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Assign function arguments to `self`

I've noticed that a common pattern I use is to assign SomeClass.__init__() arguments to self attributes of the same name. Example:

In fact it must be a common task for others as well as PyDev has a shortcut for this - if you place the cursor on the parameter list and click Ctrl+1 you're given the option to Assign parameters to attributes which will create that boilerplate code for you.

Is there a different, short and elegant way to perform this assignment?

Jonathan Livni's user avatar

10 Answers 10

You could do this, which has the virtue of simplicity:

This leaves it up to whatever code creates an instance of C to decide what the instance's attributes will be after construction, e.g.:

If you want all C objects to have a , b , and c attributes, this approach won't be useful.

(BTW, this pattern comes from Guido his own bad self, as a general solution to the problem of defining enums in Python. Create a class like the above called Enum , and then you can write code like Colors = Enum(Red=0, Green=1, Blue=2) , and henceforth use Colors.Red , Colors.Green , and Colors.Blue .)

It's a worthwhile exercise to figure out what kinds of problems you could have if you set self.__dict__ to kwargs instead of dict(kwargs) .

Robert Rossney's user avatar

I sympathize with your sense that boilerplate code is a bad thing. But in this case, I'm not sure there even could be a better alternative. Let's consider the possibilities.

If you're talking about just a few variables, then a series of self.x = x lines is easy to read. In fact, I think its explicitness makes that approach preferable from a readability standpoint. And while it might be a slight pain to type, that alone isn't quite enough to justify a new language construct that might obscure what's really going on. Certainly using vars(self).update() shenanigans would be more confusing than it's worth in this case.

On the other hand, if you're passing nine, ten, or more parameters to __init__ , you probably need to refactor anyway. So this concern really only applies to cases that involve passing, say, 5-8 parameters. Now I can see how eight lines of self.x = x would be annoying both to type and to read; but I'm not sure that the 5-8 parameter case is common enough or troublesome enough to justify using a different method. So I think that, while the concern you're raising is a good one in principle, in practice, there are other limiting issues that make it irrelevant.

To make this point more concrete, let's consider a function that takes an object, a dict, and a list of names, and assigns values from the dict to names from the list. This ensures that you're still being explicit about which variables are being assigned to self. (I would never suggest a solution to this problem that didn't call for an explicit enumeration of the variables to be assigned; that would be a rare-earth bug magnet):

Now, while not horribly unattractive, this is still harder to figure out than a straightforward series of self.x = x lines. And it's also longer and more trouble to type than one, two, and maybe even three or four lines, depending on circumstances. So you only get certain payoff starting with the five-parameter case. But that's also the exact moment that you begin to approach the limit on human short-term memory capacity (= 7 +/- 2 "chunks"). So in this case, your code is already a bit challenging to read, and this would only make it more challenging.

senderle's user avatar

Mod for @pcperini's answer:

Seth Robertson's user avatar

Your specific case could also be handled with a namedtuple :

PaulMcG's user avatar

Decorator magic!!

while defining:

Of course you could define this decorator once and use it throughout your project. Also, This decorator works on any object function, not only __init__ .

You can do it via setattr(), like:

[setattr(self, key, value) for key, value in kwargs.items()]

Is not very beautiful, but can save some space :)

So, you'll get:

Pavel Shvedov's user avatar

For that simple use-case I must say I like putting things explicitly (using the Ctrl+1 from PyDev), but sometimes I also end up using a bunch implementation, but with a class where the accepted attributes are created from attributes pre-declared in the class, so that I know what's expected (and I like it more than a namedtuple as I find it more readable -- and it won't confuse static code analysis or code-completion).

I've put on a recipe for it at: http://code.activestate.com/recipes/577999-bunch-class-created-from-attributes-in-class/

The basic idea is that you declare your class as a subclass of Bunch and it'll create those attributes in the instance (either from default or from values passed in the constructor):

Also, Alex Martelli also provided a bunch implementation: http://code.activestate.com/recipes/52308-the-simple-but-handy-collector-of-a-bunch-of-named/ with the idea of updating the instance from the arguments, but that'll confuse static code-analysis (and IMO can make things harder to follow) so, I'd only use that approach for an instance that's created locally and thrown away inside that same scope without passing it anywhere else).

Fabio Zadrozny's user avatar

I solved it for myself using locals() and __dict__ :

user3638162's user avatar

Do not use this: I was simply trying to create the answer closest to OPs initial intentions. As pointed out in comments, this relies on entirely undefined behavior , and explicitly prohibited modifications of the symbol table.

It does work though, and has been tested under extremely basic circumstances .

Using the vars() built-in function, this snippet iterates through all of the variables available in the __init__ method (which should, at this point, just be self , a , b , and c ) and set's self 's variables equal to the same, obviously ignoring the argument-reference to self (because self.self seemed like a poor decision.)

Community's user avatar

One of the problems with @user3638162's answer is that locals() contain the 'self' variable. Hence, you end up with an extra self.self . If one doesn't mind the extra self, that solution can simply be

The self can be removed after construction by del self.__dict__['self']

Alternatively, one can remove the self during construction using dictionary comprehensions introduced in Python3

Rahul Gopinath's user avatar

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pandas.DataFrame.assign #

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.

A new DataFrame with the new columns in addition to all the existing columns.

Assigning multiple columns within the same assign is possible. Later items in ‘**kwargs’ may refer to newly created or modified columns in ‘df’; items are computed and assigned into ‘df’ in order.

Where the value is a callable, evaluated on df :

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

Python Examples

Python **kwargs

Example: python **kwargs, kwargs is parameter name, kwargs is a dictionary, **kwargs with other parameters, **kwargs with *args.

Python **kwargs allows function call to pass variable number of k ey w ord (named) arg uments to the function.

The datatype of kwargs is dictionary. So, keywords and respective argument values come as key:value pairs. The number of key:value pairs in kwargs is determined only by the function call at the runtime.

Double asterisk ** before kwargs is the unpacking operator. It unpacks the arguments passed to the function, as dictionary.

In this tutorial, we will learn how to use **kwargs in a function definition to accept any number of named arguments to the function.

In this example, we will demonstrate a simple use case, just what **kwargs do. We will create a function that can accept any number of keyword arguments.

Python Program

We are calling myFunction() with four named arguments, namely a, b, c and d.

In myFunction() definition, we used kwargs parameter with ** unpacking operator, to accept any number of named arguments.

In the first named argument a = 24 , a is the keyword and 24 is the argument value. Therefore, a goes as key and 24 goes as value. In the function, a and 24 can be accessed as key:value pair from the dictionary kwargs. The same explanation holds for the remaining named arguments passed to the function.

kwargs is just a name that we have given to the parameter that can accept any number of named arguments. You can use any name instead of kwargs, that fits your requirement.

In the following example, program, we will use a different name, say computers , instead of kwargs for keyword arguments in myFunction() function definition.

We have already learnt that the datatype of kwargs is dictionary. So, you can use all the dictionary methods on kwargs.

For example, we will use dict.items() method, to iterate over keys and values simultaneoulsy.

Just a function can have multiple parameters in its definition, we can also have parameters along with **kwargs. Please remember that **kwargs is just another parameter. The only difference of **kwargs from other parameters is that, it can take multiple named arguments.

In the following example, we will define a function myFunction(), with parameters x, y and **kwargs.

While **kwargs can accept any number of named arguments, Python *args can accept any number of positional arguments.

You can use *args and **kwargs in a function definition to accept both positional arguments and named arguments, whose count is unknown.

In the following example, we will define a function with both *args and **kwargs.

Just to remind, the datatype of args is tuple, and the datatype of kwargs is dictionary.

In this tutorial of Python Examples , we learned how to use **kwargs to accept any number of named arguments in a function.

// Tutorial //

How to use *args and **kwargs in python 3.

Default avatar

By Lisa Tagliaferri

How To Use *args and **kwargs in Python 3

Introduction

In function definitions , parameters are named entities that specify an argument that a given function can accept.

When programming, you may not be aware of all the possible use cases of your code, and may want to offer more options for future programmers working with the module, or for users interacting with the code. We can pass a variable number of arguments to a function by using *args and **kwargs in our code.

Prerequisites

You should have Python 3 installed and a programming environment set up on your computer or server. If you don’t have a programming environment set up, you can refer to the installation and setup guides for a local programming environment or for a programming environment on your server appropriate for your operating system (Ubuntu, CentOS, Debian, etc.)

Understanding *args

In Python, the single-asterisk form of *args can be used as a parameter to send a non-keyworded variable-length argument list to functions. It is worth noting that the asterisk ( * ) is the important element here, as the word args is the established conventional idiom, though it is not enforced by the language.

Info: To follow along with the example code in this tutorial, open a Python interactive shell on your local system by running the python3 command. Then you can copy, paste, or edit the examples by adding them after the >>> prompt.

