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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.

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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.

Example

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

Explanation

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.

Example:

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')

Explanation

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)

Explanation:

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)

Explanation:

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)

Explanation:

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.

Conclusion

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.

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## How Outer Function Works In Numpy?

Introduction to NumPy Outer

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Syntax

Outer() is one of the predefined functions of the Numpy library, and mainly, it’s used for vector and matrix calculations; its basic syntax is as follows below.

import numpy as first x=first.ones() y=first.linspace() z=first.outer(x,y) print (z)

Above basic Python code, we have imported the numpy packages in the Python script, and it has called the pre-defined methods of the package. The vector has rows and columns for all the dimensions, releasing both 2D and 3D vectors in matrix multiplications.

How outer Function works in NumPy?

In the Numpy library, the outer is the function or product of two coordinate vectors in the matrix calculations. We use more than one vector with dimensions like any variables, and their variables are calculated using the “x” multiplication operator for calculating matrix outputs. If suppose we use the tensor type of datas like a multidimensional array of numbers, then the outer function will give the tensor as a result. It is also known and defined as tensor algebras. The outer function is also known as the outer product, and the tensor is also referred to as the tensor product. The outer function used dot products, and Kronecker products also used standard matrix multiplications.

The Numpy arrays have used both single and multi-dimensional arrays if we can pass the Python list to the arrays method in single or one-dimensional arrays. And if we pass the list of lists packages in the arrays method in multi or two-dimensional arrays. The matrix array multiplication in every inner list and the outer lists becomes the rows and columns if the number of columns equals the number of elements in each inner list. When we use arrays in the Numpy, it has some default and important pre-defined methods, which are arrange(), zeros(), and ones(), etc, while creating the NumPy arrays. Also, if we use the arrays, it takes the arguments like start index, end index, and some linearly-spaced types of numbers that can be the specified ranges. The index values are different depending on the application requirement.

Examples of NumPy Outer

Here are the following examples as mentioned below.

Example #1 import numpy as np p = np.array([3, 6, 8], float) q = np.array([4, 7, 13], float) print("The Calculation of Matrixes and vectors are.") print("p:") print(p) print("q:") print(q) print("The Outer function used in the p and q are:") print(np.outer(p, q))

Output:

Example #2

Code:

import numpy as np p = np.ones(6) q = np.linspace(-5, 3, 7) r = np.outer(p, q) print (r) x = [5, 9] y = [2, 6, 4] z = np.outer(x, y) print(z)

Output:

Example #3 import numpy as np p = np.array([[4, 3, 2, 1, 16], [-7, 4, 3, 6, 15], [-5, 2, 19, 11, 26]]) y = [2, 6, 4] print(p[:3, :7]) print(p[:6,]) print(p[:,5]) print(A[:, 4:8]) z = np.outer(p, y) prPostint(z)

Output:

In the above three examples, we described the outer function in different areas, as well as the Numpy library has used many other different methods like slicer(), inner(), ones(), etc. If the user inputs must be entered in multiple areas simultaneously, the inputs are validated in both the front and back ends. Depending on the application requirements, the inputs can be in number formats such as integer, float, or decimal point. When using the Numpy packages, it becomes essential to validate user inputs due to their specific design for integer formats and utilization of arrays and vectors for matrix calculations.

Conclusion

In this article, we have discussed some important points regarding the Numpy packages and their method, especially in the outer() function. So in the latest and future technology purpose, these Numpy packages and their methods are an important part of the technology trends.

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## 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.

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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

Code:

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)

Output:

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.

Code:

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)

Output:

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.

Code:

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)

Output:

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)

Output:

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)

Output:

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.

Conclusion

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.

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## How Does Authentication Work In Java

Introduction to Authentication Java

Authentication java is a term of the security to identity confirmation of web applications. It is a function to confirm user identification of the websites & web applications using a programming language. It confirms the users’ use and permits them to access the website, application, and software-related products using Java technology. It is a security method to identify the authorized user and give permission to use the application using the security terms of the Java language.

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It is a client and server-side function to use unique content and confirm with a security password and user identity. It is used the user id and password on the client side and accesses the server-side data with true identification using a Java programming language. It is a documentation process to keep a secure web application and use only accessible team members.

Syntax

In this syntax, the application uses a username and password for authentication.

Use web application with security and login form. This form redirects to the JSP page.

<form:form action="\${pageContext.request.contextPath}/authenticateUser"

Use a web application for authentication of the login form. This form redirects to the JSP page.

