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Visitor Method – Python Design Patterns

Visitor Method is a Behavioral Design Pattern which allows us to separate the algorithm from an object structure on which it operates. It helps us to add new features to an existing class hierarchy dynamically without changing it. All the behavioral patterns proved as the best methods to handle the communication between the objects. Similarly, it is used when we have to perform an operation on a group of similar kinds of objects. A Visitor Method consists of two parts: 

• method named as Visit() implemented by the visitor and used and called for every element of the data structure.
• Visitable classes providing Accept() methods that accept a visitor

Design Components

• Client: The Client class acts as the consumer of the classes of the visitor design pattern. It can access the data structure objects and can instruct them to accept a visitor for the future processing.
• Visitor: An Abstract class which is used to declare visit operations for all visitable classes.
• Concrete Visitor: Each Visitor will be responsible for different operations. For each type of visitor all the visit methods, declared in abstract visitor, must be implemented.
• Visitable: Accept operations is declared by this class. It also act as the entry point which enables an object to be visited by visitor.
• Concrete Visitable: These classes implement the Visitable class and defines the accept operation. The visitor object is passed to this object using the accept operation.

Problem without using Visitor Method

Imagine you are handling the Software management of GeeksforGeeks and they have started certain courses such as DSA, SDE, and STL which are definitely useful for students who are preparing for the product based companies. But how will you handle all the data of Courses, Instructors, students, classes, IDs in your database? If you go with a simple direct approach to handle such a situation, you will definitely end up with a mess only. 

Visitor-problem-diagram

Solution using Visitor Method

Let’s look at the solution to the above-described problem. The Visitor method suggests adding a new behavior in a separate class called Visitor class instead of mixing it with the already existing classes. We will pass the original object to the visitor’s method as parameters such that the method will access all the necessary information. 

UML Diagram

Following is the UML diagram for Visitor Method

UML-diagram-visitor-method

• Open/Closed principle: Introducing new behavior in class is easy which can work with objects of different classes without making changes in these classes.
• Single Responsibility Principle: Multiple versions of same behavior can be operated into the same class.
• Addition of entities: Adding an entity in Visitor Method is easy as we have to make changes in visitor class only and it will not affect the existing item.
• Updating Logic: If the logic of operation is updated, then we need to make change only in the visitor implementation rather than doing it in all the item classes.

Disadvantages

• Lots of Updates: We have to update each and every visitor whenever a class get added or removed from the primary hierarchy
• Hard to Extend: If there are too many visitor classes then it becomes really hard to extend the whole interface of the class.
• Lack of Access: Sometimes visitors might not have the access to private field of certain classes that they are supposed to work with.

Applicability

• Recursive structures: Visitor Method works really well with recursive structures like directory trees or XML structures. The Visitor object can visit each node in the recursive structure
• Performing Operations: We can use the visitor method when we have to perform operations on all the elements of the complex object like Tree.

Further Read – Visitor Method in C++

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• Design Pattern
• System Design
• python-design-pattern

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Abstract Syntax Trees in Python

By Alessandro Finamore on 20 February 2021

What is an abstract syntax tree (ast), the ast python module and its use.

• builtins popularity

Modules popularity

The module.type_ignores attribute and type comments, visiting an ast, modifying an ast.

Requirement : All examples are compatible with at least Python v3.6, except for using ast.dump() with the attribute indent= which has been added in Python v3.9.

An Abstract Syntax Tree (AST) is a data structure used to reason about the grammar of a programming language in the context of the instructions provided into source code.

For instance, compilers use ASTs when transforming source code into binary code:

Given some text source code, the compiler first tokenizes the text to identify programming language keywords, variables, literals, etc. Each token represents an “atom” of an instruction.

Tokens are then rearranged into an AST, a tree where nodes are the “atoms” of the instructions, and edges the relationships between the atoms based on the programming language grammar. For instance, the AST make explicit the presence of a function call, the related input arguments, the instructions composing the function, etc.

The compiler then can apply multiple optimizations to the AST, and ultimately converts it into binary code.

Despite their role for compilers, ASTs are useful for a broader set of use-cases. Let’s discuss this more in details.

The ast module in the Python standard library can be used to create, visit, and modify AST related to Python source code. It has been introduced in Python 2.6 , and since then it evolved alongside the Python grammar.