Let’s look at a typical function that uses two arguments:

In the code above, we built the function with x and y as arguments, and then when we call the function, we need to use numbers to correspond with x and y . In this case, we will pass the integer 5 in for x and the integer 4 in for y :

Now, we can run the above code:

We’ll receive the following output, showing that the integers 5 and 4 were multiplied as per the multiply(x,y) function:

What if, later on, we decide that we would like to multiply three numbers rather than just two? If we try to add an additional number to the function, as shown below, we’ll receive an error.

So, if you suspect that you may need to use more arguments later on, you can make use of *args as your parameter instead.

We can essentially create the same function and code that we showed in the first example, by removing x and y as function parameters, and instead replacing them with *args :

When we run this code, we’ll receive the product for each of these function calls:

Because we used *args to send a variable-length argument list to our function, we were able to pass in as many arguments as we wished into the function calls.

With *args you can create more flexible code that accepts a varied amount of non-keyworded arguments within your function.

Understanding **kwargs

The double asterisk form of **kwargs is used to pass a keyworded, variable-length argument dictionary to a function. Again, the two asterisks ( ** ) are the important element here, as the word kwargs is conventionally used, though not enforced by the language.

Like *args , **kwargs can take however many arguments you would like to supply to it. However, **kwargs differs from *args in that you will need to assign keywords.

First, let’s print out the **kwargs arguments that we pass to a function. We’ll create a short function to do this:

Next, we’ll call the function with some keyworded arguments passed into the function:

Let’s run the program above and look at the output:

Depending on the version of Python 3 you are currently using, the dictionary data type may be unordered. In Python 3.6 and above, you’ll receive the key-value pairs in order, but in earlier versions, the pairs will be output in a random order.

What is important to note is that a dictionary called kwargs is created and we can work with it just like we can work with other dictionaries.

Let’s create another short program to show how we can make use of **kwargs . Here we’ll create a function to greet a dictionary of names. First, we’ll start with a dictionary of two names:

We can now run the program and look at the output:

Again, because dictionaries may be unordered, your output may be with the name Casey first or with the name Sammy first.

Let’s now pass additional arguments to the function to show that **kwargs will accept however many arguments you would like to include:

When we run the program at this point, we’ll receive the following output, which may again be unordered:

Using **kwargs provides us with flexibility to use keyword arguments in our program. When we use **kwargs as a parameter, we don’t need to know how many arguments we would eventually like to pass to a function.

Ordering Arguments

When ordering arguments within a function or function call, arguments need to occur in a particular order:

In practice, when working with explicit positional parameters along with *args and **kwargs , your function would look like this:

And, when working with positional parameters along with named keyword parameters in addition to *args and **kwargs , your function would look like this:

It is important to keep the order of arguments in mind when creating functions so that you do not receive a syntax error in your Python code.

Using *args and **kwargs in Function Calls

We can also use *args and **kwargs to pass arguments into functions.

First, let’s look at an example with *args .

In the function above, there are three parameters defined as arg_1 , arg_ , and arg_3 . The function will print out each of these arguments. We then create a variable that is set to an iterable (in this case, a tuple ), and can pass that variable into the function with the asterisk syntax.

When we run the program with the python some_args.py command, we’ll receive the following output:

We can also modify the program above to an iterable list data type with a different variable name. Let’s also combine the *args syntax with a named parameter :

If we run the program above, it will produce the following output:

Similarly, the keyworded **kwargs arguments can be used to call a function. We will set up a variable equal to a dictionary with 3 key-value pairs (we’ll use kwargs here, but it can be called whatever you want), and pass it to a function with 3 arguments:

Let’s run the program above with the python some_kwargs.py command:

When calling a function, you can use *args and **kwargs to pass arguments.

We can use the special syntax of *args and **kwargs within a function definition in order to pass a variable number of arguments to the function.

Creating functions that accept *args and **kwargs are best used in situations where you expect that the number of inputs within the argument list will remain relatively small. The use of *args and **kwargs is primarily to provide readability and convenience, but should be done with care.

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Brilliant refresher.

Thank you for your tutorial. Simple to understand and comprehensive.

This is fascinating. Like the other commenters said, simple, brief, and explanatory. My question is what would a function invocation look like which uses each of the four argument types? You showed the function signature though I’d like to see the invocation.

This is very simple to recap and also for new starters. Lisa deserves hike for this article :-).

Thank you so much. All Digitalocean tutorials are very clear and easy to understand.

Thanks , helped me alot

I appreciated this overview. Thank you.

What is important to note is that a dictionary called **kwargs is created

The dictionary is actually just called kwargs , isn’t it?

As of 3.6 dicts stay in order. :)

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python assign kwargs to self

How to Use *args and **kwargs in Python

In this article, we'll discuss *args and **kwargs in Python along with their uses and some examples.

When writing a function, we often need to pass values to the function. These values are called function arguments .

Problem with Function Arguments

Let's define a function to add two numbers in Python. We'll write it like this:

What if you need to add three numbers? Simple, we can modify the function to accept three arguments and return their sum as:

Wasn't that quite simple? Yes, it was!

But what if we're again required to add two numbers only? Will our modified function help us get the sum? Let's see:

You see the problem?

The problem arises when we have a variable number of arguments. Should we keep modifying the function to accept the exact number of arguments? Of course not, we won't be doing this.

So there must be some other way to do it. This is where *args and **kwargs jump in.

You can use *args and **kwargs as arguments of a function when you are unsure about the number of arguments to pass in the functions.

How to Use *args in Python

*args allows us to pass a variable number of non-keyword arguments to a Python function. In the function, we should use an asterisk ( * ) before the parameter name to pass a variable number of arguments.

Thus, we're sure that these passed arguments make a tuple inside the function with the same name as the parameter excluding * .

Now let's rewrite our add() function with a variable number of arguments.

Note that the name of the argument need not necessarily be args – it can be anything. In this case it's numbers . But it's generally a standard way to use *args as the name.

How to Use **kwargs in Python

**kwargs allows us to pass a variable number of keyword arguments to a Python function. In the function, we use the double-asterisk ( ** ) before the parameter name to denote this type of argument.

Thus we see that the arguments, in this case, are passed as a dictionary and these arguments make a dictionary inside the function with name same as the parameter excluding ** .

Now, let's complete the total_fruits() function to return the total number of fruit.

Note that the name of the argument need not necessarily be kwargs – again, it can be anything. In this case, it's fruits . But it's generally a standard way to use **kwargs as the name.

In this article, we learned about two special keywords in Python – *args and **kwargs . These make a Python function flexible so it can accept a variable number of arguments and keyword arguments, respectively.

Thanks for reading!

You can find the code for this blog here .

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Sachin Pal

__init__ and __call__ In Python — What They Do And How To Use

You may have encountered the methods in Python that are prefixed and suffixed with double underscores , those methods are called “Dunder Methods” . These methods are also called “magic methods” .

Dunder methods are used to overload specific methods in order to make their behaviour unique to that class.

Python has a rich collection of built-in dunder methods and a few are listed below:

In this article, we will look at two dunder methods( __init__ and __call__ ) that are commonly used in Python classes. How do they differ, and when do we need to use them within the Python program?

__init__ Method

The __init__ method is used to create and initialize the objects of the class. When a class object is created, the task of the constructors is to assign values to the class's arguments.

The __init__ for the class Language is created in the preceding code, and it takes two arguments called lang and year .

Then we created the instance of the class Language , passed it the necessary arguments, and called the function data .

The arguments “Python” and “1991” passed inside the class are actually stored in the variables lang and year passed inside the __init__ that initializes the object.

Every time we create an object, the __init__ method is automatically invoked. We'll get different results if we create another object and pass different values.

Thus, we can conclude that the syntax of the __init__ method can be written as the following.

self - is an instance of the class. Mandatory.

arg1 and arg2 - are the parameters. We can pass as many parameters as we want or the field can also be left empty.

What if we pass more than the number of parameters that a class takes?

The above code will throw an error and prompts the following message.

The message states that two arguments were allowed, but three were passed. But we passed two arguments, not three then why did this happen?

This occurred because __init__() only accepts self and lang . When we instantiated the class with arguments, the keyword self , which represents the object's instance, was passed along with the arguments automatically.

So, when we passed the arguments "JavaScript" and "1995" , the self was automatically passed, making three arguments passed to the class Language .

__init__ with and without parameters

Python constructor( __init__ ) can be created with or without passing any parameters.

Default __init__ constructor

A constructor created without parameters other than self (a reference to the instance being constructed) is called the default constructor.

We created self.lang and self.year and assigned the default values "C++" and "1976" respectively. We accessed the lang and year by using the instance of the class object1 .

We can also override the attribute’s default value by assigning a new value before accessing the attribute from the class.

__init__ with parameters

We’ve already seen some examples where we used parameters to create the constructor. Pass the parameters to the constructor, as shown in the following example. Then we created an object or instance of the Vehicle class and passed the arguments to it. The output was then obtained by calling the vehicle function.

