Trending December 2023 # How Does The Python Dump Function Work? # Suggested January 2024 # Top 18 Popular

You are reading the article How Does The Python Dump Function Work? updated in December 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 How Does The Python Dump Function Work?

Definition of Python Dump

Python supports the json package, which allows for the execution of script files containing textual programming code. This package enables the transfer and storage of data by utilizing the functions provided by the json module. The dump function in json supports the code scripted in key-value pairs similar to the Python dictionary that is within curly brackets. The dumps function is mainly used when we want to store and transfer Python objects, and json package allows us to perform the operation efficiently.

Start Your Free Software Development Course

Web development, programming languages, Software testing & others

Syntax json.dump(object, skipkeys=False, ensure_ascii=True, indent=None, allow_nan=True, number_mode = None, datetime_mode = None, separators=None) pickle.dump(Object, Destination, pickle_protocol=None, )

json.dump represents the function that encodes and stores the Python object value into json value.

object is the filename of the input data or a value that we are passing through the dump function.

skipkeys is a parameter where we will declare Boolean values whether we want to skip the invalid dictionary keys.

ensure_ascii is a parameter where we will declare the Boolean values to ensure the output should contain ASCII values or not.

allow_nan is also a Boolean parameter that is used to allow null values.

number_mode & datetime_mode allow us to handle the type of behaviors we handle inside the function, and datetime mode allows us to handle to format of data and time instances.

The value we give first denotes the separation of a key-value pair from another key-value pair. 2nd value we give denotes the symbol which separates keys from their values.

For pickle package,

The object is the Python object we have created to be pickled

The destination is the file or data where the pickled python objected is written

Pickle_protocol refers to the version of the pickle protocol. By default, it assigns to the Python version.

How does Python Dump Function Work?

Let us discuss a basic example of how the json dump function works.

Example #1


import json # python dictionary dict_pets ={ "Dog": { "Species": "cocker spaniel", "country": "United Kingdom" }, "Cat": { "Species": "British Shorthair", "country": "United Kingdom" }, "Hamster": { "Species": "golden hamster ", "country": "Turkey" } } ## Converting output to json format pets_data = open("pet_data.json", "w") json.dump(dict_pets, pets_data)


In this example, we have created a Python dictionary with three key-value pairs, and we have converted the Python dictionary to json file format using the json package. Then, we pass the dictionary variable to the chúng tôi function, which serializes the Python object and writes the JSON output to the pets_data file. The chúng tôi function requires two positional arguments: dict_pets represents the Python object to be serialized, and pets_data is the file where the JSON output is stored or written.

Example #2

In this example, we’ll discuss the package called Pickle in Python, which helps us to serialize the Python object.


import pickle # python dictionary dict_pets ={ "Dog": { "Species": "cocker spaniel", "country": "United Kingdom" }, "Cat": { "Species": "British Shorthair", "country": "United Kingdom" }, "Hamster": { "Species": "golden hamster ", "country": "Turkey" } } ## Serializing output using pickle pets_data = open("pet_data.pickle", "wb") pickle.dump(dict_pets, pets_data)


Example #3

Let’s discuss another example where we use the json dumps() function, which is similar to the dump() function but the dumps() function allows us to convert the Python dictionary object to a string file in json format.


import json import json # python dictionary dict_pets ={ "Dog": { "Species": "cocker spaniel", "country": "United Kingdom" }, "Cat": { "Species": "British Shorthair", "country": "United Kingdom" }, "Hamster": { "Species": "golden hamster ", "country": "Turkey" } } ## Converting output to json format json_dict = json.dumps(dict_pets) print(json_dict)


Similar to the 1st example, we have created the Python dictionary with the same three key-value pairs. Here, we pass only one positional argument to the dumps() function, unlike json.dump(), which requires two positional arguments.

Since we are converting the Python object to json string format, we only require the object variable.