Even if it is part of standard library since a long time, it is not common to use it directly . Rather, you might have used it indirectly as popular tools use it under-the-hood:

code testing : mutpy is a mutation testing tool used to alters the code under testing to broaden the set of tests in an automated fashion. In practice a mutation is an artificial modification of the AST generated from the code under testing. To see how PyBites uses mutpy , check out this article .

code coverage : vulture is a static code analyzer that studies an AST to identify portion of the code not used.

code vulnerabilities : bandit uses the AST representation to identify security vulnerabilities.

code autocompletion : jedi is an IDE and text editors autocompletion tool relying on the ast module functionality to safely evaluate expressions.

code reformating : black and flake8 are two popular tools to enforce code reformatting, and they both use an AST representation of the source code to apply their formatting rules.

Using the ast module to investigate the PyBites Bite exercises

Still not convinced of the relevance of an AST? Fair enough: let’s consider a more practical, and closer to the PyBites Platform, use-case.

The PyBites Platform is currently offering 300+ Bite exercises, and the number is constantly increasing. Given the (semi)hidden intention of the platform is to offer a varied set of challenges covering different Python modules and functionalities, it starts to be more and more challenging to identify what is covered by already available exercises, and what is instead left to explore.

This is where we can take advantage of the ast module. Specifically, we can process the source code of the solution of the exercises (as provided by the authors of the challenges) and recover some statistics about their content. For instance, which are the popular modules and builtin functions used.

Here some of the results. To follow along check out this Jupyter notebook .

Builtins popularity

The histogram above shows the Python builtin calls sorted by their popularity. In other words, using the ast module one can detect when a function call has been made, and if it relates to the builtins module or not. Three colors are used to visually distinguish between exception types, the creation of base types ( int , float , bool , list , and set ), or other functions. The histogram is a normalized frequency count, i.e., the frequency of each element is cumulated across all exercises, and divided by the sum of all elements occurrence across all exercises.

A few observations:

The distribution is heavy tailed, with len() representing 13.4% of all builtin calls, while dir() being used only once.

All five base types are used, but bool() is used only in 1 challenge.

Only 5 of the standard exceptions are used, with ValueError being the most common.

Most of the builtin functions are already used by exercises, but considering the functional programming calls you can notice that map() appears while filter() does not (as indeed the common practice is to prefer list comprehension ).

The histogram above shows the ranking for modules. For simplicity we limit to report on the root modules only. If submodules are used, their frequencies are cumulated into the frequency of the respective root modules.

As before, the histogram is heavy tailed, a testament that the PyBites Bite exercises try to “cover a little bit of everything”.

We can observe the presence of non-standard modules, such as pandas and pytest , as well more ad-hoc modules such as as zodiac and fibonacci that are created for the purpose of the challenges themselves.

One can easily expand the analysis to understand the functions used in each module/submodule, as well as dive into more specific analysis. What is relevant to highlight is that the results reported here are generated with about 50 lines of Python code and using ast module. Processing the 300+ source code files with tools like awk , grep , or anything else would have been significantly harder.

Hopefully this examples gave you a rough idea of what you can achieve with an AST. The next step is to understand how to create such data structures, and investigate their composition.

Dissecting an assignment instruction using the ast module

To start familiarize with the ast module, let’s see what happens when we analyze a single instruction: one_plus_two = 1+2

This will output:

It might not be obvious at first, but the output generated by ast.dump() is actually a tree:

The words starting with capital letter are nodes of the tree.

The attributes of the nodes are either edges of the tree or metadata.

Let’s rework the output into a diagram with the following conventions:

One rectangle for each node, marking in bold the related node type.

Node attributes collecting metadata are reported in blue.

Other node attributes are annotated with their type.

Nodes are connected based on their attributes.

With this visualization at hand we can observe a few things.

The root of the tree is a Module node. In fact, even if our example is a single line program, it is still a true Python module. The node contains two attributes body and type_ignores . Let’s put the aside type_ignores for a moment and focus on body .

As a Python module contains a series of instructions, the Module.body attribute is a list of nodes, one for each instruction in the program. Our example consists of a single assignment operation, hence Module.body contains only one Assign node.

An assignment operation has a right-hand side specifying the operation to perform, and a left-hand side specifying the destination of the operation. The two sides are associated to the Assign.value and Assign.targets attributes of the Assign node.

Considering the right-hand side, the Assign.value attribute is a BinOp node, since the instruction is a binary operation between two operands, which is fully specified with three attributes:

BinOp.op is a Add node given we are performing an addition.

BinOp.left and BinOp.right are the addition operands and consist of Constant nodes, each holding the raw value in the Constant.value attribute.

Considering the left-side, as Python supports multiple assignments and tuple unpacking, the Assign.targets attribute is a list collecting the different destinations of the operation. In our case the assignment is for a single variable, so a single Name node is used. In turn, the Name node has 2 attributes:

Name.id stores the name of the variable used in the program ( "one_plus_two" ).

