Instead of first creating an object and then changing its name on the next line:

dude = Person() = "Jack"

To do this, you need to understand class initialization and instance variables. These give you the power to instantiate objects with unique attributes instead of separately modifying each object.

Class Initialization in Python

As you saw in the previous section, creating a person object with a unique name is only possible this way:

dude = Person() = "Jack"

But what you actually want is to be able to do this instead:

dude = Person("Jack")

This is possible and it is called class initialization.

To enable class initialization, you need to define a special method into your class. This method is known as a constructor or initializer and is defined with def __init__(self):.

Every class can be provided with the __init__() method. This special method runs whenever you create an object.

You can use the __init__() method to assign initial values to the object (or run other useful operations when an object is created).

The __init__() method is also known as the constructor method of the class.

In the Person class example, all the Person objects have the same name “Sophie”.

But our goal is to be able to create persons with unique names like this:

worker = Person("Jack") assistant = Person("Charlie") manager = Person("Sofie")

To make it possible, implement the__init__() method in the Person class:

class Person: def __init__(self, person_name): chúng tôi = person_name

Now, let’s test the Person class by instantiating person objects:

worker = Person("Jack") assistant = Person("Charlie") manager = Person("Sofie") print(,,


Jack Charlie Sofie

Let’s inspect the code of the Person class to understand what is going on:

The __init__() method accepts two parameters: self and person_name

self refers to the Person instance itself. This parameter has to be the first argument of any method in the class. Otherwise, the class does not know how to access its properties.

person_name is the name input that represents the name you give to a new person object.

The last line = person_name means “Assign the input person_name as the name of this person object.” is an example of an instance variable. This means that is an instance-specific (or object-specific) variable. You can create Person objects each with a different name.

To Recap

Initialization makes it possible to assign values to an object upon creation. The __init__() method is responsible for the initialization process. The method runs whenever you create a new object to set it up. This way you can for example give a name to your object when creating it.


In Python, a class is an outline for creating objects.

A Python class can store attributes and methods. These define the behavior of the class.

Also, you can initialize objects by implementing the __init__() method in the class. This way you can create objects with unique values, also known as instance variables without having to modify them separately.

Thanks for reading. Happy coding!

Further Reading

50 Python Interview Questions

Everything You Need To Know About Edge Detection

Edge detection refers to a set of mathematical techniques for detecting edges, or curves in a digital picture when the brightness of the image abruptly changes or, more formally, has discontinuities. Step detection is the issue of identifying discontinuities in one-dimensional signals, while change detection is the problem of finding signal discontinuities across time. In image processing, machine vision, and 

Prewitt Edge Detection

This is a popular edge detector that is used to identify horizontal and vertical edges in pictures.  

Sobel Edge Detection

This makes use of a filter that emphasizes the filter’s center. It is one of the most often used edge detectors, and it reduces noise while also providing distinguishing and edge response.  

Laplacian Edge Detection

The Laplacian edge detectors are different from the edge detectors previously mentioned. Only one filter is used in this technique (also called a kernel). Laplacian edge detection executes second-order derivatives in a single pass, making it susceptible to noise. Before using this approach, the picture is smoothed with Gaussian smoothing to avoid this susceptibility to noise.  

Canny Edge Detection

Edge detection refers to a set of mathematical techniques for detecting edges, or curves in a digital picture when the brightness of the image abruptly changes or, more formally, has discontinuities. Step detection is the issue of identifying discontinuities in one-dimensional signals, while change detection is the problem of finding signal discontinuities across time. In image processing, machine vision, and computer vision , edge detection is a critical technique, especially in the fields of feature identification and extraction. The goal of detecting sharp changes in picture brightness is to record significant events and changes in the world’s characteristics. Discontinuities in picture brightness are expected to correlate to discontinuities in-depth, discontinuities in surface orientation, changes in material characteristics, and fluctuations in scene light given relatively generic assumptions for an image generation model. In an ideal world, applying an edge detector to an image would result in a collection of linked curves that indicate object borders, surface marking boundaries, and curves that correspond to surface orientation discontinuities. Applying an edge detection method to a picture can minimize the quantity of data that has to be processed and therefore filter out information that isn’t as vital while retaining the image’s crucial structural features. If the edge detection stage is successful, the job of understanding the information contained in the original image may be significantly streamlined. However, such perfect edges are not always possible to get from real-life pictures of modest complexity. Edges recovered from non-trivial pictures are frequently impeded by fragmentation, which results in unconnected edge curves, missing edge segments, and false edges that do not correlate to important events in the image, complicating the process of understanding the image data. One of the most basic processes in image processing, image analysis, picture pattern recognition, and computer vision approaches is edge detection. Viewpoint-dependent or viewpoint-independent edges can be retrieved from a two-dimensional picture of a three-dimensional scene. The intrinsic features of three-dimensional objects, such as surface marks and form, are generally reflected by a perspective-independent edge. The geometry of the scene, such as objects occluding one another, is generally reflected by a perspective-dependent edge, which varies as the viewpoint changes. The border between a block of red and a block of yellow, for example, is a typical edge. A line, on the other hand, can be a tiny number of pixels of a variable hue on an otherwise constant backdrop (as can be retrieved by a ridge detector). As a result, there may be one edge on either side of a line in most cases. Edge detection may be done in a variety of ways, with Prewitt edge detection, Sobel edge detection, Laplacian edge detection, and Canny edge detection being some of the most chúng tôi is a popular edge detector that is used to identify horizontal and vertical edges in chúng tôi makes use of a filter that emphasizes the filter’s center. It is one of the most often used edge detectors, and it reduces noise while also providing distinguishing and edge chúng tôi Laplacian edge detectors are different from the edge detectors previously mentioned. Only one filter is used in this technique (also called a kernel). Laplacian edge detection executes second-order derivatives in a single pass, making it susceptible to noise. Before using this approach, the picture is smoothed with Gaussian smoothing to avoid this susceptibility to chúng tôi is the most widely utilized, highly successful, and complicated approach in comparison to many others. It’s a multi-stage method for detecting and identifying a variety of edges. The steps of the Canny edge detection method are shown below. It transforms the picture to grayscale, eliminates noise (since edge detection using derivatives is susceptible to noise), calculates the gradient (which aids in identifying the edge strength and direction), and last, turns the image to grayscale. It employs non-maximum suppression to narrow the image’s edges, a double threshold to detect the image’s strong, weak, and irrelevant pixels, and hysteresis edge tracking to help transform weak pixels into strong pixels only if they are surrounded by strong pixels.

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