Example #4

In this example, we utilize the allow_nan parameter, which we discussed earlier, to handle NaN (Not a Number) values in a Python dictionary.

import json import json # python dictionary dict_pets ={ "Dog": { "Species": "cocker spaniel", "country": "United Kingdom" }, "Cat": { "Species": "British Shorthair", "country": "United Kingdom" }, "Hamster": { "Species": "golden hamster ", "country": "Turkey" } } ## Converting output to json format json_dict = json.dumps(dict_pets) print(json_dict)


When we declare the allow_nan parameter as True

import json # python dictionary which should be dumped dict_pets ={ "Dog": { "Species": "cocker spaniel", "country": "United Kingdom", "life expectency": 20 }, "Hamster": { "Species": "golden hamster", "country": "Turkey", "life expectency": float("nan") } } ## Converting output to json format pets_data = open("pet_data.json", "w") json.dump(dict_pets, pets_data, allow_nan=True)


we can see from two codes that when we set the allow_nan parameter as True when our object has Nan values, we can dump the object to json output without any problem.


In this article, we have discussed the Python dump function in detail using various examples to get a clear understanding of the json dump function and its uses. We have also discussed the pickle package and dumps() function along with the examples, and we have discussed the usage of allow_nan parameters with an example. I hope this article helps.

Recommended Articles

This is a guide to Python Dump. Here we also discuss the definition and how the Python dump function works, along with different examples and its code implementation. You may also have a look at the following articles to learn more –

You're reading How Does The Python Dump Function Work?

How Does Vectorize Function Work In Numpy?

Introduction to NumPy Vectorize

Python provides different functions to the users. To work with vectorizing, the python library provides a numpy function. The NumPy vectorize accepts the hierarchical order of the numpy array or different objects as an input to the system and generates a single numpy array or multiple numpy arrays. After successive multiple arrays of input, the NumPy vectorize evaluates pyfunc like a python map function, and it helps to define numpy rules. We use numpy vectorization instead of a loop to increase speed. Arrays play a major role in data science, where speed matters. Basically, numpy is an open-source project. In python, numpy is faster than the list. Therefore, processing and manipulating can be done efficiently.

Start Your Free Software Development Course

Web development, programming languages, Software testing & others

Syntax of NumPy Vectorize

The syntax for NumPy Vectorize is as follows:

vectorize_funcunction = np.vectorize (function, parameter 1, parameter 2….. parameter N)

In the above syntax, vectorize_function is a function name, np.vectorize is a numpy class, and function is a user-defined function with parameters. The parameters which we are using in the numpy vector as below.

Different Parameters of Numpy vectorize are as follows.

1. pyfunc: It is used to define the function of python as well as the method, and it must be required. Therefore, it is a callable parameter.

2. otypes: The otypes mean output data type, and it is optional. In otypes, it should be specified as either a list of data types specified or a string of type code characters. For each output, there must be one data specified.

3. doc: The doc is an optional parameter to the docstring. If there is none in doc, then docstring will be pyfunc_doc_str.

4. excluded: This is an optional parameter. This parameter consists of either a set of strings or integers representing the positional or keyword arguments for the functions that will not be vectorized. A set of strings or integers will be passed directly to pyfunc unmodified.

5. cache: The cache is an optional parameter. It will cache the first function call, which generally determines the number of outputs if True and otypes are not given.

How does the vectorize function work in NumPy?

We must install Python on your  system.

We must install numpy using the pip command.

We required basic knowledge about Python.

We required basic knowledge about arrays.

We can perform different operations using the numpy vectorize function.

Let’s see how we can implement a numpy vectorize function on an array. But, first, we see what is the difference between vectorizing and non-vectorize implementation.

1. Vectorize Implementation

It is mainly related to matrices. In vectorize implementation, we execute huge algorithms like machine learning algorithms and neural language algorithms.


import numpy as np import time no = 100000 x = np.random.random(no) y = np.random.random(no) start = time.time() z =,y) end = time.time() print("Vectorize :" + str((end-start)*1000)+ 'ms')


In the above example, we implemented the numpy vectorize function using an array. In this program, we used two arrays, x, and y, with random numbers, and then we used dot product means the multiplication of x and y arrays. Also, we have calculated the total execution time of the x and y array using vectorize. Thus, the vectorize function takes minimum time for execution. Illustrate the end result of the above declaration by using the use of the following snapshot.