Name.ctx specifies how variable reference is used in the program. This can only be one of types ast.Load , ast.Remove or ast.Store , but those are always empty nodes.

The attribute Module.type_ignores in the vast majority of the cases is going to be an empty list. This is why in the sketch is colored in blue. To understand why this is the case and what is the actual purpose of the attribute, we need to make a digression.

Python 3.0 introduced annotations, and few years later those have been expanded into type hints. If you are not familiar with those concepts, check this Real Python tutorial and the official doc .

Those changes were not back ported to Python 2, which instead was using type comments as a form of annotation. For more information, see PEP 484 or this Real Python tutorial .

The attribute Module.type_ignores refers to a special type comment # type: ignore that was used to indicate to type checker (such as mypy ) to suppress errors if one was found. For legacy reasons, the ast module is still reporting on those comments, but only when asked to do so.

Let’s see an example.

Notice that the only difference with respect to the detailed analysis of the AST previously discussed is the presence of the attribute Assign.type_comment='int' . The attribute reflects the metadata provided by type comment # type: int , and is added to the AST tree Assign node because we specified type_comment=True when triggering the parsing.

However, # type: ignore is treated differently. Those type comments are stored into the Module.type_ignores attribute as TypeIgnore objects rather than being collected as metadata in the inner nodes of the tree.

The ast module APIs

The ast module is mostly a large collection of classes, one for each of the different aspects of the Python grammar. Overall, there are about 100 classes, ranging from literals, to more complex construct such as list comprehensions.

ast.AST is the base class for all other classes in the module, and it defines the following base attributes for all AST nodes:

lineno , col_offset , end_lineno , and end_col_offset are used to track the precise position of the related instruction in the source code.

_fields contains the list of attribute names (you can think that is a list of “children” names).

When dealing with an AST the trickiest part is understanding nodes and attributes semantic. In fact, there are a lot of variants and corner cases, so it is easy to get confused.

A good way to start to familiarize with an AST is to use an interactive console such as ipython similarly to what we did in the previous examples. If you are used to an IDE, both PyCharm and Visual Studio Code provide plugins to visualize an AST (notice that PyCharm uses its own version of AST called Program Structure Interface – PSI )

No matter your preferred choice, the documentation is a fundamental resource to keep at hand. Yet, a couple of remarks:

Given that the Python language is in constant evolution, make sure to use the most recent version of the Python doc .

The official documentation also suggests to consult Green Tree Snake , which indeed does a good job at complementing the official documentation on parts that that otherwise would seem “dry” of details.

Beside the classes, the ast module defines how to perform a visit of a tree, and how to do transformations.

You can visit an AST in two ways: using helper functions, or via an ast.NodeVisitor class.

Let’s starts reviewing the helper functions:

ast.walk() visit the specified node, and recursively all its descendant, but in a non specified order.

ast.iter_fields() and ast.iter_child_nodes() are similar to .items() and .keys() of a dict data structure, but applied to a specific node only, and they are not recursive.

Here some examples:

When using an ast.NodeVisitor instead, one can register specific callbacks to trigger when visiting specific node types:

In this example:

We define a class BinOpVisitor extending the ast.NodeVisitor .

We register a callback to be triggered when ast.BinOp nodes are visited. The name of callback is always visit_XYZ where XYZ is one of the predefined node types name ( BinOp in our case).

When the callback is invoked it receives the reference of the node under analysis. In this example we use the node info to print the line number of the instruction it relates to.

Finally, we invoke self.generic_visit(node) to propagate the visit on the children of the input node.

What sort of black magic happens behind the scene to trigger the callbacks? It is actually simple. A ast.NodeVisitor also defines a visit() function which is always invoked first: if the input node type matches one of the callbacks, such callback is called, otherwise generic_visit() is invoked to visit the node children. In our example we are not overwriting visit() , hence we can trigger a visit of the tree simply invoking the method:

Here the complete example:

Running the program we obtain the following output:

A ast.NodeTransformer can be used as base class for a transformers, similarly to the logic used for the visitor class. This time, rather than simply visiting the nodes, the callbacks are used to modify, replace, add new nodes.

Here an example:

We registered a callback to be triggered when handing a ast.Constant node.

The callback generates a random number between (-10, 10), creates a new ast.Constant() node with the generated value, and reports a message on the standard output.

Finally, it returns the reference of the new node.

The reference returned by the callbacks represent the node to use in the AST. In this example we are replacing the original node. When returning None instead, the visited node is removed from the tree.

To trigger the transformation, we use the same operation used for the visit. This time the visit returns the reference of the tree modified:

Here the full example:

The source code in code is the same as the one used to do a simple visit. Likewise, the process to generate the related tree.