Use Java authentication syntax using java spring frameworks. Java uses Spring security to authenticate the authority.

public class AppSecurityConfig extends AppSecurityConfigurerAdapter { @Override protected void configure(AuthenticationManagerBuilder authentic) throws Exception { UserBuilder userid = User.withDefaultPasswordEncoder(); authentic.inMemoryAuthentication() .withUser (usersid.username ("merry") .password ("[email protected]") .roles ("student")) } @Override http.authorizeRequests() .antMatchers("/") .hasRole("student") .and() .formLogin() .loginPage("/useLoginPage") .loginProcessingUrl("/authenticatationUser") .permitAll() .and() .logout().permitAll(); } } Examples of Authentication Java

Given below are the examples:

Example #1

The basic example is shown below.

Code:

File: authenticationApp.java

<form:form action="\${pageContext.request.contextPath}/authenticateUser" File: authentication.jsp

Output:

Output

Here, you see single-user authentication in a single user name.

The “Sunny” accesses only the student portal with Java authentication.

You get the single form for a single authentic user.

Example #2

Two authentications in the Java example and output are shown below.

Code:

File: authenticationApp.java

<form:form action="\${pageContext.request.contextPath}/authenticateUser" File: authentication.jsp

Output:

Output:

Explanation:

Here, you see two authentications in a single user name.

The “sam” accesses the teacher and student portal with Java authentication.

You get the single form for multiple authentic users.

Example #3

Code:

File: authenticationApp.java

<form:form action="\${pageContext.request.contextPath}/authenticateUser" File: authentication.jsp

Output:

Output:

Explanation:

Here, you see multiple authentications in a single user name.

The “Ram” accesses the teacher, student, and admin portal with Java authentication.

You get the single form for multiple authentic users.

Conclusion

Authentication in Java provides security, safety, and privacy of the data and authority. The authentication uses for accessing part of the database to respective users and authorities. It becomes easy, attractive, user-friendly, and elegant websites and web applications. This function sorts the documentation per the user’s identity and returns only the required data. It helps to get complicated information easily without disturbing others’ privacy.

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## How Does Listview Work In Javafx?

Introduction to JavaFX ListView

JavaFX ListView is a class used to choose one or more choices from the list. ListViewclass is available within scene.control.ListView package. ListView allows us to add as many elements as we want. The user may additionally add elements to ListView either horizontally or vertically. ListView can be allowed to add images to the list values. ListView is used to select single or multiple values at a time.

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Real-Time Example: While installing any application into the Windows PC, many features are there to select. But we didn’t choose all the features because we are choosing the feature because we are allowing additional software to install in the background. So, we choose the required features from the list. In this case, we can use the ListView Multi-select option.

How does ListView work in JavaFX?

Accessing JavaFX features user-defined class must extend the Application class.

Step 1: In JavaFX, creating is the first step. ListView can instantiate by using the new keyword.

Syntax:

ListFView listViewRef=new ListView();

Step 2: Adding elements or items to thelistViewRef is the second step. Items can be added in 2 ways:

Syntax:

getItems(): Used for showing the list item to the user.

2. By using ObservableList Class

Syntax:

FXCollections.observableArrayList(): Takes the all possible Typed list of items.

Note: A recommended way to add the elements to the ListView is ObservableList. Because, This ObservableList, by default observed with the ListView, allows any changes that occur inside the Observable list and updates the ListView automatically.

Step 3: The third step is to create a horizontal or vertical box to add the items.

Syntax:

HBox hBox=new HBox(); VBox vBox=new VBox();//Gives vertical box

Step 4: Creating a scene means screen to display output is the fourth step.

Syntax:

Scene screen = new Scene(hBox or vBox, length, width);

Step 5: Adding a Scene reference screen to the Stage object reference is the fifth step. We are adding an output screen to Stage. We will get this stage object reference from the start predefined JavaFX method.

Syntax:

stage.setScene(screen);

Syntax:

stage.show(); Examples of JavaFX ListView

Following are the different examples of JavaFX Listview.