__call__ method

When we invoke a function, we simply use the function name with parenthesis, such as hello() , to notify the interpreter that a function is being called. Well, we can say that it is a shorthand for hello.__call__() .

When we invoke a function in Python, the interpreter executes the __call__ method in the background.

In the above code, first, we called the function func simply as we usually do and then by using the __call__ method.

__call__ inside Python classes

The concept behind using __class__ is to call the instances of the class as if it were a function. Instances of classes can be made callable by defining a __call__ method in their class.

In this case, we called the class object example as if it were a function.

The syntax of the __call__ method is

self - reference of the object.

args and kwargs - arguments and keyword arguments.

__call__ with parameters

We passed the parameter school to the __call__ method just like we do when we create the constructor. This will allow us to pass the argument within the object of the class as we did in the above code.

__call__ with decorators

We first generated the decorator( @Demo ) for the class Demo , followed by the function decor . Then we invoked the decor function and got the result shown below.

The decorator altered the behaviour of our class Demo , and we accessed the class's attributes simply by invoking the decor function.

If we examine more closely, the function decor was supplied as an argument to the class Demo , the decor's return value was saved within the action variable, and the class was called and produced the output.

The decorator part in the above code is equivalent to the following.

The __init__ method is also called the constructor method which is used to initialize the objects of the specific class whereas the __call__ method allows us to call the object of the class like a function.

The __init__ method created without passing parameters is called the default __init__ constructor.

When we call a function using () , in actuality, the __call__ method is implemented in the function.

We’ve coded many examples to get a better understanding of both methods.

🏆 Other articles you might be interested in if you liked this one

✅ Introduction to the different types of class inheritance in Python .

✅ Build a deep learning model using the transfer learning technique .

✅ Tackle the shortage of data by using the data augmentation technique .

✅ Upload and display images on the frontend using Flask in Python .

✅ Integrate TailwindCSS into the Flask app .

✅ Avoid conflicts between dependencies by using virtual environments .

That’s all for now

Keep Coding✌✌

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I am a self-taught Python developer who loves to write on Python Programming and quite obsessed with Machine Learning.

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Python Class Constructors: Control Your Object Instantiation

Python Class Constructors: Control Your Object Instantiation

Table of Contents

Getting to Know Python’s Class Constructors

Understanding python’s instantiation process, providing custom object initializers, building flexible object initializers, providing custom object creators, subclassing immutable built-in types, returning instances of a different class, allowing only a single instance in your classes, partially emulating collections.namedtuple.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Using Python Class Constructors

Class constructors are a fundamental part of object-oriented programming in Python. They allow you to create and properly initialize objects of a given class, making those objects ready to use. Class constructors internally trigger Python’s instantiation process, which runs through two main steps: instance creation and instance initialization .

If you want to dive deeper into how Python internally constructs objects and learn how to customize the process, then this tutorial is for you.

In this tutorial, you’ll:

With this knowledge, you’ll be able to tweak the creation and initialization of objects in your custom Python classes, which will give you control over the instantiation process at a more advanced level.

To better understand the examples and concepts in this tutorial, you should be familiar with object-oriented programming and special methods in Python.

Free Bonus: Click here to get access to a free Python OOP Cheat Sheet that points you to the best tutorials, videos, and books to learn more about Object-Oriented Programming with Python.

Python’s Class Constructors and the Instantiation Process

Like many other programming languages, Python supports object-oriented programming . At the heart of Python’s object-oriented capabilities, you’ll find the class keyword, which allows you to define custom classes that can have attributes for storing data and methods for providing behaviors.

Once you have a class to work with, then you can start creating new instances or objects of that class, which is an efficient way to reuse functionality in your code.

Creating and initializing objects of a given class is a fundamental step in object-oriented programming. This step is often referred to as object construction or instantiation . The tool responsible for running this instantiation process is commonly known as a class constructor .

In Python, to construct an object of a given class, you just need to call the class with appropriate arguments, as you would call any function :

In this example, you define SomeClass using the class keyword. This class is currently empty because it doesn’t have attributes or methods. Instead, the class’s body only contains a pass statement as a placeholder statement that does nothing.

Then you create a new instance of SomeClass by calling the class with a pair of parentheses. In this example, you don’t need to pass any argument in the call because your class doesn’t take arguments yet.

In Python, when you call a class as you did in the above example, you’re calling the class constructor, which creates, initializes, and returns a new object by triggering Python’s internal instantiation process.

A final point to note is that calling a class isn’t the same as calling an instance of a class. These are two different and unrelated topics. To make a class’s instance callable, you need to implement a .__call__() special method, which has nothing to do with Python’s instantiation process.

You trigger Python’s instantiation process whenever you call a Python class to create a new instance. This process runs through two separate steps, which you can describe as follows:

To run the first step, Python classes have a special method called .__new__() , which is responsible for creating and returning a new empty object. Then another special method, .__init__() , takes the resulting object, along with the class constructor’s arguments.

The .__init__() method takes the new object as its first argument, self . Then it sets any required instance attribute to a valid state using the arguments that the class constructor passed to it.

In short, Python’s instantiation process starts with a call to the class constructor, which triggers the instance creator , .__new__() , to create a new empty object. The process continues with the instance initializer , .__init__() , which takes the constructor’s arguments to initialize the newly created object.

To explore how Python’s instantiation process works internally, consider the following example of a Point class that implements a custom version of both methods, .__new__() and .__init__() , for demonstration purposes:

Here’s a breakdown of what this code does:

Line 3 defines the Point class using the class keyword followed by the class name.

Line 4 defines the .__new__() method, which takes the class as its first argument. Note that using cls as the name of this argument is a strong convention in Python, just like using self to name the current instance is. The method also takes *args and **kwargs , which allow for passing an undefined number of initialization arguments to the underlying instance.

Line 5 prints a message when .__new__() runs the object creation step.

Line 6 creates a new Point instance by calling the parent class’s .__new__() method with cls as an argument. In this example, object is the parent class, and the call to super() gives you access to it. Then the instance is returned. This instance will be the first argument to .__init__() .

Line 8 defines .__init__() , which is responsible for the initialization step. This method takes a first argument called self , which holds a reference to the current instance. The method also takes two additional arguments, x and y . These arguments hold initial values for the instance attributes .x and .y . You need to pass suitable values for these arguments in the call to Point() , as you’ll learn in a moment.

Line 9 prints a message when .__init__() runs the object initialization step.

Lines 10 and 11 initialize .x and .y , respectively. To do this, they use the provided input arguments x and y .

Lines 13 and 14 implement the .__repr__() special method, which provides a proper string representation for your Point class.

With Point in place, you can uncover how the instantiation process works in practice. Save your code to a file called point.py and start your Python interpreter in a command-line window. Then run the following code:

Calling the Point() class constructor creates, initializes, and returns a new instance of the class. This instance is then assigned to the point variable .

In this example, the call to the constructor also lets you know the steps that Python internally runs to construct the instance. First, Python calls .__new__() and then .__init__() , resulting in a new and fully initialized instance of Point , as you confirmed at the end of the example.

To continue learning about class instantiation in Python, you can try running both steps manually:

In this example, you first call .__new__() on your Point class, passing the class itself as the first argument to the method. This call only runs the first step of the instantiation process, creating a new and empty object. Note that creating an instance this way bypasses the call to .__init__() .

Note: The code snippet above is intended to be a demonstrative example of how the instantiation process works internally. It’s not something that you would typically do in real code.

Once you have the new object, then you can initialize it by calling .__init__() with an appropriate set of arguments. After this call, your Point object is properly initialized, with all its attributes set up.

A subtle and important detail to note about .__new__() is that it can also return an instance of a class different from the class that implements the method itself. When that happens, Python doesn’t call .__init__() in the current class, because there’s no way to unambiguously know how to initialize an object of a different class.

Consider the following example, in which the .__new__() method of the B class returns an instance of the A class:

Because B.__new__() returns an instance of a different class, Python doesn’t run B.__init__() . To confirm this behavior, save the code into a file called ab_classes.py and then run the following code in an interactive Python session:

The call to the B() class constructor runs B.__new__() , which returns an instance of A instead of B . That’s why B.__init__() never runs. Note that b doesn’t have a .b_value attribute. In contrast, b does have an .a_value attribute with a value of 42 .

Now that you know the steps that Python internally takes to create instances of a given class, you’re ready to dig a little deeper into other characteristics of .__init__() , .__new__() , and the steps that they run.

Object Initialization With .__init__()

In Python, the .__init__() method is probably the most common special method that you’ll ever override in your custom classes. Almost all your classes will need a custom implementation of .__init__() . Overriding this method will allow you to initialize your objects properly.

The purpose of this initialization step is to leave your new objects in a valid state so that you can start using them right away in your code. In this section, you’ll learn the basics of writing your own .__init__() methods and how they can help you customize your classes.