Then we fix a seed for the random number generator via random.seed() , so to have consistent output when running the program multiple times.

We create a ConstantTransformer() object, and we visit it obtaining new_tree , which is a transformed version of the original tree.

To verify the transformations, we can print the AST, and “run it” by transforming into executable code. To do so, we use the helper function exec_tree() :

We start printing the content of the tree using ast.dump() as seen in previous examples.

We then apply ast.fix_missing_locations() to the tree. Each node in the AST is indeed expected to have lineno filled, but rather than filling it when doing the transformations, the helper function ast.fix_missing_locations() allows to delay this fix until the compilation is required.

Finally, the builtin function compile() is used to transform the AST to a code object, which in turn is executed calling the builtin exec() .

Here the output related to exec_tree(tree) :

The output for exec_tree(new_tree) :

The output now is “randomized”, as expected by the transformation. However, the transformation has overwritten the original tree, as new_tree and tree are the same object.

To avoid this however one simply use the copy module to clone the whole tree before triggering the transformation, or overwrite the visit() method and define the ad-hoc logic for the use-case at hand.

Keep calm and happy Python coding!

— Alessandro

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• VisIt Tutorial

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Page actions.

• View source
• 1 Command line interface (CLI) overview
• 2 Launching the CLI
• 3 A first action in the CLI
• 4 Tips about Python
• 5.1 Setting Attributes
• 5.2 Animating an isosurface
• 5.3 Using all of VisIt's Building Blocks
• 5.4 Creating a Movie of Animated Streamline Paths
• 5.5 Rendering each time step of a Dataset to a Movie
• 5.6 Animating the camera
• 5.7 Automating data analysis
• 5.8 Extracting a per-material aggregate value at each timestep.
• 6 Recording GUI Actions to Python Scripts
• 7.1 Tips for searching for help
• 8 Advanced features

Command line interface (CLI) overview

VisIt includes a rich a command line interface that is based on Python 2.7.

There are several ways to use the CLI:

• Linux: path/to/visit/bin/visit -nowin -cli -s <script.py>
• OSX: /path/to/VisIt.app/Contents/Resources/bin/visit -nowin -cli -s <script.py>
• Launch VisIt so that a visualization window is visible and interactively issue CLI commands
• Use both the standard graphical user interface (GUI) and CLI simultaneously

Launching the CLI

We will focus on the use case where we have the graphical user interface and CLI simultaneously.

To launch the CLI from the graphical user interface:

• Go to Controls->Launch CLI
• This will launch a separate terminal that has the Python interface.

Note that you can also use VisIt's Command window to submit Python code to the CLI. The Command window provides a text editor with Python syntax highlighting and an Execute button that tells VisIt to execute the script. Finally, the Command window lets you record your GUI actions into Python code that you can use in your scripts.

A first action in the CLI

• You can do also do this in the CLI, using OpenDatabase() with the path to the example.silo dataset.

Tips about Python

• Python is whitespace sensitive! This is a pain, especially when you are cut-n-pasting things.
• Python has great constructs for control and iteration, here are some examples:

Example Scripts

We will be using Python scripts in each of the following sections: You can get them by:

• Cut-n-paste-ing it into your Python interpreter
• Or copying it to a separate file and issuing: Source("/path/to/script/location/script.py")

For all of these scripts, make sure example.silo is currently open.

Setting Attributes

Each of VisIt's Plots and Operators expose set of attributes that control their behavior. In VisIt's GUI, these attributes are modified via options windows. VisIt's CLI provides a set simple Python objects that control to these attributes.

Animating an isosurface

This example demonstrates sweeping an isosurface operator to animate the display of a range of isovalues from example.silo.

Using all of VisIt's Building Blocks

Creating a movie of animated streamline paths.

This example extends the "Using all of VisIt's Building Blocks" example by

• animating the paths of the streamlines
• saving images of the animation
• finally, encoding those images into a movie

(Note: Encoding requires ffmpeg is installed and available in your PATH)

Rendering each time step of a Dataset to a Movie

This example assumes the aneurysm.visit database is already opened.

• Create a plot, render all timesteps and encode a movie.

Animating the camera

Automating data analysis, extracting a per-material aggregate value at each timestep..

See example here .

Recording GUI Actions to Python Scripts

VisIt's Commands window provides a mechanism to translate GUI actions into their equivalent Python commands.

• Open the Commands Window by selecting Controls Menu->Command
• Press the Record button
• Perform GUI actions
• Select a tab to hold the python script of your recorded actions
• Click the Stop button.
• The equivalent Python script will be placed in the tab in the Commands window.
• Note that the scripts are very verbose and contain some unnecessary commands, which can be edited out.