Example #1 – Adding Items Horizontally to the ListView

Code:

import javafx.application.Application; import javafx.scene.Scene; import javafx.scene.control.ListView; import javafx.scene.layout.HBox; import javafx.stage.Stage; public class AddingItemsListView extends Application { @Override public void start(Stage displayScreen) throws Exception { /* create list object */ /* adding items to the list view */ listViewReference.getItems().add("First Item"); listViewReference.getItems().add("Second Item"); listViewReference.getItems().add("Third Item"); listViewReference.getItems().add("Fourth Item"); listViewReference.getItems().add("Fifth Item"); /*making list view horizontal*/ listViewReference.setOrientation(Orientation.HORIZONTAL); /* creating horizontal box to add item objects */ HBox hbox = new HBox(listViewReference); /* creating scene */ Scene scene = new Scene(hbox, 400, 200); /* adding scene to stage */ displayScreen.setScene(scene); /* display scene for showing output */ displayScreen.show(); } public static void main(String[] args) { /*launch method calls internally start() method*/ Application.launch(args); } }

Output:

Explanation: As you can see from the output, items are added horizontally. You can scroll the scroll bar to see more items like in the output.

Example #2 – Adding Items Vertically to the ListView

Code:

import javafx.application.Application; import javafx.scene.Scene; import javafx.scene.control.ListView; import javafx.scene.layout.VBox; import javafx.stage.Stage; public class AddingItemsListView extends Application { @Override public void start(Stage displayScreen) throws Exception { /* create list object */ /* adding items to the list view */ listViewReference.getItems().add("First Item"); listViewReference.getItems().add("Second Item"); listViewReference.getItems().add("Third Item"); listViewReference.getItems().add("Fourth Item"); listViewReference.getItems().add("Fifth Item"); /* creating vertical box to add item objects */ VBox vBox = new VBox(listViewReference); /* creating scene */ Scene scene = new Scene(vBox, 220, 270); /* adding scene to stage */ displayScreen.setScene(scene); /* display scene for showing output */ displayScreen.show(); } public static void main(String[] args) { /*launch method calls internally start() method*/ Application.launch(args); } }

Output:

Explanation: As you can see from the output, items are added vertically. By default alignment of the list, the view is vertical.

Example #3 – Select Multiple Items from the ListView

Code:

import javafx.application.Application; import javafx.scene.Scene; import javafx.scene.control.ListView; import javafx.scene.control.SelectionMode; import javafx.scene.layout.VBox; import javafx.stage.Stage; public class SelectingMultipleItemsListView extends Application { @Override public void start(Stage displayScreen) throws Exception { /* create list object */ /* adding items to the list view */ listViewReference.getItems().add("First Item"); listViewReference.getItems().add("Second Item"); listViewReference.getItems().add("Third Item"); listViewReference.getItems().add("Fourth Item"); listViewReference.getItems().add("Fifth Item"); /*Make listview to select multiple values*/ listViewReference.getSelectionModel().setSelectionMode(SelectionMode.MULTIPLE); /* creating vertical box to add item objects */ VBox vBox = new VBox(listViewReference); /* creating scene */ Scene scene = new Scene(vBox, 220, 270); /* adding scene to stage */ displayScreen.setScene(scene); /* display scene for showing output */ displayScreen.show(); } public static void main(String[] args) { /*launch method calls internally start() method*/ Application.launch(args); } }

Output:

Example #4 – Adding Items to the ListView

Code:

import javafx.application.Application; import javafx.collections.FXCollections; import javafx.collections.ObservableList; import javafx.scene.Scene; import javafx.scene.control.ListCell; import javafx.scene.control.ListView; import javafx.scene.image.Image; import javafx.scene.image.ImageView; import javafx.scene.layout.VBox; import javafx.stage.Stage; public class AddingImagesToItemsListView extends Application { /*loading images with their paths*/ private final Image cabinetImage  = new Image("Cab.png"); private final Image docIconImage  = new Image("documenticon.png"); private final Image homeCabImage  = new Image("HomCab.png"); private final Image searchIconImage = new Image("searchicon.png"); /*image array to load all images at a time*/ private Image[] imagesArray = {cabinetImage, docIconImage, homeCabImage, searchIconImage}; @Override public void start(Stage displayScreen) throws Exception { /* create list object */ /* adding items to the list view */ "Fourth Image"); listViewReference.setItems(elements); /*setting each image to corresponding array index*/ /*view the image class to display the image*/ private ImageView displayImage = new ImageView(); @Override public void updateItem(String name, boolean empty) { super.updateItem(name, empty); if (empty) { setText(null); setGraphic(null); } else { if (name.equals("Fist Image")) displayImage.setImage(imagesArray[0]); /*setting array image to First Image*/ else if (name.equals("Second Image")) displayImage.setImage(imagesArray[1]);/*setting array image to Second Image*/ else if (name.equals("Third Image")) displayImage.setImage(imagesArray[2]);/*setting array image to Third Image*/ else if (name.equals("Fourth Image")) displayImage.setImage(imagesArray[3]);/*setting array image to Fourth Image*/ setText(name); setGraphic(displayImage); } } }); /* creating vertical box to add item objects */ VBox vBox = new VBox(listViewReference); /* creating scene */ Scene scene = new Scene(vBox, 220, 270); /* adding scene to stage */ displayScreen.setScene(scene); /* display scene for showing output */ displayScreen.show(); } public static void main(String[] args) { /* launch method calls internally start() method */ Application.launch(args); } }

Output:

Explanation:

First, paste all the images to the eclipse src folder, then give their names in the Image class.