The most bare-bones implementation of .__init__() that you can write will just take care of assigning input arguments to matching instance attributes. For example, say you’re writing a Rectangle class that requires .width and .height attributes. In that case, you can do something like this:

As you learned before, .__init__() runs the second step of the object instantiation process in Python. Its first argument, self , holds the new instance that results from calling .__new__() . The rest of the arguments to .__init__() are normally used to initialize instance attributes. In the above example, you initialized the rectangle’s .width and .height using the width and height arguments to .__init__() .

It’s important to note that, without counting self , the arguments to .__init__() are the same ones that you passed in the call to the class constructor. So, in a way, the .__init__() signature defines the signature of the class constructor.

Additionally, keep in mind that .__init__() must not explicitly return anything different from None , or you’ll get a TypeError exception:

In this example, the .__init__() method attempts to return an integer number , which ends up raising a TypeError exception at run time.

The error message in the above example says that .__init__() should return None . However, you don’t need to return None explicitly, because methods and functions without an explicit return statement just return None implicitly in Python.

With the above implementation of .__init__() , you ensure that .width and .height get initialized to a valid state when you call the class constructor with appropriate arguments. That way, your rectangles will be ready for use right after the construction process finishes.

In .__init__() , you can also run any transformation over the input arguments to properly initialize the instance attributes. For example, if your users will use Rectangle directly, then you might want to validate the supplied width and height and make sure that they’re correct before initializing the corresponding attributes:

In this updated implementation of .__init__() , you make sure that the input width and height arguments are positive numbers before initializing the corresponding .width and .height attributes. If either validation fails, then you get a ValueError .

Note: A more Pythonic technique to tackle attribute validation is to turn attributes into properties . To learn more about properties, check out Python’s property(): Add Managed Attributes to Your Classes .

Now say that you’re using inheritance to create a custom class hierarchy and reuse some functionality in your code. If your subclasses provide a .__init__() method, then this method must explicitly call the base class’s .__init__() method with appropriate arguments to ensure the correct initialization of instances. To do this, you should use the built-in super() function like in the following example:

The first line in the .__init__() method of Employee calls super().__init__() with name and birth_date as arguments. This call ensures the initialization of .name and .birth_date in the parent class, Person . This technique allows you to extend the base class with new attributes and functionality.

To wrap up this section, you should know that the base implementation of .__init__() comes from the built-in object class. This implementation is automatically called when you don’t provide an explicit .__init__() method in your classes.

You can make your objects’ initialization step flexible and versatile by tweaking the .__init__() method. To this end, one of the most popular techniques is to use optional arguments . This technique allows you to write classes in which the constructor accepts different sets of input arguments at instantiation time. Which arguments to use at a given time will depend on your specific needs and context.

As a quick example, check out the following Greeter class:

In this example, .__init__() takes a regular argument called name . It also takes an optional argument called formal , which defaults to False . Because formal has a default value, you can construct objects by relying on this value or by providing your own.

The class’s final behavior will depend on the value of formal . If this argument is False , then you’ll get an informal greeting when you call .greet() . Otherwise, you’ll get a more formal greeting.

To try Greeter out, go ahead and save the code into a greet.py file. Then open an interactive session in your working directory and run the following code:

In the first example, you create an informal_greeter object by passing a value to the name argument and relying on the default value of formal . You get an informal greeting on your screen when you call .greet() on the informal_greeter object.

In the second example, you use a name and a formal argument to instantiate Greeter . Because formal is True , the result of calling .greet() is a formal greeting.

Even though this is a toy example, it showcases how default argument values are a powerful Python feature that you can use to write flexible initializers for your classes. These initializers will allow you to instantiate your classes using different sets of arguments depending on your needs.

Okay! Now that you know the basics of .__init__() and the object initialization step, it’s time to change gears and start diving deeper into .__new__() and the object creation step.

Object Creation With .__new__()

When writing Python classes, you typically don’t need to provide your own implementation of the .__new__() special method. Most of the time, the base implementation from the built-in object class is sufficient to build an empty object of your current class.

However, there are a few interesting use cases for this method. For example, you can use .__new__() to create subclasses of immutable types, such as int , float , tuple , and str .

In the following sections, you’ll learn how to write custom implementations of .__new__() in your classes. To do this, you’ll code a few examples that’ll give you an idea of when you might need to override this method.

Typically, you’ll write a custom implementation of .__new__() only when you need to control the creation of a new instance at a low level. Now, if you need a custom implementation of this method, then you should follow a few steps:

With these three succinct steps, you’ll be able to customize the instance creation step in the Python instantiation process. Here’s an example of how you can translate these steps into Python code:

This example provides a sort of template implementation of .__new__() . As usual, .__new__() takes the current class as an argument that’s typically called cls .

Note that you’re using *args and **kwargs to make the method more flexible and maintainable by accepting any number of arguments. You should always define .__new__() with *args and **kwargs , unless you have a good reason to follow a different pattern.

In the first line of .__new__() , you call the parent class’s .__new__() method to create a new instance and allocate memory for it. To access the parent class’s .__new__() method, you use the super() function. This chain of calls takes you up to object.__new__() , which is the base implementation of .__new__() for all Python classes.

Note: The built-in object class is the default base class of all Python classes.

The next step is to customize your newly created instance. You can do whatever you need to do to customize the instance at hand. Finally, in the third step, you need to return the new instance to continue the instantiation process with the initialization step.

It’s important to note that object.__new__() itself only accepts a single argument, the class to instantiate. If you call object.__new__() with more arguments, then you get a TypeError :

In this example, you hand over *args and **kwargs as additional arguments in the call to super().__new__() . The underlying object.__new__() accepts only the class as an argument, so you get a TypeError when you instantiate the class.

However, object.__new__() still accepts and passes over extra arguments to .__init__() if your class doesn’t override .__new__() , as in the following variation of SomeClass :

In this implementation of SomeClass , you don’t override .__new__() . The object creation is then delegated to object.__new__() , which now accepts value and passes it over to SomeClass.__init__() to finalize the instantiation. Now you can create new and fully initialized instances of SomeClass , just like some_obj in the example.

Cool! Now that you know the basics of writing your own implementations of .__new__() , you’re ready to dive into a few practical examples that feature some of the most common use cases of this method in Python programming.

To kick things off, you’ll start with a use case of .__new__() that consists of subclassing an immutable built-in type. As an example, say you need to write a Distance class as a subclass of Python’s float type. Your class will have an additional attribute to store the unit that’s used to measure the distance.

Here’s a first approach to this problem, using the .__init__() method:

When you subclass an immutable built-in data type, you get an error. Part of the problem is that the value is set during creation, and it’s too late to change it during initialization. Additionally, float.__new__() is called under the hood, and it doesn’t deal with extra arguments in the same way as object.__new__() . This is what raises the error in your example.

To work around this issue, you can initialize the object at creation time with .__new__() instead of overriding .__init__() . Here’s how you can do this in practice:

In this example, .__new__() runs the three steps that you learned in the previous section. First, the method creates a new instance of the current class, cls , by calling super().__new__() . This time, the call rolls back to float.__new__() , which creates a new instance and initializes it using value as an argument. Then the method customizes the new instance by adding a .unit attribute to it. Finally, the new instance gets returned.

Note: The Distance class in the example above doesn’t provide a proper unit conversion mechanism. This means that something like Distance(10, "km") + Distance(20, "miles") won’t attempt at converting units before adding the values. If you’re interested in converting units, then check out the Pint project on PyPI .

That’s it! Now your Distance class works as expected, allowing you to use an instance attribute for storing the unit in which you’re measuring the distance. Unlike the floating-point value stored in a given instance of Distance , the .unit attribute is mutable, so you can change its value any time you like. Finally, note how a call to the dir() function reveals that your class inherits features and methods from float .

Returning an object of a different class is a requirement that can raise the need for a custom implementation of .__new__() . However, you should be careful because in this case, Python skips the initialization step entirely. So, you’ll have the responsibility of taking the newly created object into a valid state before using it in your code.

Check out the following example, in which the Pet class uses .__new__() to return instances of randomly selected classes:

In this example, Pet provides a .__new__() method that creates a new instance by randomly selecting a class from a list of existing classes.

Here’s how you can use this Pet class as a factory of pet objects:

Every time you instantiate Pet , you get a random object from a different class. This result is possible because there’s no restriction on the object that .__new__() can return. Using .__new__() in such a way transforms a class into a flexible and powerful factory of objects, not limited to instances of itself.

Finally, note how the .__init__() method of Pet never runs. That’s because Pet.__new__() always returns objects of a different class rather than of Pet itself.

Sometimes you need to implement a class that allows the creation of a single instance only. This type of class is commonly known as a singleton class. In this situation, the .__new__() method comes in handy because it can help you restrict the number of instances that a given class can have.

Note: Most experienced Python developers would argue that you don’t need to implement the singleton design pattern in Python unless you already have a working class and need to add the pattern’s functionality on top of it.