Learning the CLI

Here are some tips to help you quickly learn how to use VisIt's CLI:

• echo "dir()" | visit -cli -nowin -forceinteractivecli | tr ',' '\n' | tr -d " '" | sort
• echo "dir()" | visit -cli -nowin -forceinteractivecli | tr ',' '\n' | tr -d " '" | grep -i material
• (Type "help(AddPlot)")
• Use the GUI to Python recording featured outlined above .
• For more details, see WriteScript
• When you have a Python object, you can see all of its attributes by printing it.

Tips for searching for help

VisIt's CLI provides a large set of functions. To can limit the scope of your search using a helpers functions. One such helper is the lsearch() function in the visit_utils module:

lsearch() returns a python list of strings with the names that match the given pattern. Here is another example that each of the result strings on a separate line.

Advanced features

• You can set up your own buttons in the VisIt gui using the CLI. See here .
• You can set up callbacks in the CLI that get called whenever events happen in VisIt. See here .
• You can create your own custom Qt GUI that uses VisIt for plotting. See here .
• This page was last edited on 10 August 2017, at 02:28.
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Python User-defined Functions

A function is a block of code that performs a specific task.

Suppose we need to create a program to make a circle and color it. We can create two functions to solve this problem:

• function to create a circle
• function to color the shape

Dividing a complex problem into smaller chunks makes our program easy to understand and reuse.

• Create a Function

Let's create our first function.

Here are the different parts of the program:

Here, we have created a simple function named greet() that prints Hello World!

Note: When writing a function, pay attention to indentation, which are the spaces at the start of a code line.

In the above code, the print() statement is indented to show it's part of the function body, distinguishing the function's definition from its body.

• Calling a Function

In the above example, we have declared a function named greet() .

If we run the above code, we won't get an output.

It's because creating a function doesn't mean we are executing the code inside it. It means the code is there for us to use if we want to.

To use this function, we need to call the function.

Function Call

• Example: Python Function Call

In the above example, we have created a function named greet() . Here's how the control of the program flows:

• When the function greet() is called, the program's control transfers to the function definition.
• All the code inside the function is executed.
• The control of the program jumps to the next statement after the function call.

Arguments are inputs given to the function.

Sample Output 1

Here, we passed ' John' as an argument to the greet() function.

We can pass different arguments in each call, making the function re-usable and dynamic.

Let's call the function with a different argument.

Sample Output 2

• Example: Function to Add Two Numbers

In the above example, we have created a function named add_numbers() with arguments: num1 and num2 .

Parameters are the variables listed inside the parentheses in the function definition. They act like placeholders for the data the function can accept when we call them.

Think of parameters as the blueprint that outlines what kind of information the function expects to receive.

In this example, the print_age() function takes age as its input. However, at this stage, the actual value is not specified.

The age parameter is just a placeholder waiting for a specific value to be provided when the function is called.

Arguments are the actual values that we pass to the function when we call it.

Arguments replace the parameters when the function executes.

Here, during the function call, the argument 25 is passed to the function.

• The return Statement

We return a value from the function using the return statement.

In the above example, we have created a function named find_square() . The function accepts a number and returns the square of the number.

Note: The return statement also denotes that the function has ended. Any code after return is not executed.

• The pass Statement

The pass statement serves as a placeholder for future code, preventing errors from empty code blocks.

It's typically used where code is planned but has yet to be written.

Note : To learn more, visit Python Pass Statement .

• Python Library Functions

Python provides some built-in functions that can be directly used in our program.

We don't need to create the function, we just need to call them.

Some Python library functions are:

• print() - prints the string inside the quotation marks
• sqrt() - returns the square root of a number
• pow() - returns the power of a number

These library functions are defined inside the module. And to use them, we must include the module inside our program.

For example, sqrt() is defined inside the math module.

Note : To learn more about library functions, please visit Python Library Functions .

• Example: Python Library Function

Here, we imported a math module to use the library functions sqrt() and pow() .

More on Python Functions

In Python, functions are divided into two categories: user-defined functions and standard library functions. These two differ in several ways:

User-Defined Functions

These are the functions we create ourselves. They're like our custom tools, designed for specific tasks we have in mind.

They're not part of Python's standard toolbox, which means we have the freedom to tailor them exactly to our needs, adding a personal touch to our code.

Standard Library Functions

Think of these as Python's pre-packaged gifts. They come built-in with Python, ready to use.

These functions cover a wide range of common tasks such as mathematics, file operations , working with strings , etc.