Now you can see the output in the above image.

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## Working And Examples Of Numpy Zeros_Like Function

Introduction to NumPy zeros_like

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Syntax

The syntax for NumPy zeros_like function in Python is as follows:

numpy.zeros_like(arrayname, datatype, memoryorder, subok)

where arrayname is the name of the array whose values must be replaced with zeros without a change in the size and shape of the array,

The data type is the data type of the values stored in the array. The default datatype for the given values in the array is float. This parameter is optional.

Memoryorder represents the order in the memory. The subok represents a Boolean value that is true if the array returned by using zeros like function is a subclass of the input array and false if the returned array is the same as the original array. This parameter is optional.

Working of NumPy zeros_like function

Whenever we have an array whose values must be replaced with all zeroes and the array size and shape must be retained as the original array, we make use of a function called zeros like function in numpy.

The zeros like functions take four parameters arrayname, datatype, memoryorder, and subok, among which the datatype and subok parameters are optional.

datatype represents the data type of the value stored in the array whose name is represented by the first parameter arrayname.

memoryorder represents the order in the memory.

subok represents a Boolean value which is true if the array returned by using zeros like function is a subclass of the input array and false if the returned array is the same as the original array.

Examples of NumPy zeros_like

Different examples are mentioned below:

Example #1

Python program to demonstrate NumPy zeros like function to create an array using array function in numpy and then using zeros like function to replace the elements of the array with zeros:

#importing the package numpy import numpy as n #Creating an array by making use of array function in NumPy and storing it in a variable called orgarray orgarray = n.array([[1,2],[3,4]]) #Displaying the elements of orgarray followed by one line space by making use of n print ("The elements of the given array are:") print (orgarray) print ("n") #using zeros like function of NumPy and passing the created array as the parameter to that function to replace all the elements of the array with zeros and store it in a variable called zerosarray zerosarray = n.zeros_like(orgarray, float) #Displaying the array consisting of all zero elements print ("The array with all its elements zero after using zeros like function is as follow:") print (zerosarray)

Output:

In the above program, we are importing the package numpy, which allows us to make use of the functions array and zeros_like. Then we are creating an array called orgarray by making use of the array function in numpy. Then the elements of the array orgarray are displayed on the screen. Then we are making using zeros_like function, and the newly created array orgarray is passed as a parameter to the function to convert all the elements of the array to zeros without changing the size and shape of the array, and the resulting array is stored in a variable called zerosarray. Finally, the elements of the zerosarray are displayed on the screen.

Example #2

Python program to demonstrate NumPy zeros like function to create an array using array function in numpy and then using zeros like function to replace the elements of the array with zeros:

#importing the package numpy import numpy as n #Creating an array by making use of array function in NumPy and storing it in a variable called orgarray orgarray = n.array([[5,6],[7,8]]) #Displaying the elements of orgarray followed by one line space by making use of n print ("The elements of the given array are:") print (orgarray) print ("n") #using zeros like function of NumPy and passing the created array as the parameter to that function to replace all the elements of the array with zeros and store it in a variable called zerosarray zerosarray = n.zeros_like(orgarray, int) #Displaying the array consisting of all zero elements print ("The array with all its elements zero after using zeros like function is as follow:") print (zerosarray)

Output:

In the above program, we are importing the package numpy, which allows us to make use of the functions array and zeros_like. Then we are creating an array called orgarray by making use of the array function in numpy. Then the elements of the array orgarray are displayed on the screen. Then we are making using the zeros_like function. The newly created array orgarray is passed as a parameter to the function to convert all the elements of the array to zeros without changing the size and shape of the array. The datatype int is also passed as the parameter, which displays the zeros in the resulting array as integer values. Then the resulting array is stored in a variable called zerosarray. Finally, the elements of zerosarray are displayed on the screen.

Conclusion

In this tutorial, we understand the concept of NumPy zeros like function in Python through definition, the syntax of zeros like function, and the working of zeros like functions through programming examples and their outputs.

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