The rest of the time, you can use a module-level constant to get the same singleton functionality without having to write a relatively complex class.

Here’s an example of coding a Singleton class with a .__new__() method that allows the creation of only one instance at a time. To do this, .__new__() checks the existence of previous instances cached on a class attribute:

The Singleton class in this example has a class attribute called ._instance that defaults to None and works as a cache . The .__new__() method checks if no previous instance exists by testing the condition cls._instance is None .

Note: In the example above, Singleton doesn’t provide an implementation of .__init__() . If you ever need a class like this with a .__init__() method, then keep in mind that this method will run every time you call the Singleton() constructor. This behavior can cause weird initialization effects and bugs.

If this condition is true, then the if code block creates a new instance of Singleton and stores it to cls._instance . Finally, the method returns the new or the existing instance to the caller.

Then you instantiate Singleton twice to try to construct two different objects, first and second . If you compare the identity of these objects with the is operator, then you’ll note that both objects are the same object. The names first and second just hold references to the same Singleton object.

As a final example of how to take advantage of .__new__() in your code, you can push your Python skills and write a factory function that partially emulates collections.namedtuple() . The namedtuple() function allows you to create subclasses of tuple with the additional feature of having named fields for accessing the items in the tuple.

The code below implements a named_tuple_factory() function that partially emulates this functionality by overriding the .__new__() method of a nested class called NamedTuple :

Here’s how this factory function works line by line:

Line 3 imports itemgetter() from the operators module. This function allows you to retrieve items using their index in the containing sequence.

Line 5 defines named_tuple_factory() . This function takes a first argument called type_name , which will hold the name of the tuple subclass that you want to create. The *fields argument allows you to pass an undefined number of field names as strings .

Line 6 defines a local variable to hold the number of named fields provided by the user.

Line 8 defines a nested class called NamedTuple , which inherits from the built-in tuple class.

Line 9 provides a .__slots__ class attribute. This attribute defines a tuple for holding instance attributes. This tuple saves memory by acting as a substitute for the instance’s dictionary, .__dict__ , which would otherwise play a similar role.

Line 11 implements .__new__() with cls as its first argument. This implementation also takes the *args argument to accept an undefined number of field values.

Lines 12 to 16 define a conditional statement that checks if the number of items to store in the final tuple differs from the number of named fields. If that’s the case, then the conditional raises a TypeError with an error message.

Line 17 sets the .__name__ attribute of the current class to the value provided by type_name .

Lines 18 and 19 define a for loop that turns every named field into a property that uses itemgetter() to return the item at the target index . The loop uses the built-in setattr() function to perform this action. Note that the built-in enumerate() function provides the appropriate index value.

Line 20 returns a new instance of the current class by calling super().__new__() as usual.

Lines 22 and 23 define a .__repr__() method for your tuple subclass.

Line 25 returns the newly created NamedTuple class.

To try your named_tuple_factory() out, fire up an interactive session in the directory containing the named_tuple.py file and run the following code:

In this code snippet, you create a new Point class by calling named_tuple_factory() . The first argument in this call represents the name that the resulting class object will use. The second and third arguments are the named fields available in the resulting class.

Then you create a Point object by calling the class constructor with appropriate values for the .x and .y fields. To access the value of each named field, you can use the dot notation. You can also use indices to retrieve the values because your class is a tuple subclass.

Because tuples are immutable data types in Python, you can’t assign new values to the point’s coordinates in place . If you try to do that, then you get an AttributeError .

Finally, calling dir() with your point instance as an argument reveals that your object inherits all the attributes and methods that regular tuples have in Python.

Now you know how Python class constructors allow you to instantiate classes, so you can create concrete and ready-to-use objects in your code. In Python, class constructors internally trigger the instantiation or construction process, which goes through instance creation and instance initialization . These steps are driven by the .__new__() and .__init__() special methods.

By learning about Python’s class constructors, the instantiation process, and the .__new__() and .__init__() methods, you can now manage how your custom classes construct new instances.

In this tutorial, you learned:

Now you’re ready to take advantage of this knowledge to fine-tune your class constructors and take full control over instance creation and initialization in your object-oriented programming adventure with Python.

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python assign kwargs to self

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self in Python class

self represents the instance of the class. By using the “self”  we can access the attributes and methods of the class in python. It binds the attributes with the given arguments.

The reason you need to use self. is because Python does not use the @ syntax to refer to instance attributes. Python decided to do methods in a way that makes the instance to which the method belongs be passed automatically, but not received automatically: the first parameter of methods is the instance the method is called on.

In more clear way you can say that SELF has following Characteristic-

Self is always pointing to Current Object.

Another Example of Using SELF:

Self is the first argument to be passed in Constructor and Instance Method.

Self must be provided as a First parameter to the Instance method and constructor. If you don’t provide it, it will cause an error.

Self is a convention and not a Python keyword .

self is parameter in Instance Method and user can use another parameter name in place of it. But it is advisable to use self because it increases the readability of code, and it is also a good programming practice.

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Edward Krueger

Apr 1, 2021

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A Guide to Args, Kwargs, Packing and Unpacking in Python

How these powerful coding tools allow function flexibility.

By: Edward Krueger and Douglas Franklin .

Introduction

We’ve all heard of arguments and keyword arguments (args and kwargs) when discussing Python functions. Arguments usually consist of numerical values, while keyword arguments, as the name suggests, are semantic. When writing functions, *args and **kwargs are often passed directly into a function definition.

This function can handle any number of args and kwargs because of the asterisk(s) used in the function definition. These asterisks are packing and unpacking operators.

When this file is run, the following output is generated.

Notice that the arguments on line 5, two args and one kwarg, get correctly placed into the print statement based on their type. Also of note are the parenthesis and curly brackets in the output. Args have been packed into a tuple and kwargs a dictionary.

Before we get more into packing and unpacking values in Python, let's talk a little more about function arguments.

There are 2 kinds of arguments in Python, positional arguments and keyword arguments , the former are specified according to their position and the latter are the arguments are key-value pairs.

Arguments without defaults cannot be omitted when calling a function. They must be passed in the correct order and position.

Let's talk a little about packing and unpacking.

Packing and Unpacking

The asterisks are unpacking operators that unpack the values from iterable objects in Python. The single asterisk operator (*) commonly associated with args can be used on any iterable. The double asterisk (**) associated with kwargs can only be used on dictionaries.

These asterisks are an operator for packing and unpacking. However, you can pack and unpack without them. Check out the code below.

In this gist, we have the tuple, t , being unpacked into three variables; a , b and c . When we print these vars, we will see the individual elements of the tuple.

Let’s use this same unpacking pattern on a dictionary.

When k and v are printed, we see:

This example is a little more complicated, as you can see on line 3. We use .items() to iterate over a dictionary, tuple() to convert the dictionary to a tuple so that we can use [0] to pull the first element of the tuple. All of this packs the key-value pair into k and v .

Packing and Unpacking Operators

Now that you know a little about unpacking values in Python, let's get into the operators * and ** . Take a look at this example of both packing and unpacking with the single asterisk.

We use the same pattern of unpacking by setting three variables equal to a list. However, here we add the packing operator to the variable b . Can you guess what the print statements will look like?

We can see that a was set equal to the first element in the list, c to the last element and all the elements in between were packed into b . This could be useful when you know how many variables you want to define but are unsure how many elements you are placing into each.

The double asterisk allows us to make some otherwise complex dictionary processes occur quite elegantly. For example, we can use the double-asterisk to merge two uniquely keyed dictionaries.

When we run this file, we see the following output.

Notice that the return statement in merge_two_dictionaries is wrapped in curly brackets. This ensures our function returns a dictionary.

Now let's move to a discussion about *args and **kwargs .

Note that args is just a name . You’re not required to use the name args . All that matters here is that you use the unpacking operator ( * ). It is, however, canonical to use args . Bear in mind that the iterable object you’ll get using the unpacking operator * is not a list but a tuple .

When you use the * operator to unpack a list and pass arguments to a function, it’s exactly as though you’re passing every single argument alone. Let’s show the power of *args with a common coding interview question.

You are tasked with writing a Python function that will sum integers.

Sounds easy enough, right?

My initial code might look something like this.

Fits the bill of ‘sums integers’ for sure. Let’s say we run it against these tests. Can you see what will go wrong?

Our first two tests work no problem, but the third, summing three integers, generates an error. It looks like our sum function can only take two positional arguments.

Let’s see if we can code a better addition function using *args .

Now we have a function that can take in an arbitrary number of arguments. The sum is calculated by looping through these arguments and adding their value to total .

Now let's run it past some tests.

This new function passes all of them! When we want to take in many arguments, but we are not sure how many, *args is a great option.

Once again, you’re not required to use the name kwargs . All that matters here is that you use the unpacking operator ( ** ). It is, however, canonical to use the name kwargs .