They've been tried and tested by the Python community, ensuring efficiency and reliability.

Python allows functions to have default argument values. Default arguments are used when no explicit values are passed to these parameters during a function call.

Let's look at an example.

Here, message has the default value of Hello . When greet() is called with only one argument, message uses its default value.

Note: To learn more about default arguments in a function, please visit Python Function Arguments .

We can handle an arbitrary number of arguments using special symbols *args and **kwargs .

*args in Functions

Using *args allows a function to take any number of positional arguments.

*kwargs in Functions

Using **kwargs allows the function to accept any number of keyword arguments.

To learn more, visit Python *args and **kwargs .

• Python Recursive Function

Table of Contents

• Introduction

Before we wrap up, let’s put your knowledge of Python function to the test! Can you solve the following challenge?

Write a function to check if a given number is prime or not.

• A prime number is only divisible by 1 and itself. For example, 13 .
• Return True if the number is prime, otherwise return False .

Video: Introduction to Python Functions

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A function is a block of code which only runs when it is called.

You can pass data, known as parameters, into a function.

A function can return data as a result.

Creating a Function

In Python a function is defined using the def keyword:

Calling a Function

To call a function, use the function name followed by parenthesis:

Information can be passed into functions as arguments.

Arguments are specified after the function name, inside the parentheses. You can add as many arguments as you want, just separate them with a comma.

The following example has a function with one argument (fname). When the function is called, we pass along a first name, which is used inside the function to print the full name:

Arguments are often shortened to args in Python documentations.

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Parameters or Arguments?

The terms parameter and argument can be used for the same thing: information that are passed into a function.

From a function's perspective:

A parameter is the variable listed inside the parentheses in the function definition.

An argument is the value that is sent to the function when it is called.

Number of Arguments

By default, a function must be called with the correct number of arguments. Meaning that if your function expects 2 arguments, you have to call the function with 2 arguments, not more, and not less.

This function expects 2 arguments, and gets 2 arguments:

This function expects 2 arguments, but gets only 1:

Arbitrary Arguments, *args

If you do not know how many arguments that will be passed into your function, add a * before the parameter name in the function definition.

This way the function will receive a tuple of arguments, and can access the items accordingly:

If the number of arguments is unknown, add a * before the parameter name:

Arbitrary Arguments are often shortened to *args in Python documentations.

Keyword Arguments

You can also send arguments with the key = value syntax.

This way the order of the arguments does not matter.

The phrase Keyword Arguments are often shortened to kwargs in Python documentations.

Arbitrary Keyword Arguments, **kwargs

If you do not know how many keyword arguments that will be passed into your function, add two asterisk: ** before the parameter name in the function definition.

This way the function will receive a dictionary of arguments, and can access the items accordingly:

If the number of keyword arguments is unknown, add a double ** before the parameter name:

Arbitrary Kword Arguments are often shortened to **kwargs in Python documentations.

Default Parameter Value

The following example shows how to use a default parameter value.

If we call the function without argument, it uses the default value:

Passing a List as an Argument

You can send any data types of argument to a function (string, number, list, dictionary etc.), and it will be treated as the same data type inside the function.

E.g. if you send a List as an argument, it will still be a List when it reaches the function:

Return Values

To let a function return a value, use the return statement:

The pass Statement

function definitions cannot be empty, but if you for some reason have a function definition with no content, put in the pass statement to avoid getting an error.

Positional-Only Arguments

You can specify that a function can have ONLY positional arguments, or ONLY keyword arguments.

To specify that a function can have only positional arguments, add , / after the arguments:

Without the , / you are actually allowed to use keyword arguments even if the function expects positional arguments:

But when adding the , / you will get an error if you try to send a keyword argument:

Keyword-Only Arguments

To specify that a function can have only keyword arguments, add *, before the arguments:

Without the *, you are allowed to use positionale arguments even if the function expects keyword arguments:

But when adding the *, / you will get an error if you try to send a positional argument:

Combine Positional-Only and Keyword-Only

You can combine the two argument types in the same function.

Any argument before the / , are positional-only, and any argument after the *, are keyword-only.

Python also accepts function recursion, which means a defined function can call itself.

Recursion is a common mathematical and programming concept. It means that a function calls itself. This has the benefit of meaning that you can loop through data to reach a result.

The developer should be very careful with recursion as it can be quite easy to slip into writing a function which never terminates, or one that uses excess amounts of memory or processor power. However, when written correctly recursion can be a very efficient and mathematically-elegant approach to programming.

In this example, tri_recursion() is a function that we have defined to call itself ("recurse"). We use the k variable as the data, which decrements ( -1 ) every time we recurse. The recursion ends when the condition is not greater than 0 (i.e. when it is 0).