2. Non-Vectorize Implementation

In this implementation, we use a loop for implementation purposes non-vectorize implementation takes more time to execute as compared to vectorize implementation.


import numpy as np import time no= 100000 x=np.random.random(no) y=np.random.random(no) start = time.time() z=0 for i in range(no): z += x[i] + y[i] end=time.time() print("Loop :" + str((end-start)*1000)+ 'ms')


In the above example, we implemented a non-vectorize numpy. In this example, we used a loop for implementation. Here we have used Loop instead of Vectorize. As a result, the non-vectorize takes more time. Illustrate the end result of the above declaration by using the use of the following snapshot.

Example: numpy vectorize function

import numpy as np def func1(c, d): return c - d else: return c + d vfun = np.vectorize(func1) z=vfun([4, 3, 5, 2], 1) print(z)


In this example, we have implemented numpy vectorization. We have defined a vectorize function in which m and n are arguments. The Vectorize function used in the above example reduces the length of code. In this example, vfun directly performs the operation on arrays. Illustrate the end result of the above declaration by using the use of the following snapshot.

import numpy as np def func1(p, q): vecfunc.__doc__ vecfunc = np.vectorize(func1, doc="welcome to python") a=vecfunc.__doc__ print(a)


For vectorization, the docstring is obtained from the input function unless the docstring is specified. Illustrate the end result of the above declaration by using the use of the following snapshot.

Example: Excluded

import numpy as np def pval(x, y): _x = list(x) res = _x.pop(0) while _x: res = res*y + _x.pop(0) return res vect_pval = np.vectorize(pval, excluded=['x']) z=vect_pval(x=[2, 4, 5], y=[1, 2]) print(z)


The excluded is used to stop vectorizing over some arguments. In this example, we implement polynomials as in polyval. Finally, illustrate the end result of the above declaration by using the use of the following snapshot.

In a similar way, we can implement remaining parameters like otype and signature and perform different operations with the help of numpy vectorize.


We hope from this article you have understood about the numpy vectorize function. From the above article, we have learned the basic syntax numpy vectorize function. We have also learned how we can implement them in Python with different examples of each parameter. With the help of the vectorizing function, we reduce the execution time of the algorithm. From this article, we have learned how we can handle numpy vectorize in python.

Recommended Articles

This is a guide to NumPy Vectorize. Here we discuss How does the vectorize function work in NumPy and Examples along with the Explanation. You may also look at the following articles to learn more –

How Does Laravel Find Work?

Introduction to Laravel Find

Web development, programming languages, Software testing & others

What is Laravel Find?

With the help of the primary key that is present in Laravel, it helps in retrieving a single record of data from the database that is available for purpose of the functioning of the application to be processed. The key in the array is not used by the find option. It is also not intended to be a column name. The user needs to understand that eloquent is not the shortcut but find is.

When there is a need to retrieve any record with the help of the primary key use this code:

$user = User::find (1);

The find() method finds the Model by its $primaryKey property. If you have none set, it’s going to assume id. If you have something set up like protected $primaryKey = foo_id the find() function will go through foo_id instead of id. Laravel also expects the id to be an integer; if you are using something other than an integer (such as a string) you need to set the incrementing property on your model to false.

How does Laravel Find work?

Laravel find is capable of retrieving data from the database for the user when the user needs to obtain a concerned set of data from a larger database. With a load of too much data, it becomes difficult or even time-consuming to manually hunt for a specific set of records to work on. Most of the data in Laravel are auto-generated and they are self-coded as Laravel PHP helps in getting applications and websites ready faster with a lot of automated properties. Laravel Find is used to retrieve data with the help of primary keys that are available in Laravel. One thing that any user will have to note is that Laravel find is not a query builder as it may look to be. This misunderstanding needs to be clarified in priority.