In function definitions **kwargs works like *args , but instead of accepting positional arguments, it accepts arbitrarily many keyword arguments.

**kwargs is often used to preserve a message as it is passed between objects. We can see this is the decorator code below. If you would like to learn more about decorators, check out the article this code is from.

A Simple Way to Trace Code in Python

Use a decorator to trace your functions.

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On line 8, we see that tracefunc_closure takes **kwargs as an argument. This allows whatever we pass to tracefunc to be preserved as it is passed between objects. Let's trace a function with this decorator to see this firsthand.

Here is the print statement output.

Notice that tracefunc recognizes the dataframes as args and the keyword arguments for pd.merge as kwargs. Additionally, we didn't have to do anything to tracefunc to make it compatible with pd.merge . Tracefunc is able to take in these kwargs and pass them to pd.merge . This represents interface preservation.

We can see examples of both keyword arguments and **kwargs in the pandas documentation. Here we see that dataframe.info can take 6 different keyword arguments. In cases like this, we must use the proper keywords or place the values in thier positional order. This later case however unnecessarily sacrifices readiblilty.

The dataframe.assign takes **kwargs as its only argument.

That sounds simple, however **kwargs is very flexible. When passing key-value pairs as **kwargs to df.assign our values can be a simple list or a more complex lambda function. The list and lambda-function processing is implemented in the source code for .assign . The significance of **kwargs is that this function can take any key and use it. It doesn't matter if we name the key state or temp_f .

Whether writing functions to take an arbitrary number of arguments and keyword arguments, or to pack and unpack values from lists and dictionaries *args and **kwargs allow for some remarkably flexible, readable and useful code. Additionally, *args and **kwargs are fundamental to understanding broader programming and function writing concepts in Python.

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Python *args and **kwargs

In this article, we will learn about Python *args and **kwargs ,their uses and functions with examples.

In programming, we define a function to make a reusable code that performs similar operation. To perform that operation, we call a function with the specific value, this value is called a function argument in Python.

We would recommend you to read Python Function and Python Function Arguments .

Suppose, we define a function for addition of 3 numbers.

Example 1: Function to add 3 numbers

When we run the above program, the output will be

In above program we have adder() function with three arguments x , y and z . When we pass three values while calling adder() function, we get sum of the 3 numbers as the output.

Lets see what happens when we pass more than 3 arguments in the adder() function.

In the above program, we passed 5 arguments to the adder() function instead of 3 arguments due to which we got TypeError .

Introduction to *args and **kwargs in Python

In Python, we can pass a variable number of arguments to a function using special symbols. There are two special symbols:

We use *args and **kwargs as an argument when we are unsure about the number of arguments to pass in the functions.

As in the above example we are not sure about the number of arguments that can be passed to a function. Python has *args which allow us to pass the variable number of non keyword arguments to function.

In the function, we should use an asterisk * before the parameter name to pass variable length arguments.The arguments are passed as a tuple and these passed arguments make tuple inside the function with same name as the parameter excluding asterisk * .

Example 2: Using *args to pass the variable length arguments to the function

In the above program, we used *num as a parameter which allows us to pass variable length argument list to the adder()  function. Inside the function, we have a loop which adds the passed argument and prints the result. We passed 3 different tuples with variable length as an argument to the function.

Python passes variable length non keyword argument to function using *args but we cannot use this to pass keyword argument. For this problem Python has got a solution called **kwargs , it allows us to pass the variable length of keyword arguments to the function.

In the function, we use the double asterisk ** before the parameter name to denote this type of argument. The arguments are passed as a dictionary and these arguments make a dictionary inside function with name same as the parameter excluding double asterisk ** .

Example 3: Using **kwargs to pass the variable keyword arguments to the function 

In the above program, we have a function intro() with **data as a parameter. We passed two dictionaries with variable argument length to the intro() function. We have for loop inside intro() function which works on the data of passed dictionary and prints the value of the dictionary.

Things to Remember:

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Understanding Args and Kwargs in Python

Esther Vaati

In this tutorial, I will be focusing on arguments ( *args ) and keyword arguments ( *kwargs ) in Python.

I will teach you what args and kwargs are and, most importantly, how to use them—that is how to take in an unlimited number of arguments and keyword arguments in functions.

What Are Args?

*args are used to pass non-keyword arguments. Examples of non-keyword arguments are  fun(3,4), fun("foo","bar") .

*args  are usually used as a measure to prevent the program from crashing if we don’t know how many arguments will be passed to the function. This is used in C++ as well as other programming languages.

What Are Kwargs?

**kwargs is a dictionary of keyword arguments. The  **  allows us to pass any number of keyword arguments. A keyword argument is basically a dictionary.

An example of a keyword argument is  fun(foo=2,bar=7) .

**kwargs are just like *args except you declare the variables and the amount within the function arguments.

Where to Use Args and Kwargs

Args and kwargs are useful when you want to:

Using Args and Kwargs in Functions

Let's look at how kwargs and args are used in functions.

The function below takes in three arguments. The three arguments have been explicitly defined, so any more or less will cause an error in the program.

Let's run the function. The function will add the three numbers, giving the following output:

What if we were to pass four arguments in the function instead of the required three? We will receive an error as shown below.

This is because only three parameters were defined in the function, but we have passed four positional arguments when calling the function.

In the second example below, the * is for non-keyword arguments and gets passed into the function. Instead of having defined arguments, we replace a , b and c  with a single parameter ( *args ).

Notice how the use of *args makes it easy to use any number of arguments without having to change your code. *args provide more flexibility to your code since you can have as many arguments as you wish in the future.

More Examples

Create a simple function as shown:

Test the function using a combination of integers and strings:

What if we were to pass a list as an argument? Test the function with a list by replacing the previous arguments with a list,  l = [11,3,4,5,"tuts] .

From the above example, you can also use *args to unpack arguments that are already in a list or a tuple so that all elements in the list are passed as different parameters.

Using the same function:

Kwargs allow you to pass keyword arguments to a function. They are used when you are not sure of the number of keyword arguments that will be passed in the function.

Write a function my_func and pass in (x= 10, y =20) as keyword arguments as shown below:

Kwargs can be used for unpacking dictionary key, value pairs. This is done using the double asterisk notation ( ** ). It's important to note that each key must be matched with a value.

Here's a typical example of how it's done. The function below takes countries as keys and their capital cities as the values. It then prints out a statement which iterates over the kwargs and maps each keyword to the value assigned to it.

You can call the function with any arguments you want.

For a more complex example, suppose we have a model for a customer that looks something like this:

You can use kwargs to do both data inputs and data queries from model objects. Let's write a function view in order to create a new customer.

Here is how to perform a query of the customer we just created using kwargs.

Using Both Args and Kwargs in a Function

When using both args and kwargs in the same function definition,  *args must occur before **kwargs . 

Remember args  should come before kwargs .

I hope this tutorial has helped you understand args and kwargs.

Below are some pointers to remember when using args and kwargs:

The official Python  documentation  offers a lot of information for further study. Additionally, don’t hesitate to see what we have available for sale and for study ga-analytics#sendMarketClickEvent">in the marketplace , and don't hesitate to ask any questions and provide your valuable feedback using the feed below.

Esther Vaati

python assign kwargs to self

Home » Python Basics » Python **kwargs

Python **kwargs

Summary : in this tutorial, you’ll learn about the Python **kwargs parameters.

Introduction to the Python **kwargs parameters

In Python, a function can have a parameter preceded by two stars (**). For example: **kwwargs

The **kwargs is called a keyword parameter.

When a function has the **kwargs parameter, it can accept a variable number of keyword arguments as a dictionary .

The two stars ( ** ) are important. However, the name kwargs is by convention. Therefore, you can use any other meaningful names such as **configs and **files .

The following example defines a function called connect() that accepts a **kwargs parameter:

The following function call shows an empty dictionary to the screen:

In this example, we didn’t pass any arguments to the connect() function, the kwargs is empty dictionary.

The following calls the connect() function and passes some keyword arguments into it:

It shows the following dictionary to the screen:

Inside the connect() function, you can use the kwargs argument as a dictionary.

If you want to pass a dictionary to the function, you need to add two stars ( ** ) to the argument like this:

If a function has the **kwargs parameter and other parameters, you need to place the **kwargs after other parameters. Otherwise, you’ll get an error.

The syntax of the following connect() function is correct:

However, the syntax of this function causes a SyntaxError:

Using both *args and **kwargs arguments

The following function has both *args and **kwargs parameters:

The fn function can accept a variable number of the positional arguments. Python will pack them as a tuple and assign the tuple to the args argument.

The fn function also accepts a variable number of keyword arguments. Python will pack them as a dictionary and assign the dictionary to the kwargs argument.

For example:

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Add a new column to the DataFrame:

Definition and Usage

The assign() method adds a new column to an existing DataFrame.

Return Value

A DataFrame with the new column(s) added.