To a new developer it can take some time to work out how exactly this works, best way to find out is by testing and modifying it.

Recursion Example

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Visiting a assert statement within a functiondef using Python ast

Abstract: Learn how to visit an assert statement within a function definition using Python's abstract syntax tree (AST) module. This article provides a step-by-step guide on using the AST module to parse and analyze Python code, specifically focusing on assert statements within function definitions. By understanding how to traverse the AST and extract relevant information, you can gain insights into the behavior and structure of your Python code.

The Python ast module provides a way to visit and manipulate abstract syntax trees (AST) of Python code. In this article, we will explore how to visit an assert statement within a function definition using the ast module.

Before we dive into the details, let's first understand what an abstract syntax tree is. An abstract syntax tree is a tree-like representation of the syntactic structure of a program. It captures the hierarchical relationships between different parts of the code, such as function definitions, loops, and conditionals.

The ast module in Python allows us to analyze and modify the AST of a Python program. It provides various classes and functions that enable us to traverse and manipulate the AST nodes.

To visit an assert statement within a function definition, we need to use the ast.NodeVisitor class. This class provides methods that are called when entering or leaving a specific node in the AST.

Let's consider the following example code:

In the above code, we define a custom visitor class AssertVisitor that inherits from ast.NodeVisitor . We override the visit_FunctionDef method, which is called when entering a function definition node in the AST.

Inside the visit_FunctionDef method, we iterate over the body of the function definition and check if each statement is an instance of ast.Assert . If it is, we print the assert statement's test expression.

In our example code, the visit_FunctionDef method will print:

The self.generic_visit(node) line ensures that the visitor continues traversing the rest of the AST nodes. This is necessary to visit all the assert statements within the function definition.

Finally, we create an instance of the AssertVisitor class and call its visit method, passing the AST tree obtained from parsing the code. This triggers the traversal of the AST using our custom visitor.

Using the ast module, we can perform various operations on the AST, such as modifying the code or extracting specific information from it. This can be useful in scenarios like code analysis, refactoring, and code generation.

In this article, we explored how to visit an assert statement within a function definition using the ast module in Python. We learned how to define a custom visitor class and override the necessary methods to traverse the AST and identify the assert statements.

Tags: :  python abstract syntax tree python ast

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Nested Functions in Python

A nested function is simply a function within another function, and is sometimes called an "inner function". There are many reasons why you would want to use nested functions, and we'll go over the most common in this article.

How to define a nested function

To define a nested function, just initialize another function within a function by using the def keyword:

As you can see, the nested getFullName function has access to the outer greeting function's parameters, first and last . This is a common use case for nested functions–to serve as small helper function to a more complex outer function.

Reasons to use nested functions

While there are many valid reasons to use nested functions, among the most common are encapsulation and closures / factory functions.

Data encapsulation

There are times when you want to prevent a function or the data it has access to from being accessed from other parts of your code, so you can encapsulate it within another function.

When you nest a function like this, it's hidden from the global scope. Because of this behavior, data encapsulation is sometimes referred to as data hiding or data privacy . For example:

In the code above, the inner function is only available from within the function outer . If you try to call inner from outside the function, you'll get the error above.

Instead, you must call the outer function like so:

But what would happen if the outer function returns the inner function itself, rather than calling it like in the example above? In that case you would have what's known as a closure.

The following are the conditions that are required to be met in order to create a closure in Python:

These are the conditions you need to create a closure in Python:

There must be a nested function The inner function has to refer to a value that is defined in the enclosing scope The enclosing function has to return the nested function Source: https://stackabuse.com/python-nested-functions/

Here's a simple example of a closure:

Closures make it possible to pass data to inner functions without first passing them to outer functions with parameters like the greeting example at the beginning of the article. They also make it possible to invoke the inner function from outside of the encapsulating outer function. All this with the benefit of data encapsulation / hiding mentioned before.

Now that you understand how and why to nest functions in Python, go out and nest 'em with the best of 'em.

If you read this far, thank the author to show them you care. Say Thanks

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• 5. Data Structures
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5. Data Structures ¶

This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.

5.1. More on Lists ¶

The list data type has some more methods. Here are all of the methods of list objects:

Add an item to the end of the list. Equivalent to a[len(a):] = [x] .

Extend the list by appending all the items from the iterable. Equivalent to a[len(a):] = iterable .

Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x) .

Remove the first item from the list whose value is equal to x . It raises a ValueError if there is no such item.

Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. It raises an IndexError if the list is empty or the index is outside the list range.