The query builder that is available in Laravel provides a lot of conveniences. It also gives an interface to create and run the queries of the database. Most of the database operations can be performed in the application that is customized and prepared. It also works on all supported database systems.

The above example will help us to understand the working of Laravel Find better.

In the above image, id is ‘1’ and is having data records to be retrieved of the ‘kids’ section with the description as ‘items that are meant for kids’, The other details of time are represented as ‘null’ which means they are not available in the database with the help of Laravel Find.

Unlike ‘Where’ that helps you to get any valid database field, find is using only the primary key that is registered by default in the system to retrieve single data from the database. ‘Where’ uses the ‘get’ method in which you will get more than a single result as it does deep searching and responds with many similar kinds of data in response.

With the help of the ‘find’ method as seen in the above image when the user retrieves information, the user is bound to get an object if the record actually exists in the database. The object that will be received will be the instance of the ‘Model’ and all the database fields that are available will be made available as attributes as that of the class. In response to no data being found inside the database, the response is directly ‘null’ value.

The above image describes the use of ‘where’ model to retrieve data from the database. There can be a limit that the user can establish while using ‘where’ clause by limiting the results to a single row with the help of the ‘first’ method on the instance variable like this:

This first method responds as similar to the ‘find’ method in order to retrieve data from the database. Laravel find thereafter will search for the records from the database that will match the filter criteria that are presented by the user by using the given option but it will only return the first and primary record that will match the search criteria of the user.

Example to Implement Laravel Find

As we know, the find () method in Laravel can be used by any user along with an array of primary keys, and it will return a set of matching records from the database. For example,

$student = Students::all ();

With the help of the above data, we can get the details of all the students.

$student = Students::find (1);

The above code will help us to retrieve the data of one student recognized by ‘id’.

As we see, with a specific attribute we can find the details of a particular student.


As we come to understand, Laravel Find is used to get a single data from the data. Laravel find will look for specific data with the help of the primary keys. Unlike ‘Where’ through which similar data from the database is also retrieved for the user, ‘find’ looks for specific data. If the data is not available, it will present the ‘null’ value.

Recommended Articles

We hope that this EDUCBA information on “Laravel Find” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

How Does Java Microservices Work?

Introduction to Java Microservices

Microservices are nothing but a set of applications written in Java language, or we can say that they are typically Java tools and frameworks. Developers usually design microservices for a specified scope and interact with each other with high availability. Each microservice has limited capabilities while we are creating the architecture. The microservice architecture is analogous, so we must assemble it for the desired result.

Start Your Free Software Development Course

Key Takeaways

It is a small Java application, so each microservice is independent.

It collaborates with the agile process, so we can do a fast sprint iteration and try to fix the bugs.

It also helps us to reduce the cost due to the agile process.

It provides scalability architectures to the developer.

What are Java Microservices?

Java microservices are a group of programming applications written in the Java programming language (and regularly influence the huge useful system of different Java tools and frameworks), intended for a restricted scope that works with one another to shape a greater arrangement. As the name infers, every microservice has negligible capacities for modularity, generally creating modules or architecture. A microservices design resembles an assembling sequential construction system, where every microservice resembles a station in the mechanical production system.

How do Java Microservices Work?

Before going on to the execution, we must know what microservices is. We have already discussed the above point. We understand that each microservice performs a specific task with a limited scope, contributing to effectively achieving the desired results.

Java Microservices Frameworks

Different Java-based frameworks are available for creating Java microservices, so let’s see them one by one as follows.

Spring Boot: It is the most popular framework used to create microservices, or we can say that Java application due to simplification, easy configuration, and security application. With this framework, we can create microservices easily and iterate fast; this is one of the reasons for spring boot.

Jersey: This is another Java-based framework that is used for creating microservices. It is used for the REST web services, and this is one of the communication layers between the microservices.

Swagger: This is used to create APIs; it is also a Java base, one of the communication layers between the microservices.