This method does not change the original DataFrame.

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Python args and kwargs: A Guide

Both Python *args and **kwargs let you pass a variable number of arguments into a function. *args arguments have no keywords whereas **kwargs arguments each are associated with a keyword.

Traditionally, when you’re working with functions in Python, you need to directly state the arguments the function will accept.

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In some cases, where you may want to accept more arguments in the future, it is impractical to state all the parameters that you want to use with a function directly.

That’s where the Python *args and **kwargs keywords come in. These keywords allow you to pass an undefined number of arguments to a function.

This tutorial will discuss, with reference to examples, the basics of the *args and **kwargs keywords in Python, and how they compare.

Python *args

The Python *args method represents a varied number of arguments. It lets you pass arguments to a function when you are unsure how many arguments you want to pass.

The term “args” is a placeholder. In your function, you can replace the name of “args” with any value. You could use “numbers” to represent a list of numbers or “student” to represent a students’ details.

The syntax for the *args method is:

This function accepts a variable number of arguments. So, we could pass zero arguments, one argument, two arguments, and so on. Our function prints out to the console each argument that we pass into our function.

The value after the asterisk, “numbers” represents our arguments.

The args keyword is useful if you are performing mathematical operations, such as if you want to add up a variable amount of numbers.

*args Python Example

Let’s write a program that adds together the values of multiple arguments. This lets us perform a mathematical sum without having to use a plus sign for each value that we want to add.

Here’s an example of a function that is capable of adding together multiple arguments:

Our code returns:

First, we declare a Python function called addNumbers() , which contains the code for our number adding program. This function has one argument specified: *args.

The *args keyword is used to represent a variable number of potential arguments that could be passed into the function.

Then, we declare a Python variable called total which keeps track of the total number we are adding. Initially, this variable is set to 0.

We use a for loop to go through every argument in the args keyword, and we add each number to our total. Notice that when you are referring to the arguments stored in *args, you use the “args” keyword. Next, we print out the total number our program has calculated.

In our main program, we call the addNumbers() method twice. When we first call the function, we specify three numbers we want our function to add together. Next time we call the function, we specify four numbers we want our function to add together.

Because we used *args, we can send an unlimited number of arguments to our function. Our program works no matter how many arguments we pass.

Python **kwargs

The **kwargs keyword represents an arbitrary number of arguments that are passed to a function. **kwargs keywords are stored in a dictionary. You can access each item by referring to the keyword you associated with an argument when you passed the argument.

The syntax for **kwargs is:

This code lets us pass a variable number of keyword arguments into our function. The name **kwargs is just a substitute. You can replace “kwargs” with any term you wish.

We use the name after the asterisk as a label for our keyword arguments. In this case, our label is “record”. Values in a **kwargs method are stored in key-value pairs. This is why we need to use items() to iterate over all of the arguments we passed to our function.

**kwargs Python Example

Let’s walk through a basic example of a function using **kwargs to illustrate how the keyword works.

Suppose we are building a program that displays information about a customer’s order at a coffee shop. We want to pass the customer’s order information to a function, which will then print out the data to the console. Let’s create a function:

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First, we declare a function called printOrder() which uses the **kwargs keyword to accept a variable number of arguments. This function prints out the value of “kwargs” (which stores all the keyword arguments passed to our function).

Then, we call our function and pass three arguments: coffee, price, and size. When we pass these values, they are sent to our function as a dictionary.

Our code returned a Python dictionary with three arguments that we specified, alongside their values. If we wanted to, we could have passed even more or even fewer arguments, because **kwargs represents a variable number of arguments.

Because our kwargs are stored in a dictionary, we can access their values individually like we would with any Python dictionary.

Improving Our Code

Now, suppose we wanted to print out the details about the coffee order in a more structured way. We could do so using this code:

Let’s break down our code. First, we declare a function called printOrder() which uses the **kwargs keyword to accept keywords. Then, we created a for loop that iterates through every item in our kwargs dictionary.

We use the Python dictionary items() method to access each key and value in our dictionary.

If we wanted to pass four keywords to our program, or two, we could. This is because **kwargs supports keyworded variable length argument lists, like *args.

Python *args vs. **kwargs

The arguments sent to a function using **kwargs are stored in a dictionary structure. *args sends a list of arguments to a function.

Also, send arguments using **kwargs, you need to assign keywords to each of the values you want to send to your function. You do not need to specify keywords when you use *args.

A single asterisk denotes *args whereas **kwargs uses a double asterisk. This is an important distinction because both “args” and “kwargs” are placeholders. You can replace these words with any value.

How to Order Python Arguments

Arguments in a Python function must appear in a specific order. This order is as follows:

So, if you’re working with both *args and **kwargs, you would use this code:

In addition, if you want to specify default values for certain arguments that may arise, you can use this code:

This function expects to receive an argument called coffee , and the default value of any argument called coffee_order will be Espresso . However, you can specify as many arguments as you want with this function, because *args and **kwargs are used.

The *args and **kwargs keywords allow you to pass a variable number of arguments to a Python function. The *args keyword sends a list of values to a function. **kwargs sends a dictionary with values associated with keywords to a function.

Both of these keywords introduce more flexibility into your code. This is because you don’t have to specify a specific number of arguments upfront when writing a function.

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10 Python Kwargs Best Practices

Python kwargs are a powerful tool that can help make your code more readable and concise. Here are 10 best practices for using them.

python assign kwargs to self

Python kwargs are a powerful tool that can help make your code more readable and concise. In this article, we’ll take a look at 10 Python kwargs best practices that can help you write better code.

Kwargs are often used in Python to specify optional arguments to a function. By using kwargs, you can make your code more flexible and easier to read.

1. Use kwargs to make your code more readable

Consider the following example:

def foo(**kwargs): return kwargs[‘foo’] This function is perfectly valid, but it’s not very readable. A better way to write this would be:

def foo(*, foo): return foo By using a keyword-only argument, we’ve made it clear that the foo argument is required, and we’ve given it a more descriptive name. This makes our code easier to understand, both for ourselves and for others.

Similarly, we can use kwargs to make our code more self-documenting. For example, consider the following code:

def foo(**kwargs): # do something with kwargs This code is perfectly valid, but it doesn’t tell us anything about what it does with kwargs. A more self-documenting way to write this would be:

def foo(**kwargs): # process the keyword arguments Now it’s clear that the function is doing something with the keyword arguments, without us having to look at the implementation.

2. Don’t use kwargs for required parameters

If you use kwargs for required parameters, then your function can be called without those parameters, and that can lead to unexpected behavior. For example, consider this code:

def my_func(a, b, c): print(a, b, c) my_func(1, 2)

This code will print out “1 2 None”, because the value of c is not provided. This can obviously lead to bugs, so it’s best to avoid using kwargs for required parameters.

3. Make sure you have a default value for every keyword argument in the function definition

If you don’t have a default value for a keyword argument, and the caller of the function doesn’t provide a value for that keyword argument, Python will raise a TypeError exception.

This is problematic because it can lead to unexpected behavior, and can cause your program to crash. Therefore, it’s important to always make sure you have a default value for every keyword argument in your function definition.

Not only does this help prevent unexpected behavior, but it also makes your code more readable and easier to understand.

4. Avoid mutable default arguments

When you use a mutable data type as a default argument, that data type is created only once when the function is first defined. This can lead to unexpected behavior if the data type is modified inside the function body without the caller being aware of it.

For example, consider the following code:

def func(arg1, arg2, lst=[]): lst.append(arg1) lst.append(arg2) return lst

If we call this function like so:

result = func(1, 2)

We would expect the result to be [1, 2], but instead it’s [1, 2, 1, 2]. That’s because the default value for the lst argument is a list that is shared between all calls to the function, and each time we append to it, those changes are reflected in subsequent calls.

To avoid this issue, you should use a sentinel value as the default argument, like so:

def func(arg1, arg2, lst=None): if lst is None: lst = [] lst.append(arg1) lst.append(arg2) return lst

Now, each time we call the function, a new list will be created, and we won’t run into the same issue.

5. Be careful with variable scope when using kwargs

When you use kwargs in a function, the Python interpreter will create a new variable in the local namespace for each keyword argument. This is fine most of the time, but it can cause problems if you’re not careful.

For example, let’s say you have a function that takes two keyword arguments, and you want to use one of those arguments to control the behavior of the other. You might write something like this:

def my_function(arg1, arg2): if arg1 == ‘foo’: # do something with arg2 else: # do something else with arg2

This looks fine at first glance, but it doesn’t work the way you might expect. The problem is that when the Python interpreter sees the if statement, it creates a new variable called arg1 in the local namespace. So, when the if statement tries to access the arg1 variable, it’s actually accessing the local arg1 variable, not the keyword argument.

To fix this, you need to be explicit about which namespace you want to access. The easiest way to do this is to use the globals() function:

def my_function(arg1, arg2): if globals()[‘arg1’] == ‘foo’: # do something with arg2 else: # do something else with arg2

Now, the if statement will access the arg1 variable in the global namespace, which is what we want.