Remove all items from the list. Equivalent to del a[:] .

Return zero-based index in the list of the first item whose value is equal to x . Raises a ValueError if there is no such item.

The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the start argument.

Return the number of times x appears in the list.

Sort the items of the list in place (the arguments can be used for sort customization, see sorted() for their explanation).

Reverse the elements of the list in place.

Return a shallow copy of the list. Equivalent to a[:] .

An example that uses most of the list methods:

You might have noticed that methods like insert , remove or sort that only modify the list have no return value printed – they return the default None . [ 1 ] This is a design principle for all mutable data structures in Python.

Another thing you might notice is that not all data can be sorted or compared. For instance, [None, 'hello', 10] doesn’t sort because integers can’t be compared to strings and None can’t be compared to other types. Also, there are some types that don’t have a defined ordering relation. For example, 3+4j < 5+7j isn’t a valid comparison.

5.1.1. Using Lists as Stacks ¶

The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append() . To retrieve an item from the top of the stack, use pop() without an explicit index. For example:

5.1.2. Using Lists as Queues ¶

It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).

To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:

5.1.3. List Comprehensions ¶

List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.

For example, assume we want to create a list of squares, like:

Note that this creates (or overwrites) a variable named x that still exists after the loop completes. We can calculate the list of squares without any side effects using:

or, equivalently:

which is more concise and readable.

A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:

and it’s equivalent to:

Note how the order of the for and if statements is the same in both these snippets.

If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.

List comprehensions can contain complex expressions and nested functions:

5.1.4. Nested List Comprehensions ¶

The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.

Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:

The following list comprehension will transpose rows and columns:

As we saw in the previous section, the inner list comprehension is evaluated in the context of the for that follows it, so this example is equivalent to:

which, in turn, is the same as:

In the real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case:

See Unpacking Argument Lists for details on the asterisk in this line.

5.2. The del statement ¶

There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:

del can also be used to delete entire variables:

Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.

5.3. Tuples and Sequences ¶

We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — list, tuple, range ). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple .

A tuple consists of a number of values separated by commas, for instance:

As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.

Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are immutable , and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples ). Lists are mutable , and their elements are usually homogeneous and are accessed by iterating over the list.

A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:

The statement t = 12345, 54321, 'hello!' is an example of tuple packing : the values 12345 , 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:

This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.

5.4. Sets ¶

Python also includes a data type for sets . A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.

Curly braces or the set() function can be used to create sets. Note: to create an empty set you have to use set() , not {} ; the latter creates an empty dictionary, a data structure that we discuss in the next section.

Here is a brief demonstration:

Similarly to list comprehensions , set comprehensions are also supported:

5.5. Dictionaries ¶

Another useful data type built into Python is the dictionary (see Mapping Types — dict ). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys , which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend() .

It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {} . Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.

The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del . If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.

Performing list(d) on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use sorted(d) instead). To check whether a single key is in the dictionary, use the in keyword.

Here is a small example using a dictionary:

The dict() constructor builds dictionaries directly from sequences of key-value pairs:

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

5.6. Looping Techniques ¶

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the items() method.

When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.

To loop over two or more sequences at the same time, the entries can be paired with the zip() function.

To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.

To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.

Using set() on a sequence eliminates duplicate elements. The use of sorted() in combination with set() over a sequence is an idiomatic way to loop over unique elements of the sequence in sorted order.

It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead.

5.7. More on Conditions ¶

The conditions used in while and if statements can contain any operators, not just comparisons.

The comparison operators in and not in are membership tests that determine whether a value is in (or not in) a container. The operators is and is not compare whether two objects are really the same object. All comparison operators have the same priority, which is lower than that of all numerical operators.

Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c .

Comparisons may be combined using the Boolean operators and and or , and the outcome of a comparison (or of any other Boolean expression) may be negated with not . These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C . As always, parentheses can be used to express the desired composition.

The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C . When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.

It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,

Note that in Python, unlike C, assignment inside expressions must be done explicitly with the walrus operator := . This avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.

5.8. Comparing Sequences and Other Types ¶

Sequence objects typically may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode code point number to order individual characters. Some examples of comparisons between sequences of the same type:

Note that comparing objects of different types with < or > is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a TypeError exception.

Table of Contents

• 5.1.1. Using Lists as Stacks
• 5.1.2. Using Lists as Queues
• 5.1.3. List Comprehensions
• 5.1.4. Nested List Comprehensions
• 5.2. The del statement
• 5.3. Tuples and Sequences
• 5.5. Dictionaries
• 5.6. Looping Techniques
• 5.7. More on Conditions
• 5.8. Comparing Sequences and Other Types

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