The Architecture of Java Microservices

Microservices provide independent space to the developer and administration to build microservices design as per our requirement. A variation of primary style design orchestrates applications as an inexactly coupled help assortment. Microservice Engineering contains fine-grained administrations and lightweight conventions. Developers design microservices to address specific application requirements individually, and they communicate with each other to collectively generate the final result sets.

Let’s see an e-commerce application:

Benefits of Java Microservices

We know that microservices is nothing but a small-scale Java application, and it is based on the problem domain, which we need to implement as well. It also helps us follow the agile process, so they can do fast iteration and determine the new features within the specified schedule.

It helps the developer resolve bugs immediately, minimizing costs and fewer bugs.

Microservices are small applications, so they communicate with each other. That means microservices are independent, and the most important benefit is that developers can use any programming language to develop microservice.

Example of Java Microservices

In the above point, we can see how to use microservices with different frameworks, so here, let’s see an example with spring boot as follows.


import org.springframework.boot.*; import org.springframework.boot.autoconfigure.*; import org.springframework.stereotype.*; import org.springframework.web.bind.annotation.*; @RestController @EnableAutoConfiguration public class sampledemo { @RequestMapping("/") String index() { return "Sample demo project will be done"; } public static void main(String[] args) {, args); } }


In the above code, we try to implement the microservice in spring boot; the result is shown in the below screenshot.


Given below are the FAQs:

Q1. Is Java suitable for microservices?

Answer: The answer is yes because Java is the most popular programming language, and we can easily develop microservices in Java. Java provides the annotation syntax to the developer.

Q2. How and what types of microservices are present in Java?

Answer: There are 3 different types of microservices, such as discovery server, authentication service, and consumer microservice, used for the client.

Q3. Which is the best framework for microservices implementation?

Answer: There are different types of frameworks available to implement microservices, such as spring boot, quarks, eclipse, and Ballerina.


In this article, we saw what Java microservices are, as well as we also saw some basic key ideas of Java microservices with configuration. We also saw the uses and features and how we can use them.

Recommended Articles

This is a guide to Java Microservices. Here we discuss the introduction, how does java microservices work, frameworks, architecture, benefits & example. You may also have a look at the following articles to learn more –

How Does Web 3.0 Evolve And Function?

Web 3.0, sometimes known as Web 3, is the emerging phase of the Internet. It is based on the idea of creating a totally decentralized ecosystem. This new technological dimension believes in leveraging the power of Artificial Intelligence, Machine Learning, and the latest technologies like Blockchain to solve the problems of the present-day internet/online ecosystem.

Web 3.0 does not require “permission”. It means that central authorities don’t dictate who uses what services, nor is there a need for “trust,” referring to the idea that a mediator does not need to facilitate digital transactions between two or more parties.

The future of Web3.0

With the potential to revolutionise how we use the internet, Web 3.0 will enable services to be used anywhere, thanks to the fact that all data and information are given access to multiple smart apps. With this latest technology, professionals and recent graduates from every sector will witness an internet system that is more dynamic and interactive than the one we currently use.

And since the data is stored across several systems, there is also minimal risk of information leaks, making Web 3.0 more resilient. For example, in the healthcare industry, Web 3.0 will make automating all the incoming data a quicker, more efficient, and seamless process. Medical practitioners will then be able to rely on Web 3.0-based applications that will fasten the process of new admissions and cut down latencies in the doctor-patient relationships. For technology-related professionals, Web 3.0 will be a game-changer.

Web 3.0 in our day-to-day life

The basic idea behind the concept of Web 3 is to make the internet a smarter place. This new era of the web will make the global internet more responsive, intelligent, and capable of giving a more satisfying and tailored experience to the user. It will efficiently transform the way we know or use the internet today.

Artificial Intelligence: One of the primary features of Web 3.0 will be its ability to decipher human emotions and thoughts. Artificial intelligence (AI) will be used extensively to filter data and content and to tailor the search results according to the user’s preference.

Semantic Web: Semantics is the study of the linkages between words. The semantic web would enable the computer to analyze data and information and then it helps to decode the meaning and emotions that they are trying to convey. This will help in giving a better and more immersive internet usage experience to the users.