6. Use **kwargs if you need to handle named parameters that are not known beforehand

If you’re using **kwargs, then all of the named parameters that are passed to your function will be stored in a dictionary. This is important because it means you can access any parameter by name, and you don’t have to worry about the order in which they were passed.

Furthermore, if you need to handle default values for some of your parameters, then **kwargs is the way to go. All you need to do is check if the parameter is in the dictionary, and if not, then use the default value.

Overall, **kwargs is a very powerful tool that can make your code more flexible and easier to read. So, if you’re ever in a situation where you need to handle named parameters that are not known beforehand, then don’t hesitate to reach for **kwargs.

7. You can use *args and **kwargs together but *args must occur before **kwargs

The way Python handles function arguments is that it first assigns any positional arguments to the corresponding parameters in the function definition. Then, it assigns any keyword arguments to the parameters in the function definition.

If you were to try and use *args and **kwargs together but put **kwargs before *args, then Python would raise a TypeError because it wouldn’t know how to handle the keyword arguments.

Putting *args before **kwargs is considered best practice because it makes your code more explicit and easier to read.

8. The order of parameters does matter

When you use kwargs in a function call, the order of the parameters is important because it determines how the values are assigned to the keyword arguments. For example, consider the following code:

def my_func(a, b=1, c=2): print(a, b, c) my_func(1, c=3)

This code will output 1 3 2. Even though we explicitly set the value of c to 3, the value of b is still 1. This is because when Python sees that we have explicitly set the value of c, it will assign the value of 1 to b and the value of 3 to c.

If we had reversed the order of the parameters like this:

def my_func(a, c=2, b=1): print(a, b, c) my_func(1, c=3)

Then the code would output 1 1 3, because now Python knows to assign the value of 1 to b and the value of 3 to c.

So, when using kwargs, make sure to put the parameters in the correct order, so the values are assigned correctly.

9. Always include self as the first parameter of instance methods

When using kwargs, Python will automatically set the first parameter to the name of the instance (self). However, if you don’t include self as the first parameter, Python will raise a TypeError.

This is because Python doesn’t know what to set the first parameter to since it’s not explicitly defined. By always including self as the first parameter, you can avoid this error and ensure that your code will run smoothly.

10. Do not use kwargs for class attributes

When you use kwargs for class attributes, it can be difficult to debug because the order of the keyword arguments is not always the same. This can lead to errors that are hard to track down. In addition, using kwargs for class attributes can make your code harder to read and understand.

If you need to use keyword arguments for class attributes, it is best to use them only for optional attributes. For required attributes, it is best to use regular positional arguments.

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COMMENTS

  1. python

    You can set your kwargs arguments by updating __dict__ attribute of the instance. class ValidationRule: def __init__ (self, **kwargs): self.__dict__.update (kwargs) Share Follow answered Mar 29, 2010 at 5:40 Ruslan Spivak 1,640 1 11 5 Add a comment 3 This could be considered nicer than updating __dict__:

  2. python

    10 Answers Sorted by: 10 You could do this, which has the virtue of simplicity: >>> class C (object): def __init__ (self, **kwargs): self.__dict__ = dict (kwargs) This leaves it up to whatever code creates an instance of C to decide what the instance's attributes will be after construction, e.g.:

  3. pandas.DataFrame.assign

    pandas.DataFrame.assign # DataFrame.assign(**kwargs) [source] # Assign new columns to a DataFrame. Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten. Parameters **kwargsdict of {str: callable or Series} The column names are keywords.

  4. Python args and kwargs: Demystified

    When you execute the script above, concatenate () will iterate through the Python kwargs dictionary and concatenate all the values it finds: $ python concatenate.py RealPythonIsGreat! Like args, kwargs is just a name that can be changed to whatever you want. Again, what is important here is the use of the unpacking operator ( ** ).

  5. Python **kwargs

    Python Program def myFunction(**kwargs): for kw in kwargs: print(kw, '-', kwargs[kw]) if __name__ == "__main__": myFunction(a = 24, b = 87, c = 3, d = 46) Run Output a - 24 b - 87 c - 3 d - 46 We are calling myFunction () with four named arguments, namely a, b, c and d.

  6. How To Use *args and **kwargs in Python 3

    In Python, the single-asterisk form of *args can be used as a parameter to send a non-keyworded variable-length argument list to functions. It is worth noting that the asterisk ( *) is the important element here, as the word args is the established conventional idiom, though it is not enforced by the language.

  7. Python **kwargs

    This way the function will receive a dictionary of arguments, and can access the items accordingly: Example Get your own Python Server If the number of keyword arguments is unknown, add a double ** before the parameter name: def my_function (**kid): print("His last name is " + kid ["lname"]) my_function (fname = "Tobias", lname = "Refsnes")

  8. Automatic binding of **kwargs to variables

    method foo (self, **kwargs) I would like to avoid manually binding the variables to the values. coming through the **kwargs dictionary, That's easy: Just Don't Do It. Instead of some horribly contorted code converting key,item in kwargs into. self.key = item, just do the simple, obvious thing: self.kwargs = kwargs.

  9. *args and **kwargs in Python

    Python program to illustrate *kwargs for a variable number of keyword arguments. Here **kwargs accept keyworded variable-length argument passed by the function call. for first='Geeks' first is key and 'Geeks' is a value. in simple words, what we assign is value, and to whom we assign is key. Python3 def myFun (**kwargs):

  10. How to Use *args and **kwargs in Python

    How to Use **kwargs in Python. **kwargs allows us to pass a variable number of keyword arguments to a Python function. In the function, we use the double-asterisk ( **) before the parameter name to denote this type of argument. def total_fruits (**kwargs): print (kwargs, type (kwargs)) total_fruits (banana=5, mango=7, apple=8)

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    Source: Author()You may have encountered the methods in Python that are prefixed and suffixed with double underscores, those methods are called "Dunder Methods".These methods are also called ...

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    Here's a breakdown of what this code does: Line 3 defines the Point class using the class keyword followed by the class name.. Line 4 defines the .__new__() method, which takes the class as its first argument. Note that using cls as the name of this argument is a strong convention in Python, just like using self to name the current instance is. The method also takes *args and **kwargs, which ...

  13. self in Python class

    Self is always pointing to Current Object. Python3 class check: def __init__ (self): print("Address of self = ",id(self)) obj = check () print("Address of class object = ",id(obj)) Output Address of self = 140124194801032 Address of class object = 140124194801032 Another Example of Using SELF: Python3 class car ():

  14. A Guide to Args, Kwargs, Packing and Unpacking in Python

    A Guide to Args, Kwargs, Packing and Unpacking in Python | by Edward Krueger | Towards Data Science 500 Apologies, but something went wrong on our end. Refresh the page, check Medium 's site status, or find something interesting to read. Edward Krueger 683 Followers Data Scientist, Software Developer and Educator Follow More from Medium

  15. Python *args and **kwargs (With Examples)

    Introduction to *args and **kwargs in Python In Python, we can pass a variable number of arguments to a function using special symbols. There are two special symbols: *args (Non Keyword Arguments) **kwargs (Keyword Arguments) We use *args and **kwargs as an argument when we are unsure about the number of arguments to pass in the functions.

  16. Understanding Args and Kwargs in Python

    Conclusion. I hope this tutorial has helped you understand args and kwargs. Below are some pointers to remember when using args and kwargs: *args and **kwargs are special syntax that are used in functions to pass a variable number of arguments to a function. *args occur before **kwargs in a function definition. *args and **kwargs are best used in situations where the number of inputs will ...

  17. Python **kwargs

    Use the Python **kwargs parameter to allow the function to accept a variable number of keyword arguments. Inside the function, the kwargs argument is a dictionary that contains all keyword arguments as its name-value pairs. Precede double stars ( **) to a dictionary argument to pass it to **kwargs parameter.

  18. Pandas DataFrame assign() Method

    Learn Python Learn Java Learn C Learn C++ Learn C# Learn R Learn Kotlin Learn Go Learn Django Learn TypeScript. Server Side ... dataframe.assign(kwargs) Parameters. Parameter Description; kwargs: Required. The new column name(s) with values: Return Value. A DataFrame with the new column(s) added.

  19. Python args and kwargs: A Guide

    Python *args vs. **kwargs. The arguments sent to a function using **kwargs are stored in a dictionary structure. *args sends a list of arguments to a function. Also, send arguments using **kwargs, you need to assign keywords to each of the values you want to send to your function. You do not need to specify keywords when you use *args.

  20. 10 Python Kwargs Best Practices

    Kwargs are often used in Python to specify optional arguments to a function. By using kwargs, you can make your code more flexible and easier to read. 1. Use kwargs to make your code more readable. Consider the following example: def foo (**kwargs): return kwargs ['foo']