Ubiquity: Ubiquity means the power to be present everywhere at the same time, or in simpler words, ubiquity means omnipresence. As we know, Web 2.0, or the internet as we know it today is already quite omnipresent. Hence, Web 3.0 will simply take it a step further by making the internet more widely accessible using the Internet of Things (IoT).

3D Graphics: Web 3 or Web 3.0 will bring in new graphics technology, making the three-dimensional virtual world a reality. The use of 3D graphics will make the internet user experience more enticing and will also be helpful in transforming a variety of sectors like health, e-commerce, real estate, defense, technology, etc.

Challenges and future of Web 3.0

Web 3.0 promises verifiable, self-governing, trustless, permission-less and robust transfer of data and information across the platforms. For the transition of Web from Web2 to Web3, a greater number of people should start developing and using decentralized applications.


How Does Angelscript Work With Example

Introduction of AngelScript

Start Your Free Software Development Course

Web development, programming languages, Software testing & others

Why do we Need AngelScript?

AngelScript is needful because it is a powerful object-oriented scripting language like c/c++. It has an extremely flexible cross-platform scripting library; hence the scripts or a computer program written can work on any platform, and the scripting library is designed to improve the functionality of the script; by using it, users can extend the functionality with the help of external scripts.

It also has a feature that we can call the c and c++ functions within the angel-script environment; due to that, it is helpful to the developers to work with it, and the syntax of the angel-script class was closely followed by the syntax in c++ classes. So that it is needful for developers who are worked with c++ and they have to work on angel-script, then they can work easily.

Angelscript can be used in robotics that means the behavior rules of robotic are followed by angel-script. Also, it is useful for video game development, which increases the life of a game. So by using angle-script, get the project up, and that will run faster. And by using an external script, we can improve the functionality of the application.

How does AngelScript Work?

First, we need the application of angel-script and then have to register the interface so that script can interact with the application; the interface contains functions, variables, and classes also.

Secondly, we have to set message call back to receive information about errors, errors in human-readable form, because if the registration is not done correctly, then attention message will get as a callback message about the incorrect registration or a script has an error that fails to compile. If we need to verify the return code, there is no need to take much effort to know it because the call-back message can give information in the human-readable form so that we can correct it. These two steps are about engine configuration.

After engine configuration next step is to compile the script that should be executed. Also, need to write a function to call the registered print function and will give the output that function is stored in files.

In this step, has to write code for loading script files and to compile it, there is a CSriptbuilder define it, which help to load the file, it will perform some necessary process, and it will tell the engine to build a script module, also need to construct builder code for processing. If the code fails after this, then it will not have the memory to allocate the module. If the filename has been removed or the wrong name was given, or there is some unnecessary code has been written, then the builder was not able to load the file.

The last step is to identify the function which was defined that is to be called and have to set up the context for execution in code format. If that function could not be found, then need to write a print function to show a message of the function expecting.

Then has to create context, prepare it, and then execute it; if the execution didn’t work as expected, there is a need to write an exception code to handle the exception.

Example of AngelScript

Below is a very simple example that will give a brief overview of Handle:



It has features like c/c++ that is static-typing, that uses the same static type as we use in c/c++ to make typing dynamic, and only additional type that we need to register here in angel-script.

It has an object handle feature that provides pointers, but pointers are not safe in scripting; hence it uses object handles instead of pointers; object handles in scripting control the lifetime of the object.

It has mod support in-game logic where it increases the life of the game.

Angelscript supports native calling conventions on major platforms, but there are some platforms that only work with generic calling conventions; generic calling is pure C++ that works everywhere.

The CScriptString used in angel-script cannot return the object value in native mode, which is not compatible.

There may be a memory leak issue.


In this article, we conclude that it is a scripting language and that has information about the application to accurately communicate with the c/c++ code; it has an inbuilt library hence making scripting language in our project is easy; in this article, we have seen some reasons to develop our program in more than one language.

Recommended Articles

Update the detailed information about How Does The Python Dump Function Work? on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!